Repository: AlphacatPlus/VmambaIR
Branch: main
Commit: 51c2efbdcb50
Files: 304
Total size: 4.1 MB

Directory structure:
gitextract_1m56b8y9/

├── .gitignore
├── Deraining/
│   ├── Deraining/
│   │   ├── Datasets/
│   │   │   └── README.md
│   │   ├── Metric/
│   │   │   └── PSNR.py
│   │   ├── Options/
│   │   │   ├── Deraining_mamber28.yml
│   │   │   ├── Deraining_mamber32.yml
│   │   │   ├── Deraining_mamber33.yml
│   │   │   └── Deraining_mamber34.yml
│   │   ├── README.md
│   │   ├── download_data.py
│   │   ├── evaluate_PSNR_SSIM.m
│   │   ├── test.py
│   │   └── utils.py
│   ├── Deraining_test.sh
│   ├── Deraining_train.sh
│   ├── INSTALL.md
│   ├── LICENSE.md
│   ├── VERSION
│   ├── basicsr/
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── data_sampler.py
│   │   │   ├── data_util.py
│   │   │   ├── ffhq_dataset.py
│   │   │   ├── meta_info/
│   │   │   │   ├── meta_info_DIV2K800sub_GT.txt
│   │   │   │   ├── meta_info_REDS4_test_GT.txt
│   │   │   │   ├── meta_info_REDS_GT.txt
│   │   │   │   ├── meta_info_REDSofficial4_test_GT.txt
│   │   │   │   ├── meta_info_REDSval_official_test_GT.txt
│   │   │   │   ├── meta_info_Vimeo90K_test_GT.txt
│   │   │   │   ├── meta_info_Vimeo90K_test_fast_GT.txt
│   │   │   │   ├── meta_info_Vimeo90K_test_medium_GT.txt
│   │   │   │   ├── meta_info_Vimeo90K_test_slow_GT.txt
│   │   │   │   └── meta_info_Vimeo90K_train_GT.txt
│   │   │   ├── paired_image_dataset.py
│   │   │   ├── prefetch_dataloader.py
│   │   │   ├── reds_dataset.py
│   │   │   ├── single_image_dataset.py
│   │   │   ├── transforms.py
│   │   │   ├── video_test_dataset.py
│   │   │   └── vimeo90k_dataset.py
│   │   ├── metrics/
│   │   │   ├── __init__.py
│   │   │   ├── fid.py
│   │   │   ├── metric_util.py
│   │   │   ├── niqe.py
│   │   │   ├── niqe_pris_params.npz
│   │   │   └── psnr_ssim.py
│   │   ├── models/
│   │   │   ├── __init__.py
│   │   │   ├── archs/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── arch_util.py
│   │   │   │   ├── common.py
│   │   │   │   ├── mamber32_arch.py
│   │   │   │   ├── mamber33_arch.py
│   │   │   │   └── restormer_arch.py
│   │   │   ├── base_model.py
│   │   │   ├── image_restoration_model.py
│   │   │   ├── losses/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── loss_util.py
│   │   │   │   └── losses.py
│   │   │   └── lr_scheduler.py
│   │   ├── test.py
│   │   ├── test_deraining.py
│   │   ├── train.py
│   │   ├── utils/
│   │   │   ├── __init__.py
│   │   │   ├── bundle_submissions.py
│   │   │   ├── create_lmdb.py
│   │   │   ├── dist_util.py
│   │   │   ├── download_util.py
│   │   │   ├── face_util.py
│   │   │   ├── file_client.py
│   │   │   ├── flow_util.py
│   │   │   ├── img_util.py
│   │   │   ├── lmdb_util.py
│   │   │   ├── logger.py
│   │   │   ├── matlab_functions.py
│   │   │   ├── misc.py
│   │   │   └── options.py
│   │   ├── utils2.py
│   │   └── version.py
│   ├── pip.sh
│   ├── setup.cfg
│   ├── setup.py
│   └── train.sh
├── Mamba/
│   ├── .gitignore
│   └── kernels/
│       └── selective_scan/
│           ├── README.md
│           ├── csrc/
│           │   └── selective_scan/
│           │       ├── cub_extra.cuh
│           │       ├── cus/
│           │       │   ├── selective_scan.cpp
│           │       │   ├── selective_scan_bwd_kernel.cuh
│           │       │   ├── selective_scan_core_bwd.cu
│           │       │   ├── selective_scan_core_fwd.cu
│           │       │   └── selective_scan_fwd_kernel.cuh
│           │       ├── cusndstate/
│           │       │   ├── selective_scan_bwd_kernel_ndstate.cuh
│           │       │   ├── selective_scan_core_bwd.cu
│           │       │   ├── selective_scan_core_fwd.cu
│           │       │   ├── selective_scan_fwd_kernel_ndstate.cuh
│           │       │   ├── selective_scan_ndstate.cpp
│           │       │   └── selective_scan_ndstate.h
│           │       ├── cusnrow/
│           │       │   ├── selective_scan_bwd_kernel_nrow.cuh
│           │       │   ├── selective_scan_core_bwd.cu
│           │       │   ├── selective_scan_core_bwd2.cu
│           │       │   ├── selective_scan_core_bwd3.cu
│           │       │   ├── selective_scan_core_bwd4.cu
│           │       │   ├── selective_scan_core_fwd.cu
│           │       │   ├── selective_scan_core_fwd2.cu
│           │       │   ├── selective_scan_core_fwd3.cu
│           │       │   ├── selective_scan_core_fwd4.cu
│           │       │   ├── selective_scan_fwd_kernel_nrow.cuh
│           │       │   └── selective_scan_nrow.cpp
│           │       ├── cusoflex/
│           │       │   ├── selective_scan_bwd_kernel_oflex.cuh
│           │       │   ├── selective_scan_core_bwd.cu
│           │       │   ├── selective_scan_core_fwd.cu
│           │       │   ├── selective_scan_fwd_kernel_oflex.cuh
│           │       │   └── selective_scan_oflex.cpp
│           │       ├── reverse_scan.cuh
│           │       ├── selective_scan.h
│           │       ├── selective_scan_common.h
│           │       ├── static_switch.h
│           │       └── uninitialized_copy.cuh
│           ├── setup.py
│           └── test_selective_scan.py
├── README.md
├── RealSR/
│   ├── .gitignore
│   ├── Metric/
│   │   ├── DISTS/
│   │   │   ├── DISTS_pytorch/
│   │   │   │   ├── DISTS_pt.py
│   │   │   │   └── weights.pt
│   │   │   ├── DISTS_tensorflow/
│   │   │   │   └── DISTS_tf.py
│   │   │   ├── LICENSE
│   │   │   └── requirements.txt
│   │   ├── LPIPS.py
│   │   ├── PSNR.py
│   │   ├── dists.py
│   │   └── pip.sh
│   ├── VERSION
│   ├── VmambaIR/
│   │   ├── __init__.py
│   │   ├── archs/
│   │   │   ├── MambaRealSR11_arch.py
│   │   │   ├── __init__.py
│   │   │   ├── attention.py
│   │   │   ├── common.py
│   │   │   ├── discriminator_arch.py
│   │   │   └── srvgg_arch.py
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── data_util.py
│   │   │   ├── deblur_paired_dataset.py
│   │   │   ├── diffir_dataset.py
│   │   │   ├── diffir_paired_dataset.py
│   │   │   ├── gaussiandenoising_paired_dataset.py
│   │   │   ├── realesrgan400_dataset.py
│   │   │   ├── realesrgan_dataset.py
│   │   │   ├── realesrgan_memery_dataset.py
│   │   │   ├── realesrgan_paired_dataset.py
│   │   │   └── transforms.py
│   │   ├── losses/
│   │   │   ├── __init__.py
│   │   │   └── my_loss.py
│   │   ├── models/
│   │   │   ├── MambaRealSRGAN_model.py
│   │   │   ├── MambaRealSRGANtest_model.py
│   │   │   ├── MambaRealSR_model.py
│   │   │   ├── __init__.py
│   │   │   └── lr_scheduler.py
│   │   ├── test.py
│   │   ├── train.py
│   │   ├── train_pipeline.py
│   │   ├── utils/
│   │   │   ├── __init__.py
│   │   │   ├── bundle_submissions.py
│   │   │   ├── create_lmdb.py
│   │   │   ├── dist_util.py
│   │   │   ├── download_util.py
│   │   │   ├── face_util.py
│   │   │   ├── file_client.py
│   │   │   ├── flow_util.py
│   │   │   ├── img_util.py
│   │   │   ├── lmdb_util.py
│   │   │   ├── logger.py
│   │   │   ├── matlab_functions.py
│   │   │   ├── misc.py
│   │   │   └── options.py
│   │   ├── utils.py
│   │   └── weights/
│   │       └── README.md
│   ├── inference.py
│   ├── ldm/
│   │   ├── classifier.py
│   │   ├── lr_scheduler.py
│   │   ├── util.py
│   │   └── util2.py
│   ├── metric.sh
│   ├── options/
│   │   ├── mambaSR11GAN_x4.yml
│   │   ├── mambaSR11_x4.yml
│   │   ├── mambaSR11m_x4.yml
│   │   └── test_mambaSR11GAN_x4.yml
│   ├── pip.sh
│   ├── requirements.txt
│   ├── scripts/
│   │   ├── Metric/
│   │   │   ├── DISTS/
│   │   │   │   ├── DISTS_pytorch/
│   │   │   │   │   ├── DISTS_pt.py
│   │   │   │   │   └── weights.pt
│   │   │   │   ├── DISTS_tensorflow/
│   │   │   │   │   └── DISTS_tf.py
│   │   │   │   ├── LICENSE
│   │   │   │   └── requirements.txt
│   │   │   ├── LPIPS.py
│   │   │   ├── PSNR.py
│   │   │   ├── dists.py
│   │   │   └── pip.sh
│   │   ├── extract_subimages.py
│   │   ├── extract_subimages_DF2K.py
│   │   ├── generate_meta_info.py
│   │   ├── generate_meta_info_DF2K.py
│   │   ├── generate_meta_info_DF2K_memo.py
│   │   ├── generate_meta_info_OST.py
│   │   ├── generate_meta_info_pairdata.py
│   │   ├── generate_multiscale_DF2K.py
│   │   ├── options/
│   │   │   ├── mambaSR11GAN_x4.yml
│   │   │   ├── mambaSR11_x4.yml
│   │   │   ├── mambaSR11m_x4.yml
│   │   │   └── test_mambaSR11GAN_x4.yml
│   │   └── pytorch2onnx.py
│   ├── setup.cfg
│   ├── setup.py
│   ├── test.sh
│   ├── tests/
│   │   ├── data/
│   │   │   ├── gt.lmdb/
│   │   │   │   ├── data.mdb
│   │   │   │   ├── lock.mdb
│   │   │   │   └── meta_info.txt
│   │   │   ├── lq.lmdb/
│   │   │   │   ├── data.mdb
│   │   │   │   ├── lock.mdb
│   │   │   │   └── meta_info.txt
│   │   │   ├── meta_info_gt.txt
│   │   │   ├── meta_info_pair.txt
│   │   │   ├── test_realesrgan_dataset.yml
│   │   │   ├── test_realesrgan_model.yml
│   │   │   ├── test_realesrgan_paired_dataset.yml
│   │   │   └── test_realesrnet_model.yml
│   │   ├── test_dataset.py
│   │   ├── test_discriminator_arch.py
│   │   ├── test_model.py
│   │   └── test_utils.py
│   ├── train_S1.sh
│   └── train_S2.sh
├── SRGAN/
│   ├── .gitignore
│   ├── Metric/
│   │   ├── DISTS/
│   │   │   ├── DISTS_pytorch/
│   │   │   │   ├── DISTS_pt.py
│   │   │   │   └── weights.pt
│   │   │   ├── DISTS_tensorflow/
│   │   │   │   └── DISTS_tf.py
│   │   │   ├── LICENSE
│   │   │   └── requirements.txt
│   │   ├── LPIPS.py
│   │   ├── PSNR.py
│   │   ├── dists.py
│   │   └── pip.sh
│   ├── VERSION
│   ├── VmambaIR/
│   │   ├── __init__.py
│   │   ├── archs/
│   │   │   ├── MambaSISR6_arch.py
│   │   │   ├── __init__.py
│   │   │   ├── attention.py
│   │   │   ├── common.py
│   │   │   ├── discriminator_arch.py
│   │   │   └── srvgg_arch.py
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── data_util.py
│   │   │   ├── deblur_paired_dataset.py
│   │   │   ├── diffir_dataset.py
│   │   │   ├── diffir_paired_dataset.py
│   │   │   ├── gaussiandenoising_paired_dataset.py
│   │   │   └── transforms.py
│   │   ├── losses/
│   │   │   ├── __init__.py
│   │   │   └── my_loss.py
│   │   ├── models/
│   │   │   ├── MambaSISR2_model.py
│   │   │   ├── MambaSISRGAN_model.py
│   │   │   ├── MambaSISR_model.py
│   │   │   ├── __init__.py
│   │   │   └── lr_scheduler.py
│   │   ├── test.py
│   │   ├── train.py
│   │   ├── train_pipeline.py
│   │   ├── utils/
│   │   │   ├── __init__.py
│   │   │   ├── bundle_submissions.py
│   │   │   ├── create_lmdb.py
│   │   │   ├── dist_util.py
│   │   │   ├── download_util.py
│   │   │   ├── face_util.py
│   │   │   ├── file_client.py
│   │   │   ├── flow_util.py
│   │   │   ├── img_util.py
│   │   │   ├── lmdb_util.py
│   │   │   ├── logger.py
│   │   │   ├── matlab_functions.py
│   │   │   ├── misc.py
│   │   │   └── options.py
│   │   ├── utils.py
│   │   └── weights/
│   │       └── README.md
│   ├── ldm/
│   │   ├── classifier.py
│   │   ├── lr_scheduler.py
│   │   ├── util.py
│   │   └── util2.py
│   ├── metric.sh
│   ├── options/
│   │   ├── MambaSISR15GAN_x4.yml
│   │   ├── MambaSISR15_x4.yml
│   │   └── test_mamba15_x4.yml
│   ├── pip.sh
│   ├── requirements.txt
│   ├── scripts/
│   │   ├── extract_subimages.py
│   │   ├── extract_subimages_DF2K.py
│   │   ├── generate_meta_info.py
│   │   ├── generate_meta_info_DF2K.py
│   │   ├── generate_meta_info_OST.py
│   │   ├── generate_meta_info_pairdata.py
│   │   ├── generate_multiscale_DF2K.py
│   │   └── pytorch2onnx.py
│   ├── setup.cfg
│   ├── setup.py
│   ├── test.sh
│   ├── train_S1.sh
│   └── train_S2.sh
├── install.md
└── requirements.txt

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# global
**/__pycache__
**/_ignore
**/.dist_test
**/.pytest_cache
**/*.egg-info
**/*.TAG
**/dist
**/*.so
*.so
**/build
**/tmp
**/output
**/work_dirs
logs
ckpts
log
ckpt


Mamba/kernels/selective_scan/1.log
Mamba/kernels/selective_scan/test_selective_scan_speed.py
Mamba/kernels/selective_scan/test_selective_scan_easy.py
Mamba/kernels/selective_scan/ssmtriton.py
Mamba/kernels/selective_scan/ssmjax.py

.ipynb_checkpoints/
*.pyc
*.png
*.tif
*.jpg
*.pth
*.mat
*.npy
.DS_Store
*.state


================================================
FILE: Deraining/Deraining/Datasets/README.md
================================================
For training and testing, your directory structure should look like this
  
 `Datasets` <br/>
 `├──train`  <br/>
     `└──Rain13K`   <br/>
          `├──input`   <br/>
          `└──target`   <br/>
 `└──test`  <br/>
     `├──Test100`   <br/>
          `├──input`   <br/>
          `└──target`   <br/>
     `├──Rain100H`  <br/>
          `├──input`   <br/>
          `└──target`   <br/>
     `├──Rain100L`  <br/>
          `├──input`   <br/>
          `└──target`   <br/>
     `├──Test1200`  <br/>
          `├──input`   <br/>
          `└──target`   <br/>
     `└──Test2800`<br/>
          `├──input`   <br/>
          `└──target` 


================================================
FILE: Deraining/Deraining/Metric/PSNR.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse
from basicsr.metrics import calculate_psnr, calculate_ssim


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100H/target')
    #parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/target')
    #parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test100/target')
    #parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test1200/target')
    #parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test2800/target')
    parser.add_argument('--folder_restored', type=str, default='/mnt/bn/shiyuan-arnold/code/VmambaIR/Restoration/results/mamber32_net144000/Rain100H')
    args = parser.parse_args()
    psnr_all = []
    ssim_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png'))) #test2800:jpg others:png
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    #print('img list ',img_list)
    #print('lr list', lr_list)
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED)
        psnr=calculate_psnr(img_restored, img_gt, crop_border=4, test_y_channel=True)
        ssim=calculate_ssim(img_restored, img_gt, crop_border=4, test_y_channel=True)
        psnr_all.append(psnr)
        ssim_all.append(ssim)

    print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f}')
    print(f'Average: SSIM: {sum(ssim_all) / len(ssim_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: Deraining/Deraining/Options/Deraining_mamber28.yml
================================================
# general settings
name: Deraining_mamber28
model_type: ImageCleanModel
scale: 1
num_gpu: 8  # set num_gpu: 0 for cpu mode
manual_seed: 100

# dataset and data loader settings
datasets:
  train:
    name: TrainSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/input
    geometric_augs: true

    filename_tmpl: '{}'
    io_backend:
      type: disk

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8

    ### -------------Progressive training--------------------------
    mini_batch_sizes: [8,5,3,2,1,1]             # Batch size per gpu   
    iters: [92000,64000,48000,36000,36000,24000]
    gt_size: 384   # Max patch size for progressive training
    gt_sizes: [128,160,192,256,320,384]  # Patch sizes for progressive training.
    ### ------------------------------------------------------------

    ### ------- Training on single fixed-patch size 128x128---------
    # mini_batch_sizes: [8]   
    # iters: [300000]
    # gt_size: 128   
    # gt_sizes: [128]
    ### ------------------------------------------------------------

    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  val:
    name: ValSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/input
    io_backend:
      type: disk

# network structures
network_g:
  type: Mamber32
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [3,5,7,9]
  num_refinement_blocks: 2
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias
  dual_pixel_task: False


# path
path:
  pretrain_network_g: ~
  strict_load_g: true
  resume_state: ~

# training settings
train:
  total_iter: 300000
  warmup_iter: -1 # no warm up
  use_grad_clip: true

  # Split 300k iterations into two cycles. 
  # 1st cycle: fixed 3e-4 LR for 92k iters. 
  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
  scheduler:
    type: CosineAnnealingRestartCyclicLR
    periods: [92000, 208000]       
    restart_weights: [1,1]
    eta_mins: [0.0003,0.000001]   
  
  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  optim_g:
    type: AdamW
    lr: !!float 3e-4
    weight_decay: !!float 1e-4
    betas: [0.9, 0.999]
  
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1
    reduction: mean

# validation settings
val:
  window_size: 8
  val_freq: !!float 4e3
  save_img: false
  rgb2bgr: true
  use_image: true
  max_minibatch: 8

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 4e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500


================================================
FILE: Deraining/Deraining/Options/Deraining_mamber32.yml
================================================
# general settings
name: Deraining_mamber32
model_type: ImageCleanModel
scale: 1
num_gpu: 8  # set num_gpu: 0 for cpu mode
manual_seed: 100

# dataset and data loader settings
datasets:
  train:
    name: TrainSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/input
    geometric_augs: true

    filename_tmpl: '{}'
    io_backend:
      type: disk

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8

    ### -------------Progressive training--------------------------
    mini_batch_sizes: [8,5,3,2,1,1]             # Batch size per gpu   
    iters: [36000,24000,24000,24000,24000,24000]
    gt_size: 384   # Max patch size for progressive training
    gt_sizes: [128,160,192,256,320,384]  # Patch sizes for progressive training.
    ### ------------------------------------------------------------

    ### ------- Training on single fixed-patch size 128x128---------
    # mini_batch_sizes: [8]   
    # iters: [300000]
    # gt_size: 128   
    # gt_sizes: [128]
    ### ------------------------------------------------------------

    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  val:
    name: ValSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/input
    io_backend:
      type: disk

# network structures
network_g:
  type: Mamber32
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [3,5,7,9]
  num_refinement_blocks: 2
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias
  dual_pixel_task: False


# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/Restoration/experiments/Deraining_mamber28/models/net_g_300000.pth
  strict_load_g: true
  resume_state: ~

# training settings
train:
  total_iter: 156000
  warmup_iter: -1 # no warm up
  use_grad_clip: true

  # Split 300k iterations into two cycles. 
  # 1st cycle: fixed 3e-4 LR for 92k iters. 
  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
  scheduler:
    type: CosineAnnealingRestartCyclicLR
    periods: [48000, 96000]       
    restart_weights: [1,1]
    eta_mins: [0.000285,0.000001]   
  
  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  optim_g:
    type: AdamW
    lr: !!float 1e-4
    weight_decay: !!float 5e-5
    betas: [0.9, 0.999]
  
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1
    reduction: mean

# validation settings
val:
  window_size: 8
  val_freq: !!float 4e3
  save_img: false
  rgb2bgr: true
  use_image: true
  max_minibatch: 8

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 4e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500


================================================
FILE: Deraining/Deraining/Options/Deraining_mamber33.yml
================================================
# general settings
name: Deraining_mamber33
model_type: ImageCleanModel
scale: 1
num_gpu: 8  # set num_gpu: 0 for cpu mode
manual_seed: 100

# dataset and data loader settings
datasets:
  train:
    name: TrainSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/input
    geometric_augs: true

    filename_tmpl: '{}'
    io_backend:
      type: disk

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8

    ### -------------Progressive training--------------------------
    mini_batch_sizes: [8,5,3,2,1,1]             # Batch size per gpu   
    iters: [92000,64000,64000,64000,64000,48000]
    gt_size: 384   # Max patch size for progressive training
    gt_sizes: [128,160,192,256,320,384]  # Patch sizes for progressive training.
    ### ------------------------------------------------------------

    ### ------- Training on single fixed-patch size 128x128---------
    # mini_batch_sizes: [8]   
    # iters: [300000]
    # gt_size: 128   
    # gt_sizes: [128]
    ### ------------------------------------------------------------

    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  val:
    name: ValSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/input
    io_backend:
      type: disk

# network structures
network_g:
  type: Mamber32
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [3,5,7,9]
  num_refinement_blocks: 2
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias
  dual_pixel_task: False


# path
path:
  pretrain_network_g: ~
  strict_load_g: true
  resume_state: ~

# training settings
train:
  total_iter: 396000
  warmup_iter: -1 # no warm up
  use_grad_clip: true

  # Split 300k iterations into two cycles. 
  # 1st cycle: fixed 3e-4 LR for 92k iters. 
  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
  scheduler:
    type: CosineAnnealingRestartCyclicLR
    periods: [144000, 288000]       
    restart_weights: [1,1]
    eta_mins: [0.0003,0.000001]   
  
  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  optim_g:
    type: AdamW
    lr: !!float 3e-4
    weight_decay: !!float 1e-4
    betas: [0.9, 0.999]
  
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1
    reduction: mean

# validation settings
val:
  window_size: 8
  val_freq: !!float 4e3
  save_img: false
  rgb2bgr: true
  use_image: true
  max_minibatch: 8

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 4e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500


================================================
FILE: Deraining/Deraining/Options/Deraining_mamber34.yml
================================================
# general settings
name: Deraining_mamber34
model_type: ImageCleanModel
scale: 1
num_gpu: 8  # set num_gpu: 0 for cpu mode
manual_seed: 100

# dataset and data loader settings
datasets:
  train:
    name: TrainSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/train/Rain13K/input
    geometric_augs: true

    filename_tmpl: '{}'
    io_backend:
      type: disk

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8

    ### -------------Progressive training--------------------------
    mini_batch_sizes: [8,5,3,2,1,1]             # Batch size per gpu   
    iters: [92000,64000,64000,64000,64000,48000]
    gt_size: 384   # Max patch size for progressive training
    gt_sizes: [128,160,192,256,320,384]  # Patch sizes for progressive training.
    ### ------------------------------------------------------------

    ### ------- Training on single fixed-patch size 128x128---------
    # mini_batch_sizes: [8]   
    # iters: [300000]
    # gt_size: 128   
    # gt_sizes: [128]
    ### ------------------------------------------------------------

    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  val:
    name: ValSet
    type: Dataset_PairedImage
    dataroot_gt: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/target
    dataroot_lq: /mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Rain100L/input
    io_backend:
      type: disk

# network structures
network_g:
  type: Mamber32
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [3,5,7,9]
  num_refinement_blocks: 2
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias
  dual_pixel_task: False


# path
path:
  pretrain_network_g: ~
  strict_load_g: true
  resume_state: ~

# training settings
train:
  total_iter: 396000
  warmup_iter: -1 # no warm up
  use_grad_clip: true

  # Split 300k iterations into two cycles. 
  # 1st cycle: fixed 3e-4 LR for 92k iters. 
  # 2nd cycle: cosine annealing (3e-4 to 1e-6) for 208k iters.
  scheduler:
    type: CosineAnnealingRestartCyclicLR
    periods: [144000, 288000]       
    restart_weights: [1,1]
    eta_mins: [0.0003,0.000001]   
  
  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  optim_g:
    type: AdamW
    lr: !!float 3e-4
    weight_decay: !!float 1e-4
    betas: [0.9, 0.999]
  
  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1
    reduction: mean

# validation settings
val:
  window_size: 8
  val_freq: !!float 4e3
  save_img: false
  rgb2bgr: true
  use_image: true
  max_minibatch: 8

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 4e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500


================================================
FILE: Deraining/Deraining/README.md
================================================

## Training

1. To download Rain13K training and testing data, run
```
python download_data.py --data train-test
```

2. To train Restormer with default settings, run
```
cd Restormer
./train.sh Deraining/Options/Deraining_Restormer.yml
```

**Note:** The above training script uses 8 GPUs by default. To use any other number of GPUs, modify [Restormer/train.sh](../train.sh) and [Deraining/Options/Deraining_Restormer.yml](Options/Deraining_Restormer.yml)

## Evaluation

1. Download the pre-trained [model](https://drive.google.com/drive/folders/1ZEDDEVW0UgkpWi-N4Lj_JUoVChGXCu_u?usp=sharing) and place it in `./pretrained_models/`

2. Download test datasets (Test100, Rain100H, Rain100L, Test1200, Test2800), run 
```
python download_data.py --data test
```

3. Testing
```
python test.py
```

#### To reproduce PSNR/SSIM scores of Table 1, run

```
evaluate_PSNR_SSIM.m 
```


================================================
FILE: Deraining/Deraining/download_data.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881

## Download training and testing data for image deraining task
import os
# import gdown
import shutil

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True, help='train, test or train-test')
args = parser.parse_args()

### Google drive IDs ######
rain13k_train = '14BidJeG4nSNuFNFDf99K-7eErCq4i47t'   ## https://drive.google.com/file/d/14BidJeG4nSNuFNFDf99K-7eErCq4i47t/view?usp=sharing
rain13k_test  = '1P_-RAvltEoEhfT-9GrWRdpEi6NSswTs8'   ## https://drive.google.com/file/d/1P_-RAvltEoEhfT-9GrWRdpEi6NSswTs8/view?usp=sharing

for data in args.data.split('-'):
    if data == 'train':
        print('Rain13K Training Data!')
        # gdown.download(id=rain13k_train, output='Datasets/train.zip', quiet=False)
        os.system(f'gdrive download {rain13k_train} --path Datasets/')
        print('Extracting Rain13K data...')
        shutil.unpack_archive('Datasets/train.zip', 'Datasets')
        os.remove('Datasets/train.zip')

    if data == 'test':
        print('Download Deraining Testing Data')
        # gdown.download(id=rain13k_test, output='Datasets/test.zip', quiet=False)
        os.system(f'gdrive download {rain13k_test} --path Datasets/')
        print('Extracting test data...')
        shutil.unpack_archive('Datasets/test.zip', 'Datasets')
        os.remove('Datasets/test.zip')

   
# print('Download completed successfully!')




================================================
FILE: Deraining/Deraining/evaluate_PSNR_SSIM.m
================================================
%% Restormer: Efficient Transformer for High-Resolution Image Restoration
%% Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
%% https://arxiv.org/abs/2111.09881


clc;close all;clear all;

% datasets = {'Rain100L'};
datasets = {'Test100', 'Rain100H', 'Rain100L', 'Test2800', 'Test1200'};
num_set = length(datasets);

psnr_alldatasets = 0;
ssim_alldatasets = 0;

tic
delete(gcp('nocreate'))
parpool('local',20);

for idx_set = 1:num_set
    file_path = strcat('./results/', datasets{idx_set}, '/');
    gt_path = strcat('/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test', datasets{idx_set}, '/target/');
    path_list = [dir(strcat(file_path,'*.jpg')); dir(strcat(file_path,'*.png'))];
    gt_list = [dir(strcat(gt_path,'*.jpg')); dir(strcat(gt_path,'*.png'))];
    img_num = length(path_list);

    total_psnr = 0;
    total_ssim = 0;
    if img_num > 0 
        parfor j = 1:img_num 
           image_name = path_list(j).name;
           gt_name = gt_list(j).name;
           input = imread(strcat(file_path,image_name));
           gt = imread(strcat(gt_path, gt_name));
           ssim_val = compute_ssim(input, gt);
           psnr_val = compute_psnr(input, gt);
           total_ssim = total_ssim + ssim_val;
           total_psnr = total_psnr + psnr_val;
       end
    end
    qm_psnr = total_psnr / img_num;
    qm_ssim = total_ssim / img_num;

    fprintf('For %s dataset PSNR: %f SSIM: %f\n', datasets{idx_set}, qm_psnr, qm_ssim);

    psnr_alldatasets = psnr_alldatasets + qm_psnr;
    ssim_alldatasets = ssim_alldatasets + qm_ssim;
    
end

fprintf('For all datasets PSNR: %f SSIM: %f\n', psnr_alldatasets/num_set, ssim_alldatasets/num_set);

delete(gcp('nocreate'))
toc

function ssim_mean=compute_ssim(img1,img2)
    if size(img1, 3) == 3
        img1 = rgb2ycbcr(img1);
        img1 = img1(:, :, 1);
    end

    if size(img2, 3) == 3
        img2 = rgb2ycbcr(img2);
        img2 = img2(:, :, 1);
    end
    ssim_mean = SSIM_index(img1, img2);
end

function psnr=compute_psnr(img1,img2)
    if size(img1, 3) == 3
        img1 = rgb2ycbcr(img1);
        img1 = img1(:, :, 1);
    end

    if size(img2, 3) == 3
        img2 = rgb2ycbcr(img2);
        img2 = img2(:, :, 1);
    end

    imdff = double(img1) - double(img2);
    imdff = imdff(:);
    rmse = sqrt(mean(imdff.^2));
    psnr = 20*log10(255/rmse);
    
end

function [mssim, ssim_map] = SSIM_index(img1, img2, K, window, L)

%========================================================================
%SSIM Index, Version 1.0
%Copyright(c) 2003 Zhou Wang
%All Rights Reserved.
%
%The author is with Howard Hughes Medical Institute, and Laboratory
%for Computational Vision at Center for Neural Science and Courant
%Institute of Mathematical Sciences, New York University.
%
%----------------------------------------------------------------------
%Permission to use, copy, or modify this software and its documentation
%for educational and research purposes only and without fee is hereby
%granted, provided that this copyright notice and the original authors'
%names appear on all copies and supporting documentation. This program
%shall not be used, rewritten, or adapted as the basis of a commercial
%software or hardware product without first obtaining permission of the
%authors. The authors make no representations about the suitability of
%this software for any purpose. It is provided "as is" without express
%or implied warranty.
%----------------------------------------------------------------------
%
%This is an implementation of the algorithm for calculating the
%Structural SIMilarity (SSIM) index between two images. Please refer
%to the following paper:
%
%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
%quality assessment: From error measurement to structural similarity"
%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
%
%Kindly report any suggestions or corrections to zhouwang@ieee.org
%
%----------------------------------------------------------------------
%
%Input : (1) img1: the first image being compared
%        (2) img2: the second image being compared
%        (3) K: constants in the SSIM index formula (see the above
%            reference). defualt value: K = [0.01 0.03]
%        (4) window: local window for statistics (see the above
%            reference). default widnow is Gaussian given by
%            window = fspecial('gaussian', 11, 1.5);
%        (5) L: dynamic range of the images. default: L = 255
%
%Output: (1) mssim: the mean SSIM index value between 2 images.
%            If one of the images being compared is regarded as 
%            perfect quality, then mssim can be considered as the
%            quality measure of the other image.
%            If img1 = img2, then mssim = 1.
%        (2) ssim_map: the SSIM index map of the test image. The map
%            has a smaller size than the input images. The actual size:
%            size(img1) - size(window) + 1.
%
%Default Usage:
%   Given 2 test images img1 and img2, whose dynamic range is 0-255
%
%   [mssim ssim_map] = ssim_index(img1, img2);
%
%Advanced Usage:
%   User defined parameters. For example
%
%   K = [0.05 0.05];
%   window = ones(8);
%   L = 100;
%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
%
%See the results:
%
%   mssim                        %Gives the mssim value
%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
%
%========================================================================


if (nargin < 2 || nargin > 5)
   ssim_index = -Inf;
   ssim_map = -Inf;
   return;
end

if (size(img1) ~= size(img2))
   ssim_index = -Inf;
   ssim_map = -Inf;
   return;
end

[M N] = size(img1);

if (nargin == 2)
   if ((M < 11) || (N < 11))
	   ssim_index = -Inf;
	   ssim_map = -Inf;
      return
   end
   window = fspecial('gaussian', 11, 1.5);	%
   K(1) = 0.01;								      % default settings
   K(2) = 0.03;								      %
   L = 255;                                  %
end

if (nargin == 3)
   if ((M < 11) || (N < 11))
	   ssim_index = -Inf;
	   ssim_map = -Inf;
      return
   end
   window = fspecial('gaussian', 11, 1.5);
   L = 255;
   if (length(K) == 2)
      if (K(1) < 0 || K(2) < 0)
		   ssim_index = -Inf;
   		ssim_map = -Inf;
	   	return;
      end
   else
	   ssim_index = -Inf;
   	ssim_map = -Inf;
	   return;
   end
end

if (nargin == 4)
   [H W] = size(window);
   if ((H*W) < 4 || (H > M) || (W > N))
	   ssim_index = -Inf;
	   ssim_map = -Inf;
      return
   end
   L = 255;
   if (length(K) == 2)
      if (K(1) < 0 || K(2) < 0)
		   ssim_index = -Inf;
   		ssim_map = -Inf;
	   	return;
      end
   else
	   ssim_index = -Inf;
   	ssim_map = -Inf;
	   return;
   end
end

if (nargin == 5)
   [H W] = size(window);
   if ((H*W) < 4 || (H > M) || (W > N))
	   ssim_index = -Inf;
	   ssim_map = -Inf;
      return
   end
   if (length(K) == 2)
      if (K(1) < 0 || K(2) < 0)
		   ssim_index = -Inf;
   		ssim_map = -Inf;
	   	return;
      end
   else
	   ssim_index = -Inf;
   	ssim_map = -Inf;
	   return;
   end
end

C1 = (K(1)*L)^2;
C2 = (K(2)*L)^2;
window = window/sum(sum(window));
img1 = double(img1);
img2 = double(img2);

mu1   = filter2(window, img1, 'valid');
mu2   = filter2(window, img2, 'valid');
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;

if (C1 > 0 & C2 > 0)
   ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
else
   numerator1 = 2*mu1_mu2 + C1;
   numerator2 = 2*sigma12 + C2;
	denominator1 = mu1_sq + mu2_sq + C1;
   denominator2 = sigma1_sq + sigma2_sq + C2;
   ssim_map = ones(size(mu1));
   index = (denominator1.*denominator2 > 0);
   ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
   index = (denominator1 ~= 0) & (denominator2 == 0);
   ssim_map(index) = numerator1(index)./denominator1(index);
end

mssim = mean2(ssim_map);

end



================================================
FILE: Deraining/Deraining/test.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881



import numpy as np
import os
import argparse
from tqdm import tqdm

import torch.nn as nn
import torch
import torch.nn.functional as F
import utils

from natsort import natsorted
from glob import glob
from basicsr.models.archs.restormer_arch import Restormer
from skimage import img_as_ubyte
from pdb import set_trace as stx

parser = argparse.ArgumentParser(description='Image Deraining using Restormer')

parser.add_argument('--input_dir', default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/', type=str, help='Directory of validation images')
parser.add_argument('--result_dir', default='./results/restormer', type=str, help='Directory for results')
parser.add_argument('--weights', default='/mnt/bn/shiyuan-arnold/code/Mamber/Restormer/experiments/deraining.pth', type=str, help='Path to weights')

args = parser.parse_args()

####### Load yaml #######
yaml_file = 'Options/Deraining_Restormer.yml'
import yaml

try:
    from yaml import CLoader as Loader
except ImportError:
    from yaml import Loader

x = yaml.load(open(yaml_file, mode='r'), Loader=Loader)

s = x['network_g'].pop('type')
##########################

model_restoration = Restormer(**x['network_g'])

checkpoint = torch.load(args.weights)
model_restoration.load_state_dict(checkpoint['params'])
print("===>Testing using weights: ",args.weights)
model_restoration.cuda()
model_restoration = nn.DataParallel(model_restoration)
model_restoration.eval()


factor = 8
datasets = ['Rain100L', 'Rain100H', 'Test100', 'Test1200', 'Test2800']

for dataset in datasets:
    result_dir  = os.path.join(args.result_dir, dataset)
    os.makedirs(result_dir, exist_ok=True)

    inp_dir = os.path.join(args.input_dir, 'test', dataset, 'input')
    files = natsorted(glob(os.path.join(inp_dir, '*.png')) + glob(os.path.join(inp_dir, '*.jpg')))
    with torch.no_grad():
        for file_ in tqdm(files):
            torch.cuda.ipc_collect()
            torch.cuda.empty_cache()

            img = np.float32(utils.load_img(file_))/255.
            img = torch.from_numpy(img).permute(2,0,1)
            input_ = img.unsqueeze(0).cuda()

            # Padding in case images are not multiples of 8
            h,w = input_.shape[2], input_.shape[3]
            H,W = ((h+factor)//factor)*factor, ((w+factor)//factor)*factor
            padh = H-h if h%factor!=0 else 0
            padw = W-w if w%factor!=0 else 0
            input_ = F.pad(input_, (0,padw,0,padh), 'reflect')

            restored = model_restoration(input_)

            # Unpad images to original dimensions
            restored = restored[:,:,:h,:w]

            restored = torch.clamp(restored,0,1).cpu().detach().permute(0, 2, 3, 1).squeeze(0).numpy()

            utils.save_img((os.path.join(result_dir, os.path.splitext(os.path.split(file_)[-1])[0]+'.png')), img_as_ubyte(restored))


================================================
FILE: Deraining/Deraining/utils.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881

import numpy as np
import os
import cv2
import math

def calculate_psnr(img1, img2, border=0):
    # img1 and img2 have range [0, 255]
    #img1 = img1.squeeze()
    #img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    h, w = img1.shape[:2]
    img1 = img1[border:h-border, border:w-border]
    img2 = img2[border:h-border, border:w-border]

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))


# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
    '''calculate SSIM
    the same outputs as MATLAB's
    img1, img2: [0, 255]
    '''
    #img1 = img1.squeeze()
    #img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    h, w = img1.shape[:2]
    img1 = img1[border:h-border, border:w-border]
    img2 = img2[border:h-border, border:w-border]

    if img1.ndim == 2:
        return ssim(img1, img2)
    elif img1.ndim == 3:
        if img1.shape[2] == 3:
            ssims = []
            for i in range(3):
                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
            return np.array(ssims).mean()
        elif img1.shape[2] == 1:
            return ssim(np.squeeze(img1), np.squeeze(img2))
    else:
        raise ValueError('Wrong input image dimensions.')


def ssim(img1, img2):
    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                            (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()

def load_img(filepath):
    return cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB)

def save_img(filepath, img):
    cv2.imwrite(filepath,cv2.cvtColor(img, cv2.COLOR_RGB2BGR))

def load_gray_img(filepath):
    return np.expand_dims(cv2.imread(filepath, cv2.IMREAD_GRAYSCALE), axis=2)

def save_gray_img(filepath, img):
    cv2.imwrite(filepath, img)


================================================
FILE: Deraining/Deraining_test.sh
================================================
CUDA_VISIBLE_DEVICES=2 \
python3 ./basicsr/test_deraining.py

================================================
FILE: Deraining/Deraining_train.sh
================================================
bash ./train.sh Deraining/Options/Deraining_mamber34.yml

================================================
FILE: Deraining/INSTALL.md
================================================
# Installation

This repository is built in PyTorch 1.8.1 and tested on Ubuntu 16.04 environment (Python3.7, CUDA10.2, cuDNN7.6).
Follow these intructions

1. Clone our repository
```
git clone https://github.com/swz30/Restormer.git
cd Restormer
```

2. Make conda environment
```
conda create -n pytorch181 python=3.7
conda activate pytorch181
```

3. Install dependencies
```
conda install pytorch=1.8 torchvision cudatoolkit=10.2 -c pytorch
pip install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm
pip install einops gdown addict future lmdb numpy pyyaml requests scipy tb-nightly yapf lpips
```

4. Install basicsr
```
python setup.py develop --no_cuda_ext
```

### Download datasets from Google Drive

To be able to download datasets automatically you would need `go` and `gdrive` installed. 

1. You can install `go` with the following
```
curl -O https://storage.googleapis.com/golang/go1.11.1.linux-amd64.tar.gz
mkdir -p ~/installed
tar -C ~/installed -xzf go1.11.1.linux-amd64.tar.gz
mkdir -p ~/go
```

2. Add the lines in `~/.bashrc`
```
export GOPATH=$HOME/go
export PATH=$PATH:$HOME/go/bin:$HOME/installed/go/bin
```

3. Install `gdrive` using
```
go get github.com/prasmussen/gdrive
```

4. Close current terminal and open a new terminal. 


================================================
FILE: Deraining/LICENSE.md
================================================
## ACADEMIC PUBLIC LICENSE

### Permissions
:heavy_check_mark: Non-Commercial use
:heavy_check_mark: Modification
:heavy_check_mark: Distribution
:heavy_check_mark: Private use

### Limitations
:x: Commercial Use
:x: Liability
:x: Warranty

### Conditions
:information_source: License and copyright notice
:information_source: Same License

Restormer is free for use in noncommercial settings: at academic institutions for teaching and research use, and at non-profit research organizations.
You can use Restormer in your research, academic work, non-commercial work, projects and personal work. We only ask you to credit us appropriately. 

You have the right to use the software, to distribute copies, to receive source code, to change the software and distribute your modifications or the modified software.
If you distribute verbatim or modified copies of this software, they must be distributed under this license.
This license guarantees that you're safe when using Restormer in your work, for teaching or research.
This license guarantees that Restormer will remain available free of charge for nonprofit use.
You can modify Restormer to your purposes, and you can also share your modifications.

If you would like to use Restormer in commercial settings, contact us so we can discuss options. Send an email to waqas.zamir@inceptioniai.org




================================================
FILE: Deraining/VERSION
================================================
1.2.0


================================================
FILE: Deraining/basicsr/data/__init__.py
================================================
import importlib
import numpy as np
import random
import torch
import torch.utils.data
from functools import partial
from os import path as osp

from data.prefetch_dataloader import PrefetchDataLoader
from utils import get_root_logger, scandir
from utils.dist_util import get_dist_info

__all__ = ['create_dataset', 'create_dataloader']

# automatically scan and import dataset modules
# scan all the files under the data folder with '_dataset' in file names
data_folder = osp.dirname(osp.abspath(__file__))
dataset_filenames = [
    osp.splitext(osp.basename(v))[0] for v in scandir(data_folder)
    if v.endswith('_dataset.py')
]
# import all the dataset modules
_dataset_modules = [
    importlib.import_module(f'data.{file_name}')
    for file_name in dataset_filenames
]


def create_dataset(dataset_opt):
    """Create dataset.

    Args:
        dataset_opt (dict): Configuration for dataset. It constains:
            name (str): Dataset name.
            type (str): Dataset type.
    """
    dataset_type = dataset_opt['type']

    # dynamic instantiation
    for module in _dataset_modules:
        dataset_cls = getattr(module, dataset_type, None)
        if dataset_cls is not None:
            break
    if dataset_cls is None:
        raise ValueError(f'Dataset {dataset_type} is not found.')

    dataset = dataset_cls(dataset_opt)

    logger = get_root_logger()
    logger.info(
        f'Dataset {dataset.__class__.__name__} - {dataset_opt["name"]} '
        'is created.')
    return dataset


def create_dataloader(dataset,
                      dataset_opt,
                      num_gpu=1,
                      dist=False,
                      sampler=None,
                      seed=None):
    """Create dataloader.

    Args:
        dataset (torch.utils.data.Dataset): Dataset.
        dataset_opt (dict): Dataset options. It contains the following keys:
            phase (str): 'train' or 'val'.
            num_worker_per_gpu (int): Number of workers for each GPU.
            batch_size_per_gpu (int): Training batch size for each GPU.
        num_gpu (int): Number of GPUs. Used only in the train phase.
            Default: 1.
        dist (bool): Whether in distributed training. Used only in the train
            phase. Default: False.
        sampler (torch.utils.data.sampler): Data sampler. Default: None.
        seed (int | None): Seed. Default: None
    """
    phase = dataset_opt['phase']
    rank, _ = get_dist_info()
    if phase == 'train':
        if dist:  # distributed training
            batch_size = dataset_opt['batch_size_per_gpu']
            num_workers = dataset_opt['num_worker_per_gpu']
        else:  # non-distributed training
            multiplier = 1 if num_gpu == 0 else num_gpu
            batch_size = dataset_opt['batch_size_per_gpu'] * multiplier
            num_workers = dataset_opt['num_worker_per_gpu'] * multiplier
        dataloader_args = dict(
            dataset=dataset,
            batch_size=batch_size,
            shuffle=False,
            num_workers=num_workers,
            sampler=sampler,
            drop_last=True)
        if sampler is None:
            dataloader_args['shuffle'] = True
        dataloader_args['worker_init_fn'] = partial(
            worker_init_fn, num_workers=num_workers, rank=rank,
            seed=seed) if seed is not None else None
    elif phase in ['val', 'test']:  # validation
        dataloader_args = dict(
            dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
    else:
        raise ValueError(f'Wrong dataset phase: {phase}. '
                         "Supported ones are 'train', 'val' and 'test'.")

    dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)

    prefetch_mode = dataset_opt.get('prefetch_mode')
    if prefetch_mode == 'cpu':  # CPUPrefetcher
        num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1)
        logger = get_root_logger()
        logger.info(f'Use {prefetch_mode} prefetch dataloader: '
                    f'num_prefetch_queue = {num_prefetch_queue}')
        return PrefetchDataLoader(
            num_prefetch_queue=num_prefetch_queue, **dataloader_args)
    else:
        # prefetch_mode=None: Normal dataloader
        # prefetch_mode='cuda': dataloader for CUDAPrefetcher
        return torch.utils.data.DataLoader(**dataloader_args)


def worker_init_fn(worker_id, num_workers, rank, seed):
    # Set the worker seed to num_workers * rank + worker_id + seed
    worker_seed = num_workers * rank + worker_id + seed
    np.random.seed(worker_seed)
    random.seed(worker_seed)


================================================
FILE: Deraining/basicsr/data/data_sampler.py
================================================
import math
import torch
from torch.utils.data.sampler import Sampler


class EnlargedSampler(Sampler):
    """Sampler that restricts data loading to a subset of the dataset.

    Modified from torch.utils.data.distributed.DistributedSampler
    Support enlarging the dataset for iteration-based training, for saving
    time when restart the dataloader after each epoch

    Args:
        dataset (torch.utils.data.Dataset): Dataset used for sampling.
        num_replicas (int | None): Number of processes participating in
            the training. It is usually the world_size.
        rank (int | None): Rank of the current process within num_replicas.
        ratio (int): Enlarging ratio. Default: 1.
    """

    def __init__(self, dataset, num_replicas, rank, ratio=1):
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.epoch = 0
        self.num_samples = math.ceil(
            len(self.dataset) * ratio / self.num_replicas)
        self.total_size = self.num_samples * self.num_replicas

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)
        indices = torch.randperm(self.total_size, generator=g).tolist()

        dataset_size = len(self.dataset)
        indices = [v % dataset_size for v in indices]

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

    def set_epoch(self, epoch):
        self.epoch = epoch


================================================
FILE: Deraining/basicsr/data/data_util.py
================================================
import cv2
cv2.setNumThreads(1)
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F

from data.transforms import mod_crop
from utils import img2tensor, scandir


def read_img_seq(path, require_mod_crop=False, scale=1):
    """Read a sequence of images from a given folder path.

    Args:
        path (list[str] | str): List of image paths or image folder path.
        require_mod_crop (bool): Require mod crop for each image.
            Default: False.
        scale (int): Scale factor for mod_crop. Default: 1.

    Returns:
        Tensor: size (t, c, h, w), RGB, [0, 1].
    """
    if isinstance(path, list):
        img_paths = path
    else:
        img_paths = sorted(list(scandir(path, full_path=True)))
    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
    if require_mod_crop:
        imgs = [mod_crop(img, scale) for img in imgs]
    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
    imgs = torch.stack(imgs, dim=0)
    return imgs


def generate_frame_indices(crt_idx,
                           max_frame_num,
                           num_frames,
                           padding='reflection'):
    """Generate an index list for reading `num_frames` frames from a sequence
    of images.

    Args:
        crt_idx (int): Current center index.
        max_frame_num (int): Max number of the sequence of images (from 1).
        num_frames (int): Reading num_frames frames.
        padding (str): Padding mode, one of
            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
            Examples: current_idx = 0, num_frames = 5
            The generated frame indices under different padding mode:
            replicate: [0, 0, 0, 1, 2]
            reflection: [2, 1, 0, 1, 2]
            reflection_circle: [4, 3, 0, 1, 2]
            circle: [3, 4, 0, 1, 2]

    Returns:
        list[int]: A list of indices.
    """
    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
    assert padding in ('replicate', 'reflection', 'reflection_circle',
                       'circle'), f'Wrong padding mode: {padding}.'

    max_frame_num = max_frame_num - 1  # start from 0
    num_pad = num_frames // 2

    indices = []
    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
        if i < 0:
            if padding == 'replicate':
                pad_idx = 0
            elif padding == 'reflection':
                pad_idx = -i
            elif padding == 'reflection_circle':
                pad_idx = crt_idx + num_pad - i
            else:
                pad_idx = num_frames + i
        elif i > max_frame_num:
            if padding == 'replicate':
                pad_idx = max_frame_num
            elif padding == 'reflection':
                pad_idx = max_frame_num * 2 - i
            elif padding == 'reflection_circle':
                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
            else:
                pad_idx = i - num_frames
        else:
            pad_idx = i
        indices.append(pad_idx)
    return indices


def paired_paths_from_lmdb(folders, keys):
    """Generate paired paths from lmdb files.

    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:

    lq.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records
    1)image name (with extension),
    2)image shape,
    3)compression level, separated by a white space.
    Example: `baboon.png (120,125,3) 1`

    We use the image name without extension as the lmdb key.
    Note that we use the same key for the corresponding lq and gt images.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
            Note that this key is different from lmdb keys.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
        raise ValueError(
            f'{input_key} folder and {gt_key} folder should both in lmdb '
            f'formats. But received {input_key}: {input_folder}; '
            f'{gt_key}: {gt_folder}')
    # ensure that the two meta_info files are the same
    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
        input_lmdb_keys = [line.split('.')[0] for line in fin]
    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
        gt_lmdb_keys = [line.split('.')[0] for line in fin]
    if set(input_lmdb_keys) != set(gt_lmdb_keys):
        raise ValueError(
            f'Keys in {input_key}_folder and {gt_key}_folder are different.')
    else:
        paths = []
        for lmdb_key in sorted(input_lmdb_keys):
            paths.append(
                dict([(f'{input_key}_path', lmdb_key),
                      (f'{gt_key}_path', lmdb_key)]))
        return paths


def paired_paths_from_meta_info_file(folders, keys, meta_info_file,
                                     filename_tmpl):
    """Generate paired paths from an meta information file.

    Each line in the meta information file contains the image names and
    image shape (usually for gt), separated by a white space.

    Example of an meta information file:
    ```
    0001_s001.png (480,480,3)
    0001_s002.png (480,480,3)
    ```

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        meta_info_file (str): Path to the meta information file.
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    with open(meta_info_file, 'r') as fin:
        gt_names = [line.split(' ')[0] for line in fin]

    paths = []
    for gt_name in gt_names:
        basename, ext = osp.splitext(osp.basename(gt_name))
        input_name = f'{filename_tmpl.format(basename)}{ext}'
        input_path = osp.join(input_folder, input_name)
        gt_path = osp.join(gt_folder, gt_name)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paired_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    input_paths = list(scandir(input_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(input_paths) == len(gt_paths), (
        f'{input_key} and {gt_key} datasets have different number of images: '
        f'{len(input_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        input_path = input_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(input_path))
        input_name = f'{filename_tmpl.format(basename)}{ext_input}'
        input_path = osp.join(input_folder, input_name)
        assert input_name in input_paths, (f'{input_name} is not in '
                                           f'{input_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths

def paired_DP_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 3, (
        'The len of folders should be 3 with [inputL_folder, inputR_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 3, (
        'The len of keys should be 2 with [inputL_key, inputR_key, gt_key]. '
        f'But got {len(keys)}')
    inputL_folder, inputR_folder, gt_folder = folders
    inputL_key, inputR_key, gt_key = keys

    inputL_paths = list(scandir(inputL_folder))
    inputR_paths = list(scandir(inputR_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(inputL_paths) == len(inputR_paths) == len(gt_paths), (
        f'{inputL_key} and {inputR_key} and {gt_key} datasets have different number of images: '
        f'{len(inputL_paths)}, {len(inputR_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        inputL_path = inputL_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputL_path))
        inputL_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputL_path = osp.join(inputL_folder, inputL_name)
        assert inputL_name in inputL_paths, (f'{inputL_name} is not in '
                                           f'{inputL_key}_paths.')
        inputR_path = inputR_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputR_path))
        inputR_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputR_path = osp.join(inputR_folder, inputR_name)
        assert inputR_name in inputR_paths, (f'{inputR_name} is not in '
                                           f'{inputR_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{inputL_key}_path', inputL_path),
                  (f'{inputR_key}_path', inputR_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paths_from_folder(folder):
    """Generate paths from folder.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """

    paths = list(scandir(folder))
    paths = [osp.join(folder, path) for path in paths]
    return paths


def paths_from_lmdb(folder):
    """Generate paths from lmdb.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """
    if not folder.endswith('.lmdb'):
        raise ValueError(f'Folder {folder}folder should in lmdb format.')
    with open(osp.join(folder, 'meta_info.txt')) as fin:
        paths = [line.split('.')[0] for line in fin]
    return paths


def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
    """Generate Gaussian kernel used in `duf_downsample`.

    Args:
        kernel_size (int): Kernel size. Default: 13.
        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.

    Returns:
        np.array: The Gaussian kernel.
    """
    from scipy.ndimage import filters as filters
    kernel = np.zeros((kernel_size, kernel_size))
    # set element at the middle to one, a dirac delta
    kernel[kernel_size // 2, kernel_size // 2] = 1
    # gaussian-smooth the dirac, resulting in a gaussian filter
    return filters.gaussian_filter(kernel, sigma)


def duf_downsample(x, kernel_size=13, scale=4):
    """Downsamping with Gaussian kernel used in the DUF official code.

    Args:
        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
        kernel_size (int): Kernel size. Default: 13.
        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
            Default: 4.

    Returns:
        Tensor: DUF downsampled frames.
    """
    assert scale in (2, 3,
                     4), f'Only support scale (2, 3, 4), but got {scale}.'

    squeeze_flag = False
    if x.ndim == 4:
        squeeze_flag = True
        x = x.unsqueeze(0)
    b, t, c, h, w = x.size()
    x = x.view(-1, 1, h, w)
    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')

    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(
        0).unsqueeze(0)
    x = F.conv2d(x, gaussian_filter, stride=scale)
    x = x[:, :, 2:-2, 2:-2]
    x = x.view(b, t, c, x.size(2), x.size(3))
    if squeeze_flag:
        x = x.squeeze(0)
    return x


================================================
FILE: Deraining/basicsr/data/ffhq_dataset.py
================================================
from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from data.transforms import augment
from utils import FileClient, imfrombytes, img2tensor


class FFHQDataset(data.Dataset):
    """FFHQ dataset for StyleGAN.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            io_backend (dict): IO backend type and other kwarg.
            mean (list | tuple): Image mean.
            std (list | tuple): Image std.
            use_hflip (bool): Whether to horizontally flip.

    """

    def __init__(self, opt):
        super(FFHQDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']

        self.gt_folder = opt['dataroot_gt']
        self.mean = opt['mean']
        self.std = opt['std']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = self.gt_folder
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError("'dataroot_gt' should end with '.lmdb', "
                                 f'but received {self.gt_folder}')
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # FFHQ has 70000 images in total
            self.paths = [
                osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)
            ]

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # load gt image
        gt_path = self.paths[index]
        img_bytes = self.file_client.get(gt_path)
        img_gt = imfrombytes(img_bytes, float32=True)

        # random horizontal flip
        img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True)
        # normalize
        normalize(img_gt, self.mean, self.std, inplace=True)
        return {'gt': img_gt, 'gt_path': gt_path}

    def __len__(self):
        return len(self.paths)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt
================================================
0001_s001.png (480,480,3)
0001_s002.png (480,480,3)
0001_s003.png (480,480,3)
0001_s004.png (480,480,3)
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0800_s038.png (480,480,3)
0800_s039.png (480,480,3)
0800_s040.png (480,480,3)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
================================================
000 100 (720,1280,3)
011 100 (720,1280,3)
015 100 (720,1280,3)
020 100 (720,1280,3)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_REDS_GT.txt
================================================
000 100 (720,1280,3)
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267 100 (720,1280,3)
268 100 (720,1280,3)
269 100 (720,1280,3)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt
================================================
240 100 (720,1280,3)
241 100 (720,1280,3)
246 100 (720,1280,3)
257 100 (720,1280,3)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt
================================================
240 100 (720,1280,3)
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================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_Vimeo90K_test_GT.txt
================================================
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00096/0865 7 (256,448,3)
00096/0866 7 (256,448,3)


================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_Vimeo90K_test_fast_GT.txt
================================================
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================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_Vimeo90K_test_medium_GT.txt
================================================
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================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_Vimeo90K_test_slow_GT.txt
================================================
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================================================
FILE: Deraining/basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
================================================
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================================================
FILE: Deraining/basicsr/data/paired_image_dataset.py
================================================
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from data.data_util import (paired_paths_from_folder,
                                    paired_DP_paths_from_folder,
                                    paired_paths_from_lmdb,
                                    paired_paths_from_meta_info_file)
from data.transforms import augment, paired_random_crop, paired_random_crop_DP, random_augmentation
from utils import FileClient, imfrombytes, img2tensor, padding, padding_DP, imfrombytesDP

import random
import numpy as np
import torch
import cv2

class Dataset_PairedImage(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the
                template excludes the file extension. Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            geometric_augs (bool): Use geometric augmentations.

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(Dataset_PairedImage, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info_file' in self.opt and self.opt[
                'meta_info_file'] is not None:
            self.paths = paired_paths_from_meta_info_file(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.opt['meta_info_file'], self.filename_tmpl)
        else:
            self.paths = paired_paths_from_folder(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        try:
            img_lq = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("lq path {} not working".format(lq_path))

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)

            # flip, rotation augmentations
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)
            
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lq_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_GaussianDenoising(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            gt_size (int): Cropped patched size for gt patches.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(Dataset_GaussianDenoising, self).__init__()
        self.opt = opt

        if self.opt['phase'] == 'train':
            self.sigma_type  = opt['sigma_type']
            self.sigma_range = opt['sigma_range']
            assert self.sigma_type in ['constant', 'random', 'choice']
        else:
            self.sigma_test = opt['sigma_test']
        self.in_ch = opt['in_ch']

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None        

        self.gt_folder = opt['dataroot_gt']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            self.paths = paths_from_lmdb(self.gt_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.gt_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.gt_folder, full_path=True)))

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')

        if self.in_ch == 3:
            try:
                img_gt = imfrombytes(img_bytes, float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2RGB)
        else:
            try:
                img_gt = imfrombytes(img_bytes, flag='grayscale', float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = img_gt.copy()


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)
            # flip, rotation
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                                        bgr2rgb=False,
                                        float32=True)


            if self.sigma_type == 'constant':
                sigma_value = self.sigma_range
            elif self.sigma_type == 'random':
                sigma_value = random.uniform(self.sigma_range[0], self.sigma_range[1])
            elif self.sigma_type == 'choice':
                sigma_value = random.choice(self.sigma_range)

            noise_level = torch.FloatTensor([sigma_value])/255.0
            # noise_level_map = torch.ones((1, img_lq.size(1), img_lq.size(2))).mul_(noise_level).float()
            noise = torch.randn(img_lq.size()).mul_(noise_level).float()
            img_lq.add_(noise)

        else:            
            np.random.seed(seed=0)
            img_lq += np.random.normal(0, self.sigma_test/255.0, img_lq.shape)
            # noise_level_map = torch.ones((1, img_lq.shape[0], img_lq.shape[1])).mul_(self.sigma_test/255.0).float()

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                            bgr2rgb=False,
                            float32=True)

        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': gt_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_DefocusDeblur_DualPixel_16bit(data.Dataset):
    def __init__(self, opt):
        super(Dataset_DefocusDeblur_DualPixel_16bit, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lqL_folder, self.lqR_folder = opt['dataroot_gt'], opt['dataroot_lqL'], opt['dataroot_lqR']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        self.paths = paired_DP_paths_from_folder(
            [self.lqL_folder, self.lqR_folder, self.gt_folder], ['lqL', 'lqR', 'gt'],
            self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lqL_path = self.paths[index]['lqL_path']
        img_bytes = self.file_client.get(lqL_path, 'lqL')
        try:
            img_lqL = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqL path {} not working".format(lqL_path))

        lqR_path = self.paths[index]['lqR_path']
        img_bytes = self.file_client.get(lqR_path, 'lqR')
        try:
            img_lqR = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqR path {} not working".format(lqR_path))


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_lqL, img_lqR, img_gt = padding_DP(img_lqL, img_lqR, img_gt, gt_size)

            # random crop
            img_lqL, img_lqR, img_gt = paired_random_crop_DP(img_lqL, img_lqR, img_gt, gt_size, scale, gt_path)
            
            # flip, rotation            
            if self.geometric_augs:
                img_lqL, img_lqR, img_gt = random_augmentation(img_lqL, img_lqR, img_gt)
        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lqL, img_lqR, img_gt = img2tensor([img_lqL, img_lqR, img_gt],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lqL, self.mean, self.std, inplace=True)
            normalize(img_lqR, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        img_lq = torch.cat([img_lqL, img_lqR], 0)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lqL_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)


================================================
FILE: Deraining/basicsr/data/prefetch_dataloader.py
================================================
import queue as Queue
import threading
import torch
from torch.utils.data import DataLoader


class PrefetchGenerator(threading.Thread):
    """A general prefetch generator.

    Ref:
    https://stackoverflow.com/questions/7323664/python-generator-pre-fetch

    Args:
        generator: Python generator.
        num_prefetch_queue (int): Number of prefetch queue.
    """

    def __init__(self, generator, num_prefetch_queue):
        threading.Thread.__init__(self)
        self.queue = Queue.Queue(num_prefetch_queue)
        self.generator = generator
        self.daemon = True
        self.start()

    def run(self):
        for item in self.generator:
            self.queue.put(item)
        self.queue.put(None)

    def __next__(self):
        next_item = self.queue.get()
        if next_item is None:
            raise StopIteration
        return next_item

    def __iter__(self):
        return self


class PrefetchDataLoader(DataLoader):
    """Prefetch version of dataloader.

    Ref:
    https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#

    TODO:
    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
    ddp.

    Args:
        num_prefetch_queue (int): Number of prefetch queue.
        kwargs (dict): Other arguments for dataloader.
    """

    def __init__(self, num_prefetch_queue, **kwargs):
        self.num_prefetch_queue = num_prefetch_queue
        super(PrefetchDataLoader, self).__init__(**kwargs)

    def __iter__(self):
        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)


class CPUPrefetcher():
    """CPU prefetcher.

    Args:
        loader: Dataloader.
    """

    def __init__(self, loader):
        self.ori_loader = loader
        self.loader = iter(loader)

    def next(self):
        try:
            return next(self.loader)
        except StopIteration:
            return None

    def reset(self):
        self.loader = iter(self.ori_loader)


class CUDAPrefetcher():
    """CUDA prefetcher.

    Ref:
    https://github.com/NVIDIA/apex/issues/304#

    It may consums more GPU memory.

    Args:
        loader: Dataloader.
        opt (dict): Options.
    """

    def __init__(self, loader, opt):
        self.ori_loader = loader
        self.loader = iter(loader)
        self.opt = opt
        self.stream = torch.cuda.Stream()
        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
        self.preload()

    def preload(self):
        try:
            self.batch = next(self.loader)  # self.batch is a dict
        except StopIteration:
            self.batch = None
            return None
        # put tensors to gpu
        with torch.cuda.stream(self.stream):
            for k, v in self.batch.items():
                if torch.is_tensor(v):
                    self.batch[k] = self.batch[k].to(
                        device=self.device, non_blocking=True)

    def next(self):
        torch.cuda.current_stream().wait_stream(self.stream)
        batch = self.batch
        self.preload()
        return batch

    def reset(self):
        self.loader = iter(self.ori_loader)
        self.preload()


================================================
FILE: Deraining/basicsr/data/reds_dataset.py
================================================
import numpy as np
import random
import torch
from pathlib import Path
from torch.utils import data as data

from data.transforms import augment, paired_random_crop
from utils import FileClient, get_root_logger, imfrombytes, img2tensor
from utils.flow_util import dequantize_flow


class REDSDataset(data.Dataset):
    """REDS dataset for training.

    The keys are generated from a meta info txt file.
    basicsr/data/meta_info/meta_info_REDS_GT.txt

    Each line contains:
    1. subfolder (clip) name; 2. frame number; 3. image shape, seperated by
    a white space.
    Examples:
    000 100 (720,1280,3)
    001 100 (720,1280,3)
    ...

    Key examples: "000/00000000"
    GT (gt): Ground-Truth;
    LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.

    Args:
        opt (dict): Config for train dataset. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            dataroot_flow (str, optional): Data root path for flow.
            meta_info_file (str): Path for meta information file.
            val_partition (str): Validation partition types. 'REDS4' or
                'official'.
            io_backend (dict): IO backend type and other kwarg.

            num_frame (int): Window size for input frames.
            gt_size (int): Cropped patched size for gt patches.
            interval_list (list): Interval list for temporal augmentation.
            random_reverse (bool): Random reverse input frames.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
    """

    def __init__(self, opt):
        super(REDSDataset, self).__init__()
        self.opt = opt
        self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(
            opt['dataroot_lq'])
        self.flow_root = Path(
            opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None
        assert opt['num_frame'] % 2 == 1, (
            f'num_frame should be odd number, but got {opt["num_frame"]}')
        self.num_frame = opt['num_frame']
        self.num_half_frames = opt['num_frame'] // 2

        self.keys = []
        with open(opt['meta_info_file'], 'r') as fin:
            for line in fin:
                folder, frame_num, _ = line.split(' ')
                self.keys.extend(
                    [f'{folder}/{i:08d}' for i in range(int(frame_num))])

        # remove the video clips used in validation
        if opt['val_partition'] == 'REDS4':
            val_partition = ['000', '011', '015', '020']
        elif opt['val_partition'] == 'official':
            val_partition = [f'{v:03d}' for v in range(240, 270)]
        else:
            raise ValueError(
                f'Wrong validation partition {opt["val_partition"]}.'
                f"Supported ones are ['official', 'REDS4'].")
        self.keys = [
            v for v in self.keys if v.split('/')[0] not in val_partition
        ]

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.is_lmdb = False
        if self.io_backend_opt['type'] == 'lmdb':
            self.is_lmdb = True
            if self.flow_root is not None:
                self.io_backend_opt['db_paths'] = [
                    self.lq_root, self.gt_root, self.flow_root
                ]
                self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow']
            else:
                self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
                self.io_backend_opt['client_keys'] = ['lq', 'gt']

        # temporal augmentation configs
        self.interval_list = opt['interval_list']
        self.random_reverse = opt['random_reverse']
        interval_str = ','.join(str(x) for x in opt['interval_list'])
        logger = get_root_logger()
        logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
                    f'random reverse is {self.random_reverse}.')

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        gt_size = self.opt['gt_size']
        key = self.keys[index]
        clip_name, frame_name = key.split('/')  # key example: 000/00000000
        center_frame_idx = int(frame_name)

        # determine the neighboring frames
        interval = random.choice(self.interval_list)

        # ensure not exceeding the borders
        start_frame_idx = center_frame_idx - self.num_half_frames * interval
        end_frame_idx = center_frame_idx + self.num_half_frames * interval
        # each clip has 100 frames starting from 0 to 99
        while (start_frame_idx < 0) or (end_frame_idx > 99):
            center_frame_idx = random.randint(0, 99)
            start_frame_idx = (
                center_frame_idx - self.num_half_frames * interval)
            end_frame_idx = center_frame_idx + self.num_half_frames * interval
        frame_name = f'{center_frame_idx:08d}'
        neighbor_list = list(
            range(center_frame_idx - self.num_half_frames * interval,
                  center_frame_idx + self.num_half_frames * interval + 1,
                  interval))
        # random reverse
        if self.random_reverse and random.random() < 0.5:
            neighbor_list.reverse()

        assert len(neighbor_list) == self.num_frame, (
            f'Wrong length of neighbor list: {len(neighbor_list)}')

        # get the GT frame (as the center frame)
        if self.is_lmdb:
            img_gt_path = f'{clip_name}/{frame_name}'
        else:
            img_gt_path = self.gt_root / clip_name / f'{frame_name}.png'
        img_bytes = self.file_client.get(img_gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)

        # get the neighboring LQ frames
        img_lqs = []
        for neighbor in neighbor_list:
            if self.is_lmdb:
                img_lq_path = f'{clip_name}/{neighbor:08d}'
            else:
                img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png'
            img_bytes = self.file_client.get(img_lq_path, 'lq')
            img_lq = imfrombytes(img_bytes, float32=True)
            img_lqs.append(img_lq)

        # get flows
        if self.flow_root is not None:
            img_flows = []
            # read previous flows
            for i in range(self.num_half_frames, 0, -1):
                if self.is_lmdb:
                    flow_path = f'{clip_name}/{frame_name}_p{i}'
                else:
                    flow_path = (
                        self.flow_root / clip_name / f'{frame_name}_p{i}.png')
                img_bytes = self.file_client.get(flow_path, 'flow')
                cat_flow = imfrombytes(
                    img_bytes, flag='grayscale',
                    float32=False)  # uint8, [0, 255]
                dx, dy = np.split(cat_flow, 2, axis=0)
                flow = dequantize_flow(
                    dx, dy, max_val=20,
                    denorm=False)  # we use max_val 20 here.
                img_flows.append(flow)
            # read next flows
            for i in range(1, self.num_half_frames + 1):
                if self.is_lmdb:
                    flow_path = f'{clip_name}/{frame_name}_n{i}'
                else:
                    flow_path = (
                        self.flow_root / clip_name / f'{frame_name}_n{i}.png')
                img_bytes = self.file_client.get(flow_path, 'flow')
                cat_flow = imfrombytes(
                    img_bytes, flag='grayscale',
                    float32=False)  # uint8, [0, 255]
                dx, dy = np.split(cat_flow, 2, axis=0)
                flow = dequantize_flow(
                    dx, dy, max_val=20,
                    denorm=False)  # we use max_val 20 here.
                img_flows.append(flow)

            # for random crop, here, img_flows and img_lqs have the same
            # spatial size
            img_lqs.extend(img_flows)

        # randomly crop
        img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
                                             img_gt_path)
        if self.flow_root is not None:
            img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.
                                                                   num_frame:]

        # augmentation - flip, rotate
        img_lqs.append(img_gt)
        if self.flow_root is not None:
            img_results, img_flows = augment(img_lqs, self.opt['use_flip'],
                                             self.opt['use_rot'], img_flows)
        else:
            img_results = augment(img_lqs, self.opt['use_flip'],
                                  self.opt['use_rot'])

        img_results = img2tensor(img_results)
        img_lqs = torch.stack(img_results[0:-1], dim=0)
        img_gt = img_results[-1]

        if self.flow_root is not None:
            img_flows = img2tensor(img_flows)
            # add the zero center flow
            img_flows.insert(self.num_half_frames,
                             torch.zeros_like(img_flows[0]))
            img_flows = torch.stack(img_flows, dim=0)

        # img_lqs: (t, c, h, w)
        # img_flows: (t, 2, h, w)
        # img_gt: (c, h, w)
        # key: str
        if self.flow_root is not None:
            return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key}
        else:
            return {'lq': img_lqs, 'gt': img_gt, 'key': key}

    def __len__(self):
        return len(self.keys)


================================================
FILE: Deraining/basicsr/data/single_image_dataset.py
================================================
from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from data.data_util import paths_from_lmdb
from utils import FileClient, imfrombytes, img2tensor, scandir


class SingleImageDataset(data.Dataset):
    """Read only lq images in the test phase.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc).

    There are two modes:
    1. 'meta_info_file': Use meta information file to generate paths.
    2. 'folder': Scan folders to generate paths.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_lq (str): Data root path for lq.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
    """

    def __init__(self, opt):
        super(SingleImageDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        self.lq_folder = opt['dataroot_lq']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder]
            self.io_backend_opt['client_keys'] = ['lq']
            self.paths = paths_from_lmdb(self.lq_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.lq_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.lq_folder, full_path=True)))

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # load lq image
        lq_path = self.paths[index]
        img_bytes = self.file_client.get(lq_path, 'lq')
        img_lq = imfrombytes(img_bytes, float32=True)

        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
        return {'lq': img_lq, 'lq_path': lq_path}

    def __len__(self):
        return len(self.paths)


================================================
FILE: Deraining/basicsr/data/transforms.py
================================================
import cv2
import random
import numpy as np

def mod_crop(img, scale):
    """Mod crop images, used during testing.

    Args:
        img (ndarray): Input image.
        scale (int): Scale factor.

    Returns:
        ndarray: Result image.
    """
    img = img.copy()
    if img.ndim in (2, 3):
        h, w = img.shape[0], img.shape[1]
        h_remainder, w_remainder = h % scale, w % scale
        img = img[:h - h_remainder, :w - w_remainder, ...]
    else:
        raise ValueError(f'Wrong img ndim: {img.ndim}.')
    return img

def paired_random_crop(img_gts, img_lqs, lq_patch_size, scale, gt_path):
    """Paired random crop.

    It crops lists of lq and gt images with corresponding locations.

    Args:
        img_gts (list[ndarray] | ndarray): GT images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        lq_patch_size (int): LQ patch size.
        scale (int): Scale factor.
        gt_path (str): Path to ground-truth.

    Returns:
        list[ndarray] | ndarray: GT images and LQ images. If returned results
            only have one element, just return ndarray.
    """

    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqs, list):
        img_lqs = [img_lqs]

    h_lq, w_lq, _ = img_lqs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    gt_patch_size = int(lq_patch_size * scale)

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqs) == 1:
        img_lqs = img_lqs[0]
    return img_gts, img_lqs

def paired_random_crop_DP(img_lqLs, img_lqRs, img_gts, gt_patch_size, scale, gt_path):
    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqLs, list):
        img_lqLs = [img_lqLs]
    if not isinstance(img_lqRs, list):
        img_lqRs = [img_lqRs]

    h_lq, w_lq, _ = img_lqLs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    lq_patch_size = gt_patch_size // scale

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqLs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqLs
    ]

    img_lqRs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqRs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqLs) == 1:
        img_lqLs = img_lqLs[0]
    if len(img_lqRs) == 1:
        img_lqRs = img_lqRs[0]
    return img_lqLs, img_lqRs, img_gts


def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).

    We use vertical flip and transpose for rotation implementation.
    All the images in the list use the same augmentation.

    Args:
        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
            is an ndarray, it will be transformed to a list.
        hflip (bool): Horizontal flip. Default: True.
        rotation (bool): Ratotation. Default: True.
        flows (list[ndarray]: Flows to be augmented. If the input is an
            ndarray, it will be transformed to a list.
            Dimension is (h, w, 2). Default: None.
        return_status (bool): Return the status of flip and rotation.
            Default: False.

    Returns:
        list[ndarray] | ndarray: Augmented images and flows. If returned
            results only have one element, just return ndarray.

    """
    hflip = hflip and random.random() < 0.5
    vflip = rotation and random.random() < 0.5
    rot90 = rotation and random.random() < 0.5

    def _augment(img):
        if hflip:  # horizontal
            cv2.flip(img, 1, img)
        if vflip:  # vertical
            cv2.flip(img, 0, img)
        if rot90:
            img = img.transpose(1, 0, 2)
        return img

    def _augment_flow(flow):
        if hflip:  # horizontal
            cv2.flip(flow, 1, flow)
            flow[:, :, 0] *= -1
        if vflip:  # vertical
            cv2.flip(flow, 0, flow)
            flow[:, :, 1] *= -1
        if rot90:
            flow = flow.transpose(1, 0, 2)
            flow = flow[:, :, [1, 0]]
        return flow

    if not isinstance(imgs, list):
        imgs = [imgs]
    imgs = [_augment(img) for img in imgs]
    if len(imgs) == 1:
        imgs = imgs[0]

    if flows is not None:
        if not isinstance(flows, list):
            flows = [flows]
        flows = [_augment_flow(flow) for flow in flows]
        if len(flows) == 1:
            flows = flows[0]
        return imgs, flows
    else:
        if return_status:
            return imgs, (hflip, vflip, rot90)
        else:
            return imgs


def img_rotate(img, angle, center=None, scale=1.0):
    """Rotate image.

    Args:
        img (ndarray): Image to be rotated.
        angle (float): Rotation angle in degrees. Positive values mean
            counter-clockwise rotation.
        center (tuple[int]): Rotation center. If the center is None,
            initialize it as the center of the image. Default: None.
        scale (float): Isotropic scale factor. Default: 1.0.
    """
    (h, w) = img.shape[:2]

    if center is None:
        center = (w // 2, h // 2)

    matrix = cv2.getRotationMatrix2D(center, angle, scale)
    rotated_img = cv2.warpAffine(img, matrix, (w, h))
    return rotated_img

def data_augmentation(image, mode):
    """
    Performs data augmentation of the input image
    Input:
        image: a cv2 (OpenCV) image
        mode: int. Choice of transformation to apply to the image
                0 - no transformation
                1 - flip up and down
                2 - rotate counterwise 90 degree
                3 - rotate 90 degree and flip up and down
                4 - rotate 180 degree
                5 - rotate 180 degree and flip
                6 - rotate 270 degree
                7 - rotate 270 degree and flip
    """
    if mode == 0:
        # original
        out = image
    elif mode == 1:
        # flip up and down
        out = np.flipud(image)
    elif mode == 2:
        # rotate counterwise 90 degree
        out = np.rot90(image)
    elif mode == 3:
        # rotate 90 degree and flip up and down
        out = np.rot90(image)
        out = np.flipud(out)
    elif mode == 4:
        # rotate 180 degree
        out = np.rot90(image, k=2)
    elif mode == 5:
        # rotate 180 degree and flip
        out = np.rot90(image, k=2)
        out = np.flipud(out)
    elif mode == 6:
        # rotate 270 degree
        out = np.rot90(image, k=3)
    elif mode == 7:
        # rotate 270 degree and flip
        out = np.rot90(image, k=3)
        out = np.flipud(out)
    else:
        raise Exception('Invalid choice of image transformation')

    return out

def random_augmentation(*args):
    out = []
    flag_aug = random.randint(0,7)
    for data in args:
        out.append(data_augmentation(data, flag_aug).copy())
    return out


================================================
FILE: Deraining/basicsr/data/video_test_dataset.py
================================================
import glob
import torch
from os import path as osp
from torch.utils import data as data

from data.data_util import (duf_downsample, generate_frame_indices,
                                    read_img_seq)
from utils import get_root_logger, scandir


class VideoTestDataset(data.Dataset):
    """Video test dataset.

    Supported datasets: Vid4, REDS4, REDSofficial.
    More generally, it supports testing dataset with following structures:

    dataroot
    ├── subfolder1
        ├── frame000
        ├── frame001
        ├── ...
    ├── subfolder1
        ├── frame000
        ├── frame001
        ├── ...
    ├── ...

    For testing datasets, there is no need to prepare LMDB files.

    Args:
        opt (dict): Config for train dataset. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            io_backend (dict): IO backend type and other kwarg.
            cache_data (bool): Whether to cache testing datasets.
            name (str): Dataset name.
            meta_info_file (str): The path to the file storing the list of test
                folders. If not provided, all the folders in the dataroot will
                be used.
            num_frame (int): Window size for input frames.
            padding (str): Padding mode.
    """

    def __init__(self, opt):
        super(VideoTestDataset, self).__init__()
        self.opt = opt
        self.cache_data = opt['cache_data']
        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
        self.data_info = {
            'lq_path': [],
            'gt_path': [],
            'folder': [],
            'idx': [],
            'border': []
        }
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        assert self.io_backend_opt[
            'type'] != 'lmdb', 'No need to use lmdb during validation/test.'

        logger = get_root_logger()
        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
        self.imgs_lq, self.imgs_gt = {}, {}
        if 'meta_info_file' in opt:
            with open(opt['meta_info_file'], 'r') as fin:
                subfolders = [line.split(' ')[0] for line in fin]
                subfolders_lq = [
                    osp.join(self.lq_root, key) for key in subfolders
                ]
                subfolders_gt = [
                    osp.join(self.gt_root, key) for key in subfolders
                ]
        else:
            subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
            subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*')))

        if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']:
            for subfolder_lq, subfolder_gt in zip(subfolders_lq,
                                                  subfolders_gt):
                # get frame list for lq and gt
                subfolder_name = osp.basename(subfolder_lq)
                img_paths_lq = sorted(
                    list(scandir(subfolder_lq, full_path=True)))
                img_paths_gt = sorted(
                    list(scandir(subfolder_gt, full_path=True)))

                max_idx = len(img_paths_lq)
                assert max_idx == len(img_paths_gt), (
                    f'Different number of images in lq ({max_idx})'
                    f' and gt folders ({len(img_paths_gt)})')

                self.data_info['lq_path'].extend(img_paths_lq)
                self.data_info['gt_path'].extend(img_paths_gt)
                self.data_info['folder'].extend([subfolder_name] * max_idx)
                for i in range(max_idx):
                    self.data_info['idx'].append(f'{i}/{max_idx}')
                border_l = [0] * max_idx
                for i in range(self.opt['num_frame'] // 2):
                    border_l[i] = 1
                    border_l[max_idx - i - 1] = 1
                self.data_info['border'].extend(border_l)

                # cache data or save the frame list
                if self.cache_data:
                    logger.info(
                        f'Cache {subfolder_name} for VideoTestDataset...')
                    self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq)
                    self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt)
                else:
                    self.imgs_lq[subfolder_name] = img_paths_lq
                    self.imgs_gt[subfolder_name] = img_paths_gt
        else:
            raise ValueError(
                f'Non-supported video test dataset: {type(opt["name"])}')

    def __getitem__(self, index):
        folder = self.data_info['folder'][index]
        idx, max_idx = self.data_info['idx'][index].split('/')
        idx, max_idx = int(idx), int(max_idx)
        border = self.data_info['border'][index]
        lq_path = self.data_info['lq_path'][index]

        select_idx = generate_frame_indices(
            idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])

        if self.cache_data:
            imgs_lq = self.imgs_lq[folder].index_select(
                0, torch.LongTensor(select_idx))
            img_gt = self.imgs_gt[folder][idx]
        else:
            img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
            imgs_lq = read_img_seq(img_paths_lq)
            img_gt = read_img_seq([self.imgs_gt[folder][idx]])
            img_gt.squeeze_(0)

        return {
            'lq': imgs_lq,  # (t, c, h, w)
            'gt': img_gt,  # (c, h, w)
            'folder': folder,  # folder name
            'idx': self.data_info['idx'][index],  # e.g., 0/99
            'border': border,  # 1 for border, 0 for non-border
            'lq_path': lq_path  # center frame
        }

    def __len__(self):
        return len(self.data_info['gt_path'])


class VideoTestVimeo90KDataset(data.Dataset):
    """Video test dataset for Vimeo90k-Test dataset.

    It only keeps the center frame for testing.
    For testing datasets, there is no need to prepare LMDB files.

    Args:
        opt (dict): Config for train dataset. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            io_backend (dict): IO backend type and other kwarg.
            cache_data (bool): Whether to cache testing datasets.
            name (str): Dataset name.
            meta_info_file (str): The path to the file storing the list of test
                folders. If not provided, all the folders in the dataroot will
                be used.
            num_frame (int): Window size for input frames.
            padding (str): Padding mode.
    """

    def __init__(self, opt):
        super(VideoTestVimeo90KDataset, self).__init__()
        self.opt = opt
        self.cache_data = opt['cache_data']
        if self.cache_data:
            raise NotImplementedError(
                'cache_data in Vimeo90K-Test dataset is not implemented.')
        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
        self.data_info = {
            'lq_path': [],
            'gt_path': [],
            'folder': [],
            'idx': [],
            'border': []
        }
        neighbor_list = [
            i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])
        ]

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        assert self.io_backend_opt[
            'type'] != 'lmdb', 'No need to use lmdb during validation/test.'

        logger = get_root_logger()
        logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
        with open(opt['meta_info_file'], 'r') as fin:
            subfolders = [line.split(' ')[0] for line in fin]
        for idx, subfolder in enumerate(subfolders):
            gt_path = osp.join(self.gt_root, subfolder, 'im4.png')
            self.data_info['gt_path'].append(gt_path)
            lq_paths = [
                osp.join(self.lq_root, subfolder, f'im{i}.png')
                for i in neighbor_list
            ]
            self.data_info['lq_path'].append(lq_paths)
            self.data_info['folder'].append('vimeo90k')
            self.data_info['idx'].append(f'{idx}/{len(subfolders)}')
            self.data_info['border'].append(0)

    def __getitem__(self, index):
        lq_path = self.data_info['lq_path'][index]
        gt_path = self.data_info['gt_path'][index]
        imgs_lq = read_img_seq(lq_path)
        img_gt = read_img_seq([gt_path])
        img_gt.squeeze_(0)

        return {
            'lq': imgs_lq,  # (t, c, h, w)
            'gt': img_gt,  # (c, h, w)
            'folder': self.data_info['folder'][index],  # folder name
            'idx': self.data_info['idx'][index],  # e.g., 0/843
            'border': self.data_info['border'][index],  # 0 for non-border
            'lq_path': lq_path[self.opt['num_frame'] // 2]  # center frame
        }

    def __len__(self):
        return len(self.data_info['gt_path'])


class VideoTestDUFDataset(VideoTestDataset):
    """ Video test dataset for DUF dataset.

    Args:
        opt (dict): Config for train dataset.
            Most of keys are the same as VideoTestDataset.
            It has the follwing extra keys:

            use_duf_downsampling (bool): Whether to use duf downsampling to
                generate low-resolution frames.
            scale (bool): Scale, which will be added automatically.
    """

    def __getitem__(self, index):
        folder = self.data_info['folder'][index]
        idx, max_idx = self.data_info['idx'][index].split('/')
        idx, max_idx = int(idx), int(max_idx)
        border = self.data_info['border'][index]
        lq_path = self.data_info['lq_path'][index]

        select_idx = generate_frame_indices(
            idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])

        if self.cache_data:
            if self.opt['use_duf_downsampling']:
                # read imgs_gt to generate low-resolution frames
                imgs_lq = self.imgs_gt[folder].index_select(
                    0, torch.LongTensor(select_idx))
                imgs_lq = duf_downsample(
                    imgs_lq, kernel_size=13, scale=self.opt['scale'])
            else:
                imgs_lq = self.imgs_lq[folder].index_select(
                    0, torch.LongTensor(select_idx))
            img_gt = self.imgs_gt[folder][idx]
        else:
            if self.opt['use_duf_downsampling']:
                img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx]
                # read imgs_gt to generate low-resolution frames
                imgs_lq = read_img_seq(
                    img_paths_lq,
                    require_mod_crop=True,
                    scale=self.opt['scale'])
                imgs_lq = duf_downsample(
                    imgs_lq, kernel_size=13, scale=self.opt['scale'])
            else:
                img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
                imgs_lq = read_img_seq(img_paths_lq)
            img_gt = read_img_seq([self.imgs_gt[folder][idx]],
                                  require_mod_crop=True,
                                  scale=self.opt['scale'])
            img_gt.squeeze_(0)

        return {
            'lq': imgs_lq,  # (t, c, h, w)
            'gt': img_gt,  # (c, h, w)
            'folder': folder,  # folder name
            'idx': self.data_info['idx'][index],  # e.g., 0/99
            'border': border,  # 1 for border, 0 for non-border
            'lq_path': lq_path  # center frame
        }


class VideoRecurrentTestDataset(VideoTestDataset):
    """Video test dataset for recurrent architectures, which takes LR video
    frames as input and output corresponding HR video frames.

    Args:
        Same as VideoTestDataset.
        Unused opt:
            padding (str): Padding mode.

    """

    def __init__(self, opt):
        super(VideoRecurrentTestDataset, self).__init__(opt)
        # Find unique folder strings
        self.folders = sorted(list(set(self.data_info['folder'])))

    def __getitem__(self, index):
        folder = self.folders[index]

        if self.cache_data:
            imgs_lq = self.imgs_lq[folder]
            imgs_gt = self.imgs_gt[folder]
        else:
            raise NotImplementedError('Without cache_data is not implemented.')

        return {
            'lq': imgs_lq,
            'gt': imgs_gt,
            'folder': folder,
        }

    def __len__(self):
        return len(self.folders)


================================================
FILE: Deraining/basicsr/data/vimeo90k_dataset.py
================================================
import random
import torch
from pathlib import Path
from torch.utils import data as data

from data.transforms import augment, paired_random_crop
from utils import FileClient, get_root_logger, imfrombytes, img2tensor


class Vimeo90KDataset(data.Dataset):
    """Vimeo90K dataset for training.

    The keys are generated from a meta info txt file.
    basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt

    Each line contains:
    1. clip name; 2. frame number; 3. image shape, seperated by a white space.
    Examples:
        00001/0001 7 (256,448,3)
        00001/0002 7 (256,448,3)

    Key examples: "00001/0001"
    GT (gt): Ground-Truth;
    LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.

    The neighboring frame list for different num_frame:
    num_frame | frame list
             1 | 4
             3 | 3,4,5
             5 | 2,3,4,5,6
             7 | 1,2,3,4,5,6,7

    Args:
        opt (dict): Config for train dataset. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.

            num_frame (int): Window size for input frames.
            gt_size (int): Cropped patched size for gt patches.
            random_reverse (bool): Random reverse input frames.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
    """

    def __init__(self, opt):
        super(Vimeo90KDataset, self).__init__()
        self.opt = opt
        self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(
            opt['dataroot_lq'])

        with open(opt['meta_info_file'], 'r') as fin:
            self.keys = [line.split(' ')[0] for line in fin]

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.is_lmdb = False
        if self.io_backend_opt['type'] == 'lmdb':
            self.is_lmdb = True
            self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']

        # indices of input images
        self.neighbor_list = [
            i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])
        ]

        # temporal augmentation configs
        self.random_reverse = opt['random_reverse']
        logger = get_root_logger()
        logger.info(f'Random reverse is {self.random_reverse}.')

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # random reverse
        if self.random_reverse and random.random() < 0.5:
            self.neighbor_list.reverse()

        scale = self.opt['scale']
        gt_size = self.opt['gt_size']
        key = self.keys[index]
        clip, seq = key.split('/')  # key example: 00001/0001

        # get the GT frame (im4.png)
        if self.is_lmdb:
            img_gt_path = f'{key}/im4'
        else:
            img_gt_path = self.gt_root / clip / seq / 'im4.png'
        img_bytes = self.file_client.get(img_gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)

        # get the neighboring LQ frames
        img_lqs = []
        for neighbor in self.neighbor_list:
            if self.is_lmdb:
                img_lq_path = f'{clip}/{seq}/im{neighbor}'
            else:
                img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
            img_bytes = self.file_client.get(img_lq_path, 'lq')
            img_lq = imfrombytes(img_bytes, float32=True)
            img_lqs.append(img_lq)

        # randomly crop
        img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
                                             img_gt_path)

        # augmentation - flip, rotate
        img_lqs.append(img_gt)
        img_results = augment(img_lqs, self.opt['use_flip'],
                              self.opt['use_rot'])

        img_results = img2tensor(img_results)
        img_lqs = torch.stack(img_results[0:-1], dim=0)
        img_gt = img_results[-1]

        # img_lqs: (t, c, h, w)
        # img_gt: (c, h, w)
        # key: str
        return {'lq': img_lqs, 'gt': img_gt, 'key': key}

    def __len__(self):
        return len(self.keys)


================================================
FILE: Deraining/basicsr/metrics/__init__.py
================================================
from .niqe import calculate_niqe
from .psnr_ssim import calculate_psnr, calculate_ssim

__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe']


================================================
FILE: Deraining/basicsr/metrics/fid.py
================================================
import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from tqdm import tqdm

from models.archs.inception import InceptionV3


def load_patched_inception_v3(device='cuda',
                              resize_input=True,
                              normalize_input=False):
    # we may not resize the input, but in [rosinality/stylegan2-pytorch] it
    # does resize the input.
    inception = InceptionV3([3],
                            resize_input=resize_input,
                            normalize_input=normalize_input)
    inception = nn.DataParallel(inception).eval().to(device)
    return inception


@torch.no_grad()
def extract_inception_features(data_generator,
                               inception,
                               len_generator=None,
                               device='cuda'):
    """Extract inception features.

    Args:
        data_generator (generator): A data generator.
        inception (nn.Module): Inception model.
        len_generator (int): Length of the data_generator to show the
            progressbar. Default: None.
        device (str): Device. Default: cuda.

    Returns:
        Tensor: Extracted features.
    """
    if len_generator is not None:
        pbar = tqdm(total=len_generator, unit='batch', desc='Extract')
    else:
        pbar = None
    features = []

    for data in data_generator:
        if pbar:
            pbar.update(1)
        data = data.to(device)
        feature = inception(data)[0].view(data.shape[0], -1)
        features.append(feature.to('cpu'))
    if pbar:
        pbar.close()
    features = torch.cat(features, 0)
    return features


def calculate_fid(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """Numpy implementation of the Frechet Distance.

    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
        d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
    Stable version by Dougal J. Sutherland.

    Args:
        mu1 (np.array): The sample mean over activations.
        sigma1 (np.array): The covariance matrix over activations for
            generated samples.
        mu2 (np.array): The sample mean over activations, precalculated on an
               representative data set.
        sigma2 (np.array): The covariance matrix over activations,
            precalculated on an representative data set.

    Returns:
        float: The Frechet Distance.
    """
    assert mu1.shape == mu2.shape, 'Two mean vectors have different lengths'
    assert sigma1.shape == sigma2.shape, (
        'Two covariances have different dimensions')

    cov_sqrt, _ = linalg.sqrtm(sigma1 @ sigma2, disp=False)

    # Product might be almost singular
    if not np.isfinite(cov_sqrt).all():
        print('Product of cov matrices is singular. Adding {eps} to diagonal '
              'of cov estimates')
        offset = np.eye(sigma1.shape[0]) * eps
        cov_sqrt = linalg.sqrtm((sigma1 + offset) @ (sigma2 + offset))

    # Numerical error might give slight imaginary component
    if np.iscomplexobj(cov_sqrt):
        if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):
            m = np.max(np.abs(cov_sqrt.imag))
            raise ValueError(f'Imaginary component {m}')
        cov_sqrt = cov_sqrt.real

    mean_diff = mu1 - mu2
    mean_norm = mean_diff @ mean_diff
    trace = np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(cov_sqrt)
    fid = mean_norm + trace

    return fid


================================================
FILE: Deraining/basicsr/metrics/metric_util.py
================================================
import numpy as np

from utils.matlab_functions import bgr2ycbcr


def reorder_image(img, input_order='HWC'):
    """Reorder images to 'HWC' order.

    If the input_order is (h, w), return (h, w, 1);
    If the input_order is (c, h, w), return (h, w, c);
    If the input_order is (h, w, c), return as it is.

    Args:
        img (ndarray): Input image.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            If the input image shape is (h, w), input_order will not have
            effects. Default: 'HWC'.

    Returns:
        ndarray: reordered image.
    """

    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f'Wrong input_order {input_order}. Supported input_orders are '
            "'HWC' and 'CHW'")
    if len(img.shape) == 2:
        img = img[..., None]
    if input_order == 'CHW':
        img = img.transpose(1, 2, 0)
    return img


def to_y_channel(img):
    """Change to Y channel of YCbCr.

    Args:
        img (ndarray): Images with range [0, 255].

    Returns:
        (ndarray): Images with range [0, 255] (float type) without round.
    """
    img = img.astype(np.float32) / 255.
    if img.ndim == 3 and img.shape[2] == 3:
        img = bgr2ycbcr(img, y_only=True)
        img = img[..., None]
    return img * 255.


================================================
FILE: Deraining/basicsr/metrics/niqe.py
================================================
import cv2
import math
import numpy as np
from scipy.ndimage.filters import convolve
from scipy.special import gamma

from metrics.metric_util import reorder_image, to_y_channel


def estimate_aggd_param(block):
    """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) paramters.

    Args:
        block (ndarray): 2D Image block.

    Returns:
        tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD
            distribution (Estimating the parames in Equation 7 in the paper).
    """
    block = block.flatten()
    gam = np.arange(0.2, 10.001, 0.001)  # len = 9801
    gam_reciprocal = np.reciprocal(gam)
    r_gam = np.square(gamma(gam_reciprocal * 2)) / (
        gamma(gam_reciprocal) * gamma(gam_reciprocal * 3))

    left_std = np.sqrt(np.mean(block[block < 0]**2))
    right_std = np.sqrt(np.mean(block[block > 0]**2))
    gammahat = left_std / right_std
    rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2)
    rhatnorm = (rhat * (gammahat**3 + 1) *
                (gammahat + 1)) / ((gammahat**2 + 1)**2)
    array_position = np.argmin((r_gam - rhatnorm)**2)

    alpha = gam[array_position]
    beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
    beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha))
    return (alpha, beta_l, beta_r)


def compute_feature(block):
    """Compute features.

    Args:
        block (ndarray): 2D Image block.

    Returns:
        list: Features with length of 18.
    """
    feat = []
    alpha, beta_l, beta_r = estimate_aggd_param(block)
    feat.extend([alpha, (beta_l + beta_r) / 2])

    # distortions disturb the fairly regular structure of natural images.
    # This deviation can be captured by analyzing the sample distribution of
    # the products of pairs of adjacent coefficients computed along
    # horizontal, vertical and diagonal orientations.
    shifts = [[0, 1], [1, 0], [1, 1], [1, -1]]
    for i in range(len(shifts)):
        shifted_block = np.roll(block, shifts[i], axis=(0, 1))
        alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block)
        # Eq. 8
        mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha))
        feat.extend([alpha, mean, beta_l, beta_r])
    return feat


def niqe(img,
         mu_pris_param,
         cov_pris_param,
         gaussian_window,
         block_size_h=96,
         block_size_w=96):
    """Calculate NIQE (Natural Image Quality Evaluator) metric.

    Ref: Making a "Completely Blind" Image Quality Analyzer.
    This implementation could produce almost the same results as the official
    MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip

    Note that we do not include block overlap height and width, since they are
    always 0 in the official implementation.

    For good performance, it is advisable by the official implemtation to
    divide the distorted image in to the same size patched as used for the
    construction of multivariate Gaussian model.

    Args:
        img (ndarray): Input image whose quality needs to be computed. The
            image must be a gray or Y (of YCbCr) image with shape (h, w).
            Range [0, 255] with float type.
        mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian
            model calculated on the pristine dataset.
        cov_pris_param (ndarray): Covariance of a pre-defined multivariate
            Gaussian model calculated on the pristine dataset.
        gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the
            image.
        block_size_h (int): Height of the blocks in to which image is divided.
            Default: 96 (the official recommended value).
        block_size_w (int): Width of the blocks in to which image is divided.
            Default: 96 (the official recommended value).
    """
    assert img.ndim == 2, (
        'Input image must be a gray or Y (of YCbCr) image with shape (h, w).')
    # crop image
    h, w = img.shape
    num_block_h = math.floor(h / block_size_h)
    num_block_w = math.floor(w / block_size_w)
    img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w]

    distparam = []  # dist param is actually the multiscale features
    for scale in (1, 2):  # perform on two scales (1, 2)
        mu = convolve(img, gaussian_window, mode='nearest')
        sigma = np.sqrt(
            np.abs(
                convolve(np.square(img), gaussian_window, mode='nearest') -
                np.square(mu)))
        # normalize, as in Eq. 1 in the paper
        img_nomalized = (img - mu) / (sigma + 1)

        feat = []
        for idx_w in range(num_block_w):
            for idx_h in range(num_block_h):
                # process ecah block
                block = img_nomalized[idx_h * block_size_h //
                                      scale:(idx_h + 1) * block_size_h //
                                      scale, idx_w * block_size_w //
                                      scale:(idx_w + 1) * block_size_w //
                                      scale]
                feat.append(compute_feature(block))

        distparam.append(np.array(feat))
        # TODO: matlab bicubic downsample with anti-aliasing
        # for simplicity, now we use opencv instead, which will result in
        # a slight difference.
        if scale == 1:
            h, w = img.shape
            img = cv2.resize(
                img / 255., (w // 2, h // 2), interpolation=cv2.INTER_LINEAR)
            img = img * 255.

    distparam = np.concatenate(distparam, axis=1)

    # fit a MVG (multivariate Gaussian) model to distorted patch features
    mu_distparam = np.nanmean(distparam, axis=0)
    # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html
    distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)]
    cov_distparam = np.cov(distparam_no_nan, rowvar=False)

    # compute niqe quality, Eq. 10 in the paper
    invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2)
    quality = np.matmul(
        np.matmul((mu_pris_param - mu_distparam), invcov_param),
        np.transpose((mu_pris_param - mu_distparam)))
    quality = np.sqrt(quality)

    return quality


def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y'):
    """Calculate NIQE (Natural Image Quality Evaluator) metric.

    Ref: Making a "Completely Blind" Image Quality Analyzer.
    This implementation could produce almost the same results as the official
    MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip

    We use the official params estimated from the pristine dataset.
    We use the recommended block size (96, 96) without overlaps.

    Args:
        img (ndarray): Input image whose quality needs to be computed.
            The input image must be in range [0, 255] with float/int type.
            The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order)
            If the input order is 'HWC' or 'CHW', it will be converted to gray
            or Y (of YCbCr) image according to the ``convert_to`` argument.
        crop_border (int): Cropped pixels in each edge of an image. These
            pixels are not involved in the metric calculation.
        input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'.
            Default: 'HWC'.
        convert_to (str): Whether coverted to 'y' (of MATLAB YCbCr) or 'gray'.
            Default: 'y'.

    Returns:
        float: NIQE result.
    """

    # we use the official params estimated from the pristine dataset.
    niqe_pris_params = np.load('basicsr/metrics/niqe_pris_params.npz')
    mu_pris_param = niqe_pris_params['mu_pris_param']
    cov_pris_param = niqe_pris_params['cov_pris_param']
    gaussian_window = niqe_pris_params['gaussian_window']

    img = img.astype(np.float32)
    if input_order != 'HW':
        img = reorder_image(img, input_order=input_order)
        if convert_to == 'y':
            img = to_y_channel(img)
        elif convert_to == 'gray':
            img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255.
        img = np.squeeze(img)

    if crop_border != 0:
        img = img[crop_border:-crop_border, crop_border:-crop_border]

    niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window)

    return niqe_result


================================================
FILE: Deraining/basicsr/metrics/psnr_ssim.py
================================================
import cv2
import numpy as np

from metrics.metric_util import reorder_image, to_y_channel
import skimage.metrics
import torch


def calculate_psnr(img1,
                   img2,
                   crop_border,
                   input_order='HWC',
                   test_y_channel=False):
    """Calculate PSNR (Peak Signal-to-Noise Ratio).

    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

    Args:
        img1 (ndarray/tensor): Images with range [0, 255]/[0, 1].
        img2 (ndarray/tensor): Images with range [0, 255]/[0, 1].
        crop_border (int): Cropped pixels in each edge of an image. These
            pixels are not involved in the PSNR calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: psnr result.
    """

    assert img1.shape == img2.shape, (
        f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f'Wrong input_order {input_order}. Supported input_orders are '
            '"HWC" and "CHW"')
    if type(img1) == torch.Tensor:
        if len(img1.shape) == 4:
            img1 = img1.squeeze(0)
        img1 = img1.detach().cpu().numpy().transpose(1,2,0)
    if type(img2) == torch.Tensor:
        if len(img2.shape) == 4:
            img2 = img2.squeeze(0)
        img2 = img2.detach().cpu().numpy().transpose(1,2,0)
        
    img1 = reorder_image(img1, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    if crop_border != 0:
        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img1 = to_y_channel(img1)
        img2 = to_y_channel(img2)

    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    max_value = 1. if img1.max() <= 1 else 255.
    return 20. * np.log10(max_value / np.sqrt(mse))


def _ssim(img1, img2):
    """Calculate SSIM (structural similarity) for one channel images.

    It is called by func:`calculate_ssim`.

    Args:
        img1 (ndarray): Images with range [0, 255] with order 'HWC'.
        img2 (ndarray): Images with range [0, 255] with order 'HWC'.

    Returns:
        float: ssim result.
    """

    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) *
                (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                       (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()

def prepare_for_ssim(img, k):
    import torch
    with torch.no_grad():
        img = torch.from_numpy(img).unsqueeze(0).unsqueeze(0).float()
        conv = torch.nn.Conv2d(1, 1, k, stride=1, padding=k//2, padding_mode='reflect')
        conv.weight.requires_grad = False
        conv.weight[:, :, :, :] = 1. / (k * k)

        img = conv(img)

        img = img.squeeze(0).squeeze(0)
        img = img[0::k, 0::k]
    return img.detach().cpu().numpy()

def prepare_for_ssim_rgb(img, k):
    import torch
    with torch.no_grad():
        img = torch.from_numpy(img).float() #HxWx3

        conv = torch.nn.Conv2d(1, 1, k, stride=1, padding=k // 2, padding_mode='reflect')
        conv.weight.requires_grad = False
        conv.weight[:, :, :, :] = 1. / (k * k)

        new_img = []

        for i in range(3):
            new_img.append(conv(img[:, :, i].unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)[0::k, 0::k])

    return torch.stack(new_img, dim=2).detach().cpu().numpy()

def _3d_gaussian_calculator(img, conv3d):
    out = conv3d(img.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)
    return out

def _generate_3d_gaussian_kernel():
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())
    kernel_3 = cv2.getGaussianKernel(11, 1.5)
    kernel = torch.tensor(np.stack([window * k for k in kernel_3], axis=0))
    conv3d = torch.nn.Conv3d(1, 1, (11, 11, 11), stride=1, padding=(5, 5, 5), bias=False, padding_mode='replicate')
    conv3d.weight.requires_grad = False
    conv3d.weight[0, 0, :, :, :] = kernel
    return conv3d

def _ssim_3d(img1, img2, max_value):
    assert len(img1.shape) == 3 and len(img2.shape) == 3
    """Calculate SSIM (structural similarity) for one channel images.

    It is called by func:`calculate_ssim`.

    Args:
        img1 (ndarray): Images with range [0, 255]/[0, 1] with order 'HWC'.
        img2 (ndarray): Images with range [0, 255]/[0, 1] with order 'HWC'.

    Returns:
        float: ssim result.
    """
    C1 = (0.01 * max_value) ** 2
    C2 = (0.03 * max_value) ** 2
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    kernel = _generate_3d_gaussian_kernel().cuda()

    img1 = torch.tensor(img1).float().cuda()
    img2 = torch.tensor(img2).float().cuda()


    mu1 = _3d_gaussian_calculator(img1, kernel)
    mu2 = _3d_gaussian_calculator(img2, kernel)

    mu1_sq = mu1 ** 2
    mu2_sq = mu2 ** 2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = _3d_gaussian_calculator(img1 ** 2, kernel) - mu1_sq
    sigma2_sq = _3d_gaussian_calculator(img2 ** 2, kernel) - mu2_sq
    sigma12 = _3d_gaussian_calculator(img1*img2, kernel) - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) *
                (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                       (sigma1_sq + sigma2_sq + C2))
    return float(ssim_map.mean())

def _ssim_cly(img1, img2):
    assert len(img1.shape) == 2 and len(img2.shape) == 2
    """Calculate SSIM (structural similarity) for one channel images.

    It is called by func:`calculate_ssim`.

    Args:
        img1 (ndarray): Images with range [0, 255] with order 'HWC'.
        img2 (ndarray): Images with range [0, 255] with order 'HWC'.

    Returns:
        float: ssim result.
    """

    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    kernel = cv2.getGaussianKernel(11, 1.5)
    # print(kernel)
    window = np.outer(kernel, kernel.transpose())

    bt = cv2.BORDER_REPLICATE

    mu1 = cv2.filter2D(img1, -1, window, borderType=bt)
    mu2 = cv2.filter2D(img2, -1, window,borderType=bt)

    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window, borderType=bt) - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window, borderType=bt) - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window, borderType=bt) - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) *
                (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                       (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()


def calculate_ssim(img1,
                   img2,
                   crop_border,
                   input_order='HWC',
                   test_y_channel=False):
    """Calculate SSIM (structural similarity).

    Ref:
    Image quality assessment: From error visibility to structural similarity

    The results are the same as that of the official released MATLAB code in
    https://ece.uwaterloo.ca/~z70wang/research/ssim/.

    For three-channel images, SSIM is calculated for each channel and then
    averaged.

    Args:
        img1 (ndarray): Images with range [0, 255].
        img2 (ndarray): Images with range [0, 255].
        crop_border (int): Cropped pixels in each edge of an image. These
            pixels are not involved in the SSIM calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: ssim result.
    """

    assert img1.shape == img2.shape, (
        f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f'Wrong input_order {input_order}. Supported input_orders are '
            '"HWC" and "CHW"')

    if type(img1) == torch.Tensor:
        if len(img1.shape) == 4:
            img1 = img1.squeeze(0)
        img1 = img1.detach().cpu().numpy().transpose(1,2,0)
    if type(img2) == torch.Tensor:
        if len(img2.shape) == 4:
            img2 = img2.squeeze(0)
        img2 = img2.detach().cpu().numpy().transpose(1,2,0)

    img1 = reorder_image(img1, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    if crop_border != 0:
        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img1 = to_y_channel(img1)
        img2 = to_y_channel(img2)
        return _ssim_cly(img1[..., 0], img2[..., 0])


    ssims = []
    # ssims_before = []

    # skimage_before = skimage.metrics.structural_similarity(img1, img2, data_range=255., multichannel=True)
    # print('.._skimage',
    #       skimage.metrics.structural_similarity(img1, img2, data_range=255., multichannel=True))
    max_value = 1 if img1.max() <= 1 else 255
    with torch.no_grad():
        final_ssim = _ssim_3d(img1, img2, max_value)
        ssims.append(final_ssim)

    # for i in range(img1.shape[2]):
    #     ssims_before.append(_ssim(img1, img2))

    # print('..ssim mean , new {:.4f}  and before {:.4f} .... skimage before {:.4f}'.format(np.array(ssims).mean(), np.array(ssims_before).mean(), skimage_before))
        # ssims.append(skimage.metrics.structural_similarity(img1[..., i], img2[..., i], multichannel=False))

    return np.array(ssims).mean()


================================================
FILE: Deraining/basicsr/models/__init__.py
================================================
import importlib
from os import path as osp

from utils import get_root_logger, scandir

# automatically scan and import model modules
# scan all the files under the 'models' folder and collect files ending with
# '_model.py'
model_folder = osp.dirname(osp.abspath(__file__))
model_filenames = [
    osp.splitext(osp.basename(v))[0] for v in scandir(model_folder)
    if v.endswith('_model.py')
]
# import all the model modules
_model_modules = [
    importlib.import_module(f'models.{file_name}')
    for file_name in model_filenames
]


def create_model(opt):
    """Create model.

    Args:
        opt (dict): Configuration. It constains:
            model_type (str): Model type.
    """
    model_type = opt['model_type']

    # dynamic instantiation
    for module in _model_modules:
        model_cls = getattr(module, model_type, None)
        if model_cls is not None:
            break
    if model_cls is None:
        raise ValueError(f'Model {model_type} is not found.')

    model = model_cls(opt)

    logger = get_root_logger()
    logger.info(f'Model [{model.__class__.__name__}] is created.')
    return model


================================================
FILE: Deraining/basicsr/models/archs/__init__.py
================================================
import importlib
from os import path as osp

from utils import scandir

# automatically scan and import arch modules
# scan all the files under the 'archs' folder and collect files ending with
# '_arch.py'
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [
    osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder)
    if v.endswith('_arch.py')
]
# import all the arch modules
_arch_modules = [
    importlib.import_module(f'models.archs.{file_name}')
    for file_name in arch_filenames
]


def dynamic_instantiation(modules, cls_type, opt):
    """Dynamically instantiate class.

    Args:
        modules (list[importlib modules]): List of modules from importlib
            files.
        cls_type (str): Class type.
        opt (dict): Class initialization kwargs.

    Returns:
        class: Instantiated class.
    """

    for module in modules:
        cls_ = getattr(module, cls_type, None)
        if cls_ is not None:
            break
    if cls_ is None:
        raise ValueError(f'{cls_type} is not found.')
    return cls_(**opt)


def define_network(opt):
    network_type = opt.pop('type')
    net = dynamic_instantiation(_arch_modules, network_type, opt)
    return net


================================================
FILE: Deraining/basicsr/models/archs/arch_util.py
================================================
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm

from utils import get_root_logger

# try:
#     from models.ops.dcn import (ModulatedDeformConvPack,
#                                         modulated_deform_conv)
# except ImportError:
#     # print('Cannot import dcn. Ignore this warning if dcn is not used. '
#     #       'Otherwise install BasicSR with compiling dcn.')
#

@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
    """Initialize network weights.

    Args:
        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
        scale (float): Scale initialized weights, especially for residual
            blocks. Default: 1.
        bias_fill (float): The value to fill bias. Default: 0
        kwargs (dict): Other arguments for initialization function.
    """
    if not isinstance(module_list, list):
        module_list = [module_list]
    for module in module_list:
        for m in module.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, _BatchNorm):
                init.constant_(m.weight, 1)
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)


def make_layer(basic_block, num_basic_block, **kwarg):
    """Make layers by stacking the same blocks.

    Args:
        basic_block (nn.module): nn.module class for basic block.
        num_basic_block (int): number of blocks.

    Returns:
        nn.Sequential: Stacked blocks in nn.Sequential.
    """
    layers = []
    for _ in range(num_basic_block):
        layers.append(basic_block(**kwarg))
    return nn.Sequential(*layers)


class ResidualBlockNoBN(nn.Module):
    """Residual block without BN.

    It has a style of:
        ---Conv-ReLU-Conv-+-
         |________________|

    Args:
        num_feat (int): Channel number of intermediate features.
            Default: 64.
        res_scale (float): Residual scale. Default: 1.
        pytorch_init (bool): If set to True, use pytorch default init,
            otherwise, use default_init_weights. Default: False.
    """

    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
        super(ResidualBlockNoBN, self).__init__()
        self.res_scale = res_scale
        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.relu = nn.ReLU(inplace=True)

        if not pytorch_init:
            default_init_weights([self.conv1, self.conv2], 0.1)

    def forward(self, x):
        identity = x
        out = self.conv2(self.relu(self.conv1(x)))
        return identity + out * self.res_scale


class Upsample(nn.Sequential):
    """Upsample module.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. '
                             'Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)


def flow_warp(x,
              flow,
              interp_mode='bilinear',
              padding_mode='zeros',
              align_corners=True):
    """Warp an image or feature map with optical flow.

    Args:
        x (Tensor): Tensor with size (n, c, h, w).
        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
        padding_mode (str): 'zeros' or 'border' or 'reflection'.
            Default: 'zeros'.
        align_corners (bool): Before pytorch 1.3, the default value is
            align_corners=True. After pytorch 1.3, the default value is
            align_corners=False. Here, we use the True as default.

    Returns:
        Tensor: Warped image or feature map.
    """
    assert x.size()[-2:] == flow.size()[1:3]
    _, _, h, w = x.size()
    # create mesh grid
    grid_y, grid_x = torch.meshgrid(
        torch.arange(0, h).type_as(x),
        torch.arange(0, w).type_as(x))
    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
    grid.requires_grad = False

    vgrid = grid + flow
    # scale grid to [-1,1]
    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
    output = F.grid_sample(
        x,
        vgrid_scaled,
        mode=interp_mode,
        padding_mode=padding_mode,
        align_corners=align_corners)

    # TODO, what if align_corners=False
    return output


def resize_flow(flow,
                size_type,
                sizes,
                interp_mode='bilinear',
                align_corners=False):
    """Resize a flow according to ratio or shape.

    Args:
        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
        size_type (str): 'ratio' or 'shape'.
        sizes (list[int | float]): the ratio for resizing or the final output
            shape.
            1) The order of ratio should be [ratio_h, ratio_w]. For
            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
            ratio > 1.0).
            2) The order of output_size should be [out_h, out_w].
        interp_mode (str): The mode of interpolation for resizing.
            Default: 'bilinear'.
        align_corners (bool): Whether align corners. Default: False.

    Returns:
        Tensor: Resized flow.
    """
    _, _, flow_h, flow_w = flow.size()
    if size_type == 'ratio':
        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
    elif size_type == 'shape':
        output_h, output_w = sizes[0], sizes[1]
    else:
        raise ValueError(
            f'Size type should be ratio or shape, but got type {size_type}.')

    input_flow = flow.clone()
    ratio_h = output_h / flow_h
    ratio_w = output_w / flow_w
    input_flow[:, 0, :, :] *= ratio_w
    input_flow[:, 1, :, :] *= ratio_h
    resized_flow = F.interpolate(
        input=input_flow,
        size=(output_h, output_w),
        mode=interp_mode,
        align_corners=align_corners)
    return resized_flow


# TODO: may write a cpp file
def pixel_unshuffle(x, scale):
    """ Pixel unshuffle.

    Args:
        x (Tensor): Input feature with shape (b, c, hh, hw).
        scale (int): Downsample ratio.

    Returns:
        Tensor: the pixel unshuffled feature.
    """
    b, c, hh, hw = x.size()
    out_channel = c * (scale**2)
    assert hh % scale == 0 and hw % scale == 0
    h = hh // scale
    w = hw // scale
    x_view = x.view(b, c, h, scale, w, scale)
    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)


# class DCNv2Pack(ModulatedDeformConvPack):
#     """Modulated deformable conv for deformable alignment.
#
#     Different from the official DCNv2Pack, which generates offsets and masks
#     from the preceding features, this DCNv2Pack takes another different
#     features to generate offsets and masks.
#
#     Ref:
#         Delving Deep into Deformable Alignment in Video Super-Resolution.
#     """
#
#     def forward(self, x, feat):
#         out = self.conv_offset(feat)
#         o1, o2, mask = torch.chunk(out, 3, dim=1)
#         offset = torch.cat((o1, o2), dim=1)
#         mask = torch.sigmoid(mask)
#
#         offset_absmean = torch.mean(torch.abs(offset))
#         if offset_absmean > 50:
#             logger = get_root_logger()
#             logger.warning(
#                 f'Offset abs mean is {offset_absmean}, larger than 50.')
#
#         return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
#                                      self.stride, self.padding, self.dilation,
#                                      self.groups, self.deformable_groups)


================================================
FILE: Deraining/basicsr/models/archs/common.py
================================================
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias)

class ResBlock(nn.Module):
    def __init__(
        self, conv, n_feats, kernel_size,
        bias=True, bn=False, act=nn.LeakyReLU(0.1, inplace=True), res_scale=1):

        super(ResBlock, self).__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        # self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x)
        res += x

        return res

class MeanShift(nn.Conv2d):
    def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1):
        super(MeanShift, self).__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1)
        self.weight.data.div_(std.view(3, 1, 1, 1))
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
        self.bias.data.div_(std)
        self.weight.requires_grad = False
        self.bias.requires_grad = False


class Upsampler(nn.Sequential):
    def __init__(self, conv, scale, n_feat, act=False, bias=True):
        m = []
        if (int(scale) & (int(scale) - 1)) == 0:    # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feat, 4 * n_feat, 3, bias))
                m.append(nn.PixelShuffle(2))
                if act: m.append(act())
        elif scale == 3:
            m.append(conv(n_feat, 9 * n_feat, 3, bias))
            m.append(nn.PixelShuffle(3))
            if act: m.append(act())
        else:
            raise NotImplementedError

        super(Upsampler, self).__init__(*m)

================================================
FILE: Deraining/basicsr/models/archs/mamber32_arch.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers

from einops import rearrange, repeat

import math
import copy
from fvcore.nn import flop_count, parameter_count


import selective_scan_cuda_core as selective_scan_cuda

class SelectiveScanFn(torch.autograd.Function):

    @staticmethod
    def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
        # input_t: float, fp16, bf16; weight_t: float;
        # u, B, C, delta: input_t
        # D, delta_bias: float
        if u.stride(-1) != 1:
            u = u.contiguous()
        if delta.stride(-1) != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.stride(-1) != 1:
            B = B.contiguous()
        if C.stride(-1) != 1:
            C = C.contiguous()
        if B.dim() == 3:
            B = rearrange(B, "b dstate l -> b 1 dstate l")
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = rearrange(C, "b dstate l -> b 1 dstate l")
            ctx.squeeze_C = True
        if D is not None and (D.dtype != torch.float):
            ctx._d_dtype = D.dtype
            D = D.float()
        if delta_bias is not None and (delta_bias.dtype != torch.float):
            ctx._delta_bias_dtype = delta_bias.dtype
            delta_bias = delta_bias.float()
        
        assert u.shape[1] % (B.shape[1] * nrows) == 0 
        assert nrows in [1, 2, 3, 4] # 8+ is too slow to compile

        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
        ctx.delta_softplus = delta_softplus
        ctx.nrows = nrows
        ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
        return out

    @staticmethod
    def backward(ctx, dout, *args):
        u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
        if dout.stride(-1) != 1:
            dout = dout.contiguous()
        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
            u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
            # u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, ctx.nrows,
        )
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
        
        _dD = None
        if D is not None:
            if dD.dtype != getattr(ctx, "_d_dtype", dD.dtype):
                _dD = dD.to(ctx._d_dtype)
            else:
                _dD = dD

        _ddelta_bias = None
        if delta_bias is not None:
            if ddelta_bias.dtype != getattr(ctx, "_delta_bias_dtype", ddelta_bias.dtype):
                _ddelta_bias = ddelta_bias.to(ctx._delta_bias_dtype)
            else:
                _ddelta_bias = ddelta_bias

        return (du, ddelta, dA, dB, dC, _dD, _ddelta_bias, None, None)


def selective_scan_fn_v1(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
    """if return_last_state is True, returns (out, last_state)
    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
    not considered in the backward pass.
    """
    return SelectiveScanFn.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)


# fvcore flops =======================================

def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: r(D N)
    B: r(B N L)
    C: r(B N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32
    
    ignores:
        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] 
    """
    assert not with_complex 
    # https://github.com/state-spaces/mamba/issues/110
    flops = 9 * B * L * D * N
    if with_D:
        flops += B * D * L
    if with_Z:
        flops += B * D * L    
    return flops

def print_jit_input_names(inputs):
    print("input params: ", end=" ", flush=True)
    try: 
        for i in range(10):
            print(inputs[i].debugName(), end=" ", flush=True)
    except Exception as e:
        pass
    print("", flush=True)

def selective_scan_flop_jit(inputs, outputs):
    print_jit_input_names(inputs)
    B, D, L = inputs[0].type().sizes()
    N = inputs[2].type().sizes()[1]
    flops = flops_selective_scan_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=False, with_Group=True)
    return flops


##########################################################################
## Layer Norm

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)



##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x



class SS2D_1(nn.Module):
    def __init__(
        self,
        # basic dims ===========
        d_model=96,
        d_state=16,
        ssm_ratio=2.0,
        ssm_rank_ratio=2.0,
        dt_rank="auto",
        act_layer=nn.SiLU,
        # dwconv ===============
        d_conv=3, # < 2 means no conv 
        conv_bias=True,
        # ======================
        dropout=0.0,
        bias=False,
        # dt init ==============
        dt_min=0.001,
        dt_max=0.1,
        dt_init="random",
        dt_scale=1.0,
        dt_init_floor=1e-4,
        simple_init=False,
        # ======================
        softmax_version=False,
        forward_type="v2",
        # ======================
        **kwargs,
    ):
        """
        ssm_rank_ratio would be used in the future...
        """
        factory_kwargs = {"device": None, "dtype": None}
        super().__init__()
        d_expand = int(ssm_ratio * d_model)
        d_inner = int(min(ssm_rank_ratio, ssm_ratio) * d_model) if ssm_rank_ratio > 0 else d_expand
        self.softmax_version = softmax_version
        self.dt_rank = math.ceil(d_model / 16) if dt_rank == "auto" else dt_rank
        self.d_state = math.ceil(d_model / 6) if d_state == "auto" else d_state # 20240109
        self.d_conv = d_conv

        dc_inner = 4 
        self.dtc_rank = 6
        self.dc_state = 16
        self.conv_cin = nn.Conv2d(in_channels=1, out_channels=dc_inner, kernel_size=1, stride=1, padding=0)
        self.conv_cout = nn.Conv2d(in_channels=dc_inner, out_channels=1, kernel_size=1, stride=1, padding=0)

        self.forward_core=self.forward_corev1

        self.K = 4 if forward_type not in ["share_ssm"] else 1
        self.K2 = self.K if forward_type not in ["share_a"] else 1        
        self.KC = 2
        self.K2C = self.KC if forward_type not in ["share_a"] else 1

        self.cforward_core = self.cforward_corev1
        self.pooling = nn.AdaptiveAvgPool2d(1)
        self.channel_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # in proj =======================================
        self.in_conv = nn.Conv2d(in_channels=d_model, out_channels=d_expand * 2, kernel_size=1, stride=1, padding=0)
        self.act: nn.Module = act_layer()
        
        # conv =======================================
        if self.d_conv > 1:
            self.conv2d = nn.Conv2d(
                in_channels=d_expand,
                out_channels=d_expand,
                groups=d_expand,
                bias=conv_bias,
                kernel_size=d_conv,
                padding=(d_conv - 1) // 2,
                **factory_kwargs,
            )


        self.out_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # x proj ============================
        self.x_proj = [
            nn.Linear(d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0)) # (K, N, inner)
        del self.x_proj
        # xc proj ============================
        self.xc_proj = [
            nn.Linear(dc_inner, (self.dtc_rank + self.dc_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.KC)
        ]
        self.xc_proj_weight = nn.Parameter(torch.stack([tc.weight for tc in self.xc_proj], dim=0)) # (K, N, inner)
        del self.xc_proj


        # dt proj ============================
        self.dt_projs = [
            self.dt_init(self.dt_rank, d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0)) # (K, inner, rank)
        self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0)) # (K, inner)
        del self.dt_projs
        
        # A, D =======================================
        self.A_logs = self.A_log_init(self.d_state, d_inner, copies=self.K2, merge=True) # (K * D, N)
        self.Ds = self.D_init(d_inner, copies=self.K2, merge=True) # (K * D)

        # out proj =======================================
        self.out_conv = nn.Conv2d(in_channels=d_expand, out_channels=d_model, kernel_size=1, stride=1, padding=0)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else nn.Identity()

        self.Dsc = nn.Parameter(torch.ones((self.K2C * dc_inner)))
        self.Ac_logs = nn.Parameter(torch.randn((self.K2C * dc_inner, self.dc_state))) # A == -A_logs.exp() < 0; # 0 < exp(A * dt) < 1
        self.dtc_projs_weight = nn.Parameter(torch.randn((self.KC, dc_inner, self.dtc_rank)).contiguous())
        self.dtc_projs_bias = nn.Parameter(torch.randn((self.KC, dc_inner))) 

    @staticmethod
    def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4, **factory_kwargs):
        dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)

        # Initialize special dt projection to preserve variance at initialization
        dt_init_std = dt_rank**-0.5 * dt_scale
        if dt_init == "constant":
            nn.init.constant_(dt_proj.weight, dt_init_std)
        elif dt_init == "random":
            nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
        else:
            raise NotImplementedError

        # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
        dt = torch.exp(
            torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
            + math.log(dt_min)
        ).clamp(min=dt_init_floor)
        # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
        inv_dt = dt + torch.log(-torch.expm1(-dt))
        with torch.no_grad():
            dt_proj.bias.copy_(inv_dt)
        
        return dt_proj


    @staticmethod
    def A_log_init(d_state, d_inner, copies=-1, device=None, merge=True):
        # S4D real initialization
        A = repeat(
            torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
            "n -> d n",
            d=d_inner,
        ).contiguous()
        A_log = torch.log(A)  # Keep A_log in fp32
        if copies > 0:
            A_log = repeat(A_log, "d n -> r d n", r=copies)
            if merge:
                A_log = A_log.flatten(0, 1)
        A_log = nn.Parameter(A_log)
        A_log._no_weight_decay = True
        return A_log


    @staticmethod
    def D_init(d_inner, copies=-1, device=None, merge=True):
        # D "skip" parameter
        D = torch.ones(d_inner, device=device)
        if copies > 0:
            D = repeat(D, "n1 -> r n1", r=copies)
            if merge:
                D = D.flatten(0, 1)
        D = nn.Parameter(D)  # Keep in fp32
        D._no_weight_decay = True
        return D
    

    
    def forward_corev1(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn_v1

        B, C, H, W = x.shape
        L = H * W

        def cross_scan_2d(x):
            x_hwwh = torch.stack([x.flatten(2, 3), x.transpose(dim0=2, dim1=3).contiguous().flatten(2, 3)], dim=1)
            xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)
            return xs 
        
        if self.K == 4:
            xs = cross_scan_2d(x) 


            x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, self.x_proj_weight)
            dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
            dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dt_projs_weight)

            xs = xs.view(B, -1, L) # (b, k * d, l)
            dts = dts.contiguous().view(B, -1, L) # (b, k * d, l)
            As = -torch.exp(self.A_logs.float())  # (k * d, d_state)
            Ds = self.Ds # (k * d)
            dt_projs_bias = self.dt_projs_bias.view(-1) # (k * d)

            out_y = self.selective_scan(
                xs, dts, 
                As, Bs, Cs, Ds,
                delta_bias=dt_projs_bias,
                delta_softplus=True,
            ).view(B, 4, -1, L)


        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        y = out_y[:, 0].float() + inv_y[:, 0].float() + wh_y.float() + invwh_y.float()
        

        y = y.view(B, C, H, W)
        y = self.out_norm(y).to(x.dtype)
        
        return y

    def cforward_corev1(self, xc: torch.Tensor):
        self.selective_scanC = selective_scan_fn_v1

        b,d,h,w = xc.shape
        
        xc = self.pooling(xc)
        xc = xc.permute(0,2,1,3).contiguous()
        xc = self.conv_cin(xc)
        xc = xc.squeeze(-1)


        B, D, L = xc.shape
        D, N = self.Ac_logs.shape
        K, D, R = self.dtc_projs_weight.shape

        xsc = torch.stack([xc, torch.flip(xc, dims=[-1])], dim=1) 

        xc_dbl = torch.einsum("b k d l, k c d -> b k c l", xsc, self.xc_proj_weight)
        
        dts, Bs, Cs = torch.split(xc_dbl, [self.dtc_rank, self.dc_state, self.dc_state], dim=2)
        dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dtc_projs_weight).contiguous()

        xsc = xsc.view(B, -1, L)
        dts = dts.contiguous().view(B, -1, L).contiguous()
        As = -torch.exp(self.Ac_logs.float())
        Ds = self.Dsc
        dt_projs_bias = self.dtc_projs_bias.view(-1)

        out_y = self.selective_scanC(
            xsc, dts, 
            As, Bs, Cs, Ds,
            delta_bias=dt_projs_bias,
            delta_softplus=True,
        ).view(B, 2, -1, L)

        y = out_y[:, 0].float() + torch.flip(out_y[:, 1], dims=[-1]).float()

        y = y.unsqueeze(-1) 
        y = self.conv_cout(y) 
        y = y.transpose(dim0=1, dim1=2).contiguous()
        y = self.channel_norm(y)
        y = y.to(xc.dtype)

        
        return y


    def forward(self, x: torch.Tensor, **kwargs):
        xz = self.in_conv(x)
        x, z = xz.chunk(2, dim=1) # (b, d, h, w)
        if not self.softmax_version:
            z = self.act(z)
        x = self.act(self.conv2d(x)) # (b, d, h, w)
        y1 = self.forward_core(x)
        y2 = y1 * z
        c = self.cforward_core(y2)#x:b,d,h,w; output:b,d,1,1
        y2 = y2 + c
        out = self.out_conv(y2)
        return out


##########################################################################
class MamberBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(MamberBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = SS2D_1(d_model=dim, ssm_ratio=1)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x


##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x



##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

    def forward(self, x):
        return self.body(x)

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):
        return self.body(x)


##########################################################################
##---------- Mamber -----------------------
class Mamber32(nn.Module):
    def __init__(self, 
        inp_channels=3, 
        out_channels=3, 
        dim = 48,
        num_blocks = [6,6,7,8], 
        num_refinement_blocks = 2,
        heads = [1,2,4,8],
        ffn_expansion_factor = 2.66,
        bias = False,
        LayerNorm_type = 'WithBias',   ## Other option 'BiasFree'
        dual_pixel_task = False        ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
    ):

        super(Mamber32, self).__init__()

        #self.scale = scale

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(*[MamberBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(*[MamberBlock( dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])

        self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
        self.latent = nn.Sequential(*[MamberBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
        
        self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])


        self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.up2_1 = Upsample(int(dim*2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.refinement = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])


        #### For Dual-Pixel Defocus Deblurring Task ####
        self.dual_pixel_task = dual_pixel_task
        if self.dual_pixel_task:
            self.skip_conv = nn.Conv2d(dim, int(dim*2**1), kernel_size=1, bias=bias)
        ###########################


        self.output = nn.Conv2d(int(dim*2**1), out_channels, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, inp_img):

        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        
        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3) 

        inp_enc_level4 = self.down3_4(out_enc_level3)        
        latent = self.latent(inp_enc_level4) 
                        
        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3) 

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2) 

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)
        
        out_dec_level1 = self.refinement(out_dec_level1)


        #### For Dual-Pixel Defocus Deblurring Task ####
        if self.dual_pixel_task:
            out_dec_level1 = out_dec_level1 + self.skip_conv(inp_enc_level1)
            out_dec_level1 = self.output(out_dec_level1)
        ###########################
        else:
            out_dec_level1 = self.output(out_dec_level1) + inp_img


        return out_dec_level1


    def flops(self, shape=(3, 64, 64)):
        # shape = self.__input_shape__[1:]
        supported_ops={
            "aten::silu": None, # as relu is in _IGNORED_OPS
            "aten::neg": None, # as relu is in _IGNORED_OPS
            "aten::exp": None, # as relu is in _IGNORED_OPS
            "aten::flip": None, # as permute is in _IGNORED_OPS
            "prim::PythonOp.SelectiveScan": selective_scan_flop_jit,
        }

        model = copy.deepcopy(self)
        model.cuda().eval()

        input = torch.randn((1, *shape), device=next(model.parameters()).device)
        params = parameter_count(model)[""]
        Gflops, unsupported = flop_count(model=model, inputs=(input,), supported_ops=supported_ops)

        del model, input
        return f"params(M) {params/1e6} GFLOPs {sum(Gflops.values())}"


if __name__ == "__main__":
    print(Mamber32().flops())


================================================
FILE: Deraining/basicsr/models/archs/mamber33_arch.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers

from einops import rearrange, repeat

import math
import copy
from fvcore.nn import flop_count, parameter_count


import selective_scan_cuda_core as selective_scan_cuda

class SelectiveScanFn(torch.autograd.Function):

    @staticmethod
    def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
        # input_t: float, fp16, bf16; weight_t: float;
        # u, B, C, delta: input_t
        # D, delta_bias: float
        if u.stride(-1) != 1:
            u = u.contiguous()
        if delta.stride(-1) != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.stride(-1) != 1:
            B = B.contiguous()
        if C.stride(-1) != 1:
            C = C.contiguous()
        if B.dim() == 3:
            B = rearrange(B, "b dstate l -> b 1 dstate l")
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = rearrange(C, "b dstate l -> b 1 dstate l")
            ctx.squeeze_C = True
        if D is not None and (D.dtype != torch.float):
            ctx._d_dtype = D.dtype
            D = D.float()
        if delta_bias is not None and (delta_bias.dtype != torch.float):
            ctx._delta_bias_dtype = delta_bias.dtype
            delta_bias = delta_bias.float()
        
        assert u.shape[1] % (B.shape[1] * nrows) == 0 
        assert nrows in [1, 2, 3, 4] # 8+ is too slow to compile

        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
        ctx.delta_softplus = delta_softplus
        ctx.nrows = nrows
        ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
        return out

    @staticmethod
    def backward(ctx, dout, *args):
        u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
        if dout.stride(-1) != 1:
            dout = dout.contiguous()
        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
            u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
        )
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
        
        _dD = None
        if D is not None:
            if dD.dtype != getattr(ctx, "_d_dtype", dD.dtype):
                _dD = dD.to(ctx._d_dtype)
            else:
                _dD = dD

        _ddelta_bias = None
        if delta_bias is not None:
            if ddelta_bias.dtype != getattr(ctx, "_delta_bias_dtype", ddelta_bias.dtype):
                _ddelta_bias = ddelta_bias.to(ctx._delta_bias_dtype)
            else:
                _ddelta_bias = ddelta_bias

        return (du, ddelta, dA, dB, dC, _dD, _ddelta_bias, None, None)


def selective_scan_fn_v1(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
    """if return_last_state is True, returns (out, last_state)
    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
    not considered in the backward pass.
    """
    return SelectiveScanFn.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)


def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: r(D N)
    B: r(B N L)
    C: r(B N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32
    
    ignores:
        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] 
    """
    assert not with_complex 
    # https://github.com/state-spaces/mamba/issues/110
    flops = 9 * B * L * D * N
    if with_D:
        flops += B * D * L
    if with_Z:
        flops += B * D * L    
    return flops

def print_jit_input_names(inputs):
    print("input params: ", end=" ", flush=True)
    try: 
        for i in range(10):
            print(inputs[i].debugName(), end=" ", flush=True)
    except Exception as e:
        pass
    print("", flush=True)

def selective_scan_flop_jit(inputs, outputs):
    print_jit_input_names(inputs)
    B, D, L = inputs[0].type().sizes()
    N = inputs[2].type().sizes()[1]
    flops = flops_selective_scan_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=False, with_Group=True)
    return flops


##########################################################################
## Layer Norm

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)



##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x



class SS2D_1(nn.Module):
    def __init__(
        self,
        # basic dims ===========
        d_model=96,
        d_state=16,
        ssm_ratio=2.0,
        ssm_rank_ratio=2.0,
        dt_rank="auto",
        act_layer=nn.SiLU,
        # dwconv ===============
        d_conv=3, # < 2 means no conv 
        conv_bias=True,
        # ======================
        dropout=0.0,
        bias=False,
        # dt init ==============
        dt_min=0.001,
        dt_max=0.1,
        dt_init="random",
        dt_scale=1.0,
        dt_init_floor=1e-4,
        simple_init=False,
        # ======================
        softmax_version=False,
        forward_type="v2",
        # ======================
        **kwargs,
    ):
        """
        ssm_rank_ratio would be used in the future...
        """
        factory_kwargs = {"device": None, "dtype": None}
        super().__init__()
        d_expand = int(ssm_ratio * d_model)
        d_inner = int(min(ssm_rank_ratio, ssm_ratio) * d_model) if ssm_rank_ratio > 0 else d_expand
        self.softmax_version = softmax_version
        self.dt_rank = math.ceil(d_model / 16) if dt_rank == "auto" else dt_rank
        self.d_state = math.ceil(d_model / 6) if d_state == "auto" else d_state
        self.d_conv = d_conv

        dc_inner = 2 
        self.dtc_rank = 6
        self.dc_state = 16 
        self.conv_cin = nn.Conv2d(in_channels=1, out_channels=dc_inner, kernel_size=1, stride=1, padding=0)
        self.conv_cout = nn.Conv2d(in_channels=dc_inner, out_channels=1, kernel_size=1, stride=1, padding=0)

        self.forward_core=self.forward_corev1

        self.K = 4 if forward_type not in ["share_ssm"] else 1
        self.K2 = self.K if forward_type not in ["share_a"] else 1        
        self.KC = 2
        self.K2C = self.KC if forward_type not in ["share_a"] else 1

        self.cforward_core = self.cforward_corev1
        self.pooling = nn.AdaptiveAvgPool2d(1)
        self.channel_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # in proj =======================================
        self.in_conv = nn.Conv2d(in_channels=d_model, out_channels=d_expand * 2, kernel_size=1, stride=1, padding=0)
        self.act: nn.Module = act_layer()
        
        # conv =======================================
        if self.d_conv > 1:
            self.conv2d = nn.Conv2d(
                in_channels=d_expand,
                out_channels=d_expand,
                groups=d_expand,
                bias=conv_bias,
                kernel_size=d_conv,
                padding=(d_conv - 1) // 2,
                **factory_kwargs,
            )


        self.out_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # x proj ============================
        self.x_proj = [
            nn.Linear(d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0)) # (K, N, inner)
        del self.x_proj
        # xc proj ============================
        self.xc_proj = [
            nn.Linear(dc_inner, (self.dtc_rank + self.dc_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.KC)
        ]
        self.xc_proj_weight = nn.Parameter(torch.stack([tc.weight for tc in self.xc_proj], dim=0)) # (K, N, inner)
        del self.xc_proj


        # dt proj ============================
        self.dt_projs = [
            self.dt_init(self.dt_rank, d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0)) # (K, inner, rank)
        self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0)) # (K, inner)
        del self.dt_projs
        
        # A, D =======================================
        self.A_logs = self.A_log_init(self.d_state, d_inner, copies=self.K2, merge=True) # (K * D, N)
        self.Ds = self.D_init(d_inner, copies=self.K2, merge=True) # (K * D)

        # out proj =======================================
        self.out_conv = nn.Conv2d(in_channels=d_expand, out_channels=d_model, kernel_size=1, stride=1, padding=0)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else nn.Identity()

        self.Dsc = nn.Parameter(torch.ones((self.K2C * dc_inner)))
        self.Ac_logs = nn.Parameter(torch.randn((self.K2C * dc_inner, self.dc_state))) # A == -A_logs.exp() < 0; # 0 < exp(A * dt) < 1
        self.dtc_projs_weight = nn.Parameter(torch.randn((self.KC, dc_inner, self.dtc_rank)).contiguous())
        self.dtc_projs_bias = nn.Parameter(torch.randn((self.KC, dc_inner))) 

    @staticmethod
    def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4, **factory_kwargs):
        dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)

        # Initialize special dt projection to preserve variance at initialization
        dt_init_std = dt_rank**-0.5 * dt_scale
        if dt_init == "constant":
            nn.init.constant_(dt_proj.weight, dt_init_std)
        elif dt_init == "random":
            nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
        else:
            raise NotImplementedError

        # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
        dt = torch.exp(
            torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
            + math.log(dt_min)
        ).clamp(min=dt_init_floor)
        inv_dt = dt + torch.log(-torch.expm1(-dt))
        with torch.no_grad():
            dt_proj.bias.copy_(inv_dt)
        
        return dt_proj


    @staticmethod
    def A_log_init(d_state, d_inner, copies=-1, device=None, merge=True):
        A = repeat(
            torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
            "n -> d n",
            d=d_inner,
        ).contiguous()
        A_log = torch.log(A)  # Keep A_log in fp32
        if copies > 0:
            A_log = repeat(A_log, "d n -> r d n", r=copies)
            if merge:
                A_log = A_log.flatten(0, 1)
        A_log = nn.Parameter(A_log)
        A_log._no_weight_decay = True
        return A_log


    @staticmethod
    def D_init(d_inner, copies=-1, device=None, merge=True):
        D = torch.ones(d_inner, device=device)
        if copies > 0:
            D = repeat(D, "n1 -> r n1", r=copies)
            if merge:
                D = D.flatten(0, 1)
        D = nn.Parameter(D)  # Keep in fp32
        D._no_weight_decay = True
        return D
    

    
    def forward_corev1(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn_v1

        B, C, H, W = x.shape
        L = H * W

        def cross_scan_2d(x):
            x_hwwh = torch.stack([x.flatten(2, 3), x.transpose(dim0=2, dim1=3).contiguous().flatten(2, 3)], dim=1)
            xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)
            return xs 
        
        if self.K == 4:
            xs = cross_scan_2d(x)


            x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, self.x_proj_weight)
            dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
            dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dt_projs_weight)

            xs = xs.view(B, -1, L) # (b, k * d, l)
            dts = dts.contiguous().view(B, -1, L) # (b, k * d, l)
            As = -torch.exp(self.A_logs.float())  # (k * d, d_state)
            Ds = self.Ds # (k * d)
            dt_projs_bias = self.dt_projs_bias.view(-1) # (k * d)

            out_y = self.selective_scan(
                xs, dts, 
                As, Bs, Cs, Ds,
                delta_bias=dt_projs_bias,
                delta_softplus=True,
            ).view(B, 4, -1, L)


        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        y = out_y[:, 0].float() + inv_y[:, 0].float() + wh_y.float() + invwh_y.float()
        

        y = y.view(B, C, H, W)
        y = self.out_norm(y).to(x.dtype)
        
        return y

    def cforward_corev1(self, xc: torch.Tensor):
        self.selective_scanC = selective_scan_fn_v1

        b,d,h,w = xc.shape
        
        xc = self.pooling(xc)
        xc = xc.permute(0,2,1,3).contiguous()
        xc = self.conv_cin(xc)
        xc = xc.squeeze(-1)


        B, D, L = xc.shape
        D, N = self.Ac_logs.shape
        K, D, R = self.dtc_projs_weight.shape


        xsc = torch.stack([xc, torch.flip(xc, dims=[-1])], dim=1)

        xc_dbl = torch.einsum("b k d l, k c d -> b k c l", xsc, self.xc_proj_weight)
        
        dts, Bs, Cs = torch.split(xc_dbl, [self.dtc_rank, self.dc_state, self.dc_state], dim=2)
        dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dtc_projs_weight).contiguous()

        xsc = xsc.view(B, -1, L)
        dts = dts.contiguous().view(B, -1, L).contiguous()
        As = -torch.exp(self.Ac_logs.float())
        Ds = self.Dsc
        dt_projs_bias = self.dtc_projs_bias.view(-1)


        out_y = self.selective_scanC(
            xsc, dts, 
            As, Bs, Cs, Ds,
            delta_bias=dt_projs_bias,
            delta_softplus=True,
        ).view(B, 2, -1, L)

        y = out_y[:, 0].float() + torch.flip(out_y[:, 1], dims=[-1]).float()


        y = y.unsqueeze(-1)
        y = self.conv_cout(y)
        y = y.transpose(dim0=1, dim1=2).contiguous()
        y = self.channel_norm(y)
        y = y.to(xc.dtype)

        
        return y


    def forward(self, x: torch.Tensor, **kwargs):
        xz = self.in_conv(x)
        x, z = xz.chunk(2, dim=1)
        if not self.softmax_version:
            z = self.act(z)
        x = self.act(self.conv2d(x))
        y1 = self.forward_core(x)
        y2 = y1 * z
        c = self.cforward_core(y2)
        y3 = y2 * c
        y2 = y3 + y2
        out = self.out_conv(y2)
        return out


##########################################################################
class MamberBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(MamberBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = SS2D_1(d_model=dim, ssm_ratio=1)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x


##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x



##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

    def forward(self, x):
        return self.body(x)

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):
        return self.body(x)


##########################################################################
##---------- Mamber -----------------------
class Mamber33(nn.Module):
    def __init__(self, 
        inp_channels=3, 
        out_channels=3, 
        dim = 48,
        num_blocks = [6,6,7,8], 
        num_refinement_blocks = 2,
        heads = [1,2,4,8],
        ffn_expansion_factor = 2.66,
        bias = False,
        LayerNorm_type = 'WithBias',   ## Other option 'BiasFree'
        dual_pixel_task = False        ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
    ):

        super(Mamber33, self).__init__()

        #self.scale = scale

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(*[MamberBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])

        self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
        self.latent = nn.Sequential(*[MamberBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
        
        self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])


        self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.up2_1 = Upsample(int(dim*2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.refinement = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])


        #### For Dual-Pixel Defocus Deblurring Task ####
        self.dual_pixel_task = dual_pixel_task
        if self.dual_pixel_task:
            self.skip_conv = nn.Conv2d(dim, int(dim*2**1), kernel_size=1, bias=bias)
        ###########################


        self.output = nn.Conv2d(int(dim*2**1), out_channels, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, inp_img):

        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        
        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3) 

        inp_enc_level4 = self.down3_4(out_enc_level3)        
        latent = self.latent(inp_enc_level4) 
                        
        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3) 

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2) 

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)
        
        out_dec_level1 = self.refinement(out_dec_level1)


        #### For Dual-Pixel Defocus Deblurring Task ####
        if self.dual_pixel_task:
            out_dec_level1 = out_dec_level1 + self.skip_conv(inp_enc_level1)
            out_dec_level1 = self.output(out_dec_level1)
        ###########################
        else:
            out_dec_level1 = self.output(out_dec_level1) + inp_img


        return out_dec_level1


    def flops(self, shape=(3, 64, 64)):
        # shape = self.__input_shape__[1:]
        supported_ops={
            "aten::silu": None, # as relu is in _IGNORED_OPS
            "aten::neg": None, # as relu is in _IGNORED_OPS
            "aten::exp": None, # as relu is in _IGNORED_OPS
            "aten::flip": None, # as permute is in _IGNORED_OPS
            "prim::PythonOp.SelectiveScan": selective_scan_flop_jit,
        }

        model = copy.deepcopy(self)
        model.cuda().eval()

        input = torch.randn((1, *shape), device=next(model.parameters()).device)
        params = parameter_count(model)[""]
        Gflops, unsupported = flop_count(model=model, inputs=(input,), supported_ops=supported_ops)

        del model, input
        return f"params(M) {params/1e6} GFLOPs {sum(Gflops.values())}"


if __name__ == "__main__":
    print(Mamber33().flops())


================================================
FILE: Deraining/basicsr/models/archs/restormer_arch.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
from pdb import set_trace as stx
import numbers

from einops import rearrange

import copy
from fvcore.nn import FlopCountAnalysis, flop_count_str, flop_count, parameter_count


##########################################################################
## Layer Norm

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)



##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x



##########################################################################
## Multi-DConv Head Transposed Self-Attention (MDTA)
class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(dim*3, dim*3, kernel_size=3, stride=1, padding=1, groups=dim*3, bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
        


    def forward(self, x):
        b,c,h,w = x.shape

        qkv = self.qkv_dwconv(self.qkv(x))
        q,k,v = qkv.chunk(3, dim=1)   
        
        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.temperature
        attn = attn.softmax(dim=-1)

        out = (attn @ v)
        
        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        return out



##########################################################################
class TransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(TransformerBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = Attention(dim, num_heads, bias)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x



##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x



##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

    def forward(self, x):
        return self.body(x)

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):
        return self.body(x)

##########################################################################
##---------- Restormer -----------------------
class Restormer(nn.Module):
    def __init__(self, 
        inp_channels=3, 
        out_channels=3, 
        dim = 48,
        num_blocks = [4,6,6,8], 
        num_refinement_blocks = 4,
        heads = [1,2,4,8],
        ffn_expansion_factor = 2.66,
        bias = False,
        LayerNorm_type = 'WithBias',   ## Other option 'BiasFree'
        dual_pixel_task = False        ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
    ):

        super(Restormer, self).__init__()

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(*[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])

        self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
        self.latent = nn.Sequential(*[TransformerBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
        
        self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])


        self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.up2_1 = Upsample(int(dim*2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.refinement = nn.Sequential(*[TransformerBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])
        
        #### For Dual-Pixel Defocus Deblurring Task ####
        self.dual_pixel_task = dual_pixel_task
        if self.dual_pixel_task:
            self.skip_conv = nn.Conv2d(dim, int(dim*2**1), kernel_size=1, bias=bias)
        ###########################
            
        self.output = nn.Conv2d(int(dim*2**1), out_channels, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, inp_img):

        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        
        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3) 

        inp_enc_level4 = self.down3_4(out_enc_level3)        
        latent = self.latent(inp_enc_level4) 
                        
        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3) 

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2) 

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)
        
        out_dec_level1 = self.refinement(out_dec_level1)

        #### For Dual-Pixel Defocus Deblurring Task ####
        if self.dual_pixel_task:
            out_dec_level1 = out_dec_level1 + self.skip_conv(inp_enc_level1)
            out_dec_level1 = self.output(out_dec_level1)
        ###########################
        else:
            out_dec_level1 = self.output(out_dec_level1) + inp_img


        return out_dec_level1

    def flops(self, shape=(3, 256, 256)):
        # shape = self.__input_shape__[1:]
        supported_ops={
            "aten::silu": None, # as relu is in _IGNORED_OPS
            "aten::neg": None, # as relu is in _IGNORED_OPS
            "aten::exp": None, # as relu is in _IGNORED_OPS
            "aten::flip": None, # as permute is in _IGNORED_OPS
        }

        model = copy.deepcopy(self)
        model.cuda().eval()

        input = torch.randn((1, *shape), device=next(model.parameters()).device)
        params = parameter_count(model)[""]
        Gflops, unsupported = flop_count(model=model, inputs=(input,), supported_ops=supported_ops)

        del model, input
        return f"params(M) {params/1e6} GFLOPs {sum(Gflops.values())}"


if __name__ == "__main__":
    print(Restormer().flops())

================================================
FILE: Deraining/basicsr/models/base_model.py
================================================
import logging
import os
import torch
from collections import OrderedDict
from copy import deepcopy
from torch.nn.parallel import DataParallel, DistributedDataParallel

from models import lr_scheduler as lr_scheduler
from utils.dist_util import master_only

logger = logging.getLogger('basicsr')


class BaseModel():
    """Base model."""

    def __init__(self, opt):
        self.opt = opt
        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
        self.is_train = opt['is_train']
        self.schedulers = []
        self.optimizers = []

    def feed_data(self, data):
        pass

    def optimize_parameters(self):
        pass

    def get_current_visuals(self):
        pass

    def save(self, epoch, current_iter):
        """Save networks and training state."""
        pass

    def validation(self, dataloader, current_iter, tb_logger, save_img=False, rgb2bgr=True, use_image=True):
        """Validation function.

        Args:
            dataloader (torch.utils.data.DataLoader): Validation dataloader.
            current_iter (int): Current iteration.
            tb_logger (tensorboard logger): Tensorboard logger.
            save_img (bool): Whether to save images. Default: False.
            rgb2bgr (bool): Whether to save images using rgb2bgr. Default: True
            use_image (bool): Whether to use saved images to compute metrics (PSNR, SSIM), if not, then use data directly from network' output. Default: True
        """
        if self.opt['dist']:
            return self.dist_validation(dataloader, current_iter, tb_logger, save_img, rgb2bgr, use_image)
        else:
            return self.nondist_validation(dataloader, current_iter, tb_logger,
                                    save_img, rgb2bgr, use_image)

    def model_ema(self, decay=0.999):
        net_g = self.get_bare_model(self.net_g)

        net_g_params = dict(net_g.named_parameters())
        net_g_ema_params = dict(self.net_g_ema.named_parameters())

        for k in net_g_ema_params.keys():
            net_g_ema_params[k].data.mul_(decay).add_(
                net_g_params[k].data, alpha=1 - decay)

    def get_current_log(self):
        return self.log_dict

    def model_to_device(self, net):
        """Model to device. It also warps models with DistributedDataParallel
        or DataParallel.

        Args:
            net (nn.Module)
        """

        net = net.to(self.device)
        if self.opt['dist']:
            find_unused_parameters = self.opt.get('find_unused_parameters',
                                                  False)
            net = DistributedDataParallel(
                net,
                device_ids=[torch.cuda.current_device()],
                find_unused_parameters=find_unused_parameters)
        elif self.opt['num_gpu'] > 1:
            net = DataParallel(net)
        return net

    def setup_schedulers(self):
        """Set up schedulers."""
        train_opt = self.opt['train']
        scheduler_type = train_opt['scheduler'].pop('type')
        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.MultiStepRestartLR(optimizer,
                                                    **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingWarmupRestarts':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingWarmupRestarts(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartCyclicLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartCyclicLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'TrueCosineAnnealingLR':
            print('..', 'cosineannealingLR')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingLRWithRestart':
            print('..', 'CosineAnnealingLR_With_Restart')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingLRWithRestart(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'LinearLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.LinearLR(
                        optimizer, train_opt['total_iter']))
        elif scheduler_type == 'VibrateLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.VibrateLR(
                        optimizer, train_opt['total_iter']))
        else:
            raise NotImplementedError(
                f'Scheduler {scheduler_type} is not implemented yet.')

    def get_bare_model(self, net):
        """Get bare model, especially under wrapping with
        DistributedDataParallel or DataParallel.
        """
        if isinstance(net, (DataParallel, DistributedDataParallel)):
            net = net.module
        return net

    @master_only
    def print_network(self, net):
        """Print the str and parameter number of a network.

        Args:
            net (nn.Module)
        """
        if isinstance(net, (DataParallel, DistributedDataParallel)):
            net_cls_str = (f'{net.__class__.__name__} - '
                           f'{net.module.__class__.__name__}')
        else:
            net_cls_str = f'{net.__class__.__name__}'

        net = self.get_bare_model(net)
        net_str = str(net)
        net_params = sum(map(lambda x: x.numel(), net.parameters()))

        logger.info(
            f'Network: {net_cls_str}, with parameters: {net_params:,d}')
        logger.info(net_str)

    def _set_lr(self, lr_groups_l):
        """Set learning rate for warmup.

        Args:
            lr_groups_l (list): List for lr_groups, each for an optimizer.
        """
        for optimizer, lr_groups in zip(self.optimizers, lr_groups_l):
            for param_group, lr in zip(optimizer.param_groups, lr_groups):
                param_group['lr'] = lr

    def _get_init_lr(self):
        """Get the initial lr, which is set by the scheduler.
        """
        init_lr_groups_l = []
        for optimizer in self.optimizers:
            init_lr_groups_l.append(
                [v['initial_lr'] for v in optimizer.param_groups])
        return init_lr_groups_l

    def update_learning_rate(self, current_iter, warmup_iter=-1):
        """Update learning rate.

        Args:
            current_iter (int): Current iteration.
            warmup_iter (int)： Warmup iter numbers. -1 for no warmup.
                Default： -1.
        """
        if current_iter > 1:
            for scheduler in self.schedulers:
                scheduler.step()
        # set up warm-up learning rate
        if current_iter < warmup_iter:
            # get initial lr for each group
            init_lr_g_l = self._get_init_lr()
            # modify warming-up learning rates
            # currently only support linearly warm up
            warm_up_lr_l = []
            for init_lr_g in init_lr_g_l:
                warm_up_lr_l.append(
                    [v / warmup_iter * current_iter for v in init_lr_g])
            # set learning rate
            self._set_lr(warm_up_lr_l)

    def get_current_learning_rate(self):
        return [
            param_group['lr']
            for param_group in self.optimizers[0].param_groups
        ]

    @master_only
    def save_network(self, net, net_label, current_iter, param_key='params'):
        """Save networks.

        Args:
            net (nn.Module | list[nn.Module]): Network(s) to be saved.
            net_label (str): Network label.
            current_iter (int): Current iter number.
            param_key (str | list[str]): The parameter key(s) to save network.
                Default: 'params'.
        """
        if current_iter == -1:
            current_iter = 'latest'
        save_filename = f'{net_label}_{current_iter}.pth'
        save_path = os.path.join(self.opt['path']['models'], save_filename)

        net = net if isinstance(net, list) else [net]
        param_key = param_key if isinstance(param_key, list) else [param_key]
        assert len(net) == len(
            param_key), 'The lengths of net and param_key should be the same.'

        save_dict = {}
        for net_, param_key_ in zip(net, param_key):
            net_ = self.get_bare_model(net_)
            state_dict = net_.state_dict()
            for key, param in state_dict.items():
                if key.startswith('module.'):  # remove unnecessary 'module.'
                    key = key[7:]
                state_dict[key] = param.cpu()
            save_dict[param_key_] = state_dict

        torch.save(save_dict, save_path)

    def _print_different_keys_loading(self, crt_net, load_net, strict=True):
        """Print keys with differnet name or different size when loading models.

        1. Print keys with differnet names.
        2. If strict=False, print the same key but with different tensor size.
            It also ignore these keys with different sizes (not load).

        Args:
            crt_net (torch model): Current network.
            load_net (dict): Loaded network.
            strict (bool): Whether strictly loaded. Default: True.
        """
        crt_net = self.get_bare_model(crt_net)
        crt_net = crt_net.state_dict()
        crt_net_keys = set(crt_net.keys())
        load_net_keys = set(load_net.keys())

        if crt_net_keys != load_net_keys:
            logger.warning('Current net - loaded net:')
            for v in sorted(list(crt_net_keys - load_net_keys)):
                logger.warning(f'  {v}')
            logger.warning('Loaded net - current net:')
            for v in sorted(list(load_net_keys - crt_net_keys)):
                logger.warning(f'  {v}')

        # check the size for the same keys
        if not strict:
            common_keys = crt_net_keys & load_net_keys
            for k in common_keys:
                if crt_net[k].size() != load_net[k].size():
                    logger.warning(
                        f'Size different, ignore [{k}]: crt_net: '
                        f'{crt_net[k].shape}; load_net: {load_net[k].shape}')
                    load_net[k + '.ignore'] = load_net.pop(k)

    def load_network(self, net, load_path, strict=True, param_key='params'):
        """Load network.

        Args:
            load_path (str): The path of networks to be loaded.
            net (nn.Module): Network.
            strict (bool): Whether strictly loaded.
            param_key (str): The parameter key of loaded network. If set to
                None, use the root 'path'.
                Default: 'params'.
        """
        net = self.get_bare_model(net)
        logger.info(
            f'Loading {net.__class__.__name__} model from {load_path}.')
        load_net = torch.load(
            load_path, map_location=lambda storage, loc: storage)
        if param_key is not None:
            if param_key not in load_net and 'params' in load_net:
                param_key = 'params'
                logger.info('Loading: params_ema does not exist, use params.')
            load_net = load_net[param_key]
        print(' load net keys', load_net.keys)
        # remove unnecessary 'module.'
        for k, v in deepcopy(load_net).items():
            if k.startswith('module.'):
                load_net[k[7:]] = v
                load_net.pop(k)
        self._print_different_keys_loading(net, load_net, strict)
        net.load_state_dict(load_net, strict=strict)

    @master_only
    def save_training_state(self, epoch, current_iter):
        """Save training states during training, which will be used for
        resuming.

        Args:
            epoch (int): Current epoch.
            current_iter (int): Current iteration.
        """
        if current_iter != -1:
            state = {
                'epoch': epoch,
                'iter': current_iter,
                'optimizers': [],
                'schedulers': []
            }
            for o in self.optimizers:
                state['optimizers'].append(o.state_dict())
            for s in self.schedulers:
                state['schedulers'].append(s.state_dict())
            save_filename = f'{current_iter}.state'
            save_path = os.path.join(self.opt['path']['training_states'],
                                     save_filename)
            torch.save(state, save_path)

    def resume_training(self, resume_state):
        """Reload the optimizers and schedulers for resumed training.

        Args:
            resume_state (dict): Resume state.
        """
        resume_optimizers = resume_state['optimizers']
        resume_schedulers = resume_state['schedulers']
        assert len(resume_optimizers) == len(
            self.optimizers), 'Wrong lengths of optimizers'
        assert len(resume_schedulers) == len(
            self.schedulers), 'Wrong lengths of schedulers'
        for i, o in enumerate(resume_optimizers):
            self.optimizers[i].load_state_dict(o)
        for i, s in enumerate(resume_schedulers):
            self.schedulers[i].load_state_dict(s)

    def reduce_loss_dict(self, loss_dict):
        """reduce loss dict.

        In distributed training, it averages the losses among different GPUs .

        Args:
            loss_dict (OrderedDict): Loss dict.
        """
        with torch.no_grad():
            if self.opt['dist']:
                keys = []
                losses = []
                for name, value in loss_dict.items():
                    keys.append(name)
                    losses.append(value)
                losses = torch.stack(losses, 0)
                torch.distributed.reduce(losses, dst=0)
                if self.opt['rank'] == 0:
                    losses /= self.opt['world_size']
                loss_dict = {key: loss for key, loss in zip(keys, losses)}

            log_dict = OrderedDict()
            for name, value in loss_dict.items():
                log_dict[name] = value.mean().item()

            return log_dict


================================================
FILE: Deraining/basicsr/models/image_restoration_model.py
================================================
import importlib
import torch
from collections import OrderedDict
from copy import deepcopy
from os import path as osp
from tqdm import tqdm

from models.archs import define_network
from models.base_model import BaseModel
from utils import get_root_logger, imwrite, tensor2img

loss_module = importlib.import_module('models.losses')
metric_module = importlib.import_module('metrics')

import os
import random
import numpy as np
import cv2
import torch.nn.functional as F
from functools import partial

class Mixing_Augment:
    def __init__(self, mixup_beta, use_identity, device):
        self.dist = torch.distributions.beta.Beta(torch.tensor([mixup_beta]), torch.tensor([mixup_beta]))
        self.device = device

        self.use_identity = use_identity

        self.augments = [self.mixup]

    def mixup(self, target, input_):
        lam = self.dist.rsample((1,1)).item()
    
        r_index = torch.randperm(target.size(0)).to(self.device)
    
        target = lam * target + (1-lam) * target[r_index, :]
        input_ = lam * input_ + (1-lam) * input_[r_index, :]
    
        return target, input_

    def __call__(self, target, input_):
        if self.use_identity:
            augment = random.randint(0, len(self.augments))
            if augment < len(self.augments):
                target, input_ = self.augments[augment](target, input_)
        else:
            augment = random.randint(0, len(self.augments)-1)
            target, input_ = self.augments[augment](target, input_)
        return target, input_

class ImageCleanModel(BaseModel):
    """Base Deblur model for single image deblur."""

    def __init__(self, opt):
        super(ImageCleanModel, self).__init__(opt)

        # define network

        self.mixing_flag = self.opt['train']['mixing_augs'].get('mixup', False)
        if self.mixing_flag:
            mixup_beta       = self.opt['train']['mixing_augs'].get('mixup_beta', 1.2)
            use_identity     = self.opt['train']['mixing_augs'].get('use_identity', False)
            self.mixing_augmentation = Mixing_Augment(mixup_beta, use_identity, self.device)

        self.net_g = define_network(deepcopy(opt['network_g']))
        self.net_g = self.model_to_device(self.net_g)
        self.print_network(self.net_g)

        # load pretrained models
        load_path = self.opt['path'].get('pretrain_network_g', None)
        if load_path is not None:
            self.load_network(self.net_g, load_path,
                              self.opt['path'].get('strict_load_g', True), param_key=self.opt['path'].get('param_key', 'params'))

        if self.is_train:
            self.init_training_settings()

    def init_training_settings(self):
        self.net_g.train()
        train_opt = self.opt['train']

        self.ema_decay = train_opt.get('ema_decay', 0)
        if self.ema_decay > 0:
            logger = get_root_logger()
            logger.info(
                f'Use Exponential Moving Average with decay: {self.ema_decay}')
            # define network net_g with Exponential Moving Average (EMA)
            # net_g_ema is used only for testing on one GPU and saving
            # There is no need to wrap with DistributedDataParallel
            self.net_g_ema = define_network(self.opt['network_g']).to(
                self.device)
            # load pretrained model
            load_path = self.opt['path'].get('pretrain_network_g', None)
            if load_path is not None:
                self.load_network(self.net_g_ema, load_path,
                                  self.opt['path'].get('strict_load_g',
                                                       True), 'params_ema')
            else:
                self.model_ema(0)  # copy net_g weight
            self.net_g_ema.eval()

        # define losses
        if train_opt.get('pixel_opt'):
            pixel_type = train_opt['pixel_opt'].pop('type')
            cri_pix_cls = getattr(loss_module, pixel_type)
            self.cri_pix = cri_pix_cls(**train_opt['pixel_opt']).to(
                self.device)
        else:
            raise ValueError('pixel loss are None.')

        # set up optimizers and schedulers
        self.setup_optimizers()
        self.setup_schedulers()

    def setup_optimizers(self):
        train_opt = self.opt['train']
        optim_params = []

        for k, v in self.net_g.named_parameters():
            if v.requires_grad:
                optim_params.append(v)
            else:
                logger = get_root_logger()
                logger.warning(f'Params {k} will not be optimized.')

        optim_type = train_opt['optim_g'].pop('type')
        if optim_type == 'Adam':
            self.optimizer_g = torch.optim.Adam(optim_params, **train_opt['optim_g'])
        elif optim_type == 'AdamW':
            self.optimizer_g = torch.optim.AdamW(optim_params, **train_opt['optim_g'])
        else:
            raise NotImplementedError(
                f'optimizer {optim_type} is not supperted yet.')
        self.optimizers.append(self.optimizer_g)

    def feed_train_data(self, data):
        self.lq = data['lq'].to(self.device)
        if 'gt' in data:
            self.gt = data['gt'].to(self.device)

        if self.mixing_flag:
            self.gt, self.lq = self.mixing_augmentation(self.gt, self.lq)

    def feed_data(self, data):
        self.lq = data['lq'].to(self.device)
        if 'gt' in data:
            self.gt = data['gt'].to(self.device)

    def optimize_parameters(self, current_iter):
        self.optimizer_g.zero_grad()
        preds = self.net_g(self.lq)
        if not isinstance(preds, list):
            preds = [preds]

        self.output = preds[-1]

        loss_dict = OrderedDict()
        # pixel loss
        l_pix = 0.
        for pred in preds:
            l_pix += self.cri_pix(pred, self.gt)

        loss_dict['l_pix'] = l_pix

        l_pix.backward()
        if self.opt['train']['use_grad_clip']:
            torch.nn.utils.clip_grad_norm_(self.net_g.parameters(), 0.01)
        self.optimizer_g.step()

        self.log_dict = self.reduce_loss_dict(loss_dict)

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        self.nonpad_test(img)
        _, _, h, w = self.output.size()
        self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]

    def nonpad_test(self, img=None):
        if img is None:
            img = self.lq      
        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                pred = self.net_g_ema(img)
            if isinstance(pred, list):
                pred = pred[-1]
            self.output = pred
        else:
            self.net_g.eval()
            with torch.no_grad():
                pred = self.net_g(img)
            if isinstance(pred, list):
                pred = pred[-1]
            self.output = pred
            self.net_g.train()

    def dist_validation(self, dataloader, current_iter, tb_logger, save_img, rgb2bgr, use_image):
        if os.environ['LOCAL_RANK'] == '0':
            return self.nondist_validation(dataloader, current_iter, tb_logger, save_img, rgb2bgr, use_image)
        else:
            return 0.

    def nondist_validation(self, dataloader, current_iter, tb_logger,
                           save_img, rgb2bgr, use_image):
        dataset_name = dataloader.dataset.opt['name']
        with_metrics = self.opt['val'].get('metrics') is not None
        if with_metrics:
            self.metric_results = {
                metric: 0
                for metric in self.opt['val']['metrics'].keys()
            }
        # pbar = tqdm(total=len(dataloader), unit='image')

        window_size = self.opt['val'].get('window_size', 0)

        if window_size:
            test = partial(self.pad_test, window_size)
        else:
            test = self.nonpad_test

        cnt = 0

        for idx, val_data in enumerate(dataloader):
            img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]

            self.feed_data(val_data)
            test()

            visuals = self.get_current_visuals()
            sr_img = tensor2img([visuals['result']], rgb2bgr=rgb2bgr)
            if 'gt' in visuals:
                gt_img = tensor2img([visuals['gt']], rgb2bgr=rgb2bgr)
                del self.gt

            # tentative for out of GPU memory
            del self.lq
            del self.output
            torch.cuda.empty_cache()

            if save_img:
                
                if self.opt['is_train']:
                    
                    save_img_path = osp.join(self.opt['path']['visualization'],
                                             img_name,
                                             f'{img_name}_{current_iter}.png')
                    
                    save_gt_img_path = osp.join(self.opt['path']['visualization'],
                                             img_name,
                                             f'{img_name}_{current_iter}_gt.png')
                else:
                    
                    save_img_path = osp.join(
                        self.opt['path']['visualization'], dataset_name,
                        f'{img_name}.png')
                    save_gt_img_path = osp.join(
                        self.opt['path']['visualization'], dataset_name,
                        f'{img_name}_gt.png')
                    
                imwrite(sr_img, save_img_path)
                imwrite(gt_img, save_gt_img_path)

            if with_metrics:
                # calculate metrics
                opt_metric = deepcopy(self.opt['val']['metrics'])
                if use_image:
                    for name, opt_ in opt_metric.items():
                        metric_type = opt_.pop('type')
                        self.metric_results[name] += getattr(
                            metric_module, metric_type)(sr_img, gt_img, **opt_)
                else:
                    for name, opt_ in opt_metric.items():
                        metric_type = opt_.pop('type')
                        self.metric_results[name] += getattr(
                            metric_module, metric_type)(visuals['result'], visuals['gt'], **opt_)

            cnt += 1

        current_metric = 0.
        if with_metrics:
            for metric in self.metric_results.keys():
                self.metric_results[metric] /= cnt
                current_metric = self.metric_results[metric]

            self._log_validation_metric_values(current_iter, dataset_name,
                                               tb_logger)
        return current_metric


    def _log_validation_metric_values(self, current_iter, dataset_name,
                                      tb_logger):
        log_str = f'Validation {dataset_name},\t'
        for metric, value in self.metric_results.items():
            log_str += f'\t # {metric}: {value:.4f}'
        logger = get_root_logger()
        logger.info(log_str)
        if tb_logger:
            for metric, value in self.metric_results.items():
                tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)

    def get_current_visuals(self):
        out_dict = OrderedDict()
        out_dict['lq'] = self.lq.detach().cpu()
        out_dict['result'] = self.output.detach().cpu()
        if hasattr(self, 'gt'):
            out_dict['gt'] = self.gt.detach().cpu()
        return out_dict

    def save(self, epoch, current_iter):
        if self.ema_decay > 0:
            self.save_network([self.net_g, self.net_g_ema],
                              'net_g',
                              current_iter,
                              param_key=['params', 'params_ema'])
        else:
            self.save_network(self.net_g, 'net_g', current_iter)
        self.save_training_state(epoch, current_iter)


================================================
FILE: Deraining/basicsr/models/losses/__init__.py
================================================
from .losses import (L1Loss, MSELoss, PSNRLoss, CharbonnierLoss)

__all__ = [
    'L1Loss', 'MSELoss', 'PSNRLoss', 'CharbonnierLoss',
]


================================================
FILE: Deraining/basicsr/models/losses/loss_util.py
================================================
import functools
from torch.nn import functional as F


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are 'none', 'mean' and 'sum'.

    Returns:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    # none: 0, elementwise_mean:1, sum: 2
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    else:
        return loss.sum()


def weight_reduce_loss(loss, weight=None, reduction='mean'):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights. Default: None.
        reduction (str): Same as built-in losses of PyTorch. Options are
            'none', 'mean' and 'sum'. Default: 'mean'.

    Returns:
        Tensor: Loss values.
    """
    # if weight is specified, apply element-wise weight
    if weight is not None:
        assert weight.dim() == loss.dim()
        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight

    # if weight is not specified or reduction is sum, just reduce the loss
    if weight is None or reduction == 'sum':
        loss = reduce_loss(loss, reduction)
    # if reduction is mean, then compute mean over weight region
    elif reduction == 'mean':
        if weight.size(1) > 1:
            weight = weight.sum()
        else:
            weight = weight.sum() * loss.size(1)
        loss = loss.sum() / weight

    return loss


def weighted_loss(loss_func):
    """Create a weighted version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    **kwargs)`.

    :Example:

    >>> import torch
    >>> @weighted_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.5000)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, reduction='sum')
    tensor(3.)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
        # get element-wise loss
        loss = loss_func(pred, target, **kwargs)
        loss = weight_reduce_loss(loss, weight, reduction)
        return loss

    return wrapper


================================================
FILE: Deraining/basicsr/models/losses/losses.py
================================================
import torch
from torch import nn as nn
from torch.nn import functional as F
import numpy as np

from models.losses.loss_util import weighted_loss

_reduction_modes = ['none', 'mean', 'sum']


@weighted_loss
def l1_loss(pred, target):
    return F.l1_loss(pred, target, reduction='none')


@weighted_loss
def mse_loss(pred, target):
    return F.mse_loss(pred, target, reduction='none')


# @weighted_loss
# def charbonnier_loss(pred, target, eps=1e-12):
#     return torch.sqrt((pred - target)**2 + eps)


class L1Loss(nn.Module):
    """L1 (mean absolute error, MAE) loss.

    Args:
        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
        reduction (str): Specifies the reduction to apply to the output.
            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
    """

    def __init__(self, loss_weight=1.0, reduction='mean'):
        super(L1Loss, self).__init__()
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(f'Unsupported reduction mode: {reduction}. '
                             f'Supported ones are: {_reduction_modes}')

        self.loss_weight = loss_weight
        self.reduction = reduction

    def forward(self, pred, target, weight=None, **kwargs):
        """
        Args:
            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
                weights. Default: None.
        """
        return self.loss_weight * l1_loss(
            pred, target, weight, reduction=self.reduction)

class MSELoss(nn.Module):
    """MSE (L2) loss.

    Args:
        loss_weight (float): Loss weight for MSE loss. Default: 1.0.
        reduction (str): Specifies the reduction to apply to the output.
            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
    """

    def __init__(self, loss_weight=1.0, reduction='mean'):
        super(MSELoss, self).__init__()
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(f'Unsupported reduction mode: {reduction}. '
                             f'Supported ones are: {_reduction_modes}')

        self.loss_weight = loss_weight
        self.reduction = reduction

    def forward(self, pred, target, weight=None, **kwargs):
        """
        Args:
            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
                weights. Default: None.
        """
        return self.loss_weight * mse_loss(
            pred, target, weight, reduction=self.reduction)

class PSNRLoss(nn.Module):

    def __init__(self, loss_weight=1.0, reduction='mean', toY=False):
        super(PSNRLoss, self).__init__()
        assert reduction == 'mean'
        self.loss_weight = loss_weight
        self.scale = 10 / np.log(10)
        self.toY = toY
        self.coef = torch.tensor([65.481, 128.553, 24.966]).reshape(1, 3, 1, 1)
        self.first = True

    def forward(self, pred, target):
        assert len(pred.size()) == 4
        if self.toY:
            if self.first:
                self.coef = self.coef.to(pred.device)
                self.first = False

            pred = (pred * self.coef).sum(dim=1).unsqueeze(dim=1) + 16.
            target = (target * self.coef).sum(dim=1).unsqueeze(dim=1) + 16.

            pred, target = pred / 255., target / 255.
            pass
        assert len(pred.size()) == 4

        return self.loss_weight * self.scale * torch.log(((pred - target) ** 2).mean(dim=(1, 2, 3)) + 1e-8).mean()

class CharbonnierLoss(nn.Module):
    """Charbonnier Loss (L1)"""

    def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-3):
        super(CharbonnierLoss, self).__init__()
        self.eps = eps

    def forward(self, x, y):
        diff = x - y
        # loss = torch.sum(torch.sqrt(diff * diff + self.eps))
        loss = torch.mean(torch.sqrt((diff * diff) + (self.eps*self.eps)))
        return loss


================================================
FILE: Deraining/basicsr/models/lr_scheduler.py
================================================
import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
import torch


class MultiStepRestartLR(_LRScheduler):
    """ MultiStep with restarts learning rate scheme.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        restarts (list): Restart iterations. Default: [0].
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 milestones,
                 gamma=0.1,
                 restarts=(0, ),
                 restart_weights=(1, ),
                 last_epoch=-1):
        self.milestones = Counter(milestones)
        self.gamma = gamma
        self.restarts = restarts
        self.restart_weights = restart_weights
        assert len(self.restarts) == len(
            self.restart_weights), 'restarts and their weights do not match.'
        super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch in self.restarts:
            weight = self.restart_weights[self.restarts.index(self.last_epoch)]
            return [
                group['initial_lr'] * weight
                for group in self.optimizer.param_groups
            ]
        if self.last_epoch not in self.milestones:
            return [group['lr'] for group in self.optimizer.param_groups]
        return [
            group['lr'] * self.gamma**self.milestones[self.last_epoch]
            for group in self.optimizer.param_groups
        ]

class LinearLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(LinearLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter
        weight = (1 - process)
        # print('get lr ', [weight * group['initial_lr'] for group in self.optimizer.param_groups])
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

class VibrateLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(VibrateLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter

        f = 0.1
        if process < 3 / 8:
            f = 1 - process * 8 / 3
        elif process < 5 / 8:
            f = 0.2

        T = self.total_iter // 80
        Th = T // 2

        t = self.last_epoch % T

        f2 = t / Th
        if t >= Th:
            f2 = 2 - f2

        weight = f * f2

        if self.last_epoch < Th:
            weight = max(0.1, weight)

        # print('f {}, T {}, Th {}, t {}, f2 {}'.format(f, T, Th, t, f2))
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

def get_position_from_periods(iteration, cumulative_period):
    """Get the position from a period list.

    It will return the index of the right-closest number in the period list.
    For example, the cumulative_period = [100, 200, 300, 400],
    if iteration == 50, return 0;
    if iteration == 210, return 2;
    if iteration == 300, return 2.

    Args:
        iteration (int): Current iteration.
        cumulative_period (list[int]): Cumulative period list.

    Returns:
        int: The position of the right-closest number in the period list.
    """
    for i, period in enumerate(cumulative_period):
        if iteration <= period:
            return i


class CosineAnnealingRestartLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.

    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7

    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_min=0,
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_min = eta_min
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]

        return [
            self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]

class CosineAnnealingRestartCyclicLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.
    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7
    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.
    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_mins=(0, ),
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_mins = eta_mins
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartCyclicLR, self).__init__(optimizer, last_epoch)
        
    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]
        eta_min = self.eta_mins[idx]

        return [
            eta_min + current_weight * 0.5 * (base_lr - eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]


================================================
FILE: Deraining/basicsr/test.py
================================================
import logging
import torch
from os import path as osp

from data import create_dataloader, create_dataset
from models import create_model
from train import parse_options
from utils import (get_env_info, get_root_logger, get_time_str,
                           make_exp_dirs)
from utils.options import dict2str


def main():
    # parse options, set distributed setting, set ramdom seed
    opt = parse_options(is_train=False)

    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    # mkdir and initialize loggers
    make_exp_dirs(opt)
    log_file = osp.join(opt['path']['log'],
                        f"test_{opt['name']}_{get_time_str()}.log")
    logger = get_root_logger(
        logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
    logger.info(get_env_info())
    logger.info(dict2str(opt))

    # create test dataset and dataloader
    test_loaders = []
    for phase, dataset_opt in sorted(opt['datasets'].items()):
        test_set = create_dataset(dataset_opt)
        test_loader = create_dataloader(
            test_set,
            dataset_opt,
            num_gpu=opt['num_gpu'],
            dist=opt['dist'],
            sampler=None,
            seed=opt['manual_seed'])
        logger.info(
            f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
        test_loaders.append(test_loader)

    # create model
    model = create_model(opt)

    for test_loader in test_loaders:
        test_set_name = test_loader.dataset.opt['name']
        logger.info(f'Testing {test_set_name}...')
        rgb2bgr = opt['val'].get('rgb2bgr', True)
        # wheather use uint8 image to compute metrics
        use_image = opt['val'].get('use_image', True)
        model.validation(
            test_loader,
            current_iter=opt['name'],
            tb_logger=None,
            save_img=opt['val']['save_img'],
            rgb2bgr=rgb2bgr, use_image=use_image)


if __name__ == '__main__':
    main()


================================================
FILE: Deraining/basicsr/test_deraining.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881

import numpy as np
import os
import argparse
from tqdm import tqdm

import torch.nn as nn
import torch
import torch.nn.functional as F
import utils2

from natsort import natsorted
from glob import glob

import sys

from models.archs.mamber32_arch import Mamber32

from skimage import img_as_ubyte
from pdb import set_trace as stx

parser = argparse.ArgumentParser(description='Image Deraining using Restormer')

parser.add_argument('--input_dir', default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/', type=str, help='Directory of validation images')
parser.add_argument('--result_dir', default='./results/mamber32_net140000', type=str, help='Directory for results')
parser.add_argument('--weights', default='/mnt/bn/shiyuan-arnold/code/VmambaIR/Restoration/experiments/Deraining_mamber32/models/net_g_140000.pth', type=str, help='Path to weights')

args = parser.parse_args()

####### Load yaml #######
yaml_file = '/mnt/bn/shiyuan-arnold/code/VmambaIR/Restoration/Deraining/Options/Deraining_mamber32.yml'
import yaml

try:
    from yaml import CLoader as Loader
except ImportError:
    from yaml import Loader

x = yaml.load(open(yaml_file, mode='r'), Loader=Loader)

s = x['network_g'].pop('type')
##########################

model_restoration = Mamber32(**x['network_g'])

checkpoint = torch.load(args.weights)
model_restoration.load_state_dict(checkpoint['params'])
print("===>Testing using weights: ",args.weights)
model_restoration.cuda()
model_restoration = nn.DataParallel(model_restoration)
model_restoration.eval()


factor = 8
datasets = ['Rain100L', 'Rain100H', 'Test100', 'Test1200', 'Test2800']

for dataset in datasets:
    result_dir  = os.path.join(args.result_dir, dataset)
    os.makedirs(result_dir, exist_ok=True)

    inp_dir = os.path.join(args.input_dir, 'test', dataset, 'input')
    files = natsorted(glob(os.path.join(inp_dir, '*.png')) + glob(os.path.join(inp_dir, '*.jpg')))
    with torch.no_grad():
        for file_ in tqdm(files):
            torch.cuda.ipc_collect()
            torch.cuda.empty_cache()

            img = np.float32(utils2.load_img(file_))/255.
            img = torch.from_numpy(img).permute(2,0,1)
            input_ = img.unsqueeze(0).cuda()

            # Padding in case images are not multiples of 8
            h,w = input_.shape[2], input_.shape[3]
            H,W = ((h+factor)//factor)*factor, ((w+factor)//factor)*factor
            padh = H-h if h%factor!=0 else 0
            padw = W-w if w%factor!=0 else 0
            input_ = F.pad(input_, (0,padw,0,padh), 'reflect')

            restored = model_restoration(input_)

            # Unpad images to original dimensions
            restored = restored[:,:,:h,:w]

            restored = torch.clamp(restored,0,1).cpu().detach().permute(0, 2, 3, 1).squeeze(0).numpy()

            utils2.save_img((os.path.join(result_dir, os.path.splitext(os.path.split(file_)[-1])[0]+'.png')), img_as_ubyte(restored))


================================================
FILE: Deraining/basicsr/train.py
================================================
import argparse
import datetime
import logging
import math
import random
import time
import torch
from os import path as osp

#from basicsr.data import create_dataloader, create_dataset
from data import create_dataloader, create_dataset
from data.data_sampler import EnlargedSampler
from data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from models import create_model
from utils import (MessageLogger, check_resume, get_env_info,
                           get_root_logger, get_time_str, init_tb_logger,
                           init_wandb_logger, make_exp_dirs, mkdir_and_rename,
                           set_random_seed)
from utils.dist_util import get_dist_info, init_dist
from utils.options import dict2str, parse

import numpy as np

def parse_options(is_train=True):
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '-opt', type=str, required=True, help='Path to option YAML file.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm'],
        default='none',
        help='job launcher')
    parser.add_argument('--local-rank', type=int, default=0)
    args = parser.parse_args()
    opt = parse(args.opt, is_train=is_train)

    # distributed settings
    if args.launcher == 'none':
        opt['dist'] = False
        print('Disable distributed.', flush=True)
    else:
        opt['dist'] = True
        if args.launcher == 'slurm' and 'dist_params' in opt:
            init_dist(args.launcher, **opt['dist_params'])
        else:
            init_dist(args.launcher)
            print('init dist .. ', args.launcher)

    opt['rank'], opt['world_size'] = get_dist_info()

    # random seed
    seed = opt.get('manual_seed')
    if seed is None:
        seed = random.randint(1, 10000)
        opt['manual_seed'] = seed
    set_random_seed(seed + opt['rank'])

    return opt


def init_loggers(opt):
    log_file = osp.join(opt['path']['log'],
                        f"train_{opt['name']}_{get_time_str()}.log")
    logger = get_root_logger(
        logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
    logger.info(get_env_info())
    logger.info(dict2str(opt))

    # initialize wandb logger before tensorboard logger to allow proper sync:
    if (opt['logger'].get('wandb')
            is not None) and (opt['logger']['wandb'].get('project')
                              is not None) and ('debug' not in opt['name']):
        assert opt['logger'].get('use_tb_logger') is True, (
            'should turn on tensorboard when using wandb')
        init_wandb_logger(opt)
    tb_logger = None
    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
        tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
    return logger, tb_logger


def create_train_val_dataloader(opt, logger):
    # create train and val dataloaders
    train_loader, val_loader = None, None
    for phase, dataset_opt in opt['datasets'].items():
        if phase == 'train':
            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
            train_set = create_dataset(dataset_opt)
            train_sampler = EnlargedSampler(train_set, opt['world_size'],
                                            opt['rank'], dataset_enlarge_ratio)
            train_loader = create_dataloader(
                train_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=train_sampler,
                seed=opt['manual_seed'])

            num_iter_per_epoch = math.ceil(
                len(train_set) * dataset_enlarge_ratio /
                (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
            total_iters = int(opt['train']['total_iter'])
            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
            logger.info(
                'Training statistics:'
                f'\n\tNumber of train images: {len(train_set)}'
                f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
                f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
                f'\n\tWorld size (gpu number): {opt["world_size"]}'
                f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
                f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')

        elif phase == 'val':
            val_set = create_dataset(dataset_opt)
            val_loader = create_dataloader(
                val_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=None,
                seed=opt['manual_seed'])
            logger.info(
                f'Number of val images/folders in {dataset_opt["name"]}: '
                f'{len(val_set)}')
        else:
            raise ValueError(f'Dataset phase {phase} is not recognized.')

    return train_loader, train_sampler, val_loader, total_epochs, total_iters


def main():
    # parse options, set distributed setting, set ramdom seed
    opt = parse_options(is_train=True)

    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    # automatic resume ..
    state_folder_path = 'experiments/{}/training_states/'.format(opt['name'])
    import os
    try:
        states = os.listdir(state_folder_path)
    except:
        states = []

    resume_state = None
    if len(states) > 0:
        max_state_file = '{}.state'.format(max([int(x[0:-6]) for x in states]))
        resume_state = os.path.join(state_folder_path, max_state_file)
        opt['path']['resume_state'] = resume_state

    # load resume states if necessary
    if opt['path'].get('resume_state'):
        device_id = torch.cuda.current_device()
        resume_state = torch.load(
            opt['path']['resume_state'],
            map_location=lambda storage, loc: storage.cuda(device_id))
    else:
        resume_state = None

    # mkdir for experiments and logger
    if resume_state is None:
        make_exp_dirs(opt)
        if opt['logger'].get('use_tb_logger') and 'debug' not in opt[
                'name'] and opt['rank'] == 0:
            mkdir_and_rename(osp.join('tb_logger', opt['name']))

    # initialize loggers
    logger, tb_logger = init_loggers(opt)

    # create train and validation dataloaders
    result = create_train_val_dataloader(opt, logger)
    train_loader, train_sampler, val_loader, total_epochs, total_iters = result

    # create model
    if resume_state:  # resume training
        check_resume(opt, resume_state['iter'])
        model = create_model(opt)
        model.resume_training(resume_state)  # handle optimizers and schedulers
        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, "
                    f"iter: {resume_state['iter']}.")
        start_epoch = resume_state['epoch']
        current_iter = resume_state['iter']
    else:
        model = create_model(opt)
        start_epoch = 0
        current_iter = 0

    # create message logger (formatted outputs)
    msg_logger = MessageLogger(opt, current_iter, tb_logger)

    # dataloader prefetcher
    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
    if prefetch_mode is None or prefetch_mode == 'cpu':
        prefetcher = CPUPrefetcher(train_loader)
    elif prefetch_mode == 'cuda':
        prefetcher = CUDAPrefetcher(train_loader, opt)
        logger.info(f'Use {prefetch_mode} prefetch dataloader')
        if opt['datasets']['train'].get('pin_memory') is not True:
            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
    else:
        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.'
                         "Supported ones are: None, 'cuda', 'cpu'.")

    # training
    logger.info(
        f'Start training from epoch: {start_epoch}, iter: {current_iter}')
    data_time, iter_time = time.time(), time.time()
    start_time = time.time()

    # for epoch in range(start_epoch, total_epochs + 1):

    iters = opt['datasets']['train'].get('iters')
    batch_size = opt['datasets']['train'].get('batch_size_per_gpu')
    mini_batch_sizes = opt['datasets']['train'].get('mini_batch_sizes')
    gt_size = opt['datasets']['train'].get('gt_size')
    mini_gt_sizes = opt['datasets']['train'].get('gt_sizes')

    groups = np.array([sum(iters[0:i + 1]) for i in range(0, len(iters))])

    logger_j = [True] * len(groups)

    scale = opt['scale']

    epoch = start_epoch
    while current_iter <= total_iters:
        train_sampler.set_epoch(epoch)
        prefetcher.reset()
        train_data = prefetcher.next()

        while train_data is not None:
            data_time = time.time() - data_time

            current_iter += 1
            if current_iter > total_iters:
                break
            # update learning rate
            model.update_learning_rate(
                current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))

            
            ### ------Progressive learning ---------------------
            j = ((current_iter>groups) !=True).nonzero()[0]
            if len(j) == 0:
                bs_j = len(groups) - 1
            else:
                bs_j = j[0]

            mini_gt_size = mini_gt_sizes[bs_j]
            mini_batch_size = mini_batch_sizes[bs_j]
            
            if logger_j[bs_j]:
                logger.info('\n Updating Patch_Size to {} and Batch_Size to {} \n'.format(mini_gt_size, mini_batch_size*torch.cuda.device_count())) 
                logger_j[bs_j] = False

            lq = train_data['lq']
            gt = train_data['gt']

            if mini_batch_size < batch_size:
                indices = random.sample(range(0, batch_size), k=mini_batch_size)
                lq = lq[indices]
                gt = gt[indices]

            if mini_gt_size < gt_size:
                x0 = int((gt_size - mini_gt_size) * random.random())
                y0 = int((gt_size - mini_gt_size) * random.random())
                x1 = x0 + mini_gt_size
                y1 = y0 + mini_gt_size
                lq = lq[:,:,x0:x1,y0:y1]
                gt = gt[:,:,x0*scale:x1*scale,y0*scale:y1*scale]
            ###-------------------------------------------

            
            model.feed_train_data({'lq': lq, 'gt':gt})
            model.optimize_parameters(current_iter)

            iter_time = time.time() - iter_time
            # log
            if current_iter % opt['logger']['print_freq'] == 0:
                log_vars = {'epoch': epoch, 'iter': current_iter}
                log_vars.update({'lrs': model.get_current_learning_rate()})
                log_vars.update({'time': iter_time, 'data_time': data_time})
                log_vars.update(model.get_current_log())
                msg_logger(log_vars)

            # save models and training states
            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
                logger.info('Saving models and training states.')
                model.save(epoch, current_iter)

            # validation
            if opt.get('val') is not None and (current_iter %
                                               opt['val']['val_freq'] == 0):
                rgb2bgr = opt['val'].get('rgb2bgr', True)
                # wheather use uint8 image to compute metrics
                use_image = opt['val'].get('use_image', True)
                model.validation(val_loader, current_iter, tb_logger,
                                 opt['val']['save_img'], rgb2bgr, use_image )

            data_time = time.time()
            iter_time = time.time()
            train_data = prefetcher.next()
        # end of iter
        epoch += 1

    # end of epoch

    consumed_time = str(
        datetime.timedelta(seconds=int(time.time() - start_time)))
    logger.info(f'End of training. Time consumed: {consumed_time}')
    logger.info('Save the latest model.')
    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
    if opt.get('val') is not None:
        model.validation(val_loader, current_iter, tb_logger,
                         opt['val']['save_img'])
    if tb_logger:
        tb_logger.close()


if __name__ == '__main__':
    main()


================================================
FILE: Deraining/basicsr/utils/__init__.py
================================================
from .file_client import FileClient
from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img, padding, padding_DP, imfrombytesDP
from .logger import (MessageLogger, get_env_info, get_root_logger,
                     init_tb_logger, init_wandb_logger)
from .misc import (check_resume, get_time_str, make_exp_dirs, mkdir_and_rename,
                   scandir, scandir_SIDD, set_random_seed, sizeof_fmt)
from .create_lmdb import (create_lmdb_for_reds, create_lmdb_for_gopro, create_lmdb_for_rain13k)

__all__ = [
    # file_client.py
    'FileClient',
    # img_util.py
    'img2tensor',
    'tensor2img',
    'imfrombytes',
    'imwrite',
    'crop_border',
    # logger.py
    'MessageLogger',
    'init_tb_logger',
    'init_wandb_logger',
    'get_root_logger',
    'get_env_info',
    # misc.py
    'set_random_seed',
    'get_time_str',
    'mkdir_and_rename',
    'make_exp_dirs',
    'scandir',
    'check_resume',
    'sizeof_fmt',
    'padding',
    'padding_DP',
    'imfrombytesDP',
    'create_lmdb_for_reds',
    'create_lmdb_for_gopro',
    'create_lmdb_for_rain13k',
]


================================================
FILE: Deraining/basicsr/utils/bundle_submissions.py
================================================
 # Author: Tobias Plötz, TU Darmstadt (tobias.ploetz@visinf.tu-darmstadt.de)

 # This file is part of the implementation as described in the CVPR 2017 paper:
 # Tobias Plötz and Stefan Roth, Benchmarking Denoising Algorithms with Real Photographs.
 # Please see the file LICENSE.txt for the license governing this code.


import numpy as np
import scipy.io as sio
import os
import h5py

def bundle_submissions_raw(submission_folder,session):
    '''
    Bundles submission data for raw denoising

    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''

    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass

    israw = True
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

def bundle_submissions_srgb(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )



def bundle_submissions_srgb_v1(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

================================================
FILE: Deraining/basicsr/utils/create_lmdb.py
================================================
import argparse
from os import path as osp

from utils import scandir
from utils.lmdb_util import make_lmdb_from_imgs

def prepare_keys(folder_path, suffix='png'):
    """Prepare image path list and keys for DIV2K dataset.

    Args:
        folder_path (str): Folder path.

    Returns:
        list[str]: Image path list.
        list[str]: Key list.
    """
    print('Reading image path list ...')
    img_path_list = sorted(
        list(scandir(folder_path, suffix=suffix, recursive=False)))
    keys = [img_path.split('.{}'.format(suffix))[0] for img_path in sorted(img_path_list)]

    return img_path_list, keys

def create_lmdb_for_reds():
    folder_path = './datasets/REDS/val/sharp_300'
    lmdb_path = './datasets/REDS/val/sharp_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
    #
    folder_path = './datasets/REDS/val/blur_300'
    lmdb_path = './datasets/REDS/val/blur_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_sharp'
    lmdb_path = './datasets/REDS/train/train_sharp.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_blur_jpeg'
    lmdb_path = './datasets/REDS/train/train_blur_jpeg.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


def create_lmdb_for_gopro():
    folder_path = './datasets/GoPro/train/blur_crops'
    lmdb_path = './datasets/GoPro/train/blur_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/train/sharp_crops'
    lmdb_path = './datasets/GoPro/train/sharp_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/target'
    lmdb_path = './datasets/GoPro/test/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/input'
    lmdb_path = './datasets/GoPro/test/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_rain13k():
    folder_path = './datasets/Rain13k/train/input'
    lmdb_path = './datasets/Rain13k/train/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/Rain13k/train/target'
    lmdb_path = './datasets/Rain13k/train/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_SIDD():
    folder_path = './datasets/SIDD/train/input_crops'
    lmdb_path = './datasets/SIDD/train/input_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/train/gt_crops'
    lmdb_path = './datasets/SIDD/train/gt_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    #for val
    folder_path = './datasets/SIDD/val/input_crops'
    lmdb_path = './datasets/SIDD/val/input_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationNoisyBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationNoisyBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/val/gt_crops'
    lmdb_path = './datasets/SIDD/val/gt_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationGtBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationGtBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


================================================
FILE: Deraining/basicsr/utils/dist_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp


def init_dist(launcher, backend='nccl', **kwargs):
    if mp.get_start_method(allow_none=True) is None:
        mp.set_start_method('spawn')
    if launcher == 'pytorch':
        _init_dist_pytorch(backend, **kwargs)
    elif launcher == 'slurm':
        _init_dist_slurm(backend, **kwargs)
    else:
        raise ValueError(f'Invalid launcher type: {launcher}')


def _init_dist_pytorch(backend, **kwargs):
    rank = int(os.environ['RANK'])
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(rank % num_gpus)
    dist.init_process_group(backend=backend, **kwargs)


def _init_dist_slurm(backend, port=None):
    """Initialize slurm distributed training environment.

    If argument ``port`` is not specified, then the master port will be system
    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
    environment variable, then a default port ``29500`` will be used.

    Args:
        backend (str): Backend of torch.distributed.
        port (int, optional): Master port. Defaults to None.
    """
    proc_id = int(os.environ['SLURM_PROCID'])
    ntasks = int(os.environ['SLURM_NTASKS'])
    node_list = os.environ['SLURM_NODELIST']
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(proc_id % num_gpus)
    addr = subprocess.getoutput(
        f'scontrol show hostname {node_list} | head -n1')
    # specify master port
    if port is not None:
        os.environ['MASTER_PORT'] = str(port)
    elif 'MASTER_PORT' in os.environ:
        pass  # use MASTER_PORT in the environment variable
    else:
        # 29500 is torch.distributed default port
        os.environ['MASTER_PORT'] = '29500'
    os.environ['MASTER_ADDR'] = addr
    os.environ['WORLD_SIZE'] = str(ntasks)
    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
    os.environ['RANK'] = str(proc_id)
    dist.init_process_group(backend=backend)


def get_dist_info():
    if dist.is_available():
        initialized = dist.is_initialized()
    else:
        initialized = False
    if initialized:
        rank = dist.get_rank()
        world_size = dist.get_world_size()
    else:
        rank = 0
        world_size = 1
    return rank, world_size


def master_only(func):

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        rank, _ = get_dist_info()
        if rank == 0:
            return func(*args, **kwargs)

    return wrapper


================================================
FILE: Deraining/basicsr/utils/download_util.py
================================================
import math
import requests
from tqdm import tqdm

from .misc import sizeof_fmt


def download_file_from_google_drive(file_id, save_path):
    """Download files from google drive.

    Ref:
    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501

    Args:
        file_id (str): File id.
        save_path (str): Save path.
    """

    session = requests.Session()
    URL = 'https://docs.google.com/uc?export=download'
    params = {'id': file_id}

    response = session.get(URL, params=params, stream=True)
    token = get_confirm_token(response)
    if token:
        params['confirm'] = token
        response = session.get(URL, params=params, stream=True)

    # get file size
    response_file_size = session.get(
        URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
    if 'Content-Range' in response_file_size.headers:
        file_size = int(
            response_file_size.headers['Content-Range'].split('/')[1])
    else:
        file_size = None

    save_response_content(response, save_path, file_size)


def get_confirm_token(response):
    for key, value in response.cookies.items():
        if key.startswith('download_warning'):
            return value
    return None


def save_response_content(response,
                          destination,
                          file_size=None,
                          chunk_size=32768):
    if file_size is not None:
        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')

        readable_file_size = sizeof_fmt(file_size)
    else:
        pbar = None

    with open(destination, 'wb') as f:
        downloaded_size = 0
        for chunk in response.iter_content(chunk_size):
            downloaded_size += chunk_size
            if pbar is not None:
                pbar.update(1)
                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} '
                                     f'/ {readable_file_size}')
            if chunk:  # filter out keep-alive new chunks
                f.write(chunk)
        if pbar is not None:
            pbar.close()


================================================
FILE: Deraining/basicsr/utils/face_util.py
================================================
import cv2
import numpy as np
import os
import torch
from skimage import transform as trans

from utils import imwrite

try:
    import dlib
except ImportError:
    print('Please install dlib before testing face restoration.'
          'Reference:　https://github.com/davisking/dlib')


class FaceRestorationHelper(object):
    """Helper for the face restoration pipeline."""

    def __init__(self, upscale_factor, face_size=512):
        self.upscale_factor = upscale_factor
        self.face_size = (face_size, face_size)

        # standard 5 landmarks for FFHQ faces with 1024 x 1024
        self.face_template = np.array([[686.77227723, 488.62376238],
                                       [586.77227723, 493.59405941],
                                       [337.91089109, 488.38613861],
                                       [437.95049505, 493.51485149],
                                       [513.58415842, 678.5049505]])
        self.face_template = self.face_template / (1024 // face_size)
        # for estimation the 2D similarity transformation
        self.similarity_trans = trans.SimilarityTransform()

        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.affine_matrices = []
        self.inverse_affine_matrices = []
        self.cropped_faces = []
        self.restored_faces = []
        self.save_png = True

    def init_dlib(self, detection_path, landmark5_path, landmark68_path):
        """Initialize the dlib detectors and predictors."""
        self.face_detector = dlib.cnn_face_detection_model_v1(detection_path)
        self.shape_predictor_5 = dlib.shape_predictor(landmark5_path)
        self.shape_predictor_68 = dlib.shape_predictor(landmark68_path)

    def free_dlib_gpu_memory(self):
        del self.face_detector
        del self.shape_predictor_5
        del self.shape_predictor_68

    def read_input_image(self, img_path):
        # self.input_img is Numpy array, (h, w, c) with RGB order
        self.input_img = dlib.load_rgb_image(img_path)

    def detect_faces(self,
                     img_path,
                     upsample_num_times=1,
                     only_keep_largest=False):
        """
        Args:
            img_path (str): Image path.
            upsample_num_times (int): Upsamples the image before running the
                face detector

        Returns:
            int: Number of detected faces.
        """
        self.read_input_image(img_path)
        det_faces = self.face_detector(self.input_img, upsample_num_times)
        if len(det_faces) == 0:
            print('No face detected. Try to increase upsample_num_times.')
        else:
            if only_keep_largest:
                print('Detect several faces and only keep the largest.')
                face_areas = []
                for i in range(len(det_faces)):
                    face_area = (det_faces[i].rect.right() -
                                 det_faces[i].rect.left()) * (
                                     det_faces[i].rect.bottom() -
                                     det_faces[i].rect.top())
                    face_areas.append(face_area)
                largest_idx = face_areas.index(max(face_areas))
                self.det_faces = [det_faces[largest_idx]]
            else:
                self.det_faces = det_faces
        return len(self.det_faces)

    def get_face_landmarks_5(self):
        for face in self.det_faces:
            shape = self.shape_predictor_5(self.input_img, face.rect)
            landmark = np.array([[part.x, part.y] for part in shape.parts()])
            self.all_landmarks_5.append(landmark)
        return len(self.all_landmarks_5)

    def get_face_landmarks_68(self):
        """Get 68 densemarks for cropped images.

        Should only have one face at most in the cropped image.
        """
        num_detected_face = 0
        for idx, face in enumerate(self.cropped_faces):
            # face detection
            det_face = self.face_detector(face, 1)  # TODO: can we remove it?
            if len(det_face) == 0:
                print(f'Cannot find faces in cropped image with index {idx}.')
                self.all_landmarks_68.append(None)
            else:
                if len(det_face) > 1:
                    print('Detect several faces in the cropped face. Use the '
                          ' largest one. Note that it will also cause overlap '
                          'during paste_faces_to_input_image.')
                    face_areas = []
                    for i in range(len(det_face)):
                        face_area = (det_face[i].rect.right() -
                                     det_face[i].rect.left()) * (
                                         det_face[i].rect.bottom() -
                                         det_face[i].rect.top())
                        face_areas.append(face_area)
                    largest_idx = face_areas.index(max(face_areas))
                    face_rect = det_face[largest_idx].rect
                else:
                    face_rect = det_face[0].rect
                shape = self.shape_predictor_68(face, face_rect)
                landmark = np.array([[part.x, part.y]
                                     for part in shape.parts()])
                self.all_landmarks_68.append(landmark)
                num_detected_face += 1

        return num_detected_face

    def warp_crop_faces(self,
                        save_cropped_path=None,
                        save_inverse_affine_path=None):
        """Get affine matrix, warp and cropped faces.

        Also get inverse affine matrix for post-processing.
        """
        for idx, landmark in enumerate(self.all_landmarks_5):
            # use 5 landmarks to get affine matrix
            self.similarity_trans.estimate(landmark, self.face_template)
            affine_matrix = self.similarity_trans.params[0:2, :]
            self.affine_matrices.append(affine_matrix)
            # warp and crop faces
            cropped_face = cv2.warpAffine(self.input_img, affine_matrix,
                                          self.face_size)
            self.cropped_faces.append(cropped_face)
            # save the cropped face
            if save_cropped_path is not None:
                path, ext = os.path.splitext(save_cropped_path)
                if self.save_png:
                    save_path = f'{path}_{idx:02d}.png'
                else:
                    save_path = f'{path}_{idx:02d}{ext}'

                imwrite(
                    cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path)

            # get inverse affine matrix
            self.similarity_trans.estimate(self.face_template,
                                           landmark * self.upscale_factor)
            inverse_affine = self.similarity_trans.params[0:2, :]
            self.inverse_affine_matrices.append(inverse_affine)
            # save inverse affine matrices
            if save_inverse_affine_path is not None:
                path, _ = os.path.splitext(save_inverse_affine_path)
                save_path = f'{path}_{idx:02d}.pth'
                torch.save(inverse_affine, save_path)

    def add_restored_face(self, face):
        self.restored_faces.append(face)

    def paste_faces_to_input_image(self, save_path):
        # operate in the BGR order
        input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR)
        h, w, _ = input_img.shape
        h_up, w_up = h * self.upscale_factor, w * self.upscale_factor
        # simply resize the background
        upsample_img = cv2.resize(input_img, (w_up, h_up))
        assert len(self.restored_faces) == len(self.inverse_affine_matrices), (
            'length of restored_faces and affine_matrices are different.')
        for restored_face, inverse_affine in zip(self.restored_faces,
                                                 self.inverse_affine_matrices):
            inv_restored = cv2.warpAffine(restored_face, inverse_affine,
                                          (w_up, h_up))
            mask = np.ones((*self.face_size, 3), dtype=np.float32)
            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
            # remove the black borders
            inv_mask_erosion = cv2.erode(
                inv_mask,
                np.ones((2 * self.upscale_factor, 2 * self.upscale_factor),
                        np.uint8))
            inv_restored_remove_border = inv_mask_erosion * inv_restored
            total_face_area = np.sum(inv_mask_erosion) // 3
            # compute the fusion edge based on the area of face
            w_edge = int(total_face_area**0.5) // 20
            erosion_radius = w_edge * 2
            inv_mask_center = cv2.erode(
                inv_mask_erosion,
                np.ones((erosion_radius, erosion_radius), np.uint8))
            blur_size = w_edge * 2
            inv_soft_mask = cv2.GaussianBlur(inv_mask_center,
                                             (blur_size + 1, blur_size + 1), 0)
            upsample_img = inv_soft_mask * inv_restored_remove_border + (
                1 - inv_soft_mask) * upsample_img
        if self.save_png:
            save_path = save_path.replace('.jpg',
                                          '.png').replace('.jpeg', '.png')
        imwrite(upsample_img.astype(np.uint8), save_path)

    def clean_all(self):
        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.restored_faces = []
        self.affine_matrices = []
        self.cropped_faces = []
        self.inverse_affine_matrices = []


================================================
FILE: Deraining/basicsr/utils/file_client.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py  # noqa: E501
from abc import ABCMeta, abstractmethod


class BaseStorageBackend(metaclass=ABCMeta):
    """Abstract class of storage backends.

    All backends need to implement two apis: ``get()`` and ``get_text()``.
    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
    as texts.
    """

    @abstractmethod
    def get(self, filepath):
        pass

    @abstractmethod
    def get_text(self, filepath):
        pass


class MemcachedBackend(BaseStorageBackend):
    """Memcached storage backend.

    Attributes:
        server_list_cfg (str): Config file for memcached server list.
        client_cfg (str): Config file for memcached client.
        sys_path (str | None): Additional path to be appended to `sys.path`.
            Default: None.
    """

    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
        if sys_path is not None:
            import sys
            sys.path.append(sys_path)
        try:
            import mc
        except ImportError:
            raise ImportError(
                'Please install memcached to enable MemcachedBackend.')

        self.server_list_cfg = server_list_cfg
        self.client_cfg = client_cfg
        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
                                                      self.client_cfg)
        # mc.pyvector servers as a point which points to a memory cache
        self._mc_buffer = mc.pyvector()

    def get(self, filepath):
        filepath = str(filepath)
        import mc
        self._client.Get(filepath, self._mc_buffer)
        value_buf = mc.ConvertBuffer(self._mc_buffer)
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class HardDiskBackend(BaseStorageBackend):
    """Raw hard disks storage backend."""

    def get(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'rb') as f:
            value_buf = f.read()
        return value_buf

    def get_text(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'r') as f:
            value_buf = f.read()
        return value_buf


class LmdbBackend(BaseStorageBackend):
    """Lmdb storage backend.

    Args:
        db_paths (str | list[str]): Lmdb database paths.
        client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
        readonly (bool, optional): Lmdb environment parameter. If True,
            disallow any write operations. Default: True.
        lock (bool, optional): Lmdb environment parameter. If False, when
            concurrent access occurs, do not lock the database. Default: False.
        readahead (bool, optional): Lmdb environment parameter. If False,
            disable the OS filesystem readahead mechanism, which may improve
            random read performance when a database is larger than RAM.
            Default: False.

    Attributes:
        db_paths (list): Lmdb database path.
        _client (list): A list of several lmdb envs.
    """

    def __init__(self,
                 db_paths,
                 client_keys='default',
                 readonly=True,
                 lock=False,
                 readahead=False,
                 **kwargs):
        try:
            import lmdb
        except ImportError:
            raise ImportError('Please install lmdb to enable LmdbBackend.')

        if isinstance(client_keys, str):
            client_keys = [client_keys]

        if isinstance(db_paths, list):
            self.db_paths = [str(v) for v in db_paths]
        elif isinstance(db_paths, str):
            self.db_paths = [str(db_paths)]
        assert len(client_keys) == len(self.db_paths), (
            'client_keys and db_paths should have the same length, '
            f'but received {len(client_keys)} and {len(self.db_paths)}.')

        self._client = {}

        for client, path in zip(client_keys, self.db_paths):
            self._client[client] = lmdb.open(
                path,
                readonly=readonly,
                lock=lock,
                readahead=readahead,
                map_size=8*1024*10485760,
                # max_readers=1,
                **kwargs)

    def get(self, filepath, client_key):
        """Get values according to the filepath from one lmdb named client_key.

        Args:
            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
            client_key (str): Used for distinguishing differnet lmdb envs.
        """
        filepath = str(filepath)
        assert client_key in self._client, (f'client_key {client_key} is not '
                                            'in lmdb clients.')
        client = self._client[client_key]
        with client.begin(write=False) as txn:
            value_buf = txn.get(filepath.encode('ascii'))
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class FileClient(object):
    """A general file client to access files in different backend.

    The client loads a file or text in a specified backend from its path
    and return it as a binary file. it can also register other backend
    accessor with a given name and backend class.

    Attributes:
        backend (str): The storage backend type. Options are "disk",
            "memcached" and "lmdb".
        client (:obj:`BaseStorageBackend`): The backend object.
    """

    _backends = {
        'disk': HardDiskBackend,
        'memcached': MemcachedBackend,
        'lmdb': LmdbBackend,
    }

    def __init__(self, backend='disk', **kwargs):
        if backend not in self._backends:
            raise ValueError(
                f'Backend {backend} is not supported. Currently supported ones'
                f' are {list(self._backends.keys())}')
        self.backend = backend
        self.client = self._backends[backend](**kwargs)

    def get(self, filepath, client_key='default'):
        # client_key is used only for lmdb, where different fileclients have
        # different lmdb environments.
        if self.backend == 'lmdb':
            return self.client.get(filepath, client_key)
        else:
            return self.client.get(filepath)

    def get_text(self, filepath):
        return self.client.get_text(filepath)


================================================
FILE: Deraining/basicsr/utils/flow_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py  # noqa: E501
import cv2
import numpy as np
import os


def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
    """Read an optical flow map.

    Args:
        flow_path (ndarray or str): Flow path.
        quantize (bool): whether to read quantized pair, if set to True,
            remaining args will be passed to :func:`dequantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.

    Returns:
        ndarray: Optical flow represented as a (h, w, 2) numpy array
    """
    if quantize:
        assert concat_axis in [0, 1]
        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
        if cat_flow.ndim != 2:
            raise IOError(f'{flow_path} is not a valid quantized flow file, '
                          f'its dimension is {cat_flow.ndim}.')
        assert cat_flow.shape[concat_axis] % 2 == 0
        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
        flow = dequantize_flow(dx, dy, *args, **kwargs)
    else:
        with open(flow_path, 'rb') as f:
            try:
                header = f.read(4).decode('utf-8')
            except Exception:
                raise IOError(f'Invalid flow file: {flow_path}')
            else:
                if header != 'PIEH':
                    raise IOError(f'Invalid flow file: {flow_path}, '
                                  'header does not contain PIEH')

            w = np.fromfile(f, np.int32, 1).squeeze()
            h = np.fromfile(f, np.int32, 1).squeeze()
            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))

    return flow.astype(np.float32)


def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
    """Write optical flow to file.

    If the flow is not quantized, it will be saved as a .flo file losslessly,
    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
    will be concatenated horizontally into a single image if quantize is True.)

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        filename (str): Output filepath.
        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
            images. If set to True, remaining args will be passed to
            :func:`quantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.
    """
    if not quantize:
        with open(filename, 'wb') as f:
            f.write('PIEH'.encode('utf-8'))
            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
            flow = flow.astype(np.float32)
            flow.tofile(f)
            f.flush()
    else:
        assert concat_axis in [0, 1]
        dx, dy = quantize_flow(flow, *args, **kwargs)
        dxdy = np.concatenate((dx, dy), axis=concat_axis)
        os.makedirs(filename, exist_ok=True)
        cv2.imwrite(dxdy, filename)


def quantize_flow(flow, max_val=0.02, norm=True):
    """Quantize flow to [0, 255].

    After this step, the size of flow will be much smaller, and can be
    dumped as jpeg images.

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        max_val (float): Maximum value of flow, values beyond
                        [-max_val, max_val] will be truncated.
        norm (bool): Whether to divide flow values by image width/height.

    Returns:
        tuple[ndarray]: Quantized dx and dy.
    """
    h, w, _ = flow.shape
    dx = flow[..., 0]
    dy = flow[..., 1]
    if norm:
        dx = dx / w  # avoid inplace operations
        dy = dy / h
    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
    flow_comps = [
        quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]
    ]
    return tuple(flow_comps)


def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
    """Recover from quantized flow.

    Args:
        dx (ndarray): Quantized dx.
        dy (ndarray): Quantized dy.
        max_val (float): Maximum value used when quantizing.
        denorm (bool): Whether to multiply flow values with width/height.

    Returns:
        ndarray: Dequantized flow.
    """
    assert dx.shape == dy.shape
    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)

    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]

    if denorm:
        dx *= dx.shape[1]
        dy *= dx.shape[0]
    flow = np.dstack((dx, dy))
    return flow


def quantize(arr, min_val, max_val, levels, dtype=np.int64):
    """Quantize an array of (-inf, inf) to [0, levels-1].

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the quantized array.

    Returns:
        tuple: Quantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    arr = np.clip(arr, min_val, max_val) - min_val
    quantized_arr = np.minimum(
        np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)

    return quantized_arr


def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
    """Dequantize an array.

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the dequantized array.

    Returns:
        tuple: Dequantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val -
                                                   min_val) / levels + min_val

    return dequantized_arr


================================================
FILE: Deraining/basicsr/utils/img_util.py
================================================
import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    else:
        return _totensor(imgs, bgr2rgb, float32)


def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
    """Convert torch Tensors into image numpy arrays.

    After clamping to [min, max], values will be normalized to [0, 1].

    Args:
        tensor (Tensor or list[Tensor]): Accept shapes:
            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
            2) 3D Tensor of shape (3/1 x H x W);
            3) 2D Tensor of shape (H x W).
            Tensor channel should be in RGB order.
        rgb2bgr (bool): Whether to change rgb to bgr.
        out_type (numpy type): output types. If ``np.uint8``, transform outputs
            to uint8 type with range [0, 255]; otherwise, float type with
            range [0, 1]. Default: ``np.uint8``.
        min_max (tuple[int]): min and max values for clamp.

    Returns:
        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
        shape (H x W). The channel order is BGR.
    """
    if not (torch.is_tensor(tensor) or
            (isinstance(tensor, list)
             and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(
            f'tensor or list of tensors expected, got {type(tensor)}')

    if torch.is_tensor(tensor):
        tensor = [tensor]
    result = []
    for _tensor in tensor:
        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])

        n_dim = _tensor.dim()
        if n_dim == 4:
            img_np = make_grid(
                _tensor, nrow=int(math.sqrt(_tensor.size(0))),
                normalize=False).numpy()
            img_np = img_np.transpose(1, 2, 0)
            if rgb2bgr:
                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 3:
            img_np = _tensor.numpy()
            img_np = img_np.transpose(1, 2, 0)
            if img_np.shape[2] == 1:  # gray image
                img_np = np.squeeze(img_np, axis=2)
            else:
                if rgb2bgr:
                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 2:
            img_np = _tensor.numpy()
        else:
            raise TypeError('Only support 4D, 3D or 2D tensor. '
                            f'But received with dimension: {n_dim}')
        if out_type == np.uint8:
            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
            img_np = (img_np * 255.0).round()
        img_np = img_np.astype(out_type)
        result.append(img_np)
    if len(result) == 1:
        result = result[0]
    return result


def imfrombytes(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    imread_flags = {
        'color': cv2.IMREAD_COLOR,
        'grayscale': cv2.IMREAD_GRAYSCALE,
        'unchanged': cv2.IMREAD_UNCHANGED
    }
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, imread_flags[flag])
    if float32:
        img = img.astype(np.float32) / 255.
    return img

def imfrombytesDP(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, cv2.IMREAD_UNCHANGED)
    if float32:
        img = img.astype(np.float32) / 65535.
    return img

def padding(img_lq, img_gt, gt_size):
    h, w, _ = img_lq.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lq, img_gt

    img_lq = cv2.copyMakeBorder(img_lq, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt = cv2.copyMakeBorder(img_gt, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    if img_lq.ndim == 2:
        img_lq = np.expand_dims(img_lq, axis=2)
    if img_gt.ndim == 2:
        img_gt = np.expand_dims(img_gt, axis=2)
    return img_lq, img_gt

def padding_DP(img_lqL, img_lqR, img_gt, gt_size):
    h, w, _ = img_gt.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lqL, img_lqR, img_gt

    img_lqL = cv2.copyMakeBorder(img_lqL, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_lqR = cv2.copyMakeBorder(img_lqR, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt  = cv2.copyMakeBorder(img_gt,  0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    return img_lqL, img_lqR, img_gt

def imwrite(img, file_path, params=None, auto_mkdir=True):
    """Write image to file.

    Args:
        img (ndarray): Image array to be written.
        file_path (str): Image file path.
        params (None or list): Same as opencv's :func:`imwrite` interface.
        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
            whether to create it automatically.

    Returns:
        bool: Successful or not.
    """
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(file_path))
        os.makedirs(dir_name, exist_ok=True)
    return cv2.imwrite(file_path, img, params)


def crop_border(imgs, crop_border):
    """Crop borders of images.

    Args:
        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
        crop_border (int): Crop border for each end of height and weight.

    Returns:
        list[ndarray]: Cropped images.
    """
    if crop_border == 0:
        return imgs
    else:
        if isinstance(imgs, list):
            return [
                v[crop_border:-crop_border, crop_border:-crop_border, ...]
                for v in imgs
            ]
        else:
            return imgs[crop_border:-crop_border, crop_border:-crop_border,
                        ...]


================================================
FILE: Deraining/basicsr/utils/lmdb_util.py
================================================
import cv2
import lmdb
import sys
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def make_lmdb_from_imgs(data_path,
                        lmdb_path,
                        img_path_list,
                        keys,
                        batch=5000,
                        compress_level=1,
                        multiprocessing_read=False,
                        n_thread=40,
                        map_size=None):
    """Make lmdb from images.

    Contents of lmdb. The file structure is:
    example.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records 1)image name (with extension),
    2)image shape, and 3)compression level, separated by a white space.

    For example, the meta information could be:
    `000_00000000.png (720,1280,3) 1`, which means:
    1) image name (with extension): 000_00000000.png;
    2) image shape: (720,1280,3);
    3) compression level: 1

    We use the image name without extension as the lmdb key.

    If `multiprocessing_read` is True, it will read all the images to memory
    using multiprocessing. Thus, your server needs to have enough memory.

    Args:
        data_path (str): Data path for reading images.
        lmdb_path (str): Lmdb save path.
        img_path_list (str): Image path list.
        keys (str): Used for lmdb keys.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
        multiprocessing_read (bool): Whether use multiprocessing to read all
            the images to memory. Default: False.
        n_thread (int): For multiprocessing.
        map_size (int | None): Map size for lmdb env. If None, use the
            estimated size from images. Default: None
    """

    assert len(img_path_list) == len(keys), (
        'img_path_list and keys should have the same length, '
        f'but got {len(img_path_list)} and {len(keys)}')
    print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
    print(f'Totoal images: {len(img_path_list)}')
    if not lmdb_path.endswith('.lmdb'):
        raise ValueError("lmdb_path must end with '.lmdb'.")
    if osp.exists(lmdb_path):
        print(f'Folder {lmdb_path} already exists. Exit.')
        sys.exit(1)

    if multiprocessing_read:
        # read all the images to memory (multiprocessing)
        dataset = {}  # use dict to keep the order for multiprocessing
        shapes = {}
        print(f'Read images with multiprocessing, #thread: {n_thread} ...')
        pbar = tqdm(total=len(img_path_list), unit='image')

        def callback(arg):
            """get the image data and update pbar."""
            key, dataset[key], shapes[key] = arg
            pbar.update(1)
            pbar.set_description(f'Read {key}')

        pool = Pool(n_thread)
        for path, key in zip(img_path_list, keys):
            pool.apply_async(
                read_img_worker,
                args=(osp.join(data_path, path), key, compress_level),
                callback=callback)
        pool.close()
        pool.join()
        pbar.close()
        print(f'Finish reading {len(img_path_list)} images.')

    # create lmdb environment
    if map_size is None:
        # obtain data size for one image
        img = cv2.imread(
            osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
        _, img_byte = cv2.imencode(
            '.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
        data_size_per_img = img_byte.nbytes
        print('Data size per image is: ', data_size_per_img)
        data_size = data_size_per_img * len(img_path_list)
        map_size = data_size * 10

    env = lmdb.open(lmdb_path, map_size=map_size)

    # write data to lmdb
    pbar = tqdm(total=len(img_path_list), unit='chunk')
    txn = env.begin(write=True)
    txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
    for idx, (path, key) in enumerate(zip(img_path_list, keys)):
        pbar.update(1)
        pbar.set_description(f'Write {key}')
        key_byte = key.encode('ascii')
        if multiprocessing_read:
            img_byte = dataset[key]
            h, w, c = shapes[key]
        else:
            _, img_byte, img_shape = read_img_worker(
                osp.join(data_path, path), key, compress_level)
            h, w, c = img_shape

        txn.put(key_byte, img_byte)
        # write meta information
        txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
        if idx % batch == 0:
            txn.commit()
            txn = env.begin(write=True)
    pbar.close()
    txn.commit()
    env.close()
    txt_file.close()
    print('\nFinish writing lmdb.')


def read_img_worker(path, key, compress_level):
    """Read image worker.

    Args:
        path (str): Image path.
        key (str): Image key.
        compress_level (int): Compress level when encoding images.

    Returns:
        str: Image key.
        byte: Image byte.
        tuple[int]: Image shape.
    """

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    if img.ndim == 2:
        h, w = img.shape
        c = 1
    else:
        h, w, c = img.shape
    _, img_byte = cv2.imencode('.png', img,
                               [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
    return (key, img_byte, (h, w, c))


class LmdbMaker():
    """LMDB Maker.

    Args:
        lmdb_path (str): Lmdb save path.
        map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
    """

    def __init__(self,
                 lmdb_path,
                 map_size=1024**4,
                 batch=5000,
                 compress_level=1):
        if not lmdb_path.endswith('.lmdb'):
            raise ValueError("lmdb_path must end with '.lmdb'.")
        if osp.exists(lmdb_path):
            print(f'Folder {lmdb_path} already exists. Exit.')
            sys.exit(1)

        self.lmdb_path = lmdb_path
        self.batch = batch
        self.compress_level = compress_level
        self.env = lmdb.open(lmdb_path, map_size=map_size)
        self.txn = self.env.begin(write=True)
        self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
        self.counter = 0

    def put(self, img_byte, key, img_shape):
        self.counter += 1
        key_byte = key.encode('ascii')
        self.txn.put(key_byte, img_byte)
        # write meta information
        h, w, c = img_shape
        self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
        if self.counter % self.batch == 0:
            self.txn.commit()
            self.txn = self.env.begin(write=True)

    def close(self):
        self.txn.commit()
        self.env.close()
        self.txt_file.close()


================================================
FILE: Deraining/basicsr/utils/logger.py
================================================
import datetime
import logging
import time

from .dist_util import get_dist_info, master_only

initialized_logger = {}


class MessageLogger():
    """Message logger for printing.

    Args:
        opt (dict): Config. It contains the following keys:
            name (str): Exp name.
            logger (dict): Contains 'print_freq' (str) for logger interval.
            train (dict): Contains 'total_iter' (int) for total iters.
            use_tb_logger (bool): Use tensorboard logger.
        start_iter (int): Start iter. Default: 1.
        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
    """

    def __init__(self, opt, start_iter=1, tb_logger=None):
        self.exp_name = opt['name']
        self.interval = opt['logger']['print_freq']
        self.start_iter = start_iter
        self.max_iters = opt['train']['total_iter']
        self.use_tb_logger = opt['logger']['use_tb_logger']
        self.tb_logger = tb_logger
        self.start_time = time.time()
        self.logger = get_root_logger()

    @master_only
    def __call__(self, log_vars):
        """Format logging message.

        Args:
            log_vars (dict): It contains the following keys:
                epoch (int): Epoch number.
                iter (int): Current iter.
                lrs (list): List for learning rates.

                time (float): Iter time.
                data_time (float): Data time for each iter.
        """
        # epoch, iter, learning rates
        epoch = log_vars.pop('epoch')
        current_iter = log_vars.pop('iter')
        lrs = log_vars.pop('lrs')

        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(')
        for v in lrs:
            message += f'{v:.3e},'
        message += ')] '

        # time and estimated time
        if 'time' in log_vars.keys():
            iter_time = log_vars.pop('time')
            data_time = log_vars.pop('data_time')

            total_time = time.time() - self.start_time
            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
            message += f'[eta: {eta_str}, '
            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '

        # other items, especially losses
        for k, v in log_vars.items():
            message += f'{k}: {v:.4e} '
            # tensorboard logger
            if self.use_tb_logger and 'debug' not in self.exp_name:
                if k.startswith('l_'):
                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
                else:
                    self.tb_logger.add_scalar(k, v, current_iter)
        self.logger.info(message)


@master_only
def init_tb_logger(log_dir):
    from torch.utils.tensorboard import SummaryWriter
    tb_logger = SummaryWriter(log_dir=log_dir)
    return tb_logger


@master_only
def init_wandb_logger(opt):
    """We now only use wandb to sync tensorboard log."""
    import wandb
    logger = logging.getLogger('basicsr')

    project = opt['logger']['wandb']['project']
    resume_id = opt['logger']['wandb'].get('resume_id')
    if resume_id:
        wandb_id = resume_id
        resume = 'allow'
        logger.warning(f'Resume wandb logger with id={wandb_id}.')
    else:
        wandb_id = wandb.util.generate_id()
        resume = 'never'

    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)

    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')


def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
    """Get the root logger.

    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If `log_file` is specified, a FileHandler will
    also be added.

    Args:
        logger_name (str): root logger name. Default: 'basicsr'.
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.

    Returns:
        logging.Logger: The root logger.
    """
    logger = logging.getLogger(logger_name)
    # if the logger has been initialized, just return it
    if logger_name in initialized_logger:
        return logger

    format_str = '%(asctime)s %(levelname)s: %(message)s'
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(logging.Formatter(format_str))
    logger.addHandler(stream_handler)
    logger.propagate = False
    rank, _ = get_dist_info()
    if rank != 0:
        logger.setLevel('ERROR')
    elif log_file is not None:
        logger.setLevel(log_level)
        # add file handler
        file_handler = logging.FileHandler(log_file, 'w')
        file_handler.setFormatter(logging.Formatter(format_str))
        file_handler.setLevel(log_level)
        logger.addHandler(file_handler)
    initialized_logger[logger_name] = True
    return logger


def get_env_info():
    """Get environment information.

    Currently, only log the software version.
    """
    import torch
    import torchvision

    from version import __version__
    msg = r"""
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    """
    msg += ('\nVersion Information: '
            f'\n\tBasicSR: {__version__}'
            f'\n\tPyTorch: {torch.__version__}'
            f'\n\tTorchVision: {torchvision.__version__}')
    return msg

================================================
FILE: Deraining/basicsr/utils/matlab_functions.py
================================================
import math
import numpy as np
import torch


def cubic(x):
    """cubic function used for calculate_weights_indices."""
    absx = torch.abs(x)
    absx2 = absx**2
    absx3 = absx**3
    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx +
                                      2) * (((absx > 1) *
                                             (absx <= 2)).type_as(absx))


def calculate_weights_indices(in_length, out_length, scale, kernel,
                              kernel_width, antialiasing):
    """Calculate weights and indices, used for imresize function.

    Args:
        in_length (int): Input length.
        out_length (int): Output length.
        scale (float): Scale factor.
        kernel_width (int): Kernel width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
    """

    if (scale < 1) and antialiasing:
        # Use a modified kernel (larger kernel width) to simultaneously
        # interpolate and antialias
        kernel_width = kernel_width / scale

    # Output-space coordinates
    x = torch.linspace(1, out_length, out_length)

    # Input-space coordinates. Calculate the inverse mapping such that 0.5
    # in output space maps to 0.5 in input space, and 0.5 + scale in output
    # space maps to 1.5 in input space.
    u = x / scale + 0.5 * (1 - 1 / scale)

    # What is the left-most pixel that can be involved in the computation?
    left = torch.floor(u - kernel_width / 2)

    # What is the maximum number of pixels that can be involved in the
    # computation?  Note: it's OK to use an extra pixel here; if the
    # corresponding weights are all zero, it will be eliminated at the end
    # of this function.
    p = math.ceil(kernel_width) + 2

    # The indices of the input pixels involved in computing the k-th output
    # pixel are in row k of the indices matrix.
    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(
        0, p - 1, p).view(1, p).expand(out_length, p)

    # The weights used to compute the k-th output pixel are in row k of the
    # weights matrix.
    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices

    # apply cubic kernel
    if (scale < 1) and antialiasing:
        weights = scale * cubic(distance_to_center * scale)
    else:
        weights = cubic(distance_to_center)

    # Normalize the weights matrix so that each row sums to 1.
    weights_sum = torch.sum(weights, 1).view(out_length, 1)
    weights = weights / weights_sum.expand(out_length, p)

    # If a column in weights is all zero, get rid of it. only consider the
    # first and last column.
    weights_zero_tmp = torch.sum((weights == 0), 0)
    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 1, p - 2)
        weights = weights.narrow(1, 1, p - 2)
    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 0, p - 2)
        weights = weights.narrow(1, 0, p - 2)
    weights = weights.contiguous()
    indices = indices.contiguous()
    sym_len_s = -indices.min() + 1
    sym_len_e = indices.max() - in_length
    indices = indices + sym_len_s - 1
    return weights, indices, int(sym_len_s), int(sym_len_e)


@torch.no_grad()
def imresize(img, scale, antialiasing=True):
    """imresize function same as MATLAB.

    It now only supports bicubic.
    The same scale applies for both height and width.

    Args:
        img (Tensor | Numpy array):
            Tensor: Input image with shape (c, h, w), [0, 1] range.
            Numpy: Input image with shape (h, w, c), [0, 1] range.
        scale (float): Scale factor. The same scale applies for both height
            and width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
            Default: True.

    Returns:
        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
    """
    if type(img).__module__ == np.__name__:  # numpy type
        numpy_type = True
        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
    else:
        numpy_type = False

    in_c, in_h, in_w = img.size()
    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
    kernel_width = 4
    kernel = 'cubic'

    # get weights and indices
    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(
        in_h, out_h, scale, kernel, kernel_width, antialiasing)
    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(
        in_w, out_w, scale, kernel, kernel_width, antialiasing)
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)

    sym_patch = img[:, :sym_len_hs, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)

    sym_patch = img[:, -sym_len_he:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(in_c, out_h, in_w)
    kernel_width = weights_h.size(1)
    for i in range(out_h):
        idx = int(indices_h[i][0])
        for j in range(in_c):
            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(
                0, 1).mv(weights_h[i])

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)

    sym_patch = out_1[:, :, :sym_len_ws]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, :, -sym_len_we:]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(in_c, out_h, out_w)
    kernel_width = weights_w.size(1)
    for i in range(out_w):
        idx = int(indices_w[i][0])
        for j in range(in_c):
            out_2[j, :, i] = out_1_aug[j, :,
                                       idx:idx + kernel_width].mv(weights_w[i])

    if numpy_type:
        out_2 = out_2.numpy().transpose(1, 2, 0)
    return out_2


def rgb2ycbcr(img, y_only=False):
    """Convert a RGB image to YCbCr image.

    This function produces the same results as Matlab's `rgb2ycbcr` function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
    else:
        out_img = np.matmul(
            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
                  [24.966, 112.0, -18.214]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def bgr2ycbcr(img, y_only=False):
    """Convert a BGR image to YCbCr image.

    The bgr version of rgb2ycbcr.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
    else:
        out_img = np.matmul(
            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2rgb(img):
    """Convert a YCbCr image to RGB image.

    This function produces the same results as Matlab's ycbcr2rgb function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted RGB image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0, -0.00153632, 0.00791071],
                              [0.00625893, -0.00318811, 0]]) * 255.0 + [
                                  -222.921, 135.576, -276.836
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2bgr(img):
    """Convert a YCbCr image to BGR image.

    The bgr version of ycbcr2rgb.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted BGR image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0.00791071, -0.00153632, 0],
                              [0, -0.00318811, 0.00625893]]) * 255.0 + [
                                  -276.836, 135.576, -222.921
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def _convert_input_type_range(img):
    """Convert the type and range of the input image.

    It converts the input image to np.float32 type and range of [0, 1].
    It is mainly used for pre-processing the input image in colorspace
    convertion functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        (ndarray): The converted image with type of np.float32 and range of
            [0, 1].
    """
    img_type = img.dtype
    img = img.astype(np.float32)
    if img_type == np.float32:
        pass
    elif img_type == np.uint8:
        img /= 255.
    else:
        raise TypeError('The img type should be np.float32 or np.uint8, '
                        f'but got {img_type}')
    return img


def _convert_output_type_range(img, dst_type):
    """Convert the type and range of the image according to dst_type.

    It converts the image to desired type and range. If `dst_type` is np.uint8,
    images will be converted to np.uint8 type with range [0, 255]. If
    `dst_type` is np.float32, it converts the image to np.float32 type with
    range [0, 1].
    It is mainly used for post-processing images in colorspace convertion
    functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The image to be converted with np.float32 type and
            range [0, 255].
        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
            converts the image to np.uint8 type with range [0, 255]. If
            dst_type is np.float32, it converts the image to np.float32 type
            with range [0, 1].

    Returns:
        (ndarray): The converted image with desired type and range.
    """
    if dst_type not in (np.uint8, np.float32):
        raise TypeError('The dst_type should be np.float32 or np.uint8, '
                        f'but got {dst_type}')
    if dst_type == np.uint8:
        img = img.round()
    else:
        img /= 255.
    return img.astype(dst_type)


================================================
FILE: Deraining/basicsr/utils/misc.py
================================================
import numpy as np
import os
import random
import time
import torch
from os import path as osp

from .dist_util import master_only
from .logger import get_root_logger


def set_random_seed(seed):
    """Set random seeds."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def get_time_str():
    return time.strftime('%Y%m%d_%H%M%S', time.localtime())


def mkdir_and_rename(path):
    """mkdirs. If path exists, rename it with timestamp and create a new one.

    Args:
        path (str): Folder path.
    """
    if osp.exists(path):
        new_name = path + '_archived_' + get_time_str()
        print(f'Path already exists. Rename it to {new_name}', flush=True)
        os.rename(path, new_name)
    os.makedirs(path, exist_ok=True)


@master_only
def make_exp_dirs(opt):
    """Make dirs for experiments."""
    path_opt = opt['path'].copy()
    if opt['is_train']:
        mkdir_and_rename(path_opt.pop('experiments_root'))
    else:
        mkdir_and_rename(path_opt.pop('results_root'))
    for key, path in path_opt.items():
        if ('strict_load' not in key) and ('pretrain_network'
                                           not in key) and ('resume'
                                                            not in key):
            os.makedirs(path, exist_ok=True)


def scandir(dir_path, suffix=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        suffix (str | tuple(str), optional): File suffix that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
        raise TypeError('"suffix" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, suffix, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if suffix is None:
                    yield return_path
                elif return_path.endswith(suffix):
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, suffix=suffix, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, suffix=suffix, recursive=recursive)

def scandir_SIDD(dir_path, keywords=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        keywords (str | tuple(str), optional): File keywords that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (keywords is not None) and not isinstance(keywords, (str, tuple)):
        raise TypeError('"keywords" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, keywords, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if keywords is None:
                    yield return_path
                elif return_path.find(keywords) > 0:
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, keywords=keywords, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, keywords=keywords, recursive=recursive)

def check_resume(opt, resume_iter):
    """Check resume states and pretrain_network paths.

    Args:
        opt (dict): Options.
        resume_iter (int): Resume iteration.
    """
    logger = get_root_logger()
    if opt['path']['resume_state']:
        # get all the networks
        networks = [key for key in opt.keys() if key.startswith('network_')]
        flag_pretrain = False
        for network in networks:
            if opt['path'].get(f'pretrain_{network}') is not None:
                flag_pretrain = True
        if flag_pretrain:
            logger.warning(
                'pretrain_network path will be ignored during resuming.')
        # set pretrained model paths
        for network in networks:
            name = f'pretrain_{network}'
            basename = network.replace('network_', '')
            if opt['path'].get('ignore_resume_networks') is None or (
                    basename not in opt['path']['ignore_resume_networks']):
                opt['path'][name] = osp.join(
                    opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
                logger.info(f"Set {name} to {opt['path'][name]}")


def sizeof_fmt(size, suffix='B'):
    """Get human readable file size.

    Args:
        size (int): File size.
        suffix (str): Suffix. Default: 'B'.

    Return:
        str: Formated file siz.
    """
    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
        if abs(size) < 1024.0:
            return f'{size:3.1f} {unit}{suffix}'
        size /= 1024.0
    return f'{size:3.1f} Y{suffix}'


================================================
FILE: Deraining/basicsr/utils/options.py
================================================
import yaml
from collections import OrderedDict
from os import path as osp


def ordered_yaml():
    """Support OrderedDict for yaml.

    Returns:
        yaml Loader and Dumper.
    """
    try:
        from yaml import CDumper as Dumper
        from yaml import CLoader as Loader
    except ImportError:
        from yaml import Dumper, Loader

    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG

    def dict_representer(dumper, data):
        return dumper.represent_dict(data.items())

    def dict_constructor(loader, node):
        return OrderedDict(loader.construct_pairs(node))

    Dumper.add_representer(OrderedDict, dict_representer)
    Loader.add_constructor(_mapping_tag, dict_constructor)
    return Loader, Dumper


def parse(opt_path, is_train=True):
    """Parse option file.

    Args:
        opt_path (str): Option file path.
        is_train (str): Indicate whether in training or not. Default: True.

    Returns:
        (dict): Options.
    """
    with open(opt_path, mode='r') as f:
        Loader, _ = ordered_yaml()
        opt = yaml.load(f, Loader=Loader)

    opt['is_train'] = is_train

    # datasets
    for phase, dataset in opt['datasets'].items():
        # for several datasets, e.g., test_1, test_2
        phase = phase.split('_')[0]
        dataset['phase'] = phase
        if 'scale' in opt:
            dataset['scale'] = opt['scale']
        if dataset.get('dataroot_gt') is not None:
            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
        if dataset.get('dataroot_lq') is not None:
            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])

    # paths
    for key, val in opt['path'].items():
        if (val is not None) and ('resume_state' in key
                                  or 'pretrain_network' in key):
            opt['path'][key] = osp.expanduser(val)
    opt['path']['root'] = osp.abspath(
        osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
    if is_train:
        experiments_root = osp.join(opt['path']['root'], 'experiments',
                                    opt['name'])
        opt['path']['experiments_root'] = experiments_root
        opt['path']['models'] = osp.join(experiments_root, 'models')
        opt['path']['training_states'] = osp.join(experiments_root,
                                                  'training_states')
        opt['path']['log'] = experiments_root
        opt['path']['visualization'] = osp.join(experiments_root,
                                                'visualization')

        # change some options for debug mode
        if 'debug' in opt['name']:
            if 'val' in opt:
                opt['val']['val_freq'] = 8
            opt['logger']['print_freq'] = 1
            opt['logger']['save_checkpoint_freq'] = 8
    else:  # test
        results_root = osp.join(opt['path']['root'], 'results', opt['name'])
        opt['path']['results_root'] = results_root
        opt['path']['log'] = results_root
        opt['path']['visualization'] = osp.join(results_root, 'visualization')

    return opt


def dict2str(opt, indent_level=1):
    """dict to string for printing options.

    Args:
        opt (dict): Option dict.
        indent_level (int): Indent level. Default: 1.

    Return:
        (str): Option string for printing.
    """
    msg = '\n'
    for k, v in opt.items():
        if isinstance(v, dict):
            msg += ' ' * (indent_level * 2) + k + ':['
            msg += dict2str(v, indent_level + 1)
            msg += ' ' * (indent_level * 2) + ']\n'
        else:
            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
    return msg


================================================
FILE: Deraining/basicsr/utils2.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881

import numpy as np
import os
import cv2
import math

def calculate_psnr(img1, img2, border=0):
    # img1 and img2 have range [0, 255]
    #img1 = img1.squeeze()
    #img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    h, w = img1.shape[:2]
    img1 = img1[border:h-border, border:w-border]
    img2 = img2[border:h-border, border:w-border]

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))


# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
    '''calculate SSIM
    the same outputs as MATLAB's
    img1, img2: [0, 255]
    '''
    #img1 = img1.squeeze()
    #img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError('Input images must have the same dimensions.')
    h, w = img1.shape[:2]
    img1 = img1[border:h-border, border:w-border]
    img2 = img2[border:h-border, border:w-border]

    if img1.ndim == 2:
        return ssim(img1, img2)
    elif img1.ndim == 3:
        if img1.shape[2] == 3:
            ssims = []
            for i in range(3):
                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
            return np.array(ssims).mean()
        elif img1.shape[2] == 1:
            return ssim(np.squeeze(img1), np.squeeze(img2))
    else:
        raise ValueError('Wrong input image dimensions.')


def ssim(img1, img2):
    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                            (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()

def load_img(filepath):
    return cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB)

def save_img(filepath, img):
    cv2.imwrite(filepath,cv2.cvtColor(img, cv2.COLOR_RGB2BGR))

def load_gray_img(filepath):
    return np.expand_dims(cv2.imread(filepath, cv2.IMREAD_GRAYSCALE), axis=2)

def save_gray_img(filepath, img):
    cv2.imwrite(filepath, img)


================================================
FILE: Deraining/basicsr/version.py
================================================
# GENERATED VERSION FILE
# TIME: Tue Jun 18 21:15:31 2024
__version__ = '1.2.0+ad047fd'
short_version = '1.2.0'
version_info = (1, 2, 0)


================================================
FILE: Deraining/pip.sh
================================================
pip3 install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm
pip3 install einops gdown addict future lmdb numpy pyyaml requests scipy tb-nightly yapf lpips


================================================
FILE: Deraining/setup.cfg
================================================
[flake8]
ignore =
    # line break before binary operator (W503)
    W503,
    # line break after binary operator (W504)
    W504,
max-line-length=79

[yapf]
based_on_style = pep8
blank_line_before_nested_class_or_def = true
split_before_expression_after_opening_paren = true

[isort]
line_length = 79
multi_line_output = 0
known_standard_library = pkg_resources,setuptools
known_first_party = basicsr
known_third_party = PIL,cv2,lmdb,numpy,requests,scipy,skimage,torch,torchvision,tqdm,yaml
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY


================================================
FILE: Deraining/setup.py
================================================
#!/usr/bin/env python

from setuptools import find_packages, setup

import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
                                       CUDAExtension)

version_file = 'basicsr/version.py'


def readme():
    return ''
    # with open('README.md', encoding='utf-8') as f:
    #     content = f.read()
    # return content


def get_git_hash():

    def _minimal_ext_cmd(cmd):
        # construct minimal environment
        env = {}
        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
            v = os.environ.get(k)
            if v is not None:
                env[k] = v
        # LANGUAGE is used on win32
        env['LANGUAGE'] = 'C'
        env['LANG'] = 'C'
        env['LC_ALL'] = 'C'
        out = subprocess.Popen(
            cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
        return out

    try:
        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
        sha = out.strip().decode('ascii')
    except OSError:
        sha = 'unknown'

    return sha


def get_hash():
    if os.path.exists('.git'):
        sha = get_git_hash()[:7]
    elif os.path.exists(version_file):
        try:
            from basicsr.version import __version__
            sha = __version__.split('+')[-1]
        except ImportError:
            raise ImportError('Unable to get git version')
    else:
        sha = 'unknown'

    return sha


def write_version_py():
    content = """# GENERATED VERSION FILE
# TIME: {}
__version__ = '{}'
short_version = '{}'
version_info = ({})
"""
    sha = get_hash()
    with open('VERSION', 'r') as f:
        SHORT_VERSION = f.read().strip()
    VERSION_INFO = ', '.join(
        [x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
    VERSION = SHORT_VERSION + '+' + sha

    version_file_str = content.format(time.asctime(), VERSION, SHORT_VERSION,
                                      VERSION_INFO)
    with open(version_file, 'w') as f:
        f.write(version_file_str)


def get_version():
    with open(version_file, 'r') as f:
        exec(compile(f.read(), version_file, 'exec'))
    return locals()['__version__']


def make_cuda_ext(name, module, sources, sources_cuda=None):
    if sources_cuda is None:
        sources_cuda = []
    define_macros = []
    extra_compile_args = {'cxx': []}

    if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
        define_macros += [('WITH_CUDA', None)]
        extension = CUDAExtension
        extra_compile_args['nvcc'] = [
            '-D__CUDA_NO_HALF_OPERATORS__',
            '-D__CUDA_NO_HALF_CONVERSIONS__',
            '-D__CUDA_NO_HALF2_OPERATORS__',
        ]
        sources += sources_cuda
    else:
        print(f'Compiling {name} without CUDA')
        extension = CppExtension

    return extension(
        name=f'{module}.{name}',
        sources=[os.path.join(*module.split('.'), p) for p in sources],
        define_macros=define_macros,
        extra_compile_args=extra_compile_args)


def get_requirements(filename='requirements.txt'):
    return []
    here = os.path.dirname(os.path.realpath(__file__))
    with open(os.path.join(here, filename), 'r') as f:
        requires = [line.replace('\n', '') for line in f.readlines()]
    return requires


if __name__ == '__main__':
    if '--no_cuda_ext' in sys.argv:
        ext_modules = []
        sys.argv.remove('--no_cuda_ext')
    else:
        ext_modules = [
            make_cuda_ext(
                name='deform_conv_ext',
                module='basicsr.models.ops.dcn',
                sources=['src/deform_conv_ext.cpp'],
                sources_cuda=[
                    'src/deform_conv_cuda.cpp',
                    'src/deform_conv_cuda_kernel.cu'
                ]),
            make_cuda_ext(
                name='fused_act_ext',
                module='basicsr.models.ops.fused_act',
                sources=['src/fused_bias_act.cpp'],
                sources_cuda=['src/fused_bias_act_kernel.cu']),
            make_cuda_ext(
                name='upfirdn2d_ext',
                module='basicsr.models.ops.upfirdn2d',
                sources=['src/upfirdn2d.cpp'],
                sources_cuda=['src/upfirdn2d_kernel.cu']),
        ]

    write_version_py()
    setup(
        name='basicsr',
        version=get_version(),
        description='Open Source Image and Video Super-Resolution Toolbox',
        long_description=readme(),
        author='Xintao Wang',
        author_email='xintao.wang@outlook.com',
        keywords='computer vision, restoration, super resolution',
        url='https://github.com/xinntao/BasicSR',
        packages=find_packages(
            exclude=('options', 'datasets', 'experiments', 'results',
                     'tb_logger', 'wandb')),
        classifiers=[
            'Development Status :: 4 - Beta',
            'License :: OSI Approved :: Apache Software License',
            'Operating System :: OS Independent',
            'Programming Language :: Python :: 3',
            'Programming Language :: Python :: 3.7',
            'Programming Language :: Python :: 3.8',
        ],
        license='Apache License 2.0',
        setup_requires=['cython', 'numpy'],
        install_requires=get_requirements(),
        ext_modules=ext_modules,
        cmdclass={'build_ext': BuildExtension},
        zip_safe=False)


================================================
FILE: Deraining/train.sh
================================================
#!/usr/bin/env bash

CONFIG=$1

python3 -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt $CONFIG --launcher pytorch

================================================
FILE: Mamba/.gitignore
================================================
# global
**/__pycache__
**/_ignore
**/.dist_test
**/.pytest_cache
**/*.egg-info
**/*.TAG
**/dist
**/*.so
*.so
**/build
**/tmp
**/output
**/work_dirs
logs
ckpts
log
ckpt


kernels/selective_scan/1.log
kernels/selective_scan/test_selective_scan_speed.py
kernels/selective_scan/test_selective_scan_easy.py
kernels/selective_scan/ssmtriton.py
test.sh
kernels/selective_scan/ssmjax.py


================================================
FILE: Mamba/kernels/selective_scan/README.md
================================================
# mamba-mini
An efficient implementation of selective scan in one file, works with both cpu and gpu, with corresponding mathematical derivation. It is probably the code which is the most close to selective_scan_cuda in mamba.

### mathematical derivation
![image](../assets/derivation.png)

### code
```python
import torch
def selective_scan_easy(us, dts, As, Bs, Cs, Ds, delta_bias=None, delta_softplus=False, return_last_state=False, chunksize=64):
    """
    # B: batch_size, G: groups, D: dim, N: state dim, L: seqlen
    us: B, G * D, L 
    dts: B, G * D, L
    As: G * D, N
    Bs: B, G, N, L
    Cs: B, G, N, L
    Ds: G * D
    delta_bias: G * D
    # chunksize can be any as you like. But as the chunksize raises, hs may get None, as exp(sum(delta) A) is really small
    """
    def selective_scan_chunk(us, dts, As, Bs, Cs, hprefix):
        """
        partial(h) / partial(t) = Ah + Bu; y = Ch + Du;
        => partial(h*exp(-At)) / partial(t) = Bu*exp(-At);
        => h_t = h_0 + sum_{0}_{t}_{Bu*exp(A(t-v)) dv};
        => h_b = exp(A(dt_a + ... + dt_{b-1})) * (h_a + sum_{a}_{b-1}_{Bu*exp(-A(dt_a + ... + dt_i)) dt_i});
           y_i = C_i*h_i + D*u_i
        """
        """
        us, dts: (L, B, G, D) # L is chunk_size
        As: (G, D, N)
        Bs, Cs: (L, B, G, N)
        Ds: (G, D)
        hprefix: (B, G, D, N)
        """
        ts = dts.cumsum(dim=0)
        Ats = torch.einsum("gdn,lbgd->lbgdn", As, ts).exp()
        scale = Ats[-1].detach()
        rAts = Ats / scale
        duts = dts * us
        dtBus = torch.einsum("lbgd,lbgn->lbgdn", duts, Bs)
        hs_tmp = rAts * (dtBus / rAts).cumsum(dim=0) 
        hs = hs_tmp + Ats * hprefix.unsqueeze(0)
        ys = torch.einsum("lbgn,lbgdn->lbgd", Cs, hs) 
        return ys, hs
    
    inp_dtype = us.dtype
    has_D = Ds is not None

    dts = dts.float()
    if delta_bias is not None:
        dts = dts + delta_bias.view(1, -1, 1).float()
    if delta_softplus:
        dts = torch.nn.functional.softplus(dts)
    
    if len(Bs.shape) == 3:
        Bs = Bs.unsqueeze(1)
    if len(Cs.shape) == 3:
        Cs = Cs.unsqueeze(1)
    B, G, N, L = Bs.shape
    us = us.view(B, G, -1, L).permute(3, 0, 1, 2).float()
    dts = dts.view(B, G, -1, L).permute(3, 0, 1, 2).float()
    As = As.view(G, -1, N).float()
    Bs = Bs.permute(3, 0, 1, 2).float()
    Cs = Cs.permute(3, 0, 1, 2).float()
    Ds = Ds.view(G, -1).float() if has_D else None
    D = As.shape[1]
    
    oys = []
    # ohs = []
    hprefix = us.new_zeros((B, G, D, N), dtype=torch.float)
    for i in range(0, L - 1, chunksize):
        ys, hs = selective_scan_chunk(
            us[i:i + chunksize], dts[i:i + chunksize], 
            As, Bs[i:i + chunksize], Cs[i:i + chunksize], hprefix, 
        )
        oys.append(ys)
        # ohs.append(hs)
        hprefix = hs[-1]

    oys = torch.cat(oys, dim=0)
    # ohs = torch.cat(ohs, dim=0)
    if has_D:
        oys = oys + Ds * us
    oys = oys.permute(1, 2, 3, 0).view(B, -1, L)
    oys = oys.to(inp_dtype)
    # hprefix = hprefix.to(inp_dtype)

    return oys if not return_last_state else (oys, hprefix.view(B, G * D, N))

```

### to test
```bash
pytest test_selective_scan.py
```


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cub_extra.cuh
================================================
// WarpMask is copied from /usr/local/cuda-12.1/include/cub/util_ptx.cuh
// PowerOfTwo is copied from /usr/local/cuda-12.1/include/cub/util_type.cuh

#pragma once

#include <cub/util_type.cuh>
#include <cub/util_arch.cuh>
#include <cub/util_namespace.cuh>
#include <cub/util_debug.cuh>

/**
 * \brief Statically determine if N is a power-of-two
 */
 template <int N>
 struct PowerOfTwo
 {
     enum { VALUE = ((N & (N - 1)) == 0) };
 };
 

/**
 * @brief Returns the warp mask for a warp of @p LOGICAL_WARP_THREADS threads
 *
 * @par
 * If the number of threads assigned to the virtual warp is not a power of two,
 * it's assumed that only one virtual warp exists.
 *
 * @tparam LOGICAL_WARP_THREADS <b>[optional]</b> The number of threads per
 *                              "logical" warp (may be less than the number of
 *                              hardware warp threads).
 * @param warp_id Id of virtual warp within architectural warp
 */
 template <int LOGICAL_WARP_THREADS, int LEGACY_PTX_ARCH = 0>
 __host__ __device__ __forceinline__
 unsigned int WarpMask(unsigned int warp_id)
 {
   constexpr bool is_pow_of_two = PowerOfTwo<LOGICAL_WARP_THREADS>::VALUE;
   constexpr bool is_arch_warp  = LOGICAL_WARP_THREADS == CUB_WARP_THREADS(0);
 
   unsigned int member_mask = 0xFFFFFFFFu >>
                              (CUB_WARP_THREADS(0) - LOGICAL_WARP_THREADS);
 
   if (is_pow_of_two && !is_arch_warp)
   {
     member_mask <<= warp_id * LOGICAL_WARP_THREADS;
   }
 
   return member_mask;
 }
 

================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <vector>

#include "selective_scan.h"
#define MAX_DSTATE 256

#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
using weight_t = float;

#define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...)                    \
    if (ITYPE == at::ScalarType::Half) {                                            \
        using input_t = at::Half;                                                   \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::BFloat16) {                                 \
        using input_t = at::BFloat16;                                               \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::Float)  {                                   \
        using input_t = float;                                                      \
        __VA_ARGS__();                                                              \
    } else {                                                                        \
        AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
    }

template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream);

template <int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream);

void set_ssm_params_fwd(SSMParamsBase &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        bool delta_softplus) {

    // Reset the parameters
    memset(&params, 0, sizeof(params));

    params.batch = batch;
    params.dim = dim;
    params.seqlen = seqlen;
    params.dstate = dstate;
    params.n_groups = n_groups;
    params.n_chunks = n_chunks;
    params.dim_ngroups_ratio = dim / n_groups;

    params.delta_softplus = delta_softplus;

    // Set the pointers and strides.
    params.u_ptr = u.data_ptr();
    params.delta_ptr = delta.data_ptr();
    params.A_ptr = A.data_ptr();
    params.B_ptr = B.data_ptr();
    params.C_ptr = C.data_ptr();
    params.D_ptr = D_ptr;
    params.delta_bias_ptr = delta_bias_ptr;
    params.out_ptr = out.data_ptr();
    params.x_ptr = x_ptr;

    // All stride are in elements, not bytes.
    params.A_d_stride = A.stride(0);
    params.A_dstate_stride = A.stride(1);
    params.B_batch_stride = B.stride(0);
    params.B_group_stride = B.stride(1);
    params.B_dstate_stride = B.stride(2);
    params.C_batch_stride = C.stride(0);
    params.C_group_stride = C.stride(1);
    params.C_dstate_stride = C.stride(2);
    params.u_batch_stride = u.stride(0);
    params.u_d_stride = u.stride(1);
    params.delta_batch_stride = delta.stride(0);
    params.delta_d_stride = delta.stride(1);

    params.out_batch_stride = out.stride(0);
    params.out_d_stride = out.stride(1);
}

void set_ssm_params_bwd(SSMParamsBwd &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        const at::Tensor dout,
                        const at::Tensor du,
                        const at::Tensor ddelta,
                        const at::Tensor dA,
                        const at::Tensor dB,
                        const at::Tensor dC,
                        void* dD_ptr,
                        void* ddelta_bias_ptr,
                        bool delta_softplus) {
    // Pass in "dout" instead of "out", we're not gonna use "out" unless we have z
    set_ssm_params_fwd(params, batch, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, dout,
                       D_ptr, delta_bias_ptr, x_ptr, delta_softplus);

    // Set the pointers and strides.
    params.dout_ptr = dout.data_ptr();
    params.du_ptr = du.data_ptr();
    params.dA_ptr = dA.data_ptr();
    params.dB_ptr = dB.data_ptr();
    params.dC_ptr = dC.data_ptr();
    params.dD_ptr = dD_ptr;
    params.ddelta_ptr = ddelta.data_ptr();
    params.ddelta_bias_ptr = ddelta_bias_ptr;
    // All stride are in elements, not bytes.
    params.dout_batch_stride = dout.stride(0);
    params.dout_d_stride = dout.stride(1);
    params.dA_d_stride = dA.stride(0);
    params.dA_dstate_stride = dA.stride(1);
    params.dB_batch_stride = dB.stride(0);
    params.dB_group_stride = dB.stride(1);
    params.dB_dstate_stride = dB.stride(2);
    params.dC_batch_stride = dC.stride(0);
    params.dC_group_stride = dC.stride(1);
    params.dC_dstate_stride = dC.stride(2);
    params.du_batch_stride = du.stride(0);
    params.du_d_stride = du.stride(1);
    params.ddelta_batch_stride = ddelta.stride(0);
    params.ddelta_d_stride = ddelta.stride(1);

}

std::vector<at::Tensor>
selective_scan_fwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");
    TORCH_CHECK(dstate <= MAX_DSTATE, "selective_scan only supports state dimension <= 256");

    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    const int n_chunks = (seqlen + 2048 - 1) / 2048; // max is 128 * 16 = 2048 in fwd_kernel
    at::Tensor out = torch::empty_like(delta);
    at::Tensor x;
    x = torch::empty({batch_size, dim, n_chunks, dstate * 2}, u.options().dtype(weight_type));

    SSMParamsBase params;
    set_ssm_params_fwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x.data_ptr(),
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_fwd", [&] {
        selective_scan_fwd_cuda<1, input_t, weight_t>(params, stream);
    });
    std::vector<at::Tensor> result = {out, x};
    return result;
}

std::vector<at::Tensor>
selective_scan_bwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  const at::Tensor &dout,
                  const c10::optional<at::Tensor> &x_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);
    TORCH_CHECK(dout.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());
    TORCH_CHECK(dout.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);
    TORCH_CHECK(dout.stride(-1) == 1 || dout.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");
    TORCH_CHECK(dstate <= MAX_DSTATE, "selective_scan only supports state dimension <= 256");
    
    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);
    CHECK_SHAPE(dout, batch_size, dim, seqlen);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    at::Tensor out;
    const int n_chunks = (seqlen + 2048 - 1) / 2048;
    // const int n_chunks = (seqlen + 1024 - 1) / 1024;
    if (n_chunks > 1) { TORCH_CHECK(x_.has_value()); }
    if (x_.has_value()) {
        auto x = x_.value();
        TORCH_CHECK(x.scalar_type() == weight_type);
        TORCH_CHECK(x.is_cuda());
        TORCH_CHECK(x.is_contiguous());
        CHECK_SHAPE(x, batch_size, dim, n_chunks, 2 * dstate);
    }

    at::Tensor du = torch::empty_like(u);
    at::Tensor ddelta = torch::empty_like(delta);
    at::Tensor dA = torch::zeros_like(A);
    at::Tensor dB = torch::zeros_like(B, B.options().dtype(torch::kFloat32));
    at::Tensor dC = torch::zeros_like(C, C.options().dtype(torch::kFloat32));
    at::Tensor dD;
    if (D_.has_value()) { dD = torch::zeros_like(D_.value()); }
    at::Tensor ddelta_bias;
    if (delta_bias_.has_value()) { ddelta_bias = torch::zeros_like(delta_bias_.value()); }

    SSMParamsBwd params;
    set_ssm_params_bwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x_.has_value() ? x_.value().data_ptr() : nullptr,
                       dout, du, ddelta, dA, dB, dC,
                       D_.has_value() ? dD.data_ptr() : nullptr,
                       delta_bias_.has_value() ? ddelta_bias.data_ptr() : nullptr,
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_bwd", [&] {
        selective_scan_bwd_cuda<1, input_t, weight_t>(params, stream);
    });
    std::vector<at::Tensor> result = {du, ddelta, dA, dB.to(B.dtype()), dC.to(C.dtype()), dD, ddelta_bias};
    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("fwd", &selective_scan_fwd, "Selective scan forward");
    m.def("bwd", &selective_scan_bwd, "Selective scan backward");
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_bwd_kernel.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
#include <ATen/cuda/Atomic.cuh>  // For atomicAdd on complex

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>
#include <cub/block/block_reduce.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "reverse_scan.cuh"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, bool kDeltaSoftplus_, typename input_t_, typename weight_t_>
struct Selective_Scan_bwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    static constexpr int kNItems = kNItems_;
    static constexpr int MaxDState = MAX_DSTATE;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;
    static constexpr bool kDeltaSoftplus = kDeltaSoftplus_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads with float improves occupancy.
    // For complex this would lead to massive register spilling, so we keep it at 2.
    static constexpr int kMinBlocks = kNThreads == 128 && 3;
    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    using BlockReverseScanT = BlockReverseScan<scan_t, kNThreads>;
    using BlockReduceT = cub::BlockReduce<scan_t, kNThreads>;
    using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
    using BlockExchangeT = cub::BlockExchange<float, kNThreads, kNItems>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemExchangeSize = 2 * sizeof(typename BlockExchangeT::TempStorage);
    static constexpr int kSmemReduceSize = sizeof(typename BlockReduceT::TempStorage);
    static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize + kSmemReduceSize + sizeof(typename BlockScanT::TempStorage) + sizeof(typename BlockReverseScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_bwd_kernel(SSMParamsBwd params) {
    constexpr bool kDeltaSoftplus = Ktraits::kDeltaSoftplus;
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_exchange = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    auto& smem_exchange1 = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize + sizeof(typename Ktraits::BlockExchangeT::TempStorage));
    auto& smem_reduce = *reinterpret_cast<typename Ktraits::BlockReduceT::TempStorage*>(reinterpret_cast<char *>(&smem_exchange) + Ktraits::kSmemExchangeSize);
    auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(&smem_reduce);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(reinterpret_cast<char *>(&smem_reduce) + Ktraits::kSmemReduceSize);
    auto& smem_reverse_scan = *reinterpret_cast<typename Ktraits::BlockReverseScanT::TempStorage*>(reinterpret_cast<char *>(&smem_scan) + sizeof(typename Ktraits::BlockScanT::TempStorage));
    weight_t *smem_delta_a = reinterpret_cast<weight_t *>(smem_ + Ktraits::kSmemSize);
    scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + 2 * Ktraits::MaxDState + kNThreads);
    weight_t *smem_da = reinterpret_cast<weight_t *>(smem_running_postfix + Ktraits::MaxDState);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;
    input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
        + dim_id * params.dout_d_stride;
    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    weight_t *dA = reinterpret_cast<weight_t *>(params.dA_ptr) + dim_id * params.dA_d_stride;
    weight_t *dB = reinterpret_cast<weight_t *>(params.dB_ptr)
        + (batch_id * params.dB_batch_stride + group_id * params.dB_group_stride);
    weight_t *dC = reinterpret_cast<weight_t *>(params.dC_ptr)
        + (batch_id * params.dC_batch_stride + group_id * params.dC_group_stride);
    float *dD = params.dD_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.dD_ptr) + dim_id;
    float D_val = params.D_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.D_ptr)[dim_id];
    float *ddelta_bias = params.ddelta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.ddelta_bias_ptr) + dim_id;
    float delta_bias = params.delta_bias_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    scan_t *x = params.x_ptr == nullptr
        ? nullptr
        : reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * (params.n_chunks) * params.dstate;
    float dD_val = 0;
    float ddelta_bias_val = 0;

    constexpr int kChunkSize = kNThreads * kNItems;
    u += (params.n_chunks - 1) * kChunkSize;
    delta += (params.n_chunks - 1) * kChunkSize;
    dout += (params.n_chunks - 1) * kChunkSize;
    Bvar += (params.n_chunks - 1) * kChunkSize;
    Cvar += (params.n_chunks - 1) * kChunkSize;
    for (int chunk = params.n_chunks - 1; chunk >= 0; --chunk) {
        input_t u_vals[kNItems];
        input_t delta_vals_load[kNItems];
        input_t dout_vals_load[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        load_input<Ktraits>(dout, dout_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        u -= kChunkSize;
        // Will reload delta at the same location if kDeltaSoftplus
        if constexpr (!kDeltaSoftplus) { delta -= kChunkSize; }
        dout -= kChunkSize;

        float dout_vals[kNItems], delta_vals[kNItems];
        float du_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            dout_vals[i] = float(dout_vals_load[i]);
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if constexpr (kDeltaSoftplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
        }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { du_vals[i] = D_val * dout_vals[i]; }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { dD_val += dout_vals[i] * float(u_vals[i]); }

        float ddelta_vals[kNItems] = {0};
        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            constexpr float kLog2e = M_LOG2E;
            weight_t A_val = A[state_idx * params.A_dstate_stride];
            weight_t A_scaled = A_val * kLog2e;
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            scan_t thread_data[kNItems], thread_reverse_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float delta_a_exp = exp2f(delta_vals[i] * A_scaled);
                thread_data[i] = make_float2(delta_a_exp, delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                if (i == 0) {
                    smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * Ktraits::MaxDState: threadIdx.x + 2 * Ktraits::MaxDState] = delta_a_exp;
                } else {
                    thread_reverse_data[i - 1].x = delta_a_exp;
                }
                thread_reverse_data[i].y = dout_vals[i] * C_vals[i];
            }
            __syncthreads();
            thread_reverse_data[kNItems - 1].x = threadIdx.x == kNThreads - 1
                ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * Ktraits::MaxDState])
                    : smem_delta_a[threadIdx.x + 1 + 2 * Ktraits::MaxDState];
            // Initialize running total
            scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[(chunk - 1) * params.dstate + state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
            Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
                thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
            );
            if (threadIdx.x == 0) { smem_running_postfix[state_idx] = postfix_op.running_prefix; }
            weight_t dA_val = 0;
            weight_t dB_vals[kNItems], dC_vals[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float dx = thread_reverse_data[i].y;
                const float ddelta_u = dx * B_vals[i];
                du_vals[i] += ddelta_u * delta_vals[i];
                const float a = thread_data[i].y - (delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                ddelta_vals[i] += ddelta_u * float(u_vals[i]) + dx * A_val * a;
                dA_val += dx * delta_vals[i] * a;
                dB_vals[i] = dx * delta_vals[i] * float(u_vals[i]);
                dC_vals[i] = dout_vals[i] * thread_data[i].y;
            }
            // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
            Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals, dB_vals);
            auto &smem_exchange_C = smem_exchange1;
            Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals, dC_vals);
            const int seqlen_remaining = params.seqlen - chunk * kChunkSize - threadIdx.x;
            weight_t *dB_cur = dB + state_idx * params.dB_dstate_stride + chunk * kChunkSize + threadIdx.x;
            weight_t *dC_cur = dC + state_idx * params.dC_dstate_stride + chunk * kChunkSize + threadIdx.x;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                if (i * kNThreads < seqlen_remaining) {
                    { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals[i]); }
                    { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals[i]); }
                }
            }
            dA_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dA_val);
            if (threadIdx.x == 0) {
                smem_da[state_idx] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[state_idx];
            }
        }

        if constexpr (kDeltaSoftplus) {
            input_t delta_vals_load[kNItems];
            __syncthreads();
            load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
            delta -= kChunkSize;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                float delta_val = float(delta_vals_load[i]) + delta_bias;
                float delta_val_neg_exp = expf(-delta_val);
                ddelta_vals[i] = delta_val <= 20.f
                    ? ddelta_vals[i] / (1.f + delta_val_neg_exp)
                        : ddelta_vals[i];
            }
        }
        
        __syncthreads();
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { ddelta_bias_val += ddelta_vals[i]; }

        input_t *du = reinterpret_cast<input_t *>(params.du_ptr) + batch_id * params.du_batch_stride
            + dim_id * params.du_d_stride + chunk * kChunkSize;
        input_t *ddelta = reinterpret_cast<input_t *>(params.ddelta_ptr) + batch_id * params.ddelta_batch_stride
            + dim_id * params.ddelta_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output<Ktraits>(du, du_vals, smem_store, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        store_output<Ktraits>(ddelta, ddelta_vals, smem_store, params.seqlen - chunk * kChunkSize);
        Bvar -= kChunkSize;
        Cvar -= kChunkSize;
    }

    if (params.dD_ptr != nullptr) {
        __syncthreads();
        dD_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dD_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(dD, dD_val); }
    }
    if (params.ddelta_bias_ptr != nullptr) {
        __syncthreads();
        ddelta_bias_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(ddelta_bias_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(ddelta_bias, ddelta_bias_val); }
    }
    __syncthreads();
    for (int state_idx = threadIdx.x; state_idx < params.dstate; state_idx += blockDim.x) {
        gpuAtomicAdd(&(dA[state_idx * params.dA_dstate_stride]), smem_da[state_idx]);
    }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
            using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, kDeltaSoftplus, input_t, weight_t>;
            constexpr int kSmemSize = Ktraits::kSmemSize + Ktraits::MaxDState * sizeof(typename Ktraits::scan_t) + (kNThreads + 4 * Ktraits::MaxDState) * sizeof(typename Ktraits::weight_t);
            // printf("smem_size = %d\n", kSmemSize);
            dim3 grid(params.batch, params.dim);
            auto kernel = &selective_scan_bwd_kernel<Ktraits>;
            if (kSmemSize >= 48 * 1024) {
                C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            }
            kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
        });
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_bwd_launch<32, 4, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_bwd_launch<32, 8, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_bwd_launch<32, 16, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_bwd_launch<64, 16, input_t, weight_t>(params, stream);
    } else {
        selective_scan_bwd_launch<128, 16, input_t, weight_t>(params, stream);
    }
}

================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_bwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel.cuh"

template void selective_scan_bwd_cuda<1, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_fwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel.cuh"

template void selective_scan_fwd_cuda<1, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_fwd_kernel.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, typename input_t_, typename weight_t_>
struct Selective_Scan_fwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads improves occupancy.
    static constexpr int kMinBlocks = kNThreads < 128 ? 5 : 3;
    static constexpr int kNItems = kNItems_;
    static constexpr int MaxDState = MAX_DSTATE;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;

    static constexpr bool kDirectIO = kIsEvenLen && kNLoads == 1;

    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE : cub::BLOCK_LOAD_DIRECT>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE  : cub::BLOCK_LOAD_DIRECT>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_STORE_WARP_TRANSPOSE : cub::BLOCK_STORE_DIRECT>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemSize = kSmemIOSize + sizeof(typename BlockScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_fwd_kernel(SSMParamsBase params) {
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    constexpr bool kDirectIO = Ktraits::kDirectIO;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    scan_t *smem_running_prefix = reinterpret_cast<scan_t *>(smem_ + Ktraits::kSmemSize);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;
    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    scan_t *x = reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * params.n_chunks * params.dstate;

    float D_val = 0; // attention!
    if (params.D_ptr != nullptr) {
        D_val = reinterpret_cast<float *>(params.D_ptr)[dim_id];
    }
    float delta_bias = 0;
    if (params.delta_bias_ptr != nullptr) {
        delta_bias = reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    }

    constexpr int kChunkSize = kNThreads * kNItems;
    for (int chunk = 0; chunk < params.n_chunks; ++chunk) {
        input_t u_vals[kNItems], delta_vals_load[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        if constexpr (!kDirectIO) { __syncthreads(); }
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        u += kChunkSize;
        delta += kChunkSize;

        float delta_vals[kNItems], delta_u_vals[kNItems], out_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            float u_val = float(u_vals[i]);
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if (params.delta_softplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
            delta_u_vals[i] = delta_vals[i] * u_val;
            out_vals[i] = D_val * u_val;
        }

        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            constexpr float kLog2e = M_LOG2E;
            weight_t A_val = A[state_idx * params.A_dstate_stride];
            A_val *= kLog2e;
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight1, (params.seqlen - chunk * kChunkSize));
            __syncthreads();
            scan_t thread_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                thread_data[i] = make_float2(exp2f(delta_vals[i] * A_val), B_vals[i] * delta_u_vals[i]);
                if constexpr (!Ktraits::kIsEvenLen) {  // So that the last state is correct
                    if (threadIdx.x * kNItems + i >= params.seqlen - chunk * kChunkSize) {
                        thread_data[i] = make_float2(1.f, 0.f);
                    }
                }
            }
            // Initialize running total
            scan_t running_prefix;
            // If we use WARP_SCAN then all lane 0 of all warps (not just thread 0) needs to read
            running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? smem_running_prefix[state_idx] : make_float2(1.f, 0.f);
            // running_prefix = chunk > 0 && threadIdx.x == 0 ? smem_running_prefix[state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            // There's a syncthreads in the scan op, so we don't need to sync here.
            // Unless there's only 1 warp, but then it's the same thread (0) reading and writing.
            if (threadIdx.x == 0) {
                smem_running_prefix[state_idx] = prefix_op.running_prefix;
                x[chunk * params.dstate + state_idx] = prefix_op.running_prefix;
            }
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                out_vals[i] += thread_data[i].y * C_vals[i];
            }
        }

        input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
            + dim_id * params.out_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output<Ktraits>(out, out_vals, smem_store, params.seqlen - chunk * kChunkSize);
        Bvar += kChunkSize;
        Cvar += kChunkSize;
    }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        using Ktraits = Selective_Scan_fwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, input_t, weight_t>;
        constexpr int kSmemSize = Ktraits::kSmemSize + Ktraits::MaxDState * sizeof(typename Ktraits::scan_t);
        // printf("smem_size = %d\n", kSmemSize);
        dim3 grid(params.batch, params.dim);
        auto kernel = &selective_scan_fwd_kernel<Ktraits>;
        if (kSmemSize >= 48 * 1024) {
            C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
        }
        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_fwd_launch<32, 4, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_fwd_launch<32, 8, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_fwd_launch<32, 16, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_fwd_launch<64, 16, input_t, weight_t>(params, stream);
    } else {
        selective_scan_fwd_launch<128, 16, input_t, weight_t>(params, stream);
    }
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_bwd_kernel_ndstate.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
#include <ATen/cuda/Atomic.cuh>  // For atomicAdd on complex

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>
#include <cub/block/block_reduce.cuh>

#include "selective_scan_ndstate.h"
#include "selective_scan_common.h"
#include "reverse_scan.cuh"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, bool kDeltaSoftplus_, typename input_t_, typename weight_t_>
struct Selective_Scan_bwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    static constexpr int kNItems = kNItems_;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;
    static constexpr bool kDeltaSoftplus = kDeltaSoftplus_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads with float improves occupancy.
    // For complex this would lead to massive register spilling, so we keep it at 2.
    static constexpr int kMinBlocks = kNThreads == 128 && 3;
    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    using BlockReverseScanT = BlockReverseScan<scan_t, kNThreads>;
    using BlockReduceT = cub::BlockReduce<scan_t, kNThreads>;
    using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
    using BlockExchangeT = cub::BlockExchange<float, kNThreads, kNItems>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemExchangeSize = 2 * sizeof(typename BlockExchangeT::TempStorage);
    static constexpr int kSmemReduceSize = sizeof(typename BlockReduceT::TempStorage);
    static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize + kSmemReduceSize + sizeof(typename BlockScanT::TempStorage) + sizeof(typename BlockReverseScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_bwd_kernel(SSMParamsBwd params) {
    constexpr bool kDeltaSoftplus = Ktraits::kDeltaSoftplus;
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_exchange = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    auto& smem_exchange1 = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize + sizeof(typename Ktraits::BlockExchangeT::TempStorage));
    auto& smem_reduce = *reinterpret_cast<typename Ktraits::BlockReduceT::TempStorage*>(reinterpret_cast<char *>(&smem_exchange) + Ktraits::kSmemExchangeSize);
    auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(&smem_reduce);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(reinterpret_cast<char *>(&smem_reduce) + Ktraits::kSmemReduceSize);
    auto& smem_reverse_scan = *reinterpret_cast<typename Ktraits::BlockReverseScanT::TempStorage*>(reinterpret_cast<char *>(&smem_scan) + sizeof(typename Ktraits::BlockScanT::TempStorage));
    weight_t *smem_delta_a = reinterpret_cast<weight_t *>(smem_ + Ktraits::kSmemSize);
    scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + 2 + kNThreads);
    weight_t *smem_da = reinterpret_cast<weight_t *>(smem_running_postfix + 1);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;
    input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
        + dim_id * params.dout_d_stride;
    weight_t A_val = reinterpret_cast<weight_t *>(params.A_ptr)[dim_id];
    constexpr float kLog2e = M_LOG2E;
    weight_t A_scaled = A_val * kLog2e;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    weight_t *dA = reinterpret_cast<weight_t *>(params.dA_ptr) + dim_id;
    weight_t *dB = reinterpret_cast<weight_t *>(params.dB_ptr)
        + (batch_id * params.dB_batch_stride + group_id * params.dB_group_stride);
    weight_t *dC = reinterpret_cast<weight_t *>(params.dC_ptr)
        + (batch_id * params.dC_batch_stride + group_id * params.dC_group_stride);
    float *dD = params.dD_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.dD_ptr) + dim_id;
    float D_val = params.D_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.D_ptr)[dim_id];
    float *ddelta_bias = params.ddelta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.ddelta_bias_ptr) + dim_id;
    float delta_bias = params.delta_bias_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    scan_t *x = params.x_ptr == nullptr
        ? nullptr
        : reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * (params.n_chunks);
    float dD_val = 0;
    float ddelta_bias_val = 0;

    constexpr int kChunkSize = kNThreads * kNItems;
    u += (params.n_chunks - 1) * kChunkSize;
    delta += (params.n_chunks - 1) * kChunkSize;
    dout += (params.n_chunks - 1) * kChunkSize;
    Bvar += (params.n_chunks - 1) * kChunkSize;
    Cvar += (params.n_chunks - 1) * kChunkSize;
    for (int chunk = params.n_chunks - 1; chunk >= 0; --chunk) {
        input_t u_vals[kNItems];
        input_t delta_vals_load[kNItems];
        input_t dout_vals_load[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        load_input<Ktraits>(dout, dout_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        u -= kChunkSize;
        // Will reload delta at the same location if kDeltaSoftplus
        if constexpr (!kDeltaSoftplus) { delta -= kChunkSize; }
        dout -= kChunkSize;

        float dout_vals[kNItems], delta_vals[kNItems];
        float du_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            dout_vals[i] = float(dout_vals_load[i]);
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if constexpr (kDeltaSoftplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
        }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { du_vals[i] = D_val * dout_vals[i]; }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { dD_val += dout_vals[i] * float(u_vals[i]); }

        float ddelta_vals[kNItems] = {0};
        __syncthreads();
        {
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            scan_t thread_data[kNItems], thread_reverse_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float delta_a_exp = exp2f(delta_vals[i] * A_scaled);
                thread_data[i] = make_float2(delta_a_exp, delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                if (i == 0) {
                    smem_delta_a[threadIdx.x == 0 ? (chunk % 2): threadIdx.x + 2] = delta_a_exp;
                } else {
                    thread_reverse_data[i - 1].x = delta_a_exp;
                }
                thread_reverse_data[i].y = dout_vals[i] * C_vals[i];
            }
            __syncthreads();
            thread_reverse_data[kNItems - 1].x = threadIdx.x == kNThreads - 1
                ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[(chunk + 1) % 2])
                    : smem_delta_a[threadIdx.x + 1 + 2];
            // Initialize running total
            scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[chunk - 1] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[0] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
            Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
                thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
            );
            if (threadIdx.x == 0) { smem_running_postfix[0] = postfix_op.running_prefix; }
            weight_t dA_val = 0;
            weight_t dB_vals[kNItems], dC_vals[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float dx = thread_reverse_data[i].y;
                const float ddelta_u = dx * B_vals[i];
                du_vals[i] += ddelta_u * delta_vals[i];
                const float a = thread_data[i].y - (delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                ddelta_vals[i] += ddelta_u * float(u_vals[i]) + dx * A_val * a;
                dA_val += dx * delta_vals[i] * a;
                dB_vals[i] = dx * delta_vals[i] * float(u_vals[i]);
                dC_vals[i] = dout_vals[i] * thread_data[i].y;
            }
            // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
            Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals, dB_vals);
            auto &smem_exchange_C = smem_exchange1;
            Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals, dC_vals);
            const int seqlen_remaining = params.seqlen - chunk * kChunkSize - threadIdx.x;
            weight_t *dB_cur = dB + chunk * kChunkSize + threadIdx.x;
            weight_t *dC_cur = dC + chunk * kChunkSize + threadIdx.x;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                if (i * kNThreads < seqlen_remaining) {
                    { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals[i]); }
                    { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals[i]); }
                }
            }
            dA_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dA_val);
            if (threadIdx.x == 0) {
                smem_da[0] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[0];
            }
        }

        if constexpr (kDeltaSoftplus) {
            input_t delta_vals_load[kNItems];
            __syncthreads();
            load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
            delta -= kChunkSize;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                float delta_val = float(delta_vals_load[i]) + delta_bias;
                float delta_val_neg_exp = expf(-delta_val);
                ddelta_vals[i] = delta_val <= 20.f
                    ? ddelta_vals[i] / (1.f + delta_val_neg_exp)
                        : ddelta_vals[i];
            }
        }
        
        __syncthreads();
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { ddelta_bias_val += ddelta_vals[i]; }

        input_t *du = reinterpret_cast<input_t *>(params.du_ptr) + batch_id * params.du_batch_stride
            + dim_id * params.du_d_stride + chunk * kChunkSize;
        input_t *ddelta = reinterpret_cast<input_t *>(params.ddelta_ptr) + batch_id * params.ddelta_batch_stride
            + dim_id * params.ddelta_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output<Ktraits>(du, du_vals, smem_store, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        store_output<Ktraits>(ddelta, ddelta_vals, smem_store, params.seqlen - chunk * kChunkSize);
        Bvar -= kChunkSize;
        Cvar -= kChunkSize;
    }

    if (params.dD_ptr != nullptr) {
        __syncthreads();
        dD_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dD_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(dD, dD_val); }
    }
    if (params.ddelta_bias_ptr != nullptr) {
        __syncthreads();
        ddelta_bias_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(ddelta_bias_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(ddelta_bias, ddelta_bias_val); }
    }
    __syncthreads();
    if (threadIdx.x == 0) { gpuAtomicAdd(dA, smem_da[0]); }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
            using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, kDeltaSoftplus, input_t, weight_t>;
            constexpr int kSmemSize = Ktraits::kSmemSize + sizeof(typename Ktraits::scan_t) + (kNThreads + 4) * sizeof(typename Ktraits::weight_t);
            // printf("smem_size = %d\n", kSmemSize);
            dim3 grid(params.batch, params.dim);
            auto kernel = &selective_scan_bwd_kernel<Ktraits>;
            if (kSmemSize >= 48 * 1024) {
                C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            }
            kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
        });
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_bwd_launch<32, 4, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_bwd_launch<32, 8, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_bwd_launch<32, 16, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_bwd_launch<64, 16, input_t, weight_t>(params, stream);
    } else {
        selective_scan_bwd_launch<128, 16, input_t, weight_t>(params, stream);
    }
}

================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_core_bwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_ndstate.cuh"

template void selective_scan_bwd_cuda<1, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_core_fwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_ndstate.cuh"

template void selective_scan_fwd_cuda<1, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_fwd_kernel_ndstate.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>

#include "selective_scan_ndstate.h"
#include "selective_scan_common.h"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, typename input_t_, typename weight_t_>
struct Selective_Scan_fwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads improves occupancy.
    static constexpr int kMinBlocks = kNThreads < 128 ? 5 : 3;
    static constexpr int kNItems = kNItems_;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;

    static constexpr bool kDirectIO = kIsEvenLen && kNLoads == 1;

    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE : cub::BLOCK_LOAD_DIRECT>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE  : cub::BLOCK_LOAD_DIRECT>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_STORE_WARP_TRANSPOSE : cub::BLOCK_STORE_DIRECT>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemSize = kSmemIOSize + sizeof(typename BlockScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_fwd_kernel(SSMParamsBase params) {
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    constexpr bool kDirectIO = Ktraits::kDirectIO;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    scan_t *smem_running_prefix = reinterpret_cast<scan_t *>(smem_ + Ktraits::kSmemSize);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;
    constexpr float kLog2e = M_LOG2E;
    weight_t A_val = reinterpret_cast<weight_t *>(params.A_ptr)[dim_id] * kLog2e;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    scan_t *x = reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * params.n_chunks;

    float D_val = 0; // attention!
    if (params.D_ptr != nullptr) {
        D_val = reinterpret_cast<float *>(params.D_ptr)[dim_id];
    }
    float delta_bias = 0;
    if (params.delta_bias_ptr != nullptr) {
        delta_bias = reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    }

    constexpr int kChunkSize = kNThreads * kNItems;
    for (int chunk = 0; chunk < params.n_chunks; ++chunk) {
        input_t u_vals[kNItems], delta_vals_load[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        if constexpr (!kDirectIO) { __syncthreads(); }
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        u += kChunkSize;
        delta += kChunkSize;

        float delta_vals[kNItems], delta_u_vals[kNItems], out_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            float u_val = float(u_vals[i]);
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if (params.delta_softplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
            delta_u_vals[i] = delta_vals[i] * u_val;
            out_vals[i] = D_val * u_val;
        }

        __syncthreads();
        {
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            __syncthreads();
            scan_t thread_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                thread_data[i] = make_float2(exp2f(delta_vals[i] * A_val), B_vals[i] * delta_u_vals[i]);
                if constexpr (!Ktraits::kIsEvenLen) {  // So that the last state is correct
                    if (threadIdx.x * kNItems + i >= params.seqlen - chunk * kChunkSize) {
                        thread_data[i] = make_float2(1.f, 0.f);
                    }
                }
            }
            // Initialize running total
            scan_t running_prefix;
            // If we use WARP_SCAN then all lane 0 of all warps (not just thread 0) needs to read
            running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? smem_running_prefix[0] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            // There's a syncthreads in the scan op, so we don't need to sync here.
            // Unless there's only 1 warp, but then it's the same thread (0) reading and writing.
            if (threadIdx.x == 0) {
                smem_running_prefix[0] = prefix_op.running_prefix;
                x[chunk] = prefix_op.running_prefix;
            }
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                out_vals[i] += thread_data[i].y * C_vals[i];
            }
        }

        input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
            + dim_id * params.out_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output<Ktraits>(out, out_vals, smem_store, params.seqlen - chunk * kChunkSize);
        Bvar += kChunkSize;
        Cvar += kChunkSize;
    }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        using Ktraits = Selective_Scan_fwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, input_t, weight_t>;
        constexpr int kSmemSize = Ktraits::kSmemSize + sizeof(typename Ktraits::scan_t);
        // printf("smem_size = %d\n", kSmemSize);
        dim3 grid(params.batch, params.dim);
        auto kernel = &selective_scan_fwd_kernel<Ktraits>;
        if (kSmemSize >= 48 * 1024) {
            C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
        }
        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_fwd_launch<32, 4, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_fwd_launch<32, 8, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_fwd_launch<32, 16, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_fwd_launch<64, 16, input_t, weight_t>(params, stream);
    } else {
        selective_scan_fwd_launch<128, 16, input_t, weight_t>(params, stream);
    }
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_ndstate.cpp
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <vector>

#include "selective_scan_ndstate.h"
#define MAX_DSTATE 256

#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
using weight_t = float;

#define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...)                    \
    if (ITYPE == at::ScalarType::Half) {                                            \
        using input_t = at::Half;                                                   \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::BFloat16) {                                 \
        using input_t = at::BFloat16;                                               \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::Float)  {                                   \
        using input_t = float;                                                      \
        __VA_ARGS__();                                                              \
    } else {                                                                        \
        AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
    }

template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream);

template <int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream);

void set_ssm_params_fwd(SSMParamsBase &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        bool delta_softplus) {

    // Reset the parameters
    memset(&params, 0, sizeof(params));

    params.batch = batch;
    params.dim = dim;
    params.seqlen = seqlen;
    params.n_groups = n_groups;
    params.n_chunks = n_chunks;
    params.dim_ngroups_ratio = dim / n_groups;

    params.delta_softplus = delta_softplus;

    // Set the pointers and strides.
    params.u_ptr = u.data_ptr();
    params.delta_ptr = delta.data_ptr();
    params.A_ptr = A.data_ptr();
    params.B_ptr = B.data_ptr();
    params.C_ptr = C.data_ptr();
    params.D_ptr = D_ptr;
    params.delta_bias_ptr = delta_bias_ptr;
    params.out_ptr = out.data_ptr();
    params.x_ptr = x_ptr;

    // All stride are in elements, not bytes.
    params.A_d_stride = A.stride(0);
    params.B_batch_stride = B.stride(0);
    params.B_group_stride = B.stride(1);
    params.C_batch_stride = C.stride(0);
    params.C_group_stride = C.stride(1);
    params.u_batch_stride = u.stride(0);
    params.u_d_stride = u.stride(1);
    params.delta_batch_stride = delta.stride(0);
    params.delta_d_stride = delta.stride(1);

    params.out_batch_stride = out.stride(0);
    params.out_d_stride = out.stride(1);
}

void set_ssm_params_bwd(SSMParamsBwd &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        const at::Tensor dout,
                        const at::Tensor du,
                        const at::Tensor ddelta,
                        const at::Tensor dA,
                        const at::Tensor dB,
                        const at::Tensor dC,
                        void* dD_ptr,
                        void* ddelta_bias_ptr,
                        bool delta_softplus) {
    // Pass in "dout" instead of "out", we're not gonna use "out" unless we have z
    set_ssm_params_fwd(params, batch, dim, seqlen, n_groups, n_chunks,
                       u, delta, A, B, C, dout,
                       D_ptr, delta_bias_ptr, x_ptr, delta_softplus);

    // Set the pointers and strides.
    params.dout_ptr = dout.data_ptr();
    params.du_ptr = du.data_ptr();
    params.dA_ptr = dA.data_ptr();
    params.dB_ptr = dB.data_ptr();
    params.dC_ptr = dC.data_ptr();
    params.dD_ptr = dD_ptr;
    params.ddelta_ptr = ddelta.data_ptr();
    params.ddelta_bias_ptr = ddelta_bias_ptr;
    // All stride are in elements, not bytes.
    params.dout_batch_stride = dout.stride(0);
    params.dout_d_stride = dout.stride(1);
    params.dA_d_stride = dA.stride(0);
    params.dB_batch_stride = dB.stride(0);
    params.dB_group_stride = dB.stride(1);
    params.dC_batch_stride = dC.stride(0);
    params.dC_group_stride = dC.stride(1);
    params.du_batch_stride = du.stride(0);
    params.du_d_stride = du.stride(1);
    params.ddelta_batch_stride = ddelta.stride(0);
    params.ddelta_d_stride = ddelta.stride(1);

}

std::vector<at::Tensor>
selective_scan_fwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");

    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim);
    CHECK_SHAPE(B, batch_size, n_groups, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    const int n_chunks = (seqlen + 2048 - 1) / 2048; // max is 128 * 16 = 2048 in fwd_kernel
    at::Tensor out = torch::empty_like(delta);
    at::Tensor x;
    x = torch::empty({batch_size, dim, n_chunks, 1 * 2}, u.options().dtype(weight_type));

    SSMParamsBase params;
    set_ssm_params_fwd(params, batch_size, dim, seqlen, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x.data_ptr(),
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_fwd", [&] {
        selective_scan_fwd_cuda<1, input_t, weight_t>(params, stream);
    });
    std::vector<at::Tensor> result = {out, x};
    return result;
}

std::vector<at::Tensor>
selective_scan_bwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  const at::Tensor &dout,
                  const c10::optional<at::Tensor> &x_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);
    TORCH_CHECK(dout.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());
    TORCH_CHECK(dout.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);
    TORCH_CHECK(dout.stride(-1) == 1 || dout.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");
    
    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim);
    CHECK_SHAPE(B, batch_size, n_groups, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);
    CHECK_SHAPE(dout, batch_size, dim, seqlen);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    at::Tensor out;
    const int n_chunks = (seqlen + 2048 - 1) / 2048;
    // const int n_chunks = (seqlen + 1024 - 1) / 1024;
    if (n_chunks > 1) { TORCH_CHECK(x_.has_value()); }
    if (x_.has_value()) {
        auto x = x_.value();
        TORCH_CHECK(x.scalar_type() == weight_type);
        TORCH_CHECK(x.is_cuda());
        TORCH_CHECK(x.is_contiguous());
        CHECK_SHAPE(x, batch_size, dim, n_chunks, 2 * 1);
    }

    at::Tensor du = torch::empty_like(u);
    at::Tensor ddelta = torch::empty_like(delta);
    at::Tensor dA = torch::zeros_like(A);
    at::Tensor dB = torch::zeros_like(B, B.options().dtype(torch::kFloat32));
    at::Tensor dC = torch::zeros_like(C, C.options().dtype(torch::kFloat32));
    at::Tensor dD;
    if (D_.has_value()) { dD = torch::zeros_like(D_.value()); }
    at::Tensor ddelta_bias;
    if (delta_bias_.has_value()) { ddelta_bias = torch::zeros_like(delta_bias_.value()); }

    SSMParamsBwd params;
    set_ssm_params_bwd(params, batch_size, dim, seqlen, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x_.has_value() ? x_.value().data_ptr() : nullptr,
                       dout, du, ddelta, dA, dB, dC,
                       D_.has_value() ? dD.data_ptr() : nullptr,
                       delta_bias_.has_value() ? ddelta_bias.data_ptr() : nullptr,
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_bwd", [&] {
        selective_scan_bwd_cuda<1, input_t, weight_t>(params, stream);
    });
    std::vector<at::Tensor> result = {du, ddelta, dA, dB.to(B.dtype()), dC.to(C.dtype()), dD, ddelta_bias};
    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("fwd", &selective_scan_fwd, "Selective scan forward");
    m.def("bwd", &selective_scan_bwd, "Selective scan backward");
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusndstate/selective_scan_ndstate.h
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

////////////////////////////////////////////////////////////////////////////////////////////////////

struct SSMScanParamsBase {
    using index_t = uint32_t;

    int batch, seqlen, n_chunks;
    index_t a_batch_stride;
    index_t b_batch_stride;
    index_t out_batch_stride;

    // Common data pointers.
    void *__restrict__ a_ptr;
    void *__restrict__ b_ptr;
    void *__restrict__ out_ptr;
    void *__restrict__ x_ptr;
};

////////////////////////////////////////////////////////////////////////////////////////////////////

struct SSMParamsBase {
    using index_t = uint32_t;

    int batch, dim, seqlen, n_groups, n_chunks;
    int dim_ngroups_ratio;

    bool delta_softplus;

    index_t A_d_stride;
    index_t B_batch_stride;
    index_t B_d_stride;
    index_t B_group_stride;
    index_t C_batch_stride;
    index_t C_d_stride;
    index_t C_group_stride;
    index_t u_batch_stride;
    index_t u_d_stride;
    index_t delta_batch_stride;
    index_t delta_d_stride;
    index_t out_batch_stride;
    index_t out_d_stride;

    // Common data pointers.
    void *__restrict__ A_ptr;
    void *__restrict__ B_ptr;
    void *__restrict__ C_ptr;
    void *__restrict__ D_ptr;
    void *__restrict__ u_ptr;
    void *__restrict__ delta_ptr;
    void *__restrict__ delta_bias_ptr;
    void *__restrict__ out_ptr;
    void *__restrict__ x_ptr;
};

struct SSMParamsBwd: public SSMParamsBase {
    index_t dout_batch_stride;
    index_t dout_d_stride;
    index_t dA_d_stride;
    index_t dB_batch_stride;
    index_t dB_group_stride;
    index_t dB_d_stride;
    index_t dC_batch_stride;
    index_t dC_group_stride;
    index_t dC_d_stride;
    index_t du_batch_stride;
    index_t du_d_stride;
    index_t ddelta_batch_stride;
    index_t ddelta_d_stride;

    // Common data pointers.
    void *__restrict__ dout_ptr;
    void *__restrict__ dA_ptr;
    void *__restrict__ dB_ptr;
    void *__restrict__ dC_ptr;
    void *__restrict__ dD_ptr;
    void *__restrict__ du_ptr;
    void *__restrict__ ddelta_ptr;
    void *__restrict__ ddelta_bias_ptr;
};


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_bwd_kernel_nrow.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
#include <ATen/cuda/Atomic.cuh>  // For atomicAdd on complex

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>
#include <cub/block/block_reduce.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "reverse_scan.cuh"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, int kNRows_, bool kIsEvenLen_, bool kDeltaSoftplus_, typename input_t_, typename weight_t_>
struct Selective_Scan_bwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    static constexpr int kNItems = kNItems_;
    static constexpr int kNRows = kNRows_;
    static constexpr int MaxDState = MAX_DSTATE / kNRows_;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;
    static constexpr bool kDeltaSoftplus = kDeltaSoftplus_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads with float improves occupancy.
    // For complex this would lead to massive register spilling, so we keep it at 2.
    static constexpr int kMinBlocks = kNThreads == 128 && 3;
    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    using BlockReverseScanT = BlockReverseScan<scan_t, kNThreads>;
    using BlockReduceT = cub::BlockReduce<scan_t, kNThreads>;
    using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
    using BlockExchangeT = cub::BlockExchange<float, kNThreads, kNItems>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemExchangeSize = 2 * sizeof(typename BlockExchangeT::TempStorage);
    static constexpr int kSmemReduceSize = sizeof(typename BlockReduceT::TempStorage);
    static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize + kSmemReduceSize + sizeof(typename BlockScanT::TempStorage) + sizeof(typename BlockReverseScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_bwd_kernel(SSMParamsBwd params) {
    constexpr bool kDeltaSoftplus = Ktraits::kDeltaSoftplus;
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    constexpr int kNRows = Ktraits::kNRows;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_exchange = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    auto& smem_exchange1 = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize + sizeof(typename Ktraits::BlockExchangeT::TempStorage));
    auto& smem_reduce = *reinterpret_cast<typename Ktraits::BlockReduceT::TempStorage*>(reinterpret_cast<char *>(&smem_exchange) + Ktraits::kSmemExchangeSize);
    auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(&smem_reduce);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(reinterpret_cast<char *>(&smem_reduce) + Ktraits::kSmemReduceSize);
    auto& smem_reverse_scan = *reinterpret_cast<typename Ktraits::BlockReverseScanT::TempStorage*>(reinterpret_cast<char *>(&smem_scan) + sizeof(typename Ktraits::BlockScanT::TempStorage));
    weight_t *smem_delta_a = reinterpret_cast<weight_t *>(smem_ + Ktraits::kSmemSize);
    // scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + kNRows * (2 * Ktraits::MaxDState + kNThreads));
    scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + kNRows * 2 * Ktraits::MaxDState + kNThreads);
    weight_t *smem_da = reinterpret_cast<weight_t *>(smem_running_postfix + kNRows * Ktraits::MaxDState);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int dim_id_nrow = dim_id * kNRows;
    const int group_id = dim_id_nrow / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id_nrow * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id_nrow * params.delta_d_stride;
    input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
        + dim_id_nrow * params.dout_d_stride;
    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id_nrow * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    weight_t *dA = reinterpret_cast<weight_t *>(params.dA_ptr) + dim_id_nrow * params.dA_d_stride;
    weight_t *dB = reinterpret_cast<weight_t *>(params.dB_ptr)
        + (batch_id * params.dB_batch_stride + group_id * params.dB_group_stride);
    weight_t *dC = reinterpret_cast<weight_t *>(params.dC_ptr)
        + (batch_id * params.dC_batch_stride + group_id * params.dC_group_stride);
    float *dD = params.dD_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.dD_ptr) + dim_id_nrow;
    float *D_val = params.D_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.D_ptr) + dim_id_nrow;
    float *ddelta_bias = params.ddelta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.ddelta_bias_ptr) + dim_id_nrow;
    float *delta_bias = params.delta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.delta_bias_ptr) + dim_id_nrow;
    scan_t *x = params.x_ptr == nullptr
        ? nullptr
        : reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id_nrow) * (params.n_chunks) * params.dstate;
    float dD_val[kNRows] = {0};
    float ddelta_bias_val[kNRows] = {0};

    constexpr int kChunkSize = kNThreads * kNItems;
    u += (params.n_chunks - 1) * kChunkSize;
    delta += (params.n_chunks - 1) * kChunkSize;
    dout += (params.n_chunks - 1) * kChunkSize;
    Bvar += (params.n_chunks - 1) * kChunkSize;
    Cvar += (params.n_chunks - 1) * kChunkSize;
    for (int chunk = params.n_chunks - 1; chunk >= 0; --chunk) {
        input_t u_vals[kNRows][kNItems];
        input_t delta_vals_load[kNRows][kNItems];
        input_t dout_vals_load[kNRows][kNItems];
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            __syncthreads();
            load_input<Ktraits>(u + r * params.u_d_stride, u_vals[r], smem_load, params.seqlen - chunk * kChunkSize);
            __syncthreads();
            load_input<Ktraits>(delta + r * params.delta_d_stride, delta_vals_load[r], smem_load, params.seqlen - chunk * kChunkSize);
            __syncthreads();
            load_input<Ktraits>(dout + r * params.dout_d_stride, dout_vals_load[r], smem_load, params.seqlen - chunk * kChunkSize);
        }
        u -= kChunkSize;
        // Will reload delta at the same location if kDeltaSoftplus
        if constexpr (!kDeltaSoftplus) { delta -= kChunkSize; }
        dout -= kChunkSize;

        float dout_vals[kNRows][kNItems], delta_vals[kNRows][kNItems];
        float du_vals[kNRows][kNItems];
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                dout_vals[r][i] = float(dout_vals_load[r][i]);
                delta_vals[r][i] = float(delta_vals_load[r][i]) + (delta_bias == nullptr? 0: delta_bias[r]);
                if constexpr (kDeltaSoftplus) {
                    delta_vals[r][i] = delta_vals[r][i] <= 20.f ? log1pf(expf(delta_vals[r][i])) : delta_vals[r][i];
                }
            }
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) { du_vals[r][i] = (D_val == nullptr? 0: D_val[r]) * dout_vals[r][i]; }
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) { dD_val[r] += dout_vals[r][i] * float(u_vals[r][i]); }
        }

        float ddelta_vals[kNRows][kNItems] = {0};
        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            weight_t A_val[kNRows];
            weight_t A_scaled[kNRows];
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                A_val[r] = A[state_idx * params.A_dstate_stride + r * params.A_d_stride];
                constexpr float kLog2e = M_LOG2E;
                A_scaled[r] = A_val[r] * kLog2e;
            }
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                scan_t thread_data[kNItems], thread_reverse_data[kNItems];
                #pragma unroll
                for (int i = 0; i < kNItems; ++i) {
                    const float delta_a_exp = exp2f(delta_vals[r][i] * A_scaled[r]);
                    thread_data[i] = make_float2(delta_a_exp, delta_vals[r][i] * float(u_vals[r][i]) * B_vals[i]);
                    if (i == 0) {
                        // smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * Ktraits::MaxDState + r * (2 * Ktraits::MaxDState + kNThreads) : threadIdx.x + 2 * Ktraits::MaxDState + r * (2 * Ktraits::MaxDState + kNThreads)] = delta_a_exp;
                        smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * Ktraits::MaxDState + r * 2 * Ktraits::MaxDState : threadIdx.x + kNRows * 2 * Ktraits::MaxDState] = delta_a_exp;
                        
                    } else {
                        thread_reverse_data[i - 1].x = delta_a_exp;
                    }
                    thread_reverse_data[i].y = dout_vals[r][i] * C_vals[i];
                }
                __syncthreads();
                thread_reverse_data[kNItems - 1].x = threadIdx.x == kNThreads - 1
                    // ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * Ktraits::MaxDState + r * (2 * Ktraits::MaxDState + kNThreads)])
                    // : smem_delta_a[threadIdx.x + 1 + 2 * Ktraits::MaxDState + r * (2 * Ktraits::MaxDState + kNThreads)];
                    ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * Ktraits::MaxDState + r * 2 * Ktraits::MaxDState])
                    : smem_delta_a[threadIdx.x + 1 + kNRows * 2 * Ktraits::MaxDState];
                // Initialize running total
                scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[(r * params.n_chunks + chunk - 1) * params.dstate + state_idx] : make_float2(1.f, 0.f);
                SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
                Ktraits::BlockScanT(smem_scan).InclusiveScan(
                    thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
                );
                scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[state_idx + r * Ktraits::MaxDState] : make_float2(1.f, 0.f);
                SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
                Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
                    thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
                );
                if (threadIdx.x == 0) { smem_running_postfix[state_idx + r * Ktraits::MaxDState] = postfix_op.running_prefix; }
                weight_t dA_val = 0;
                weight_t dB_vals[kNItems], dC_vals[kNItems];
                #pragma unroll
                for (int i = 0; i < kNItems; ++i) {
                    const float dx = thread_reverse_data[i].y;
                    const float ddelta_u = dx * B_vals[i];
                    du_vals[r][i] += ddelta_u * delta_vals[r][i];
                    const float a = thread_data[i].y - (delta_vals[r][i] * float(u_vals[r][i]) * B_vals[i]);
                    ddelta_vals[r][i] += ddelta_u * float(u_vals[r][i]) + dx * A_val[r] * a;
                    dA_val += dx * delta_vals[r][i] * a;
                    dB_vals[i] = dx * delta_vals[r][i] * float(u_vals[r][i]);
                    dC_vals[i] = dout_vals[r][i] * thread_data[i].y;
                }
                // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
                    Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals, dB_vals);
                    auto &smem_exchange_C = smem_exchange1;
                    Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals, dC_vals);
                    const int seqlen_remaining = params.seqlen - chunk * kChunkSize - threadIdx.x;
                    weight_t *dB_cur = dB + state_idx * params.dB_dstate_stride + chunk * kChunkSize + threadIdx.x;
                    weight_t *dC_cur = dC + state_idx * params.dC_dstate_stride + chunk * kChunkSize + threadIdx.x;
                    #pragma unroll
                    for (int i = 0; i < kNItems; ++i) {
                        if (i * kNThreads < seqlen_remaining) {
                            { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals[i]); }
                            { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals[i]); }
                        }
                    }
                dA_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dA_val);
                if (threadIdx.x == 0) {
                    smem_da[state_idx + r * Ktraits::MaxDState] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[state_idx + r * Ktraits::MaxDState];
                }
            }
        }

        if constexpr (kDeltaSoftplus) {
            input_t delta_vals_load[kNRows][kNItems];
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                __syncthreads();
                load_input<Ktraits>(delta + r * params.delta_d_stride, delta_vals_load[r], smem_load, params.seqlen - chunk * kChunkSize);
            }
            delta -= kChunkSize;
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                #pragma unroll
                for (int i = 0; i < kNItems; ++i) {
                    float delta_val = float(delta_vals_load[r][i]) + (delta_bias == nullptr? 0: delta_bias[r]);
                    float delta_val_neg_exp = expf(-delta_val);
                    ddelta_vals[r][i] = delta_val <= 20.f
                        ? ddelta_vals[r][i] / (1.f + delta_val_neg_exp)
                        : ddelta_vals[r][i];
                }
            }
        }
        
        __syncthreads();
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) { ddelta_bias_val[r] += ddelta_vals[r][i]; }
        }

        input_t *du = reinterpret_cast<input_t *>(params.du_ptr) + batch_id * params.du_batch_stride
            + dim_id_nrow * params.du_d_stride + chunk * kChunkSize;
        input_t *ddelta = reinterpret_cast<input_t *>(params.ddelta_ptr) + batch_id * params.ddelta_batch_stride
            + dim_id_nrow * params.ddelta_d_stride + chunk * kChunkSize;
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            __syncthreads();
            store_output<Ktraits>(du + r * params.du_d_stride, du_vals[r], smem_store, params.seqlen - chunk * kChunkSize);
            __syncthreads();
            store_output<Ktraits>(ddelta + r * params.ddelta_d_stride, ddelta_vals[r], smem_store, params.seqlen - chunk * kChunkSize);
        }

        Bvar -= kChunkSize;
        Cvar -= kChunkSize;
    }

    #pragma unroll
    for (int r = 0; r < kNRows; ++r) {
        if (params.dD_ptr != nullptr) {
            __syncthreads();
            dD_val[r] = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dD_val[r]);
            if (threadIdx.x == 0) { gpuAtomicAdd(&(dD[r]), dD_val[r]); }
        }
        if (params.ddelta_bias_ptr != nullptr) {
            __syncthreads();
            ddelta_bias_val[r] = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(ddelta_bias_val[r]);
            if (threadIdx.x == 0) { gpuAtomicAdd(&(ddelta_bias[r]), ddelta_bias_val[r]); }
        }
        __syncthreads();
        for (int state_idx = threadIdx.x; state_idx < params.dstate; state_idx += blockDim.x) {
            gpuAtomicAdd(&(dA[state_idx * params.dA_dstate_stride + r * params.dA_d_stride]), smem_da[state_idx + r * Ktraits::MaxDState]);
        }
    }
}

template<int kNThreads, int kNItems, int kNRows, typename input_t, typename weight_t>
void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
            using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kNRows, kIsEvenLen, kDeltaSoftplus, input_t, weight_t>;
            constexpr int kSmemSize = Ktraits::kSmemSize + kNRows * Ktraits::MaxDState * sizeof(typename Ktraits::scan_t) + (kNThreads + 4 * kNRows * Ktraits::MaxDState) * sizeof(typename Ktraits::weight_t);
            // printf("smem_size = %d\n", kSmemSize);
            dim3 grid(params.batch, params.dim / kNRows);
            auto kernel = &selective_scan_bwd_kernel<Ktraits>;
            if (kSmemSize >= 48 * 1024) {
                C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            }
            kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
        });
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_bwd_launch<32, 4, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_bwd_launch<32, 8, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_bwd_launch<32, 16, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_bwd_launch<64, 16, knrows, input_t, weight_t>(params, stream);
    } else {
        selective_scan_bwd_launch<128, 16, knrows, input_t, weight_t>(params, stream);
    }
}

================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_bwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_nrow.cuh"

template void selective_scan_bwd_cuda<1, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_bwd2.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_nrow.cuh"

template void selective_scan_bwd_cuda<2, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<2, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<2, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_bwd3.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_nrow.cuh"

template void selective_scan_bwd_cuda<3, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<3, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<3, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_bwd4.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_nrow.cuh"

template void selective_scan_bwd_cuda<4, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<4, at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<4, at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_fwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_nrow.cuh"

template void selective_scan_fwd_cuda<1, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_fwd2.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_nrow.cuh"

template void selective_scan_fwd_cuda<2, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<2, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<2, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_fwd3.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_nrow.cuh"

template void selective_scan_fwd_cuda<3, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<3, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<3, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_core_fwd4.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_nrow.cuh"

template void selective_scan_fwd_cuda<4, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<4, at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<4, at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_fwd_kernel_nrow.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, int kNRows_, bool kIsEvenLen_, typename input_t_, typename weight_t_>
struct Selective_Scan_fwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    static constexpr int kNThreads = kNThreads_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads improves occupancy.
    static constexpr int kMinBlocks = kNThreads < 128 ? 5 : 3;
    static constexpr int kNItems = kNItems_;
    static constexpr int kNRows = kNRows_;
    static constexpr int MaxDState = MAX_DSTATE / kNRows;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;

    static constexpr bool kDirectIO = kIsEvenLen && kNLoads == 1;

    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE : cub::BLOCK_LOAD_DIRECT>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE  : cub::BLOCK_LOAD_DIRECT>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_STORE_WARP_TRANSPOSE : cub::BLOCK_STORE_DIRECT>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemSize = kSmemIOSize + sizeof(typename BlockScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_fwd_kernel(SSMParamsBase params) {
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    constexpr int kNRows = Ktraits::kNRows;
    constexpr bool kDirectIO = Ktraits::kDirectIO;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    scan_t *smem_running_prefix = reinterpret_cast<scan_t *>(smem_ + Ktraits::kSmemSize);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int dim_id_nrow = dim_id * kNRows;
    const int group_id = dim_id_nrow / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id_nrow * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id_nrow * params.delta_d_stride;
    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id_nrow * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    scan_t *x = reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id_nrow) * params.n_chunks * params.dstate;

    float D_val[kNRows] = {0};
    if (params.D_ptr != nullptr) {
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            D_val[r] = reinterpret_cast<float *>(params.D_ptr)[dim_id_nrow + r];
        }
    }
    float delta_bias[kNRows] = {0};
    if (params.delta_bias_ptr != nullptr) {
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            delta_bias[r] = reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id_nrow + r];
        }
    }

    constexpr int kChunkSize = kNThreads * kNItems;
    for (int chunk = 0; chunk < params.n_chunks; ++chunk) {
        input_t u_vals[kNRows][kNItems], delta_vals_load[kNRows][kNItems];
        __syncthreads();
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            if constexpr (!kDirectIO) {
                if (r > 0) { __syncthreads(); }
            }
            load_input<Ktraits>(u + r * params.u_d_stride, u_vals[r], smem_load, params.seqlen - chunk * kChunkSize);
            if constexpr (!kDirectIO) { __syncthreads(); }
            load_input<Ktraits>(delta + r * params.delta_d_stride, delta_vals_load[r], smem_load, params.seqlen - chunk * kChunkSize);
        }
        u += kChunkSize;
        delta += kChunkSize;

        float delta_vals[kNRows][kNItems], delta_u_vals[kNRows][kNItems], out_vals[kNRows][kNItems];
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                float u_val = float(u_vals[r][i]);
                delta_vals[r][i] = float(delta_vals_load[r][i]) + delta_bias[r];
                if (params.delta_softplus) {
                    delta_vals[r][i] = delta_vals[r][i] <= 20.f ? log1pf(expf(delta_vals[r][i])) : delta_vals[r][i];
                }
                delta_u_vals[r][i] = delta_vals[r][i] * u_val;
                out_vals[r][i] = D_val[r] * u_val;
            }
        }

        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            weight_t A_val[kNRows];
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                A_val[r] = A[state_idx * params.A_dstate_stride + r * params.A_d_stride];
                // Multiply the real part of A with LOG2E so we can use exp2f instead of expf.
                constexpr float kLog2e = M_LOG2E;
                A_val[r] *= kLog2e;
            }
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            #pragma unroll
            for (int r = 0; r < kNRows; ++r) {
                if (r > 0) { __syncthreads(); }  // Scan could be using the same smem
                scan_t thread_data[kNItems];
                #pragma unroll
                for (int i = 0; i < kNItems; ++i) {
                    thread_data[i] = make_float2(exp2f(delta_vals[r][i] * A_val[r]),
                                                 B_vals[i] * delta_u_vals[r][i]);
                    if constexpr (!Ktraits::kIsEvenLen) {  // So that the last state is correct
                        if (threadIdx.x * kNItems + i >= params.seqlen - chunk * kChunkSize) {
                            thread_data[i] = make_float2(1.f, 0.f);
                        }
                    }
                }
                // Initialize running total
                scan_t running_prefix;
                // If we use WARP_SCAN then all lane 0 of all warps (not just thread 0) needs to read
                running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? smem_running_prefix[state_idx + r * Ktraits::MaxDState] : make_float2(1.f, 0.f);
                // running_prefix = chunk > 0 && threadIdx.x == 0 ? smem_running_prefix[state_idx] : make_float2(1.f, 0.f);
                SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
                Ktraits::BlockScanT(smem_scan).InclusiveScan(
                    thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
                );
                // There's a syncthreads in the scan op, so we don't need to sync here.
                // Unless there's only 1 warp, but then it's the same thread (0) reading and writing.
                if (threadIdx.x == 0) {
                    smem_running_prefix[state_idx + r * Ktraits::MaxDState] = prefix_op.running_prefix;
                    x[(r * params.n_chunks + chunk) * params.dstate + state_idx] = prefix_op.running_prefix;
                }
                #pragma unroll
                for (int i = 0; i < kNItems; ++i) {
                    out_vals[r][i] += thread_data[i].y * C_vals[i];
                }
            }
        }

        input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
            + dim_id_nrow * params.out_d_stride + chunk * kChunkSize;
        __syncthreads();
        #pragma unroll
        for (int r = 0; r < kNRows; ++r) {
            if constexpr (!kDirectIO) {
                if (r > 0) { __syncthreads(); }
            }
            store_output<Ktraits>(out + r * params.out_d_stride, out_vals[r], smem_store, params.seqlen - chunk * kChunkSize);
        }

        Bvar += kChunkSize;
        Cvar += kChunkSize;
    }
}

template<int kNThreads, int kNItems, int kNRows, typename input_t, typename weight_t>
void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        using Ktraits = Selective_Scan_fwd_kernel_traits<kNThreads, kNItems, kNRows, kIsEvenLen, input_t, weight_t>;
        constexpr int kSmemSize = Ktraits::kSmemSize + kNRows * Ktraits::MaxDState * sizeof(typename Ktraits::scan_t);
        // printf("smem_size = %d\n", kSmemSize);
        dim3 grid(params.batch, params.dim / kNRows);
        auto kernel = &selective_scan_fwd_kernel<Ktraits>;
        if (kSmemSize >= 48 * 1024) {
            C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
        }
        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    });
}

template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_fwd_launch<32, 4, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_fwd_launch<32, 8, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_fwd_launch<32, 16, knrows, input_t, weight_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_fwd_launch<64, 16, knrows, input_t, weight_t>(params, stream);
    } else {
        selective_scan_fwd_launch<128, 16, knrows, input_t, weight_t>(params, stream);
    }
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusnrow/selective_scan_nrow.cpp
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <vector>

#include "selective_scan.h"
#define MAX_DSTATE 256

#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
using weight_t = float;

#define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...)                    \
    if (ITYPE == at::ScalarType::Half) {                                            \
        using input_t = at::Half;                                                   \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::BFloat16) {                                 \
        using input_t = at::BFloat16;                                               \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::Float)  {                                   \
        using input_t = float;                                                      \
        __VA_ARGS__();                                                              \
    } else {                                                                        \
        AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
    }

#define INT_SWITCH(INT, NAME, ...) [&] {                         \
    if (INT == 2) {constexpr int NAME = 2; __VA_ARGS__(); }      \
    else if (INT == 3) {constexpr int NAME = 3; __VA_ARGS__(); } \
    else if (INT == 4) {constexpr int NAME = 4; __VA_ARGS__(); } \
    else {constexpr int NAME = 1; __VA_ARGS__(); }               \
}()                                                              \


template<int knrows, typename input_t, typename weight_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream);

template <int knrows, typename input_t, typename weight_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream);

void set_ssm_params_fwd(SSMParamsBase &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        bool delta_softplus) {

    // Reset the parameters
    memset(&params, 0, sizeof(params));

    params.batch = batch;
    params.dim = dim;
    params.seqlen = seqlen;
    params.dstate = dstate;
    params.n_groups = n_groups;
    params.n_chunks = n_chunks;
    params.dim_ngroups_ratio = dim / n_groups;

    params.delta_softplus = delta_softplus;

    // Set the pointers and strides.
    params.u_ptr = u.data_ptr();
    params.delta_ptr = delta.data_ptr();
    params.A_ptr = A.data_ptr();
    params.B_ptr = B.data_ptr();
    params.C_ptr = C.data_ptr();
    params.D_ptr = D_ptr;
    params.delta_bias_ptr = delta_bias_ptr;
    params.out_ptr = out.data_ptr();
    params.x_ptr = x_ptr;

    // All stride are in elements, not bytes.
    params.A_d_stride = A.stride(0);
    params.A_dstate_stride = A.stride(1);
    params.B_batch_stride = B.stride(0);
    params.B_group_stride = B.stride(1);
    params.B_dstate_stride = B.stride(2);
    params.C_batch_stride = C.stride(0);
    params.C_group_stride = C.stride(1);
    params.C_dstate_stride = C.stride(2);
    params.u_batch_stride = u.stride(0);
    params.u_d_stride = u.stride(1);
    params.delta_batch_stride = delta.stride(0);
    params.delta_d_stride = delta.stride(1);

    params.out_batch_stride = out.stride(0);
    params.out_d_stride = out.stride(1);
}

void set_ssm_params_bwd(SSMParamsBwd &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        const at::Tensor dout,
                        const at::Tensor du,
                        const at::Tensor ddelta,
                        const at::Tensor dA,
                        const at::Tensor dB,
                        const at::Tensor dC,
                        void* dD_ptr,
                        void* ddelta_bias_ptr,
                        bool delta_softplus) {
    // Pass in "dout" instead of "out", we're not gonna use "out" unless we have z
    set_ssm_params_fwd(params, batch, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, dout,
                       D_ptr, delta_bias_ptr, x_ptr, delta_softplus);

    // Set the pointers and strides.
    params.dout_ptr = dout.data_ptr();
    params.du_ptr = du.data_ptr();
    params.dA_ptr = dA.data_ptr();
    params.dB_ptr = dB.data_ptr();
    params.dC_ptr = dC.data_ptr();
    params.dD_ptr = dD_ptr;
    params.ddelta_ptr = ddelta.data_ptr();
    params.ddelta_bias_ptr = ddelta_bias_ptr;
    // All stride are in elements, not bytes.
    params.dout_batch_stride = dout.stride(0);
    params.dout_d_stride = dout.stride(1);
    params.dA_d_stride = dA.stride(0);
    params.dA_dstate_stride = dA.stride(1);
    params.dB_batch_stride = dB.stride(0);
    params.dB_group_stride = dB.stride(1);
    params.dB_dstate_stride = dB.stride(2);
    params.dC_batch_stride = dC.stride(0);
    params.dC_group_stride = dC.stride(1);
    params.dC_dstate_stride = dC.stride(2);
    params.du_batch_stride = du.stride(0);
    params.du_d_stride = du.stride(1);
    params.ddelta_batch_stride = ddelta.stride(0);
    params.ddelta_d_stride = ddelta.stride(1);

}

std::vector<at::Tensor>
selective_scan_fwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % (n_groups * nrows) == 0, "dims should be dividable by n_groups * nrows");
    TORCH_CHECK(dstate <= MAX_DSTATE / nrows, "selective_scan only supports state dimension <= 256 / nrows");

    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    const int n_chunks = (seqlen + 2048 - 1) / 2048; // max is 128 * 16 = 2048 in fwd_kernel
    at::Tensor out = torch::empty_like(delta);
    at::Tensor x;
    x = torch::empty({batch_size, dim, n_chunks, dstate * 2}, u.options().dtype(weight_type));

    SSMParamsBase params;
    set_ssm_params_fwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x.data_ptr(),
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_fwd", [&] {
        INT_SWITCH(nrows, kNRows, [&] {
            selective_scan_fwd_cuda<kNRows, input_t, weight_t>(params, stream);
        });
    });
    std::vector<at::Tensor> result = {out, x};
    return result;
}

std::vector<at::Tensor>
selective_scan_bwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  const at::Tensor &dout,
                  const c10::optional<at::Tensor> &x_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);
    TORCH_CHECK(dout.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());
    TORCH_CHECK(dout.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);
    TORCH_CHECK(dout.stride(-1) == 1 || dout.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % (n_groups * nrows) == 0, "dims should be dividable by n_groups * nrows");
    TORCH_CHECK(dstate <= MAX_DSTATE / nrows, "selective_scan only supports state dimension <= 256 / nrows");
    
    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);
    CHECK_SHAPE(dout, batch_size, dim, seqlen);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    at::Tensor out;
    const int n_chunks = (seqlen + 2048 - 1) / 2048;
    // const int n_chunks = (seqlen + 1024 - 1) / 1024;
    if (n_chunks > 1) { TORCH_CHECK(x_.has_value()); }
    if (x_.has_value()) {
        auto x = x_.value();
        TORCH_CHECK(x.scalar_type() == weight_type);
        TORCH_CHECK(x.is_cuda());
        TORCH_CHECK(x.is_contiguous());
        CHECK_SHAPE(x, batch_size, dim, n_chunks, 2 * dstate);
    }

    at::Tensor du = torch::empty_like(u);
    at::Tensor ddelta = torch::empty_like(delta);
    at::Tensor dA = torch::zeros_like(A);
    at::Tensor dB = torch::zeros_like(B, B.options().dtype(torch::kFloat32));
    at::Tensor dC = torch::zeros_like(C, C.options().dtype(torch::kFloat32));
    at::Tensor dD;
    if (D_.has_value()) { dD = torch::zeros_like(D_.value()); }
    at::Tensor ddelta_bias;
    if (delta_bias_.has_value()) { ddelta_bias = torch::zeros_like(delta_bias_.value()); }

    SSMParamsBwd params;
    set_ssm_params_bwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x_.has_value() ? x_.value().data_ptr() : nullptr,
                       dout, du, ddelta, dA, dB, dC,
                       D_.has_value() ? dD.data_ptr() : nullptr,
                       delta_bias_.has_value() ? ddelta_bias.data_ptr() : nullptr,
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_bwd", [&] {
        // constexpr int kNRows = 1;
        INT_SWITCH(nrows, kNRows, [&] {
            selective_scan_bwd_cuda<kNRows, input_t, weight_t>(params, stream);
        });
    });
    std::vector<at::Tensor> result = {du, ddelta, dA, dB.to(B.dtype()), dC.to(C.dtype()), dD, ddelta_bias};
    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("fwd", &selective_scan_fwd, "Selective scan forward");
    m.def("bwd", &selective_scan_bwd, "Selective scan backward");
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusoflex/selective_scan_bwd_kernel_oflex.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
#include <ATen/cuda/Atomic.cuh>  // For atomicAdd on complex

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>
#include <cub/block/block_reduce.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "reverse_scan.cuh"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, bool kDeltaSoftplus_, typename input_t_, typename weight_t_, typename output_t_>
struct Selective_Scan_bwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    using output_t = output_t_;

    static constexpr int kNThreads = kNThreads_;
    static constexpr int kNItems = kNItems_;
    static constexpr int MaxDState = MAX_DSTATE;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;
    static constexpr bool kDeltaSoftplus = kDeltaSoftplus_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads with float improves occupancy.
    // For complex this would lead to massive register spilling, so we keep it at 2.
    static constexpr int kMinBlocks = kNThreads == 128 && 3;
    static constexpr int kNLoadsOutput = sizeof(output_t) * kNLoads / kNBytes;
    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadOutputT = cub::BlockLoad<output_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadOutputVecT = cub::BlockLoad<vec_t, kNThreads, kNLoadsOutput, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    using BlockReverseScanT = BlockReverseScan<scan_t, kNThreads>;
    using BlockReduceT = cub::BlockReduce<scan_t, kNThreads>;
    using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
    using BlockExchangeT = cub::BlockExchange<float, kNThreads, kNItems>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockLoadOutputT::TempStorage),
                                                 sizeof(typename BlockLoadOutputVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage)});
    static constexpr int kSmemExchangeSize = 2 * sizeof(typename BlockExchangeT::TempStorage);
    static constexpr int kSmemReduceSize = sizeof(typename BlockReduceT::TempStorage);
    static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize + kSmemReduceSize + sizeof(typename BlockScanT::TempStorage) + sizeof(typename BlockReverseScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_bwd_kernel(SSMParamsBwd params) {
    constexpr bool kDeltaSoftplus = Ktraits::kDeltaSoftplus;
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using output_t = typename Ktraits::output_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load1 = reinterpret_cast<typename Ktraits::BlockLoadOutputT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_exchange = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    auto& smem_exchange1 = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize + sizeof(typename Ktraits::BlockExchangeT::TempStorage));
    auto& smem_reduce = *reinterpret_cast<typename Ktraits::BlockReduceT::TempStorage*>(reinterpret_cast<char *>(&smem_exchange) + Ktraits::kSmemExchangeSize);
    auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(&smem_reduce);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(reinterpret_cast<char *>(&smem_reduce) + Ktraits::kSmemReduceSize);
    auto& smem_reverse_scan = *reinterpret_cast<typename Ktraits::BlockReverseScanT::TempStorage*>(reinterpret_cast<char *>(&smem_scan) + sizeof(typename Ktraits::BlockScanT::TempStorage));
    weight_t *smem_delta_a = reinterpret_cast<weight_t *>(smem_ + Ktraits::kSmemSize);
    scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + 2 * Ktraits::MaxDState + kNThreads);
    weight_t *smem_da = reinterpret_cast<weight_t *>(smem_running_postfix + Ktraits::MaxDState);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;

    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    weight_t *dA = reinterpret_cast<weight_t *>(params.dA_ptr) + dim_id * params.dA_d_stride;
    weight_t *dB = reinterpret_cast<weight_t *>(params.dB_ptr)
        + (batch_id * params.dB_batch_stride + group_id * params.dB_group_stride);
    weight_t *dC = reinterpret_cast<weight_t *>(params.dC_ptr)
        + (batch_id * params.dC_batch_stride + group_id * params.dC_group_stride);
    float *dD = params.dD_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.dD_ptr) + dim_id;
    float D_val = params.D_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.D_ptr)[dim_id];
    float *ddelta_bias = params.ddelta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.ddelta_bias_ptr) + dim_id;
    float delta_bias = params.delta_bias_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    scan_t *x = params.x_ptr == nullptr
        ? nullptr
        : reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * (params.n_chunks) * params.dstate;
    float dD_val = 0;
    float ddelta_bias_val = 0;

    output_t *dout = reinterpret_cast<output_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride + dim_id * params.dout_d_stride;

    constexpr int kChunkSize = kNThreads * kNItems;
    u += (params.n_chunks - 1) * kChunkSize;
    delta += (params.n_chunks - 1) * kChunkSize;
    dout += (params.n_chunks - 1) * kChunkSize;
    Bvar += (params.n_chunks - 1) * kChunkSize;
    Cvar += (params.n_chunks - 1) * kChunkSize;
    for (int chunk = params.n_chunks - 1; chunk >= 0; --chunk) {
        input_t u_vals[kNItems];
        input_t delta_vals_load[kNItems];
        float dout_vals[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        if constexpr (std::is_same_v<output_t, input_t>) {
            input_t dout_vals_load[kNItems];
            load_input<Ktraits>(reinterpret_cast<input_t *>(dout), dout_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
            Converter<typename Ktraits::input_t, kNItems>::to_float(dout_vals_load, dout_vals);
        } else {
            static_assert(std::is_same_v<output_t, float>);
            load_output<Ktraits>(dout, dout_vals, smem_load1, params.seqlen - chunk * kChunkSize);
        }
        u -= kChunkSize;
        // Will reload delta at the same location if kDeltaSoftplus
        if constexpr (!kDeltaSoftplus) { delta -= kChunkSize; }
        dout -= kChunkSize;

        float delta_vals[kNItems];
        float du_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if constexpr (kDeltaSoftplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
        }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { du_vals[i] = D_val * dout_vals[i]; }
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { dD_val += dout_vals[i] * float(u_vals[i]); }

        float ddelta_vals[kNItems] = {0};
        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            constexpr float kLog2e = M_LOG2E;
            weight_t A_val = A[state_idx * params.A_dstate_stride];
            weight_t A_scaled = A_val * kLog2e;
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            auto &smem_load_weight_C = smem_load_weight1;
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight_C, (params.seqlen - chunk * kChunkSize));
            scan_t thread_data[kNItems], thread_reverse_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float delta_a_exp = exp2f(delta_vals[i] * A_scaled);
                thread_data[i] = make_float2(delta_a_exp, delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                if (i == 0) {
                    smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * Ktraits::MaxDState: threadIdx.x + 2 * Ktraits::MaxDState] = delta_a_exp;
                } else {
                    thread_reverse_data[i - 1].x = delta_a_exp;
                }
                thread_reverse_data[i].y = dout_vals[i] * C_vals[i];
            }
            __syncthreads();
            thread_reverse_data[kNItems - 1].x = threadIdx.x == kNThreads - 1
                ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * Ktraits::MaxDState])
                    : smem_delta_a[threadIdx.x + 1 + 2 * Ktraits::MaxDState];
            // Initialize running total
            scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[(chunk - 1) * params.dstate + state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
            Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
                thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
            );
            if (threadIdx.x == 0) { smem_running_postfix[state_idx] = postfix_op.running_prefix; }
            weight_t dA_val = 0;
            weight_t dB_vals[kNItems], dC_vals[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                const float dx = thread_reverse_data[i].y;
                const float ddelta_u = dx * B_vals[i];
                du_vals[i] += ddelta_u * delta_vals[i];
                const float a = thread_data[i].y - (delta_vals[i] * float(u_vals[i]) * B_vals[i]);
                ddelta_vals[i] += ddelta_u * float(u_vals[i]) + dx * A_val * a;
                dA_val += dx * delta_vals[i] * a;
                dB_vals[i] = dx * delta_vals[i] * float(u_vals[i]);
                dC_vals[i] = dout_vals[i] * thread_data[i].y;
            }
            // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
            Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals, dB_vals);
            auto &smem_exchange_C = smem_exchange1;
            Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals, dC_vals);
            const int seqlen_remaining = params.seqlen - chunk * kChunkSize - threadIdx.x;
            weight_t *dB_cur = dB + state_idx * params.dB_dstate_stride + chunk * kChunkSize + threadIdx.x;
            weight_t *dC_cur = dC + state_idx * params.dC_dstate_stride + chunk * kChunkSize + threadIdx.x;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                if (i * kNThreads < seqlen_remaining) {
                    { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals[i]); }
                    { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals[i]); }
                }
            }
            dA_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dA_val);
            if (threadIdx.x == 0) {
                smem_da[state_idx] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[state_idx];
            }
        }

        if constexpr (kDeltaSoftplus) {
            input_t delta_vals_load[kNItems];
            __syncthreads();
            load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
            delta -= kChunkSize;
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                float delta_val = float(delta_vals_load[i]) + delta_bias;
                float delta_val_neg_exp = expf(-delta_val);
                ddelta_vals[i] = delta_val <= 20.f
                    ? ddelta_vals[i] / (1.f + delta_val_neg_exp)
                        : ddelta_vals[i];
            }
        }
        
        __syncthreads();
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) { ddelta_bias_val += ddelta_vals[i]; }

        input_t *du = reinterpret_cast<input_t *>(params.du_ptr) + batch_id * params.du_batch_stride
            + dim_id * params.du_d_stride + chunk * kChunkSize;
        input_t *ddelta = reinterpret_cast<input_t *>(params.ddelta_ptr) + batch_id * params.ddelta_batch_stride
            + dim_id * params.ddelta_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output<Ktraits>(du, du_vals, smem_store, params.seqlen - chunk * kChunkSize);
        __syncthreads();
        store_output<Ktraits>(ddelta, ddelta_vals, smem_store, params.seqlen - chunk * kChunkSize);
        Bvar -= kChunkSize;
        Cvar -= kChunkSize;
    }

    if (params.dD_ptr != nullptr) {
        __syncthreads();
        dD_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dD_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(dD, dD_val); }
    }
    if (params.ddelta_bias_ptr != nullptr) {
        __syncthreads();
        ddelta_bias_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(ddelta_bias_val);
        if (threadIdx.x == 0) { gpuAtomicAdd(ddelta_bias, ddelta_bias_val); }
    }
    __syncthreads();
    for (int state_idx = threadIdx.x; state_idx < params.dstate; state_idx += blockDim.x) {
        gpuAtomicAdd(&(dA[state_idx * params.dA_dstate_stride]), smem_da[state_idx]);
    }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t, typename output_t>
void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
            using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, kDeltaSoftplus, input_t, weight_t, output_t>;
            constexpr int kSmemSize = Ktraits::kSmemSize + Ktraits::MaxDState * sizeof(typename Ktraits::scan_t) + (kNThreads + 4 * Ktraits::MaxDState) * sizeof(typename Ktraits::weight_t);
            // printf("smem_size = %d\n", kSmemSize);
            dim3 grid(params.batch, params.dim);
            auto kernel = &selective_scan_bwd_kernel<Ktraits>;
            if (kSmemSize >= 48 * 1024) {
                C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
            }
            kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
        });
    });
}

template<int knrows, typename input_t, typename weight_t, typename output_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_bwd_launch<32, 4, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_bwd_launch<32, 8, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_bwd_launch<32, 16, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_bwd_launch<64, 16, input_t, weight_t, output_t>(params, stream);
    } else {
        selective_scan_bwd_launch<128, 16, input_t, weight_t, output_t>(params, stream);
    }
}



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusoflex/selective_scan_core_bwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_bwd_kernel_oflex.cuh"

template void selective_scan_bwd_cuda<1, float, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::Half, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::BFloat16, float, float>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::Half, float, at::Half>(SSMParamsBwd &params, cudaStream_t stream);
template void selective_scan_bwd_cuda<1, at::BFloat16, float, at::BFloat16>(SSMParamsBwd &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusoflex/selective_scan_core_fwd.cu
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/
#include "selective_scan_fwd_kernel_oflex.cuh"

template void selective_scan_fwd_cuda<1, float, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::Half, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::BFloat16, float, float>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::Half, float, at::Half>(SSMParamsBase &params, cudaStream_t stream);
template void selective_scan_fwd_cuda<1, at::BFloat16, float, at::BFloat16>(SSMParamsBase &params, cudaStream_t stream);



================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusoflex/selective_scan_fwd_kernel_oflex.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include <c10/cuda/CUDAException.h>  // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_scan.cuh>

#include "selective_scan.h"
#include "selective_scan_common.h"
#include "static_switch.h"

template<int kNThreads_, int kNItems_, bool kIsEvenLen_, typename input_t_, typename weight_t_, typename output_t_>
struct Selective_Scan_fwd_kernel_traits {
    static_assert(kNItems_ % 4 == 0);
    using input_t = input_t_;
    using weight_t = weight_t_;
    using output_t = output_t_;
    static constexpr int kNThreads = kNThreads_;
    // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads improves occupancy.
    static constexpr int kMinBlocks = kNThreads < 128 ? 5 : 3;
    static constexpr int kNItems = kNItems_;
    static constexpr int MaxDState = MAX_DSTATE;
    static constexpr int kNBytes = sizeof(input_t);
    static_assert(kNBytes == 2 || kNBytes == 4);
    static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
    static_assert(kNItems % kNElts == 0);
    static constexpr int kNLoads = kNItems / kNElts;
    static constexpr bool kIsEvenLen = kIsEvenLen_;
    static constexpr bool kDirectIO = kIsEvenLen && kNLoads == 1;
    static constexpr int kNLoadsOutput = sizeof(output_t) * kNLoads / kNBytes;
    static constexpr bool kDirectIOOutput = kDirectIO && (kNLoadsOutput == 1);
    using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
    using scan_t = float2;
    using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE : cub::BLOCK_LOAD_DIRECT>;
    using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
    using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_LOAD_WARP_TRANSPOSE  : cub::BLOCK_LOAD_DIRECT>;
    using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads,
        !kDirectIO ? cub::BLOCK_STORE_WARP_TRANSPOSE : cub::BLOCK_STORE_DIRECT>;
    using BlockStoreOutputT = cub::BlockStore<output_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
    using BlockStoreOutputVecT = cub::BlockStore<vec_t, kNThreads, kNLoadsOutput,
        !kDirectIOOutput ? cub::BLOCK_STORE_WARP_TRANSPOSE  : cub::BLOCK_STORE_DIRECT>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
    // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
    using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
    static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
                                                 sizeof(typename BlockLoadVecT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightT::TempStorage),
                                                 2 * sizeof(typename BlockLoadWeightVecT::TempStorage),
                                                 sizeof(typename BlockStoreT::TempStorage),
                                                 sizeof(typename BlockStoreVecT::TempStorage),
                                                 sizeof(typename BlockStoreOutputT::TempStorage),
                                                 sizeof(typename BlockStoreOutputVecT::TempStorage)});
    static constexpr int kSmemSize = kSmemIOSize + sizeof(typename BlockScanT::TempStorage);
};

template<typename Ktraits>
__global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
void selective_scan_fwd_kernel(SSMParamsBase params) {
    constexpr int kNThreads = Ktraits::kNThreads;
    constexpr int kNItems = Ktraits::kNItems;
    constexpr bool kDirectIO = Ktraits::kDirectIO;
    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using output_t = typename Ktraits::output_t;
    using scan_t = typename Ktraits::scan_t;

    // Shared memory.
    extern __shared__ char smem_[];
    auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
    auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
    auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
    auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
    auto& smem_store1 = reinterpret_cast<typename Ktraits::BlockStoreOutputT::TempStorage&>(smem_);
    auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
    scan_t *smem_running_prefix = reinterpret_cast<scan_t *>(smem_ + Ktraits::kSmemSize);

    const int batch_id = blockIdx.x;
    const int dim_id = blockIdx.y;
    const int group_id = dim_id / (params.dim_ngroups_ratio);
    input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
        + dim_id * params.u_d_stride;
    input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
        + dim_id * params.delta_d_stride;
    weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id * params.A_d_stride;
    input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
    input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
    scan_t *x = reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * params.n_chunks * params.dstate;

    float D_val = 0; // attention!
    if (params.D_ptr != nullptr) {
        D_val = reinterpret_cast<float *>(params.D_ptr)[dim_id];
    }
    float delta_bias = 0;
    if (params.delta_bias_ptr != nullptr) {
        delta_bias = reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
    }

    constexpr int kChunkSize = kNThreads * kNItems;
    for (int chunk = 0; chunk < params.n_chunks; ++chunk) {
        input_t u_vals[kNItems], delta_vals_load[kNItems];
        __syncthreads();
        load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
        if constexpr (!kDirectIO) { __syncthreads(); }
        load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
        u += kChunkSize;
        delta += kChunkSize;

        float delta_vals[kNItems], delta_u_vals[kNItems], out_vals[kNItems];
        #pragma unroll
        for (int i = 0; i < kNItems; ++i) {
            float u_val = float(u_vals[i]);
            delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
            if (params.delta_softplus) {
                delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
            }
            delta_u_vals[i] = delta_vals[i] * u_val;
            out_vals[i] = D_val * u_val;
        }

        __syncthreads();
        for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
            constexpr float kLog2e = M_LOG2E;
            weight_t A_val = A[state_idx * params.A_dstate_stride];
            A_val *= kLog2e;
            weight_t B_vals[kNItems], C_vals[kNItems];
            load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
                    smem_load_weight, (params.seqlen - chunk * kChunkSize));
            load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
                    smem_load_weight1, (params.seqlen - chunk * kChunkSize));
            __syncthreads();
            scan_t thread_data[kNItems];
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                thread_data[i] = make_float2(exp2f(delta_vals[i] * A_val), B_vals[i] * delta_u_vals[i]);
                if constexpr (!Ktraits::kIsEvenLen) {  // So that the last state is correct
                    if (threadIdx.x * kNItems + i >= params.seqlen - chunk * kChunkSize) {
                        thread_data[i] = make_float2(1.f, 0.f);
                    }
                }
            }
            // Initialize running total
            scan_t running_prefix;
            // If we use WARP_SCAN then all lane 0 of all warps (not just thread 0) needs to read
            running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? smem_running_prefix[state_idx] : make_float2(1.f, 0.f);
            // running_prefix = chunk > 0 && threadIdx.x == 0 ? smem_running_prefix[state_idx] : make_float2(1.f, 0.f);
            SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
            Ktraits::BlockScanT(smem_scan).InclusiveScan(
                thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
            );
            // There's a syncthreads in the scan op, so we don't need to sync here.
            // Unless there's only 1 warp, but then it's the same thread (0) reading and writing.
            if (threadIdx.x == 0) {
                smem_running_prefix[state_idx] = prefix_op.running_prefix;
                x[chunk * params.dstate + state_idx] = prefix_op.running_prefix;
            }
            #pragma unroll
            for (int i = 0; i < kNItems; ++i) {
                out_vals[i] += thread_data[i].y * C_vals[i];
            }
        }

        output_t *out = reinterpret_cast<output_t *>(params.out_ptr) + batch_id * params.out_batch_stride
            + dim_id * params.out_d_stride + chunk * kChunkSize;
        __syncthreads();
        store_output1<Ktraits>(out, out_vals, smem_store1, params.seqlen - chunk * kChunkSize);
        Bvar += kChunkSize;
        Cvar += kChunkSize;
    }
}

template<int kNThreads, int kNItems, typename input_t, typename weight_t, typename output_t>
void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        using Ktraits = Selective_Scan_fwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, input_t, weight_t, output_t>;
        constexpr int kSmemSize = Ktraits::kSmemSize + Ktraits::MaxDState * sizeof(typename Ktraits::scan_t);
        // printf("smem_size = %d\n", kSmemSize);
        dim3 grid(params.batch, params.dim);
        auto kernel = &selective_scan_fwd_kernel<Ktraits>;
        if (kSmemSize >= 48 * 1024) {
            C10_CUDA_CHECK(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
        }
        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    });
}

template<int knrows, typename input_t, typename weight_t, typename output_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream) {
    if (params.seqlen <= 128) {
        selective_scan_fwd_launch<32, 4, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 256) {
        selective_scan_fwd_launch<32, 8, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 512) {
        selective_scan_fwd_launch<32, 16, input_t, weight_t, output_t>(params, stream);
    } else if (params.seqlen <= 1024) {
        selective_scan_fwd_launch<64, 16, input_t, weight_t, output_t>(params, stream);
    } else {
        selective_scan_fwd_launch<128, 16, input_t, weight_t, output_t>(params, stream);
    }
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/cusoflex/selective_scan_oflex.cpp
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <vector>

#include "selective_scan.h"
#define MAX_DSTATE 256

#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
using weight_t = float;

#define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...)                    \
    if (ITYPE == at::ScalarType::Half) {                                            \
        using input_t = at::Half;                                                   \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::BFloat16) {                                 \
        using input_t = at::BFloat16;                                               \
        __VA_ARGS__();                                                              \
    } else if (ITYPE == at::ScalarType::Float)  {                                   \
        using input_t = float;                                                      \
        __VA_ARGS__();                                                              \
    } else {                                                                        \
        AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
    }

template<int knrows, typename input_t, typename weight_t, typename output_t>
void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream);

template <int knrows, typename input_t, typename weight_t, typename output_t>
void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream);

void set_ssm_params_fwd(SSMParamsBase &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        bool delta_softplus) {

    // Reset the parameters
    memset(&params, 0, sizeof(params));

    params.batch = batch;
    params.dim = dim;
    params.seqlen = seqlen;
    params.dstate = dstate;
    params.n_groups = n_groups;
    params.n_chunks = n_chunks;
    params.dim_ngroups_ratio = dim / n_groups;

    params.delta_softplus = delta_softplus;

    // Set the pointers and strides.
    params.u_ptr = u.data_ptr();
    params.delta_ptr = delta.data_ptr();
    params.A_ptr = A.data_ptr();
    params.B_ptr = B.data_ptr();
    params.C_ptr = C.data_ptr();
    params.D_ptr = D_ptr;
    params.delta_bias_ptr = delta_bias_ptr;
    params.out_ptr = out.data_ptr();
    params.x_ptr = x_ptr;

    // All stride are in elements, not bytes.
    params.A_d_stride = A.stride(0);
    params.A_dstate_stride = A.stride(1);
    params.B_batch_stride = B.stride(0);
    params.B_group_stride = B.stride(1);
    params.B_dstate_stride = B.stride(2);
    params.C_batch_stride = C.stride(0);
    params.C_group_stride = C.stride(1);
    params.C_dstate_stride = C.stride(2);
    params.u_batch_stride = u.stride(0);
    params.u_d_stride = u.stride(1);
    params.delta_batch_stride = delta.stride(0);
    params.delta_d_stride = delta.stride(1);

    params.out_batch_stride = out.stride(0);
    params.out_d_stride = out.stride(1);
}

void set_ssm_params_bwd(SSMParamsBwd &params,
                        // sizes
                        const size_t batch,
                        const size_t dim,
                        const size_t seqlen,
                        const size_t dstate,
                        const size_t n_groups,
                        const size_t n_chunks,
                        // device pointers
                        const at::Tensor u,
                        const at::Tensor delta,
                        const at::Tensor A,
                        const at::Tensor B,
                        const at::Tensor C,
                        const at::Tensor out,
                        void* D_ptr,
                        void* delta_bias_ptr,
                        void* x_ptr,
                        const at::Tensor dout,
                        const at::Tensor du,
                        const at::Tensor ddelta,
                        const at::Tensor dA,
                        const at::Tensor dB,
                        const at::Tensor dC,
                        void* dD_ptr,
                        void* ddelta_bias_ptr,
                        bool delta_softplus) {
    // Pass in "dout" instead of "out", we're not gonna use "out" unless we have z
    set_ssm_params_fwd(params, batch, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, dout,
                       D_ptr, delta_bias_ptr, x_ptr, delta_softplus);

    // Set the pointers and strides.
    params.dout_ptr = dout.data_ptr();
    params.du_ptr = du.data_ptr();
    params.dA_ptr = dA.data_ptr();
    params.dB_ptr = dB.data_ptr();
    params.dC_ptr = dC.data_ptr();
    params.dD_ptr = dD_ptr;
    params.ddelta_ptr = ddelta.data_ptr();
    params.ddelta_bias_ptr = ddelta_bias_ptr;
    // All stride are in elements, not bytes.
    params.dout_batch_stride = dout.stride(0);
    params.dout_d_stride = dout.stride(1);
    params.dA_d_stride = dA.stride(0);
    params.dA_dstate_stride = dA.stride(1);
    params.dB_batch_stride = dB.stride(0);
    params.dB_group_stride = dB.stride(1);
    params.dB_dstate_stride = dB.stride(2);
    params.dC_batch_stride = dC.stride(0);
    params.dC_group_stride = dC.stride(1);
    params.dC_dstate_stride = dC.stride(2);
    params.du_batch_stride = du.stride(0);
    params.du_d_stride = du.stride(1);
    params.ddelta_batch_stride = ddelta.stride(0);
    params.ddelta_d_stride = ddelta.stride(1);

}

std::vector<at::Tensor>
selective_scan_fwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  bool delta_softplus,
                  int nrows,
                  bool out_float
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");
    TORCH_CHECK(dstate <= MAX_DSTATE, "selective_scan only supports state dimension <= 256");

    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    const int n_chunks = (seqlen + 2048 - 1) / 2048; // max is 128 * 16 = 2048 in fwd_kernel
    at::Tensor out = torch::empty({batch_size, dim, seqlen}, u.options().dtype(out_float? (at::ScalarType::Float): input_type));
    at::Tensor x = torch::empty({batch_size, dim, n_chunks, dstate * 2}, u.options().dtype(weight_type));

    SSMParamsBase params;
    set_ssm_params_fwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x.data_ptr(),
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_fwd", [&] {
        if (!out_float) {
            selective_scan_fwd_cuda<1, input_t, weight_t, input_t>(params, stream);
        } else {
            selective_scan_fwd_cuda<1, input_t, weight_t, float>(params, stream);
        }
    });
    std::vector<at::Tensor> result = {out, x};
    return result;
}

std::vector<at::Tensor>
selective_scan_bwd(const at::Tensor &u, const at::Tensor &delta,
                  const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
                  const c10::optional<at::Tensor> &D_,
                  const c10::optional<at::Tensor> &delta_bias_,
                  const at::Tensor &dout,
                  const c10::optional<at::Tensor> &x_,
                  bool delta_softplus,
                  int nrows
                  ) {
    auto input_type = u.scalar_type();
    auto weight_type = A.scalar_type();
    auto output_type = dout.scalar_type();
    TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
    TORCH_CHECK(weight_type == at::ScalarType::Float);
    TORCH_CHECK(output_type == input_type || output_type == at::ScalarType::Float);

    TORCH_CHECK(delta.scalar_type() == input_type);
    TORCH_CHECK(B.scalar_type() == input_type);
    TORCH_CHECK(C.scalar_type() == input_type);

    TORCH_CHECK(u.is_cuda());
    TORCH_CHECK(delta.is_cuda());
    TORCH_CHECK(A.is_cuda());
    TORCH_CHECK(B.is_cuda());
    TORCH_CHECK(C.is_cuda());
    TORCH_CHECK(dout.is_cuda());

    TORCH_CHECK(u.stride(-1) == 1 || u.size(-1) == 1);
    TORCH_CHECK(delta.stride(-1) == 1 || delta.size(-1) == 1);
    TORCH_CHECK(dout.stride(-1) == 1 || dout.size(-1) == 1);

    const auto sizes = u.sizes();
    const int batch_size = sizes[0];
    const int dim = sizes[1];
    const int seqlen = sizes[2];
    const int dstate = A.size(1);
    const int n_groups = B.size(1);

    TORCH_CHECK(dim % n_groups == 0, "dims should be dividable by n_groups");
    TORCH_CHECK(dstate <= MAX_DSTATE, "selective_scan only supports state dimension <= 256");
    
    CHECK_SHAPE(u, batch_size, dim, seqlen);
    CHECK_SHAPE(delta, batch_size, dim, seqlen);
    CHECK_SHAPE(A, dim, dstate);
    CHECK_SHAPE(B, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(B.stride(-1) == 1 || B.size(-1) == 1);
    CHECK_SHAPE(C, batch_size, n_groups, dstate, seqlen);
    TORCH_CHECK(C.stride(-1) == 1 || C.size(-1) == 1);
    CHECK_SHAPE(dout, batch_size, dim, seqlen);

    if (D_.has_value()) {
        auto D = D_.value();
        TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(D.is_cuda());
        TORCH_CHECK(D.stride(-1) == 1 || D.size(-1) == 1);
        CHECK_SHAPE(D, dim);
    }

    if (delta_bias_.has_value()) {
        auto delta_bias = delta_bias_.value();
        TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
        TORCH_CHECK(delta_bias.is_cuda());
        TORCH_CHECK(delta_bias.stride(-1) == 1 || delta_bias.size(-1) == 1);
        CHECK_SHAPE(delta_bias, dim);
    }

    at::Tensor out;
    const int n_chunks = (seqlen + 2048 - 1) / 2048;
    // const int n_chunks = (seqlen + 1024 - 1) / 1024;
    if (n_chunks > 1) { TORCH_CHECK(x_.has_value()); }
    if (x_.has_value()) {
        auto x = x_.value();
        TORCH_CHECK(x.scalar_type() == weight_type);
        TORCH_CHECK(x.is_cuda());
        TORCH_CHECK(x.is_contiguous());
        CHECK_SHAPE(x, batch_size, dim, n_chunks, 2 * dstate);
    }

    at::Tensor du = torch::empty_like(u);
    at::Tensor ddelta = torch::empty_like(delta);
    at::Tensor dA = torch::zeros_like(A);
    at::Tensor dB = torch::zeros_like(B, B.options().dtype(torch::kFloat32));
    at::Tensor dC = torch::zeros_like(C, C.options().dtype(torch::kFloat32));
    at::Tensor dD;
    if (D_.has_value()) { dD = torch::zeros_like(D_.value()); }
    at::Tensor ddelta_bias;
    if (delta_bias_.has_value()) { ddelta_bias = torch::zeros_like(delta_bias_.value()); }

    SSMParamsBwd params;
    set_ssm_params_bwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks,
                       u, delta, A, B, C, out,
                       D_.has_value() ? D_.value().data_ptr() : nullptr,
                       delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
                       x_.has_value() ? x_.value().data_ptr() : nullptr,
                       dout, du, ddelta, dA, dB, dC,
                       D_.has_value() ? dD.data_ptr() : nullptr,
                       delta_bias_.has_value() ? ddelta_bias.data_ptr() : nullptr,
                       delta_softplus);

    // Otherwise the kernel will be launched from cuda:0 device
    // Cast to char to avoid compiler warning about narrowing
    at::cuda::CUDAGuard device_guard{(char)u.get_device()};
    auto stream = at::cuda::getCurrentCUDAStream().stream();
    DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_bwd", [&] {
        if (output_type == input_type) {
            selective_scan_bwd_cuda<1, input_t, weight_t, input_t>(params, stream);
        } else {
            selective_scan_bwd_cuda<1, input_t, weight_t, float>(params, stream);
        }
    });
    std::vector<at::Tensor> result = {du, ddelta, dA, dB.to(B.dtype()), dC.to(C.dtype()), dD, ddelta_bias};
    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("fwd", &selective_scan_fwd, "Selective scan forward");
    m.def("bwd", &selective_scan_bwd, "Selective scan backward");
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/reverse_scan.cuh
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <cub/config.cuh>

#include <cub/util_ptx.cuh>
#include <cub/util_type.cuh>
#include <cub/block/block_raking_layout.cuh>
// #include <cub/detail/uninitialized_copy.cuh>
#include "uninitialized_copy.cuh"
#include "cub_extra.cuh"

/**
 * Perform a reverse sequential reduction over \p LENGTH elements of the \p input array.  The aggregate is returned.
 */
template <
    int         LENGTH,
    typename    T,
    typename    ReductionOp>
__device__ __forceinline__ T ThreadReverseReduce(const T (&input)[LENGTH], ReductionOp reduction_op) {
    static_assert(LENGTH > 0);
    T retval = input[LENGTH - 1];
    #pragma unroll
    for (int i = LENGTH - 2; i >= 0; --i) { retval = reduction_op(retval, input[i]); }
    return retval;
}

/**
 * Perform a sequential inclusive postfix reverse scan over the statically-sized \p input array, seeded with the specified \p postfix.  The aggregate is returned.
 */
template <
    int         LENGTH,
    typename    T,
    typename    ScanOp>
__device__ __forceinline__ T ThreadReverseScanInclusive(
    const T (&input)[LENGTH],
    T (&output)[LENGTH],
    ScanOp scan_op,
    const T postfix)
{
    T inclusive = postfix;
    #pragma unroll
    for (int i = LENGTH - 1; i >= 0; --i) {
        inclusive = scan_op(inclusive, input[i]);
        output[i] = inclusive;
    }
}

/**
 * Perform a sequential exclusive postfix reverse scan over the statically-sized \p input array, seeded with the specified \p postfix.  The aggregate is returned.
 */
template <
    int         LENGTH,
    typename    T,
    typename    ScanOp>
__device__ __forceinline__ T ThreadReverseScanExclusive(
    const T (&input)[LENGTH],
    T (&output)[LENGTH],
    ScanOp scan_op,
    const T postfix)
{
    // Careful, output maybe be aliased to input
    T exclusive = postfix;
    T inclusive;
    #pragma unroll
    for (int i = LENGTH - 1; i >= 0; --i) {
        inclusive = scan_op(exclusive, input[i]);
        output[i] = exclusive;
        exclusive = inclusive;
    }
    return inclusive;
}


/**
 * \brief WarpReverseScan provides SHFL-based variants of parallel postfix scan of items partitioned across a CUDA thread warp.
 *
 * LOGICAL_WARP_THREADS must be a power-of-two
 */
template <
    typename    T,                      ///< Data type being scanned
    int         LOGICAL_WARP_THREADS    ///< Number of threads per logical warp
    >
struct WarpReverseScan {
    //---------------------------------------------------------------------
    // Constants and type definitions
    //---------------------------------------------------------------------

    /// Whether the logical warp size and the PTX warp size coincide
    static constexpr bool IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(0));
    /// The number of warp scan steps
    static constexpr int STEPS = cub::Log2<LOGICAL_WARP_THREADS>::VALUE;
    static_assert(LOGICAL_WARP_THREADS == 1 << STEPS);


    //---------------------------------------------------------------------
    // Thread fields
    //---------------------------------------------------------------------

    /// Lane index in logical warp
    unsigned int lane_id;

    /// Logical warp index in 32-thread physical warp
    unsigned int warp_id;

    /// 32-thread physical warp member mask of logical warp
    unsigned int member_mask;

    //---------------------------------------------------------------------
    // Construction
    //---------------------------------------------------------------------

    /// Constructor
    explicit __device__ __forceinline__
    WarpReverseScan()
        : lane_id(cub::LaneId())
        , warp_id(IS_ARCH_WARP ? 0 : (lane_id / LOGICAL_WARP_THREADS))
        // , member_mask(cub::WarpMask<LOGICAL_WARP_THREADS>(warp_id))
        , member_mask(WarpMask<LOGICAL_WARP_THREADS>(warp_id))
    {
        if (!IS_ARCH_WARP) {
            lane_id = lane_id % LOGICAL_WARP_THREADS;
        }
    }


    /// Broadcast
    __device__ __forceinline__ T Broadcast(
        T               input,              ///< [in] The value to broadcast
        int             src_lane)           ///< [in] Which warp lane is to do the broadcasting
    {
        return cub::ShuffleIndex<LOGICAL_WARP_THREADS>(input, src_lane, member_mask);
    }


    /// Inclusive scan
    template <typename ScanOpT>
    __device__ __forceinline__ void InclusiveReverseScan(
        T               input,              ///< [in] Calling thread's input item.
        T               &inclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
        ScanOpT         scan_op)            ///< [in] Binary scan operator
    {
        inclusive_output = input;
        #pragma unroll
        for (int STEP = 0; STEP < STEPS; STEP++) {
            int offset = 1 << STEP;
            T temp = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
                inclusive_output, offset, LOGICAL_WARP_THREADS - 1, member_mask
            );
            // Perform scan op if from a valid peer
            inclusive_output = static_cast<int>(lane_id) >= LOGICAL_WARP_THREADS - offset
                ? inclusive_output : scan_op(temp, inclusive_output);
        }
    }

    /// Exclusive scan
    // Get exclusive from inclusive
    template <typename ScanOpT>
    __device__ __forceinline__ void ExclusiveReverseScan(
        T              input,              ///< [in] Calling thread's input item.
        T              &exclusive_output,  ///< [out] Calling thread's output item.  May be aliased with \p input.
        ScanOpT        scan_op,            ///< [in] Binary scan operator
        T              &warp_aggregate)    ///< [out] Warp-wide aggregate reduction of input items.
    {
        T inclusive_output;
        InclusiveReverseScan(input, inclusive_output, scan_op);
        warp_aggregate = cub::ShuffleIndex<LOGICAL_WARP_THREADS>(inclusive_output, 0, member_mask);
        // initial value unknown
        exclusive_output = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
            inclusive_output, 1, LOGICAL_WARP_THREADS - 1, member_mask
        );
    }

    /**
     * \brief Computes both inclusive and exclusive reverse scans using the specified binary scan functor across the calling warp.  Because no initial value is supplied, the \p exclusive_output computed for the last <em>warp-lane</em> is undefined.
     */
    template <typename ScanOpT>
    __device__ __forceinline__ void ReverseScan(
        T               input,              ///< [in] Calling thread's input item.
        T               &inclusive_output,  ///< [out] Calling thread's inclusive-scan output item.
        T               &exclusive_output,  ///< [out] Calling thread's exclusive-scan output item.
        ScanOpT         scan_op)            ///< [in] Binary scan operator
    {
        InclusiveReverseScan(input, inclusive_output, scan_op);
        // initial value unknown
        exclusive_output = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
            inclusive_output, 1, LOGICAL_WARP_THREADS - 1, member_mask
        );
    }

};

/**
 * \brief BlockReverseScan provides variants of raking-based parallel postfix scan across a CUDA thread block.
 */
template <
    typename    T,              ///< Data type being scanned
    int         BLOCK_DIM_X,    ///< The thread block length in threads along the X dimension
    bool        MEMOIZE=false   ///< Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure
    >
struct BlockReverseScan {
    //---------------------------------------------------------------------
    // Types and constants
    //---------------------------------------------------------------------

    /// Constants
    /// The thread block size in threads
    static constexpr int BLOCK_THREADS = BLOCK_DIM_X;

    /// Layout type for padded thread block raking grid
    using BlockRakingLayout = cub::BlockRakingLayout<T, BLOCK_THREADS>;
    // The number of reduction elements is not a multiple of the number of raking threads for now
    static_assert(BlockRakingLayout::UNGUARDED);

    /// Number of raking threads
    static constexpr int RAKING_THREADS = BlockRakingLayout::RAKING_THREADS;
    /// Number of raking elements per warp synchronous raking thread
    static constexpr int SEGMENT_LENGTH = BlockRakingLayout::SEGMENT_LENGTH;
    /// Cooperative work can be entirely warp synchronous
    static constexpr bool WARP_SYNCHRONOUS = (int(BLOCK_THREADS) == int(RAKING_THREADS));

    ///  WarpReverseScan utility type
    using WarpReverseScan = WarpReverseScan<T, RAKING_THREADS>;

    /// Shared memory storage layout type
    struct _TempStorage {
        typename BlockRakingLayout::TempStorage raking_grid;     ///< Padded thread block raking grid
    };


    /// Alias wrapper allowing storage to be unioned
    struct TempStorage : cub::Uninitialized<_TempStorage> {};


    //---------------------------------------------------------------------
    // Per-thread fields
    //---------------------------------------------------------------------

    // Thread fields
    _TempStorage    &temp_storage;
    unsigned int    linear_tid;
    T               cached_segment[SEGMENT_LENGTH];


    //---------------------------------------------------------------------
    // Utility methods
    //---------------------------------------------------------------------

    /// Performs upsweep raking reduction, returning the aggregate
    template <typename ScanOp>
    __device__ __forceinline__ T Upsweep(ScanOp scan_op) {
        T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
        // Read data into registers
        #pragma unroll
        for (int i = 0; i < SEGMENT_LENGTH; ++i) { cached_segment[i] = smem_raking_ptr[i]; }
        T raking_partial = cached_segment[SEGMENT_LENGTH - 1];
        #pragma unroll
        for (int i = SEGMENT_LENGTH - 2; i >= 0; --i) {
            raking_partial = scan_op(raking_partial, cached_segment[i]);
        }
        return raking_partial;
    }


    /// Performs exclusive downsweep raking scan
    template <typename ScanOp>
    __device__ __forceinline__ void ExclusiveDownsweep(
        ScanOp          scan_op,
        T               raking_partial)
    {
        T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
        // Read data back into registers
        if (!MEMOIZE) {
            #pragma unroll
            for (int i = 0; i < SEGMENT_LENGTH; ++i) { cached_segment[i] = smem_raking_ptr[i]; }
        }
        ThreadReverseScanExclusive(cached_segment, cached_segment, scan_op, raking_partial);
        // Write data back to smem
        #pragma unroll
        for (int i = 0; i < SEGMENT_LENGTH; ++i) { smem_raking_ptr[i] = cached_segment[i]; }
    }


    //---------------------------------------------------------------------
    // Constructors
    //---------------------------------------------------------------------

    /// Constructor
    __device__ __forceinline__ BlockReverseScan(
        TempStorage &temp_storage)
    :
        temp_storage(temp_storage.Alias()),
        linear_tid(cub::RowMajorTid(BLOCK_DIM_X, 1, 1))
    {}


    /// Computes an exclusive thread block-wide postfix scan using the specified binary \p scan_op functor.  Each thread contributes one input element.  the call-back functor \p block_postfix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically postfixes the thread block's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
    template <
        typename ScanOp,
        typename BlockPostfixCallbackOp>
    __device__ __forceinline__ void ExclusiveReverseScan(
        T                       input,                          ///< [in] Calling thread's input item
        T                       &exclusive_output,              ///< [out] Calling thread's output item (may be aliased to \p input)
        ScanOp                  scan_op,                        ///< [in] Binary scan operator
        BlockPostfixCallbackOp  &block_postfix_callback_op)     ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a thread block-wide postfix to be applied to all inputs.
    {
        if (WARP_SYNCHRONOUS) {
            // Short-circuit directly to warp-synchronous scan
            T block_aggregate;
            WarpReverseScan warp_scan;
            warp_scan.ExclusiveReverseScan(input, exclusive_output, scan_op, block_aggregate);
            // Obtain warp-wide postfix in lane0, then broadcast to other lanes
            T block_postfix = block_postfix_callback_op(block_aggregate);
            block_postfix = warp_scan.Broadcast(block_postfix, 0);
            exclusive_output = linear_tid == BLOCK_THREADS - 1 ? block_postfix : scan_op(block_postfix, exclusive_output);
        } else {
            // Place thread partial into shared memory raking grid
            T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
            detail::uninitialized_copy(placement_ptr, input);
            cub::CTA_SYNC();
            // Reduce parallelism down to just raking threads
            if (linear_tid < RAKING_THREADS) {
                WarpReverseScan warp_scan;
                // Raking upsweep reduction across shared partials
                T upsweep_partial = Upsweep(scan_op);
                // Warp-synchronous scan
                T exclusive_partial, block_aggregate;
                warp_scan.ExclusiveReverseScan(upsweep_partial, exclusive_partial, scan_op, block_aggregate);
                // Obtain block-wide postfix in lane0, then broadcast to other lanes
                T block_postfix = block_postfix_callback_op(block_aggregate);
                block_postfix = warp_scan.Broadcast(block_postfix, 0);
                // Update postfix with warpscan exclusive partial
                T downsweep_postfix = linear_tid == RAKING_THREADS - 1
                    ? block_postfix : scan_op(block_postfix, exclusive_partial);
                // Exclusive raking downsweep scan
                ExclusiveDownsweep(scan_op, downsweep_postfix);
            }
            cub::CTA_SYNC();
            // Grab thread postfix from shared memory
            exclusive_output = *placement_ptr;

            // // Compute warp scan in each warp.
            // // The exclusive output from the last lane in each warp is invalid.
            // T inclusive_output;
            // WarpReverseScan warp_scan;
            // warp_scan.ReverseScan(input, inclusive_output, exclusive_output, scan_op);

            // // Compute the warp-wide postfix and block-wide aggregate for each warp.  Warp postfix for the last warp is invalid.
            // T block_aggregate;
            // T warp_postfix = ComputeWarpPostfix(scan_op, inclusive_output, block_aggregate);

            // // Apply warp postfix to our lane's partial
            // if (warp_id != 0) {
            //     exclusive_output = scan_op(warp_postfix, exclusive_output);
            //     if (lane_id == 0) { exclusive_output = warp_postfix; }
            // }

            // // Use the first warp to determine the thread block postfix, returning the result in lane0
            // if (warp_id == 0) {
            //     T block_postfix = block_postfix_callback_op(block_aggregate);
            //     if (lane_id == 0) {
            //         // Share the postfix with all threads
            //         detail::uninitialized_copy(&temp_storage.block_postfix,
            //                                   block_postfix);

            //         exclusive_output = block_postfix; // The block postfix is the exclusive output for tid0
            //     }
            // }

            // cub::CTA_SYNC();

            // // Incorporate thread block postfix into outputs
            // T block_postfix = temp_storage.block_postfix;
            // if (linear_tid > 0) { exclusive_output = scan_op(block_postfix, exclusive_output); }
        }
    }


    /**
     * \brief Computes an inclusive block-wide postfix scan using the specified binary \p scan_op functor.  Each thread contributes an array of consecutive input elements.  the call-back functor \p block_postfix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically postfixes the thread block's scan inputs.  Also provides every thread with the block-wide \p block_aggregate of all inputs.
     */
    template <
        int             ITEMS_PER_THREAD,
        typename        ScanOp,
        typename        BlockPostfixCallbackOp>
    __device__ __forceinline__ void InclusiveReverseScan(
        T                       (&input)[ITEMS_PER_THREAD],     ///< [in] Calling thread's input items
        T                       (&output)[ITEMS_PER_THREAD],    ///< [out] Calling thread's output items (may be aliased to \p input)
        ScanOp                  scan_op,                        ///< [in] Binary scan functor
        BlockPostfixCallbackOp   &block_postfix_callback_op)    ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide postfix to be applied to the logical input sequence.
    {
        // Reduce consecutive thread items in registers
        T thread_postfix = ThreadReverseReduce(input, scan_op);
        // Exclusive thread block-scan
        ExclusiveReverseScan(thread_postfix, thread_postfix, scan_op, block_postfix_callback_op);
        // Inclusive scan in registers with postfix as seed
        ThreadReverseScanInclusive(input, output, scan_op, thread_postfix);
    }

};

================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/selective_scan.h
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

////////////////////////////////////////////////////////////////////////////////////////////////////

struct SSMScanParamsBase {
    using index_t = uint32_t;

    int batch, seqlen, n_chunks;
    index_t a_batch_stride;
    index_t b_batch_stride;
    index_t out_batch_stride;

    // Common data pointers.
    void *__restrict__ a_ptr;
    void *__restrict__ b_ptr;
    void *__restrict__ out_ptr;
    void *__restrict__ x_ptr;
};

////////////////////////////////////////////////////////////////////////////////////////////////////

struct SSMParamsBase {
    using index_t = uint32_t;

    int batch, dim, seqlen, dstate, n_groups, n_chunks;
    int dim_ngroups_ratio;

    bool delta_softplus;

    index_t A_d_stride;
    index_t A_dstate_stride;
    index_t B_batch_stride;
    index_t B_d_stride;
    index_t B_dstate_stride;
    index_t B_group_stride;
    index_t C_batch_stride;
    index_t C_d_stride;
    index_t C_dstate_stride;
    index_t C_group_stride;
    index_t u_batch_stride;
    index_t u_d_stride;
    index_t delta_batch_stride;
    index_t delta_d_stride;
    index_t out_batch_stride;
    index_t out_d_stride;

    // Common data pointers.
    void *__restrict__ A_ptr;
    void *__restrict__ B_ptr;
    void *__restrict__ C_ptr;
    void *__restrict__ D_ptr;
    void *__restrict__ u_ptr;
    void *__restrict__ delta_ptr;
    void *__restrict__ delta_bias_ptr;
    void *__restrict__ out_ptr;
    void *__restrict__ x_ptr;
};

struct SSMParamsBwd: public SSMParamsBase {
    index_t dout_batch_stride;
    index_t dout_d_stride;
    index_t dA_d_stride;
    index_t dA_dstate_stride;
    index_t dB_batch_stride;
    index_t dB_group_stride;
    index_t dB_d_stride;
    index_t dB_dstate_stride;
    index_t dC_batch_stride;
    index_t dC_group_stride;
    index_t dC_d_stride;
    index_t dC_dstate_stride;
    index_t du_batch_stride;
    index_t du_d_stride;
    index_t ddelta_batch_stride;
    index_t ddelta_d_stride;

    // Common data pointers.
    void *__restrict__ dout_ptr;
    void *__restrict__ dA_ptr;
    void *__restrict__ dB_ptr;
    void *__restrict__ dC_ptr;
    void *__restrict__ dD_ptr;
    void *__restrict__ du_ptr;
    void *__restrict__ ddelta_ptr;
    void *__restrict__ ddelta_bias_ptr;
};


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/selective_scan_common.h
================================================
/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <cuda_bf16.h>
#include <cuda_fp16.h>
#include <c10/util/complex.h>  // For scalar_value_type

#define MAX_DSTATE 256

inline __device__ float2 operator+(const float2 & a, const float2 & b){
    return {a.x + b.x, a.y + b.y};
}

inline __device__ float3 operator+(const float3 &a, const float3 &b) {
  return {a.x + b.x, a.y + b.y, a.z + b.z};
}

inline __device__ float4 operator+(const float4 & a, const float4 & b){
    return {a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w};
}

////////////////////////////////////////////////////////////////////////////////////////////////////

template<int BYTES> struct BytesToType {};

template<> struct BytesToType<16> {
    using Type = uint4;
    static_assert(sizeof(Type) == 16);
};

template<> struct BytesToType<8> {
    using Type = uint64_t;
    static_assert(sizeof(Type) == 8);
};

template<> struct BytesToType<4> {
    using Type = uint32_t;
    static_assert(sizeof(Type) == 4);
};

template<> struct BytesToType<2> {
    using Type = uint16_t;
    static_assert(sizeof(Type) == 2);
};

template<> struct BytesToType<1> {
    using Type = uint8_t;
    static_assert(sizeof(Type) == 1);
};

////////////////////////////////////////////////////////////////////////////////////////////////////

template<typename scalar_t, int N>
struct Converter{
    static inline __device__ void to_float(const scalar_t (&src)[N], float (&dst)[N]) {
        #pragma unroll
        for (int i = 0; i < N; ++i) { dst[i] = src[i]; }
    }
};

template<int N>
struct Converter<at::Half, N>{
    static inline __device__ void to_float(const at::Half (&src)[N], float (&dst)[N]) {
        static_assert(N % 2 == 0);
        auto &src2 = reinterpret_cast<const half2 (&)[N / 2]>(src);
        auto &dst2 = reinterpret_cast<float2 (&)[N / 2]>(dst);
        #pragma unroll
        for (int i = 0; i < N / 2; ++i) { dst2[i] = __half22float2(src2[i]); }
    }
};

#if __CUDA_ARCH__ >= 800
template<int N>
struct Converter<at::BFloat16, N>{
    static inline __device__ void to_float(const at::BFloat16 (&src)[N], float (&dst)[N]) {
        static_assert(N % 2 == 0);
        auto &src2 = reinterpret_cast<const nv_bfloat162 (&)[N / 2]>(src);
        auto &dst2 = reinterpret_cast<float2 (&)[N / 2]>(dst);
        #pragma unroll
        for (int i = 0; i < N / 2; ++i) { dst2[i] = __bfloat1622float2(src2[i]); }
    }
};
#endif

////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename scalar_t> struct SSMScanOp;

template<>
struct SSMScanOp<float> {
    __device__ __forceinline__ float2 operator()(const float2 &ab0, const float2 &ab1) const {
        return make_float2(ab1.x * ab0.x, ab1.x * ab0.y + ab1.y);
    }
};

// A stateful callback functor that maintains a running prefix to be applied
// during consecutive scan operations.
template <typename scalar_t> struct SSMScanPrefixCallbackOp {
    using scan_t = std::conditional_t<std::is_same_v<scalar_t, float>, float2, float4>;
    scan_t running_prefix;
    // Constructor
    __device__ SSMScanPrefixCallbackOp(scan_t running_prefix_) : running_prefix(running_prefix_) {}
    // Callback operator to be entered by the first warp of threads in the block.
    // Thread-0 is responsible for returning a value for seeding the block-wide scan.
    __device__ scan_t operator()(scan_t block_aggregate) {
        scan_t old_prefix = running_prefix;
        running_prefix = SSMScanOp<scalar_t>()(running_prefix, block_aggregate);
        return old_prefix;
    }
};

////////////////////////////////////////////////////////////////////////////////////////////////////

template<typename Ktraits>
inline __device__ void load_input(typename Ktraits::input_t *u,
                                  typename Ktraits::input_t (&u_vals)[Ktraits::kNItems],
                                  typename Ktraits::BlockLoadT::TempStorage &smem_load,
                                  int seqlen) {
    if constexpr (Ktraits::kIsEvenLen) {
        auto& smem_load_vec = reinterpret_cast<typename Ktraits::BlockLoadVecT::TempStorage&>(smem_load);
        using vec_t = typename Ktraits::vec_t;
        Ktraits::BlockLoadVecT(smem_load_vec).Load(
            reinterpret_cast<vec_t*>(u),
            reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(u_vals)
       );
    } else {
        Ktraits::BlockLoadT(smem_load).Load(u, u_vals, seqlen, 0.f);
    }
}

template<typename Ktraits>
inline __device__ void load_weight(typename Ktraits::input_t *Bvar,
                                   typename Ktraits::weight_t (&B_vals)[Ktraits::kNItems],
                                   typename Ktraits::BlockLoadWeightT::TempStorage &smem_load_weight,
                                   int seqlen) {
    constexpr int kNItems = Ktraits::kNItems;
    typename Ktraits::input_t B_vals_load[kNItems];
    if constexpr (Ktraits::kIsEvenLen) {
        auto& smem_load_weight_vec = reinterpret_cast<typename Ktraits::BlockLoadWeightVecT::TempStorage&>(smem_load_weight);
        using vec_t = typename Ktraits::vec_t;
        Ktraits::BlockLoadWeightVecT(smem_load_weight_vec).Load(
            reinterpret_cast<vec_t*>(Bvar),
            reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(B_vals_load)
      );
    } else {
        Ktraits::BlockLoadWeightT(smem_load_weight).Load(Bvar, B_vals_load, seqlen, 0.f);
    }
    // #pragma unroll
    // for (int i = 0; i < kNItems; ++i) { B_vals[i] = B_vals_load[i]; }
    Converter<typename Ktraits::input_t, kNItems>::to_float(B_vals_load, B_vals);
}

template<typename Ktraits>
inline __device__ void store_output(typename Ktraits::input_t *out,
                                    const float (&out_vals)[Ktraits::kNItems],
                                    typename Ktraits::BlockStoreT::TempStorage &smem_store,
                                    int seqlen) {
    typename Ktraits::input_t write_vals[Ktraits::kNItems];
    #pragma unroll
    for (int i = 0; i < Ktraits::kNItems; ++i) { write_vals[i] = out_vals[i]; }
    if constexpr (Ktraits::kIsEvenLen) {
        auto& smem_store_vec = reinterpret_cast<typename Ktraits::BlockStoreVecT::TempStorage&>(smem_store);
        using vec_t = typename Ktraits::vec_t;
        Ktraits::BlockStoreVecT(smem_store_vec).Store(
            reinterpret_cast<vec_t*>(out),
            reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(write_vals)
       );
    } else {
        Ktraits::BlockStoreT(smem_store).Store(out, write_vals, seqlen);
    }
}

template<typename Ktraits>
inline __device__ void store_output1(typename Ktraits::output_t *out,
                                    const float (&out_vals)[Ktraits::kNItems],
                                    typename Ktraits::BlockStoreOutputT::TempStorage &smem_store,
                                    int seqlen) {
    typename Ktraits::output_t write_vals[Ktraits::kNItems];
    #pragma unroll
    for (int i = 0; i < Ktraits::kNItems; ++i) { write_vals[i] = out_vals[i]; }
    if constexpr (Ktraits::kIsEvenLen) {
        auto& smem_store_vec = reinterpret_cast<typename Ktraits::BlockStoreOutputVecT::TempStorage&>(smem_store);
        using vec_t = typename Ktraits::vec_t;
        Ktraits::BlockStoreOutputVecT(smem_store_vec).Store(
            reinterpret_cast<vec_t*>(out),
            reinterpret_cast<vec_t(&)[Ktraits::kNLoadsOutput]>(write_vals)
       );
    } else {
        Ktraits::BlockStoreOutputT(smem_store).Store(out, write_vals, seqlen);
    }
}

template<typename Ktraits>
inline __device__ void load_output(typename Ktraits::output_t *u,
                                  typename Ktraits::output_t (&u_vals)[Ktraits::kNItems],
                                  typename Ktraits::BlockLoadOutputT::TempStorage &smem_load,
                                  int seqlen) {
    if constexpr (Ktraits::kIsEvenLen) {
        auto& smem_load_vec = reinterpret_cast<typename Ktraits::BlockLoadOutputVecT::TempStorage&>(smem_load);
        using vec_t = typename Ktraits::vec_t;
        Ktraits::BlockLoadOutputVecT(smem_load_vec).Load(
            reinterpret_cast<vec_t*>(u),
            reinterpret_cast<vec_t(&)[Ktraits::kNLoadsOutput]>(u_vals)
       );
    } else {
        Ktraits::BlockLoadOutputT(smem_load).Load(u, u_vals, seqlen, 0.f);
    }
}


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/static_switch.h
================================================
// Inspired by https://github.com/NVIDIA/DALI/blob/main/include/dali/core/static_switch.h
// and https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Dispatch.h

#pragma once

/// @param COND       - a boolean expression to switch by
/// @param CONST_NAME - a name given for the constexpr bool variable.
/// @param ...       - code to execute for true and false
///
/// Usage:
/// ```
/// BOOL_SWITCH(flag, BoolConst, [&] {
///     some_function<BoolConst>(...);
/// });
/// ```
#define BOOL_SWITCH(COND, CONST_NAME, ...)                                           \
    [&] {                                                                            \
        if (COND) {                                                                  \
            constexpr bool CONST_NAME = true;                                        \
            return __VA_ARGS__();                                                    \
        } else {                                                                     \
            constexpr bool CONST_NAME = false;                                       \
            return __VA_ARGS__();                                                    \
        }                                                                            \
    }()


================================================
FILE: Mamba/kernels/selective_scan/csrc/selective_scan/uninitialized_copy.cuh
================================================
/******************************************************************************
 * Copyright (c) 2011-2022, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *       notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *       notice, this list of conditions and the following disclaimer in the
 *       documentation and/or other materials provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the
 *       names of its contributors may be used to endorse or promote products
 *       derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
 * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 ******************************************************************************/

#pragma once

#include <cub/config.cuh>

#include <cuda/std/type_traits>


namespace detail
{

#if defined(_NVHPC_CUDA)
template <typename T, typename U>
__host__ __device__ void uninitialized_copy(T *ptr, U &&val)
{
  // NVBug 3384810
  new (ptr) T(::cuda::std::forward<U>(val));
}
#else
template <typename T,
          typename U,
          typename ::cuda::std::enable_if<
            ::cuda::std::is_trivially_copyable<T>::value,
            int
          >::type = 0>
__host__ __device__ void uninitialized_copy(T *ptr, U &&val)
{
  *ptr = ::cuda::std::forward<U>(val);
}

template <typename T,
         typename U,
         typename ::cuda::std::enable_if<
           !::cuda::std::is_trivially_copyable<T>::value,
           int
         >::type = 0>
__host__ __device__ void uninitialized_copy(T *ptr, U &&val)
{
  new (ptr) T(::cuda::std::forward<U>(val));
}
#endif

} // namespace detail


================================================
FILE: Mamba/kernels/selective_scan/setup.py
================================================
# Modified by $@#Anonymous#@$ #20240123
# Copyright (c) 2023, Albert Gu, Tri Dao.
import sys
import warnings
import os
import re
import ast
from pathlib import Path
from packaging.version import parse, Version
import platform
import shutil

from setuptools import setup, find_packages
import subprocess
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

import torch
from torch.utils.cpp_extension import (
    BuildExtension,
    CppExtension,
    CUDAExtension,
    CUDA_HOME,
)

# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("FORCE_CXX11_ABI", "FALSE") == "TRUE"

def get_cuda_bare_metal_version(cuda_dir):
    raw_output = subprocess.check_output(
        [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
    )
    output = raw_output.split()
    release_idx = output.index("release") + 1
    bare_metal_version = parse(output[release_idx].split(",")[0])

    return raw_output, bare_metal_version

MODES = ["core", "ndstate", "oflex"]
# MODES = ["core", "ndstate", "oflex", "nrow"]

def get_ext():
    cc_flag = []

    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
    print("\n\nCUDA_HOME = {}\n\n".format(CUDA_HOME))

    # Check, if CUDA11 is installed for compute capability 8.0
    multi_threads = True
    gencode_sm90 = False
    if CUDA_HOME is not None:
        _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
        print("CUDA version: ", bare_metal_version, flush=True)
        if bare_metal_version >= Version("11.8"):
            gencode_sm90 = True
        if bare_metal_version < Version("11.6"):
            warnings.warn("CUDA version ealier than 11.6 may leads to performance mismatch.")
        if bare_metal_version < Version("11.2"):
            multi_threads = False
            
    cc_flag.extend(["-gencode", "arch=compute_70,code=sm_70"])
    cc_flag.extend(["-gencode", "arch=compute_80,code=sm_80"])
    if gencode_sm90:
        cc_flag.extend(["-gencode", "arch=compute_90,code=sm_90"])
    if multi_threads:
        cc_flag.extend(["--threads", "4"])

    # HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
    # torch._C._GLIBCXX_USE_CXX11_ABI
    # https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
    if FORCE_CXX11_ABI:
        torch._C._GLIBCXX_USE_CXX11_ABI = True

    sources = dict(
        core=[
            "csrc/selective_scan/cus/selective_scan.cpp",
            "csrc/selective_scan/cus/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cus/selective_scan_core_bwd.cu",
        ],
        nrow=[
            "csrc/selective_scan/cusnrow/selective_scan_nrow.cpp",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd2.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd3.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd4.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd2.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd3.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd4.cu",
        ],
        ndstate=[
            "csrc/selective_scan/cusndstate/selective_scan_ndstate.cpp",
            "csrc/selective_scan/cusndstate/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusndstate/selective_scan_core_bwd.cu",
        ],
        oflex=[
            "csrc/selective_scan/cusoflex/selective_scan_oflex.cpp",
            "csrc/selective_scan/cusoflex/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusoflex/selective_scan_core_bwd.cu",
        ],
    )

    names = dict(
        core="selective_scan_cuda_core",
        nrow="selective_scan_cuda_nrow",
        ndstate="selective_scan_cuda_ndstate",
        oflex="selective_scan_cuda_oflex",
    )

    ext_modules = [
        CUDAExtension(
            name=names.get(MODE, None),
            sources=sources.get(MODE, None),
            extra_compile_args={
                "cxx": ["-O3", "-std=c++17"],
                "nvcc": [
                            "-O3",
                            "-std=c++17",
                            "-U__CUDA_NO_HALF_OPERATORS__",
                            "-U__CUDA_NO_HALF_CONVERSIONS__",
                            "-U__CUDA_NO_BFLOAT16_OPERATORS__",
                            "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
                            "-U__CUDA_NO_BFLOAT162_OPERATORS__",
                            "-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
                            "--expt-relaxed-constexpr",
                            "--expt-extended-lambda",
                            "--use_fast_math",
                            "--ptxas-options=-v",
                            "-lineinfo",
                        ]
                        + cc_flag
            },
            include_dirs=[Path(this_dir) / "csrc" / "selective_scan"],
        )
        for MODE in MODES
    ]

    return ext_modules

ext_modules = get_ext()
setup(
    name="selective_scan",
    version="0.0.2",
    packages=[],
    author="Tri Dao, Albert Gu, $@#Anonymous#@$ ",
    author_email="tri@tridao.me, agu@cs.cmu.edu, $@#Anonymous#EMAIL@$",
    description="selective scan",
    long_description="",
    long_description_content_type="text/markdown",
    url="https://github.com/state-spaces/mamba",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: BSD License",
        "Operating System :: Unix",
    ],
    ext_modules=ext_modules,
    cmdclass={"bdist_wheel": _bdist_wheel, "build_ext": BuildExtension} if ext_modules else {"bdist_wheel": _bdist_wheel,},
    python_requires=">=3.7",
    install_requires=[
        "torch",
        "packaging",
        "ninja",
        "einops",
    ],
)


================================================
FILE: Mamba/kernels/selective_scan/test_selective_scan.py
================================================
# Modified by $@#Anonymous#@$ #20240123
# Copyright (C) 2023, Tri Dao, Albert Gu.

import math
import torch
import torch.nn.functional as F
import pytest
import torch
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
from einops import rearrange, repeat
import time
from functools import partial

SSOFLEX_FLOAT = True


def build_selective_scan_fn(selective_scan_cuda: object = None, mode="mamba_ssm", tag=None):
    MODE = mode

    class SelectiveScanFn(torch.autograd.Function):
        @staticmethod
        def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False, nrows=1, backnrows=-1):
            if u.stride(-1) != 1:
                u = u.contiguous()
            if delta.stride(-1) != 1:
                delta = delta.contiguous()
            if D is not None:
                D = D.contiguous()
            if B.stride(-1) != 1:
                B = B.contiguous()
            if C.stride(-1) != 1:
                C = C.contiguous()
            if z is not None and z.stride(-1) != 1:
                z = z.contiguous()
            if B.dim() == 3:
                B = rearrange(B, "b dstate l -> b 1 dstate l")
                ctx.squeeze_B = True
            if C.dim() == 3:
                C = rearrange(C, "b dstate l -> b 1 dstate l")
                ctx.squeeze_C = True
            if D is not None and (D.dtype != torch.float):
                ctx._d_dtype = D.dtype
                D = D.float()
            if delta_bias is not None and (delta_bias.dtype != torch.float):
                ctx._delta_bias_dtype = delta_bias.dtype
                delta_bias = delta_bias.float()

            assert u.shape[1] % (B.shape[1] * nrows) == 0 
            assert nrows in [1, 2, 3, 4] # 8+ is too slow to compile

            if backnrows > 0:
                assert u.shape[1] % (B.shape[1] * backnrows) == 0 
                assert backnrows in [1, 2, 3, 4] # 8+ is too slow to compile
            else:
                backnrows = nrows
            ctx.backnrows = backnrows
            
            if MODE in ["mamba_ssm"]:
                out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus)
            elif MODE in ["ssoflex"]:
                out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows, SSOFLEX_FLOAT)
            elif MODE in ["sscore"]:
                out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
            elif MODE in ["sstest"]:
                out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus, nrows)
            elif MODE in ["sscorendstate"]:
                assert A.shape[-1] == 1 and B.shape[2] == 1 and C.shape[2] == 1
                A = A.view(-1)
                B = B.squeeze(2)
                C = C.squeeze(2)
                out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, 1)
            else:
                raise NotImplementedError

            ctx.delta_softplus = delta_softplus
            ctx.has_z = z is not None

            last_state = x[:, :, -1, 1::2]  # (batch, dim, dstate)
            if not ctx.has_z:
                ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
                return out if not return_last_state else (out, last_state)
            else:
                ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
                if MODE in ["mamba_ssm", "sstest"]:
                    out_z = rest[0]
                    return out_z if not return_last_state else (out_z, last_state)
                elif MODE in ["sscore", "ssoflex"]:
                    return out if not return_last_state else (out, last_state)

        @staticmethod
        def backward(ctx, dout, *args):
            if not ctx.has_z:
                u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
                z = None
                out = None
            else:
                u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensors
            if dout.stride(-1) != 1:
                dout = dout.contiguous()
            # The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
            # backward of selective_scan_cuda with the backward of chunk).
            # Here we just pass in None and dz will be allocated in the C++ code.
            if MODE in ["mamba_ssm"]:
                du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
                    u, delta, A, B, C, D, z, delta_bias, dout, x, out, None, ctx.delta_softplus,
                    False # option to recompute out_z, not used here
                )
            elif MODE in ["sstest"]:
                du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
                    u, delta, A, B, C, D, z, delta_bias, dout, x, out, None, ctx.delta_softplus,
                    False, ctx.backnrows  # option to recompute out_z, not used here
                )
            elif MODE in ["sscore", "ssoflex"]:
                du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
                    u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, ctx.backnrows
                )
            elif MODE in ["sscorendstate"]:
                du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
                    u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
                )
                dA = dA.unsqueeze(1)
                dB = dB.unsqueeze(2)
                dC = dC.unsqueeze(2)
            else:
                raise NotImplementedError
            
            dz = rest[0] if ctx.has_z else None
            dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
            dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
            
            _dD = None
            if D is not None:
                if dD.dtype != getattr(ctx, "_d_dtype", dD.dtype):
                    _dD = dD.to(ctx._d_dtype)
                else:
                    _dD = dD

            _ddelta_bias = None
            if delta_bias is not None:
                if ddelta_bias.dtype != getattr(ctx, "_delta_bias_dtype", ddelta_bias.dtype):
                    _ddelta_bias = ddelta_bias.to(ctx._delta_bias_dtype)
                else:
                    _ddelta_bias = ddelta_bias

            return (du, ddelta, dA, dB, dC,
                        dD if D is not None else None,
                        dz,
                        ddelta_bias if delta_bias is not None else None,
                        None, None, None, None)

    def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False, nrows=1, backnrows=-1):
        """if return_last_state is True, returns (out, last_state)
        last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
        not considered in the backward pass.
        """
        outs = SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state, nrows, backnrows)
        if mode in ["ssoflex"]:
            return outs.to(u.dtype) if not return_last_state else (outs[0].to(u.dtype), outs[1])
        else:
            return outs

    selective_scan_fn.__repr__ = lambda *_ :f"selective_scan_fn | {mode} | {tag}"

    return selective_scan_fn


def selective_scan_ref(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
                      return_last_state=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: c(D N) or r(D N)
    B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
    C: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32

    out: r(B D L)
    last_state (optional): r(B D dstate) or c(B D dstate)
    """
    dtype_in = u.dtype
    u = u.float()
    delta = delta.float()
    if delta_bias is not None:
        delta = delta + delta_bias[..., None].float()
    if delta_softplus:
        delta = F.softplus(delta)
    batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
    is_variable_B = B.dim() >= 3
    is_variable_C = C.dim() >= 3
    if A.is_complex():
        if is_variable_B:
            B = torch.view_as_complex(rearrange(B.float(), "... (L two) -> ... L two", two=2))
        if is_variable_C:
            C = torch.view_as_complex(rearrange(C.float(), "... (L two) -> ... L two", two=2))
    else:
        B = B.float()
        C = C.float()
    x = A.new_zeros((batch, dim, dstate))
    ys = []
    deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
    if not is_variable_B:
        deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
    else:
        if B.dim() == 3:
            deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
        else:
            B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
            deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
    if is_variable_C and C.dim() == 4:
        C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
    last_state = None
    for i in range(u.shape[2]):
        x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
        if not is_variable_C:
            y = torch.einsum('bdn,dn->bd', x, C)
        else:
            if C.dim() == 3:
                y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
            else:
                y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
        if i == u.shape[2] - 1:
            last_state = x
        if y.is_complex():
            y = y.real * 2
        ys.append(y)
    y = torch.stack(ys, dim=2) # (batch dim L)
    out = y if D is None else y + u * rearrange(D, "d -> d 1")
    if z is not None:
        out = out * F.silu(z)
    out = out.to(dtype=dtype_in)
    return out if not return_last_state else (out, last_state)


def selective_scan_ref_v2(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
                      return_last_state=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: c(D N) or r(D N)
    B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
    C: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32

    out: r(B D L)
    last_state (optional): r(B D dstate) or c(B D dstate)
    """
    dtype_in = u.dtype
    A = A.to(dtype_in)
    B = B.to(dtype_in)
    C = C.to(dtype_in)
    D = D.to(dtype_in) if D is not None else None
    z = z.to(dtype_in) if z is not None else None
    delta = delta.to(dtype_in) if delta is not None else None
    delta_bias = delta_bias.to(dtype_in) if delta_bias is not None else None

    if delta_bias is not None:
        delta = delta + delta_bias[..., None]
    if delta_softplus:
        delta = F.softplus(delta)
    batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
    is_variable_B = B.dim() >= 3
    is_variable_C = C.dim() >= 3
    if A.is_complex():
        if is_variable_B:
            B = torch.view_as_complex(rearrange(B, "... (L two) -> ... L two", two=2))
        if is_variable_C:
            C = torch.view_as_complex(rearrange(C, "... (L two) -> ... L two", two=2))
    x = A.new_zeros((batch, dim, dstate))
    ys = []
    deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
    if not is_variable_B:
        deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
    else:
        if B.dim() == 3:
            deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
        else:
            B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
            deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
    if is_variable_C and C.dim() == 4:
        C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
    last_state = None
    for i in range(u.shape[2]):
        x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
        if not is_variable_C:
            y = torch.einsum('bdn,dn->bd', x, C)
        else:
            if C.dim() == 3:
                y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
            else:
                y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
        if i == u.shape[2] - 1:
            last_state = x
        if y.is_complex():
            y = y.real * 2
        ys.append(y)
    y = torch.stack(ys, dim=2) # (batch dim L)
    out = y if D is None else y + u * rearrange(D, "d -> d 1")
    if z is not None:
        out = out * F.silu(z)
    out = out.to(dtype=dtype_in)
    return out if not return_last_state else (out, last_state.float())


def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False, *args, **kwargs):
    return selective_scan_ref_v2(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)

# MODE = None
# MODE = "mamba_ssm"
# MODE = "sscore"
# MODE = "ssoflex"
# MODE = "sstest"
# MODE = "mamba_ssm_sscore" # 1344 items pass
# MODE = "mamba_ssm_sscorendstate" # 1344 items pass
MODE = "mamba_ssm_ssoflex" # 1344 items pass

if MODE in ["mamba_ssm"]:
    import selective_scan_cuda
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda, mode=MODE)
    selective_scan_ref = selective_scan_ref
elif MODE in ["ssoflex"]:
    import selective_scan_cuda_oflex
    selective_scan_cuda = selective_scan_cuda_oflex
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_oflex, mode=MODE)
    selective_scan_ref = selective_scan_ref
elif MODE in ["sscore"]:
    import selective_scan_cuda_core
    selective_scan_cuda = selective_scan_cuda_core
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_core, mode=MODE)
    selective_scan_ref = selective_scan_ref
elif MODE in ["sstest"]:
    import selective_scan_cuda_test
    selective_scan_cuda = selective_scan_cuda_test
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_test, mode=MODE)
    selective_scan_ref = selective_scan_ref
elif MODE in ["mamba_ssm_sscore"]:
    import selective_scan_cuda_core
    import selective_scan_cuda
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_core, mode="sscore")
    selective_scan_ref = build_selective_scan_fn(selective_scan_cuda, mode="mamba_ssm")
elif MODE in ["mamba_ssm_sstest"]:
    import selective_scan_cuda_test
    import selective_scan_cuda
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_test, mode="sstest")
    selective_scan_ref = build_selective_scan_fn(selective_scan_cuda, mode="mamba_ssm")
elif MODE in ["mamba_ssm_sscorendstate"]:
    import selective_scan_cuda_core
    import selective_scan_cuda
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_core, mode="sscorendstate")
    selective_scan_ref = build_selective_scan_fn(selective_scan_cuda, mode="mamba_ssm")
elif MODE in ["mamba_ssm_ssoflex"]:
    import selective_scan_cuda_oflex
    import selective_scan_cuda
    selective_scan_fn = build_selective_scan_fn(selective_scan_cuda_oflex, mode="ssoflex")
    selective_scan_ref = build_selective_scan_fn(selective_scan_cuda, mode="mamba_ssm")
else:
    selective_scan_cuda = None


print("use MODE:", MODE)
DSTATE = [1]
DIM = [768]
BATCHSIZE = [2]
# DSTATE = [1] if MODE in ["mamba_ssm_sscorendstate", "sscorendstate"] else [8]
NROWS = [1,2,3,4]
IDTYPE = MODE in [None]

# @pytest.mark.parametrize('wtype', [torch.float32, torch.complex64])
@pytest.mark.parametrize('wtype', [torch.float32])
@pytest.mark.parametrize('itype', [torch.float32, torch.float16, torch.bfloat16])
@pytest.mark.parametrize('seqlen', [64, 128, 256, 512, 1024, 2048, 4096])
@pytest.mark.parametrize("return_last_state", [True])
@pytest.mark.parametrize('has_delta_bias', [False, True])
@pytest.mark.parametrize('delta_softplus', [False, True])
# @pytest.mark.parametrize('has_z', [False, True])
@pytest.mark.parametrize('has_z', [False])
@pytest.mark.parametrize('has_D', [False, True])
@pytest.mark.parametrize("varBC_groups", [1, 2])
# @pytest.mark.parametrize("is_variable_C", [False, True])
@pytest.mark.parametrize("is_variable_C", [True])
# @pytest.mark.parametrize("is_variable_B", [False, True])
@pytest.mark.parametrize("is_variable_B", [True])
@pytest.mark.parametrize("nrows", NROWS)
@pytest.mark.parametrize("batch_size", BATCHSIZE)
@pytest.mark.parametrize("dim", DIM)
@pytest.mark.parametrize("dstate", DSTATE)
def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_D, has_z, has_delta_bias,
                        delta_softplus, return_last_state, seqlen, itype, wtype, nrows, batch_size, dim, dstate):
    wtype = itype if IDTYPE else wtype
    print(f'method: {selective_scan_cuda}')
    if varBC_groups > 1 and (not is_variable_B or not is_variable_C):
        pytest.skip()  # This config is not applicable
    device = 'cuda'
    rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3)
    if itype == torch.bfloat16:
        rtol, atol = 3e-2, 5e-2
    rtolw, atolw = (1e-3, 1e-3)
    if has_z:  # If we have z, the errors on the weights seem higher
        rtolw = max(rtolw, rtol)
        atolw = max(atolw, atol)
    # set seed
    torch.random.manual_seed(0)
    # batch_size = 2
    # dim = 24
    # dstate = 8
    is_complex = wtype == torch.complex64
    A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_()
    if not is_variable_B:
        B_shape = (dim, dstate)
    elif varBC_groups == 1:
        B_shape = (batch_size, dstate, seqlen if not is_complex else seqlen * 2)
    else:
        B_shape = (batch_size, varBC_groups, dstate, seqlen if not is_complex else seqlen * 2)
    B = torch.randn(*B_shape, device=device, dtype=wtype if not is_variable_B else itype,
                    requires_grad=True)
    if not is_variable_C:
        C_shape = (dim, dstate)
    elif varBC_groups == 1:
        C_shape = (batch_size, dstate, seqlen if not is_complex else seqlen * 2)
    else:
        C_shape = (batch_size, varBC_groups, dstate, seqlen if not is_complex else seqlen * 2)
    C = torch.randn(*C_shape, device=device, dtype=wtype if not is_variable_C else itype,
                    requires_grad=True)
    if has_D:
        D = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
    else:
        D = None
    if has_z:
        z = torch.randn(batch_size, dim, seqlen, device=device, dtype=itype, requires_grad=True)
    else:
        z = None
    if has_delta_bias:
        delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)).requires_grad_()
    else:
        delta_bias = None
    u = torch.randn(batch_size, dim, seqlen, device=device, dtype=itype, requires_grad=True)
    delta = (0.5 * torch.rand(batch_size, dim, seqlen, device=device, dtype=itype)).requires_grad_()
    A_ref = A.detach().clone().requires_grad_()
    B_ref = B.detach().clone().requires_grad_()
    C_ref = C.detach().clone().requires_grad_()
    D_ref = D.detach().clone().requires_grad_() if D is not None else None
    z_ref = z.detach().clone().requires_grad_() if z is not None else None
    u_ref = u.detach().clone().requires_grad_()
    delta_ref = delta.detach().clone().requires_grad_()
    delta_bias_ref = delta_bias.detach().clone().requires_grad_() if delta_bias is not None else None
    out, *rest = selective_scan_fn(
        u, delta, A, B, C, D, z=z,
        delta_bias=delta_bias, delta_softplus=delta_softplus,
        return_last_state=return_last_state, nrows=nrows
    )
    if return_last_state:
        state = rest[0]
    out_ref, *rest = selective_scan_ref(
        u_ref, delta_ref, A_ref, B_ref, C_ref, D_ref, z=z_ref,
        delta_bias=delta_bias_ref, delta_softplus=delta_softplus,
        return_last_state=return_last_state
    )
    if return_last_state:
        state_ref = rest[0]
    # dA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
    # dt_u = delta * u

    print(f'Output max diff: {(out - out_ref).abs().max().item()}')
    print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
    assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
    if return_last_state:
        print(f'State max diff: {(state - state_ref).abs().max().item()}')
        assert torch.allclose(state, state_ref, rtol=rtol, atol=atol)

    g = torch.randn_like(out)
    out_ref.backward(g)
    out.backward(g)

    print(f'du max diff: {(u.grad - u_ref.grad).abs().max().item()}')
    print(f'ddelta max diff: {(delta.grad - delta_ref.grad).abs().max().item()}')
    print(f'dA max diff: {(A.grad - A_ref.grad).abs().max().item()}')
    print(f'dB max diff: {(B.grad - B_ref.grad).abs().max().item()}')
    print(f'dC max diff: {(C.grad - C_ref.grad).abs().max().item()}')
    if has_D:
        print(f'dD max diff: {(D.grad - D_ref.grad).abs().max().item()}')
    if has_z:
        print(f'dz max diff: {(z.grad - z_ref.grad).abs().max().item()}')
    if has_delta_bias:
        print(f'ddelta_bias max diff: {(delta_bias.grad - delta_bias_ref.grad).abs().max().item()}')

    assert torch.allclose(u.grad, u_ref.grad.to(dtype=itype), rtol=rtol * 2, atol=atol * 2)
    assert torch.allclose(delta.grad, delta_ref.grad.to(dtype=itype), rtol=rtol * 5, atol=atol * 10)
    assert torch.allclose(A.grad, A_ref.grad, rtol=rtolw, atol=atolw * 5)
    assert torch.allclose(B.grad, B_ref.grad, rtol=rtolw if not is_variable_B else rtol,
                          atol=atolw if not is_variable_B else atol)
    assert torch.allclose(C.grad, C_ref.grad, rtol=rtolw if not is_variable_C else rtol,
                          atol=atolw if not is_variable_C else atol)
    if has_D:
        assert torch.allclose(D.grad, D_ref.grad, rtol=rtolw, atol=atolw)
    if has_z:
        assert torch.allclose(z.grad, z_ref.grad, rtol=rtolw, atol=atolw)
    if has_delta_bias:
        assert torch.allclose(delta_bias.grad, delta_bias_ref.grad, rtol=rtolw, atol=atolw)

# test_selective_scan(True, True, 2, True, False, True, True, True, 64, torch.float32, torch.float32, 1, 2, 24, 1)



================================================
FILE: README.md
================================================
<div align="center">
<p align="center"> <img src="figs/logo.png" width="200px"> </p>
</div>

# VmambaIR: Visual State Space Model for Image Restoration

[Yuan Shi](https://github.com/shiyuan7), [Bin Xia](https://github.com/Zj-BinXia), [Xiaoyu Jin](https://github.com/xyjin01), Xing Wang, Tianyu Zhao, Xin Xia, Xuefeng Xiao, and [Wenming Yang](https://scholar.google.com/citations?user=vsE4nKcAAAAJ&hl=zh-CN), "VmambaIR: Visual State Space Model for Image Restoration", arXiv, 2024

[[arXiv](https://arxiv.org/abs/2403.11423)] [[supplementary material]()] [[visual results]()]

#### 🔥🔥🔥 News

- **2024-03-18:** This repo is released.
- **2025-05-08:** We released the pretrained models [Google Drive](https://drive.google.com/drive/folders/10Pogbp2hkCadGPcuCrlxaZXHt68JAQ3d?usp=sharing) .

---

> **Abstract:** Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have limitations in capturing long-range dependencies. DMs require large prior models and computationally intensive denoising steps. Transformers have powerful modeling capabilities but face challenges due to quadratic complexity with input image size. To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks. We utilize a Unet architecture to stack our proposed Omni Selective Scan (OSS) blocks, consisting of an OSS module and an Efficient Feed-Forward Network (EFFN). Our proposed omni selective scan mechanism overcomes the unidirectional modeling limitation of SSMs by efficiently modeling image information flows in all six directions. Furthermore, we conducted a comprehensive evaluation of our VmambaIR across multiple image restoration tasks, including image deraining, single image super-resolution, and real-world image super-resolution. Extensive experimental results demonstrate that our proposed VmambaIR achieves state-of-the-art (SOTA) performance with much fewer computational resources and parameters. Our research highlights the potential of state space models as promising alternatives to the transformer and CNN architectures in serving as foundational frameworks for next-generation low-level visual tasks.

![](figs/Snipaste_2024-03-18_21-18-39.png)

---
Single Image Super-Resolution

[<img src="figs/Snipaste_2024-03-18_22-28-23.png" height="216"/>](https://imgsli.com/MjQ4MjI5) [<img src="figs/Snipaste_2024-03-18_22-22-34.png" height="216"/>](https://imgsli.com/MjQ4MjI2) [<img src="figs/Snipaste_2024-03-18_22-25-10.png" height="216"/>](https://imgsli.com/MjQ4MjI3) [<img src="figs/Snipaste_2024-03-18_22-26-41.png" height="216"/>](https://imgsli.com/MjQ4MjI4)


Real-World Image Super-Resolution

[<img src="figs/Snipaste_2024-03-18_22-29-05.png" height="216"/>](https://imgsli.com/MjQ4MjMw) [<img src="figs/Snipaste_2024-03-18_22-34-00.png" height="216"/>](https://imgsli.com/MjQ4MjMx) [<img src="figs/Snipaste_2024-03-18_22-34-25.png" height="216"/>](https://imgsli.com/MjQ4MjMy) [<img src="figs/Snipaste_2024-03-18_22-36-36.png" height="216"/>](https://imgsli.com/MjQ4MjM0)


Image Deraining

[<img src="figs/Snipaste_2024-03-18_22-37-57.png" height="216"/>](https://imgsli.com/MjQ4MjM3) [<img src="figs/Snipaste_2024-03-18_22-40-59.png" height="216"/>](https://imgsli.com/MjQ4MjM5) [<img src="figs/Snipaste_2024-03-18_22-42-11.png" height="216"/>](https://imgsli.com/MjQ4MjQz) [<img src="figs/Snipaste_2024-03-18_22-44-20.png" height="216"/>](https://imgsli.com/MjQ4MjQ1)

---


## 🔗 Contents

1. Datasets
1. [Models](#Model)
1. [Installation](#Installation)
1. [Results](#results)
1. [Citation](#citation)
1. [Acknowledgements](#acknowledgements)

## <a name="results"></a>🔎 Results

We achieved state-of-the-art performance on multiple image restoration tasks. Detailed results can be found in the paper.

<details>
<summary>Evaluation on Single Image Super-Resolution (click to expand)</summary>


- quantitative comparisons in Table 1 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-51-46.png">
</p>



- visual comparison in Figure 5 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-53-07.png">
</p>
</details>



<details>
<summary>Evaluation on Real-World Image Super-Resolution (click to expand)</summary>



- quantitative comparisons in Table 2 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-54-35.png">
</p>


- visual comparison in Figure 6 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-55-06.png">
</p>

</details>


<details>
<summary>Evaluation on Image Deraining (click to expand)</summary>



- quantitative comparisons in Table 2 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-56-16.png">
</p>


- visual comparison in Figure 6 of the main paper

<p align="center">
  <img width="900" src="figs/Snipaste_2024-03-18_21-56-24.png">
</p>

</details>


## <a name="Installation"></a> 🔧 Installation

This repository is built in PyTorch 2.3.0 and tested on Debian 11 environment (Python3.9, CUDA12.1).
Follow these intructions

1. Clone our repository
```
git clone https://github.com/AlphacatPlus/VmambaIR.git
cd VmambaIR
```

2. Make conda environment
```
conda create -n vmambair python=3.9
conda activate vmambair
```

3. Install dependencies
```
cd VmambaIR
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/

```

4. Install mamba
```
cd Mamba
cd kernels/selective_scan && pip install .
```


## <a name="Model"></a>📃 Model
Our models are available here [Google Drive](https://drive.google.com/drive/folders/10Pogbp2hkCadGPcuCrlxaZXHt68JAQ3d?usp=sharing). 



## <a name="citation"></a>📎 Citation

If you find the code helpful in your resarch or work, please cite the following paper(s).

```
@article{shi2024vmambair,
  title={VmambaIR: Visual State Space Model for Image Restoration},
  author={Shi, Yuan and Xia, Bin and Jin, Xiaoyu and Wang, Xing and Zhao, Tianyu and Xia, Xin and Xiao, Xuefeng and Yang, Wenming},
  journal={arXiv preprint arXiv:2403.11423},
  year={2024}
}
```

## <a name="acknowledgements"></a>💡 Acknowledgements

This code is built on [BasicSR](https://github.com/XPixelGroup/BasicSR), [Vmamba](https://github.com/MzeroMiko/VMamba), [Restormer](https://github.com/swz30/Restormer) and [DiffIR](https://github.com/Zj-BinXia/DiffIR). We thank them all for their excellent work.



================================================
FILE: RealSR/.gitignore
================================================
# ignored folders
datasets/*
experiments/*
results/*
tb_logger/*
wandb/*
tmp/*
realesrgan/weights/*

version.py

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
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share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
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.coverage
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nosetests.xml
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*.cover
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# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
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# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

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.ropeproject

# mkdocs documentation
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# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/


================================================
FILE: RealSR/Metric/DISTS/DISTS_pytorch/DISTS_pt.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('scripts/metrics/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'

    # others
    # data_root = '/data1/liangjie/BasicSR_ALL/results/'
    # ref_root = '/data1/liangjie/BasicSR_ALL/datasets/'
    # ref_dirs = ['SISR_Test_matlab/Set5mod12', 'SISR_Test_matlab/Set14mod12', 'SISR_Test_matlab/Manga109mod12', 'SISR_Test_matlab/BSDS100mod12', 'SISR_Test_matlab/General100mod12', 'SISR_Test/Urban100', 'DIV2K/DIV2K_valid_HR/']
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SRGAN_official', 'ESRGAN_official', 'NatSR_official', 'USRGAN_official', 'SPSR_official', 'SPSR_DF2K', 'ESRGAN_ours_DIV2K', 'ESRGAN_ours_DIV2K_ema', 'ESRGAN_ours_DF2K', 'ESRGAN_ours_DF2K_ema']

    # SFTGAN
    # data_root = '/data1/liangjie/BasicSR_ALL/results'
    # ref_root = '/data1/liangjie/BasicSR_ALL/results/SFTGAN_official'
    # ref_dirs = ['GT'] * 7
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SFTGAN_official']

    # new
    data_root = 'results/'
    ref_root = 'datasets/'
    ref_dirs = ['DIV2K/DIV2K_valid_HR/']
    datasets = ['DIV2K100']
    img_dirs = ['ESRGAN_ours_DISTS_300k/visualization/']

    logoverall_path = 'results/table_logs/' + 'DISTS_orisize_DISTStrain225k.txt'

    for index in range(len(ref_dirs)):
        ref_dir = os.path.join(ref_root, ref_dirs[index])
        for method in img_dirs:
            img_dir = os.path.join(data_root, method, datasets[index])

            img_list = sorted(glob.glob(os.path.join(img_dir, '*')))

            log_path = 'results/table_logs/' + img_dir.replace('/', '_') + '_DISTS_orisize.txt'

            DISTS_all = []

            for i, img_path in enumerate(img_list):
                file_name = img_path.split('/')[-1]
                if 'DIV2K100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:4] + '.png')
                elif 'Urban100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:7] + '.png')
                elif 'SFTGAN' in img_dir:
                    gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_gt.png')
                    if 'Urban100' in img_dir:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_' + file_name.split('_')[1] + '_gt.png')
                else:
                    if '_' in file_name:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '.png')
                    else:
                        gt_path = os.path.join(ref_dir, file_name)

                ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
                dist = prepare_image(Image.open(img_path).convert("RGB"), resize=False)
                assert ref.shape == dist.shape
                device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                model = DISTS().to(device)
                ref = ref.to(device)
                dist = dist.to(device)
                score = model(ref, dist)
                DISTS_all.append(score.item())
                log = f'{i + 1:3d}: {file_name:25}. \tDISTS: {score.item():.6f}.'
                with open(log_path, 'a') as f:
                    f.write(log + '\n')
                # print(log)

            log = f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}'
            with open(log_path, 'a') as f:
                f.write(log + '\n')
            log_overall = method + '__' + datasets[index] + '__' + log
            with open(logoverall_path, 'a') as f:
                f.write(log_overall + '\n')
            print(log_overall)



================================================
FILE: RealSR/Metric/DISTS/DISTS_tensorflow/DISTS_tf.py
================================================
# This is a tensorflow implementation of DISTS metric.
# Requirements: python >= 3.6, tensorflow-gpu >= 1.15

import tensorflow.compat.v1 as tf
import numpy as np
import time
import scipy.io as scio
from PIL import Image
import argparse
# tf.enable_eager_execution()
tf.disable_eager_execution()

class DISTS():
    def __init__(self):
        self.parameters = scio.loadmat('../weights/net_param.mat')
        self.chns = [3,64,128,256,512,512]
        self.mean = tf.constant(self.parameters['vgg_mean'], dtype=tf.float32, shape=(1,1,1,3),name="img_mean")
        self.std = tf.constant(self.parameters['vgg_std'], dtype=tf.float32, shape=(1,1,1,3),name="img_std")
        # self.alpha = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="alpha")
        # self.beta = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="beta")
        self.weights = scio.loadmat('../weights/alpha_beta.mat')
        self.alpha = tf.constant(np.reshape(self.weights['alpha'],(1,1,1,sum(self.chns))),name="alpha")
        self.beta = tf.constant(np.reshape(self.weights['beta'],(1,1,1,sum(self.chns))),name="beta")

    def get_features(self, img):

        x = (img - self.mean)/self.std

        self.conv1_1 = self.conv_layer(x, "conv1_1")
        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
        self.pool1 = self.pool_layer(self.conv1_2, name="pool_1")

        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
        self.pool2 = self.pool_layer(self.conv2_2, name="pool_2")

        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
        self.pool3 = self.pool_layer(self.conv3_3, name="pool_3")

        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
        self.pool4 = self.pool_layer(self.conv4_3, name="pool_4")

        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")

        return [img, self.conv1_2,self.conv2_2,self.conv3_3,self.conv4_3,self.conv5_3]

    def conv_layer(self, input, name):
        with tf.variable_scope(name) as _:
            filter = self.get_conv_filter(name)
            conv = tf.nn.conv2d(input, filter, strides=1, padding="SAME")
            bias = self.get_bias(name)
            conv = tf.nn.relu(tf.nn.bias_add(conv, bias))
            return conv

    def pool_layer(self, input, name):
        # return tf.nn.max_pool(input, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
        with tf.variable_scope(name) as _:
            filter = tf.squeeze(tf.constant(self.parameters['L2'+name], name = "filter"),3)
            conv = tf.nn.conv2d(input**2, filter, strides=2, padding=[[0, 0], [1, 0], [1, 0], [0, 0]])
            return tf.sqrt(tf.maximum(conv, 1e-12))     

    def get_conv_filter(self, name):
        return tf.constant(self.parameters[name+'_weight'], name = "filter")

    def get_bias(self, name):
        return tf.constant(np.squeeze(self.parameters[name+'_bias']), name = "bias")

    def get_score(self, img1, img2):
        feats0 = self.get_features(img1)
        feats1 = self.get_features(img2)
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = tf.reduce_sum(self.alpha) + tf.reduce_sum(self.beta)
        alpha = tf.split(self.alpha/w_sum, self.chns, axis=3)
        beta = tf.split(self.beta/w_sum, self.chns, axis=3)
        for k in range(len(self.chns)):
            x_mean = tf.reduce_mean(feats0[k],[1,2], keepdims=True)
            y_mean = tf.reduce_mean(feats1[k],[1,2], keepdims=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+tf.reduce_sum(alpha[k]*S1, 3, keepdims=True)
            x_var = tf.reduce_mean((feats0[k]-x_mean)**2,[1,2], keepdims=True)
            y_var = tf.reduce_mean((feats1[k]-y_mean)**2,[1,2], keepdims=True)
            xy_cov = tf.reduce_mean(feats0[k]*feats1[k],[1,2], keepdims=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+tf.reduce_sum(beta[k]*S2, 3, keepdims=True)

        dist = 1-tf.squeeze(dist1+dist2)
        return dist


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--ref', type=str, default='../images/r0.png')
    parser.add_argument('--dist', type=str, default='../images/r1.png')
    args = parser.parse_args()
    model = DISTS()

    ref = np.array(Image.open(args.ref).convert("RGB"))
    ref = np.expand_dims(ref,axis=0)/255.
    dist = np.array(Image.open(args.dist).convert("RGB"))
    dist = np.expand_dims(dist,axis=0)/255.

    x = tf.placeholder(dtype=tf.float32, shape=ref.shape, name= "ref")
    y = tf.placeholder(dtype=tf.float32, shape=dist.shape, name= "dist")
    score = model.get_score(x,y)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        score = sess.run(score, feed_dict={x: ref, y: dist})
        print(score)




================================================
FILE: RealSR/Metric/DISTS/LICENSE
================================================
MIT License

Copyright (c) 2020 Keyan Ding

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: RealSR/Metric/DISTS/requirements.txt
================================================
torch>=1.0

================================================
FILE: RealSR/Metric/LPIPS.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    loss_fn_vgg = lpips.LPIPS(net='vgg').cuda(0)
    lpips_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    mean = [0.5, 0.5, 0.5]
    std = [0.5, 0.5, 0.5]
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED).astype(
            np.float32) / 255.

        img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)
        # norm to [-1, 1]
        normalize(img_gt, mean, std, inplace=True)
        normalize(img_restored, mean, std, inplace=True)

        # calculate lpips
        lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(0), img_gt.unsqueeze(0).cuda(0)).cpu().data.numpy()[0,0,0,0]
        # print(lpips_val)
        lpips_all.append(lpips_val)

    print(f'Average: LPIPS: {sum(lpips_all) / len(lpips_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: RealSR/Metric/PSNR.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse
from basicsr.metrics import calculate_psnr, calculate_ssim


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test2800/target')
    parser.add_argument('--folder_restored', type=str, default='/mnt/bn/shiyuan-arnold/code/Mamber/Restormer/Deraining/results/Test2800')
    args = parser.parse_args()
    psnr_all = []
    ssim_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED)
        psnr=calculate_psnr(img_restored, img_gt, crop_border=4, test_y_channel=True)
        ssim=calculate_ssim(img_restored, img_gt, crop_border=4, test_y_channel=True)
        psnr_all.append(psnr)
        ssim_all.append(ssim)

    print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f}')
    print(f'Average: SSIM: {sum(ssim_all) / len(ssim_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: RealSR/Metric/dists.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F
import argparse
import os.path as osp

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-RealSR/Metric/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))


    DISTS_all = []

    for i, (gt_path, lr_path) in enumerate(zip(img_list,lr_list)):
        ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
        dist = prepare_image(Image.open(lr_path).convert("RGB"), resize=False)
        assert ref.shape == dist.shape
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model = DISTS().to(device)
        ref = ref.to(device)
        dist = dist.to(device)
        score = model(ref, dist)
        DISTS_all.append(score.item())
        log = f'{i + 1:3d}:\tDISTS: {score.item():.6f}.'
        print(log)
        # with open(log_path, 'a') as f:
        #     f.write(log + '\n')
        # # print(log)

    print(f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}')




================================================
FILE: RealSR/Metric/pip.sh
================================================
pip install lpips --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install basicsr --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com


================================================
FILE: RealSR/VERSION
================================================
0.2.5.0


================================================
FILE: RealSR/VmambaIR/__init__.py
================================================
# flake8: noqa
from .losses import *
from .archs import *
from .data import *
from .models import *
from .utils import *
from .version import *


================================================
FILE: RealSR/VmambaIR/archs/MambaRealSR11_arch.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers

from einops import rearrange, repeat

import VmambaIR.archs.common as common

from basicsr.utils.registry import ARCH_REGISTRY

import math
import copy
from fvcore.nn import flop_count, parameter_count

try:
    "sscore acts the same as mamba_ssm"
    SSMODE = "sscore"
    import selective_scan_cuda_core
except Exception as e:
    print(e, flush=True)
    "you should install mamba_ssm to use this"
    SSMODE = "mamba_ssm"
    import selective_scan_cuda


import selective_scan_cuda_core as selective_scan_cuda

class SelectiveScanFn(torch.autograd.Function):

    @staticmethod
    def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
        # input_t: float, fp16, bf16; weight_t: float;
        # u, B, C, delta: input_t
        # D, delta_bias: float
        if u.stride(-1) != 1:
            u = u.contiguous()
        if delta.stride(-1) != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.stride(-1) != 1:
            B = B.contiguous()
        if C.stride(-1) != 1:
            C = C.contiguous()
        if B.dim() == 3:
            B = rearrange(B, "b dstate l -> b 1 dstate l")
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = rearrange(C, "b dstate l -> b 1 dstate l")
            ctx.squeeze_C = True
        if D is not None and (D.dtype != torch.float):
            ctx._d_dtype = D.dtype
            D = D.float()
        if delta_bias is not None and (delta_bias.dtype != torch.float):
            ctx._delta_bias_dtype = delta_bias.dtype
            delta_bias = delta_bias.float()
        
        assert u.shape[1] % (B.shape[1] * nrows) == 0 
        assert nrows in [1, 2, 3, 4] # 8+ is too slow to compile

        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
        ctx.delta_softplus = delta_softplus
        ctx.nrows = nrows
        ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
        return out

    @staticmethod
    def backward(ctx, dout, *args):
        u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
        if dout.stride(-1) != 1:
            dout = dout.contiguous()
        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
            u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
        )
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
        
        _dD = None
        if D is not None:
            if dD.dtype != getattr(ctx, "_d_dtype", dD.dtype):
                _dD = dD.to(ctx._d_dtype)
            else:
                _dD = dD

        _ddelta_bias = None
        if delta_bias is not None:
            if ddelta_bias.dtype != getattr(ctx, "_delta_bias_dtype", ddelta_bias.dtype):
                _ddelta_bias = ddelta_bias.to(ctx._delta_bias_dtype)
            else:
                _ddelta_bias = ddelta_bias

        return (du, ddelta, dA, dB, dC, _dD, _ddelta_bias, None, None)


def selective_scan_fn_v1(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
    """if return_last_state is True, returns (out, last_state)
    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
    not considered in the backward pass.
    """
    return SelectiveScanFn.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)


# fvcore flops =======================================
def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: r(D N)
    B: r(B N L)
    C: r(B N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32
    
    ignores:
        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] 
    """
    assert not with_complex 
    # https://github.com/state-spaces/mamba/issues/110
    flops = 9 * B * L * D * N
    if with_D:
        flops += B * D * L
    if with_Z:
        flops += B * D * L    
    return flops

def print_jit_input_names(inputs):
    print("input params: ", end=" ", flush=True)
    try: 
        for i in range(10):
            print(inputs[i].debugName(), end=" ", flush=True)
    except Exception as e:
        pass
    print("", flush=True)

def selective_scan_flop_jit(inputs, outputs):
    print_jit_input_names(inputs)
    B, D, L = inputs[0].type().sizes()
    N = inputs[2].type().sizes()[1]
    flops = flops_selective_scan_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=False, with_Group=True)
    return flops


##########################################################################
## Layer Norm
def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)



##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x



##########################################################################
## Multi-DConv Head Transposed Self-Attention (MDTA)
class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(dim*3, dim*3, kernel_size=3, stride=1, padding=1, groups=dim*3, bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
        


    def forward(self, x):
        b,c,h,w = x.shape

        qkv = self.qkv_dwconv(self.qkv(x))
        q,k,v = qkv.chunk(3, dim=1)   
        
        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.temperature
        attn = attn.softmax(dim=-1)

        out = (attn @ v)
        
        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        return out

class SelectiveScan(torch.autograd.Function):
    
    @staticmethod
    @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
    def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
        assert nrows in [1, 2, 3, 4], f"{nrows}" # 8+ is too slow to compile
        assert u.shape[1] % (B.shape[1] * nrows) == 0, f"{nrows}, {u.shape}, {B.shape}"
        ctx.delta_softplus = delta_softplus
        ctx.nrows = nrows
        # all in float
        if u.stride(-1) != 1:
            u = u.contiguous()
        if delta.stride(-1) != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.stride(-1) != 1:
            B = B.contiguous()
        if C.stride(-1) != 1:
            C = C.contiguous()
        if B.dim() == 3:
            B = B.unsqueeze(dim=1)
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = C.unsqueeze(dim=1)
            ctx.squeeze_C = True
        
        if SSMODE == "mamba_ssm":
            out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, None, delta_bias, delta_softplus)
        else:
            out, x, *rest = selective_scan_cuda_core.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
        
        ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
        return out
    
    @staticmethod
    @torch.cuda.amp.custom_bwd
    def backward(ctx, dout, *args):
        u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
        if dout.stride(-1) != 1:
            dout = dout.contiguous()
        
        if SSMODE == "mamba_ssm":
            du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
                u, delta, A, B, C, D, None, delta_bias, dout, x, None, None, ctx.delta_softplus,
                False  # option to recompute out_z, not used here
            )
        else:
            du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda_core.bwd(
                u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
                # u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, ctx.nrows,
            )
        
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
        return (du, ddelta, dA, dB, dC, dD, ddelta_bias, None, None)


class CrossScan(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x: torch.Tensor):
        B, C, H, W = x.shape
        ctx.shape = (B, C, H, W)
        xs = x.new_empty((B, 4, C, H * W))
        xs[:, 0] = x.flatten(2, 3)
        xs[:, 1] = x.transpose(dim0=2, dim1=3).flatten(2, 3)
        xs[:, 2:4] = torch.flip(xs[:, 0:2], dims=[-1])
        return xs
    
    @staticmethod
    def backward(ctx, ys: torch.Tensor):
        # out: (b, k, d, l)
        B, C, H, W = ctx.shape
        L = H * W
        ys = ys[:, 0:2] + ys[:, 2:4].flip(dims=[-1]).view(B, 2, -1, L)
        y = ys[:, 0] + ys[:, 1].view(B, -1, W, H).transpose(dim0=2, dim1=3).contiguous().view(B, -1, L)
        return y.view(B, -1, H, W)


class CrossMerge(torch.autograd.Function):
    @staticmethod
    def forward(ctx, ys: torch.Tensor):
        B, K, D, H, W = ys.shape
        ctx.shape = (H, W)
        ys = ys.view(B, K, D, -1)
        ys = ys[:, 0:2] + ys[:, 2:4].flip(dims=[-1]).view(B, 2, D, -1)
        y = ys[:, 0] + ys[:, 1].view(B, -1, W, H).transpose(dim0=2, dim1=3).contiguous().view(B, D, -1)
        #output: B,D,L
        return y
    
    @staticmethod
    def backward(ctx, x: torch.Tensor):
        # B, D, L = x.shape
        # out: (b, k, d, l)
        H, W = ctx.shape
        B, C, L = x.shape
        xs = x.new_empty((B, 4, C, L))
        xs[:, 0] = x
        xs[:, 1] = x.view(B, C, H, W).transpose(dim0=2, dim1=3).flatten(2, 3)
        xs[:, 2:4] = torch.flip(xs[:, 0:2], dims=[-1])
        xs = xs.view(B, 4, C, H, W)
        return xs, None, None


def cross_selective_scan(
    x: torch.Tensor=None, 
    x_proj_weight: torch.Tensor=None,
    x_proj_bias: torch.Tensor=None,
    dt_projs_weight: torch.Tensor=None,
    dt_projs_bias: torch.Tensor=None,
    A_logs: torch.Tensor=None,
    Ds: torch.Tensor=None,
    out_norm: torch.nn.Module=None,
    softmax_version=False,
    nrows = -1,
    delta_softplus = True,
    to_dtype=True,
):
    B, D, H, W = x.shape
    D, N = A_logs.shape
    K, D, R = dt_projs_weight.shape
    L = H * W

    if nrows < 1:
        if D % 4 == 0:
            nrows = 4
        elif D % 3 == 0:
            nrows = 3
        elif D % 2 == 0:
            nrows = 2
        else:
            nrows = 1
    
    xs = CrossScan.apply(x) #xs: b,4,d,hw
    
    x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, x_proj_weight) #b,4,c,hw
    if x_proj_bias is not None:
        x_dbl = x_dbl + x_proj_bias.view(1, K, -1, 1)
    dts, Bs, Cs = torch.split(x_dbl, [R, N, N], dim=2)
    dts = torch.einsum("b k r l, k d r -> b k d l", dts, dt_projs_weight)
    xs = xs.view(B, -1, L).to(torch.float)
    dts = dts.contiguous().view(B, -1, L).to(torch.float)
    As = -torch.exp(A_logs.to(torch.float)) # (k * c, d_state)
    Bs = Bs.contiguous().to(torch.float)
    Cs = Cs.contiguous().to(torch.float)
    Ds = Ds.to(torch.float) # (K * c)
    delta_bias = dt_projs_bias.view(-1).to(torch.float)
     
    def selective_scan(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=True, nrows=1):
        return SelectiveScan.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
    
    ys: torch.Tensor = selective_scan(
        xs, dts, As, Bs, Cs, Ds, delta_bias, delta_softplus, nrows,
    ).view(B, K, -1, H, W)
    
    y: torch.Tensor = CrossMerge.apply(ys)

    if softmax_version:
        y = y.softmax(dim=-1)
        if to_dtype:
            y = y.to(x.dtype)
        y = y.transpose(dim0=1, dim1=2).contiguous().view(B, H, W, -1)
    else:
        y = y.view(B, D, H, W)
        y = out_norm(y)
        if to_dtype:
            y = y.to(x.dtype)
    return y


#channel scan
class CrossScanC(torch.autograd.Function):
    #input:b,d,l output:b,2,d,l
    @staticmethod
    def forward(ctx, x: torch.Tensor):
        #input x : b,1,c
        B, D, L = x.shape
        ctx.shape = (B, D, L)
        xs = x.new_empty((B, 2, D, L))
        xs[:, 0] = x
        xs[:, 1] = torch.flip(x, dims=[-1])
        return xs
    
    @staticmethod
    def backward(ctx, ys: torch.Tensor):
        # ys: (b, 2, d, l)
        # get: b,1,c
        B, D, L = ctx.shape
        y = ys[:, 0] + ys[:, 1].flip(dims=[-1])
        return y


class CrossMergeC(torch.autograd.Function):
    #input b,k,d,l  output:b,d,l
    @staticmethod
    def forward(ctx, ys: torch.Tensor):
        #output : b,d,l

        y = ys[:, 0] + ys[:, 1].flip(dims=[-1])
        return y
    
    @staticmethod
    def backward(ctx, x: torch.Tensor):
        # out: (b, k, d, l)
        B, D, L = x.shape
        xs = x.new_empty((B, 2, D, L))
        xs[:, 0] = x
        xs[:, 1] = torch.flip(x, dims=[-1])
        return xs, None, None


def cross_selective_scanC(
    x: torch.Tensor=None, 
    x_proj_weight: torch.Tensor=None,
    x_proj_bias: torch.Tensor=None,
    dt_projs_weight: torch.Tensor=None,
    dt_projs_bias: torch.Tensor=None,
    A_logs: torch.Tensor=None,
    Ds: torch.Tensor=None,
    out_norm: torch.nn.Module=None,
    softmax_version=False,
    nrows = -1,
    delta_softplus = True,
    to_dtype=True,
):
    B, D, L = x.shape #b,1,c
    D, N = A_logs.shape
    K, D, R = dt_projs_weight.shape

    if nrows < 1:
        if D % 4 == 0:
            nrows = 4
        elif D % 3 == 0:
            nrows = 3
        elif D % 2 == 0:
            nrows = 2
        else:
            nrows = 1
    
    xs = CrossScanC.apply(x) #input:b,d,l output:b,2,d,l
    
    x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, x_proj_weight) #b,4,c,hw
    if x_proj_bias is not None:
        x_dbl = x_dbl + x_proj_bias.view(1, K, -1, 1)
    dts, Bs, Cs = torch.split(x_dbl, [R, N, N], dim=2)
    dts = torch.einsum("b k r l, k d r -> b k d l", dts, dt_projs_weight)
    xs = xs.view(B, -1, L).to(torch.float)
    dts = dts.contiguous().view(B, -1, L).to(torch.float)
    As = -torch.exp(A_logs.to(torch.float)) # (k * c, d_state)
    Bs = Bs.contiguous().to(torch.float)
    Cs = Cs.contiguous().to(torch.float)
    Ds = Ds.to(torch.float) # (K * c)
    delta_bias = dt_projs_bias.view(-1).to(torch.float)
     
    def selective_scan(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=True, nrows=1):
        return SelectiveScan.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
    
    ys: torch.Tensor = selective_scan(
        xs, dts, As, Bs, Cs, Ds, delta_bias, delta_softplus, nrows,
    ).view(B, K, -1, L)
    
    y: torch.Tensor = CrossMergeC.apply(ys) #input b,k,d,l  output:b,d,l

    y = y.transpose(dim0=1, dim1=2).unsqueeze(2).contiguous()
    y = out_norm(y)
    if to_dtype:
        y = y.to(x.dtype)
    return y





class SS2D_1(nn.Module):
    def __init__(
        self,
        # basic dims ===========
        d_model=96,
        d_state=16,
        ssm_ratio=2.0,
        ssm_rank_ratio=2.0,
        dt_rank="auto",
        act_layer=nn.SiLU,
        # dwconv ===============
        d_conv=3, # < 2 means no conv 
        conv_bias=True,
        # ======================
        dropout=0.0,
        bias=False,
        # dt init ==============
        dt_min=0.001,
        dt_max=0.1,
        dt_init="random",
        dt_scale=1.0,
        dt_init_floor=1e-4,
        simple_init=False,
        # ======================
        softmax_version=False,
        forward_type="v2",
        # ======================
        **kwargs,
    ):
        """
        ssm_rank_ratio would be used in the future...
        """
        factory_kwargs = {"device": None, "dtype": None}
        super().__init__()
        d_expand = int(ssm_ratio * d_model)
        d_inner = int(min(ssm_rank_ratio, ssm_ratio) * d_model) if ssm_rank_ratio > 0 else d_expand
        self.softmax_version = softmax_version
        self.dt_rank = math.ceil(d_model / 16) if dt_rank == "auto" else dt_rank
        self.d_state = math.ceil(d_model / 6) if d_state == "auto" else d_state # 20240109
        self.d_conv = d_conv


        self.forward_core=self.forward_corev1

        self.K = 4 if forward_type not in ["share_ssm"] else 1
        self.K2 = self.K if forward_type not in ["share_a"] else 1
        self.KC = 2
        self.K2C = self.KC if forward_type not in ["share_a"] else 1

        self.cforward_core = self.cforward_corev2
        self.pooling = nn.AdaptiveAvgPool2d(1)
        self.channel_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # in proj =======================================
        self.in_conv = nn.Conv2d(in_channels=d_model, out_channels=d_expand * 2, kernel_size=1, stride=1, padding=0)
        self.act: nn.Module = act_layer()
        
        # conv =======================================
        if self.d_conv > 1:
            self.conv2d = nn.Conv2d(
                in_channels=d_expand,
                out_channels=d_expand,
                groups=d_expand,
                bias=conv_bias,
                kernel_size=d_conv,
                padding=(d_conv - 1) // 2,
                **factory_kwargs,
            )

        # rank ratio =====================================
        self.ssm_low_rank = False
        if d_inner < d_expand:
            self.ssm_low_rank = True
            self.in_rank = nn.Conv2d(d_expand, d_inner, kernel_size=1, bias=False, **factory_kwargs)
            self.out_rank = nn.Linear(d_inner, d_expand, bias=False, **factory_kwargs)
        if not self.softmax_version:
            #self.out_norm = nn.LayerNorm(d_inner)
            self.out_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # x proj ============================
        self.x_proj = [
            nn.Linear(d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0)) # (K, N, inner)
        del self.x_proj
        # xc proj ============================
        self.xc_proj = [
            nn.Linear(1, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.KC)
        ]
        self.xc_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.xc_proj], dim=0)) # (K, N, inner)
        del self.xc_proj


        # dt proj ============================
        self.dt_projs = [
            self.dt_init(self.dt_rank, d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0)) # (K, inner, rank)
        self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0)) # (K, inner)
        del self.dt_projs
        # dtc proj ============================
        self.dtc_projs = [
            self.dt_init(self.dt_rank, 1, dt_scale, dt_init, dt_min, dt_max, dt_init_floor, **factory_kwargs)
            for _ in range(self.KC)
        ]
        self.dtc_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dtc_projs], dim=0)) # (K, inner, rank)
        self.dtc_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dtc_projs], dim=0)) # (K, inner)
        del self.dtc_projs
        
        # A, D =======================================
        self.A_logs = self.A_log_init(self.d_state, d_inner, copies=self.K2, merge=True) # (K * D, N)
        self.Ds = self.D_init(d_inner, copies=self.K2, merge=True) # (K * D)
        # Ac, Dc =======================================
        self.Ac_logs = self.A_log_init(self.d_state, 1, copies=self.K2C, merge=True) # (K * D, N)
        self.Dsc = self.D_init(1, copies=self.K2C, merge=True) # (K * D)

        # out proj =======================================
        self.out_conv = nn.Conv2d(in_channels=d_expand, out_channels=d_model, kernel_size=1, stride=1, padding=0)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else nn.Identity()

        if simple_init:
            self.Ds = nn.Parameter(torch.ones((self.K2 * d_inner)))
            self.A_logs = nn.Parameter(torch.randn((self.K2 * d_inner, self.d_state))) # A == -A_logs.exp() < 0; # 0 < exp(A * dt) < 1
            self.dt_projs_weight = nn.Parameter(torch.randn((self.K, d_inner, self.dt_rank)))
            self.dt_projs_bias = nn.Parameter(torch.randn((self.K, d_inner))) 

    @staticmethod
    def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4, **factory_kwargs):
        dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)

        # Initialize special dt projection to preserve variance at initialization
        dt_init_std = dt_rank**-0.5 * dt_scale
        if dt_init == "constant":
            nn.init.constant_(dt_proj.weight, dt_init_std)
        elif dt_init == "random":
            nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
        else:
            raise NotImplementedError

        # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
        dt = torch.exp(
            torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
            + math.log(dt_min)
        ).clamp(min=dt_init_floor)
        # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
        inv_dt = dt + torch.log(-torch.expm1(-dt))
        with torch.no_grad():
            dt_proj.bias.copy_(inv_dt)
        
        return dt_proj

    @staticmethod
    def A_log_init(d_state, d_inner, copies=-1, device=None, merge=True):
        # S4D real initialization
        A = repeat(
            torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
            "n -> d n",
            d=d_inner,
        ).contiguous()
        A_log = torch.log(A)  # Keep A_log in fp32
        if copies > 0:
            A_log = repeat(A_log, "d n -> r d n", r=copies)
            if merge:
                A_log = A_log.flatten(0, 1)
        A_log = nn.Parameter(A_log)
        A_log._no_weight_decay = True
        return A_log

    @staticmethod
    def D_init(d_inner, copies=-1, device=None, merge=True):
        # D "skip" parameter
        D = torch.ones(d_inner, device=device)
        if copies > 0:
            D = repeat(D, "n1 -> r n1", r=copies)
            if merge:
                D = D.flatten(0, 1)
        D = nn.Parameter(D)  # Keep in fp32
        D._no_weight_decay = True
        return D
    
    def forward_corev1(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn_v1

        B, C, H, W = x.shape
        L = H * W

        def cross_scan_2d(x):
            x_hwwh = torch.stack([x.flatten(2, 3), x.transpose(dim0=2, dim1=3).contiguous().flatten(2, 3)], dim=1) 
            xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)
            return xs 
        
        if self.K == 4:
            xs = cross_scan_2d(x)

            x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, self.x_proj_weight)
            dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
            dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dt_projs_weight)

            xs = xs.view(B, -1, L) # (b, k * d, l)
            dts = dts.contiguous().view(B, -1, L) # (b, k * d, l)
            As = -torch.exp(self.A_logs.float())  # (k * d, d_state)
            Ds = self.Ds # (k * d)
            dt_projs_bias = self.dt_projs_bias.view(-1) # (k * d)


            out_y = self.selective_scan(
                xs, dts, 
                As, Bs, Cs, Ds,
                delta_bias=dt_projs_bias,
                delta_softplus=True,
            ).view(B, 4, -1, L)

        elif self.K == 1:
            x_dbl = torch.einsum("b d l, c d -> b c l", x.view(B, -1, L), self.x_proj_weight[0])
            dt, BC = torch.split(x_dbl, [self.dt_rank, 2 * self.d_state], dim=1)
            dt = torch.einsum("b r l, d r -> b d l", dt, self.dt_projs_weight[0])
            x_dt_BC = torch.cat([x, dt.view(B, -1, H, W), BC.view(B, -1, H, W)], dim=1) # (b, -1, h, w)

            x_dt_BCs = cross_scan_2d(x_dt_BC) # (b, k, d, l)
            xs, dts, Bs, Cs = torch.split(x_dt_BCs, [self.d_inner, self.d_inner, self.d_state, self.d_state], dim=2)

            xs = xs.contiguous().view(B, -1, L) # (b, k * d, l)
            dts = dts.contiguous().view(B, -1, L) # (b, k * d, l)
            As = -torch.exp(self.A_logs.float()).repeat(4, 1) # (k * d, d_state)
            Ds = self.Ds.repeat(4) # (k * d)
            dt_projs_bias = self.dt_projs_bias.view(-1).repeat(4) # (k * d)

            out_y = self.selective_scan(
                xs, dts, 
                As, Bs, Cs, Ds,
                delta_bias=dt_projs_bias,
                delta_softplus=True,
            ).view(B, 4, -1, L)

        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        y = out_y[:, 0].float() + inv_y[:, 0].float() + wh_y.float() + invwh_y.float()
        
        if self.softmax_version:
            y = torch.softmax(y, dim=-1).to(x.dtype)
            y = torch.transpose(y, dim0=1, dim1=2).contiguous().view(B, H, W, -1)
        else:
            y = y.view(B, C, H, W)
            y = self.out_norm(y).to(x.dtype)
        
        return y

    def forward_corev2(self, x: torch.Tensor, nrows=-1, channel_first=False):
        nrows = 1
        if not channel_first:
            x = x.permute(0, 3, 1, 2).contiguous()
        if self.ssm_low_rank:
            x = self.in_rank(x)
        x = cross_selective_scan(
            x, self.x_proj_weight, None, self.dt_projs_weight, self.dt_projs_bias,
            self.A_logs, self.Ds, getattr(self, "out_norm", None), self.softmax_version, 
            nrows=nrows, delta_softplus=True,
        )
        if self.ssm_low_rank:
            x = self.out_rank(x)
        return x

    def cforward_corev2(self, xc: torch.Tensor, nrows=-1, channel_first=False):
        nrows = 1
        if not channel_first:
            xc = xc.permute(0, 3, 1, 2).contiguous()
        b,d,h,w = xc.shape
        xc = self.pooling(xc).view(b, -1, d) #b,1,d
        xc = cross_selective_scanC(
            xc, self.xc_proj_weight, None, self.dtc_projs_weight, self.dtc_projs_bias,
            self.Ac_logs, self.Dsc, self.channel_norm, self.softmax_version, 
            nrows=nrows, delta_softplus=True,
        )
        return xc

    def forward(self, x: torch.Tensor, **kwargs):
        xz = self.in_conv(x)
        x, z = xz.chunk(2, dim=1) # (b, d, h, w)
        if not self.softmax_version:
            z = self.act(z)
        x = self.act(self.conv2d(x)) # (b, d, h, w)
        y1 = self.forward_core(x)
        y2 = y1 * z
        c = self.cforward_core(y2, channel_first=True)
        y3 = y2 * c
        y2 = y3 + y2
        out = self.out_conv(y2)
        return out


##########################################################################
class MamberBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(MamberBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = SS2D_1(d_model=dim, ssm_ratio=1)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x



##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x



##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

    def forward(self, x):
        return self.body(x)

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):
        return self.body(x)

##########################################################################
##---------- Mamber -----------------------
@ARCH_REGISTRY.register()
class MambaRealSR11(nn.Module):
    def __init__(self, 
        inp_channels=3, 
        out_channels=3, 
        scale= 4,
        dim = 48,
        num_blocks = [6,2,2,1], 
        num_refinement_blocks = 6,
        heads = [1,2,4,8],
        ffn_expansion_factor = 2.66,
        bias = False,
        LayerNorm_type = 'WithBias',   ## Other option 'BiasFree'
    ):

        super(MambaRealSR11, self).__init__()

        self.scale = scale

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(*[MamberBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])

        self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
        self.latent = nn.Sequential(*[MamberBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
        
        self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])


        self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.up2_1 = Upsample(int(dim*2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.refinement = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])
        

        modules_tail = [common.Upsampler(common.default_conv, 4, int(dim*2**1), act=False),
                        common.default_conv(int(dim*2**1), out_channels, 3)]
        self.tail = nn.Sequential(*modules_tail)


    def forward(self, inp_img):

        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        
        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3) 

        inp_enc_level4 = self.down3_4(out_enc_level3)        
        latent = self.latent(inp_enc_level4) 
                        
        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3) 

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2) 

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)
        
        out_dec_level1 = self.refinement(out_dec_level1)

        out_dec_level1 = self.tail(out_dec_level1) + F.interpolate(inp_img, scale_factor=self.scale, mode='nearest') 


        return out_dec_level1


    def flops(self, shape=(3, 64, 64)):
        supported_ops={
            "aten::silu": None, # as relu is in _IGNORED_OPS
            "aten::neg": None, # as relu is in _IGNORED_OPS
            "aten::exp": None, # as relu is in _IGNORED_OPS
            "aten::flip": None, # as permute is in _IGNORED_OPS
            "prim::PythonOp.SelectiveScan": selective_scan_flop_jit,
        }

        model = copy.deepcopy(self)
        model.cuda().eval()

        input = torch.randn((1, *shape), device=next(model.parameters()).device)
        params = parameter_count(model)[""]
        Gflops, unsupported = flop_count(model=model, inputs=(input,), supported_ops=supported_ops)

        del model, input
        #return sum(Gflops.values()) * 1e9
        return f"params(M) {params/1e6} GFLOPs {sum(Gflops.values())}"


if __name__ == "__main__":
    print(MambaRealSR11().flops())


================================================
FILE: RealSR/VmambaIR/archs/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'VmambaIR.archs.{file_name}') for file_name in arch_filenames]


================================================
FILE: RealSR/VmambaIR/archs/attention.py
================================================
import torch.nn as nn
import math
import torch as th
class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads
        self.scale=math.sqrt(10)

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        #scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * self.scale, k * self.scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads
        self.scale=math.sqrt(10)

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        #scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * self.scale).view(bs * self.n_heads, ch, length),
            (k * self.scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

class AttentionBlock(nn.Module):
    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.qkv = nn.Conv2d(channels,channels*3,3,padding=1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)


    def forward(self, x):
        res = self.qkv(x[0])
        b, c, *spatial = res.shape
        res = res.reshape(b, c, -1)
        h = self.attention(res)
        b, c, *spatial = x[0].shape
        h= h.reshape(b, c, *spatial)
        return [x[0] + h,x[1]]

================================================
FILE: RealSR/VmambaIR/archs/common.py
================================================
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias)

class ResBlock(nn.Module):
    def __init__(
        self, conv, n_feats, kernel_size,
        bias=True, bn=False, act=nn.LeakyReLU(0.1, inplace=True), res_scale=1):

        super(ResBlock, self).__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        # self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x)
        res += x

        return res

class MeanShift(nn.Conv2d):
    def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1):
        super(MeanShift, self).__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1)
        self.weight.data.div_(std.view(3, 1, 1, 1))
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
        self.bias.data.div_(std)
        self.weight.requires_grad = False
        self.bias.requires_grad = False


class Upsampler(nn.Sequential):
    def __init__(self, conv, scale, n_feat, act=False, bias=True):
        m = []
        if (int(scale) & (int(scale) - 1)) == 0:    # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feat, 4 * n_feat, 3, bias))
                m.append(nn.PixelShuffle(2))
                if act: m.append(act())
        elif scale == 3:
            m.append(conv(n_feat, 9 * n_feat, 3, bias))
            m.append(nn.PixelShuffle(3))
            if act: m.append(act())
        else:
            raise NotImplementedError

        super(Upsampler, self).__init__(*m)

================================================
FILE: RealSR/VmambaIR/archs/discriminator_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm


@ARCH_REGISTRY.register()
class UNetDiscriminatorSN(nn.Module):
    """Defines a U-Net discriminator with spectral normalization (SN)

    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    Arg:
        num_in_ch (int): Channel number of inputs. Default: 3.
        num_feat (int): Channel number of base intermediate features. Default: 64.
        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
    """

    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
        super(UNetDiscriminatorSN, self).__init__()
        self.skip_connection = skip_connection
        norm = spectral_norm
        # the first convolution
        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
        # downsample
        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
        # upsample
        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
        # extra convolutions
        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)

    def forward(self, x):
        # downsample
        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
        x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)

        # upsample
        x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
        x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x4 = x4 + x2
        x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
        x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x5 = x5 + x1
        x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
        x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x6 = x6 + x0

        # extra convolutions
        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
        out = self.conv9(out)

        return out


================================================
FILE: RealSR/VmambaIR/archs/srvgg_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F


@ARCH_REGISTRY.register()
class SRVGGNetCompact(nn.Module):
    """A compact VGG-style network structure for super-resolution.

    It is a compact network structure, which performs upsampling in the last layer and no convolution is
    conducted on the HR feature space.

    Args:
        num_in_ch (int): Channel number of inputs. Default: 3.
        num_out_ch (int): Channel number of outputs. Default: 3.
        num_feat (int): Channel number of intermediate features. Default: 64.
        num_conv (int): Number of convolution layers in the body network. Default: 16.
        upscale (int): Upsampling factor. Default: 4.
        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
    """

    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
        super(SRVGGNetCompact, self).__init__()
        self.num_in_ch = num_in_ch
        self.num_out_ch = num_out_ch
        self.num_feat = num_feat
        self.num_conv = num_conv
        self.upscale = upscale
        self.act_type = act_type

        self.body = nn.ModuleList()
        # the first conv
        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
        # the first activation
        if act_type == 'relu':
            activation = nn.ReLU(inplace=True)
        elif act_type == 'prelu':
            activation = nn.PReLU(num_parameters=num_feat)
        elif act_type == 'leakyrelu':
            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.body.append(activation)

        # the body structure
        for _ in range(num_conv):
            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
            # activation
            if act_type == 'relu':
                activation = nn.ReLU(inplace=True)
            elif act_type == 'prelu':
                activation = nn.PReLU(num_parameters=num_feat)
            elif act_type == 'leakyrelu':
                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
            self.body.append(activation)

        # the last conv
        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
        # upsample
        self.upsampler = nn.PixelShuffle(upscale)

    def forward(self, x):
        out = x
        for i in range(0, len(self.body)):
            out = self.body[i](out)

        out = self.upsampler(out)
        # add the nearest upsampled image, so that the network learns the residual
        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
        out += base
        return out


================================================
FILE: RealSR/VmambaIR/data/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import dataset modules for registry
# scan all the files that end with '_dataset.py' under the data folder
data_folder = osp.dirname(osp.abspath(__file__))
dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
# import all the dataset modules
_dataset_modules = [importlib.import_module(f'VmambaIR.data.{file_name}') for file_name in dataset_filenames]


================================================
FILE: RealSR/VmambaIR/data/data_util.py
================================================
import cv2
cv2.setNumThreads(1)
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F

from VmambaIR.data.transforms import mod_crop
from VmambaIR.utils import img2tensor, scandir


def read_img_seq(path, require_mod_crop=False, scale=1):
    """Read a sequence of images from a given folder path.

    Args:
        path (list[str] | str): List of image paths or image folder path.
        require_mod_crop (bool): Require mod crop for each image.
            Default: False.
        scale (int): Scale factor for mod_crop. Default: 1.

    Returns:
        Tensor: size (t, c, h, w), RGB, [0, 1].
    """
    if isinstance(path, list):
        img_paths = path
    else:
        img_paths = sorted(list(scandir(path, full_path=True)))
    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
    if require_mod_crop:
        imgs = [mod_crop(img, scale) for img in imgs]
    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
    imgs = torch.stack(imgs, dim=0)
    return imgs


def generate_frame_indices(crt_idx,
                           max_frame_num,
                           num_frames,
                           padding='reflection'):
    """Generate an index list for reading `num_frames` frames from a sequence
    of images.

    Args:
        crt_idx (int): Current center index.
        max_frame_num (int): Max number of the sequence of images (from 1).
        num_frames (int): Reading num_frames frames.
        padding (str): Padding mode, one of
            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
            Examples: current_idx = 0, num_frames = 5
            The generated frame indices under different padding mode:
            replicate: [0, 0, 0, 1, 2]
            reflection: [2, 1, 0, 1, 2]
            reflection_circle: [4, 3, 0, 1, 2]
            circle: [3, 4, 0, 1, 2]

    Returns:
        list[int]: A list of indices.
    """
    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
    assert padding in ('replicate', 'reflection', 'reflection_circle',
                       'circle'), f'Wrong padding mode: {padding}.'

    max_frame_num = max_frame_num - 1  # start from 0
    num_pad = num_frames // 2

    indices = []
    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
        if i < 0:
            if padding == 'replicate':
                pad_idx = 0
            elif padding == 'reflection':
                pad_idx = -i
            elif padding == 'reflection_circle':
                pad_idx = crt_idx + num_pad - i
            else:
                pad_idx = num_frames + i
        elif i > max_frame_num:
            if padding == 'replicate':
                pad_idx = max_frame_num
            elif padding == 'reflection':
                pad_idx = max_frame_num * 2 - i
            elif padding == 'reflection_circle':
                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
            else:
                pad_idx = i - num_frames
        else:
            pad_idx = i
        indices.append(pad_idx)
    return indices


def paired_paths_from_lmdb(folders, keys):
    """Generate paired paths from lmdb files.

    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:

    lq.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records
    1)image name (with extension),
    2)image shape,
    3)compression level, separated by a white space.
    Example: `baboon.png (120,125,3) 1`

    We use the image name without extension as the lmdb key.
    Note that we use the same key for the corresponding lq and gt images.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
            Note that this key is different from lmdb keys.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
        raise ValueError(
            f'{input_key} folder and {gt_key} folder should both in lmdb '
            f'formats. But received {input_key}: {input_folder}; '
            f'{gt_key}: {gt_folder}')
    # ensure that the two meta_info files are the same
    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
        input_lmdb_keys = [line.split('.')[0] for line in fin]
    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
        gt_lmdb_keys = [line.split('.')[0] for line in fin]
    if set(input_lmdb_keys) != set(gt_lmdb_keys):
        raise ValueError(
            f'Keys in {input_key}_folder and {gt_key}_folder are different.')
    else:
        paths = []
        for lmdb_key in sorted(input_lmdb_keys):
            paths.append(
                dict([(f'{input_key}_path', lmdb_key),
                      (f'{gt_key}_path', lmdb_key)]))
        return paths


def paired_paths_from_meta_info_file(folders, keys, meta_info_file,
                                     filename_tmpl):
    """Generate paired paths from an meta information file.

    Each line in the meta information file contains the image names and
    image shape (usually for gt), separated by a white space.

    Example of an meta information file:
    ```
    0001_s001.png (480,480,3)
    0001_s002.png (480,480,3)
    ```

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        meta_info_file (str): Path to the meta information file.
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    with open(meta_info_file, 'r') as fin:
        gt_names = [line.split(' ')[0] for line in fin]

    paths = []
    for gt_name in gt_names:
        basename, ext = osp.splitext(osp.basename(gt_name))
        input_name = f'{filename_tmpl.format(basename)}{ext}'
        input_path = osp.join(input_folder, input_name)
        gt_path = osp.join(gt_folder, gt_name)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paired_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    input_paths = list(scandir(input_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(input_paths) == len(gt_paths), (
        f'{input_key} and {gt_key} datasets have different number of images: '
        f'{len(input_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        input_path = input_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(input_path))
        input_name = f'{filename_tmpl.format(basename)}{ext_input}'
        input_path = osp.join(input_folder, input_name)
        assert input_name in input_paths, (f'{input_name} is not in '
                                           f'{input_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths

def paired_DP_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 3, (
        'The len of folders should be 3 with [inputL_folder, inputR_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 3, (
        'The len of keys should be 2 with [inputL_key, inputR_key, gt_key]. '
        f'But got {len(keys)}')
    inputL_folder, inputR_folder, gt_folder = folders
    inputL_key, inputR_key, gt_key = keys

    inputL_paths = list(scandir(inputL_folder))
    inputR_paths = list(scandir(inputR_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(inputL_paths) == len(inputR_paths) == len(gt_paths), (
        f'{inputL_key} and {inputR_key} and {gt_key} datasets have different number of images: '
        f'{len(inputL_paths)}, {len(inputR_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        inputL_path = inputL_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputL_path))
        inputL_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputL_path = osp.join(inputL_folder, inputL_name)
        assert inputL_name in inputL_paths, (f'{inputL_name} is not in '
                                           f'{inputL_key}_paths.')
        inputR_path = inputR_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputR_path))
        inputR_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputR_path = osp.join(inputR_folder, inputR_name)
        assert inputR_name in inputR_paths, (f'{inputR_name} is not in '
                                           f'{inputR_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{inputL_key}_path', inputL_path),
                  (f'{inputR_key}_path', inputR_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paths_from_folder(folder):
    """Generate paths from folder.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """

    paths = list(scandir(folder))
    paths = [osp.join(folder, path) for path in paths]
    return paths


def paths_from_lmdb(folder):
    """Generate paths from lmdb.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """
    if not folder.endswith('.lmdb'):
        raise ValueError(f'Folder {folder}folder should in lmdb format.')
    with open(osp.join(folder, 'meta_info.txt')) as fin:
        paths = [line.split('.')[0] for line in fin]
    return paths


def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
    """Generate Gaussian kernel used in `duf_downsample`.

    Args:
        kernel_size (int): Kernel size. Default: 13.
        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.

    Returns:
        np.array: The Gaussian kernel.
    """
    from scipy.ndimage import filters as filters
    kernel = np.zeros((kernel_size, kernel_size))
    # set element at the middle to one, a dirac delta
    kernel[kernel_size // 2, kernel_size // 2] = 1
    # gaussian-smooth the dirac, resulting in a gaussian filter
    return filters.gaussian_filter(kernel, sigma)


def duf_downsample(x, kernel_size=13, scale=4):
    """Downsamping with Gaussian kernel used in the DUF official code.

    Args:
        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
        kernel_size (int): Kernel size. Default: 13.
        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
            Default: 4.

    Returns:
        Tensor: DUF downsampled frames.
    """
    assert scale in (2, 3,
                     4), f'Only support scale (2, 3, 4), but got {scale}.'

    squeeze_flag = False
    if x.ndim == 4:
        squeeze_flag = True
        x = x.unsqueeze(0)
    b, t, c, h, w = x.size()
    x = x.view(-1, 1, h, w)
    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')

    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(
        0).unsqueeze(0)
    x = F.conv2d(x, gaussian_filter, stride=scale)
    x = x[:, :, 2:-2, 2:-2]
    x = x.view(b, t, c, x.size(2), x.size(3))
    if squeeze_flag:
        x = x.squeeze(0)
    return x


================================================
FILE: RealSR/VmambaIR/data/deblur_paired_dataset.py
================================================
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from VmambaIR.data.data_util import (paired_paths_from_folder,
                                    paired_DP_paths_from_folder,
                                    paired_paths_from_lmdb,
                                    paired_paths_from_meta_info_file)
from VmambaIR.data.transforms import augment, paired_random_crop, paired_random_crop_DP, random_augmentation
from VmambaIR.utils import FileClient, imfrombytes, img2tensor, padding, padding_DP, imfrombytesDP
from basicsr.utils.registry import DATASET_REGISTRY
import random
import numpy as np
import torch
import cv2

@DATASET_REGISTRY.register()
class DeblurPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the
                template excludes the file extension. Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            geometric_augs (bool): Use geometric augmentations.

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(DeblurPairedDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info_file' in self.opt and self.opt[
                'meta_info_file'] is not None:
            self.paths = paired_paths_from_meta_info_file(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.opt['meta_info_file'], self.filename_tmpl)
        else:
            self.paths = paired_paths_from_folder(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        try:
            img_lq = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("lq path {} not working".format(lq_path))

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)

            # flip, rotation augmentations
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)
            
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lq_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_GaussianDenoising(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            gt_size (int): Cropped patched size for gt patches.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(Dataset_GaussianDenoising, self).__init__()
        self.opt = opt

        if self.opt['phase'] == 'train':
            self.sigma_type  = opt['sigma_type']
            self.sigma_range = opt['sigma_range']
            assert self.sigma_type in ['constant', 'random', 'choice']
        else:
            self.sigma_test = opt['sigma_test']
        self.in_ch = opt['in_ch']

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None        

        self.gt_folder = opt['dataroot_gt']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            self.paths = paths_from_lmdb(self.gt_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.gt_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.gt_folder, full_path=True)))

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')

        if self.in_ch == 3:
            try:
                img_gt = imfrombytes(img_bytes, float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2RGB)
        else:
            try:
                img_gt = imfrombytes(img_bytes, flag='grayscale', float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = img_gt.copy()


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)
            # flip, rotation
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                                        bgr2rgb=False,
                                        float32=True)


            if self.sigma_type == 'constant':
                sigma_value = self.sigma_range
            elif self.sigma_type == 'random':
                sigma_value = random.uniform(self.sigma_range[0], self.sigma_range[1])
            elif self.sigma_type == 'choice':
                sigma_value = random.choice(self.sigma_range)

            noise_level = torch.FloatTensor([sigma_value])/255.0
            # noise_level_map = torch.ones((1, img_lq.size(1), img_lq.size(2))).mul_(noise_level).float()
            noise = torch.randn(img_lq.size()).mul_(noise_level).float()
            img_lq.add_(noise)

        else:            
            np.random.seed(seed=0)
            img_lq += np.random.normal(0, self.sigma_test/255.0, img_lq.shape)
            # noise_level_map = torch.ones((1, img_lq.shape[0], img_lq.shape[1])).mul_(self.sigma_test/255.0).float()

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                            bgr2rgb=False,
                            float32=True)

        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': gt_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_DefocusDeblur_DualPixel_16bit(data.Dataset):
    def __init__(self, opt):
        super(Dataset_DefocusDeblur_DualPixel_16bit, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lqL_folder, self.lqR_folder = opt['dataroot_gt'], opt['dataroot_lqL'], opt['dataroot_lqR']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        self.paths = paired_DP_paths_from_folder(
            [self.lqL_folder, self.lqR_folder, self.gt_folder], ['lqL', 'lqR', 'gt'],
            self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lqL_path = self.paths[index]['lqL_path']
        img_bytes = self.file_client.get(lqL_path, 'lqL')
        try:
            img_lqL = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqL path {} not working".format(lqL_path))

        lqR_path = self.paths[index]['lqR_path']
        img_bytes = self.file_client.get(lqR_path, 'lqR')
        try:
            img_lqR = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqR path {} not working".format(lqR_path))


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_lqL, img_lqR, img_gt = padding_DP(img_lqL, img_lqR, img_gt, gt_size)

            # random crop
            img_lqL, img_lqR, img_gt = paired_random_crop_DP(img_lqL, img_lqR, img_gt, gt_size, scale, gt_path)
            
            # flip, rotation            
            if self.geometric_augs:
                img_lqL, img_lqR, img_gt = random_augmentation(img_lqL, img_lqR, img_gt)
        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lqL, img_lqR, img_gt = img2tensor([img_lqL, img_lqR, img_gt],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lqL, self.mean, self.std, inplace=True)
            normalize(img_lqR, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        img_lq = torch.cat([img_lqL, img_lqR], 0)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lqL_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)


================================================
FILE: RealSR/VmambaIR/data/diffir_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data


@DATASET_REGISTRY.register()
class DiffIRGANDataset(data.Dataset):
    """Dataset used for KDSRGAN model:
    KDSRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(DiffIRGANDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']
        self.gt_size = self.opt['gt_size']
        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # -------------------------------- Load gt images -------------------------------- #
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        # avoid errors caused by high latency in reading files
        retry = 3
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
            except (IOError, OSError) as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
                index = random.randint(0, self.__len__())
                gt_path = self.paths[index]
                time.sleep(1)  # sleep 1s for occasional server congestion
            else:
                break
            finally:
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = self.gt_size
        # pad
        if h < crop_pad_size or w < crop_pad_size:
            pad_h = max(0, crop_pad_size - h)
            pad_w = max(0, crop_pad_size - w)
            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
        # crop
        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
            h, w = img_gt.shape[0:2]
            # randomly choose top and left coordinates
            top = random.randint(0, h - crop_pad_size)
            left = random.randint(0, w - crop_pad_size)
            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]

        return_d = {'gt': img_gt,'gt_path': gt_path}
        return return_d

    def __len__(self):
        return len(self.paths)


================================================
FILE: RealSR/VmambaIR/data/diffir_paired_dataset.py
================================================
import os
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
from torchvision.transforms.functional import normalize


@DATASET_REGISTRY.register()
class DiffIRGANPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
                Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(DiffIRGANPairedDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        # mean and std for normalizing the input images
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None

        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip() for line in fin]
            self.paths = []
            for path in paths:
                gt_path, lq_path = path.split(', ')
                gt_path = os.path.join(self.gt_folder, gt_path)
                lq_path = os.path.join(self.lq_folder, lq_path)
                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
        else:
            # disk backend
            # it will scan the whole folder to get meta info
            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']

        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)
        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        img_lq = imfrombytes(img_bytes, float32=True)

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
            # flip, rotation
            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}

    def __len__(self):
        return len(self.paths)


================================================
FILE: RealSR/VmambaIR/data/gaussiandenoising_paired_dataset.py
================================================
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from VmambaIR.data.data_util import (paired_paths_from_folder,
                                    paired_DP_paths_from_folder,
                                    paired_paths_from_lmdb,
                                    paired_paths_from_meta_info_file)
from VmambaIR.data.transforms import augment, paired_random_crop, paired_random_crop_DP, random_augmentation
from VmambaIR.utils import FileClient, imfrombytes, img2tensor, padding, padding_DP, imfrombytesDP,scandir
from basicsr.utils.registry import DATASET_REGISTRY
import random
import numpy as np
import torch
import cv2


@DATASET_REGISTRY.register()
class GaussianDenoisingPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            gt_size (int): Cropped patched size for gt patches.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(GaussianDenoisingPairedDataset, self).__init__()
        self.opt = opt

        if self.opt['phase'] == 'train':
            self.sigma_type  = opt['sigma_type']
            self.sigma_range = opt['sigma_range']
            assert self.sigma_type in ['constant', 'random', 'choice']
        else:
            self.sigma_test = opt['sigma_test']
        self.in_ch = opt['in_ch']

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None        

        self.gt_folder = opt['dataroot_gt']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            self.paths = paths_from_lmdb(self.gt_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.gt_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.gt_folder, full_path=True)))

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        # gt_path = self.paths[index]['gt_path']
        gt_path = self.paths[index]
        img_bytes = self.file_client.get(gt_path, 'gt')

        if self.in_ch == 3:
            try:
                img_gt = imfrombytes(img_bytes, float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2RGB)
        else:
            try:
                img_gt = imfrombytes(img_bytes, flag='grayscale', float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = img_gt.copy()


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)
            # flip, rotation
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                                        bgr2rgb=False,
                                        float32=True)


            if self.sigma_type == 'constant':
                sigma_value = self.sigma_range
            elif self.sigma_type == 'random':
                sigma_value = random.uniform(self.sigma_range[0], self.sigma_range[1])
            elif self.sigma_type == 'choice':
                sigma_value = random.choice(self.sigma_range)

            noise_level = torch.FloatTensor([sigma_value])/255.0
            # noise_level_map = torch.ones((1, img_lq.size(1), img_lq.size(2))).mul_(noise_level).float()
            noise = torch.randn(img_lq.size()).mul_(noise_level).float()
            img_lq.add_(noise)

        else:            
            np.random.seed(seed=0)
            img_lq += np.random.normal(0, self.sigma_test/255.0, img_lq.shape)
            # noise_level_map = torch.ones((1, img_lq.shape[0], img_lq.shape[1])).mul_(self.sigma_test/255.0).float()

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                            bgr2rgb=False,
                            float32=True)

        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': gt_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_DefocusDeblur_DualPixel_16bit(data.Dataset):
    def __init__(self, opt):
        super(Dataset_DefocusDeblur_DualPixel_16bit, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lqL_folder, self.lqR_folder = opt['dataroot_gt'], opt['dataroot_lqL'], opt['dataroot_lqR']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        self.paths = paired_DP_paths_from_folder(
            [self.lqL_folder, self.lqR_folder, self.gt_folder], ['lqL', 'lqR', 'gt'],
            self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lqL_path = self.paths[index]['lqL_path']
        img_bytes = self.file_client.get(lqL_path, 'lqL')
        try:
            img_lqL = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqL path {} not working".format(lqL_path))

        lqR_path = self.paths[index]['lqR_path']
        img_bytes = self.file_client.get(lqR_path, 'lqR')
        try:
            img_lqR = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqR path {} not working".format(lqR_path))


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_lqL, img_lqR, img_gt = padding_DP(img_lqL, img_lqR, img_gt, gt_size)

            # random crop
            img_lqL, img_lqR, img_gt = paired_random_crop_DP(img_lqL, img_lqR, img_gt, gt_size, scale, gt_path)
            
            # flip, rotation            
            if self.geometric_augs:
                img_lqL, img_lqR, img_gt = random_augmentation(img_lqL, img_lqR, img_gt)
        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lqL, img_lqR, img_gt = img2tensor([img_lqL, img_lqR, img_gt],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lqL, self.mean, self.std, inplace=True)
            normalize(img_lqR, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        img_lq = torch.cat([img_lqL, img_lqR], 0)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lqL_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)


================================================
FILE: RealSR/VmambaIR/data/realesrgan400_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data


@DATASET_REGISTRY.register()
class RealESRGANDataset400(data.Dataset):
    """Dataset used for Real-ESRGAN model:
    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(RealESRGANDataset400, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

        # blur settings for the first degradation
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
        self.blur_sigma = opt['blur_sigma']
        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters

        # blur settings for the second degradation
        self.blur_kernel_size2 = opt['blur_kernel_size2']
        self.kernel_list2 = opt['kernel_list2']
        self.kernel_prob2 = opt['kernel_prob2']
        self.blur_sigma2 = opt['blur_sigma2']
        self.betag_range2 = opt['betag_range2']
        self.betap_range2 = opt['betap_range2']
        self.sinc_prob2 = opt['sinc_prob2']

        # a final sinc filter
        self.final_sinc_prob = opt['final_sinc_prob']

        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
        # TODO: kernel range is now hard-coded, should be in the configure file
        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
        self.pulse_tensor[10, 10] = 1

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # -------------------------------- Load gt images -------------------------------- #
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        # avoid errors caused by high latency in reading files
        retry = 3
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
            except Exception as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
                index = random.randint(0, self.__len__())
                gt_path = self.paths[index]
                time.sleep(1)  # sleep 1s for occasional server congestion
            else:
                break
            finally:
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = 400
        # pad
        if h < crop_pad_size or w < crop_pad_size:
            pad_h = max(0, crop_pad_size - h)
            pad_w = max(0, crop_pad_size - w)
            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
        # crop
        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
            h, w = img_gt.shape[0:2]
            # randomly choose top and left coordinates
            top = random.randint(0, h - crop_pad_size)
            left = random.randint(0, w - crop_pad_size)
            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]

        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob']:
            # this sinc filter setting is for kernels ranging from [7, 21]
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel = random_mixed_kernels(
                self.kernel_list,
                self.kernel_prob,
                kernel_size,
                self.blur_sigma,
                self.blur_sigma, [-math.pi, math.pi],
                self.betag_range,
                self.betap_range,
                noise_range=None)
        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob2']:
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel2 = random_mixed_kernels(
                self.kernel_list2,
                self.kernel_prob2,
                kernel_size,
                self.blur_sigma2,
                self.blur_sigma2, [-math.pi, math.pi],
                self.betag_range2,
                self.betap_range2,
                noise_range=None)

        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------------------- the final sinc kernel ------------------------------------- #
        if np.random.uniform() < self.opt['final_sinc_prob']:
            kernel_size = random.choice(self.kernel_range)
            omega_c = np.random.uniform(np.pi / 3, np.pi)
            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
            sinc_kernel = torch.FloatTensor(sinc_kernel)
        else:
            sinc_kernel = self.pulse_tensor

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
        kernel = torch.FloatTensor(kernel)
        kernel2 = torch.FloatTensor(kernel2)

        return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
        return return_d

    def __len__(self):
        return len(self.paths)

================================================
FILE: RealSR/VmambaIR/data/realesrgan_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data


@DATASET_REGISTRY.register()
class RealESRGANDataset(data.Dataset):
    """Dataset used for Real-ESRGAN model:
    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(RealESRGANDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

        # blur settings for the first degradation
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
        self.blur_sigma = opt['blur_sigma']
        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters

        # blur settings for the second degradation
        self.blur_kernel_size2 = opt['blur_kernel_size2']
        self.kernel_list2 = opt['kernel_list2']
        self.kernel_prob2 = opt['kernel_prob2']
        self.blur_sigma2 = opt['blur_sigma2']
        self.betag_range2 = opt['betag_range2']
        self.betap_range2 = opt['betap_range2']
        self.sinc_prob2 = opt['sinc_prob2']

        # a final sinc filter
        self.final_sinc_prob = opt['final_sinc_prob']

        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
        # TODO: kernel range is now hard-coded, should be in the configure file
        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
        self.pulse_tensor[10, 10] = 1

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # -------------------------------- Load gt images -------------------------------- #
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        # avoid errors caused by high latency in reading files
        retry = 3
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
            except Exception as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
                index = random.randint(0, self.__len__())
                gt_path = self.paths[index]
                time.sleep(1)  # sleep 1s for occasional server congestion
            else:
                break
            finally:
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = 600
        # pad
        if h < crop_pad_size or w < crop_pad_size:
            pad_h = max(0, crop_pad_size - h)
            pad_w = max(0, crop_pad_size - w)
            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
        # crop
        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
            h, w = img_gt.shape[0:2]
            # randomly choose top and left coordinates
            top = random.randint(0, h - crop_pad_size)
            left = random.randint(0, w - crop_pad_size)
            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]

        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob']:
            # this sinc filter setting is for kernels ranging from [7, 21]
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel = random_mixed_kernels(
                self.kernel_list,
                self.kernel_prob,
                kernel_size,
                self.blur_sigma,
                self.blur_sigma, [-math.pi, math.pi],
                self.betag_range,
                self.betap_range,
                noise_range=None)
        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob2']:
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel2 = random_mixed_kernels(
                self.kernel_list2,
                self.kernel_prob2,
                kernel_size,
                self.blur_sigma2,
                self.blur_sigma2, [-math.pi, math.pi],
                self.betag_range2,
                self.betap_range2,
                noise_range=None)

        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------------------- the final sinc kernel ------------------------------------- #
        if np.random.uniform() < self.opt['final_sinc_prob']:
            kernel_size = random.choice(self.kernel_range)
            omega_c = np.random.uniform(np.pi / 3, np.pi)
            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
            sinc_kernel = torch.FloatTensor(sinc_kernel)
        else:
            sinc_kernel = self.pulse_tensor

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
        kernel = torch.FloatTensor(kernel)
        kernel2 = torch.FloatTensor(kernel2)

        return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
        return return_d

    def __len__(self):
        return len(self.paths)

================================================
FILE: RealSR/VmambaIR/data/realesrgan_memery_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data


@DATASET_REGISTRY.register()
class RealESRGANDataset_memory(data.Dataset):
    """Dataset used for Real-ESRGAN model:
    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(RealESRGANDataset_memory, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

        # blur settings for the first degradation
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
        self.blur_sigma = opt['blur_sigma']
        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters

        # blur settings for the second degradation
        self.blur_kernel_size2 = opt['blur_kernel_size2']
        self.kernel_list2 = opt['kernel_list2']
        self.kernel_prob2 = opt['kernel_prob2']
        self.blur_sigma2 = opt['blur_sigma2']
        self.betag_range2 = opt['betag_range2']
        self.betap_range2 = opt['betap_range2']
        self.sinc_prob2 = opt['sinc_prob2']

        # a final sinc filter
        self.final_sinc_prob = opt['final_sinc_prob']

        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
        # TODO: kernel range is now hard-coded, should be in the configure file
        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
        self.pulse_tensor[10, 10] = 1

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # -------------------------------- Load gt images -------------------------------- #
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        # avoid errors caused by high latency in reading files
        retry = 3
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
            except Exception as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
                index = random.randint(0, self.__len__())
                gt_path = self.paths[index]
                time.sleep(1)  # sleep 1s for occasional server congestion
            else:
                break
            finally:
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = 600
        # pad
        if h < crop_pad_size or w < crop_pad_size:
            pad_h = max(0, crop_pad_size - h)
            pad_w = max(0, crop_pad_size - w)
            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
        # crop
        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
            h, w = img_gt.shape[0:2]
            # randomly choose top and left coordinates
            top = random.randint(0, h - crop_pad_size)
            left = random.randint(0, w - crop_pad_size)
            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]

        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob']:
            # this sinc filter setting is for kernels ranging from [7, 21]
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel = random_mixed_kernels(
                self.kernel_list,
                self.kernel_prob,
                kernel_size,
                self.blur_sigma,
                self.blur_sigma, [-math.pi, math.pi],
                self.betag_range,
                self.betap_range,
                noise_range=None)
        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
        kernel_size = random.choice(self.kernel_range)
        if np.random.uniform() < self.opt['sinc_prob2']:
            if kernel_size < 13:
                omega_c = np.random.uniform(np.pi / 3, np.pi)
            else:
                omega_c = np.random.uniform(np.pi / 5, np.pi)
            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
        else:
            kernel2 = random_mixed_kernels(
                self.kernel_list2,
                self.kernel_prob2,
                kernel_size,
                self.blur_sigma2,
                self.blur_sigma2, [-math.pi, math.pi],
                self.betag_range2,
                self.betap_range2,
                noise_range=None)

        # pad kernel
        pad_size = (21 - kernel_size) // 2
        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))

        # ------------------------------------- the final sinc kernel ------------------------------------- #
        if np.random.uniform() < self.opt['final_sinc_prob']:
            kernel_size = random.choice(self.kernel_range)
            omega_c = np.random.uniform(np.pi / 3, np.pi)
            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
            sinc_kernel = torch.FloatTensor(sinc_kernel)
        else:
            sinc_kernel = self.pulse_tensor

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
        kernel = torch.FloatTensor(kernel)
        kernel2 = torch.FloatTensor(kernel2)

        return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
        return return_d

    def __len__(self):
        return len(self.paths)

================================================
FILE: RealSR/VmambaIR/data/realesrgan_paired_dataset.py
================================================
import os
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
from torchvision.transforms.functional import normalize


@DATASET_REGISTRY.register()
class RealESRGANPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
                Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(RealESRGANPairedDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        # mean and std for normalizing the input images
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None

        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip() for line in fin]
            self.paths = []
            for path in paths:
                gt_path, lq_path = path.split(', ')
                gt_path = os.path.join(self.gt_folder, gt_path)
                lq_path = os.path.join(self.lq_folder, lq_path)
                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
        else:
            # disk backend
            # it will scan the whole folder to get meta info
            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']

        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)
        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        img_lq = imfrombytes(img_bytes, float32=True)

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
            # flip, rotation
            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}

    def __len__(self):
        return len(self.paths)

================================================
FILE: RealSR/VmambaIR/data/transforms.py
================================================
import cv2
import random
import numpy as np

def mod_crop(img, scale):
    """Mod crop images, used during testing.

    Args:
        img (ndarray): Input image.
        scale (int): Scale factor.

    Returns:
        ndarray: Result image.
    """
    img = img.copy()
    if img.ndim in (2, 3):
        h, w = img.shape[0], img.shape[1]
        h_remainder, w_remainder = h % scale, w % scale
        img = img[:h - h_remainder, :w - w_remainder, ...]
    else:
        raise ValueError(f'Wrong img ndim: {img.ndim}.')
    return img

def paired_random_crop(img_gts, img_lqs, lq_patch_size, scale, gt_path):
    """Paired random crop.

    It crops lists of lq and gt images with corresponding locations.

    Args:
        img_gts (list[ndarray] | ndarray): GT images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        lq_patch_size (int): LQ patch size.
        scale (int): Scale factor.
        gt_path (str): Path to ground-truth.

    Returns:
        list[ndarray] | ndarray: GT images and LQ images. If returned results
            only have one element, just return ndarray.
    """

    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqs, list):
        img_lqs = [img_lqs]

    h_lq, w_lq, _ = img_lqs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    gt_patch_size = int(lq_patch_size * scale)

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqs) == 1:
        img_lqs = img_lqs[0]
    return img_gts, img_lqs

def paired_random_crop_DP(img_lqLs, img_lqRs, img_gts, gt_patch_size, scale, gt_path):
    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqLs, list):
        img_lqLs = [img_lqLs]
    if not isinstance(img_lqRs, list):
        img_lqRs = [img_lqRs]

    h_lq, w_lq, _ = img_lqLs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    lq_patch_size = gt_patch_size // scale

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqLs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqLs
    ]

    img_lqRs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqRs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqLs) == 1:
        img_lqLs = img_lqLs[0]
    if len(img_lqRs) == 1:
        img_lqRs = img_lqRs[0]
    return img_lqLs, img_lqRs, img_gts


def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).

    We use vertical flip and transpose for rotation implementation.
    All the images in the list use the same augmentation.

    Args:
        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
            is an ndarray, it will be transformed to a list.
        hflip (bool): Horizontal flip. Default: True.
        rotation (bool): Ratotation. Default: True.
        flows (list[ndarray]: Flows to be augmented. If the input is an
            ndarray, it will be transformed to a list.
            Dimension is (h, w, 2). Default: None.
        return_status (bool): Return the status of flip and rotation.
            Default: False.

    Returns:
        list[ndarray] | ndarray: Augmented images and flows. If returned
            results only have one element, just return ndarray.

    """
    hflip = hflip and random.random() < 0.5
    vflip = rotation and random.random() < 0.5
    rot90 = rotation and random.random() < 0.5

    def _augment(img):
        if hflip:  # horizontal
            cv2.flip(img, 1, img)
        if vflip:  # vertical
            cv2.flip(img, 0, img)
        if rot90:
            img = img.transpose(1, 0, 2)
        return img

    def _augment_flow(flow):
        if hflip:  # horizontal
            cv2.flip(flow, 1, flow)
            flow[:, :, 0] *= -1
        if vflip:  # vertical
            cv2.flip(flow, 0, flow)
            flow[:, :, 1] *= -1
        if rot90:
            flow = flow.transpose(1, 0, 2)
            flow = flow[:, :, [1, 0]]
        return flow

    if not isinstance(imgs, list):
        imgs = [imgs]
    imgs = [_augment(img) for img in imgs]
    if len(imgs) == 1:
        imgs = imgs[0]

    if flows is not None:
        if not isinstance(flows, list):
            flows = [flows]
        flows = [_augment_flow(flow) for flow in flows]
        if len(flows) == 1:
            flows = flows[0]
        return imgs, flows
    else:
        if return_status:
            return imgs, (hflip, vflip, rot90)
        else:
            return imgs


def img_rotate(img, angle, center=None, scale=1.0):
    """Rotate image.

    Args:
        img (ndarray): Image to be rotated.
        angle (float): Rotation angle in degrees. Positive values mean
            counter-clockwise rotation.
        center (tuple[int]): Rotation center. If the center is None,
            initialize it as the center of the image. Default: None.
        scale (float): Isotropic scale factor. Default: 1.0.
    """
    (h, w) = img.shape[:2]

    if center is None:
        center = (w // 2, h // 2)

    matrix = cv2.getRotationMatrix2D(center, angle, scale)
    rotated_img = cv2.warpAffine(img, matrix, (w, h))
    return rotated_img

def data_augmentation(image, mode):
    """
    Performs data augmentation of the input image
    Input:
        image: a cv2 (OpenCV) image
        mode: int. Choice of transformation to apply to the image
                0 - no transformation
                1 - flip up and down
                2 - rotate counterwise 90 degree
                3 - rotate 90 degree and flip up and down
                4 - rotate 180 degree
                5 - rotate 180 degree and flip
                6 - rotate 270 degree
                7 - rotate 270 degree and flip
    """
    if mode == 0:
        # original
        out = image
    elif mode == 1:
        # flip up and down
        out = np.flipud(image)
    elif mode == 2:
        # rotate counterwise 90 degree
        out = np.rot90(image)
    elif mode == 3:
        # rotate 90 degree and flip up and down
        out = np.rot90(image)
        out = np.flipud(out)
    elif mode == 4:
        # rotate 180 degree
        out = np.rot90(image, k=2)
    elif mode == 5:
        # rotate 180 degree and flip
        out = np.rot90(image, k=2)
        out = np.flipud(out)
    elif mode == 6:
        # rotate 270 degree
        out = np.rot90(image, k=3)
    elif mode == 7:
        # rotate 270 degree and flip
        out = np.rot90(image, k=3)
        out = np.flipud(out)
    else:
        raise Exception('Invalid choice of image transformation')

    return out

def random_augmentation(*args):
    out = []
    flag_aug = random.randint(0,7)
    for data in args:
        out.append(data_augmentation(data, flag_aug).copy())
    return out


================================================
FILE: RealSR/VmambaIR/losses/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_loss.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'VmambaIR.losses.{file_name}') for file_name in arch_filenames]


================================================
FILE: RealSR/VmambaIR/losses/my_loss.py
================================================
import torch
from torch import nn as nn
from torch.nn import functional as F
from basicsr.utils.registry import LOSS_REGISTRY


@LOSS_REGISTRY.register()
class KDLoss(nn.Module):
    """
    Args:
        loss_weight (float): Loss weight for KD loss. Default: 1.0.
    """

    def __init__(self, loss_weight=1.0, temperature = 0.15):
        super(KDLoss, self).__init__()
    
        self.loss_weight = loss_weight
        self.temperature = temperature

    def forward(self, S1_fea, S2_fea):
        """
        Args:
            S1_fea (List): contain shape (N, L) vector. 
            S2_fea (List): contain shape (N, L) vector.
            weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None.
        """
        loss_KD_dis = 0
        loss_KD_abs = 0
        for i in range(len(S1_fea)):
            S2_distance = F.log_softmax(S2_fea[i] / self.temperature, dim=1)
            S1_distance = F.softmax(S1_fea[i].detach()/ self.temperature, dim=1)
            loss_KD_dis += F.kl_div(
                        S2_distance, S1_distance, reduction='batchmean')
            loss_KD_abs += nn.L1Loss()(S2_fea[i], S1_fea[i].detach())
        return self.loss_weight * loss_KD_dis, self.loss_weight * loss_KD_abs

                

================================================
FILE: RealSR/VmambaIR/models/MambaRealSRGAN_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F
from basicsr.archs import build_network
from basicsr.utils import get_root_logger
from basicsr.losses import build_loss
from torch import nn

@MODEL_REGISTRY.register()
class MambaRealSRGAN(SRGANModel):
    """

    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaRealSRGAN, self).__init__(opt)
        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)

        #self.net_g_S1 = build_network(opt['network_S1'])
        #self.net_g_S1 = self.model_to_device(self.net_g_S1)
        load_path = self.opt['path'].get('pretrain_network_S1', None)

        if load_path is not None:
            param_key = self.opt['path'].get('param_key_g', 'params')
            #self.load_network(self.net_g_S1, load_path, True, param_key)

        #self.net_g_S1.eval()
        #if self.opt['dist']:
            #self.model_Es1 = self.net_g_S1.module.E
        #else:
            #self.model_Es1 = self.net_g_S1.module.E

        self.pixel_unshuffle = nn.PixelUnshuffle(opt["scale"])
        if self.is_train:
            #self.encoder_iter = opt["train"]["encoder_iter"]
            #self.lr_encoder = opt["train"]["lr_encoder"]
            self.lr_sr = opt["train"]["lr_sr"]
            #self.gamma_encoder = opt["train"]["gamma_encoder"]
            self.gamma_sr = opt["train"]["gamma_sr"]
            #self.lr_decay_encoder = opt["train"]["lr_decay_encoder"]
            self.lr_decay_sr = opt["train"]["lr_decay_sr"]

    def setup_optimizers(self):
        train_opt = self.opt['train']
        optim_params = []
        for k, v in self.net_g.named_parameters():
            if v.requires_grad:
                optim_params.append(v)
            else:
                logger = get_root_logger()
                logger.warning(f'Params {k} will not be optimized in the second stage.')

        optim_type_g = train_opt['optim_g'].pop('type')
        self.optimizer_g = self.get_optimizer(optim_type_g, optim_params, **train_opt['optim_g'])
        self.optimizers.append(self.optimizer_g)

        # optimizer d
        optim_type = train_opt['optim_d'].pop('type')
        self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
        self.optimizers.append(self.optimizer_d)

        #parms=[]
        #for k,v in self.net_g.named_parameters():
        #    if "denoise" in k or "condition" in k:
        #        parms.append(v)
        #self.optimizer_e = self.get_optimizer(optim_type_g, parms, **train_opt['optim_g'])
        #self.optimizers.append(self.optimizer_e)


    def init_training_settings(self):
        train_opt = self.opt['train']
        super(MambaRealSRGAN, self).init_training_settings()
        

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
        """It is the training pair pool for increasing the diversity in a batch.

        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

            self.lq = lq_dequeue
            self.gt = gt_dequeue
        else:
            # only do enqueue
            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
            self.queue_ptr = self.queue_ptr + b

    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            self.gt_usm = self.usm_sharpener(self.gt)

            self.kernel1 = data['kernel1'].to(self.device)
            self.kernel2 = data['kernel2'].to(self.device)
            self.sinc_kernel = data['sinc_kernel'].to(self.device)

            ori_h, ori_w = self.gt.size()[2:4]

            # ----------------------- The first degradation process ----------------------- #
            # blur
            if self.opt['l1_gt_usm']:
                out = filter2D(self.gt_usm, self.kernel1)
            else:
                out = filter2D(self.gt, self.kernel1)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
            # blur
            if np.random.uniform() < self.opt['second_blur_prob']:
                out = filter2D(out, self.kernel2)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range2'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range2'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)

            # JPEG compression + the final sinc filter
            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
            # as one operation.
            # We consider two orders:
            #   1. [resize back + sinc filter] + JPEG compression
            #   2. JPEG compression + [resize back + sinc filter]
            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
            if np.random.uniform() < 0.5:
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
            else:
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)

            # clamp and round
            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.

            # random crop
            gt_size = self.opt['gt_size']
            (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
                                                                 self.opt['scale'])

            # training pair pool
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaRealSRGAN, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True
        
    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,gt,mod_pad_h,mod_pad_w

    def test(self):
        window_size = self.opt['val'].get('window_size', 0)
        if window_size:
            lq,gt,mod_pad_h,mod_pad_w=self.pad_test(window_size)
        else:
            lq=self.lq
            gt=self.gt

        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                self.output = self.net_g_ema(lq)
        else:
            self.net_g.eval()
            with torch.no_grad():
                self.output = self.net_g(lq)
            self.net_g.train()

        if window_size:
            scale = self.opt.get('scale', 1)
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]


    def optimize_parameters(self, current_iter):
        lr = self.lr_sr * (self.gamma_sr ** ((current_iter ) // self.lr_decay_sr))
        for param_group in self.optimizer_g.param_groups:
            param_group['lr'] = lr 
        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
        if self.opt['l1_gt_usm'] is False:
            l1_gt = self.gt
        if self.opt['percep_gt_usm'] is False:
            percep_gt = self.gt
        if self.opt['gan_gt_usm'] is False:
            gan_gt = self.gt
        
        #_, S1_IPR = self.model_Es1(self.lq,l1_gt)

        # optimize net_g
        for p in self.net_d.parameters():
            p.requires_grad = False

        self.optimizer_g.zero_grad()
        #self.output, pred_IPR_list = self.net_g(self.lq)
        self.output = self.net_g(self.lq)

        l_g_total = 0
        loss_dict = OrderedDict()
        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
            # pixel loss
            if self.cri_pix:
                l_g_pix = self.cri_pix(self.output, l1_gt)
                l_g_total += l_g_pix
                loss_dict['l_g_pix'] = l_g_pix
            

            # perceptual loss
            if self.cri_perceptual:
                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
                if l_g_percep is not None:
                    l_g_total += l_g_percep
                    loss_dict['l_g_percep'] = l_g_percep
                if l_g_style is not None:
                    l_g_total += l_g_style
                    loss_dict['l_g_style'] = l_g_style
            # gan loss
            fake_g_pred = self.net_d(self.output)
            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
            l_g_total += l_g_gan
            loss_dict['l_g_gan'] = l_g_gan

            l_g_total.backward()
            self.optimizer_g.step()

        # optimize net_d
        for p in self.net_d.parameters():
            p.requires_grad = True

        self.optimizer_d.zero_grad()
        # real
        real_d_pred = self.net_d(gan_gt)
        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
        loss_dict['l_d_real'] = l_d_real
        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
        l_d_real.backward()
        # fake
        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
        loss_dict['l_d_fake'] = l_d_fake
        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
        l_d_fake.backward()
        self.optimizer_d.step()

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

        self.log_dict = self.reduce_loss_dict(loss_dict)


================================================
FILE: RealSR/VmambaIR/models/MambaRealSRGANtest_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F
from basicsr.archs import build_network
from basicsr.utils import get_root_logger
from basicsr.losses import build_loss
from torch import nn

@MODEL_REGISTRY.register()
class MambaRealSRGANtest(SRGANModel):
    """

    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaRealSRGANtest, self).__init__(opt)
        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)

        #self.net_g_S1 = build_network(opt['network_S1'])
        #self.net_g_S1 = self.model_to_device(self.net_g_S1)
        load_path = self.opt['path'].get('pretrain_network_S1', None)

        if load_path is not None:
            param_key = self.opt['path'].get('param_key_g', 'params')
            #self.load_network(self.net_g_S1, load_path, True, param_key)

        #self.net_g_S1.eval()
        #if self.opt['dist']:
            #self.model_Es1 = self.net_g_S1.module.E
        #else:
            #self.model_Es1 = self.net_g_S1.module.E

        self.pixel_unshuffle = nn.PixelUnshuffle(opt["scale"])
        if self.is_train:
            #self.encoder_iter = opt["train"]["encoder_iter"]
            #self.lr_encoder = opt["train"]["lr_encoder"]
            self.lr_sr = opt["train"]["lr_sr"]
            #self.gamma_encoder = opt["train"]["gamma_encoder"]
            self.gamma_sr = opt["train"]["gamma_sr"]
            #self.lr_decay_encoder = opt["train"]["lr_decay_encoder"]
            self.lr_decay_sr = opt["train"]["lr_decay_sr"]

    def setup_optimizers(self):
        train_opt = self.opt['train']
        optim_params = []
        for k, v in self.net_g.named_parameters():
            if v.requires_grad:
                optim_params.append(v)
            else:
                logger = get_root_logger()
                logger.warning(f'Params {k} will not be optimized in the second stage.')

        optim_type_g = train_opt['optim_g'].pop('type')
        self.optimizer_g = self.get_optimizer(optim_type_g, optim_params, **train_opt['optim_g'])
        self.optimizers.append(self.optimizer_g)

        # optimizer d
        optim_type = train_opt['optim_d'].pop('type')
        self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
        self.optimizers.append(self.optimizer_d)

        #parms=[]
        #for k,v in self.net_g.named_parameters():
        #    if "denoise" in k or "condition" in k:
        #        parms.append(v)
        #self.optimizer_e = self.get_optimizer(optim_type_g, parms, **train_opt['optim_g'])
        #self.optimizers.append(self.optimizer_e)


    def init_training_settings(self):
        train_opt = self.opt['train']
        super(MambaRealSRGANtest, self).init_training_settings()
        

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
        """It is the training pair pool for increasing the diversity in a batch.

        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

            self.lq = lq_dequeue
            self.gt = gt_dequeue
        else:
            # only do enqueue
            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
            self.queue_ptr = self.queue_ptr + b

    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            self.gt_usm = self.usm_sharpener(self.gt)

            self.kernel1 = data['kernel1'].to(self.device)
            self.kernel2 = data['kernel2'].to(self.device)
            self.sinc_kernel = data['sinc_kernel'].to(self.device)

            ori_h, ori_w = self.gt.size()[2:4]

            # ----------------------- The first degradation process ----------------------- #
            # blur
            if self.opt['l1_gt_usm']:
                out = filter2D(self.gt_usm, self.kernel1)
            else:
                out = filter2D(self.gt, self.kernel1)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
            # blur
            if np.random.uniform() < self.opt['second_blur_prob']:
                out = filter2D(out, self.kernel2)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range2'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range2'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)

            # JPEG compression + the final sinc filter
            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
            # as one operation.
            # We consider two orders:
            #   1. [resize back + sinc filter] + JPEG compression
            #   2. JPEG compression + [resize back + sinc filter]
            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
            if np.random.uniform() < 0.5:
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
            else:
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)

            # clamp and round
            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.

            # random crop
            gt_size = self.opt['gt_size']
            (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
                                                                 self.opt['scale'])

            # training pair pool
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaRealSRGANtest, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True
        
    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        #gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,mod_pad_h,mod_pad_w

    def test(self):
        window_size = self.opt['val'].get('window_size', 0)
        if window_size:
            lq,mod_pad_h,mod_pad_w=self.pad_test(window_size)
        else:
            lq=self.lq
            #gt=self.gt

        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                self.output = self.net_g_ema(lq)
        else:
            self.net_g.eval()
            with torch.no_grad():
                self.output = self.net_g(lq)
            self.net_g.train()

        if window_size:
            scale = self.opt.get('scale', 1)
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]


    def optimize_parameters(self, current_iter):
        lr = self.lr_sr * (self.gamma_sr ** ((current_iter ) // self.lr_decay_sr))
        for param_group in self.optimizer_g.param_groups:
            param_group['lr'] = lr 
        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
        if self.opt['l1_gt_usm'] is False:
            l1_gt = self.gt
        if self.opt['percep_gt_usm'] is False:
            percep_gt = self.gt
        if self.opt['gan_gt_usm'] is False:
            gan_gt = self.gt
        
        #_, S1_IPR = self.model_Es1(self.lq,l1_gt)

        # optimize net_g
        for p in self.net_d.parameters():
            p.requires_grad = False

        self.optimizer_g.zero_grad()
        #self.output, pred_IPR_list = self.net_g(self.lq)
        self.output = self.net_g(self.lq)

        l_g_total = 0
        loss_dict = OrderedDict()
        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
            # pixel loss
            if self.cri_pix:
                l_g_pix = self.cri_pix(self.output, l1_gt)
                l_g_total += l_g_pix
                loss_dict['l_g_pix'] = l_g_pix
            

            # perceptual loss
            if self.cri_perceptual:
                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
                if l_g_percep is not None:
                    l_g_total += l_g_percep
                    loss_dict['l_g_percep'] = l_g_percep
                if l_g_style is not None:
                    l_g_total += l_g_style
                    loss_dict['l_g_style'] = l_g_style
            # gan loss
            fake_g_pred = self.net_d(self.output)
            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
            l_g_total += l_g_gan
            loss_dict['l_g_gan'] = l_g_gan

            l_g_total.backward()
            self.optimizer_g.step()

        # optimize net_d
        for p in self.net_d.parameters():
            p.requires_grad = True

        self.optimizer_d.zero_grad()
        # real
        real_d_pred = self.net_d(gan_gt)
        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
        loss_dict['l_d_real'] = l_d_real
        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
        l_d_real.backward()
        # fake
        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
        loss_dict['l_d_fake'] = l_d_fake
        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
        l_d_fake.backward()
        self.optimizer_d.step()

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

        self.log_dict = self.reduce_loss_dict(loss_dict)


================================================
FILE: RealSR/VmambaIR/models/MambaRealSR_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.sr_model import SRModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from torch.nn import functional as F
from collections import OrderedDict
from VmambaIR.models import lr_scheduler as lr_scheduler

class Mixing_Augment:
    def __init__(self, mixup_beta, use_identity, device):
        self.dist = torch.distributions.beta.Beta(torch.tensor([mixup_beta]), torch.tensor([mixup_beta]))
        self.device = device

        self.use_identity = use_identity

        self.augments = [self.mixup]

    def mixup(self, target, input_):
        lam = self.dist.rsample((1,1)).item()
    
        r_index = torch.randperm(target.size(0)).to(self.device)
    
        target = lam * target + (1-lam) * target[r_index, :]
        input_ = lam * input_ + (1-lam) * input_[r_index, :]
    
        return target, input_

    def __call__(self, target, input_):
        if self.use_identity:
            augment = random.randint(0, len(self.augments))
            if augment < len(self.augments):
                target, input_ = self.augments[augment](target, input_)
        else:
            augment = random.randint(0, len(self.augments)-1)
            target, input_ = self.augments[augment](target, input_)
        return target, input_

@MODEL_REGISTRY.register()
class MambaRealSR(SRModel):
    """
    It is trained without GAN losses.
    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaRealSR, self).__init__(opt)
        self.scale = self.opt.get('scale', 1)
        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)


    def setup_schedulers(self):
        """Set up schedulers."""
        train_opt = self.opt['train']
        scheduler_type = train_opt['scheduler'].pop('type')
        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.MultiStepRestartLR(optimizer,
                                                    **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingWarmupRestarts':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingWarmupRestarts(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartCyclicLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartCyclicLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'TrueCosineAnnealingLR':
            print('..', 'cosineannealingLR')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingLRWithRestart':
            print('..', 'CosineAnnealingLR_With_Restart')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingLRWithRestart(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'LinearLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.LinearLR(
                        optimizer, train_opt['total_iter']))
        elif scheduler_type == 'VibrateLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.VibrateLR(
                        optimizer, train_opt['total_iter']))
        else:
            raise NotImplementedError(
                f'Scheduler {scheduler_type} is not implemented yet.')


    @torch.no_grad()
    def _dequeue_and_enqueue(self):
        """It is the training pair pool for increasing the diversity in a batch.

        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
        to increase the degradation diversity in a batch.
        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

            self.lq = lq_dequeue
            self.gt = gt_dequeue
        else:
            # only do enqueue
            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
            self.queue_ptr = self.queue_ptr + b

    @torch.no_grad()
    def feed_data(self, data):
        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            # USM sharpen the GT images
            if self.opt['gt_usm'] is True:
                self.gt = self.usm_sharpener(self.gt)

            self.kernel1 = data['kernel1'].to(self.device)
            self.kernel2 = data['kernel2'].to(self.device)
            self.sinc_kernel = data['sinc_kernel'].to(self.device)

            ori_h, ori_w = self.gt.size()[2:4]

            # ----------------------- The first degradation process ----------------------- #
            # blur
            out = filter2D(self.gt, self.kernel1)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
            # blur
            if np.random.uniform() < self.opt['second_blur_prob']:
                out = filter2D(out, self.kernel2)
            # random resize
            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
            if updown_type == 'up':
                scale = np.random.uniform(1, self.opt['resize_range2'][1])
            elif updown_type == 'down':
                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
            else:
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
            else:
                out = random_add_poisson_noise_pt(
                    out,
                    scale_range=self.opt['poisson_scale_range2'],
                    gray_prob=gray_noise_prob,
                    clip=True,
                    rounds=False)

            # JPEG compression + the final sinc filter
            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
            # as one operation.
            # We consider two orders:
            #   1. [resize back + sinc filter] + JPEG compression
            #   2. JPEG compression + [resize back + sinc filter]
            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
            if np.random.uniform() < 0.5:
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
            else:
                # JPEG compression
                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
                out = torch.clamp(out, 0, 1)
                out = self.jpeger(out, quality=jpeg_p)
                # resize back + the final sinc filter
                mode = random.choice(['area', 'bilinear', 'bicubic'])
                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
                out = filter2D(out, self.sinc_kernel)

            # clamp and round
            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.

            # random crop
            gt_size = self.opt['gt_size']
            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])

            # training pair pool
            self._dequeue_and_enqueue()
            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)
                # self.gt = self.usm_sharpener(self.gt)


    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaRealSR, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True

    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,gt,mod_pad_h,mod_pad_w

    def test(self):
        window_size = self.opt['val'].get('window_size', 0)
        if window_size:
            lq,gt,mod_pad_h,mod_pad_w=self.pad_test(window_size)
        else:
            lq=self.lq
            gt=self.gt
        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                self.output = self.net_g_ema(lq)
        else:
            self.net_g.eval()
            with torch.no_grad():
                self.output = self.net_g(lq)
            self.net_g.train()
        if window_size:
            scale = self.opt.get('scale', 1)
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]

    def optimize_parameters(self, current_iter):
        self.optimizer_g.zero_grad()
        #self.output, _ = self.net_g(self.lq, self.gt)
        self.output = self.net_g(self.lq)

        l_total = 0
        loss_dict = OrderedDict()
        # pixel loss
        if self.cri_pix:
            l_pix = self.cri_pix(self.output, self.gt)
            l_total += l_pix
            loss_dict['l_pix'] = l_pix
        # perceptual loss
        if self.cri_perceptual:
            l_percep, l_style = self.cri_perceptual(self.output, self.gt)
            if l_percep is not None:
                l_total += l_percep
                loss_dict['l_percep'] = l_percep
            if l_style is not None:
                l_total += l_style
                loss_dict['l_style'] = l_style

        l_total.backward()
        self.optimizer_g.step()

        self.log_dict = self.reduce_loss_dict(loss_dict)

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)


================================================
FILE: RealSR/VmambaIR/models/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import model modules for registry
# scan all the files that end with '_model.py' under the model folder
model_folder = osp.dirname(osp.abspath(__file__))
model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
# import all the model modules
_model_modules = [importlib.import_module(f'VmambaIR.models.{file_name}') for file_name in model_filenames]


================================================
FILE: RealSR/VmambaIR/models/lr_scheduler.py
================================================
import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
import torch


class MultiStepRestartLR(_LRScheduler):
    """ MultiStep with restarts learning rate scheme.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        restarts (list): Restart iterations. Default: [0].
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 milestones,
                 gamma=0.1,
                 restarts=(0, ),
                 restart_weights=(1, ),
                 last_epoch=-1):
        self.milestones = Counter(milestones)
        self.gamma = gamma
        self.restarts = restarts
        self.restart_weights = restart_weights
        assert len(self.restarts) == len(
            self.restart_weights), 'restarts and their weights do not match.'
        super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch in self.restarts:
            weight = self.restart_weights[self.restarts.index(self.last_epoch)]
            return [
                group['initial_lr'] * weight
                for group in self.optimizer.param_groups
            ]
        if self.last_epoch not in self.milestones:
            return [group['lr'] for group in self.optimizer.param_groups]
        return [
            group['lr'] * self.gamma**self.milestones[self.last_epoch]
            for group in self.optimizer.param_groups
        ]

class LinearLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(LinearLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter
        weight = (1 - process)
        # print('get lr ', [weight * group['initial_lr'] for group in self.optimizer.param_groups])
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

class VibrateLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(VibrateLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter

        f = 0.1
        if process < 3 / 8:
            f = 1 - process * 8 / 3
        elif process < 5 / 8:
            f = 0.2

        T = self.total_iter // 80
        Th = T // 2

        t = self.last_epoch % T

        f2 = t / Th
        if t >= Th:
            f2 = 2 - f2

        weight = f * f2

        if self.last_epoch < Th:
            weight = max(0.1, weight)

        # print('f {}, T {}, Th {}, t {}, f2 {}'.format(f, T, Th, t, f2))
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

def get_position_from_periods(iteration, cumulative_period):
    """Get the position from a period list.

    It will return the index of the right-closest number in the period list.
    For example, the cumulative_period = [100, 200, 300, 400],
    if iteration == 50, return 0;
    if iteration == 210, return 2;
    if iteration == 300, return 2.

    Args:
        iteration (int): Current iteration.
        cumulative_period (list[int]): Cumulative period list.

    Returns:
        int: The position of the right-closest number in the period list.
    """
    for i, period in enumerate(cumulative_period):
        if iteration <= period:
            return i


class CosineAnnealingRestartLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.

    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7

    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_min=0,
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_min = eta_min
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]

        return [
            self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]

class CosineAnnealingRestartCyclicLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.
    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7
    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.
    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_mins=(0, ),
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_mins = eta_mins
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartCyclicLR, self).__init__(optimizer, last_epoch)
        
    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]
        eta_min = self.eta_mins[idx]

        return [
            eta_min + current_weight * 0.5 * (base_lr - eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]


================================================
FILE: RealSR/VmambaIR/test.py
================================================
# flake8: noqa
import sys
import os.path as osp

root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
sys.path.append(root_path)

from basicsr.test import test_pipeline

import VmambaIR.archs
import VmambaIR.data
import VmambaIR.models

if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    test_pipeline(root_path)


================================================
FILE: RealSR/VmambaIR/train.py
================================================
# flake8: noqa
import sys
import os.path as osp

root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
sys.path.append(root_path)

from VmambaIR.train_pipeline import train_pipeline

import VmambaIR.archs
import VmambaIR.data
import VmambaIR.models
import VmambaIR.losses
import warnings

warnings.filterwarnings("ignore")

if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    train_pipeline(root_path)


================================================
FILE: RealSR/VmambaIR/train_pipeline.py
================================================
import datetime
import logging
import math
import time
import torch
from os import path as osp

from basicsr.data import build_dataloader, build_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from basicsr.models import build_model
from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str,
                           init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir)
from basicsr.utils.options import copy_opt_file, dict2str, parse_options
import numpy as np
import random

#import inspect
#model_file_path = inspect.getfile(parse_options)
#print('model path:',model_file_path)


def init_tb_loggers(opt):
    # initialize wandb logger before tensorboard logger to allow proper sync
    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project')
                                                     is not None) and ('debug' not in opt['name']):
        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
        init_wandb_logger(opt)
    tb_logger = None
    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
        tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name']))
    return tb_logger


def create_train_val_dataloader(opt, logger):
    # create train and val dataloaders
    train_loader, val_loaders = None, []
    for phase, dataset_opt in opt['datasets'].items():
        if phase == 'train':
            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
            train_set = build_dataset(dataset_opt)
            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
            train_loader = build_dataloader(
                train_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=train_sampler,
                seed=opt['manual_seed'])

            num_iter_per_epoch = math.ceil(
                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
            total_iters = int(opt['train']['total_iter'])
            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
            logger.info('Training statistics:'
                        f'\n\tNumber of train images: {len(train_set)}'
                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
        elif phase.split('_')[0] == 'val':
            val_set = build_dataset(dataset_opt)
            val_loader = build_dataloader(
                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}')
            val_loaders.append(val_loader)
        else:
            raise ValueError(f'Dataset phase {phase} is not recognized.')

    return train_loader, train_sampler, val_loaders, total_epochs, total_iters


def load_resume_state(opt):
    resume_state_path = None
    if opt['auto_resume']:
        state_path = osp.join('experiments', opt['name'], 'training_states')
        if osp.isdir(state_path):
            states = list(scandir(state_path, suffix='state', recursive=False, full_path=False))
            if len(states) != 0:
                states = [float(v.split('.state')[0]) for v in states]
                resume_state_path = osp.join(state_path, f'{max(states):.0f}.state')
                opt['path']['resume_state'] = resume_state_path
    else:
        if opt['path'].get('resume_state'):
            resume_state_path = opt['path']['resume_state']

    if resume_state_path is None:
        resume_state = None
    else:
        device_id = torch.cuda.current_device()
        resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id))
        check_resume(opt, resume_state['iter'])
    return resume_state


def train_pipeline(root_path):
    # parse options, set distributed setting, set random seed
    opt, args = parse_options(root_path, is_train=True)
    opt['root_path'] = root_path

    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    # load resume states if necessary
    resume_state = load_resume_state(opt)
    # mkdir for experiments and logger
    if resume_state is None:
        make_exp_dirs(opt)
        if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0:
            mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name']))

    # copy the yml file to the experiment root
    copy_opt_file(args.opt, opt['path']['experiments_root'])

    # WARNING: should not use get_root_logger in the above codes, including the called functions
    # Otherwise the logger will not be properly initialized
    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log")
    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
    logger.info(get_env_info())
    logger.info(dict2str(opt))
    # initialize wandb and tb loggers
    tb_logger = init_tb_loggers(opt)

    # create train and validation dataloaders
    result = create_train_val_dataloader(opt, logger)
    train_loader, train_sampler, val_loaders, total_epochs, total_iters = result

    # create model
    model = build_model(opt)
    if resume_state:  # resume training
        model.resume_training(resume_state)  # handle optimizers and schedulers
        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.")
        start_epoch = resume_state['epoch']
        current_iter = resume_state['iter']
    else:
        start_epoch = 0
        current_iter = 0

    # create message logger (formatted outputs)
    msg_logger = MessageLogger(opt, current_iter, tb_logger)

    # dataloader prefetcher
    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
    if prefetch_mode is None or prefetch_mode == 'cpu':
        prefetcher = CPUPrefetcher(train_loader)
    elif prefetch_mode == 'cuda':
        prefetcher = CUDAPrefetcher(train_loader, opt)
        logger.info(f'Use {prefetch_mode} prefetch dataloader')
        if opt['datasets']['train'].get('pin_memory') is not True:
            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
    else:
        raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.")


    # iters = opt['datasets']['train'].get('iters')
    # batch_size = opt['datasets']['train'].get('batch_size_per_gpu')
    # mini_batch_sizes = opt['datasets']['train'].get('mini_batch_sizes')
    # gt_size = opt['datasets']['train'].get('gt_size')
    # mini_gt_sizes = opt['datasets']['train'].get('gt_sizes')

    # groups = np.array([sum(iters[0:i + 1]) for i in range(0, len(iters))])
    # logger_j = [True] * len(groups)
    # training
    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
    # for val_loader in val_loaders:
    #     model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
    data_timer, iter_timer = AvgTimer(), AvgTimer()
    start_time = time.time()

    for epoch in range(start_epoch, total_epochs + 1):
        train_sampler.set_epoch(epoch)
        prefetcher.reset()
        train_data = prefetcher.next()

        while train_data is not None:
            data_timer.record()

            current_iter += 1
            if current_iter > total_iters:
                break
            # update learning rate
            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
         


            # training
            # model.feed_data({'lq': lq, 'gt':gt})
            model.feed_data(train_data)
            model.optimize_parameters(current_iter)
            iter_timer.record()
            if current_iter == 1:
                # reset start time in msg_logger for more accurate eta_time
                # not work in resume mode
                msg_logger.reset_start_time()
            # log
            if current_iter % opt['logger']['print_freq'] == 0:
                log_vars = {'epoch': epoch, 'iter': current_iter}
                log_vars.update({'lrs': model.get_current_learning_rate()})
                log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()})
                log_vars.update(model.get_current_log())
                msg_logger(log_vars)

            # save models and training states
            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
                logger.info('Saving models and training states.')
                model.save(epoch, current_iter)

            # validation
            if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0):
                if len(val_loaders) > 1:
                    logger.warning('Multiple validation datasets are *only* supported by SRModel.')
                for val_loader in val_loaders:
                    model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])

            data_timer.start()
            iter_timer.start()
            train_data = prefetcher.next()
        # end of iter

    # end of epoch

    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
    logger.info(f'End of training. Time consumed: {consumed_time}')
    logger.info('Save the latest model.')
    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
    if opt.get('val') is not None:
        for val_loader in val_loaders:
            model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
    if tb_logger:
        tb_logger.close()


if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    train_pipeline(root_path)

================================================
FILE: RealSR/VmambaIR/utils/__init__.py
================================================
from .file_client import FileClient
from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img, padding, padding_DP, imfrombytesDP
from .logger import (MessageLogger, get_env_info, get_root_logger,
                     init_tb_logger, init_wandb_logger)
from .misc import (check_resume, get_time_str, make_exp_dirs, mkdir_and_rename,
                   scandir, scandir_SIDD, set_random_seed, sizeof_fmt)
from .create_lmdb import (create_lmdb_for_reds, create_lmdb_for_gopro, create_lmdb_for_rain13k)

__all__ = [
    # file_client.py
    'FileClient',
    # img_util.py
    'img2tensor',
    'tensor2img',
    'imfrombytes',
    'imwrite',
    'crop_border',
    # logger.py
    'MessageLogger',
    'init_tb_logger',
    'init_wandb_logger',
    'get_root_logger',
    'get_env_info',
    # misc.py
    'set_random_seed',
    'get_time_str',
    'mkdir_and_rename',
    'make_exp_dirs',
    'scandir',
    'check_resume',
    'sizeof_fmt',
    'padding',
    'padding_DP',
    'imfrombytesDP',
    'create_lmdb_for_reds',
    'create_lmdb_for_gopro',
    'create_lmdb_for_rain13k',
]


================================================
FILE: RealSR/VmambaIR/utils/bundle_submissions.py
================================================
 # Author: Tobias Plötz, TU Darmstadt (tobias.ploetz@visinf.tu-darmstadt.de)

 # This file is part of the implementation as described in the CVPR 2017 paper:
 # Tobias Plötz and Stefan Roth, Benchmarking Denoising Algorithms with Real Photographs.
 # Please see the file LICENSE.txt for the license governing this code.


import numpy as np
import scipy.io as sio
import os
import h5py

def bundle_submissions_raw(submission_folder,session):
    '''
    Bundles submission data for raw denoising

    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''

    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass

    israw = True
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

def bundle_submissions_srgb(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )



def bundle_submissions_srgb_v1(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

================================================
FILE: RealSR/VmambaIR/utils/create_lmdb.py
================================================
import argparse
from os import path as osp

from VmambaIR.utils import scandir
from VmambaIR.utils.lmdb_util import make_lmdb_from_imgs

def prepare_keys(folder_path, suffix='png'):
    """Prepare image path list and keys for DIV2K dataset.

    Args:
        folder_path (str): Folder path.

    Returns:
        list[str]: Image path list.
        list[str]: Key list.
    """
    print('Reading image path list ...')
    img_path_list = sorted(
        list(scandir(folder_path, suffix=suffix, recursive=False)))
    keys = [img_path.split('.{}'.format(suffix))[0] for img_path in sorted(img_path_list)]

    return img_path_list, keys

def create_lmdb_for_reds():
    folder_path = './datasets/REDS/val/sharp_300'
    lmdb_path = './datasets/REDS/val/sharp_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
    #
    folder_path = './datasets/REDS/val/blur_300'
    lmdb_path = './datasets/REDS/val/blur_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_sharp'
    lmdb_path = './datasets/REDS/train/train_sharp.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_blur_jpeg'
    lmdb_path = './datasets/REDS/train/train_blur_jpeg.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


def create_lmdb_for_gopro():
    folder_path = './datasets/GoPro/train/blur_crops'
    lmdb_path = './datasets/GoPro/train/blur_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/train/sharp_crops'
    lmdb_path = './datasets/GoPro/train/sharp_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/target'
    lmdb_path = './datasets/GoPro/test/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/input'
    lmdb_path = './datasets/GoPro/test/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_rain13k():
    folder_path = './datasets/Rain13k/train/input'
    lmdb_path = './datasets/Rain13k/train/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/Rain13k/train/target'
    lmdb_path = './datasets/Rain13k/train/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_SIDD():
    folder_path = './datasets/SIDD/train/input_crops'
    lmdb_path = './datasets/SIDD/train/input_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/train/gt_crops'
    lmdb_path = './datasets/SIDD/train/gt_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    #for val
    folder_path = './datasets/SIDD/val/input_crops'
    lmdb_path = './datasets/SIDD/val/input_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationNoisyBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationNoisyBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/val/gt_crops'
    lmdb_path = './datasets/SIDD/val/gt_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationGtBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationGtBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


================================================
FILE: RealSR/VmambaIR/utils/dist_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp


def init_dist(launcher, backend='nccl', **kwargs):
    if mp.get_start_method(allow_none=True) is None:
        mp.set_start_method('spawn')
    if launcher == 'pytorch':
        _init_dist_pytorch(backend, **kwargs)
    elif launcher == 'slurm':
        _init_dist_slurm(backend, **kwargs)
    else:
        raise ValueError(f'Invalid launcher type: {launcher}')


def _init_dist_pytorch(backend, **kwargs):
    rank = int(os.environ['RANK'])
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(rank % num_gpus)
    dist.init_process_group(backend=backend, **kwargs)


def _init_dist_slurm(backend, port=None):
    """Initialize slurm distributed training environment.

    If argument ``port`` is not specified, then the master port will be system
    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
    environment variable, then a default port ``29500`` will be used.

    Args:
        backend (str): Backend of torch.distributed.
        port (int, optional): Master port. Defaults to None.
    """
    proc_id = int(os.environ['SLURM_PROCID'])
    ntasks = int(os.environ['SLURM_NTASKS'])
    node_list = os.environ['SLURM_NODELIST']
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(proc_id % num_gpus)
    addr = subprocess.getoutput(
        f'scontrol show hostname {node_list} | head -n1')
    # specify master port
    if port is not None:
        os.environ['MASTER_PORT'] = str(port)
    elif 'MASTER_PORT' in os.environ:
        pass  # use MASTER_PORT in the environment variable
    else:
        # 29500 is torch.distributed default port
        os.environ['MASTER_PORT'] = '29500'
    os.environ['MASTER_ADDR'] = addr
    os.environ['WORLD_SIZE'] = str(ntasks)
    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
    os.environ['RANK'] = str(proc_id)
    dist.init_process_group(backend=backend)


def get_dist_info():
    if dist.is_available():
        initialized = dist.is_initialized()
    else:
        initialized = False
    if initialized:
        rank = dist.get_rank()
        world_size = dist.get_world_size()
    else:
        rank = 0
        world_size = 1
    return rank, world_size


def master_only(func):

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        rank, _ = get_dist_info()
        if rank == 0:
            return func(*args, **kwargs)

    return wrapper


================================================
FILE: RealSR/VmambaIR/utils/download_util.py
================================================
import math
import requests
from tqdm import tqdm

from .misc import sizeof_fmt


def download_file_from_google_drive(file_id, save_path):
    """Download files from google drive.

    Ref:
    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501

    Args:
        file_id (str): File id.
        save_path (str): Save path.
    """

    session = requests.Session()
    URL = 'https://docs.google.com/uc?export=download'
    params = {'id': file_id}

    response = session.get(URL, params=params, stream=True)
    token = get_confirm_token(response)
    if token:
        params['confirm'] = token
        response = session.get(URL, params=params, stream=True)

    # get file size
    response_file_size = session.get(
        URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
    if 'Content-Range' in response_file_size.headers:
        file_size = int(
            response_file_size.headers['Content-Range'].split('/')[1])
    else:
        file_size = None

    save_response_content(response, save_path, file_size)


def get_confirm_token(response):
    for key, value in response.cookies.items():
        if key.startswith('download_warning'):
            return value
    return None


def save_response_content(response,
                          destination,
                          file_size=None,
                          chunk_size=32768):
    if file_size is not None:
        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')

        readable_file_size = sizeof_fmt(file_size)
    else:
        pbar = None

    with open(destination, 'wb') as f:
        downloaded_size = 0
        for chunk in response.iter_content(chunk_size):
            downloaded_size += chunk_size
            if pbar is not None:
                pbar.update(1)
                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} '
                                     f'/ {readable_file_size}')
            if chunk:  # filter out keep-alive new chunks
                f.write(chunk)
        if pbar is not None:
            pbar.close()


================================================
FILE: RealSR/VmambaIR/utils/face_util.py
================================================
import cv2
import numpy as np
import os
import torch
from skimage import transform as trans

from VmambaIR.utils import imwrite

try:
    import dlib
except ImportError:
    print('Please install dlib before testing face restoration.'
          'Reference:　https://github.com/davisking/dlib')


class FaceRestorationHelper(object):
    """Helper for the face restoration pipeline."""

    def __init__(self, upscale_factor, face_size=512):
        self.upscale_factor = upscale_factor
        self.face_size = (face_size, face_size)

        # standard 5 landmarks for FFHQ faces with 1024 x 1024
        self.face_template = np.array([[686.77227723, 488.62376238],
                                       [586.77227723, 493.59405941],
                                       [337.91089109, 488.38613861],
                                       [437.95049505, 493.51485149],
                                       [513.58415842, 678.5049505]])
        self.face_template = self.face_template / (1024 // face_size)
        # for estimation the 2D similarity transformation
        self.similarity_trans = trans.SimilarityTransform()

        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.affine_matrices = []
        self.inverse_affine_matrices = []
        self.cropped_faces = []
        self.restored_faces = []
        self.save_png = True

    def init_dlib(self, detection_path, landmark5_path, landmark68_path):
        """Initialize the dlib detectors and predictors."""
        self.face_detector = dlib.cnn_face_detection_model_v1(detection_path)
        self.shape_predictor_5 = dlib.shape_predictor(landmark5_path)
        self.shape_predictor_68 = dlib.shape_predictor(landmark68_path)

    def free_dlib_gpu_memory(self):
        del self.face_detector
        del self.shape_predictor_5
        del self.shape_predictor_68

    def read_input_image(self, img_path):
        # self.input_img is Numpy array, (h, w, c) with RGB order
        self.input_img = dlib.load_rgb_image(img_path)

    def detect_faces(self,
                     img_path,
                     upsample_num_times=1,
                     only_keep_largest=False):
        """
        Args:
            img_path (str): Image path.
            upsample_num_times (int): Upsamples the image before running the
                face detector

        Returns:
            int: Number of detected faces.
        """
        self.read_input_image(img_path)
        det_faces = self.face_detector(self.input_img, upsample_num_times)
        if len(det_faces) == 0:
            print('No face detected. Try to increase upsample_num_times.')
        else:
            if only_keep_largest:
                print('Detect several faces and only keep the largest.')
                face_areas = []
                for i in range(len(det_faces)):
                    face_area = (det_faces[i].rect.right() -
                                 det_faces[i].rect.left()) * (
                                     det_faces[i].rect.bottom() -
                                     det_faces[i].rect.top())
                    face_areas.append(face_area)
                largest_idx = face_areas.index(max(face_areas))
                self.det_faces = [det_faces[largest_idx]]
            else:
                self.det_faces = det_faces
        return len(self.det_faces)

    def get_face_landmarks_5(self):
        for face in self.det_faces:
            shape = self.shape_predictor_5(self.input_img, face.rect)
            landmark = np.array([[part.x, part.y] for part in shape.parts()])
            self.all_landmarks_5.append(landmark)
        return len(self.all_landmarks_5)

    def get_face_landmarks_68(self):
        """Get 68 densemarks for cropped images.

        Should only have one face at most in the cropped image.
        """
        num_detected_face = 0
        for idx, face in enumerate(self.cropped_faces):
            # face detection
            det_face = self.face_detector(face, 1)  # TODO: can we remove it?
            if len(det_face) == 0:
                print(f'Cannot find faces in cropped image with index {idx}.')
                self.all_landmarks_68.append(None)
            else:
                if len(det_face) > 1:
                    print('Detect several faces in the cropped face. Use the '
                          ' largest one. Note that it will also cause overlap '
                          'during paste_faces_to_input_image.')
                    face_areas = []
                    for i in range(len(det_face)):
                        face_area = (det_face[i].rect.right() -
                                     det_face[i].rect.left()) * (
                                         det_face[i].rect.bottom() -
                                         det_face[i].rect.top())
                        face_areas.append(face_area)
                    largest_idx = face_areas.index(max(face_areas))
                    face_rect = det_face[largest_idx].rect
                else:
                    face_rect = det_face[0].rect
                shape = self.shape_predictor_68(face, face_rect)
                landmark = np.array([[part.x, part.y]
                                     for part in shape.parts()])
                self.all_landmarks_68.append(landmark)
                num_detected_face += 1

        return num_detected_face

    def warp_crop_faces(self,
                        save_cropped_path=None,
                        save_inverse_affine_path=None):
        """Get affine matrix, warp and cropped faces.

        Also get inverse affine matrix for post-processing.
        """
        for idx, landmark in enumerate(self.all_landmarks_5):
            # use 5 landmarks to get affine matrix
            self.similarity_trans.estimate(landmark, self.face_template)
            affine_matrix = self.similarity_trans.params[0:2, :]
            self.affine_matrices.append(affine_matrix)
            # warp and crop faces
            cropped_face = cv2.warpAffine(self.input_img, affine_matrix,
                                          self.face_size)
            self.cropped_faces.append(cropped_face)
            # save the cropped face
            if save_cropped_path is not None:
                path, ext = os.path.splitext(save_cropped_path)
                if self.save_png:
                    save_path = f'{path}_{idx:02d}.png'
                else:
                    save_path = f'{path}_{idx:02d}{ext}'

                imwrite(
                    cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path)

            # get inverse affine matrix
            self.similarity_trans.estimate(self.face_template,
                                           landmark * self.upscale_factor)
            inverse_affine = self.similarity_trans.params[0:2, :]
            self.inverse_affine_matrices.append(inverse_affine)
            # save inverse affine matrices
            if save_inverse_affine_path is not None:
                path, _ = os.path.splitext(save_inverse_affine_path)
                save_path = f'{path}_{idx:02d}.pth'
                torch.save(inverse_affine, save_path)

    def add_restored_face(self, face):
        self.restored_faces.append(face)

    def paste_faces_to_input_image(self, save_path):
        # operate in the BGR order
        input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR)
        h, w, _ = input_img.shape
        h_up, w_up = h * self.upscale_factor, w * self.upscale_factor
        # simply resize the background
        upsample_img = cv2.resize(input_img, (w_up, h_up))
        assert len(self.restored_faces) == len(self.inverse_affine_matrices), (
            'length of restored_faces and affine_matrices are different.')
        for restored_face, inverse_affine in zip(self.restored_faces,
                                                 self.inverse_affine_matrices):
            inv_restored = cv2.warpAffine(restored_face, inverse_affine,
                                          (w_up, h_up))
            mask = np.ones((*self.face_size, 3), dtype=np.float32)
            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
            # remove the black borders
            inv_mask_erosion = cv2.erode(
                inv_mask,
                np.ones((2 * self.upscale_factor, 2 * self.upscale_factor),
                        np.uint8))
            inv_restored_remove_border = inv_mask_erosion * inv_restored
            total_face_area = np.sum(inv_mask_erosion) // 3
            # compute the fusion edge based on the area of face
            w_edge = int(total_face_area**0.5) // 20
            erosion_radius = w_edge * 2
            inv_mask_center = cv2.erode(
                inv_mask_erosion,
                np.ones((erosion_radius, erosion_radius), np.uint8))
            blur_size = w_edge * 2
            inv_soft_mask = cv2.GaussianBlur(inv_mask_center,
                                             (blur_size + 1, blur_size + 1), 0)
            upsample_img = inv_soft_mask * inv_restored_remove_border + (
                1 - inv_soft_mask) * upsample_img
        if self.save_png:
            save_path = save_path.replace('.jpg',
                                          '.png').replace('.jpeg', '.png')
        imwrite(upsample_img.astype(np.uint8), save_path)

    def clean_all(self):
        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.restored_faces = []
        self.affine_matrices = []
        self.cropped_faces = []
        self.inverse_affine_matrices = []


================================================
FILE: RealSR/VmambaIR/utils/file_client.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py  # noqa: E501
from abc import ABCMeta, abstractmethod


class BaseStorageBackend(metaclass=ABCMeta):
    """Abstract class of storage backends.

    All backends need to implement two apis: ``get()`` and ``get_text()``.
    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
    as texts.
    """

    @abstractmethod
    def get(self, filepath):
        pass

    @abstractmethod
    def get_text(self, filepath):
        pass


class MemcachedBackend(BaseStorageBackend):
    """Memcached storage backend.

    Attributes:
        server_list_cfg (str): Config file for memcached server list.
        client_cfg (str): Config file for memcached client.
        sys_path (str | None): Additional path to be appended to `sys.path`.
            Default: None.
    """

    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
        if sys_path is not None:
            import sys
            sys.path.append(sys_path)
        try:
            import mc
        except ImportError:
            raise ImportError(
                'Please install memcached to enable MemcachedBackend.')

        self.server_list_cfg = server_list_cfg
        self.client_cfg = client_cfg
        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
                                                      self.client_cfg)
        # mc.pyvector servers as a point which points to a memory cache
        self._mc_buffer = mc.pyvector()

    def get(self, filepath):
        filepath = str(filepath)
        import mc
        self._client.Get(filepath, self._mc_buffer)
        value_buf = mc.ConvertBuffer(self._mc_buffer)
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class HardDiskBackend(BaseStorageBackend):
    """Raw hard disks storage backend."""

    def get(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'rb') as f:
            value_buf = f.read()
        return value_buf

    def get_text(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'r') as f:
            value_buf = f.read()
        return value_buf


class LmdbBackend(BaseStorageBackend):
    """Lmdb storage backend.

    Args:
        db_paths (str | list[str]): Lmdb database paths.
        client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
        readonly (bool, optional): Lmdb environment parameter. If True,
            disallow any write operations. Default: True.
        lock (bool, optional): Lmdb environment parameter. If False, when
            concurrent access occurs, do not lock the database. Default: False.
        readahead (bool, optional): Lmdb environment parameter. If False,
            disable the OS filesystem readahead mechanism, which may improve
            random read performance when a database is larger than RAM.
            Default: False.

    Attributes:
        db_paths (list): Lmdb database path.
        _client (list): A list of several lmdb envs.
    """

    def __init__(self,
                 db_paths,
                 client_keys='default',
                 readonly=True,
                 lock=False,
                 readahead=False,
                 **kwargs):
        try:
            import lmdb
        except ImportError:
            raise ImportError('Please install lmdb to enable LmdbBackend.')

        if isinstance(client_keys, str):
            client_keys = [client_keys]

        if isinstance(db_paths, list):
            self.db_paths = [str(v) for v in db_paths]
        elif isinstance(db_paths, str):
            self.db_paths = [str(db_paths)]
        assert len(client_keys) == len(self.db_paths), (
            'client_keys and db_paths should have the same length, '
            f'but received {len(client_keys)} and {len(self.db_paths)}.')

        self._client = {}

        for client, path in zip(client_keys, self.db_paths):
            self._client[client] = lmdb.open(
                path,
                readonly=readonly,
                lock=lock,
                readahead=readahead,
                map_size=8*1024*10485760,
                # max_readers=1,
                **kwargs)

    def get(self, filepath, client_key):
        """Get values according to the filepath from one lmdb named client_key.

        Args:
            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
            client_key (str): Used for distinguishing differnet lmdb envs.
        """
        filepath = str(filepath)
        assert client_key in self._client, (f'client_key {client_key} is not '
                                            'in lmdb clients.')
        client = self._client[client_key]
        with client.begin(write=False) as txn:
            value_buf = txn.get(filepath.encode('ascii'))
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class FileClient(object):
    """A general file client to access files in different backend.

    The client loads a file or text in a specified backend from its path
    and return it as a binary file. it can also register other backend
    accessor with a given name and backend class.

    Attributes:
        backend (str): The storage backend type. Options are "disk",
            "memcached" and "lmdb".
        client (:obj:`BaseStorageBackend`): The backend object.
    """

    _backends = {
        'disk': HardDiskBackend,
        'memcached': MemcachedBackend,
        'lmdb': LmdbBackend,
    }

    def __init__(self, backend='disk', **kwargs):
        if backend not in self._backends:
            raise ValueError(
                f'Backend {backend} is not supported. Currently supported ones'
                f' are {list(self._backends.keys())}')
        self.backend = backend
        self.client = self._backends[backend](**kwargs)

    def get(self, filepath, client_key='default'):
        # client_key is used only for lmdb, where different fileclients have
        # different lmdb environments.
        if self.backend == 'lmdb':
            return self.client.get(filepath, client_key)
        else:
            return self.client.get(filepath)

    def get_text(self, filepath):
        return self.client.get_text(filepath)


================================================
FILE: RealSR/VmambaIR/utils/flow_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py  # noqa: E501
import cv2
import numpy as np
import os


def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
    """Read an optical flow map.

    Args:
        flow_path (ndarray or str): Flow path.
        quantize (bool): whether to read quantized pair, if set to True,
            remaining args will be passed to :func:`dequantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.

    Returns:
        ndarray: Optical flow represented as a (h, w, 2) numpy array
    """
    if quantize:
        assert concat_axis in [0, 1]
        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
        if cat_flow.ndim != 2:
            raise IOError(f'{flow_path} is not a valid quantized flow file, '
                          f'its dimension is {cat_flow.ndim}.')
        assert cat_flow.shape[concat_axis] % 2 == 0
        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
        flow = dequantize_flow(dx, dy, *args, **kwargs)
    else:
        with open(flow_path, 'rb') as f:
            try:
                header = f.read(4).decode('utf-8')
            except Exception:
                raise IOError(f'Invalid flow file: {flow_path}')
            else:
                if header != 'PIEH':
                    raise IOError(f'Invalid flow file: {flow_path}, '
                                  'header does not contain PIEH')

            w = np.fromfile(f, np.int32, 1).squeeze()
            h = np.fromfile(f, np.int32, 1).squeeze()
            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))

    return flow.astype(np.float32)


def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
    """Write optical flow to file.

    If the flow is not quantized, it will be saved as a .flo file losslessly,
    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
    will be concatenated horizontally into a single image if quantize is True.)

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        filename (str): Output filepath.
        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
            images. If set to True, remaining args will be passed to
            :func:`quantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.
    """
    if not quantize:
        with open(filename, 'wb') as f:
            f.write('PIEH'.encode('utf-8'))
            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
            flow = flow.astype(np.float32)
            flow.tofile(f)
            f.flush()
    else:
        assert concat_axis in [0, 1]
        dx, dy = quantize_flow(flow, *args, **kwargs)
        dxdy = np.concatenate((dx, dy), axis=concat_axis)
        os.makedirs(filename, exist_ok=True)
        cv2.imwrite(dxdy, filename)


def quantize_flow(flow, max_val=0.02, norm=True):
    """Quantize flow to [0, 255].

    After this step, the size of flow will be much smaller, and can be
    dumped as jpeg images.

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        max_val (float): Maximum value of flow, values beyond
                        [-max_val, max_val] will be truncated.
        norm (bool): Whether to divide flow values by image width/height.

    Returns:
        tuple[ndarray]: Quantized dx and dy.
    """
    h, w, _ = flow.shape
    dx = flow[..., 0]
    dy = flow[..., 1]
    if norm:
        dx = dx / w  # avoid inplace operations
        dy = dy / h
    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
    flow_comps = [
        quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]
    ]
    return tuple(flow_comps)


def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
    """Recover from quantized flow.

    Args:
        dx (ndarray): Quantized dx.
        dy (ndarray): Quantized dy.
        max_val (float): Maximum value used when quantizing.
        denorm (bool): Whether to multiply flow values with width/height.

    Returns:
        ndarray: Dequantized flow.
    """
    assert dx.shape == dy.shape
    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)

    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]

    if denorm:
        dx *= dx.shape[1]
        dy *= dx.shape[0]
    flow = np.dstack((dx, dy))
    return flow


def quantize(arr, min_val, max_val, levels, dtype=np.int64):
    """Quantize an array of (-inf, inf) to [0, levels-1].

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the quantized array.

    Returns:
        tuple: Quantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    arr = np.clip(arr, min_val, max_val) - min_val
    quantized_arr = np.minimum(
        np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)

    return quantized_arr


def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
    """Dequantize an array.

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the dequantized array.

    Returns:
        tuple: Dequantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val -
                                                   min_val) / levels + min_val

    return dequantized_arr


================================================
FILE: RealSR/VmambaIR/utils/img_util.py
================================================
import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    else:
        return _totensor(imgs, bgr2rgb, float32)


def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
    """Convert torch Tensors into image numpy arrays.

    After clamping to [min, max], values will be normalized to [0, 1].

    Args:
        tensor (Tensor or list[Tensor]): Accept shapes:
            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
            2) 3D Tensor of shape (3/1 x H x W);
            3) 2D Tensor of shape (H x W).
            Tensor channel should be in RGB order.
        rgb2bgr (bool): Whether to change rgb to bgr.
        out_type (numpy type): output types. If ``np.uint8``, transform outputs
            to uint8 type with range [0, 255]; otherwise, float type with
            range [0, 1]. Default: ``np.uint8``.
        min_max (tuple[int]): min and max values for clamp.

    Returns:
        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
        shape (H x W). The channel order is BGR.
    """
    if not (torch.is_tensor(tensor) or
            (isinstance(tensor, list)
             and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(
            f'tensor or list of tensors expected, got {type(tensor)}')

    if torch.is_tensor(tensor):
        tensor = [tensor]
    result = []
    for _tensor in tensor:
        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])

        n_dim = _tensor.dim()
        if n_dim == 4:
            img_np = make_grid(
                _tensor, nrow=int(math.sqrt(_tensor.size(0))),
                normalize=False).numpy()
            img_np = img_np.transpose(1, 2, 0)
            if rgb2bgr:
                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 3:
            img_np = _tensor.numpy()
            img_np = img_np.transpose(1, 2, 0)
            if img_np.shape[2] == 1:  # gray image
                img_np = np.squeeze(img_np, axis=2)
            else:
                if rgb2bgr:
                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 2:
            img_np = _tensor.numpy()
        else:
            raise TypeError('Only support 4D, 3D or 2D tensor. '
                            f'But received with dimension: {n_dim}')
        if out_type == np.uint8:
            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
            img_np = (img_np * 255.0).round()
        img_np = img_np.astype(out_type)
        result.append(img_np)
    if len(result) == 1:
        result = result[0]
    return result


def imfrombytes(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    imread_flags = {
        'color': cv2.IMREAD_COLOR,
        'grayscale': cv2.IMREAD_GRAYSCALE,
        'unchanged': cv2.IMREAD_UNCHANGED
    }
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, imread_flags[flag])
    if float32:
        img = img.astype(np.float32) / 255.
    return img

def imfrombytesDP(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, cv2.IMREAD_UNCHANGED)
    if float32:
        img = img.astype(np.float32) / 65535.
    return img

def padding(img_lq, img_gt, gt_size):
    h, w, _ = img_lq.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lq, img_gt

    img_lq = cv2.copyMakeBorder(img_lq, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt = cv2.copyMakeBorder(img_gt, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    if img_lq.ndim == 2:
        img_lq = np.expand_dims(img_lq, axis=2)
    if img_gt.ndim == 2:
        img_gt = np.expand_dims(img_gt, axis=2)
    return img_lq, img_gt

def padding_DP(img_lqL, img_lqR, img_gt, gt_size):
    h, w, _ = img_gt.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lqL, img_lqR, img_gt

    img_lqL = cv2.copyMakeBorder(img_lqL, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_lqR = cv2.copyMakeBorder(img_lqR, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt  = cv2.copyMakeBorder(img_gt,  0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    return img_lqL, img_lqR, img_gt

def imwrite(img, file_path, params=None, auto_mkdir=True):
    """Write image to file.

    Args:
        img (ndarray): Image array to be written.
        file_path (str): Image file path.
        params (None or list): Same as opencv's :func:`imwrite` interface.
        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
            whether to create it automatically.

    Returns:
        bool: Successful or not.
    """
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(file_path))
        os.makedirs(dir_name, exist_ok=True)
    return cv2.imwrite(file_path, img, params)


def crop_border(imgs, crop_border):
    """Crop borders of images.

    Args:
        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
        crop_border (int): Crop border for each end of height and weight.

    Returns:
        list[ndarray]: Cropped images.
    """
    if crop_border == 0:
        return imgs
    else:
        if isinstance(imgs, list):
            return [
                v[crop_border:-crop_border, crop_border:-crop_border, ...]
                for v in imgs
            ]
        else:
            return imgs[crop_border:-crop_border, crop_border:-crop_border,
                        ...]


================================================
FILE: RealSR/VmambaIR/utils/lmdb_util.py
================================================
import cv2
import lmdb
import sys
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def make_lmdb_from_imgs(data_path,
                        lmdb_path,
                        img_path_list,
                        keys,
                        batch=5000,
                        compress_level=1,
                        multiprocessing_read=False,
                        n_thread=40,
                        map_size=None):
    """Make lmdb from images.

    Contents of lmdb. The file structure is:
    example.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records 1)image name (with extension),
    2)image shape, and 3)compression level, separated by a white space.

    For example, the meta information could be:
    `000_00000000.png (720,1280,3) 1`, which means:
    1) image name (with extension): 000_00000000.png;
    2) image shape: (720,1280,3);
    3) compression level: 1

    We use the image name without extension as the lmdb key.

    If `multiprocessing_read` is True, it will read all the images to memory
    using multiprocessing. Thus, your server needs to have enough memory.

    Args:
        data_path (str): Data path for reading images.
        lmdb_path (str): Lmdb save path.
        img_path_list (str): Image path list.
        keys (str): Used for lmdb keys.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
        multiprocessing_read (bool): Whether use multiprocessing to read all
            the images to memory. Default: False.
        n_thread (int): For multiprocessing.
        map_size (int | None): Map size for lmdb env. If None, use the
            estimated size from images. Default: None
    """

    assert len(img_path_list) == len(keys), (
        'img_path_list and keys should have the same length, '
        f'but got {len(img_path_list)} and {len(keys)}')
    print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
    print(f'Totoal images: {len(img_path_list)}')
    if not lmdb_path.endswith('.lmdb'):
        raise ValueError("lmdb_path must end with '.lmdb'.")
    if osp.exists(lmdb_path):
        print(f'Folder {lmdb_path} already exists. Exit.')
        sys.exit(1)

    if multiprocessing_read:
        # read all the images to memory (multiprocessing)
        dataset = {}  # use dict to keep the order for multiprocessing
        shapes = {}
        print(f'Read images with multiprocessing, #thread: {n_thread} ...')
        pbar = tqdm(total=len(img_path_list), unit='image')

        def callback(arg):
            """get the image data and update pbar."""
            key, dataset[key], shapes[key] = arg
            pbar.update(1)
            pbar.set_description(f'Read {key}')

        pool = Pool(n_thread)
        for path, key in zip(img_path_list, keys):
            pool.apply_async(
                read_img_worker,
                args=(osp.join(data_path, path), key, compress_level),
                callback=callback)
        pool.close()
        pool.join()
        pbar.close()
        print(f'Finish reading {len(img_path_list)} images.')

    # create lmdb environment
    if map_size is None:
        # obtain data size for one image
        img = cv2.imread(
            osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
        _, img_byte = cv2.imencode(
            '.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
        data_size_per_img = img_byte.nbytes
        print('Data size per image is: ', data_size_per_img)
        data_size = data_size_per_img * len(img_path_list)
        map_size = data_size * 10

    env = lmdb.open(lmdb_path, map_size=map_size)

    # write data to lmdb
    pbar = tqdm(total=len(img_path_list), unit='chunk')
    txn = env.begin(write=True)
    txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
    for idx, (path, key) in enumerate(zip(img_path_list, keys)):
        pbar.update(1)
        pbar.set_description(f'Write {key}')
        key_byte = key.encode('ascii')
        if multiprocessing_read:
            img_byte = dataset[key]
            h, w, c = shapes[key]
        else:
            _, img_byte, img_shape = read_img_worker(
                osp.join(data_path, path), key, compress_level)
            h, w, c = img_shape

        txn.put(key_byte, img_byte)
        # write meta information
        txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
        if idx % batch == 0:
            txn.commit()
            txn = env.begin(write=True)
    pbar.close()
    txn.commit()
    env.close()
    txt_file.close()
    print('\nFinish writing lmdb.')


def read_img_worker(path, key, compress_level):
    """Read image worker.

    Args:
        path (str): Image path.
        key (str): Image key.
        compress_level (int): Compress level when encoding images.

    Returns:
        str: Image key.
        byte: Image byte.
        tuple[int]: Image shape.
    """

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    if img.ndim == 2:
        h, w = img.shape
        c = 1
    else:
        h, w, c = img.shape
    _, img_byte = cv2.imencode('.png', img,
                               [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
    return (key, img_byte, (h, w, c))


class LmdbMaker():
    """LMDB Maker.

    Args:
        lmdb_path (str): Lmdb save path.
        map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
    """

    def __init__(self,
                 lmdb_path,
                 map_size=1024**4,
                 batch=5000,
                 compress_level=1):
        if not lmdb_path.endswith('.lmdb'):
            raise ValueError("lmdb_path must end with '.lmdb'.")
        if osp.exists(lmdb_path):
            print(f'Folder {lmdb_path} already exists. Exit.')
            sys.exit(1)

        self.lmdb_path = lmdb_path
        self.batch = batch
        self.compress_level = compress_level
        self.env = lmdb.open(lmdb_path, map_size=map_size)
        self.txn = self.env.begin(write=True)
        self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
        self.counter = 0

    def put(self, img_byte, key, img_shape):
        self.counter += 1
        key_byte = key.encode('ascii')
        self.txn.put(key_byte, img_byte)
        # write meta information
        h, w, c = img_shape
        self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
        if self.counter % self.batch == 0:
            self.txn.commit()
            self.txn = self.env.begin(write=True)

    def close(self):
        self.txn.commit()
        self.env.close()
        self.txt_file.close()


================================================
FILE: RealSR/VmambaIR/utils/logger.py
================================================
import datetime
import logging
import time

from .dist_util import get_dist_info, master_only

initialized_logger = {}


class MessageLogger():
    """Message logger for printing.

    Args:
        opt (dict): Config. It contains the following keys:
            name (str): Exp name.
            logger (dict): Contains 'print_freq' (str) for logger interval.
            train (dict): Contains 'total_iter' (int) for total iters.
            use_tb_logger (bool): Use tensorboard logger.
        start_iter (int): Start iter. Default: 1.
        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
    """

    def __init__(self, opt, start_iter=1, tb_logger=None):
        self.exp_name = opt['name']
        self.interval = opt['logger']['print_freq']
        self.start_iter = start_iter
        self.max_iters = opt['train']['total_iter']
        self.use_tb_logger = opt['logger']['use_tb_logger']
        self.tb_logger = tb_logger
        self.start_time = time.time()
        self.logger = get_root_logger()

    @master_only
    def __call__(self, log_vars):
        """Format logging message.

        Args:
            log_vars (dict): It contains the following keys:
                epoch (int): Epoch number.
                iter (int): Current iter.
                lrs (list): List for learning rates.

                time (float): Iter time.
                data_time (float): Data time for each iter.
        """
        # epoch, iter, learning rates
        epoch = log_vars.pop('epoch')
        current_iter = log_vars.pop('iter')
        lrs = log_vars.pop('lrs')

        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(')
        for v in lrs:
            message += f'{v:.3e},'
        message += ')] '

        # time and estimated time
        if 'time' in log_vars.keys():
            iter_time = log_vars.pop('time')
            data_time = log_vars.pop('data_time')

            total_time = time.time() - self.start_time
            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
            message += f'[eta: {eta_str}, '
            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '

        # other items, especially losses
        for k, v in log_vars.items():
            message += f'{k}: {v:.4e} '
            # tensorboard logger
            if self.use_tb_logger and 'debug' not in self.exp_name:
                if k.startswith('l_'):
                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
                else:
                    self.tb_logger.add_scalar(k, v, current_iter)
        self.logger.info(message)


@master_only
def init_tb_logger(log_dir):
    from torch.utils.tensorboard import SummaryWriter
    tb_logger = SummaryWriter(log_dir=log_dir)
    return tb_logger


@master_only
def init_wandb_logger(opt):
    """We now only use wandb to sync tensorboard log."""
    import wandb
    logger = logging.getLogger('basicsr')

    project = opt['logger']['wandb']['project']
    resume_id = opt['logger']['wandb'].get('resume_id')
    if resume_id:
        wandb_id = resume_id
        resume = 'allow'
        logger.warning(f'Resume wandb logger with id={wandb_id}.')
    else:
        wandb_id = wandb.util.generate_id()
        resume = 'never'

    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)

    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')


def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
    """Get the root logger.

    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If `log_file` is specified, a FileHandler will
    also be added.

    Args:
        logger_name (str): root logger name. Default: 'basicsr'.
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.

    Returns:
        logging.Logger: The root logger.
    """
    logger = logging.getLogger(logger_name)
    # if the logger has been initialized, just return it
    if logger_name in initialized_logger:
        return logger

    format_str = '%(asctime)s %(levelname)s: %(message)s'
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(logging.Formatter(format_str))
    logger.addHandler(stream_handler)
    logger.propagate = False
    rank, _ = get_dist_info()
    if rank != 0:
        logger.setLevel('ERROR')
    elif log_file is not None:
        logger.setLevel(log_level)
        # add file handler
        file_handler = logging.FileHandler(log_file, 'w')
        file_handler.setFormatter(logging.Formatter(format_str))
        file_handler.setLevel(log_level)
        logger.addHandler(file_handler)
    initialized_logger[logger_name] = True
    return logger


def get_env_info():
    """Get environment information.

    Currently, only log the software version.
    """
    import torch
    import torchvision

    from basicsr.version import __version__
    msg = r"""
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    """
    msg += ('\nVersion Information: '
            f'\n\tBasicSR: {__version__}'
            f'\n\tPyTorch: {torch.__version__}'
            f'\n\tTorchVision: {torchvision.__version__}')
    return msg

================================================
FILE: RealSR/VmambaIR/utils/matlab_functions.py
================================================
import math
import numpy as np
import torch


def cubic(x):
    """cubic function used for calculate_weights_indices."""
    absx = torch.abs(x)
    absx2 = absx**2
    absx3 = absx**3
    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx +
                                      2) * (((absx > 1) *
                                             (absx <= 2)).type_as(absx))


def calculate_weights_indices(in_length, out_length, scale, kernel,
                              kernel_width, antialiasing):
    """Calculate weights and indices, used for imresize function.

    Args:
        in_length (int): Input length.
        out_length (int): Output length.
        scale (float): Scale factor.
        kernel_width (int): Kernel width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
    """

    if (scale < 1) and antialiasing:
        # Use a modified kernel (larger kernel width) to simultaneously
        # interpolate and antialias
        kernel_width = kernel_width / scale

    # Output-space coordinates
    x = torch.linspace(1, out_length, out_length)

    # Input-space coordinates. Calculate the inverse mapping such that 0.5
    # in output space maps to 0.5 in input space, and 0.5 + scale in output
    # space maps to 1.5 in input space.
    u = x / scale + 0.5 * (1 - 1 / scale)

    # What is the left-most pixel that can be involved in the computation?
    left = torch.floor(u - kernel_width / 2)

    # What is the maximum number of pixels that can be involved in the
    # computation?  Note: it's OK to use an extra pixel here; if the
    # corresponding weights are all zero, it will be eliminated at the end
    # of this function.
    p = math.ceil(kernel_width) + 2

    # The indices of the input pixels involved in computing the k-th output
    # pixel are in row k of the indices matrix.
    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(
        0, p - 1, p).view(1, p).expand(out_length, p)

    # The weights used to compute the k-th output pixel are in row k of the
    # weights matrix.
    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices

    # apply cubic kernel
    if (scale < 1) and antialiasing:
        weights = scale * cubic(distance_to_center * scale)
    else:
        weights = cubic(distance_to_center)

    # Normalize the weights matrix so that each row sums to 1.
    weights_sum = torch.sum(weights, 1).view(out_length, 1)
    weights = weights / weights_sum.expand(out_length, p)

    # If a column in weights is all zero, get rid of it. only consider the
    # first and last column.
    weights_zero_tmp = torch.sum((weights == 0), 0)
    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 1, p - 2)
        weights = weights.narrow(1, 1, p - 2)
    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 0, p - 2)
        weights = weights.narrow(1, 0, p - 2)
    weights = weights.contiguous()
    indices = indices.contiguous()
    sym_len_s = -indices.min() + 1
    sym_len_e = indices.max() - in_length
    indices = indices + sym_len_s - 1
    return weights, indices, int(sym_len_s), int(sym_len_e)


@torch.no_grad()
def imresize(img, scale, antialiasing=True):
    """imresize function same as MATLAB.

    It now only supports bicubic.
    The same scale applies for both height and width.

    Args:
        img (Tensor | Numpy array):
            Tensor: Input image with shape (c, h, w), [0, 1] range.
            Numpy: Input image with shape (h, w, c), [0, 1] range.
        scale (float): Scale factor. The same scale applies for both height
            and width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
            Default: True.

    Returns:
        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
    """
    if type(img).__module__ == np.__name__:  # numpy type
        numpy_type = True
        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
    else:
        numpy_type = False

    in_c, in_h, in_w = img.size()
    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
    kernel_width = 4
    kernel = 'cubic'

    # get weights and indices
    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(
        in_h, out_h, scale, kernel, kernel_width, antialiasing)
    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(
        in_w, out_w, scale, kernel, kernel_width, antialiasing)
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)

    sym_patch = img[:, :sym_len_hs, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)

    sym_patch = img[:, -sym_len_he:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(in_c, out_h, in_w)
    kernel_width = weights_h.size(1)
    for i in range(out_h):
        idx = int(indices_h[i][0])
        for j in range(in_c):
            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(
                0, 1).mv(weights_h[i])

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)

    sym_patch = out_1[:, :, :sym_len_ws]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, :, -sym_len_we:]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(in_c, out_h, out_w)
    kernel_width = weights_w.size(1)
    for i in range(out_w):
        idx = int(indices_w[i][0])
        for j in range(in_c):
            out_2[j, :, i] = out_1_aug[j, :,
                                       idx:idx + kernel_width].mv(weights_w[i])

    if numpy_type:
        out_2 = out_2.numpy().transpose(1, 2, 0)
    return out_2


def rgb2ycbcr(img, y_only=False):
    """Convert a RGB image to YCbCr image.

    This function produces the same results as Matlab's `rgb2ycbcr` function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
    else:
        out_img = np.matmul(
            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
                  [24.966, 112.0, -18.214]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def bgr2ycbcr(img, y_only=False):
    """Convert a BGR image to YCbCr image.

    The bgr version of rgb2ycbcr.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
    else:
        out_img = np.matmul(
            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2rgb(img):
    """Convert a YCbCr image to RGB image.

    This function produces the same results as Matlab's ycbcr2rgb function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted RGB image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0, -0.00153632, 0.00791071],
                              [0.00625893, -0.00318811, 0]]) * 255.0 + [
                                  -222.921, 135.576, -276.836
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2bgr(img):
    """Convert a YCbCr image to BGR image.

    The bgr version of ycbcr2rgb.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted BGR image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0.00791071, -0.00153632, 0],
                              [0, -0.00318811, 0.00625893]]) * 255.0 + [
                                  -276.836, 135.576, -222.921
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def _convert_input_type_range(img):
    """Convert the type and range of the input image.

    It converts the input image to np.float32 type and range of [0, 1].
    It is mainly used for pre-processing the input image in colorspace
    convertion functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        (ndarray): The converted image with type of np.float32 and range of
            [0, 1].
    """
    img_type = img.dtype
    img = img.astype(np.float32)
    if img_type == np.float32:
        pass
    elif img_type == np.uint8:
        img /= 255.
    else:
        raise TypeError('The img type should be np.float32 or np.uint8, '
                        f'but got {img_type}')
    return img


def _convert_output_type_range(img, dst_type):
    """Convert the type and range of the image according to dst_type.

    It converts the image to desired type and range. If `dst_type` is np.uint8,
    images will be converted to np.uint8 type with range [0, 255]. If
    `dst_type` is np.float32, it converts the image to np.float32 type with
    range [0, 1].
    It is mainly used for post-processing images in colorspace convertion
    functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The image to be converted with np.float32 type and
            range [0, 255].
        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
            converts the image to np.uint8 type with range [0, 255]. If
            dst_type is np.float32, it converts the image to np.float32 type
            with range [0, 1].

    Returns:
        (ndarray): The converted image with desired type and range.
    """
    if dst_type not in (np.uint8, np.float32):
        raise TypeError('The dst_type should be np.float32 or np.uint8, '
                        f'but got {dst_type}')
    if dst_type == np.uint8:
        img = img.round()
    else:
        img /= 255.
    return img.astype(dst_type)


================================================
FILE: RealSR/VmambaIR/utils/misc.py
================================================
import numpy as np
import os
import random
import time
import torch
from os import path as osp

from .dist_util import master_only
from .logger import get_root_logger


def set_random_seed(seed):
    """Set random seeds."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def get_time_str():
    return time.strftime('%Y%m%d_%H%M%S', time.localtime())


def mkdir_and_rename(path):
    """mkdirs. If path exists, rename it with timestamp and create a new one.

    Args:
        path (str): Folder path.
    """
    if osp.exists(path):
        new_name = path + '_archived_' + get_time_str()
        print(f'Path already exists. Rename it to {new_name}', flush=True)
        os.rename(path, new_name)
    os.makedirs(path, exist_ok=True)


@master_only
def make_exp_dirs(opt):
    """Make dirs for experiments."""
    path_opt = opt['path'].copy()
    if opt['is_train']:
        mkdir_and_rename(path_opt.pop('experiments_root'))
    else:
        mkdir_and_rename(path_opt.pop('results_root'))
    for key, path in path_opt.items():
        if ('strict_load' not in key) and ('pretrain_network'
                                           not in key) and ('resume'
                                                            not in key):
            os.makedirs(path, exist_ok=True)


def scandir(dir_path, suffix=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        suffix (str | tuple(str), optional): File suffix that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
        raise TypeError('"suffix" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, suffix, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if suffix is None:
                    yield return_path
                elif return_path.endswith(suffix):
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, suffix=suffix, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, suffix=suffix, recursive=recursive)

def scandir_SIDD(dir_path, keywords=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        keywords (str | tuple(str), optional): File keywords that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (keywords is not None) and not isinstance(keywords, (str, tuple)):
        raise TypeError('"keywords" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, keywords, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if keywords is None:
                    yield return_path
                elif return_path.find(keywords) > 0:
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, keywords=keywords, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, keywords=keywords, recursive=recursive)

def check_resume(opt, resume_iter):
    """Check resume states and pretrain_network paths.

    Args:
        opt (dict): Options.
        resume_iter (int): Resume iteration.
    """
    logger = get_root_logger()
    if opt['path']['resume_state']:
        # get all the networks
        networks = [key for key in opt.keys() if key.startswith('network_')]
        flag_pretrain = False
        for network in networks:
            if opt['path'].get(f'pretrain_{network}') is not None:
                flag_pretrain = True
        if flag_pretrain:
            logger.warning(
                'pretrain_network path will be ignored during resuming.')
        # set pretrained model paths
        for network in networks:
            name = f'pretrain_{network}'
            basename = network.replace('network_', '')
            if opt['path'].get('ignore_resume_networks') is None or (
                    basename not in opt['path']['ignore_resume_networks']):
                opt['path'][name] = osp.join(
                    opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
                logger.info(f"Set {name} to {opt['path'][name]}")


def sizeof_fmt(size, suffix='B'):
    """Get human readable file size.

    Args:
        size (int): File size.
        suffix (str): Suffix. Default: 'B'.

    Return:
        str: Formated file siz.
    """
    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
        if abs(size) < 1024.0:
            return f'{size:3.1f} {unit}{suffix}'
        size /= 1024.0
    return f'{size:3.1f} Y{suffix}'


================================================
FILE: RealSR/VmambaIR/utils/options.py
================================================
import yaml
from collections import OrderedDict
from os import path as osp


def ordered_yaml():
    """Support OrderedDict for yaml.

    Returns:
        yaml Loader and Dumper.
    """
    try:
        from yaml import CDumper as Dumper
        from yaml import CLoader as Loader
    except ImportError:
        from yaml import Dumper, Loader

    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG

    def dict_representer(dumper, data):
        return dumper.represent_dict(data.items())

    def dict_constructor(loader, node):
        return OrderedDict(loader.construct_pairs(node))

    Dumper.add_representer(OrderedDict, dict_representer)
    Loader.add_constructor(_mapping_tag, dict_constructor)
    return Loader, Dumper


def parse(opt_path, is_train=True):
    """Parse option file.

    Args:
        opt_path (str): Option file path.
        is_train (str): Indicate whether in training or not. Default: True.

    Returns:
        (dict): Options.
    """
    with open(opt_path, mode='r') as f:
        Loader, _ = ordered_yaml()
        opt = yaml.load(f, Loader=Loader)

    opt['is_train'] = is_train

    # datasets
    for phase, dataset in opt['datasets'].items():
        # for several datasets, e.g., test_1, test_2
        phase = phase.split('_')[0]
        dataset['phase'] = phase
        if 'scale' in opt:
            dataset['scale'] = opt['scale']
        if dataset.get('dataroot_gt') is not None:
            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
        if dataset.get('dataroot_lq') is not None:
            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])

    # paths
    for key, val in opt['path'].items():
        if (val is not None) and ('resume_state' in key
                                  or 'pretrain_network' in key):
            opt['path'][key] = osp.expanduser(val)
    opt['path']['root'] = osp.abspath(
        osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
    if is_train:
        experiments_root = osp.join(opt['path']['root'], 'experiments',
                                    opt['name'])
        opt['path']['experiments_root'] = experiments_root
        opt['path']['models'] = osp.join(experiments_root, 'models')
        opt['path']['training_states'] = osp.join(experiments_root,
                                                  'training_states')
        opt['path']['log'] = experiments_root
        opt['path']['visualization'] = osp.join(experiments_root,
                                                'visualization')

        # change some options for debug mode
        if 'debug' in opt['name']:
            if 'val' in opt:
                opt['val']['val_freq'] = 8
            opt['logger']['print_freq'] = 1
            opt['logger']['save_checkpoint_freq'] = 8
    else:  # test
        results_root = osp.join(opt['path']['root'], 'results', opt['name'])
        opt['path']['results_root'] = results_root
        opt['path']['log'] = results_root
        opt['path']['visualization'] = osp.join(results_root, 'visualization')

    return opt


def dict2str(opt, indent_level=1):
    """dict to string for printing options.

    Args:
        opt (dict): Option dict.
        indent_level (int): Indent level. Default: 1.

    Return:
        (str): Option string for printing.
    """
    msg = '\n'
    for k, v in opt.items():
        if isinstance(v, dict):
            msg += ' ' * (indent_level * 2) + k + ':['
            msg += dict2str(v, indent_level + 1)
            msg += ' ' * (indent_level * 2) + ']\n'
        else:
            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
    return msg


================================================
FILE: RealSR/VmambaIR/utils.py
================================================
import cv2
import math
import numpy as np
import os
import queue
import threading
import torch
from basicsr.utils.download_util import load_file_from_url
from torch.nn import functional as F

ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))


class RealESRGANer():
    """A helper class for upsampling images with RealESRGAN.

    Args:
        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
        model (nn.Module): The defined network. Default: None.
        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
            input images into tiles, and then process each of them. Finally, they will be merged into one image.
            0 denotes for do not use tile. Default: 0.
        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
        half (float): Whether to use half precision during inference. Default: False.
    """

    def __init__(self,
                 scale,
                 model_path,
                 model=None,
                 tile=0,
                 tile_pad=10,
                 pre_pad=10,
                 half=False,
                 device=None,
                 gpu_id=None):
        self.scale = scale
        self.tile_size = tile
        self.tile_pad = tile_pad
        self.pre_pad = pre_pad
        self.mod_scale = None
        self.half = half

        # initialize model
        if gpu_id:
            self.device = torch.device(
                f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
        else:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
        # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
        if model_path.startswith('https://'):
            model_path = load_file_from_url(
                url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
        loadnet = torch.load(model_path, map_location=torch.device('cpu'))
        # prefer to use params_ema
        if 'params_ema' in loadnet:
            keyname = 'params_ema'
        else:
            keyname = 'params'
        model.load_state_dict(loadnet[keyname], strict=True)
        model.eval()
        self.model = model.to(self.device)
        if self.half:
            self.model = self.model.half()

    def pre_process(self, img):
        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
            self.img = self.img.half()

        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
            self.mod_scale = 4
        if self.mod_scale is not None:
            self.mod_pad_h, self.mod_pad_w = 0, 0
            _, _, h, w = self.img.size()
            if (h % self.mod_scale != 0):
                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
            if (w % self.mod_scale != 0):
                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')

    def process(self):
        # model inference
        self.output = self.model(self.img)

    def tile_process(self):
        """It will first crop input images to tiles, and then process each tile.
        Finally, all the processed tiles are merged into one images.

        Modified from: https://github.com/ata4/esrgan-launcher
        """
        batch, channel, height, width = self.img.shape
        output_height = height * self.scale
        output_width = width * self.scale
        output_shape = (batch, channel, output_height, output_width)

        # start with black image
        self.output = self.img.new_zeros(output_shape)
        tiles_x = math.ceil(width / self.tile_size)
        tiles_y = math.ceil(height / self.tile_size)

        # loop over all tiles
        for y in range(tiles_y):
            for x in range(tiles_x):
                # extract tile from input image
                ofs_x = x * self.tile_size
                ofs_y = y * self.tile_size
                # input tile area on total image
                input_start_x = ofs_x
                input_end_x = min(ofs_x + self.tile_size, width)
                input_start_y = ofs_y
                input_end_y = min(ofs_y + self.tile_size, height)

                # input tile area on total image with padding
                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
                input_end_x_pad = min(input_end_x + self.tile_pad, width)
                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
                input_end_y_pad = min(input_end_y + self.tile_pad, height)

                # input tile dimensions
                input_tile_width = input_end_x - input_start_x
                input_tile_height = input_end_y - input_start_y
                tile_idx = y * tiles_x + x + 1
                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]

                # upscale tile
                try:
                    with torch.no_grad():
                        output_tile = self.model(input_tile)
                except RuntimeError as error:
                    print('Error', error)
                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')

                # output tile area on total image
                output_start_x = input_start_x * self.scale
                output_end_x = input_end_x * self.scale
                output_start_y = input_start_y * self.scale
                output_end_y = input_end_y * self.scale

                # output tile area without padding
                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale

                # put tile into output image
                self.output[:, :, output_start_y:output_end_y,
                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
                                                                       output_start_x_tile:output_end_x_tile]

    def post_process(self):
        # remove extra pad
        if self.mod_scale is not None:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
        # remove prepad
        if self.pre_pad != 0:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
        return self.output

    @torch.no_grad()
    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
        h_input, w_input = img.shape[0:2]
        # img: numpy
        img = img.astype(np.float32)
        if np.max(img) > 256:  # 16-bit image
            max_range = 65535
            print('\tInput is a 16-bit image')
        else:
            max_range = 255
        img = img / max_range
        if len(img.shape) == 2:  # gray image
            img_mode = 'L'
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
        elif img.shape[2] == 4:  # RGBA image with alpha channel
            img_mode = 'RGBA'
            alpha = img[:, :, 3]
            img = img[:, :, 0:3]
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            if alpha_upsampler == 'realesrgan':
                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
        else:
            img_mode = 'RGB'
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # ------------------- process image (without the alpha channel) ------------------- #
        self.pre_process(img)
        if self.tile_size > 0:
            self.tile_process()
        else:
            self.process()
        output_img = self.post_process()
        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
        if img_mode == 'L':
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)

        # ------------------- process the alpha channel if necessary ------------------- #
        if img_mode == 'RGBA':
            if alpha_upsampler == 'realesrgan':
                self.pre_process(alpha)
                if self.tile_size > 0:
                    self.tile_process()
                else:
                    self.process()
                output_alpha = self.post_process()
                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
            else:  # use the cv2 resize for alpha channel
                h, w = alpha.shape[0:2]
                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)

            # merge the alpha channel
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
            output_img[:, :, 3] = output_alpha

        # ------------------------------ return ------------------------------ #
        if max_range == 65535:  # 16-bit image
            output = (output_img * 65535.0).round().astype(np.uint16)
        else:
            output = (output_img * 255.0).round().astype(np.uint8)

        if outscale is not None and outscale != float(self.scale):
            output = cv2.resize(
                output, (
                    int(w_input * outscale),
                    int(h_input * outscale),
                ), interpolation=cv2.INTER_LANCZOS4)

        return output, img_mode


class PrefetchReader(threading.Thread):
    """Prefetch images.

    Args:
        img_list (list[str]): A image list of image paths to be read.
        num_prefetch_queue (int): Number of prefetch queue.
    """

    def __init__(self, img_list, num_prefetch_queue):
        super().__init__()
        self.que = queue.Queue(num_prefetch_queue)
        self.img_list = img_list

    def run(self):
        for img_path in self.img_list:
            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
            self.que.put(img)

        self.que.put(None)

    def __next__(self):
        next_item = self.que.get()
        if next_item is None:
            raise StopIteration
        return next_item

    def __iter__(self):
        return self


class IOConsumer(threading.Thread):

    def __init__(self, opt, que, qid):
        super().__init__()
        self._queue = que
        self.qid = qid
        self.opt = opt

    def run(self):
        while True:
            msg = self._queue.get()
            if isinstance(msg, str) and msg == 'quit':
                break

            output = msg['output']
            save_path = msg['save_path']
            cv2.imwrite(save_path, output)
        print(f'IO worker {self.qid} is done.')


================================================
FILE: RealSR/VmambaIR/weights/README.md
================================================
# Weights

Put the downloaded weights to this folder.


================================================
FILE: RealSR/inference.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.utils import  img2tensor
from torch.utils import data as data
import cv2
from basicsr.utils.img_util import tensor2img
from DiffIR.archs.S2_arch import DiffIRS2
from basicsr.utils import  img2tensor
import argparse
from torch.nn import functional as F


def pad_test(lq,scale):
    if scale==1:
        window_size = 32
    elif scale==2:
        window_size = 16
    else:
        window_size = 8      
    mod_pad_h, mod_pad_w = 0, 0
    _, _, h, w = lq.size()
    if h % window_size != 0:
        mod_pad_h = window_size - h % window_size
    if w % window_size != 0:
        mod_pad_w = window_size - w % window_size
    lq = F.pad(lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
    return lq,mod_pad_h,mod_pad_w

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--scale', type=int, default=4)
    parser.add_argument('--model_path', type=str, default='./experiments/DiffIRS2-GAN.pth')
    parser.add_argument('--im_path', type=str, default='/mnt/bn/xiabinpaint/dataset/NTIRE2020-Track1/track1-valid-input')
    parser.add_argument('--res_path', type=str, default='./outputs/')
    args = parser.parse_args()

    os.makedirs(args.res_path, exist_ok=True)
    model = DiffIRS2( n_encoder_res= 9, dim= 64, scale=args.scale,num_blocks= [13,1,1,1],num_refinement_blocks= 13,heads= [1,2,4,8], ffn_expansion_factor= 2.2,LayerNorm_type= "BiasFree")
    loadnet = torch.load(args.model_path, map_location=torch.device('cpu'))
    model.load_state_dict(loadnet['params_ema'], strict=True)
    model.to('cuda:0')
    model.eval()

    im_list = os.listdir(args.im_path)
    im_list.sort()
    im_list = [name for name in im_list if name.endswith('.png')]


    for name in im_list:
        path = os.path.join(args.im_path, name)
        im = cv2.imread(path)
        im = img2tensor(im)
        im = im.unsqueeze(0).cuda(0)/255.
        lq,mod_pad_h,mod_pad_w= pad_test(im,args.scale)
        with torch.no_grad():
            sr = model(lq)
        _, _, h, w = sr.size()
        sr = sr[:, :, 0:h - mod_pad_h * args.scale, 0:w - mod_pad_w * args.scale]
        im_sr = tensor2img(sr, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1))
        save_path = os.path.join(args.res_path, name.split('.')[0]+'_out.png')
        cv2.imwrite(save_path, im_sr)
        print(save_path)


================================================
FILE: RealSR/ldm/classifier.py
================================================
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted

from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config

__models__ = {
    'class_label': EncoderUNetModel,
    'segmentation': UNetModel
}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class NoisyLatentImageClassifier(pl.LightningModule):

    def __init__(self,
                 diffusion_path,
                 num_classes,
                 ckpt_path=None,
                 pool='attention',
                 label_key=None,
                 diffusion_ckpt_path=None,
                 scheduler_config=None,
                 weight_decay=1.e-2,
                 log_steps=10,
                 monitor='val/loss',
                 *args,
                 **kwargs):
        super().__init__(*args, **kwargs)
        self.num_classes = num_classes
        # get latest config of diffusion model
        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
        self.diffusion_config = OmegaConf.load(diffusion_config).model
        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
        self.load_diffusion()

        self.monitor = monitor
        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
        self.log_steps = log_steps

        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
            else self.diffusion_model.cond_stage_key

        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'

        if self.label_key not in __models__:
            raise NotImplementedError()

        self.load_classifier(ckpt_path, pool)

        self.scheduler_config = scheduler_config
        self.use_scheduler = self.scheduler_config is not None
        self.weight_decay = weight_decay

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
            sd, strict=False)
        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def load_diffusion(self):
        model = instantiate_from_config(self.diffusion_config)
        self.diffusion_model = model.eval()
        self.diffusion_model.train = disabled_train
        for param in self.diffusion_model.parameters():
            param.requires_grad = False

    def load_classifier(self, ckpt_path, pool):
        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
        model_config.out_channels = self.num_classes
        if self.label_key == 'class_label':
            model_config.pool = pool

        self.model = __models__[self.label_key](**model_config)
        if ckpt_path is not None:
            print('#####################################################################')
            print(f'load from ckpt "{ckpt_path}"')
            print('#####################################################################')
            self.init_from_ckpt(ckpt_path)

    @torch.no_grad()
    def get_x_noisy(self, x, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x))
        continuous_sqrt_alpha_cumprod = None
        if self.diffusion_model.use_continuous_noise:
            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
            # todo: make sure t+1 is correct here

        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)

    def forward(self, x_noisy, t, *args, **kwargs):
        return self.model(x_noisy, t)

    @torch.no_grad()
    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, 'b h w c -> b c h w')
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    @torch.no_grad()
    def get_conditioning(self, batch, k=None):
        if k is None:
            k = self.label_key
        assert k is not None, 'Needs to provide label key'

        targets = batch[k].to(self.device)

        if self.label_key == 'segmentation':
            targets = rearrange(targets, 'b h w c -> b c h w')
            for down in range(self.numd):
                h, w = targets.shape[-2:]
                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')

            # targets = rearrange(targets,'b c h w -> b h w c')

        return targets

    def compute_top_k(self, logits, labels, k, reduction="mean"):
        _, top_ks = torch.topk(logits, k, dim=1)
        if reduction == "mean":
            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
        elif reduction == "none":
            return (top_ks == labels[:, None]).float().sum(dim=-1)

    def on_train_epoch_start(self):
        # save some memory
        self.diffusion_model.model.to('cpu')

    @torch.no_grad()
    def write_logs(self, loss, logits, targets):
        log_prefix = 'train' if self.training else 'val'
        log = {}
        log[f"{log_prefix}/loss"] = loss.mean()
        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
            logits, targets, k=1, reduction="mean"
        )
        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
            logits, targets, k=5, reduction="mean"
        )

        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
        lr = self.optimizers().param_groups[0]['lr']
        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)

    def shared_step(self, batch, t=None):
        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
        targets = self.get_conditioning(batch)
        if targets.dim() == 4:
            targets = targets.argmax(dim=1)
        if t is None:
            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
        else:
            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
        x_noisy = self.get_x_noisy(x, t)
        logits = self(x_noisy, t)

        loss = F.cross_entropy(logits, targets, reduction='none')

        self.write_logs(loss.detach(), logits.detach(), targets.detach())

        loss = loss.mean()
        return loss, logits, x_noisy, targets

    def training_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)
        return loss

    def reset_noise_accs(self):
        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}

    def on_validation_start(self):
        self.reset_noise_accs()

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)

        for t in self.noisy_acc:
            _, logits, _, targets = self.shared_step(batch, t)
            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))

        return loss

    def configure_optimizers(self):
        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)

        if self.use_scheduler:
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                }]
            return [optimizer], scheduler

        return optimizer

    @torch.no_grad()
    def log_images(self, batch, N=8, *args, **kwargs):
        log = dict()
        x = self.get_input(batch, self.diffusion_model.first_stage_key)
        log['inputs'] = x

        y = self.get_conditioning(batch)

        if self.label_key == 'class_label':
            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
            log['labels'] = y

        if ismap(y):
            log['labels'] = self.diffusion_model.to_rgb(y)

            for step in range(self.log_steps):
                current_time = step * self.log_time_interval

                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)

                log[f'inputs@t{current_time}'] = x_noisy

                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
                pred = rearrange(pred, 'b h w c -> b c h w')

                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)

        for key in log:
            log[key] = log[key][:N]

        return log


================================================
FILE: RealSR/ldm/lr_scheduler.py
================================================
import numpy as np


class LambdaWarmUpCosineScheduler:
    """
    note: use with a base_lr of 1.0
    """
    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
        self.lr_warm_up_steps = warm_up_steps
        self.lr_start = lr_start
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.lr_max_decay_steps = max_decay_steps
        self.last_lr = 0.
        self.verbosity_interval = verbosity_interval

    def schedule(self, n, **kwargs):
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
        if n < self.lr_warm_up_steps:
            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
            self.last_lr = lr
            return lr
        else:
            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
            t = min(t, 1.0)
            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
                    1 + np.cos(t * np.pi))
            self.last_lr = lr
            return lr

    def __call__(self, n, **kwargs):
        return self.schedule(n,**kwargs)


class LambdaWarmUpCosineScheduler2:
    """
    supports repeated iterations, configurable via lists
    note: use with a base_lr of 1.0.
    """
    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
        self.lr_warm_up_steps = warm_up_steps
        self.f_start = f_start
        self.f_min = f_min
        self.f_max = f_max
        self.cycle_lengths = cycle_lengths
        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
        self.last_f = 0.
        self.verbosity_interval = verbosity_interval

    def find_in_interval(self, n):
        interval = 0
        for cl in self.cum_cycles[1:]:
            if n <= cl:
                return interval
            interval += 1

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                                                       f"current cycle {cycle}")
        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
            t = min(t, 1.0)
            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
                    1 + np.cos(t * np.pi))
            self.last_f = f
            return f

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                                                       f"current cycle {cycle}")

        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
            self.last_f = f
            return f



================================================
FILE: RealSR/ldm/util.py
================================================
import importlib

import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial

import multiprocessing as mp
from threading import Thread
from queue import Queue

from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont


def log_txt_as_img(wh, xc, size=10):
    # wh a tuple of (width, height)
    # xc a list of captions to plot
    b = len(xc)
    txts = list()
    for bi in range(b):
        txt = Image.new("RGB", wh, color="white")
        draw = ImageDraw.Draw(txt)
        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
        nc = int(40 * (wh[0] / 256))
        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))

        try:
            draw.text((0, 0), lines, fill="black", font=font)
        except UnicodeEncodeError:
            print("Cant encode string for logging. Skipping.")

        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
        txts.append(txt)
    txts = np.stack(txts)
    txts = torch.tensor(txts)
    return txts


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def exists(x):
    return x is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def mean_flat(tensor):
    """
    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
    return total_params


def instantiate_from_config(config):
    if not "target" in config:
        if config == '__is_first_stage__':
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
    # create dummy dataset instance

    # run prefetching
    if idx_to_fn:
        res = func(data, worker_id=idx)
    else:
        res = func(data)
    Q.put([idx, res])
    Q.put("Done")


def parallel_data_prefetch(
        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
):
    # if target_data_type not in ["ndarray", "list"]:
    #     raise ValueError(
    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
    #     )
    if isinstance(data, np.ndarray) and target_data_type == "list":
        raise ValueError("list expected but function got ndarray.")
    elif isinstance(data, abc.Iterable):
        if isinstance(data, dict):
            print(
                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
            )
            data = list(data.values())
        if target_data_type == "ndarray":
            data = np.asarray(data)
        else:
            data = list(data)
    else:
        raise TypeError(
            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
        )

    if cpu_intensive:
        Q = mp.Queue(1000)
        proc = mp.Process
    else:
        Q = Queue(1000)
        proc = Thread
    # spawn processes
    if target_data_type == "ndarray":
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(np.array_split(data, n_proc))
        ]
    else:
        step = (
            int(len(data) / n_proc + 1)
            if len(data) % n_proc != 0
            else int(len(data) / n_proc)
        )
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(
                [data[i: i + step] for i in range(0, len(data), step)]
            )
        ]
    processes = []
    for i in range(n_proc):
        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
        processes += [p]

    # start processes
    print(f"Start prefetching...")
    import time

    start = time.time()
    gather_res = [[] for _ in range(n_proc)]
    try:
        for p in processes:
            p.start()

        k = 0
        while k < n_proc:
            # get result
            res = Q.get()
            if res == "Done":
                k += 1
            else:
                gather_res[res[0]] = res[1]

    except Exception as e:
        print("Exception: ", e)
        for p in processes:
            p.terminate()

        raise e
    finally:
        for p in processes:
            p.join()
        print(f"Prefetching complete. [{time.time() - start} sec.]")

    if target_data_type == 'ndarray':
        if not isinstance(gather_res[0], np.ndarray):
            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)

        # order outputs
        return np.concatenate(gather_res, axis=0)
    elif target_data_type == 'list':
        out = []
        for r in gather_res:
            out.extend(r)
        return out
    else:
        return gather_res


================================================
FILE: RealSR/ldm/util2.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!


import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat

from ldm.util import instantiate_from_config


def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
    if schedule == "linear":
        betas = (
                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
        )

    elif schedule == "cosine":
        timesteps = (
                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * np.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = np.clip(betas, a_min=0, a_max=0.999)

    elif schedule == "sqrt_linear":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
    elif schedule == "sqrt":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
    else:
        raise ValueError(f"schedule '{schedule}' unknown.")
    return betas.numpy()


def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
    if ddim_discr_method == 'uniform':
        c = num_ddpm_timesteps // num_ddim_timesteps
        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
    elif ddim_discr_method == 'quad':
        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
    else:
        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')

    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
    # add one to get the final alpha values right (the ones from first scale to data during sampling)
    steps_out = ddim_timesteps + 1
    if verbose:
        print(f'Selected timesteps for ddim sampler: {steps_out}')
    return steps_out


def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
    # select alphas for computing the variance schedule
    alphas = alphacums[ddim_timesteps]
    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())

    # according the the formula provided in https://arxiv.org/abs/2010.02502
    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
    if verbose:
        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
        print(f'For the chosen value of eta, which is {eta}, '
              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
    return sigmas, alphas, alphas_prev


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_tensors = list(args[:length])
        ctx.input_params = list(args[length:])

        with torch.no_grad():
            output_tensors = ctx.run_function(*ctx.input_tensors)
        return output_tensors

    @staticmethod
    def backward(ctx, *output_grads):
        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
        with torch.enable_grad():
            # Fixes a bug where the first op in run_function modifies the
            # Tensor storage in place, which is not allowed for detach()'d
            # Tensors.
            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
            output_tensors = ctx.run_function(*shallow_copies)
        input_grads = torch.autograd.grad(
            output_tensors,
            ctx.input_tensors + ctx.input_params,
            output_grads,
            allow_unused=True,
        )
        del ctx.input_tensors
        del ctx.input_params
        del output_tensors
        return (None, None) + input_grads


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=timesteps.device)
        args = timesteps[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    else:
        embedding = repeat(timesteps, 'b -> b d', d=dim)
    return embedding


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def normalization(channels):
    """
    Make a standard normalization layer.
    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNorm32(32, channels)


# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)

def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


class HybridConditioner(nn.Module):

    def __init__(self, c_concat_config, c_crossattn_config):
        super().__init__()
        self.concat_conditioner = instantiate_from_config(c_concat_config)
        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)

    def forward(self, c_concat, c_crossattn):
        c_concat = self.concat_conditioner(c_concat)
        c_crossattn = self.crossattn_conditioner(c_crossattn)
        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}


def noise_like(shape, device, repeat=False):
    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
    noise = lambda: torch.randn(shape, device=device)
    return repeat_noise() if repeat else noise()

================================================
FILE: RealSR/metric.sh
================================================

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-gt
#folder_restored=/mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/results/test_mambaSR11GAN2_archived_20240623_200741/visualization/NTIRE2020-Track1

folder_gt=/mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-gt-clean
#folder_restored=/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-RealSR/results/test_mambaSR11GAN_AIM/visualization/AIM19
folder_restored=/mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/results/test_mambaSR11GAN2/visualization/AIM19

python3  Metric/LPIPS.py \
    --folder_gt $folder_gt  \
    --folder_restored $folder_restored

python3  Metric/PSNR.py \
    --folder_gt $folder_gt  \
    --folder_restored $folder_restored


================================================
FILE: RealSR/options/mambaSR11GAN_x4.yml
================================================
# general settings
name: MambaRealSR11GAN
model_type: MambaRealSRGAN
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
# USM the ground-truth
l1_gt_usm: False
percep_gt_usm: False
gan_gt_usm: False

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 256
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K+OST
    type: RealESRGANDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale+OST_sub.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 256
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 9
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: NTIRE2020-Track1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-gt
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
    io_backend:
      type: disk


# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/experiments/MambaRealSR11/models/net_g_500000.pth
  param_key_g: params_ema
  strict_load_g: True
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]
    
  optim_d:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [400000]
    gamma: 0.5

  total_iter: 400000
  lr_sr: !!float 1e-4
  gamma_sr: 0.5
  lr_decay_sr: 300000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
  # perceptual loss (content and style losses)
  perceptual_opt:
    type: PerceptualLoss
    layer_weights:
      # before relu
      'conv1_2': 0.1
      'conv2_2': 0.1
      'conv3_4': 1
      'conv4_4': 1
      'conv5_4': 1
    vgg_type: vgg19
    use_input_norm: true
    perceptual_weight: !!float 1.0
    style_weight: 0
    range_norm: false
    criterion: l1
  # gan loss
  gan_opt:
    type: GANLoss
    gan_type: vanilla
    real_label_val: 1.0
    fake_label_val: 0.0
    loss_weight: !!float 1.0

  net_d_iters: 1
  net_d_init_iters: 0
  

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 1e4
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 4
      test_y_channel: true

    ssim: # metric name
      type: calculate_ssim
      crop_border: 4
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 1e4
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/options/mambaSR11_x4.yml
================================================
# general settings
name: MambaRealSR11
model_type: MambaRealSR
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
gt_usm: False  # USM the ground-truth

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 256
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K+OST
    type: RealESRGANDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale+OST_sub.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 256
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 9
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: NTIRE2020-Track1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-gt
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999

  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [250000,350000]
    gamma: 0.5

  total_iter: 500000
  warmup_iter: -1  # no warm up

  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 5e3
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/options/mambaSR11m_x4.yml
================================================
# general settings
name: MambaRealSR11m
model_type: MambaRealSR
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
gt_usm: False  # USM the ground-truth

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 32
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K
    type: RealESRGANDataset_memory
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DIV2K_train_HR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DIV2k_meta.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 32
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 1
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: test1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-gt-clean
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-input-noisy
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999

  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [250000,350000]
    gamma: 0.5

  total_iter: 500000
  warmup_iter: -1  # no warm up

  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 5e3
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/options/test_mambaSR11GAN_x4.yml
================================================
# general settings
name: test_mambaSR11GAN2
model_type: MambaRealSRGANtest
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0



# dataset and data loader settings
datasets:
  #test_1:
  #  name: NTIRE2020-Track1
  #  type: SingleImageDataset
  #  dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
  #  io_backend:
  #    type: disk

  test_2:
    name: AIM19
    type: SingleImageDataset
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-input-noisy
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/experiments/MambaRealSR11GAN/models/net_g_360000.pth
  param_key_g: params_ema
  strict_load_g: False



val:
  window_size: 8
  save_img: True
  suffix: ~  # add suffix to saved images, if None, use exp name







================================================
FILE: RealSR/pip.sh
================================================
sudo apt-get update
sudo apt install tmux
sudo apt install libgl1-mesa-glx
pip3 install basicsr 
pip3 install -r requirements.txt 
pip3 install pandas 
sudo python3 setup.py develop
#sudo pip uninstall bytedmetrics
pip3 install einops 
pip3 install lpips 

pip3 install torchsummary 

================================================
FILE: RealSR/requirements.txt
================================================
basicsr>=1.3.3.11
facexlib>=0.2.0.3
gfpgan>=0.2.1
numpy
opencv-python
Pillow
torch>=1.7
torchvision
tqdm


================================================
FILE: RealSR/scripts/Metric/DISTS/DISTS_pytorch/DISTS_pt.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('scripts/metrics/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'

    # others
    # data_root = '/data1/liangjie/BasicSR_ALL/results/'
    # ref_root = '/data1/liangjie/BasicSR_ALL/datasets/'
    # ref_dirs = ['SISR_Test_matlab/Set5mod12', 'SISR_Test_matlab/Set14mod12', 'SISR_Test_matlab/Manga109mod12', 'SISR_Test_matlab/BSDS100mod12', 'SISR_Test_matlab/General100mod12', 'SISR_Test/Urban100', 'DIV2K/DIV2K_valid_HR/']
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SRGAN_official', 'ESRGAN_official', 'NatSR_official', 'USRGAN_official', 'SPSR_official', 'SPSR_DF2K', 'ESRGAN_ours_DIV2K', 'ESRGAN_ours_DIV2K_ema', 'ESRGAN_ours_DF2K', 'ESRGAN_ours_DF2K_ema']

    # SFTGAN
    # data_root = '/data1/liangjie/BasicSR_ALL/results'
    # ref_root = '/data1/liangjie/BasicSR_ALL/results/SFTGAN_official'
    # ref_dirs = ['GT'] * 7
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SFTGAN_official']

    # new
    data_root = 'results/'
    ref_root = 'datasets/'
    ref_dirs = ['DIV2K/DIV2K_valid_HR/']
    datasets = ['DIV2K100']
    img_dirs = ['ESRGAN_ours_DISTS_300k/visualization/']

    logoverall_path = 'results/table_logs/' + 'DISTS_orisize_DISTStrain225k.txt'

    for index in range(len(ref_dirs)):
        ref_dir = os.path.join(ref_root, ref_dirs[index])
        for method in img_dirs:
            img_dir = os.path.join(data_root, method, datasets[index])

            img_list = sorted(glob.glob(os.path.join(img_dir, '*')))

            log_path = 'results/table_logs/' + img_dir.replace('/', '_') + '_DISTS_orisize.txt'

            DISTS_all = []

            for i, img_path in enumerate(img_list):
                file_name = img_path.split('/')[-1]
                if 'DIV2K100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:4] + '.png')
                elif 'Urban100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:7] + '.png')
                elif 'SFTGAN' in img_dir:
                    gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_gt.png')
                    if 'Urban100' in img_dir:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_' + file_name.split('_')[1] + '_gt.png')
                else:
                    if '_' in file_name:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '.png')
                    else:
                        gt_path = os.path.join(ref_dir, file_name)

                ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
                dist = prepare_image(Image.open(img_path).convert("RGB"), resize=False)
                assert ref.shape == dist.shape
                device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                model = DISTS().to(device)
                ref = ref.to(device)
                dist = dist.to(device)
                score = model(ref, dist)
                DISTS_all.append(score.item())
                log = f'{i + 1:3d}: {file_name:25}. \tDISTS: {score.item():.6f}.'
                with open(log_path, 'a') as f:
                    f.write(log + '\n')
                # print(log)

            log = f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}'
            with open(log_path, 'a') as f:
                f.write(log + '\n')
            log_overall = method + '__' + datasets[index] + '__' + log
            with open(logoverall_path, 'a') as f:
                f.write(log_overall + '\n')
            print(log_overall)



================================================
FILE: RealSR/scripts/Metric/DISTS/DISTS_tensorflow/DISTS_tf.py
================================================
# This is a tensorflow implementation of DISTS metric.
# Requirements: python >= 3.6, tensorflow-gpu >= 1.15

import tensorflow.compat.v1 as tf
import numpy as np
import time
import scipy.io as scio
from PIL import Image
import argparse
# tf.enable_eager_execution()
tf.disable_eager_execution()

class DISTS():
    def __init__(self):
        self.parameters = scio.loadmat('../weights/net_param.mat')
        self.chns = [3,64,128,256,512,512]
        self.mean = tf.constant(self.parameters['vgg_mean'], dtype=tf.float32, shape=(1,1,1,3),name="img_mean")
        self.std = tf.constant(self.parameters['vgg_std'], dtype=tf.float32, shape=(1,1,1,3),name="img_std")
        # self.alpha = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="alpha")
        # self.beta = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="beta")
        self.weights = scio.loadmat('../weights/alpha_beta.mat')
        self.alpha = tf.constant(np.reshape(self.weights['alpha'],(1,1,1,sum(self.chns))),name="alpha")
        self.beta = tf.constant(np.reshape(self.weights['beta'],(1,1,1,sum(self.chns))),name="beta")

    def get_features(self, img):

        x = (img - self.mean)/self.std

        self.conv1_1 = self.conv_layer(x, "conv1_1")
        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
        self.pool1 = self.pool_layer(self.conv1_2, name="pool_1")

        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
        self.pool2 = self.pool_layer(self.conv2_2, name="pool_2")

        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
        self.pool3 = self.pool_layer(self.conv3_3, name="pool_3")

        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
        self.pool4 = self.pool_layer(self.conv4_3, name="pool_4")

        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")

        return [img, self.conv1_2,self.conv2_2,self.conv3_3,self.conv4_3,self.conv5_3]

    def conv_layer(self, input, name):
        with tf.variable_scope(name) as _:
            filter = self.get_conv_filter(name)
            conv = tf.nn.conv2d(input, filter, strides=1, padding="SAME")
            bias = self.get_bias(name)
            conv = tf.nn.relu(tf.nn.bias_add(conv, bias))
            return conv

    def pool_layer(self, input, name):
        # return tf.nn.max_pool(input, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
        with tf.variable_scope(name) as _:
            filter = tf.squeeze(tf.constant(self.parameters['L2'+name], name = "filter"),3)
            conv = tf.nn.conv2d(input**2, filter, strides=2, padding=[[0, 0], [1, 0], [1, 0], [0, 0]])
            return tf.sqrt(tf.maximum(conv, 1e-12))     

    def get_conv_filter(self, name):
        return tf.constant(self.parameters[name+'_weight'], name = "filter")

    def get_bias(self, name):
        return tf.constant(np.squeeze(self.parameters[name+'_bias']), name = "bias")

    def get_score(self, img1, img2):
        feats0 = self.get_features(img1)
        feats1 = self.get_features(img2)
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = tf.reduce_sum(self.alpha) + tf.reduce_sum(self.beta)
        alpha = tf.split(self.alpha/w_sum, self.chns, axis=3)
        beta = tf.split(self.beta/w_sum, self.chns, axis=3)
        for k in range(len(self.chns)):
            x_mean = tf.reduce_mean(feats0[k],[1,2], keepdims=True)
            y_mean = tf.reduce_mean(feats1[k],[1,2], keepdims=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+tf.reduce_sum(alpha[k]*S1, 3, keepdims=True)
            x_var = tf.reduce_mean((feats0[k]-x_mean)**2,[1,2], keepdims=True)
            y_var = tf.reduce_mean((feats1[k]-y_mean)**2,[1,2], keepdims=True)
            xy_cov = tf.reduce_mean(feats0[k]*feats1[k],[1,2], keepdims=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+tf.reduce_sum(beta[k]*S2, 3, keepdims=True)

        dist = 1-tf.squeeze(dist1+dist2)
        return dist


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--ref', type=str, default='../images/r0.png')
    parser.add_argument('--dist', type=str, default='../images/r1.png')
    args = parser.parse_args()
    model = DISTS()

    ref = np.array(Image.open(args.ref).convert("RGB"))
    ref = np.expand_dims(ref,axis=0)/255.
    dist = np.array(Image.open(args.dist).convert("RGB"))
    dist = np.expand_dims(dist,axis=0)/255.

    x = tf.placeholder(dtype=tf.float32, shape=ref.shape, name= "ref")
    y = tf.placeholder(dtype=tf.float32, shape=dist.shape, name= "dist")
    score = model.get_score(x,y)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        score = sess.run(score, feed_dict={x: ref, y: dist})
        print(score)




================================================
FILE: RealSR/scripts/Metric/DISTS/LICENSE
================================================
MIT License

Copyright (c) 2020 Keyan Ding

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: RealSR/scripts/Metric/DISTS/requirements.txt
================================================
torch>=1.0

================================================
FILE: RealSR/scripts/Metric/LPIPS.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    loss_fn_vgg = lpips.LPIPS(net='vgg').cuda(0)
    lpips_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    mean = [0.5, 0.5, 0.5]
    std = [0.5, 0.5, 0.5]
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED).astype(
            np.float32) / 255.

        img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)
        # norm to [-1, 1]
        normalize(img_gt, mean, std, inplace=True)
        normalize(img_restored, mean, std, inplace=True)

        # calculate lpips
        lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(0), img_gt.unsqueeze(0).cuda(0)).cpu().data.numpy()[0,0,0,0]
        # print(lpips_val)
        lpips_all.append(lpips_val)

    print(f'Average: LPIPS: {sum(lpips_all) / len(lpips_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: RealSR/scripts/Metric/PSNR.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse
from basicsr.metrics import calculate_psnr, calculate_ssim


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test2800/target')
    parser.add_argument('--folder_restored', type=str, default='/mnt/bn/shiyuan-arnold/code/Mamber/Restormer/Deraining/results/Test2800')
    args = parser.parse_args()
    psnr_all = []
    ssim_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED)
        psnr=calculate_psnr(img_restored, img_gt, crop_border=4, test_y_channel=True)
        ssim=calculate_ssim(img_restored, img_gt, crop_border=4, test_y_channel=True)
        psnr_all.append(psnr)
        ssim_all.append(ssim)

    print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f}')
    print(f'Average: SSIM: {sum(ssim_all) / len(ssim_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: RealSR/scripts/Metric/dists.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F
import argparse
import os.path as osp

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-RealSR/Metric/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))


    DISTS_all = []

    for i, (gt_path, lr_path) in enumerate(zip(img_list,lr_list)):
        ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
        dist = prepare_image(Image.open(lr_path).convert("RGB"), resize=False)
        assert ref.shape == dist.shape
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model = DISTS().to(device)
        ref = ref.to(device)
        dist = dist.to(device)
        score = model(ref, dist)
        DISTS_all.append(score.item())
        log = f'{i + 1:3d}:\tDISTS: {score.item():.6f}.'
        print(log)
        # with open(log_path, 'a') as f:
        #     f.write(log + '\n')
        # # print(log)

    print(f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}')




================================================
FILE: RealSR/scripts/Metric/pip.sh
================================================
pip install lpips --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install basicsr --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com


================================================
FILE: RealSR/scripts/extract_subimages.py
================================================
import argparse
import cv2
import numpy as np
import os
import sys
from basicsr.utils import scandir
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def main(args):
    """A multi-thread tool to crop large images to sub-images for faster IO.

    opt (dict): Configuration dict. It contains:
        n_thread (int): Thread number.
        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
            and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2.
        input_folder (str): Path to the input folder.
        save_folder (str): Path to save folder.
        crop_size (int): Crop size.
        step (int): Step for overlapped sliding window.
        thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.

    Usage:
        For each folder, run this script.
        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
        After process, each sub_folder should have the same number of subimages.
        Remember to modify opt configurations according to your settings.
    """

    opt = {}
    opt['n_thread'] = args.n_thread
    opt['compression_level'] = args.compression_level
    opt['input_folder'] = args.input
    opt['save_folder'] = args.output
    opt['crop_size'] = args.crop_size
    opt['step'] = args.step
    opt['thresh_size'] = args.thresh_size
    extract_subimages(opt)


def extract_subimages(opt):
    """Crop images to subimages.

    Args:
        opt (dict): Configuration dict. It contains:
            input_folder (str): Path to the input folder.
            save_folder (str): Path to save folder.
            n_thread (int): Thread number.
    """
    input_folder = opt['input_folder']
    save_folder = opt['save_folder']
    if not osp.exists(save_folder):
        os.makedirs(save_folder)
        print(f'mkdir {save_folder} ...')
    else:
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)

    # scan all images
    img_list = list(scandir(input_folder, full_path=True))

    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
    pool = Pool(opt['n_thread'])
    for path in img_list:
        pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
    pool.close()
    pool.join()
    pbar.close()
    print('All processes done.')


def worker(path, opt):
    """Worker for each process.

    Args:
        path (str): Image path.
        opt (dict): Configuration dict. It contains:
            crop_size (int): Crop size.
            step (int): Step for overlapped sliding window.
            thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
            save_folder (str): Path to save folder.
            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.

    Returns:
        process_info (str): Process information displayed in progress bar.
    """
    crop_size = opt['crop_size']
    step = opt['step']
    thresh_size = opt['thresh_size']
    img_name, extension = osp.splitext(osp.basename(path))

    # remove the x2, x3, x4 and x8 in the filename for DIV2K
    img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)

    h, w = img.shape[0:2]
    h_space = np.arange(0, h - crop_size + 1, step)
    if h - (h_space[-1] + crop_size) > thresh_size:
        h_space = np.append(h_space, h - crop_size)
    w_space = np.arange(0, w - crop_size + 1, step)
    if w - (w_space[-1] + crop_size) > thresh_size:
        w_space = np.append(w_space, w - crop_size)

    index = 0
    for x in h_space:
        for y in w_space:
            index += 1
            cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
            cropped_img = np.ascontiguousarray(cropped_img)
            cv2.imwrite(
                osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
                [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
    process_info = f'Processing {img_name} ...'
    return process_info


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale_sub', help='Output folder')
    parser.add_argument('--crop_size', type=int, default=400, help='Crop size')
    parser.add_argument('--step', type=int, default=200, help='Step for overlapped sliding window')
    parser.add_argument(
        '--thresh_size',
        type=int,
        default=0,
        help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
    parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
    parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
    args = parser.parse_args()

    main(args)


================================================
FILE: RealSR/scripts/extract_subimages_DF2K.py
================================================
import argparse
import cv2
import numpy as np
import os
import sys
from basicsr.utils import scandir
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def main(args):
    """A multi-thread tool to crop large images to sub-images for faster IO.

    opt (dict): Configuration dict. It contains:
        n_thread (int): Thread number.
        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
            and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2.
        input_folder (str): Path to the input folder.
        save_folder (str): Path to save folder.
        crop_size (int): Crop size.
        step (int): Step for overlapped sliding window.
        thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.

    Usage:
        For each folder, run this script.
        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
        After process, each sub_folder should have the same number of subimages.
        Remember to modify opt configurations according to your settings.
    """

    opt = {}
    opt['n_thread'] = args.n_thread
    opt['compression_level'] = args.compression_level
    opt['input_folder'] = args.input
    opt['save_folder'] = args.output
    opt['crop_size'] = args.crop_size
    opt['step'] = args.step
    opt['thresh_size'] = args.thresh_size
    extract_subimages(opt)


def extract_subimages(opt):
    """Crop images to subimages.

    Args:
        opt (dict): Configuration dict. It contains:
            input_folder (str): Path to the input folder.
            save_folder (str): Path to save folder.
            n_thread (int): Thread number.
    """
    input_folder = opt['input_folder']
    save_folder = opt['save_folder']
    if not osp.exists(save_folder):
        os.makedirs(save_folder)
        print(f'mkdir {save_folder} ...')
    else:
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)

    # scan all images
    img_list = list(scandir(input_folder, full_path=True))

    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
    pool = Pool(opt['n_thread'])
    for path in img_list:
        pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
    pool.close()
    pool.join()
    pbar.close()
    print('All processes done.')


def worker(path, opt):
    """Worker for each process.

    Args:
        path (str): Image path.
        opt (dict): Configuration dict. It contains:
            crop_size (int): Crop size.
            step (int): Step for overlapped sliding window.
            thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
            save_folder (str): Path to save folder.
            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.

    Returns:
        process_info (str): Process information displayed in progress bar.
    """
    crop_size = opt['crop_size']
    step = opt['step']
    thresh_size = opt['thresh_size']
    img_name, extension = osp.splitext(osp.basename(path))

    # remove the x2, x3, x4 and x8 in the filename for DIV2K
    img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)

    h, w = img.shape[0:2]
    h_space = np.arange(0, h - crop_size + 1, step)
    if h - (h_space[-1] + crop_size) > thresh_size:
        h_space = np.append(h_space, h - crop_size)
    w_space = np.arange(0, w - crop_size + 1, step)
    if w - (w_space[-1] + crop_size) > thresh_size:
        w_space = np.append(w_space, w - crop_size)

    index = 0
    for x in h_space:
        for y in w_space:
            index += 1
            cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
            cropped_img = np.ascontiguousarray(cropped_img)
            cv2.imwrite(
                osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
                [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
    process_info = f'Processing {img_name} ...'
    return process_info


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/HR', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_sub', help='Output folder')
    parser.add_argument('--crop_size', type=int, default=480, help='Crop size')
    parser.add_argument('--step', type=int, default=240, help='Step for overlapped sliding window')
    parser.add_argument(
        '--thresh_size',
        type=int,
        default=0,
        help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
    parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
    parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
    args = parser.parse_args()

    main(args)


================================================
FILE: RealSR/scripts/generate_meta_info.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['datasets/DF2K', 'datasets/DF2K'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: RealSR/scripts/generate_meta_info_DF2K.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_sub', '/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale_sub'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset', '/mnt/bn/xiabinpaint/dataset'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/xiabinpaint/ICCV-SR/KDSR-GAN/datasets/meta_info/meta_info_DF2Kmultiscale_sub.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: RealSR/scripts/generate_meta_info_DF2K_memo.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_sub', '/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale_sub'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset', '/mnt/bn/xiabinpaint/dataset'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/xiabinpaint/ICCV-SR/KDSR-GAN/datasets/meta_info/meta_info_DF2Kmultiscale_sub.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: RealSR/scripts/generate_meta_info_OST.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset/OST/train/HR'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/xiabinpaint/ICCV-SR/KDSR-GAN/datasets/meta_info/meta_info_OST.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: RealSR/scripts/generate_meta_info_pairdata.py
================================================
import argparse
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    # sca images
    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))

    assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
                                                    f'{len(img_paths_gt)} and {len(img_paths_lq)}.')

    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
        # get the relative paths
        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
        print(f'{img_name_gt}, {img_name_lq}')
        txt_file.write(f'{img_name_gt}, {img_name_lq}\n')


if __name__ == '__main__':
    """This script is used to generate meta info (txt file) for paired images.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
        help='Input folder, should be [gt_folder, lq_folder]')
    parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
        help='txt path for meta info')
    args = parser.parse_args()

    assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
    assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
    for i in range(2):
        if args.input[i].endswith('/'):
            args.input[i] = args.input[i][:-1]
        if args.root[i] is None:
            args.root[i] = os.path.dirname(args.input[i])

    main(args)


================================================
FILE: RealSR/scripts/generate_multiscale_DF2K.py
================================================
import argparse
import glob
import os
from PIL import Image


def main(args):
    # For DF2K, we consider the following three scales,
    # and the smallest image whose shortest edge is 400
    scale_list = [0.75, 0.5, 1 / 3]
    shortest_edge = 400

    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
    for path in path_list:
        print(path)
        basename = os.path.splitext(os.path.basename(path))[0]

        img = Image.open(path)
        width, height = img.size
        for idx, scale in enumerate(scale_list):
            print(f'\t{scale:.2f}')
            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))

        # save the smallest image which the shortest edge is 400
        if width < height:
            ratio = height / width
            width = shortest_edge
            height = int(width * ratio)
        else:
            ratio = width / height
            height = shortest_edge
            width = int(height * ratio)
        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))


if __name__ == '__main__':
    """Generate multi-scale versions for GT images with LANCZOS resampling.
    It is now used for DF2K dataset (DIV2K + Flickr 2K)
    """
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bd/dlspace-hl-256g-0001/datasets/DF2K/HR', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bd/dlspace-hl-256g-0001/datasets/DF2K/DF2K_multiscale', help='Output folder')
    args = parser.parse_args()
    os.makedirs(args.output, exist_ok=True)
    main(args)


================================================
FILE: RealSR/scripts/options/mambaSR11GAN_x4.yml
================================================
# general settings
name: MambaRealSR11GAN
model_type: MambaRealSRGAN
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
# USM the ground-truth
l1_gt_usm: False
percep_gt_usm: False
gan_gt_usm: False

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 256
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K+OST
    type: RealESRGANDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale+OST_sub.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 256
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 9
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: NTIRE2020-Track1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-gt
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
    io_backend:
      type: disk


# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/experiments/MambaRealSR11/models/net_g_500000.pth
  param_key_g: params_ema
  strict_load_g: True
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]
    
  optim_d:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [400000]
    gamma: 0.5

  total_iter: 400000
  lr_sr: !!float 1e-4
  gamma_sr: 0.5
  lr_decay_sr: 300000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
  # perceptual loss (content and style losses)
  perceptual_opt:
    type: PerceptualLoss
    layer_weights:
      # before relu
      'conv1_2': 0.1
      'conv2_2': 0.1
      'conv3_4': 1
      'conv4_4': 1
      'conv5_4': 1
    vgg_type: vgg19
    use_input_norm: true
    perceptual_weight: !!float 1.0
    style_weight: 0
    range_norm: false
    criterion: l1
  # gan loss
  gan_opt:
    type: GANLoss
    gan_type: vanilla
    real_label_val: 1.0
    fake_label_val: 0.0
    loss_weight: !!float 1.0

  net_d_iters: 1
  net_d_init_iters: 0
  

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 1e4
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 4
      test_y_channel: true

    ssim: # metric name
      type: calculate_ssim
      crop_border: 4
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 1e4
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/scripts/options/mambaSR11_x4.yml
================================================
# general settings
name: MambaRealSR11
model_type: MambaRealSR
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
gt_usm: False  # USM the ground-truth

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 256
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K+OST
    type: RealESRGANDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale+OST_sub.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 256
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 9
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: NTIRE2020-Track1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-gt
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999

  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [250000,350000]
    gamma: 0.5

  total_iter: 500000
  warmup_iter: -1  # no warm up

  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 5e3
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/scripts/options/mambaSR11m_x4.yml
================================================
# general settings
name: MambaRealSR11m
model_type: MambaRealSR
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
gt_usm: False  # USM the ground-truth

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]

gt_size: 32
queue_size: 180

# dataset and data loader settings
datasets:
  train:
    name: DF2K
    type: RealESRGANDataset_memory
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DIV2K_train_HR
    meta_info: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DIV2k_meta.txt
    io_backend:
      type: disk

    blur_kernel_size: 21
    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob: 0.1
    blur_sigma: [0.2, 3]
    betag_range: [0.5, 4]
    betap_range: [1, 2]

    blur_kernel_size2: 21
    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
    sinc_prob2: 0.1
    blur_sigma2: [0.2, 1.5]
    betag_range2: [0.5, 4]
    betap_range2: [1, 2]

    final_sinc_prob: 0.8

    gt_size: 32
    use_hflip: True
    use_rot: False

    # data loader
    use_shuffle: true
    num_worker_per_gpu: 12
    batch_size_per_gpu: 1
    dataset_enlarge_ratio: 1
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: test1
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-gt-clean
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-input-noisy
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999

  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [250000,350000]
    gamma: 0.5

  total_iter: 500000
  warmup_iter: -1  # no warm up

  mixing_augs:
    mixup: false
    mixup_beta: 1.2
    use_identity: true

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 5e3
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 5e3
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: RealSR/scripts/options/test_mambaSR11GAN_x4.yml
================================================
# general settings
name: test_mambaSR11GAN2
model_type: MambaRealSRGANtest
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0



# dataset and data loader settings
datasets:
  #test_1:
  #  name: NTIRE2020-Track1
  #  type: SingleImageDataset
  #  dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/NTIRE2020/track1-valid-input
  #  io_backend:
  #    type: disk

  test_2:
    name: AIM19
    type: SingleImageDataset
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/AIM19/AIM19/valid-input-noisy
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaRealSR11
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [6,2,2,1]
  num_refinement_blocks: 6
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/RealSR/experiments/MambaRealSR11GAN/models/net_g_360000.pth
  param_key_g: params_ema
  strict_load_g: False



val:
  window_size: 8
  save_img: True
  suffix: ~  # add suffix to saved images, if None, use exp name







================================================
FILE: RealSR/scripts/pytorch2onnx.py
================================================
import argparse
import torch
import torch.onnx
from DiffIR.archs.S2_arch import DiffIRS2


def main(args):
    # An instance of the model
    model = DiffIRS2( n_encoder_res= 9, dim= 64, scale=args.scale,num_blocks= [13,1,1,1],num_refinement_blocks= 13,heads= [1,2,4,8], ffn_expansion_factor= 2.2,LayerNorm_type= "BiasFree")
    loadnet = torch.load(args.model_path, map_location=torch.device('cpu'))
    model.load_state_dict(loadnet['params_ema'], strict=True)
    # set the train mode to false since we will only run the forward pass.
    model.train(False)
    model.cpu().eval()

    # An example input
    x = torch.rand(1, 3, 64, 64)
    # Export the model
    with torch.no_grad():
        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
    print(torch_out.shape)


if __name__ == '__main__':
    """Convert pytorch model to onnx models"""
    parser = argparse.ArgumentParser()
    parser.add_argument('--scale', type=int, default=4)
    parser.add_argument('--model_path', type=str, default='./experiments/DiffIRS2-GANv2.pth')
    parser.add_argument('--output', type=str, default='DiffIRS2-GANv2-x4.onnx', help='Output onnx path')
    args = parser.parse_args()

    main(args)


================================================
FILE: RealSR/setup.cfg
================================================
[flake8]
ignore =
    # line break before binary operator (W503)
    W503,
    # line break after binary operator (W504)
    W504,
max-line-length=120

[yapf]
based_on_style = pep8
column_limit = 120
blank_line_before_nested_class_or_def = true
split_before_expression_after_opening_paren = true

[isort]
line_length = 120
multi_line_output = 0
known_standard_library = pkg_resources,setuptools
known_first_party = realesrgan
known_third_party = PIL,basicsr,cv2,numpy,pytest,torch,torchvision,tqdm,yaml
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY

[codespell]
skip = .git,./docs/build
count =
quiet-level = 3

[aliases]
test=pytest

[tool:pytest]
addopts=tests/


================================================
FILE: RealSR/setup.py
================================================
#!/usr/bin/env python

from setuptools import find_packages, setup

import os
import subprocess
import time

version_file = 'VmambaIR/version.py'


def readme():
    with open('README.md', encoding='utf-8') as f:
        content = f.read()
    return content


def get_git_hash():

    def _minimal_ext_cmd(cmd):
        # construct minimal environment
        env = {}
        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
            v = os.environ.get(k)
            if v is not None:
                env[k] = v
        # LANGUAGE is used on win32
        env['LANGUAGE'] = 'C'
        env['LANG'] = 'C'
        env['LC_ALL'] = 'C'
        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
        return out

    try:
        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
        sha = out.strip().decode('ascii')
    except OSError:
        sha = 'unknown'

    return sha


def get_hash():
    if os.path.exists('.git'):
        sha = get_git_hash()[:7]
    else:
        sha = 'unknown'

    return sha


def write_version_py():
    content = """# GENERATED VERSION FILE
# TIME: {}
__version__ = '{}'
__gitsha__ = '{}'
version_info = ({})
"""
    sha = get_hash()
    with open('VERSION', 'r') as f:
        SHORT_VERSION = f.read().strip()
    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])

    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
    with open(version_file, 'w') as f:
        f.write(version_file_str)


def get_version():
    with open(version_file, 'r') as f:
        exec(compile(f.read(), version_file, 'exec'))
    return locals()['__version__']


def get_requirements(filename='requirements.txt'):
    here = os.path.dirname(os.path.realpath(__file__))
    with open(os.path.join(here, filename), 'r') as f:
        requires = [line.replace('\n', '') for line in f.readlines()]
    return requires


if __name__ == '__main__':
    write_version_py()
    setup(
        name='realesrgan',
        version=get_version(),
        description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
        long_description=readme(),
        long_description_content_type='text/markdown',
        author='Xintao Wang',
        author_email='xintao.wang@outlook.com',
        keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
        url='https://github.com/xinntao/Real-ESRGAN',
        include_package_data=True,
        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
        classifiers=[
            'Development Status :: 4 - Beta',
            'License :: OSI Approved :: Apache Software License',
            'Operating System :: OS Independent',
            'Programming Language :: Python :: 3',
            'Programming Language :: Python :: 3.7',
            'Programming Language :: Python :: 3.8',
        ],
        license='BSD-3-Clause License',
        setup_requires=['cython', 'numpy'],
        install_requires=get_requirements(),
        zip_safe=False)


================================================
FILE: RealSR/test.sh
================================================


CUDA_VISIBLE_DEVICES=1 python3  VmambaIR/test.py -opt options/test_mambaSR11GAN_x4.yml


================================================
FILE: RealSR/tests/data/gt.lmdb/meta_info.txt
================================================
baboon.png (480,500,3) 1
comic.png (360,240,3) 1


================================================
FILE: RealSR/tests/data/lq.lmdb/meta_info.txt
================================================
baboon.png (120,125,3) 1
comic.png (80,60,3) 1


================================================
FILE: RealSR/tests/data/meta_info_gt.txt
================================================
baboon.png
comic.png


================================================
FILE: RealSR/tests/data/meta_info_pair.txt
================================================
gt/baboon.png, lq/baboon.png
gt/comic.png, lq/comic.png


================================================
FILE: RealSR/tests/data/test_realesrgan_dataset.yml
================================================
name: Demo
type: RealESRGANDataset
dataroot_gt: tests/data/gt
meta_info: tests/data/meta_info_gt.txt
io_backend:
  type: disk

blur_kernel_size: 21
kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
sinc_prob: 1
blur_sigma: [0.2, 3]
betag_range: [0.5, 4]
betap_range: [1, 2]

blur_kernel_size2: 21
kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
sinc_prob2: 1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]

final_sinc_prob: 1

gt_size: 128
use_hflip: True
use_rot: False


================================================
FILE: RealSR/tests/data/test_realesrgan_model.yml
================================================
scale: 4
num_gpu: 1
manual_seed: 0
is_train: True
dist: False

# ----------------- options for synthesizing training data ----------------- #
# USM the ground-truth
l1_gt_usm: True
percep_gt_usm: True
gan_gt_usm: False

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 1
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 1
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 1
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 1
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 1
jpeg_range2: [30, 95]

gt_size: 32
queue_size: 1

# network structures
network_g:
  type: RRDBNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 4
  num_block: 1
  num_grow_ch: 2

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 2
  skip_connection: True

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]
  optim_d:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [400000]
    gamma: 0.5

  total_iter: 400000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
  # perceptual loss (content and style losses)
  perceptual_opt:
    type: PerceptualLoss
    layer_weights:
      # before relu
      'conv1_2': 0.1
      'conv2_2': 0.1
      'conv3_4': 1
      'conv4_4': 1
      'conv5_4': 1
    vgg_type: vgg19
    use_input_norm: true
    perceptual_weight: !!float 1.0
    style_weight: 0
    range_norm: false
    criterion: l1
  # gan loss
  gan_opt:
    type: GANLoss
    gan_type: vanilla
    real_label_val: 1.0
    fake_label_val: 0.0
    loss_weight: !!float 1e-1

  net_d_iters: 1
  net_d_init_iters: 0


# validation settings
val:
  val_freq: !!float 5e3
  save_img: False


================================================
FILE: RealSR/tests/data/test_realesrgan_paired_dataset.yml
================================================
name: Demo
type: RealESRGANPairedDataset
scale: 4
dataroot_gt: tests/data
dataroot_lq: tests/data
meta_info: tests/data/meta_info_pair.txt
io_backend:
  type: disk

phase: train
gt_size: 128
use_hflip: True
use_rot: False


================================================
FILE: RealSR/tests/data/test_realesrnet_model.yml
================================================
scale: 4
num_gpu: 1
manual_seed: 0
is_train: True
dist: False

# ----------------- options for synthesizing training data ----------------- #
gt_usm: True  # USM the ground-truth

# the first degradation process
resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 1
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 1
jpeg_range: [30, 95]

# the second degradation process
second_blur_prob: 1
resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 1
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 1
jpeg_range2: [30, 95]

gt_size: 32
queue_size: 1

# network structures
network_g:
  type: RRDBNet
  num_in_ch: 3
  num_out_ch: 3
  num_feat: 4
  num_block: 1
  num_grow_ch: 2

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [1000000]
    gamma: 0.5

  total_iter: 1000000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean


# validation settings
val:
  val_freq: !!float 5e3
  save_img: False


================================================
FILE: RealSR/tests/test_dataset.py
================================================
import pytest
import yaml

from realesrgan.data.realesrgan_dataset import RealESRGANDataset
from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset


def test_realesrgan_dataset():

    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)

    dataset = RealESRGANDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info
    assert dataset.kernel_list == [
        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
    ]  # correct initialization the degradation configurations
    assert dataset.betag_range2 == [0.5, 4]

    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'tests/data/gt/baboon.png'

    # ------------------ test lmdb backend -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['io_backend']['type'] = 'lmdb'

    dataset = RealESRGANDataset(opt)
    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
    assert len(dataset.paths) == 2  # whether to read correct meta info
    assert dataset.kernel_list == [
        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
    ]  # correct initialization the degradation configurations
    assert dataset.betag_range2 == [0.5, 4]

    # test __getitem__
    result = dataset.__getitem__(1)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'comic'

    # ------------------ test with sinc_prob = 0 -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['io_backend']['type'] = 'lmdb'
    opt['sinc_prob'] = 0
    opt['sinc_prob2'] = 0
    opt['final_sinc_prob'] = 0
    dataset = RealESRGANDataset(opt)
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 400, 400)
    assert result['kernel1'].shape == (21, 21)
    assert result['kernel2'].shape == (21, 21)
    assert result['sinc_kernel'].shape == (21, 21)
    assert result['gt_path'] == 'baboon'

    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
    with pytest.raises(ValueError):
        opt['dataroot_gt'] = 'tests/data/gt'
        opt['io_backend']['type'] = 'lmdb'
        dataset = RealESRGANDataset(opt)


def test_realesrgan_paired_dataset():

    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)

    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info

    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
    assert result['gt_path'] == 'tests/data/gt/baboon.png'
    assert result['lq_path'] == 'tests/data/lq/baboon.png'

    # ------------------ test lmdb backend -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
    opt['io_backend']['type'] = 'lmdb'

    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info

    # test __getitem__
    result = dataset.__getitem__(1)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)
    assert result['gt_path'] == 'comic'
    assert result['lq_path'] == 'comic'

    # ------------------ test paired_paths_from_folder -------------------- #
    opt['dataroot_gt'] = 'tests/data/gt'
    opt['dataroot_lq'] = 'tests/data/lq'
    opt['io_backend'] = dict(type='disk')
    opt['meta_info'] = None

    dataset = RealESRGANPairedDataset(opt)
    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
    assert len(dataset) == 2  # whether to read correct meta info

    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)

    # ------------------ test normalization -------------------- #
    dataset.mean = [0.5, 0.5, 0.5]
    dataset.std = [0.5, 0.5, 0.5]
    # test __getitem__
    result = dataset.__getitem__(0)
    # check returned keys
    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
    assert set(expected_keys).issubset(set(result.keys()))
    # check shape and contents
    assert result['gt'].shape == (3, 128, 128)
    assert result['lq'].shape == (3, 32, 32)


================================================
FILE: RealSR/tests/test_discriminator_arch.py
================================================
import torch

from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN


def test_unetdiscriminatorsn():
    """Test arch: UNetDiscriminatorSN."""

    # model init and forward (cpu)
    net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
    img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    output = net(img)
    assert output.shape == (1, 1, 32, 32)

    # model init and forward (gpu)
    if torch.cuda.is_available():
        net.cuda()
        output = net(img.cuda())
        assert output.shape == (1, 1, 32, 32)


================================================
FILE: RealSR/tests/test_model.py
================================================
import torch
import yaml
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.data.paired_image_dataset import PairedImageDataset
from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss

from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
from realesrgan.models.realesrgan_model import RealESRGANModel
from realesrgan.models.realesrnet_model import RealESRNetModel


def test_realesrnet_model():
    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)

    # build model
    model = RealESRNetModel(opt)
    # test attributes
    assert model.__class__.__name__ == 'RealESRNetModel'
    assert isinstance(model.net_g, RRDBNet)
    assert isinstance(model.cri_pix, L1Loss)
    assert isinstance(model.optimizers[0], torch.optim.Adam)

    # prepare data
    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
    model.feed_data(data)
    # check dequeue
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)

    # change probability to test if-else
    model.opt['gaussian_noise_prob'] = 0
    model.opt['gray_noise_prob'] = 0
    model.opt['second_blur_prob'] = 0
    model.opt['gaussian_noise_prob2'] = 0
    model.opt['gray_noise_prob2'] = 0
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)

    # ----------------- test nondist_validation -------------------- #
    # construct dataloader
    dataset_opt = dict(
        name='Demo',
        dataroot_gt='tests/data/gt',
        dataroot_lq='tests/data/lq',
        io_backend=dict(type='disk'),
        scale=4,
        phase='val')
    dataset = PairedImageDataset(dataset_opt)
    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
    assert model.is_train is True
    model.nondist_validation(dataloader, 1, None, False)
    assert model.is_train is True


def test_realesrgan_model():
    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
        opt = yaml.load(f, Loader=yaml.FullLoader)

    # build model
    model = RealESRGANModel(opt)
    # test attributes
    assert model.__class__.__name__ == 'RealESRGANModel'
    assert isinstance(model.net_g, RRDBNet)  # generator
    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
    assert isinstance(model.cri_pix, L1Loss)
    assert isinstance(model.cri_perceptual, PerceptualLoss)
    assert isinstance(model.cri_gan, GANLoss)
    assert isinstance(model.optimizers[0], torch.optim.Adam)
    assert isinstance(model.optimizers[1], torch.optim.Adam)

    # prepare data
    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
    model.feed_data(data)
    # check dequeue
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)

    # change probability to test if-else
    model.opt['gaussian_noise_prob'] = 0
    model.opt['gray_noise_prob'] = 0
    model.opt['second_blur_prob'] = 0
    model.opt['gaussian_noise_prob2'] = 0
    model.opt['gray_noise_prob2'] = 0
    model.feed_data(data)
    # check data shape
    assert model.lq.shape == (1, 3, 8, 8)
    assert model.gt.shape == (1, 3, 32, 32)

    # ----------------- test nondist_validation -------------------- #
    # construct dataloader
    dataset_opt = dict(
        name='Demo',
        dataroot_gt='tests/data/gt',
        dataroot_lq='tests/data/lq',
        io_backend=dict(type='disk'),
        scale=4,
        phase='val')
    dataset = PairedImageDataset(dataset_opt)
    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
    assert model.is_train is True
    model.nondist_validation(dataloader, 1, None, False)
    assert model.is_train is True

    # ----------------- test optimize_parameters -------------------- #
    model.feed_data(data)
    model.optimize_parameters(1)
    assert model.output.shape == (1, 3, 32, 32)
    assert isinstance(model.log_dict, dict)
    # check returned keys
    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
    assert set(expected_keys).issubset(set(model.log_dict.keys()))


================================================
FILE: RealSR/tests/test_utils.py
================================================
import numpy as np
from basicsr.archs.rrdbnet_arch import RRDBNet

from realesrgan.utils import RealESRGANer


def test_realesrganer():
    # initialize with default model
    restorer = RealESRGANer(
        scale=4,
        model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth',
        model=None,
        tile=10,
        tile_pad=10,
        pre_pad=2,
        half=False)
    assert isinstance(restorer.model, RRDBNet)
    assert restorer.half is False
    # initialize with user-defined model
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
    restorer = RealESRGANer(
        scale=4,
        model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth',
        model=model,
        tile=10,
        tile_pad=10,
        pre_pad=2,
        half=True)
    # test attribute
    assert isinstance(restorer.model, RRDBNet)
    assert restorer.half is True

    # ------------------ test pre_process ---------------- #
    img = np.random.random((12, 12, 3)).astype(np.float32)
    restorer.pre_process(img)
    assert restorer.img.shape == (1, 3, 14, 14)
    # with modcrop
    restorer.scale = 1
    restorer.pre_process(img)
    assert restorer.img.shape == (1, 3, 16, 16)

    # ------------------ test process ---------------- #
    restorer.process()
    assert restorer.output.shape == (1, 3, 64, 64)

    # ------------------ test post_process ---------------- #
    restorer.mod_scale = 4
    output = restorer.post_process()
    assert output.shape == (1, 3, 60, 60)

    # ------------------ test tile_process ---------------- #
    restorer.scale = 4
    img = np.random.random((12, 12, 3)).astype(np.float32)
    restorer.pre_process(img)
    restorer.tile_process()
    assert restorer.output.shape == (1, 3, 64, 64)

    # ------------------ test enhance ---------------- #
    img = np.random.random((12, 12, 3)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (24, 24, 3)
    assert result[1] == 'RGB'

    # ------------------ test enhance with 16-bit image---------------- #
    img = np.random.random((4, 4, 3)).astype(np.uint16) + 512
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8, 3)
    assert result[1] == 'RGB'

    # ------------------ test enhance with gray image---------------- #
    img = np.random.random((4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8)
    assert result[1] == 'L'

    # ------------------ test enhance with RGBA---------------- #
    img = np.random.random((4, 4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2)
    assert result[0].shape == (8, 8, 4)
    assert result[1] == 'RGBA'

    # ------------------ test enhance with RGBA, alpha_upsampler---------------- #
    restorer.tile_size = 0
    img = np.random.random((4, 4, 4)).astype(np.float32)
    result = restorer.enhance(img, outscale=2, alpha_upsampler=None)
    assert result[0].shape == (8, 8, 4)
    assert result[1] == 'RGBA'


================================================
FILE: RealSR/train_S1.sh
================================================

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 -m torch.distributed.launch --nproc_per_node=8 \
    --master_port=7310 \
    --use_env \
    VmambaIR/train.py \
    -opt options/mambaSR11_x4.yml \
    --launcher pytorch 


================================================
FILE: RealSR/train_S2.sh
================================================

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 -m torch.distributed.launch --nproc_per_node=8 \
    --master_port=7310 \
    --use_env \
    VmambaIR/train.py \
    -opt options/mambaSR11GAN_x4.yml \
    --launcher pytorch 


================================================
FILE: SRGAN/.gitignore
================================================
# ignored folders
datasets/*
experiments/*
results/*
tb_logger/*
wandb/*
tmp/*
realesrgan/weights/*

version.py

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/


================================================
FILE: SRGAN/Metric/DISTS/DISTS_pytorch/DISTS_pt.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('scripts/metrics/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'

    # others
    # data_root = '/data1/liangjie/BasicSR_ALL/results/'
    # ref_root = '/data1/liangjie/BasicSR_ALL/datasets/'
    # ref_dirs = ['SISR_Test_matlab/Set5mod12', 'SISR_Test_matlab/Set14mod12', 'SISR_Test_matlab/Manga109mod12', 'SISR_Test_matlab/BSDS100mod12', 'SISR_Test_matlab/General100mod12', 'SISR_Test/Urban100', 'DIV2K/DIV2K_valid_HR/']
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SRGAN_official', 'ESRGAN_official', 'NatSR_official', 'USRGAN_official', 'SPSR_official', 'SPSR_DF2K', 'ESRGAN_ours_DIV2K', 'ESRGAN_ours_DIV2K_ema', 'ESRGAN_ours_DF2K', 'ESRGAN_ours_DF2K_ema']

    # SFTGAN
    # data_root = '/data1/liangjie/BasicSR_ALL/results'
    # ref_root = '/data1/liangjie/BasicSR_ALL/results/SFTGAN_official'
    # ref_dirs = ['GT'] * 7
    # datasets = ['Set5', 'Set14', 'Manga109', 'BSDS100', 'General100', 'Urban100', 'DIV2K100']
    # img_dirs = ['SFTGAN_official']

    # new
    data_root = 'results/'
    ref_root = 'datasets/'
    ref_dirs = ['DIV2K/DIV2K_valid_HR/']
    datasets = ['DIV2K100']
    img_dirs = ['ESRGAN_ours_DISTS_300k/visualization/']

    logoverall_path = 'results/table_logs/' + 'DISTS_orisize_DISTStrain225k.txt'

    for index in range(len(ref_dirs)):
        ref_dir = os.path.join(ref_root, ref_dirs[index])
        for method in img_dirs:
            img_dir = os.path.join(data_root, method, datasets[index])

            img_list = sorted(glob.glob(os.path.join(img_dir, '*')))

            log_path = 'results/table_logs/' + img_dir.replace('/', '_') + '_DISTS_orisize.txt'

            DISTS_all = []

            for i, img_path in enumerate(img_list):
                file_name = img_path.split('/')[-1]
                if 'DIV2K100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:4] + '.png')
                elif 'Urban100' in img_dir and 'SFTGAN' not in img_dir:
                    gt_path = os.path.join(ref_dir, file_name[:7] + '.png')
                elif 'SFTGAN' in img_dir:
                    gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_gt.png')
                    if 'Urban100' in img_dir:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '_' + file_name.split('_')[1] + '_gt.png')
                else:
                    if '_' in file_name:
                        gt_path = os.path.join(ref_dir, file_name.split('_')[0] + '.png')
                    else:
                        gt_path = os.path.join(ref_dir, file_name)

                ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
                dist = prepare_image(Image.open(img_path).convert("RGB"), resize=False)
                assert ref.shape == dist.shape
                device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                model = DISTS().to(device)
                ref = ref.to(device)
                dist = dist.to(device)
                score = model(ref, dist)
                DISTS_all.append(score.item())
                log = f'{i + 1:3d}: {file_name:25}. \tDISTS: {score.item():.6f}.'
                with open(log_path, 'a') as f:
                    f.write(log + '\n')
                # print(log)

            log = f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}'
            with open(log_path, 'a') as f:
                f.write(log + '\n')
            log_overall = method + '__' + datasets[index] + '__' + log
            with open(logoverall_path, 'a') as f:
                f.write(log_overall + '\n')
            print(log_overall)



================================================
FILE: SRGAN/Metric/DISTS/DISTS_tensorflow/DISTS_tf.py
================================================
# This is a tensorflow implementation of DISTS metric.
# Requirements: python >= 3.6, tensorflow-gpu >= 1.15

import tensorflow.compat.v1 as tf
import numpy as np
import time
import scipy.io as scio
from PIL import Image
import argparse
# tf.enable_eager_execution()
tf.disable_eager_execution()

class DISTS():
    def __init__(self):
        self.parameters = scio.loadmat('../weights/net_param.mat')
        self.chns = [3,64,128,256,512,512]
        self.mean = tf.constant(self.parameters['vgg_mean'], dtype=tf.float32, shape=(1,1,1,3),name="img_mean")
        self.std = tf.constant(self.parameters['vgg_std'], dtype=tf.float32, shape=(1,1,1,3),name="img_std")
        # self.alpha = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="alpha")
        # self.beta = tf.Variable(tf.random_normal(shape=(1,1,1,sum(self.chns)), mean=0.1, stddev=0.01),name="beta")
        self.weights = scio.loadmat('../weights/alpha_beta.mat')
        self.alpha = tf.constant(np.reshape(self.weights['alpha'],(1,1,1,sum(self.chns))),name="alpha")
        self.beta = tf.constant(np.reshape(self.weights['beta'],(1,1,1,sum(self.chns))),name="beta")

    def get_features(self, img):

        x = (img - self.mean)/self.std

        self.conv1_1 = self.conv_layer(x, "conv1_1")
        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
        self.pool1 = self.pool_layer(self.conv1_2, name="pool_1")

        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
        self.pool2 = self.pool_layer(self.conv2_2, name="pool_2")

        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
        self.pool3 = self.pool_layer(self.conv3_3, name="pool_3")

        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
        self.pool4 = self.pool_layer(self.conv4_3, name="pool_4")

        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")

        return [img, self.conv1_2,self.conv2_2,self.conv3_3,self.conv4_3,self.conv5_3]

    def conv_layer(self, input, name):
        with tf.variable_scope(name) as _:
            filter = self.get_conv_filter(name)
            conv = tf.nn.conv2d(input, filter, strides=1, padding="SAME")
            bias = self.get_bias(name)
            conv = tf.nn.relu(tf.nn.bias_add(conv, bias))
            return conv

    def pool_layer(self, input, name):
        # return tf.nn.max_pool(input, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
        with tf.variable_scope(name) as _:
            filter = tf.squeeze(tf.constant(self.parameters['L2'+name], name = "filter"),3)
            conv = tf.nn.conv2d(input**2, filter, strides=2, padding=[[0, 0], [1, 0], [1, 0], [0, 0]])
            return tf.sqrt(tf.maximum(conv, 1e-12))     

    def get_conv_filter(self, name):
        return tf.constant(self.parameters[name+'_weight'], name = "filter")

    def get_bias(self, name):
        return tf.constant(np.squeeze(self.parameters[name+'_bias']), name = "bias")

    def get_score(self, img1, img2):
        feats0 = self.get_features(img1)
        feats1 = self.get_features(img2)
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = tf.reduce_sum(self.alpha) + tf.reduce_sum(self.beta)
        alpha = tf.split(self.alpha/w_sum, self.chns, axis=3)
        beta = tf.split(self.beta/w_sum, self.chns, axis=3)
        for k in range(len(self.chns)):
            x_mean = tf.reduce_mean(feats0[k],[1,2], keepdims=True)
            y_mean = tf.reduce_mean(feats1[k],[1,2], keepdims=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+tf.reduce_sum(alpha[k]*S1, 3, keepdims=True)
            x_var = tf.reduce_mean((feats0[k]-x_mean)**2,[1,2], keepdims=True)
            y_var = tf.reduce_mean((feats1[k]-y_mean)**2,[1,2], keepdims=True)
            xy_cov = tf.reduce_mean(feats0[k]*feats1[k],[1,2], keepdims=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+tf.reduce_sum(beta[k]*S2, 3, keepdims=True)

        dist = 1-tf.squeeze(dist1+dist2)
        return dist


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--ref', type=str, default='../images/r0.png')
    parser.add_argument('--dist', type=str, default='../images/r1.png')
    args = parser.parse_args()
    model = DISTS()

    ref = np.array(Image.open(args.ref).convert("RGB"))
    ref = np.expand_dims(ref,axis=0)/255.
    dist = np.array(Image.open(args.dist).convert("RGB"))
    dist = np.expand_dims(dist,axis=0)/255.

    x = tf.placeholder(dtype=tf.float32, shape=ref.shape, name= "ref")
    y = tf.placeholder(dtype=tf.float32, shape=dist.shape, name= "dist")
    score = model.get_score(x,y)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        score = sess.run(score, feed_dict={x: ref, y: dist})
        print(score)




================================================
FILE: SRGAN/Metric/DISTS/LICENSE
================================================
MIT License

Copyright (c) 2020 Keyan Ding

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: SRGAN/Metric/DISTS/requirements.txt
================================================
torch>=1.0

================================================
FILE: SRGAN/Metric/LPIPS.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    loss_fn_vgg = lpips.LPIPS(net='vgg').cuda(0)
    lpips_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    mean = [0.5, 0.5, 0.5]
    std = [0.5, 0.5, 0.5]
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED).astype(
            np.float32) / 255.

        img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)
        # norm to [-1, 1]
        normalize(img_gt, mean, std, inplace=True)
        normalize(img_restored, mean, std, inplace=True)

        # calculate lpips
        lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(0), img_gt.unsqueeze(0).cuda(0)).cpu().data.numpy()[0,0,0,0]
        # print(lpips_val)
        lpips_all.append(lpips_val)

    print(f'Average: LPIPS: {sum(lpips_all) / len(lpips_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: SRGAN/Metric/PSNR.py
================================================
import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
import lpips
import argparse
from basicsr.metrics import calculate_psnr, calculate_ssim


def main():
    # Configurations
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/mnt/bn/shiyuan-arnold/code/Restormer/Deraining/Datasets/test/Test2800/target')
    parser.add_argument('--folder_restored', type=str, default='/mnt/bn/shiyuan-arnold/code/Mamber/Restormer/Deraining/results/Test2800')
    args = parser.parse_args()
    psnr_all = []
    ssim_all = []
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))
    for i, (img_path, lr_path) in enumerate(zip(img_list,lr_list)):
        basename, ext = osp.splitext(osp.basename(img_path))
        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
        img_restored = cv2.imread(osp.join(lr_path), cv2.IMREAD_UNCHANGED)
        psnr=calculate_psnr(img_restored, img_gt, crop_border=4, test_y_channel=True)
        ssim=calculate_ssim(img_restored, img_gt, crop_border=4, test_y_channel=True)
        psnr_all.append(psnr)
        ssim_all.append(ssim)

    print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f}')
    print(f'Average: SSIM: {sum(ssim_all) / len(ssim_all):.6f}')


if __name__ == '__main__':
    main()


================================================
FILE: SRGAN/Metric/dists.py
================================================
# This is a pytoch implementation of DISTS metric.
# Requirements: python >= 3.6, pytorch >= 1.0

import numpy as np
import os,sys
import torch
from torchvision import models,transforms
import torch.nn as nn
import torch.nn.functional as F
import argparse
import os.path as osp

class L2pooling(nn.Module):
    def __init__(self, filter_size=5, stride=2, channels=None, pad_off=0):
        super(L2pooling, self).__init__()
        self.padding = (filter_size - 2 )//2
        self.stride = stride
        self.channels = channels
        a = np.hanning(filter_size)[1:-1]
        g = torch.Tensor(a[:,None]*a[None,:])
        g = g/torch.sum(g)
        self.register_buffer('filter', g[None,None,:,:].repeat((self.channels,1,1,1)))

    def forward(self, input):
        input = input**2
        out = F.conv2d(input, self.filter, stride=self.stride, padding=self.padding, groups=input.shape[1])
        return (out+1e-12).sqrt()

class DISTS(torch.nn.Module):
    def __init__(self, load_weights=True):
        super(DISTS, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=True).features
        self.stage1 = torch.nn.Sequential()
        self.stage2 = torch.nn.Sequential()
        self.stage3 = torch.nn.Sequential()
        self.stage4 = torch.nn.Sequential()
        self.stage5 = torch.nn.Sequential()
        for x in range(0,4):
            self.stage1.add_module(str(x), vgg_pretrained_features[x])
        self.stage2.add_module(str(4), L2pooling(channels=64))
        for x in range(5, 9):
            self.stage2.add_module(str(x), vgg_pretrained_features[x])
        self.stage3.add_module(str(9), L2pooling(channels=128))
        for x in range(10, 16):
            self.stage3.add_module(str(x), vgg_pretrained_features[x])
        self.stage4.add_module(str(16), L2pooling(channels=256))
        for x in range(17, 23):
            self.stage4.add_module(str(x), vgg_pretrained_features[x])
        self.stage5.add_module(str(23), L2pooling(channels=512))
        for x in range(24, 30):
            self.stage5.add_module(str(x), vgg_pretrained_features[x])
    
        for param in self.parameters():
            param.requires_grad = False

        self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1,-1,1,1))
        self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1,-1,1,1))

        self.chns = [3,64,128,256,512,512]
        self.register_parameter("alpha", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.register_parameter("beta", nn.Parameter(torch.randn(1, sum(self.chns),1,1)))
        self.alpha.data.normal_(0.1,0.01)
        self.beta.data.normal_(0.1,0.01)
        if load_weights:
            # weights = torch.load(os.path.join(sys.prefix, 'weights.pt'))
            weights = torch.load('/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-RealSR/Metric/DISTS/DISTS_pytorch/weights.pt')
            self.alpha.data = weights['alpha']
            self.beta.data = weights['beta']
        
    def forward_once(self, x):
        h = (x-self.mean)/self.std
        h = self.stage1(h)
        h_relu1_2 = h
        h = self.stage2(h)
        h_relu2_2 = h
        h = self.stage3(h)
        h_relu3_3 = h
        h = self.stage4(h)
        h_relu4_3 = h
        h = self.stage5(h)
        h_relu5_3 = h
        return [x,h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]

    def forward(self, x, y, require_grad=False, batch_average=False):
        if require_grad:
            feats0 = self.forward_once(x)
            feats1 = self.forward_once(y)   
        else:
            with torch.no_grad():
                feats0 = self.forward_once(x)
                feats1 = self.forward_once(y) 
        dist1 = 0 
        dist2 = 0 
        c1 = 1e-6
        c2 = 1e-6
        w_sum = self.alpha.sum() + self.beta.sum()
        alpha = torch.split(self.alpha/w_sum, self.chns, dim=1)
        beta = torch.split(self.beta/w_sum, self.chns, dim=1)
        for k in range(len(self.chns)):
            x_mean = feats0[k].mean([2,3], keepdim=True)
            y_mean = feats1[k].mean([2,3], keepdim=True)
            S1 = (2*x_mean*y_mean+c1)/(x_mean**2+y_mean**2+c1)
            dist1 = dist1+(alpha[k]*S1).sum(1,keepdim=True)

            x_var = ((feats0[k]-x_mean)**2).mean([2,3], keepdim=True)
            y_var = ((feats1[k]-y_mean)**2).mean([2,3], keepdim=True)
            xy_cov = (feats0[k]*feats1[k]).mean([2,3],keepdim=True) - x_mean*y_mean
            S2 = (2*xy_cov+c2)/(x_var+y_var+c2)
            dist2 = dist2+(beta[k]*S2).sum(1,keepdim=True)

        score = 1 - (dist1+dist2).squeeze()
        if batch_average:
            return score.mean()
        else:
            return score

def prepare_image(image, resize=True):
    if resize and min(image.size) > 256:
        image = transforms.functional.resize(image, 256)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

if __name__ == '__main__':

    from PIL import Image
    import glob

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    parser = argparse.ArgumentParser()
    parser.add_argument('--folder_gt', type=str, default='/root/results/NTIRE2020-Track1')
    parser.add_argument('--folder_restored', type=str, default='/root/datasets/NTIRE2020-Track1/track1-valid-gt')
    args = parser.parse_args()
    img_list = sorted(glob.glob(osp.join(args.folder_gt, '*.png')))
    lr_list = sorted(glob.glob(osp.join(args.folder_restored, '*.png')))


    DISTS_all = []

    for i, (gt_path, lr_path) in enumerate(zip(img_list,lr_list)):
        ref = prepare_image(Image.open(gt_path).convert("RGB"), resize=False)
        dist = prepare_image(Image.open(lr_path).convert("RGB"), resize=False)
        assert ref.shape == dist.shape
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model = DISTS().to(device)
        ref = ref.to(device)
        dist = dist.to(device)
        score = model(ref, dist)
        DISTS_all.append(score.item())
        log = f'{i + 1:3d}:\tDISTS: {score.item():.6f}.'
        print(log)
        # with open(log_path, 'a') as f:
        #     f.write(log + '\n')
        # # print(log)

    print(f'Average: DISTS: {sum(DISTS_all) / len(DISTS_all):.6f}')




================================================
FILE: SRGAN/Metric/pip.sh
================================================
pip install lpips --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install basicsr --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com


================================================
FILE: SRGAN/VERSION
================================================
0.2.5.0


================================================
FILE: SRGAN/VmambaIR/__init__.py
================================================
# flake8: noqa
from .losses import *
from .archs import *
from .data import *
from .models import *
from .utils import *
#from .version import *


================================================
FILE: SRGAN/VmambaIR/archs/MambaSISR6_arch.py
================================================
## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers

from einops import rearrange, repeat

import VmambaIR.archs.common as common

from basicsr.utils.registry import ARCH_REGISTRY

import math
import copy
from fvcore.nn import flop_count, parameter_count


import selective_scan_cuda_core as selective_scan_cuda

class SelectiveScanFn(torch.autograd.Function):

    @staticmethod
    def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
        # input_t: float, fp16, bf16; weight_t: float;
        # u, B, C, delta: input_t
        # D, delta_bias: float
        if u.stride(-1) != 1:
            u = u.contiguous()
        if delta.stride(-1) != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.stride(-1) != 1:
            B = B.contiguous()
        if C.stride(-1) != 1:
            C = C.contiguous()
        if B.dim() == 3:
            B = rearrange(B, "b dstate l -> b 1 dstate l")
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = rearrange(C, "b dstate l -> b 1 dstate l")
            ctx.squeeze_C = True
        if D is not None and (D.dtype != torch.float):
            ctx._d_dtype = D.dtype
            D = D.float()
        if delta_bias is not None and (delta_bias.dtype != torch.float):
            ctx._delta_bias_dtype = delta_bias.dtype
            delta_bias = delta_bias.float()
        
        assert u.shape[1] % (B.shape[1] * nrows) == 0 
        assert nrows in [1, 2, 3, 4] # 8+ is too slow to compile

        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)
        ctx.delta_softplus = delta_softplus
        ctx.nrows = nrows
        ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
        return out

    @staticmethod
    def backward(ctx, dout, *args):
        u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
        if dout.stride(-1) != 1:
            dout = dout.contiguous()
        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
            u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
        )
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
        
        _dD = None
        if D is not None:
            if dD.dtype != getattr(ctx, "_d_dtype", dD.dtype):
                _dD = dD.to(ctx._d_dtype)
            else:
                _dD = dD

        _ddelta_bias = None
        if delta_bias is not None:
            if ddelta_bias.dtype != getattr(ctx, "_delta_bias_dtype", ddelta_bias.dtype):
                _ddelta_bias = ddelta_bias.to(ctx._delta_bias_dtype)
            else:
                _ddelta_bias = ddelta_bias

        return (du, ddelta, dA, dB, dC, _dD, _ddelta_bias, None, None)


def selective_scan_fn_v1(u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, nrows=1):
    """if return_last_state is True, returns (out, last_state)
    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
    not considered in the backward pass.
    """
    return SelectiveScanFn.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows)


# fvcore flops =======================================

def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: r(D N)
    B: r(B N L)
    C: r(B N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32
    
    ignores:
        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] 
    """
    assert not with_complex 
    # https://github.com/state-spaces/mamba/issues/110
    flops = 9 * B * L * D * N
    if with_D:
        flops += B * D * L
    if with_Z:
        flops += B * D * L    
    return flops

def print_jit_input_names(inputs):
    print("input params: ", end=" ", flush=True)
    try: 
        for i in range(10):
            print(inputs[i].debugName(), end=" ", flush=True)
    except Exception as e:
        pass
    print("", flush=True)

def selective_scan_flop_jit(inputs, outputs):
    print_jit_input_names(inputs)
    B, D, L = inputs[0].type().sizes()
    N = inputs[2].type().sizes()[1]
    flops = flops_selective_scan_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=False, with_Group=True)
    return flops


##########################################################################
## Layer Norm

def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')

def to_4d(x,h,w):
    return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)

class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma+1e-5) * self.weight

class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type =='BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)



##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features*2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x



class SS2D_1(nn.Module):
    def __init__(
        self,
        # basic dims ===========
        d_model=96,
        d_state=16,
        ssm_ratio=2.0,
        ssm_rank_ratio=2.0,
        dt_rank="auto",
        act_layer=nn.SiLU,
        # dwconv ===============
        d_conv=3, # < 2 means no conv 
        conv_bias=True,
        # ======================
        dropout=0.0,
        bias=False,
        # dt init ==============
        dt_min=0.001,
        dt_max=0.1,
        dt_init="random",
        dt_scale=1.0,
        dt_init_floor=1e-4,
        simple_init=False,
        # ======================
        softmax_version=False,
        forward_type="v2",
        # ======================
        **kwargs,
    ):
        """
        ssm_rank_ratio would be used in the future...
        """
        factory_kwargs = {"device": None, "dtype": None}
        super().__init__()
        d_expand = int(ssm_ratio * d_model)
        d_inner = int(min(ssm_rank_ratio, ssm_ratio) * d_model) if ssm_rank_ratio > 0 else d_expand
        self.softmax_version = softmax_version
        self.dt_rank = math.ceil(d_model / 16) if dt_rank == "auto" else dt_rank
        self.d_state = math.ceil(d_model / 6) if d_state == "auto" else d_state
        self.d_conv = d_conv

        dc_inner = 4 
        self.dtc_rank = 6
        self.dc_state = 16
        self.conv_cin = nn.Conv2d(in_channels=1, out_channels=dc_inner, kernel_size=1, stride=1, padding=0)
        self.conv_cout = nn.Conv2d(in_channels=dc_inner, out_channels=1, kernel_size=1, stride=1, padding=0)

        self.forward_core=self.forward_corev1

        self.K = 4 if forward_type not in ["share_ssm"] else 1
        self.K2 = self.K if forward_type not in ["share_a"] else 1        
        self.KC = 2
        self.K2C = self.KC if forward_type not in ["share_a"] else 1

        self.cforward_core = self.cforward_corev1
        self.pooling = nn.AdaptiveAvgPool2d(1)
        self.channel_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # in proj =======================================
        self.in_conv = nn.Conv2d(in_channels=d_model, out_channels=d_expand * 2, kernel_size=1, stride=1, padding=0)
        self.act: nn.Module = act_layer()
        
        # conv =======================================
        if self.d_conv > 1:
            self.conv2d = nn.Conv2d(
                in_channels=d_expand,
                out_channels=d_expand,
                groups=d_expand,
                bias=conv_bias,
                kernel_size=d_conv,
                padding=(d_conv - 1) // 2,
                **factory_kwargs,
            )


        self.out_norm = LayerNorm(d_inner, LayerNorm_type='WithBias')

        # x proj ============================
        self.x_proj = [
            nn.Linear(d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0))
        del self.x_proj
        # xc proj ============================
        self.xc_proj = [
            nn.Linear(dc_inner, (self.dtc_rank + self.dc_state * 2), bias=False, **factory_kwargs)
            for _ in range(self.KC)
        ]
        self.xc_proj_weight = nn.Parameter(torch.stack([tc.weight for tc in self.xc_proj], dim=0))
        del self.xc_proj


        # dt proj ============================
        self.dt_projs = [
            self.dt_init(self.dt_rank, d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor, **factory_kwargs)
            for _ in range(self.K)
        ]
        self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0))
        self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0))
        del self.dt_projs
        
        # A, D =======================================
        self.A_logs = self.A_log_init(self.d_state, d_inner, copies=self.K2, merge=True) # (K * D, N)
        self.Ds = self.D_init(d_inner, copies=self.K2, merge=True) # (K * D)

        # out proj =======================================
        self.out_conv = nn.Conv2d(in_channels=d_expand, out_channels=d_model, kernel_size=1, stride=1, padding=0)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else nn.Identity()

        self.Dsc = nn.Parameter(torch.ones((self.K2C * dc_inner)))
        self.Ac_logs = nn.Parameter(torch.randn((self.K2C * dc_inner, self.dc_state))) # A == -A_logs.exp() < 0; # 0 < exp(A * dt) < 1
        self.dtc_projs_weight = nn.Parameter(torch.randn((self.KC, dc_inner, self.dtc_rank)).contiguous())
        self.dtc_projs_bias = nn.Parameter(torch.randn((self.KC, dc_inner))) 

    @staticmethod
    def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4, **factory_kwargs):
        dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)

        # Initialize special dt projection to preserve variance at initialization
        dt_init_std = dt_rank**-0.5 * dt_scale
        if dt_init == "constant":
            nn.init.constant_(dt_proj.weight, dt_init_std)
        elif dt_init == "random":
            nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
        else:
            raise NotImplementedError

        # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
        dt = torch.exp(
            torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
            + math.log(dt_min)
        ).clamp(min=dt_init_floor)
        # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
        inv_dt = dt + torch.log(-torch.expm1(-dt))
        with torch.no_grad():
            dt_proj.bias.copy_(inv_dt)
        
        return dt_proj


    @staticmethod
    def A_log_init(d_state, d_inner, copies=-1, device=None, merge=True):
        # S4D real initialization
        A = repeat(
            torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
            "n -> d n",
            d=d_inner,
        ).contiguous()
        A_log = torch.log(A)  # Keep A_log in fp32
        if copies > 0:
            A_log = repeat(A_log, "d n -> r d n", r=copies)
            if merge:
                A_log = A_log.flatten(0, 1)
        A_log = nn.Parameter(A_log)
        A_log._no_weight_decay = True
        return A_log


    @staticmethod
    def D_init(d_inner, copies=-1, device=None, merge=True):
        # D "skip" parameter
        D = torch.ones(d_inner, device=device)
        if copies > 0:
            D = repeat(D, "n1 -> r n1", r=copies)
            if merge:
                D = D.flatten(0, 1)
        D = nn.Parameter(D)  # Keep in fp32
        D._no_weight_decay = True
        return D
    

    
    def forward_corev1(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn_v1

        B, C, H, W = x.shape
        L = H * W

        def cross_scan_2d(x):
            x_hwwh = torch.stack([x.flatten(2, 3), x.transpose(dim0=2, dim1=3).contiguous().flatten(2, 3)], dim=1)
            xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)
            return xs 
        
        if self.K == 4:
            xs = cross_scan_2d(x)

            x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs, self.x_proj_weight)
            dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
            dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dt_projs_weight)

            xs = xs.view(B, -1, L)
            dts = dts.contiguous().view(B, -1, L)
            As = -torch.exp(self.A_logs.float())
            Ds = self.Ds
            dt_projs_bias = self.dt_projs_bias.view(-1)

            out_y = self.selective_scan(
                xs, dts, 
                As, Bs, Cs, Ds,
                delta_bias=dt_projs_bias,
                delta_softplus=True,
            ).view(B, 4, -1, L)


        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        y = out_y[:, 0].float() + inv_y[:, 0].float() + wh_y.float() + invwh_y.float()
        

        y = y.view(B, C, H, W)
        y = self.out_norm(y).to(x.dtype)
        
        return y

    def cforward_corev1(self, xc: torch.Tensor):
        self.selective_scanC = selective_scan_fn_v1

        b,d,h,w = xc.shape
        
        xc = self.pooling(xc)
        xc = xc.permute(0,2,1,3).contiguous()
        xc = self.conv_cin(xc)
        xc = xc.squeeze(-1)


        B, D, L = xc.shape 
        D, N = self.Ac_logs.shape 
        K, D, R = self.dtc_projs_weight.shape 

        xsc = torch.stack([xc, torch.flip(xc, dims=[-1])], dim=1) 

        xc_dbl = torch.einsum("b k d l, k c d -> b k c l", xsc, self.xc_proj_weight) #8,2,1,96; 2,38,1 ->8,2,38,96
        
        dts, Bs, Cs = torch.split(xc_dbl, [self.dtc_rank, self.dc_state, self.dc_state], dim=2) # 8,2,38,96-> 6,16,16
        dts = torch.einsum("b k r l, k d r -> b k d l", dts, self.dtc_projs_weight).contiguous()

        xsc = xsc.view(B, -1, L) # (b, k * d, l) 8,2,96
        dts = dts.contiguous().view(B, -1, L).contiguous() # (b, k * d, l) 8,2,96
        As = -torch.exp(self.Ac_logs.float())  # (k * d, d_state) 2,16
        Ds = self.Dsc # (k * d) 2 
        dt_projs_bias = self.dtc_projs_bias.view(-1) # (k * d)2

        out_y = self.selective_scanC(
            xsc, dts, 
            As, Bs, Cs, Ds,
            delta_bias=dt_projs_bias,
            delta_softplus=True,
        ).view(B, 2, -1, L)

        y = out_y[:, 0].float() + torch.flip(out_y[:, 1], dims=[-1]).float()


        y = y.unsqueeze(-1) 
        y = self.conv_cout(y) 
        y = y.transpose(dim0=1, dim1=2).contiguous()
        y = self.channel_norm(y)
        y = y.to(xc.dtype)

        
        return y


    def forward(self, x: torch.Tensor, **kwargs):
        xz = self.in_conv(x)
        x, z = xz.chunk(2, dim=1) # (b, d, h, w)
        if not self.softmax_version:
            z = self.act(z)
        x = self.act(self.conv2d(x)) # (b, d, h, w)
        y1 = self.forward_core(x)
        y2 = y1 * z
        c = self.cforward_core(y2)
        y3 = y2 * c
        y2 = y3 + y2
        out = self.out_conv(y2)
        return out


##########################################################################
class MamberBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(MamberBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = SS2D_1(d_model=dim, ssm_ratio=1)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x


##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x



##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat//2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelUnshuffle(2))

    def forward(self, x):
        return self.body(x)

class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat*2, kernel_size=3, stride=1, padding=1, bias=False),
                                  nn.PixelShuffle(2))

    def forward(self, x):
        return self.body(x)

##########################################################################
##---------- Mamber -----------------------
@ARCH_REGISTRY.register()
class MambaSISR6(nn.Module):
    def __init__(self, 
        inp_channels=3, 
        out_channels=3, 
        scale= 4,
        dim = 48,
        num_blocks = [6,2,2,1], 
        num_refinement_blocks = 6,
        heads = [1,2,4,8],
        ffn_expansion_factor = 2.66,
        bias = False,
        LayerNorm_type = 'WithBias',   ## Other option 'BiasFree'
    ):

        super(MambaSISR6, self).__init__()

        self.scale = scale

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(*[MamberBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.down1_2 = Downsample(dim) ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.down2_3 = Downsample(int(dim*2**1)) ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])

        self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
        self.latent = nn.Sequential(*[MamberBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
        
        self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(*[MamberBlock(dim=int(dim*2**2), num_heads=heads[2], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[2])])


        self.up3_2 = Upsample(int(dim*2**2)) ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim*2**2), int(dim*2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        
        self.up2_1 = Upsample(int(dim*2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        
        self.refinement = nn.Sequential(*[MamberBlock(dim=int(dim*2**1), num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_refinement_blocks)])
        

        modules_tail = [common.Upsampler(common.default_conv, 4, int(dim*2**1), act=False),
                        common.default_conv(int(dim*2**1), out_channels, 3)]
        self.tail = nn.Sequential(*modules_tail)


    def forward(self, inp_img):

        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        
        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3) 

        inp_enc_level4 = self.down3_4(out_enc_level3)        
        latent = self.latent(inp_enc_level4) 
                        
        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3) 

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2) 

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)
        
        out_dec_level1 = self.refinement(out_dec_level1)

        out_dec_level1 = self.tail(out_dec_level1) + F.interpolate(inp_img, scale_factor=self.scale, mode='nearest') 


        return out_dec_level1


    def flops(self, shape=(3, 64, 64)):
        # shape = self.__input_shape__[1:]
        supported_ops={
            "aten::silu": None, # as relu is in _IGNORED_OPS
            "aten::neg": None, # as relu is in _IGNORED_OPS
            "aten::exp": None, # as relu is in _IGNORED_OPS
            "aten::flip": None, # as permute is in _IGNORED_OPS
            "prim::PythonOp.SelectiveScan": selective_scan_flop_jit,
        }

        model = copy.deepcopy(self)
        model.cuda().eval()

        input = torch.randn((1, *shape), device=next(model.parameters()).device)
        params = parameter_count(model)[""]
        Gflops, unsupported = flop_count(model=model, inputs=(input,), supported_ops=supported_ops)

        del model, input
        return f"params(M) {params/1e6} GFLOPs {sum(Gflops.values())}"


if __name__ == "__main__":
    print(MambaSISR6().flops())


================================================
FILE: SRGAN/VmambaIR/archs/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'VmambaIR.archs.{file_name}') for file_name in arch_filenames]


================================================
FILE: SRGAN/VmambaIR/archs/attention.py
================================================
import torch.nn as nn
import math
import torch as th
class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads
        self.scale=math.sqrt(10)

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        #scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * self.scale, k * self.scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads
        self.scale=math.sqrt(10)

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        #scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * self.scale).view(bs * self.n_heads, ch, length),
            (k * self.scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

class AttentionBlock(nn.Module):
    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.qkv = nn.Conv2d(channels,channels*3,3,padding=1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)


    def forward(self, x):
        res = self.qkv(x[0])
        b, c, *spatial = res.shape
        res = res.reshape(b, c, -1)
        h = self.attention(res)
        b, c, *spatial = x[0].shape
        h= h.reshape(b, c, *spatial)
        return [x[0] + h,x[1]]

================================================
FILE: SRGAN/VmambaIR/archs/common.py
================================================
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias)

class ResBlock(nn.Module):
    def __init__(
        self, conv, n_feats, kernel_size,
        bias=True, bn=False, act=nn.LeakyReLU(0.1, inplace=True), res_scale=1):

        super(ResBlock, self).__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        # self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x)
        res += x

        return res

class MeanShift(nn.Conv2d):
    def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1):
        super(MeanShift, self).__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1)
        self.weight.data.div_(std.view(3, 1, 1, 1))
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
        self.bias.data.div_(std)
        self.weight.requires_grad = False
        self.bias.requires_grad = False


class Upsampler(nn.Sequential):
    def __init__(self, conv, scale, n_feat, act=False, bias=True):
        m = []
        if (int(scale) & (int(scale) - 1)) == 0:    # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feat, 4 * n_feat, 3, bias))
                m.append(nn.PixelShuffle(2))
                if act: m.append(act())
        elif scale == 3:
            m.append(conv(n_feat, 9 * n_feat, 3, bias))
            m.append(nn.PixelShuffle(3))
            if act: m.append(act())
        else:
            raise NotImplementedError

        super(Upsampler, self).__init__(*m)

================================================
FILE: SRGAN/VmambaIR/archs/discriminator_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm


@ARCH_REGISTRY.register()
class UNetDiscriminatorSN(nn.Module):
    """Defines a U-Net discriminator with spectral normalization (SN)

    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    Arg:
        num_in_ch (int): Channel number of inputs. Default: 3.
        num_feat (int): Channel number of base intermediate features. Default: 64.
        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
    """

    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
        super(UNetDiscriminatorSN, self).__init__()
        self.skip_connection = skip_connection
        norm = spectral_norm
        # the first convolution
        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
        # downsample
        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
        # upsample
        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
        # extra convolutions
        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)

    def forward(self, x):
        # downsample
        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
        x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)

        # upsample
        x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
        x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x4 = x4 + x2
        x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
        x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x5 = x5 + x1
        x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
        x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)

        if self.skip_connection:
            x6 = x6 + x0

        # extra convolutions
        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
        out = self.conv9(out)

        return out


================================================
FILE: SRGAN/VmambaIR/archs/srvgg_arch.py
================================================
from basicsr.utils.registry import ARCH_REGISTRY
from torch import nn as nn
from torch.nn import functional as F


@ARCH_REGISTRY.register()
class SRVGGNetCompact(nn.Module):
    """A compact VGG-style network structure for super-resolution.

    It is a compact network structure, which performs upsampling in the last layer and no convolution is
    conducted on the HR feature space.

    Args:
        num_in_ch (int): Channel number of inputs. Default: 3.
        num_out_ch (int): Channel number of outputs. Default: 3.
        num_feat (int): Channel number of intermediate features. Default: 64.
        num_conv (int): Number of convolution layers in the body network. Default: 16.
        upscale (int): Upsampling factor. Default: 4.
        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
    """

    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
        super(SRVGGNetCompact, self).__init__()
        self.num_in_ch = num_in_ch
        self.num_out_ch = num_out_ch
        self.num_feat = num_feat
        self.num_conv = num_conv
        self.upscale = upscale
        self.act_type = act_type

        self.body = nn.ModuleList()
        # the first conv
        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
        # the first activation
        if act_type == 'relu':
            activation = nn.ReLU(inplace=True)
        elif act_type == 'prelu':
            activation = nn.PReLU(num_parameters=num_feat)
        elif act_type == 'leakyrelu':
            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.body.append(activation)

        # the body structure
        for _ in range(num_conv):
            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
            # activation
            if act_type == 'relu':
                activation = nn.ReLU(inplace=True)
            elif act_type == 'prelu':
                activation = nn.PReLU(num_parameters=num_feat)
            elif act_type == 'leakyrelu':
                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
            self.body.append(activation)

        # the last conv
        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
        # upsample
        self.upsampler = nn.PixelShuffle(upscale)

    def forward(self, x):
        out = x
        for i in range(0, len(self.body)):
            out = self.body[i](out)

        out = self.upsampler(out)
        # add the nearest upsampled image, so that the network learns the residual
        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
        out += base
        return out


================================================
FILE: SRGAN/VmambaIR/data/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import dataset modules for registry
# scan all the files that end with '_dataset.py' under the data folder
data_folder = osp.dirname(osp.abspath(__file__))
dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
# import all the dataset modules
_dataset_modules = [importlib.import_module(f'VmambaIR.data.{file_name}') for file_name in dataset_filenames]


================================================
FILE: SRGAN/VmambaIR/data/data_util.py
================================================
import cv2
cv2.setNumThreads(1)
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F

from VmambaIR.data.transforms import mod_crop
from VmambaIR.utils import img2tensor, scandir


def read_img_seq(path, require_mod_crop=False, scale=1):
    """Read a sequence of images from a given folder path.

    Args:
        path (list[str] | str): List of image paths or image folder path.
        require_mod_crop (bool): Require mod crop for each image.
            Default: False.
        scale (int): Scale factor for mod_crop. Default: 1.

    Returns:
        Tensor: size (t, c, h, w), RGB, [0, 1].
    """
    if isinstance(path, list):
        img_paths = path
    else:
        img_paths = sorted(list(scandir(path, full_path=True)))
    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
    if require_mod_crop:
        imgs = [mod_crop(img, scale) for img in imgs]
    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
    imgs = torch.stack(imgs, dim=0)
    return imgs


def generate_frame_indices(crt_idx,
                           max_frame_num,
                           num_frames,
                           padding='reflection'):
    """Generate an index list for reading `num_frames` frames from a sequence
    of images.

    Args:
        crt_idx (int): Current center index.
        max_frame_num (int): Max number of the sequence of images (from 1).
        num_frames (int): Reading num_frames frames.
        padding (str): Padding mode, one of
            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
            Examples: current_idx = 0, num_frames = 5
            The generated frame indices under different padding mode:
            replicate: [0, 0, 0, 1, 2]
            reflection: [2, 1, 0, 1, 2]
            reflection_circle: [4, 3, 0, 1, 2]
            circle: [3, 4, 0, 1, 2]

    Returns:
        list[int]: A list of indices.
    """
    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
    assert padding in ('replicate', 'reflection', 'reflection_circle',
                       'circle'), f'Wrong padding mode: {padding}.'

    max_frame_num = max_frame_num - 1  # start from 0
    num_pad = num_frames // 2

    indices = []
    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
        if i < 0:
            if padding == 'replicate':
                pad_idx = 0
            elif padding == 'reflection':
                pad_idx = -i
            elif padding == 'reflection_circle':
                pad_idx = crt_idx + num_pad - i
            else:
                pad_idx = num_frames + i
        elif i > max_frame_num:
            if padding == 'replicate':
                pad_idx = max_frame_num
            elif padding == 'reflection':
                pad_idx = max_frame_num * 2 - i
            elif padding == 'reflection_circle':
                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
            else:
                pad_idx = i - num_frames
        else:
            pad_idx = i
        indices.append(pad_idx)
    return indices


def paired_paths_from_lmdb(folders, keys):
    """Generate paired paths from lmdb files.

    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:

    lq.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records
    1)image name (with extension),
    2)image shape,
    3)compression level, separated by a white space.
    Example: `baboon.png (120,125,3) 1`

    We use the image name without extension as the lmdb key.
    Note that we use the same key for the corresponding lq and gt images.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
            Note that this key is different from lmdb keys.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
        raise ValueError(
            f'{input_key} folder and {gt_key} folder should both in lmdb '
            f'formats. But received {input_key}: {input_folder}; '
            f'{gt_key}: {gt_folder}')
    # ensure that the two meta_info files are the same
    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
        input_lmdb_keys = [line.split('.')[0] for line in fin]
    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
        gt_lmdb_keys = [line.split('.')[0] for line in fin]
    if set(input_lmdb_keys) != set(gt_lmdb_keys):
        raise ValueError(
            f'Keys in {input_key}_folder and {gt_key}_folder are different.')
    else:
        paths = []
        for lmdb_key in sorted(input_lmdb_keys):
            paths.append(
                dict([(f'{input_key}_path', lmdb_key),
                      (f'{gt_key}_path', lmdb_key)]))
        return paths


def paired_paths_from_meta_info_file(folders, keys, meta_info_file,
                                     filename_tmpl):
    """Generate paired paths from an meta information file.

    Each line in the meta information file contains the image names and
    image shape (usually for gt), separated by a white space.

    Example of an meta information file:
    ```
    0001_s001.png (480,480,3)
    0001_s002.png (480,480,3)
    ```

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        meta_info_file (str): Path to the meta information file.
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    with open(meta_info_file, 'r') as fin:
        gt_names = [line.split(' ')[0] for line in fin]

    paths = []
    for gt_name in gt_names:
        basename, ext = osp.splitext(osp.basename(gt_name))
        input_name = f'{filename_tmpl.format(basename)}{ext}'
        input_path = osp.join(input_folder, input_name)
        gt_path = osp.join(gt_folder, gt_name)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paired_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 2, (
        'The len of folders should be 2 with [input_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 2, (
        'The len of keys should be 2 with [input_key, gt_key]. '
        f'But got {len(keys)}')
    input_folder, gt_folder = folders
    input_key, gt_key = keys

    input_paths = list(scandir(input_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(input_paths) == len(gt_paths), (
        f'{input_key} and {gt_key} datasets have different number of images: '
        f'{len(input_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        input_path = input_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(input_path))
        input_name = f'{filename_tmpl.format(basename)}{ext_input}'
        input_path = osp.join(input_folder, input_name)
        assert input_name in input_paths, (f'{input_name} is not in '
                                           f'{input_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{input_key}_path', input_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths

def paired_DP_paths_from_folder(folders, keys, filename_tmpl):
    """Generate paired paths from folders.

    Args:
        folders (list[str]): A list of folder path. The order of list should
            be [input_folder, gt_folder].
        keys (list[str]): A list of keys identifying folders. The order should
            be in consistent with folders, e.g., ['lq', 'gt'].
        filename_tmpl (str): Template for each filename. Note that the
            template excludes the file extension. Usually the filename_tmpl is
            for files in the input folder.

    Returns:
        list[str]: Returned path list.
    """
    assert len(folders) == 3, (
        'The len of folders should be 3 with [inputL_folder, inputR_folder, gt_folder]. '
        f'But got {len(folders)}')
    assert len(keys) == 3, (
        'The len of keys should be 2 with [inputL_key, inputR_key, gt_key]. '
        f'But got {len(keys)}')
    inputL_folder, inputR_folder, gt_folder = folders
    inputL_key, inputR_key, gt_key = keys

    inputL_paths = list(scandir(inputL_folder))
    inputR_paths = list(scandir(inputR_folder))
    gt_paths = list(scandir(gt_folder))
    assert len(inputL_paths) == len(inputR_paths) == len(gt_paths), (
        f'{inputL_key} and {inputR_key} and {gt_key} datasets have different number of images: '
        f'{len(inputL_paths)}, {len(inputR_paths)}, {len(gt_paths)}.')
    paths = []
    for idx in range(len(gt_paths)):
        gt_path = gt_paths[idx]
        basename, ext = osp.splitext(osp.basename(gt_path))
        inputL_path = inputL_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputL_path))
        inputL_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputL_path = osp.join(inputL_folder, inputL_name)
        assert inputL_name in inputL_paths, (f'{inputL_name} is not in '
                                           f'{inputL_key}_paths.')
        inputR_path = inputR_paths[idx]
        basename_input, ext_input = osp.splitext(osp.basename(inputR_path))
        inputR_name = f'{filename_tmpl.format(basename)}{ext_input}'
        inputR_path = osp.join(inputR_folder, inputR_name)
        assert inputR_name in inputR_paths, (f'{inputR_name} is not in '
                                           f'{inputR_key}_paths.')
        gt_path = osp.join(gt_folder, gt_path)
        paths.append(
            dict([(f'{inputL_key}_path', inputL_path),
                  (f'{inputR_key}_path', inputR_path),
                  (f'{gt_key}_path', gt_path)]))
    return paths


def paths_from_folder(folder):
    """Generate paths from folder.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """

    paths = list(scandir(folder))
    paths = [osp.join(folder, path) for path in paths]
    return paths


def paths_from_lmdb(folder):
    """Generate paths from lmdb.

    Args:
        folder (str): Folder path.

    Returns:
        list[str]: Returned path list.
    """
    if not folder.endswith('.lmdb'):
        raise ValueError(f'Folder {folder}folder should in lmdb format.')
    with open(osp.join(folder, 'meta_info.txt')) as fin:
        paths = [line.split('.')[0] for line in fin]
    return paths


def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
    """Generate Gaussian kernel used in `duf_downsample`.

    Args:
        kernel_size (int): Kernel size. Default: 13.
        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.

    Returns:
        np.array: The Gaussian kernel.
    """
    from scipy.ndimage import filters as filters
    kernel = np.zeros((kernel_size, kernel_size))
    # set element at the middle to one, a dirac delta
    kernel[kernel_size // 2, kernel_size // 2] = 1
    # gaussian-smooth the dirac, resulting in a gaussian filter
    return filters.gaussian_filter(kernel, sigma)


def duf_downsample(x, kernel_size=13, scale=4):
    """Downsamping with Gaussian kernel used in the DUF official code.

    Args:
        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
        kernel_size (int): Kernel size. Default: 13.
        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
            Default: 4.

    Returns:
        Tensor: DUF downsampled frames.
    """
    assert scale in (2, 3,
                     4), f'Only support scale (2, 3, 4), but got {scale}.'

    squeeze_flag = False
    if x.ndim == 4:
        squeeze_flag = True
        x = x.unsqueeze(0)
    b, t, c, h, w = x.size()
    x = x.view(-1, 1, h, w)
    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')

    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(
        0).unsqueeze(0)
    x = F.conv2d(x, gaussian_filter, stride=scale)
    x = x[:, :, 2:-2, 2:-2]
    x = x.view(b, t, c, x.size(2), x.size(3))
    if squeeze_flag:
        x = x.squeeze(0)
    return x


================================================
FILE: SRGAN/VmambaIR/data/deblur_paired_dataset.py
================================================
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from VmambaIR.data.data_util import (paired_paths_from_folder,
                                    paired_DP_paths_from_folder,
                                    paired_paths_from_lmdb,
                                    paired_paths_from_meta_info_file)
from VmambaIR.data.transforms import augment, paired_random_crop, paired_random_crop_DP, random_augmentation
from VmambaIR.utils import FileClient, imfrombytes, img2tensor, padding, padding_DP, imfrombytesDP
from basicsr.utils.registry import DATASET_REGISTRY
import random
import numpy as np
import torch
import cv2

@DATASET_REGISTRY.register()
class DeblurPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the
                template excludes the file extension. Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            geometric_augs (bool): Use geometric augmentations.

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(DeblurPairedDataset, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info_file' in self.opt and self.opt[
                'meta_info_file'] is not None:
            self.paths = paired_paths_from_meta_info_file(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.opt['meta_info_file'], self.filename_tmpl)
        else:
            self.paths = paired_paths_from_folder(
                [self.lq_folder, self.gt_folder], ['lq', 'gt'],
                self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        try:
            img_lq = imfrombytes(img_bytes, float32=True)
        except:
            raise Exception("lq path {} not working".format(lq_path))

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)

            # flip, rotation augmentations
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)
            
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lq_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_GaussianDenoising(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            gt_size (int): Cropped patched size for gt patches.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(Dataset_GaussianDenoising, self).__init__()
        self.opt = opt

        if self.opt['phase'] == 'train':
            self.sigma_type  = opt['sigma_type']
            self.sigma_range = opt['sigma_range']
            assert self.sigma_type in ['constant', 'random', 'choice']
        else:
            self.sigma_test = opt['sigma_test']
        self.in_ch = opt['in_ch']

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None        

        self.gt_folder = opt['dataroot_gt']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            self.paths = paths_from_lmdb(self.gt_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.gt_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.gt_folder, full_path=True)))

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')

        if self.in_ch == 3:
            try:
                img_gt = imfrombytes(img_bytes, float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2RGB)
        else:
            try:
                img_gt = imfrombytes(img_bytes, flag='grayscale', float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = img_gt.copy()


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)
            # flip, rotation
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                                        bgr2rgb=False,
                                        float32=True)


            if self.sigma_type == 'constant':
                sigma_value = self.sigma_range
            elif self.sigma_type == 'random':
                sigma_value = random.uniform(self.sigma_range[0], self.sigma_range[1])
            elif self.sigma_type == 'choice':
                sigma_value = random.choice(self.sigma_range)

            noise_level = torch.FloatTensor([sigma_value])/255.0
            # noise_level_map = torch.ones((1, img_lq.size(1), img_lq.size(2))).mul_(noise_level).float()
            noise = torch.randn(img_lq.size()).mul_(noise_level).float()
            img_lq.add_(noise)

        else:            
            np.random.seed(seed=0)
            img_lq += np.random.normal(0, self.sigma_test/255.0, img_lq.shape)
            # noise_level_map = torch.ones((1, img_lq.shape[0], img_lq.shape[1])).mul_(self.sigma_test/255.0).float()

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                            bgr2rgb=False,
                            float32=True)

        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': gt_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_DefocusDeblur_DualPixel_16bit(data.Dataset):
    def __init__(self, opt):
        super(Dataset_DefocusDeblur_DualPixel_16bit, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lqL_folder, self.lqR_folder = opt['dataroot_gt'], opt['dataroot_lqL'], opt['dataroot_lqR']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        self.paths = paired_DP_paths_from_folder(
            [self.lqL_folder, self.lqR_folder, self.gt_folder], ['lqL', 'lqR', 'gt'],
            self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lqL_path = self.paths[index]['lqL_path']
        img_bytes = self.file_client.get(lqL_path, 'lqL')
        try:
            img_lqL = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqL path {} not working".format(lqL_path))

        lqR_path = self.paths[index]['lqR_path']
        img_bytes = self.file_client.get(lqR_path, 'lqR')
        try:
            img_lqR = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqR path {} not working".format(lqR_path))


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_lqL, img_lqR, img_gt = padding_DP(img_lqL, img_lqR, img_gt, gt_size)

            # random crop
            img_lqL, img_lqR, img_gt = paired_random_crop_DP(img_lqL, img_lqR, img_gt, gt_size, scale, gt_path)
            
            # flip, rotation            
            if self.geometric_augs:
                img_lqL, img_lqR, img_gt = random_augmentation(img_lqL, img_lqR, img_gt)
        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lqL, img_lqR, img_gt = img2tensor([img_lqL, img_lqR, img_gt],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lqL, self.mean, self.std, inplace=True)
            normalize(img_lqR, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        img_lq = torch.cat([img_lqL, img_lqR], 0)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lqL_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)


================================================
FILE: SRGAN/VmambaIR/data/diffir_dataset.py
================================================
import cv2
import math
import numpy as np
import os
import os.path as osp
import random
import time
import torch
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data


@DATASET_REGISTRY.register()
class DiffIRGANDataset(data.Dataset):
    """Dataset used for KDSRGAN model:
    DiffIRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It loads gt (Ground-Truth) images, and augments them.
    It also generates blur kernels and sinc kernels for generating low-quality images.
    Note that the low-quality images are processed in tensors on GPUS for faster processing.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(DiffIRGANDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']
        self.gt_size = self.opt['gt_size']
        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            if not self.gt_folder.endswith('.lmdb'):
                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # -------------------------------- Load gt images -------------------------------- #
        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
        gt_path = self.paths[index]
        # avoid errors caused by high latency in reading files
        retry = 3
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
            except (IOError, OSError) as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
                index = random.randint(0, self.__len__())
                gt_path = self.paths[index]
                time.sleep(1)  # sleep 1s for occasional server congestion
            else:
                break
            finally:
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

        # crop or pad to 400
        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = self.gt_size
        # pad
        if h < crop_pad_size or w < crop_pad_size:
            pad_h = max(0, crop_pad_size - h)
            pad_w = max(0, crop_pad_size - w)
            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
        # crop
        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
            h, w = img_gt.shape[0:2]
            # randomly choose top and left coordinates
            top = random.randint(0, h - crop_pad_size)
            left = random.randint(0, w - crop_pad_size)
            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]

        return_d = {'gt': img_gt,'gt_path': gt_path}
        return return_d

    def __len__(self):
        return len(self.paths)


================================================
FILE: SRGAN/VmambaIR/data/diffir_paired_dataset.py
================================================
import os
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
from torchvision.transforms.functional import normalize


@DATASET_REGISTRY.register()
class DiffIRGANPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            dataroot_lq (str): Data root path for lq.
            meta_info (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
                Default: '{}'.
            gt_size (int): Cropped patched size for gt patches.
            use_hflip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(DiffIRGANPairedDataset, self).__init__()
        self.opt = opt
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        # mean and std for normalizing the input images
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None

        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'

        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']
            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
            # disk backend with meta_info
            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip() for line in fin]
            self.paths = []
            for path in paths:
                gt_path, lq_path = path.split(', ')
                gt_path = os.path.join(self.gt_folder, gt_path)
                lq_path = os.path.join(self.lq_folder, lq_path)
                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
        else:
            # disk backend
            # it will scan the whole folder to get meta info
            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']

        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)
        lq_path = self.paths[index]['lq_path']
        img_bytes = self.file_client.get(lq_path, 'lq')
        img_lq = imfrombytes(img_bytes, float32=True)

        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
            # flip, rotation
            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])

        # BGR to RGB, HWC to CHW, numpy to tensor
        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lq, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}

    def __len__(self):
        return len(self.paths)


================================================
FILE: SRGAN/VmambaIR/data/gaussiandenoising_paired_dataset.py
================================================
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from VmambaIR.data.data_util import (paired_paths_from_folder,
                                    paired_DP_paths_from_folder,
                                    paired_paths_from_lmdb,
                                    paired_paths_from_meta_info_file)
from VmambaIR.data.transforms import augment, paired_random_crop, paired_random_crop_DP, random_augmentation
from VmambaIR.utils import FileClient, imfrombytes, img2tensor, padding, padding_DP, imfrombytesDP,scandir
from basicsr.utils.registry import DATASET_REGISTRY
import random
import numpy as np
import torch
import cv2


@DATASET_REGISTRY.register()
class GaussianDenoisingPairedDataset(data.Dataset):
    """Paired image dataset for image restoration.

    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
    GT image pairs.

    There are three modes:
    1. 'lmdb': Use lmdb files.
        If opt['io_backend'] == lmdb.
    2. 'meta_info_file': Use meta information file to generate paths.
        If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
    3. 'folder': Scan folders to generate paths.
        The rest.

    Args:
        opt (dict): Config for train datasets. It contains the following keys:
            dataroot_gt (str): Data root path for gt.
            meta_info_file (str): Path for meta information file.
            io_backend (dict): IO backend type and other kwarg.
            gt_size (int): Cropped patched size for gt patches.
            use_flip (bool): Use horizontal flips.
            use_rot (bool): Use rotation (use vertical flip and transposing h
                and w for implementation).

            scale (bool): Scale, which will be added automatically.
            phase (str): 'train' or 'val'.
    """

    def __init__(self, opt):
        super(GaussianDenoisingPairedDataset, self).__init__()
        self.opt = opt

        if self.opt['phase'] == 'train':
            self.sigma_type  = opt['sigma_type']
            self.sigma_range = opt['sigma_range']
            assert self.sigma_type in ['constant', 'random', 'choice']
        else:
            self.sigma_test = opt['sigma_test']
        self.in_ch = opt['in_ch']

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None        

        self.gt_folder = opt['dataroot_gt']

        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
            self.paths = paths_from_lmdb(self.gt_folder)
        elif 'meta_info_file' in self.opt:
            with open(self.opt['meta_info_file'], 'r') as fin:
                self.paths = [
                    osp.join(self.gt_folder,
                             line.split(' ')[0]) for line in fin
                ]
        else:
            self.paths = sorted(list(scandir(self.gt_folder, full_path=True)))

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        # gt_path = self.paths[index]['gt_path']
        gt_path = self.paths[index]
        img_bytes = self.file_client.get(gt_path, 'gt')

        if self.in_ch == 3:
            try:
                img_gt = imfrombytes(img_bytes, float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2RGB)
        else:
            try:
                img_gt = imfrombytes(img_bytes, flag='grayscale', float32=True)
            except:
                raise Exception("gt path {} not working".format(gt_path))

            img_gt = np.expand_dims(img_gt, axis=2)
        img_lq = img_gt.copy()


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_gt, img_lq = padding(img_gt, img_lq, gt_size)

            # random crop
            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
                                                gt_path)
            # flip, rotation
            if self.geometric_augs:
                img_gt, img_lq = random_augmentation(img_gt, img_lq)

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                                        bgr2rgb=False,
                                        float32=True)


            if self.sigma_type == 'constant':
                sigma_value = self.sigma_range
            elif self.sigma_type == 'random':
                sigma_value = random.uniform(self.sigma_range[0], self.sigma_range[1])
            elif self.sigma_type == 'choice':
                sigma_value = random.choice(self.sigma_range)

            noise_level = torch.FloatTensor([sigma_value])/255.0
            # noise_level_map = torch.ones((1, img_lq.size(1), img_lq.size(2))).mul_(noise_level).float()
            noise = torch.randn(img_lq.size()).mul_(noise_level).float()
            img_lq.add_(noise)

        else:            
            np.random.seed(seed=0)
            img_lq += np.random.normal(0, self.sigma_test/255.0, img_lq.shape)
            # noise_level_map = torch.ones((1, img_lq.shape[0], img_lq.shape[1])).mul_(self.sigma_test/255.0).float()

            img_gt, img_lq = img2tensor([img_gt, img_lq],
                            bgr2rgb=False,
                            float32=True)

        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': gt_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)

class Dataset_DefocusDeblur_DualPixel_16bit(data.Dataset):
    def __init__(self, opt):
        super(Dataset_DefocusDeblur_DualPixel_16bit, self).__init__()
        self.opt = opt
        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.mean = opt['mean'] if 'mean' in opt else None
        self.std = opt['std'] if 'std' in opt else None
        
        self.gt_folder, self.lqL_folder, self.lqR_folder = opt['dataroot_gt'], opt['dataroot_lqL'], opt['dataroot_lqR']
        if 'filename_tmpl' in opt:
            self.filename_tmpl = opt['filename_tmpl']
        else:
            self.filename_tmpl = '{}'

        self.paths = paired_DP_paths_from_folder(
            [self.lqL_folder, self.lqR_folder, self.gt_folder], ['lqL', 'lqR', 'gt'],
            self.filename_tmpl)

        if self.opt['phase'] == 'train':
            self.geometric_augs = self.opt['geometric_augs']

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(
                self.io_backend_opt.pop('type'), **self.io_backend_opt)

        scale = self.opt['scale']
        index = index % len(self.paths)
        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
        # image range: [0, 1], float32.
        gt_path = self.paths[index]['gt_path']
        img_bytes = self.file_client.get(gt_path, 'gt')
        try:
            img_gt = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("gt path {} not working".format(gt_path))

        lqL_path = self.paths[index]['lqL_path']
        img_bytes = self.file_client.get(lqL_path, 'lqL')
        try:
            img_lqL = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqL path {} not working".format(lqL_path))

        lqR_path = self.paths[index]['lqR_path']
        img_bytes = self.file_client.get(lqR_path, 'lqR')
        try:
            img_lqR = imfrombytesDP(img_bytes, float32=True)
        except:
            raise Exception("lqR path {} not working".format(lqR_path))


        # augmentation for training
        if self.opt['phase'] == 'train':
            gt_size = self.opt['gt_size']
            # padding
            img_lqL, img_lqR, img_gt = padding_DP(img_lqL, img_lqR, img_gt, gt_size)

            # random crop
            img_lqL, img_lqR, img_gt = paired_random_crop_DP(img_lqL, img_lqR, img_gt, gt_size, scale, gt_path)
            
            # flip, rotation            
            if self.geometric_augs:
                img_lqL, img_lqR, img_gt = random_augmentation(img_lqL, img_lqR, img_gt)
        # TODO: color space transform
        # BGR to RGB, HWC to CHW, numpy to tensor
        img_lqL, img_lqR, img_gt = img2tensor([img_lqL, img_lqR, img_gt],
                                    bgr2rgb=True,
                                    float32=True)
        # normalize
        if self.mean is not None or self.std is not None:
            normalize(img_lqL, self.mean, self.std, inplace=True)
            normalize(img_lqR, self.mean, self.std, inplace=True)
            normalize(img_gt, self.mean, self.std, inplace=True)

        img_lq = torch.cat([img_lqL, img_lqR], 0)
        
        return {
            'lq': img_lq,
            'gt': img_gt,
            'lq_path': lqL_path,
            'gt_path': gt_path
        }

    def __len__(self):
        return len(self.paths)


================================================
FILE: SRGAN/VmambaIR/data/transforms.py
================================================
import cv2
import random
import numpy as np

def mod_crop(img, scale):
    """Mod crop images, used during testing.

    Args:
        img (ndarray): Input image.
        scale (int): Scale factor.

    Returns:
        ndarray: Result image.
    """
    img = img.copy()
    if img.ndim in (2, 3):
        h, w = img.shape[0], img.shape[1]
        h_remainder, w_remainder = h % scale, w % scale
        img = img[:h - h_remainder, :w - w_remainder, ...]
    else:
        raise ValueError(f'Wrong img ndim: {img.ndim}.')
    return img

def paired_random_crop(img_gts, img_lqs, lq_patch_size, scale, gt_path):
    """Paired random crop.

    It crops lists of lq and gt images with corresponding locations.

    Args:
        img_gts (list[ndarray] | ndarray): GT images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
            should have the same shape. If the input is an ndarray, it will
            be transformed to a list containing itself.
        lq_patch_size (int): LQ patch size.
        scale (int): Scale factor.
        gt_path (str): Path to ground-truth.

    Returns:
        list[ndarray] | ndarray: GT images and LQ images. If returned results
            only have one element, just return ndarray.
    """

    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqs, list):
        img_lqs = [img_lqs]

    h_lq, w_lq, _ = img_lqs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    gt_patch_size = int(lq_patch_size * scale)

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqs) == 1:
        img_lqs = img_lqs[0]
    return img_gts, img_lqs

def paired_random_crop_DP(img_lqLs, img_lqRs, img_gts, gt_patch_size, scale, gt_path):
    if not isinstance(img_gts, list):
        img_gts = [img_gts]
    if not isinstance(img_lqLs, list):
        img_lqLs = [img_lqLs]
    if not isinstance(img_lqRs, list):
        img_lqRs = [img_lqRs]

    h_lq, w_lq, _ = img_lqLs[0].shape
    h_gt, w_gt, _ = img_gts[0].shape
    lq_patch_size = gt_patch_size // scale

    if h_gt != h_lq * scale or w_gt != w_lq * scale:
        raise ValueError(
            f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
            f'multiplication of LQ ({h_lq}, {w_lq}).')
    if h_lq < lq_patch_size or w_lq < lq_patch_size:
        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
                         f'({lq_patch_size}, {lq_patch_size}). '
                         f'Please remove {gt_path}.')

    # randomly choose top and left coordinates for lq patch
    top = random.randint(0, h_lq - lq_patch_size)
    left = random.randint(0, w_lq - lq_patch_size)

    # crop lq patch
    img_lqLs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqLs
    ]

    img_lqRs = [
        v[top:top + lq_patch_size, left:left + lq_patch_size, ...]
        for v in img_lqRs
    ]

    # crop corresponding gt patch
    top_gt, left_gt = int(top * scale), int(left * scale)
    img_gts = [
        v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...]
        for v in img_gts
    ]
    if len(img_gts) == 1:
        img_gts = img_gts[0]
    if len(img_lqLs) == 1:
        img_lqLs = img_lqLs[0]
    if len(img_lqRs) == 1:
        img_lqRs = img_lqRs[0]
    return img_lqLs, img_lqRs, img_gts


def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).

    We use vertical flip and transpose for rotation implementation.
    All the images in the list use the same augmentation.

    Args:
        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
            is an ndarray, it will be transformed to a list.
        hflip (bool): Horizontal flip. Default: True.
        rotation (bool): Ratotation. Default: True.
        flows (list[ndarray]: Flows to be augmented. If the input is an
            ndarray, it will be transformed to a list.
            Dimension is (h, w, 2). Default: None.
        return_status (bool): Return the status of flip and rotation.
            Default: False.

    Returns:
        list[ndarray] | ndarray: Augmented images and flows. If returned
            results only have one element, just return ndarray.

    """
    hflip = hflip and random.random() < 0.5
    vflip = rotation and random.random() < 0.5
    rot90 = rotation and random.random() < 0.5

    def _augment(img):
        if hflip:  # horizontal
            cv2.flip(img, 1, img)
        if vflip:  # vertical
            cv2.flip(img, 0, img)
        if rot90:
            img = img.transpose(1, 0, 2)
        return img

    def _augment_flow(flow):
        if hflip:  # horizontal
            cv2.flip(flow, 1, flow)
            flow[:, :, 0] *= -1
        if vflip:  # vertical
            cv2.flip(flow, 0, flow)
            flow[:, :, 1] *= -1
        if rot90:
            flow = flow.transpose(1, 0, 2)
            flow = flow[:, :, [1, 0]]
        return flow

    if not isinstance(imgs, list):
        imgs = [imgs]
    imgs = [_augment(img) for img in imgs]
    if len(imgs) == 1:
        imgs = imgs[0]

    if flows is not None:
        if not isinstance(flows, list):
            flows = [flows]
        flows = [_augment_flow(flow) for flow in flows]
        if len(flows) == 1:
            flows = flows[0]
        return imgs, flows
    else:
        if return_status:
            return imgs, (hflip, vflip, rot90)
        else:
            return imgs


def img_rotate(img, angle, center=None, scale=1.0):
    """Rotate image.

    Args:
        img (ndarray): Image to be rotated.
        angle (float): Rotation angle in degrees. Positive values mean
            counter-clockwise rotation.
        center (tuple[int]): Rotation center. If the center is None,
            initialize it as the center of the image. Default: None.
        scale (float): Isotropic scale factor. Default: 1.0.
    """
    (h, w) = img.shape[:2]

    if center is None:
        center = (w // 2, h // 2)

    matrix = cv2.getRotationMatrix2D(center, angle, scale)
    rotated_img = cv2.warpAffine(img, matrix, (w, h))
    return rotated_img

def data_augmentation(image, mode):
    """
    Performs data augmentation of the input image
    Input:
        image: a cv2 (OpenCV) image
        mode: int. Choice of transformation to apply to the image
                0 - no transformation
                1 - flip up and down
                2 - rotate counterwise 90 degree
                3 - rotate 90 degree and flip up and down
                4 - rotate 180 degree
                5 - rotate 180 degree and flip
                6 - rotate 270 degree
                7 - rotate 270 degree and flip
    """
    if mode == 0:
        # original
        out = image
    elif mode == 1:
        # flip up and down
        out = np.flipud(image)
    elif mode == 2:
        # rotate counterwise 90 degree
        out = np.rot90(image)
    elif mode == 3:
        # rotate 90 degree and flip up and down
        out = np.rot90(image)
        out = np.flipud(out)
    elif mode == 4:
        # rotate 180 degree
        out = np.rot90(image, k=2)
    elif mode == 5:
        # rotate 180 degree and flip
        out = np.rot90(image, k=2)
        out = np.flipud(out)
    elif mode == 6:
        # rotate 270 degree
        out = np.rot90(image, k=3)
    elif mode == 7:
        # rotate 270 degree and flip
        out = np.rot90(image, k=3)
        out = np.flipud(out)
    else:
        raise Exception('Invalid choice of image transformation')

    return out

def random_augmentation(*args):
    out = []
    flag_aug = random.randint(0,7)
    for data in args:
        out.append(data_augmentation(data, flag_aug).copy())
    return out


================================================
FILE: SRGAN/VmambaIR/losses/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_loss.py')]
# import all the arch modules
_arch_modules = [importlib.import_module(f'VmambaIR.losses.{file_name}') for file_name in arch_filenames]


================================================
FILE: SRGAN/VmambaIR/losses/my_loss.py
================================================
import torch
from torch import nn as nn
from torch.nn import functional as F
from basicsr.utils.registry import LOSS_REGISTRY


@LOSS_REGISTRY.register()
class KDLoss(nn.Module):
    """
    Args:
        loss_weight (float): Loss weight for KD loss. Default: 1.0.
    """

    def __init__(self, loss_weight=1.0, temperature = 0.15):
        super(KDLoss, self).__init__()
    
        self.loss_weight = loss_weight
        self.temperature = temperature

    def forward(self, S1_fea, S2_fea):
        """
        Args:
            S1_fea (List): contain shape (N, L) vector. 
            S2_fea (List): contain shape (N, L) vector.
            weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None.
        """
        loss_KD_dis = 0
        loss_KD_abs = 0
        for i in range(len(S1_fea)):
            S2_distance = F.log_softmax(S2_fea[i] / self.temperature, dim=1)
            S1_distance = F.softmax(S1_fea[i].detach()/ self.temperature, dim=1)
            loss_KD_dis += F.kl_div(
                        S2_distance, S1_distance, reduction='batchmean')
            loss_KD_abs += nn.L1Loss()(S2_fea[i], S1_fea[i].detach())
        return self.loss_weight * loss_KD_dis, self.loss_weight * loss_KD_abs                

================================================
FILE: SRGAN/VmambaIR/models/MambaSISR2_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.sr_model import SRModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from torch.nn import functional as F
from collections import OrderedDict
from VmambaIR.models import lr_scheduler as lr_scheduler


@MODEL_REGISTRY.register()
class MambaSISRModel2(SRModel):
    """
    It is trained without GAN losses.
    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaSISRModel2, self).__init__(opt)
        self.scale = self.opt.get('scale', 1)

    def setup_schedulers(self):
        """Set up schedulers."""
        train_opt = self.opt['train']
        scheduler_type = train_opt['scheduler'].pop('type')
        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.MultiStepRestartLR(optimizer,
                                                    **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingWarmupRestarts':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingWarmupRestarts(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartCyclicLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartCyclicLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'TrueCosineAnnealingLR':
            print('..', 'cosineannealingLR')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingLRWithRestart':
            print('..', 'CosineAnnealingLR_With_Restart')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingLRWithRestart(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'LinearLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.LinearLR(
                        optimizer, train_opt['total_iter']))
        elif scheduler_type == 'VibrateLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.VibrateLR(
                        optimizer, train_opt['total_iter']))
        else:
            raise NotImplementedError(
                f'Scheduler {scheduler_type} is not implemented yet.')


    def feed_data(self, data):
        self.gt = data['gt'].to(self.device)
        self.lq=data['lq'] .to(self.device)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaSISRModel2, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True

    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,gt,mod_pad_h,mod_pad_w

    def test(self):
        _, C, h, w = self.lq.size()
        split_h = 64
        split_w = 64
        mod_pad_h, mod_pad_w = 0, 0
        if h % split_h != 0:
            mod_pad_h = (h // split_h + 1) *split_h - h
        if w % split_w != 0:
            mod_pad_w = (w // split_w + 1) *split_w - w
        img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        _, _, H, W = img.size()
        #split_h = H // split_token_h  # height of each partition
        #split_w = W // split_token_w  # width of each partition
        # overlapping
        shave_h = 0 #split_h // 10
        shave_w = 0 #split_w // 10
        scale = self.opt.get('scale', 1)
        ral = H // split_h
        row = W // split_w
        slices = []  # list of partition borders
        for i in range(ral):
            for j in range(row):
                if i == 0 and i == ral - 1:
                    top = slice(i * split_h, (i + 1) * split_h)
                elif i == 0:
                    top = slice(i*split_h, (i+1)*split_h+shave_h)
                elif i == ral - 1:
                    top = slice(i*split_h-shave_h, (i+1)*split_h)
                else:
                    top = slice(i*split_h-shave_h, (i+1)*split_h+shave_h)
                if j == 0 and j == row - 1:
                    left = slice(j*split_w, (j+1)*split_w)
                elif j == 0:
                    left = slice(j*split_w, (j+1)*split_w+shave_w)
                elif j == row - 1:
                    left = slice(j*split_w-shave_w, (j+1)*split_w)
                else:
                    left = slice(j*split_w-shave_w, (j+1)*split_w+shave_w)
                temp = (top, left)
                slices.append(temp)
        img_chops = []  # list of partitions
        for temp in slices:
            top, left = temp
            img_chops.append(img[..., top, left])
        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                outputs = []
                for chop in img_chops:
                    #print("chop", chop.size())
                    out = self.net_g_ema(chop)  # image processing of each partition
                    outputs.append(out)
                _img = torch.zeros(1, C, H * scale, W * scale)
                # merge
                for i in range(ral):
                    for j in range(row):
                        top = slice(i * split_h * scale, (i + 1) * split_h * scale)
                        left = slice(j * split_w * scale, (j + 1) * split_w * scale)
                        if i == 0:
                            _top = slice(0, split_h * scale)
                        else:
                            _top = slice(shave_h*scale, (shave_h+split_h)*scale)
                        if j == 0:
                            _left = slice(0, split_w*scale)
                        else:
                            _left = slice(shave_w*scale, (shave_w+split_w)*scale)
                        _img[..., top, left] = outputs[i * row + j][..., _top, _left]
                self.output = _img
        else:
            self.net_g.eval()
            with torch.no_grad():
                outputs = []
                for chop in img_chops:
                    #print("chop", chop.size())
                    out = self.net_g(chop)  # image processing of each partition
                    outputs.append(out)
                _img = torch.zeros(1, C, H * scale, W * scale)
                # merge
                for i in range(ral):
                    for j in range(row):
                        top = slice(i * split_h * scale, (i + 1) * split_h * scale)
                        left = slice(j * split_w * scale, (j + 1) * split_w * scale)
                        if i == 0:
                            _top = slice(0, split_h * scale)
                        else:
                            _top = slice(shave_h * scale, (shave_h + split_h) * scale)
                        if j == 0:
                            _left = slice(0, split_w * scale)
                        else:
                            _left = slice(shave_w * scale, (shave_w + split_w) * scale)
                        _img[..., top, left] = outputs[i * row + j][..., _top, _left]
                self.output = _img
            self.net_g.train()
        _, _, h, w = self.output.size()
        self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]

    def optimize_parameters(self, current_iter):
        self.optimizer_g.zero_grad()
        self.output = self.net_g(self.lq)

        l_total = 0
        loss_dict = OrderedDict()
        # pixel loss
        if self.cri_pix:
            l_pix = self.cri_pix(self.output, self.gt)
            l_total += l_pix
            loss_dict['l_pix'] = l_pix
        # perceptual loss
        if self.cri_perceptual:
            l_percep, l_style = self.cri_perceptual(self.output, self.gt)
            if l_percep is not None:
                l_total += l_percep
                loss_dict['l_percep'] = l_percep
            if l_style is not None:
                l_total += l_style
                loss_dict['l_style'] = l_style

        l_total.backward()
        self.optimizer_g.step()

        self.log_dict = self.reduce_loss_dict(loss_dict)

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)


================================================
FILE: SRGAN/VmambaIR/models/MambaSISRGAN_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F
from basicsr.archs import build_network
from basicsr.utils import get_root_logger
from basicsr.losses import build_loss
from torch import nn


@MODEL_REGISTRY.register()
class MambaSISRGANModel(SRGANModel):
    """

    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaSISRGANModel, self).__init__(opt)
        self.scale = self.opt.get('scale', 1)

        #self.net_g_S1 = build_network(opt['network_S1'])
        #self.net_g_S1 = self.model_to_device(self.net_g_S1)
        #load_path = self.opt['path'].get('pretrain_network_S1', None)

        #if load_path is not None:
            #param_key = self.opt['path'].get('param_key_g', 'params')
            #self.load_network(self.net_g_S1, load_path, True, param_key)

        #self.net_g_S1.eval()
        #if self.opt['dist']:
            #self.model_Es1 = self.net_g_S1.module.E
        #else:
            #self.model_Es1 = self.net_g_S1.module.E

        if self.is_train:
            #self.encoder_iter = opt["train"]["encoder_iter"]
            #self.lr_encoder = opt["train"]["lr_encoder"]
            self.lr_sr = opt["train"]["lr_sr"]
            #self.gamma_encoder = opt["train"]["gamma_encoder"]
            self.gamma_sr = opt["train"]["gamma_sr"]
            #self.lr_decay_encoder = opt["train"]["lr_decay_encoder"]
            self.lr_decay_sr = opt["train"]["lr_decay_sr"]

    def init_training_settings(self):
        train_opt = self.opt['train']

        if train_opt.get('kd_opt'):
            self.cri_kd = build_loss(train_opt['kd_opt']).to(self.device)
        else:
            self.cri_kd = None
        super(MambaSISRGANModel, self).init_training_settings()
        

    @torch.no_grad()
    def feed_data(self, data):
        self.gt = data['gt'].to(self.device)
        self.lq = data['lq'].to(self.device)


    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaSISRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True
    
    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,gt,mod_pad_h,mod_pad_w

    def test(self):
        window_size = self.opt['val'].get('window_size', 0)
        if window_size:
            lq,gt,mod_pad_h,mod_pad_w=self.pad_test(window_size)
        else:
            lq=self.lq
            gt=self.gt
        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                self.output = self.net_g_ema(lq)
        else:
            self.net_g.eval()
            with torch.no_grad():
                self.output = self.net_g(lq)
            self.net_g.train()
        if window_size:
            scale = self.opt.get('scale', 1)
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]


    def optimize_parameters(self, current_iter):
        #lr = self.lr_sr * (self.gamma_sr ** ((current_iter ) // self.lr_decay_sr))
        # for param_group in self.optimizer_g.param_groups:
        #     param_group['lr'] = 5e-5 

    
        #_, S1_IPR = self.model_Es1(self.lq,self.gt)

        # optimize net_g
        for p in self.net_d.parameters():
            p.requires_grad = False

        self.optimizer_g.zero_grad()
        #self.output, pred_IPR_list = self.net_g(self.lq,S1_IPR[0])
        self.output = self.net_g(self.lq)

        l_g_total = 0
        loss_dict = OrderedDict()
        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
            # pixel loss
            if self.cri_pix:
                l_g_pix = self.cri_pix(self.output, self.gt)
                l_g_total += l_g_pix
                loss_dict['l_g_pix'] = l_g_pix
            

            # perceptual loss
            if self.cri_perceptual:
                l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
                if l_g_percep is not None:
                    l_g_total += l_g_percep
                    loss_dict['l_g_percep'] = l_g_percep
                if l_g_style is not None:
                    l_g_total += l_g_style
                    loss_dict['l_g_style'] = l_g_style
            # gan loss
            fake_g_pred = self.net_d(self.output)
            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
            l_g_total += l_g_gan
            loss_dict['l_g_gan'] = l_g_gan

            l_g_total.backward()
            self.optimizer_g.step()

        # optimize net_d
        for p in self.net_d.parameters():
            p.requires_grad = True

        self.optimizer_d.zero_grad()
        # real
        real_d_pred = self.net_d(self.gt)
        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
        loss_dict['l_d_real'] = l_d_real
        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
        l_d_real.backward()
        # fake
        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
        loss_dict['l_d_fake'] = l_d_fake
        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
        l_d_fake.backward()
        self.optimizer_d.step()

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

        self.log_dict = self.reduce_loss_dict(loss_dict)


================================================
FILE: SRGAN/VmambaIR/models/MambaSISR_model.py
================================================
import numpy as np
import random
import torch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
from basicsr.data.transforms import paired_random_crop
from basicsr.models.sr_model import SRModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from torch.nn import functional as F
from collections import OrderedDict
from VmambaIR.models import lr_scheduler as lr_scheduler


@MODEL_REGISTRY.register()
class MambaSISRModel(SRModel):
    """
    It is trained without GAN losses.
    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(MambaSISRModel, self).__init__(opt)
        self.scale = self.opt.get('scale', 1)

    def setup_schedulers(self):
        """Set up schedulers."""
        train_opt = self.opt['train']
        scheduler_type = train_opt['scheduler'].pop('type')
        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.MultiStepRestartLR(optimizer,
                                                    **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingWarmupRestarts':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingWarmupRestarts(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingRestartCyclicLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingRestartCyclicLR(
                        optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'TrueCosineAnnealingLR':
            print('..', 'cosineannealingLR')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'CosineAnnealingLRWithRestart':
            print('..', 'CosineAnnealingLR_With_Restart')
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.CosineAnnealingLRWithRestart(optimizer, **train_opt['scheduler']))
        elif scheduler_type == 'LinearLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.LinearLR(
                        optimizer, train_opt['total_iter']))
        elif scheduler_type == 'VibrateLR':
            for optimizer in self.optimizers:
                self.schedulers.append(
                    lr_scheduler.VibrateLR(
                        optimizer, train_opt['total_iter']))
        else:
            raise NotImplementedError(
                f'Scheduler {scheduler_type} is not implemented yet.')


    def feed_data(self, data):
        self.gt = data['gt'].to(self.device)
        self.lq=data['lq'] .to(self.device)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(MambaSISRModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True

    def pad_test(self, window_size):        
        scale = self.opt.get('scale', 1)
        mod_pad_h, mod_pad_w = 0, 0
        _, _, h, w = self.lq.size()
        if h % window_size != 0:
            mod_pad_h = window_size - h % window_size
        if w % window_size != 0:
            mod_pad_w = window_size - w % window_size
        lq = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        gt = F.pad(self.gt, (0, mod_pad_w*scale, 0, mod_pad_h*scale), 'reflect')
        return lq,gt,mod_pad_h,mod_pad_w

    def test(self):
        window_size = self.opt['val'].get('window_size', 0)
        if window_size:
            lq,gt,mod_pad_h,mod_pad_w=self.pad_test(window_size)
        else:
            lq=self.lq
            gt=self.gt
        if hasattr(self, 'net_g_ema'):
            self.net_g_ema.eval()
            with torch.no_grad():
                self.output = self.net_g_ema(lq)
        else:
            self.net_g.eval()
            with torch.no_grad():
                self.output = self.net_g(lq)
            self.net_g.train()
        if window_size:
            scale = self.opt.get('scale', 1)
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]

    def optimize_parameters(self, current_iter):
        self.optimizer_g.zero_grad()
        self.output = self.net_g(self.lq)

        l_total = 0
        loss_dict = OrderedDict()
        # pixel loss
        if self.cri_pix:
            l_pix = self.cri_pix(self.output, self.gt)
            l_total += l_pix
            loss_dict['l_pix'] = l_pix
        # perceptual loss
        if self.cri_perceptual:
            l_percep, l_style = self.cri_perceptual(self.output, self.gt)
            if l_percep is not None:
                l_total += l_percep
                loss_dict['l_percep'] = l_percep
            if l_style is not None:
                l_total += l_style
                loss_dict['l_style'] = l_style

        l_total.backward()
        self.optimizer_g.step()

        self.log_dict = self.reduce_loss_dict(loss_dict)

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)


================================================
FILE: SRGAN/VmambaIR/models/__init__.py
================================================
import importlib
from basicsr.utils import scandir
from os import path as osp

# automatically scan and import model modules for registry
# scan all the files that end with '_model.py' under the model folder
model_folder = osp.dirname(osp.abspath(__file__))
model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
# import all the model modules
_model_modules = [importlib.import_module(f'VmambaIR.models.{file_name}') for file_name in model_filenames]


================================================
FILE: SRGAN/VmambaIR/models/lr_scheduler.py
================================================
import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
import torch


class MultiStepRestartLR(_LRScheduler):
    """ MultiStep with restarts learning rate scheme.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        restarts (list): Restart iterations. Default: [0].
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 milestones,
                 gamma=0.1,
                 restarts=(0, ),
                 restart_weights=(1, ),
                 last_epoch=-1):
        self.milestones = Counter(milestones)
        self.gamma = gamma
        self.restarts = restarts
        self.restart_weights = restart_weights
        assert len(self.restarts) == len(
            self.restart_weights), 'restarts and their weights do not match.'
        super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch in self.restarts:
            weight = self.restart_weights[self.restarts.index(self.last_epoch)]
            return [
                group['initial_lr'] * weight
                for group in self.optimizer.param_groups
            ]
        if self.last_epoch not in self.milestones:
            return [group['lr'] for group in self.optimizer.param_groups]
        return [
            group['lr'] * self.gamma**self.milestones[self.last_epoch]
            for group in self.optimizer.param_groups
        ]

class LinearLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(LinearLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter
        weight = (1 - process)
        # print('get lr ', [weight * group['initial_lr'] for group in self.optimizer.param_groups])
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

class VibrateLR(_LRScheduler):
    """

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        milestones (list): Iterations that will decrease learning rate.
        gamma (float): Decrease ratio. Default: 0.1.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 total_iter,
                 last_epoch=-1):
        self.total_iter = total_iter
        super(VibrateLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        process = self.last_epoch / self.total_iter

        f = 0.1
        if process < 3 / 8:
            f = 1 - process * 8 / 3
        elif process < 5 / 8:
            f = 0.2

        T = self.total_iter // 80
        Th = T // 2

        t = self.last_epoch % T

        f2 = t / Th
        if t >= Th:
            f2 = 2 - f2

        weight = f * f2

        if self.last_epoch < Th:
            weight = max(0.1, weight)

        # print('f {}, T {}, Th {}, t {}, f2 {}'.format(f, T, Th, t, f2))
        return [weight * group['initial_lr'] for group in self.optimizer.param_groups]

def get_position_from_periods(iteration, cumulative_period):
    """Get the position from a period list.

    It will return the index of the right-closest number in the period list.
    For example, the cumulative_period = [100, 200, 300, 400],
    if iteration == 50, return 0;
    if iteration == 210, return 2;
    if iteration == 300, return 2.

    Args:
        iteration (int): Current iteration.
        cumulative_period (list[int]): Cumulative period list.

    Returns:
        int: The position of the right-closest number in the period list.
    """
    for i, period in enumerate(cumulative_period):
        if iteration <= period:
            return i


class CosineAnnealingRestartLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.

    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7

    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.

    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_min=0,
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_min = eta_min
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]

        return [
            self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]

class CosineAnnealingRestartCyclicLR(_LRScheduler):
    """ Cosine annealing with restarts learning rate scheme.
    An example of config:
    periods = [10, 10, 10, 10]
    restart_weights = [1, 0.5, 0.5, 0.5]
    eta_min=1e-7
    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
    scheduler will restart with the weights in restart_weights.
    Args:
        optimizer (torch.nn.optimizer): Torch optimizer.
        periods (list): Period for each cosine anneling cycle.
        restart_weights (list): Restart weights at each restart iteration.
            Default: [1].
        eta_min (float): The mimimum lr. Default: 0.
        last_epoch (int): Used in _LRScheduler. Default: -1.
    """

    def __init__(self,
                 optimizer,
                 periods,
                 restart_weights=(1, ),
                 eta_mins=(0, ),
                 last_epoch=-1):
        self.periods = periods
        self.restart_weights = restart_weights
        self.eta_mins = eta_mins
        assert (len(self.periods) == len(self.restart_weights)
                ), 'periods and restart_weights should have the same length.'
        self.cumulative_period = [
            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
        ]
        super(CosineAnnealingRestartCyclicLR, self).__init__(optimizer, last_epoch)
        
    def get_lr(self):
        idx = get_position_from_periods(self.last_epoch,
                                        self.cumulative_period)
        current_weight = self.restart_weights[idx]
        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
        current_period = self.periods[idx]
        eta_min = self.eta_mins[idx]

        return [
            eta_min + current_weight * 0.5 * (base_lr - eta_min) *
            (1 + math.cos(math.pi * (
                (self.last_epoch - nearest_restart) / current_period)))
            for base_lr in self.base_lrs
        ]


================================================
FILE: SRGAN/VmambaIR/test.py
================================================
# flake8: noqa
import sys
import os.path as osp

root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
sys.path.append(root_path)

from basicsr.test import test_pipeline

import VmambaIR.archs
import VmambaIR.data
import VmambaIR.models

if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    test_pipeline(root_path)


================================================
FILE: SRGAN/VmambaIR/train.py
================================================
# flake8: noqa
import sys
import os.path as osp

root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
sys.path.append(root_path)


from VmambaIR.train_pipeline import train_pipeline

import VmambaIR.archs
import VmambaIR.data
import VmambaIR.models
import VmambaIR.losses
import warnings

warnings.filterwarnings("ignore")

if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    train_pipeline(root_path)


================================================
FILE: SRGAN/VmambaIR/train_pipeline.py
================================================
import datetime
import logging
import math
import time
import torch
from os import path as osp

from basicsr.data import build_dataloader, build_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from basicsr.models import build_model
from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str,
                           init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir)
from basicsr.utils.options import copy_opt_file, dict2str, parse_options
import numpy as np
import random

def init_tb_loggers(opt):
    # initialize wandb logger before tensorboard logger to allow proper sync
    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project')
                                                     is not None) and ('debug' not in opt['name']):
        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
        init_wandb_logger(opt)
    tb_logger = None
    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
        tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name']))
    return tb_logger


def create_train_val_dataloader(opt, logger):
    # create train and val dataloaders
    train_loader, val_loaders = None, []
    for phase, dataset_opt in opt['datasets'].items():
        if phase == 'train':
            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
            train_set = build_dataset(dataset_opt)
            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
            train_loader = build_dataloader(
                train_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=train_sampler,
                seed=opt['manual_seed'])

            num_iter_per_epoch = math.ceil(
                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
            total_iters = int(opt['train']['total_iter'])
            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
            logger.info('Training statistics:'
                        f'\n\tNumber of train images: {len(train_set)}'
                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
        elif phase.split('_')[0] == 'val':
            val_set = build_dataset(dataset_opt)
            val_loader = build_dataloader(
                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}')
            val_loaders.append(val_loader)
        else:
            raise ValueError(f'Dataset phase {phase} is not recognized.')

    return train_loader, train_sampler, val_loaders, total_epochs, total_iters


def load_resume_state(opt):
    resume_state_path = None
    if opt['auto_resume']:
        state_path = osp.join('experiments', opt['name'], 'training_states')
        if osp.isdir(state_path):
            states = list(scandir(state_path, suffix='state', recursive=False, full_path=False))
            if len(states) != 0:
                states = [float(v.split('.state')[0]) for v in states]
                resume_state_path = osp.join(state_path, f'{max(states):.0f}.state')
                opt['path']['resume_state'] = resume_state_path
    else:
        if opt['path'].get('resume_state'):
            resume_state_path = opt['path']['resume_state']

    if resume_state_path is None:
        resume_state = None
    else:
        device_id = torch.cuda.current_device()
        resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id))
        check_resume(opt, resume_state['iter'])
    return resume_state


def train_pipeline(root_path):
    # parse options, set distributed setting, set random seed
    opt, args = parse_options(root_path, is_train=True)
    opt['root_path'] = root_path

    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    # load resume states if necessary
    resume_state = load_resume_state(opt)
    # mkdir for experiments and logger
    if resume_state is None:
        make_exp_dirs(opt)
        if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0:
            mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name']))

    # copy the yml file to the experiment root
    copy_opt_file(args.opt, opt['path']['experiments_root'])

    # WARNING: should not use get_root_logger in the above codes, including the called functions
    # Otherwise the logger will not be properly initialized
    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log")
    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
    logger.info(get_env_info())
    logger.info(dict2str(opt))
    # initialize wandb and tb loggers
    tb_logger = init_tb_loggers(opt)

    # create train and validation dataloaders
    result = create_train_val_dataloader(opt, logger)
    train_loader, train_sampler, val_loaders, total_epochs, total_iters = result

    # create model
    model = build_model(opt)
    if resume_state:  # resume training
        model.resume_training(resume_state)  # handle optimizers and schedulers
        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.")
        start_epoch = resume_state['epoch']
        current_iter = resume_state['iter']
    else:
        start_epoch = 0
        current_iter = 0

    # create message logger (formatted outputs)
    msg_logger = MessageLogger(opt, current_iter, tb_logger)

    # dataloader prefetcher
    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
    if prefetch_mode is None or prefetch_mode == 'cpu':
        prefetcher = CPUPrefetcher(train_loader)
    elif prefetch_mode == 'cuda':
        prefetcher = CUDAPrefetcher(train_loader, opt)
        logger.info(f'Use {prefetch_mode} prefetch dataloader')
        if opt['datasets']['train'].get('pin_memory') is not True:
            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
    else:
        raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.")


    # iters = opt['datasets']['train'].get('iters')
    # batch_size = opt['datasets']['train'].get('batch_size_per_gpu')
    # mini_batch_sizes = opt['datasets']['train'].get('mini_batch_sizes')
    # gt_size = opt['datasets']['train'].get('gt_size')
    # mini_gt_sizes = opt['datasets']['train'].get('gt_sizes')

    # groups = np.array([sum(iters[0:i + 1]) for i in range(0, len(iters))])
    # logger_j = [True] * len(groups)
    # training
    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
    for val_loader in val_loaders:
        model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
    data_timer, iter_timer = AvgTimer(), AvgTimer()
    start_time = time.time()

    for epoch in range(start_epoch, total_epochs + 1):
        train_sampler.set_epoch(epoch)
        prefetcher.reset()
        train_data = prefetcher.next()

        while train_data is not None:
            data_timer.record()

            current_iter += 1
            if current_iter > total_iters:
                break
            # update learning rate
            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
         


            # training
            # model.feed_data({'lq': lq, 'gt':gt})
            model.feed_data(train_data)
            model.optimize_parameters(current_iter)
            iter_timer.record()
            if current_iter == 1:
                # reset start time in msg_logger for more accurate eta_time
                # not work in resume mode
                msg_logger.reset_start_time()
            # log
            if current_iter % opt['logger']['print_freq'] == 0:
                log_vars = {'epoch': epoch, 'iter': current_iter}
                log_vars.update({'lrs': model.get_current_learning_rate()})
                log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()})
                log_vars.update(model.get_current_log())
                msg_logger(log_vars)

            # save models and training states
            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
                logger.info('Saving models and training states.')
                model.save(epoch, current_iter)

            # validation
            if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0):
                if len(val_loaders) > 1:
                    logger.warning('Multiple validation datasets are *only* supported by SRModel.')
                for val_loader in val_loaders:
                    model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])

            data_timer.start()
            iter_timer.start()
            train_data = prefetcher.next()
        # end of iter

    # end of epoch

    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
    logger.info(f'End of training. Time consumed: {consumed_time}')
    logger.info('Save the latest model.')
    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
    if opt.get('val') is not None:
        for val_loader in val_loaders:
            model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
    if tb_logger:
        tb_logger.close()


if __name__ == '__main__':
    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
    train_pipeline(root_path)

================================================
FILE: SRGAN/VmambaIR/utils/__init__.py
================================================
from .file_client import FileClient
from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img, padding, padding_DP, imfrombytesDP
from .logger import (MessageLogger, get_env_info, get_root_logger,
                     init_tb_logger, init_wandb_logger)
from .misc import (check_resume, get_time_str, make_exp_dirs, mkdir_and_rename,
                   scandir, scandir_SIDD, set_random_seed, sizeof_fmt)
from .create_lmdb import (create_lmdb_for_reds, create_lmdb_for_gopro, create_lmdb_for_rain13k)

__all__ = [
    # file_client.py
    'FileClient',
    # img_util.py
    'img2tensor',
    'tensor2img',
    'imfrombytes',
    'imwrite',
    'crop_border',
    # logger.py
    'MessageLogger',
    'init_tb_logger',
    'init_wandb_logger',
    'get_root_logger',
    'get_env_info',
    # misc.py
    'set_random_seed',
    'get_time_str',
    'mkdir_and_rename',
    'make_exp_dirs',
    'scandir',
    'check_resume',
    'sizeof_fmt',
    'padding',
    'padding_DP',
    'imfrombytesDP',
    'create_lmdb_for_reds',
    'create_lmdb_for_gopro',
    'create_lmdb_for_rain13k',
]


================================================
FILE: SRGAN/VmambaIR/utils/bundle_submissions.py
================================================
 # Author: Tobias Plötz, TU Darmstadt (tobias.ploetz@visinf.tu-darmstadt.de)

 # This file is part of the implementation as described in the CVPR 2017 paper:
 # Tobias Plötz and Stefan Roth, Benchmarking Denoising Algorithms with Real Photographs.
 # Please see the file LICENSE.txt for the license governing this code.


import numpy as np
import scipy.io as sio
import os
import h5py

def bundle_submissions_raw(submission_folder,session):
    '''
    Bundles submission data for raw denoising

    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''

    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass

    israw = True
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

def bundle_submissions_srgb(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%02d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )



def bundle_submissions_srgb_v1(submission_folder,session):
    '''
    Bundles submission data for sRGB denoising
    
    submission_folder Folder where denoised images reside

    Output is written to <submission_folder>/bundled/. Please submit
    the content of this folder.
    '''
    out_folder = os.path.join(submission_folder, session)
    # out_folder = os.path.join(submission_folder, "bundled/")
    try:
        os.mkdir(out_folder)
    except:pass
    israw = False
    eval_version="1.0"

    for i in range(50):
        Idenoised = np.zeros((20,), dtype=np.object)
        for bb in range(20):
            filename = '%04d_%d.mat'%(i+1,bb+1)
            s = sio.loadmat(os.path.join(submission_folder,filename))
            Idenoised_crop = s["Idenoised_crop"]
            Idenoised[bb] = Idenoised_crop
        filename = '%04d.mat'%(i+1)
        sio.savemat(os.path.join(out_folder, filename),
                    {"Idenoised": Idenoised,
                     "israw": israw,
                     "eval_version": eval_version},
                    )

================================================
FILE: SRGAN/VmambaIR/utils/create_lmdb.py
================================================
import argparse
from os import path as osp

from VmambaIR.utils import scandir
from VmambaIR.utils.lmdb_util import make_lmdb_from_imgs

def prepare_keys(folder_path, suffix='png'):
    """Prepare image path list and keys for DIV2K dataset.

    Args:
        folder_path (str): Folder path.

    Returns:
        list[str]: Image path list.
        list[str]: Key list.
    """
    print('Reading image path list ...')
    img_path_list = sorted(
        list(scandir(folder_path, suffix=suffix, recursive=False)))
    keys = [img_path.split('.{}'.format(suffix))[0] for img_path in sorted(img_path_list)]

    return img_path_list, keys

def create_lmdb_for_reds():
    folder_path = './datasets/REDS/val/sharp_300'
    lmdb_path = './datasets/REDS/val/sharp_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
    #
    folder_path = './datasets/REDS/val/blur_300'
    lmdb_path = './datasets/REDS/val/blur_300.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_sharp'
    lmdb_path = './datasets/REDS/train/train_sharp.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/REDS/train/train_blur_jpeg'
    lmdb_path = './datasets/REDS/train/train_blur_jpeg.lmdb'
    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


def create_lmdb_for_gopro():
    folder_path = './datasets/GoPro/train/blur_crops'
    lmdb_path = './datasets/GoPro/train/blur_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/train/sharp_crops'
    lmdb_path = './datasets/GoPro/train/sharp_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/target'
    lmdb_path = './datasets/GoPro/test/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/GoPro/test/input'
    lmdb_path = './datasets/GoPro/test/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_rain13k():
    folder_path = './datasets/Rain13k/train/input'
    lmdb_path = './datasets/Rain13k/train/input.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/Rain13k/train/target'
    lmdb_path = './datasets/Rain13k/train/target.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'jpg')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

def create_lmdb_for_SIDD():
    folder_path = './datasets/SIDD/train/input_crops'
    lmdb_path = './datasets/SIDD/train/input_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/train/gt_crops'
    lmdb_path = './datasets/SIDD/train/gt_crops.lmdb'

    img_path_list, keys = prepare_keys(folder_path, 'PNG')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    #for val
    folder_path = './datasets/SIDD/val/input_crops'
    lmdb_path = './datasets/SIDD/val/input_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationNoisyBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationNoisyBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)

    folder_path = './datasets/SIDD/val/gt_crops'
    lmdb_path = './datasets/SIDD/val/gt_crops.lmdb'
    mat_path = './datasets/SIDD/ValidationGtBlocksSrgb.mat'
    if not osp.exists(folder_path):
        os.makedirs(folder_path)
    assert  osp.exists(mat_path)
    data = scio.loadmat(mat_path)['ValidationGtBlocksSrgb']
    N, B, H ,W, C = data.shape
    data = data.reshape(N*B, H, W, C)
    for i in tqdm(range(N*B)):
        cv2.imwrite(osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)), cv2.cvtColor(data[i,...], cv2.COLOR_RGB2BGR)) 
    img_path_list, keys = prepare_keys(folder_path, 'png')
    make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)


================================================
FILE: SRGAN/VmambaIR/utils/dist_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp


def init_dist(launcher, backend='nccl', **kwargs):
    if mp.get_start_method(allow_none=True) is None:
        mp.set_start_method('spawn')
    if launcher == 'pytorch':
        _init_dist_pytorch(backend, **kwargs)
    elif launcher == 'slurm':
        _init_dist_slurm(backend, **kwargs)
    else:
        raise ValueError(f'Invalid launcher type: {launcher}')


def _init_dist_pytorch(backend, **kwargs):
    rank = int(os.environ['RANK'])
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(rank % num_gpus)
    dist.init_process_group(backend=backend, **kwargs)


def _init_dist_slurm(backend, port=None):
    """Initialize slurm distributed training environment.

    If argument ``port`` is not specified, then the master port will be system
    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
    environment variable, then a default port ``29500`` will be used.

    Args:
        backend (str): Backend of torch.distributed.
        port (int, optional): Master port. Defaults to None.
    """
    proc_id = int(os.environ['SLURM_PROCID'])
    ntasks = int(os.environ['SLURM_NTASKS'])
    node_list = os.environ['SLURM_NODELIST']
    num_gpus = torch.cuda.device_count()
    torch.cuda.set_device(proc_id % num_gpus)
    addr = subprocess.getoutput(
        f'scontrol show hostname {node_list} | head -n1')
    # specify master port
    if port is not None:
        os.environ['MASTER_PORT'] = str(port)
    elif 'MASTER_PORT' in os.environ:
        pass  # use MASTER_PORT in the environment variable
    else:
        # 29500 is torch.distributed default port
        os.environ['MASTER_PORT'] = '29500'
    os.environ['MASTER_ADDR'] = addr
    os.environ['WORLD_SIZE'] = str(ntasks)
    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
    os.environ['RANK'] = str(proc_id)
    dist.init_process_group(backend=backend)


def get_dist_info():
    if dist.is_available():
        initialized = dist.is_initialized()
    else:
        initialized = False
    if initialized:
        rank = dist.get_rank()
        world_size = dist.get_world_size()
    else:
        rank = 0
        world_size = 1
    return rank, world_size


def master_only(func):

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        rank, _ = get_dist_info()
        if rank == 0:
            return func(*args, **kwargs)

    return wrapper


================================================
FILE: SRGAN/VmambaIR/utils/download_util.py
================================================
import math
import requests
from tqdm import tqdm

from .misc import sizeof_fmt


def download_file_from_google_drive(file_id, save_path):
    """Download files from google drive.

    Ref:
    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501

    Args:
        file_id (str): File id.
        save_path (str): Save path.
    """

    session = requests.Session()
    URL = 'https://docs.google.com/uc?export=download'
    params = {'id': file_id}

    response = session.get(URL, params=params, stream=True)
    token = get_confirm_token(response)
    if token:
        params['confirm'] = token
        response = session.get(URL, params=params, stream=True)

    # get file size
    response_file_size = session.get(
        URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
    if 'Content-Range' in response_file_size.headers:
        file_size = int(
            response_file_size.headers['Content-Range'].split('/')[1])
    else:
        file_size = None

    save_response_content(response, save_path, file_size)


def get_confirm_token(response):
    for key, value in response.cookies.items():
        if key.startswith('download_warning'):
            return value
    return None


def save_response_content(response,
                          destination,
                          file_size=None,
                          chunk_size=32768):
    if file_size is not None:
        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')

        readable_file_size = sizeof_fmt(file_size)
    else:
        pbar = None

    with open(destination, 'wb') as f:
        downloaded_size = 0
        for chunk in response.iter_content(chunk_size):
            downloaded_size += chunk_size
            if pbar is not None:
                pbar.update(1)
                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} '
                                     f'/ {readable_file_size}')
            if chunk:  # filter out keep-alive new chunks
                f.write(chunk)
        if pbar is not None:
            pbar.close()


================================================
FILE: SRGAN/VmambaIR/utils/face_util.py
================================================
import cv2
import numpy as np
import os
import torch
from skimage import transform as trans

from VmambaIR.utils import imwrite

try:
    import dlib
except ImportError:
    print('Please install dlib before testing face restoration.'
          'Reference:　https://github.com/davisking/dlib')


class FaceRestorationHelper(object):
    """Helper for the face restoration pipeline."""

    def __init__(self, upscale_factor, face_size=512):
        self.upscale_factor = upscale_factor
        self.face_size = (face_size, face_size)

        # standard 5 landmarks for FFHQ faces with 1024 x 1024
        self.face_template = np.array([[686.77227723, 488.62376238],
                                       [586.77227723, 493.59405941],
                                       [337.91089109, 488.38613861],
                                       [437.95049505, 493.51485149],
                                       [513.58415842, 678.5049505]])
        self.face_template = self.face_template / (1024 // face_size)
        # for estimation the 2D similarity transformation
        self.similarity_trans = trans.SimilarityTransform()

        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.affine_matrices = []
        self.inverse_affine_matrices = []
        self.cropped_faces = []
        self.restored_faces = []
        self.save_png = True

    def init_dlib(self, detection_path, landmark5_path, landmark68_path):
        """Initialize the dlib detectors and predictors."""
        self.face_detector = dlib.cnn_face_detection_model_v1(detection_path)
        self.shape_predictor_5 = dlib.shape_predictor(landmark5_path)
        self.shape_predictor_68 = dlib.shape_predictor(landmark68_path)

    def free_dlib_gpu_memory(self):
        del self.face_detector
        del self.shape_predictor_5
        del self.shape_predictor_68

    def read_input_image(self, img_path):
        # self.input_img is Numpy array, (h, w, c) with RGB order
        self.input_img = dlib.load_rgb_image(img_path)

    def detect_faces(self,
                     img_path,
                     upsample_num_times=1,
                     only_keep_largest=False):
        """
        Args:
            img_path (str): Image path.
            upsample_num_times (int): Upsamples the image before running the
                face detector

        Returns:
            int: Number of detected faces.
        """
        self.read_input_image(img_path)
        det_faces = self.face_detector(self.input_img, upsample_num_times)
        if len(det_faces) == 0:
            print('No face detected. Try to increase upsample_num_times.')
        else:
            if only_keep_largest:
                print('Detect several faces and only keep the largest.')
                face_areas = []
                for i in range(len(det_faces)):
                    face_area = (det_faces[i].rect.right() -
                                 det_faces[i].rect.left()) * (
                                     det_faces[i].rect.bottom() -
                                     det_faces[i].rect.top())
                    face_areas.append(face_area)
                largest_idx = face_areas.index(max(face_areas))
                self.det_faces = [det_faces[largest_idx]]
            else:
                self.det_faces = det_faces
        return len(self.det_faces)

    def get_face_landmarks_5(self):
        for face in self.det_faces:
            shape = self.shape_predictor_5(self.input_img, face.rect)
            landmark = np.array([[part.x, part.y] for part in shape.parts()])
            self.all_landmarks_5.append(landmark)
        return len(self.all_landmarks_5)

    def get_face_landmarks_68(self):
        """Get 68 densemarks for cropped images.

        Should only have one face at most in the cropped image.
        """
        num_detected_face = 0
        for idx, face in enumerate(self.cropped_faces):
            # face detection
            det_face = self.face_detector(face, 1)  # TODO: can we remove it?
            if len(det_face) == 0:
                print(f'Cannot find faces in cropped image with index {idx}.')
                self.all_landmarks_68.append(None)
            else:
                if len(det_face) > 1:
                    print('Detect several faces in the cropped face. Use the '
                          ' largest one. Note that it will also cause overlap '
                          'during paste_faces_to_input_image.')
                    face_areas = []
                    for i in range(len(det_face)):
                        face_area = (det_face[i].rect.right() -
                                     det_face[i].rect.left()) * (
                                         det_face[i].rect.bottom() -
                                         det_face[i].rect.top())
                        face_areas.append(face_area)
                    largest_idx = face_areas.index(max(face_areas))
                    face_rect = det_face[largest_idx].rect
                else:
                    face_rect = det_face[0].rect
                shape = self.shape_predictor_68(face, face_rect)
                landmark = np.array([[part.x, part.y]
                                     for part in shape.parts()])
                self.all_landmarks_68.append(landmark)
                num_detected_face += 1

        return num_detected_face

    def warp_crop_faces(self,
                        save_cropped_path=None,
                        save_inverse_affine_path=None):
        """Get affine matrix, warp and cropped faces.

        Also get inverse affine matrix for post-processing.
        """
        for idx, landmark in enumerate(self.all_landmarks_5):
            # use 5 landmarks to get affine matrix
            self.similarity_trans.estimate(landmark, self.face_template)
            affine_matrix = self.similarity_trans.params[0:2, :]
            self.affine_matrices.append(affine_matrix)
            # warp and crop faces
            cropped_face = cv2.warpAffine(self.input_img, affine_matrix,
                                          self.face_size)
            self.cropped_faces.append(cropped_face)
            # save the cropped face
            if save_cropped_path is not None:
                path, ext = os.path.splitext(save_cropped_path)
                if self.save_png:
                    save_path = f'{path}_{idx:02d}.png'
                else:
                    save_path = f'{path}_{idx:02d}{ext}'

                imwrite(
                    cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path)

            # get inverse affine matrix
            self.similarity_trans.estimate(self.face_template,
                                           landmark * self.upscale_factor)
            inverse_affine = self.similarity_trans.params[0:2, :]
            self.inverse_affine_matrices.append(inverse_affine)
            # save inverse affine matrices
            if save_inverse_affine_path is not None:
                path, _ = os.path.splitext(save_inverse_affine_path)
                save_path = f'{path}_{idx:02d}.pth'
                torch.save(inverse_affine, save_path)

    def add_restored_face(self, face):
        self.restored_faces.append(face)

    def paste_faces_to_input_image(self, save_path):
        # operate in the BGR order
        input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR)
        h, w, _ = input_img.shape
        h_up, w_up = h * self.upscale_factor, w * self.upscale_factor
        # simply resize the background
        upsample_img = cv2.resize(input_img, (w_up, h_up))
        assert len(self.restored_faces) == len(self.inverse_affine_matrices), (
            'length of restored_faces and affine_matrices are different.')
        for restored_face, inverse_affine in zip(self.restored_faces,
                                                 self.inverse_affine_matrices):
            inv_restored = cv2.warpAffine(restored_face, inverse_affine,
                                          (w_up, h_up))
            mask = np.ones((*self.face_size, 3), dtype=np.float32)
            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
            # remove the black borders
            inv_mask_erosion = cv2.erode(
                inv_mask,
                np.ones((2 * self.upscale_factor, 2 * self.upscale_factor),
                        np.uint8))
            inv_restored_remove_border = inv_mask_erosion * inv_restored
            total_face_area = np.sum(inv_mask_erosion) // 3
            # compute the fusion edge based on the area of face
            w_edge = int(total_face_area**0.5) // 20
            erosion_radius = w_edge * 2
            inv_mask_center = cv2.erode(
                inv_mask_erosion,
                np.ones((erosion_radius, erosion_radius), np.uint8))
            blur_size = w_edge * 2
            inv_soft_mask = cv2.GaussianBlur(inv_mask_center,
                                             (blur_size + 1, blur_size + 1), 0)
            upsample_img = inv_soft_mask * inv_restored_remove_border + (
                1 - inv_soft_mask) * upsample_img
        if self.save_png:
            save_path = save_path.replace('.jpg',
                                          '.png').replace('.jpeg', '.png')
        imwrite(upsample_img.astype(np.uint8), save_path)

    def clean_all(self):
        self.all_landmarks_5 = []
        self.all_landmarks_68 = []
        self.restored_faces = []
        self.affine_matrices = []
        self.cropped_faces = []
        self.inverse_affine_matrices = []


================================================
FILE: SRGAN/VmambaIR/utils/file_client.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py  # noqa: E501
from abc import ABCMeta, abstractmethod


class BaseStorageBackend(metaclass=ABCMeta):
    """Abstract class of storage backends.

    All backends need to implement two apis: ``get()`` and ``get_text()``.
    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
    as texts.
    """

    @abstractmethod
    def get(self, filepath):
        pass

    @abstractmethod
    def get_text(self, filepath):
        pass


class MemcachedBackend(BaseStorageBackend):
    """Memcached storage backend.

    Attributes:
        server_list_cfg (str): Config file for memcached server list.
        client_cfg (str): Config file for memcached client.
        sys_path (str | None): Additional path to be appended to `sys.path`.
            Default: None.
    """

    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
        if sys_path is not None:
            import sys
            sys.path.append(sys_path)
        try:
            import mc
        except ImportError:
            raise ImportError(
                'Please install memcached to enable MemcachedBackend.')

        self.server_list_cfg = server_list_cfg
        self.client_cfg = client_cfg
        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
                                                      self.client_cfg)
        # mc.pyvector servers as a point which points to a memory cache
        self._mc_buffer = mc.pyvector()

    def get(self, filepath):
        filepath = str(filepath)
        import mc
        self._client.Get(filepath, self._mc_buffer)
        value_buf = mc.ConvertBuffer(self._mc_buffer)
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class HardDiskBackend(BaseStorageBackend):
    """Raw hard disks storage backend."""

    def get(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'rb') as f:
            value_buf = f.read()
        return value_buf

    def get_text(self, filepath):
        filepath = str(filepath)
        with open(filepath, 'r') as f:
            value_buf = f.read()
        return value_buf


class LmdbBackend(BaseStorageBackend):
    """Lmdb storage backend.

    Args:
        db_paths (str | list[str]): Lmdb database paths.
        client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
        readonly (bool, optional): Lmdb environment parameter. If True,
            disallow any write operations. Default: True.
        lock (bool, optional): Lmdb environment parameter. If False, when
            concurrent access occurs, do not lock the database. Default: False.
        readahead (bool, optional): Lmdb environment parameter. If False,
            disable the OS filesystem readahead mechanism, which may improve
            random read performance when a database is larger than RAM.
            Default: False.

    Attributes:
        db_paths (list): Lmdb database path.
        _client (list): A list of several lmdb envs.
    """

    def __init__(self,
                 db_paths,
                 client_keys='default',
                 readonly=True,
                 lock=False,
                 readahead=False,
                 **kwargs):
        try:
            import lmdb
        except ImportError:
            raise ImportError('Please install lmdb to enable LmdbBackend.')

        if isinstance(client_keys, str):
            client_keys = [client_keys]

        if isinstance(db_paths, list):
            self.db_paths = [str(v) for v in db_paths]
        elif isinstance(db_paths, str):
            self.db_paths = [str(db_paths)]
        assert len(client_keys) == len(self.db_paths), (
            'client_keys and db_paths should have the same length, '
            f'but received {len(client_keys)} and {len(self.db_paths)}.')

        self._client = {}

        for client, path in zip(client_keys, self.db_paths):
            self._client[client] = lmdb.open(
                path,
                readonly=readonly,
                lock=lock,
                readahead=readahead,
                map_size=8*1024*10485760,
                # max_readers=1,
                **kwargs)

    def get(self, filepath, client_key):
        """Get values according to the filepath from one lmdb named client_key.

        Args:
            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
            client_key (str): Used for distinguishing differnet lmdb envs.
        """
        filepath = str(filepath)
        assert client_key in self._client, (f'client_key {client_key} is not '
                                            'in lmdb clients.')
        client = self._client[client_key]
        with client.begin(write=False) as txn:
            value_buf = txn.get(filepath.encode('ascii'))
        return value_buf

    def get_text(self, filepath):
        raise NotImplementedError


class FileClient(object):
    """A general file client to access files in different backend.

    The client loads a file or text in a specified backend from its path
    and return it as a binary file. it can also register other backend
    accessor with a given name and backend class.

    Attributes:
        backend (str): The storage backend type. Options are "disk",
            "memcached" and "lmdb".
        client (:obj:`BaseStorageBackend`): The backend object.
    """

    _backends = {
        'disk': HardDiskBackend,
        'memcached': MemcachedBackend,
        'lmdb': LmdbBackend,
    }

    def __init__(self, backend='disk', **kwargs):
        if backend not in self._backends:
            raise ValueError(
                f'Backend {backend} is not supported. Currently supported ones'
                f' are {list(self._backends.keys())}')
        self.backend = backend
        self.client = self._backends[backend](**kwargs)

    def get(self, filepath, client_key='default'):
        # client_key is used only for lmdb, where different fileclients have
        # different lmdb environments.
        if self.backend == 'lmdb':
            return self.client.get(filepath, client_key)
        else:
            return self.client.get(filepath)

    def get_text(self, filepath):
        return self.client.get_text(filepath)


================================================
FILE: SRGAN/VmambaIR/utils/flow_util.py
================================================
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py  # noqa: E501
import cv2
import numpy as np
import os


def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
    """Read an optical flow map.

    Args:
        flow_path (ndarray or str): Flow path.
        quantize (bool): whether to read quantized pair, if set to True,
            remaining args will be passed to :func:`dequantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.

    Returns:
        ndarray: Optical flow represented as a (h, w, 2) numpy array
    """
    if quantize:
        assert concat_axis in [0, 1]
        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
        if cat_flow.ndim != 2:
            raise IOError(f'{flow_path} is not a valid quantized flow file, '
                          f'its dimension is {cat_flow.ndim}.')
        assert cat_flow.shape[concat_axis] % 2 == 0
        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
        flow = dequantize_flow(dx, dy, *args, **kwargs)
    else:
        with open(flow_path, 'rb') as f:
            try:
                header = f.read(4).decode('utf-8')
            except Exception:
                raise IOError(f'Invalid flow file: {flow_path}')
            else:
                if header != 'PIEH':
                    raise IOError(f'Invalid flow file: {flow_path}, '
                                  'header does not contain PIEH')

            w = np.fromfile(f, np.int32, 1).squeeze()
            h = np.fromfile(f, np.int32, 1).squeeze()
            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))

    return flow.astype(np.float32)


def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
    """Write optical flow to file.

    If the flow is not quantized, it will be saved as a .flo file losslessly,
    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
    will be concatenated horizontally into a single image if quantize is True.)

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        filename (str): Output filepath.
        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
            images. If set to True, remaining args will be passed to
            :func:`quantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.
    """
    if not quantize:
        with open(filename, 'wb') as f:
            f.write('PIEH'.encode('utf-8'))
            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
            flow = flow.astype(np.float32)
            flow.tofile(f)
            f.flush()
    else:
        assert concat_axis in [0, 1]
        dx, dy = quantize_flow(flow, *args, **kwargs)
        dxdy = np.concatenate((dx, dy), axis=concat_axis)
        os.makedirs(filename, exist_ok=True)
        cv2.imwrite(dxdy, filename)


def quantize_flow(flow, max_val=0.02, norm=True):
    """Quantize flow to [0, 255].

    After this step, the size of flow will be much smaller, and can be
    dumped as jpeg images.

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        max_val (float): Maximum value of flow, values beyond
                        [-max_val, max_val] will be truncated.
        norm (bool): Whether to divide flow values by image width/height.

    Returns:
        tuple[ndarray]: Quantized dx and dy.
    """
    h, w, _ = flow.shape
    dx = flow[..., 0]
    dy = flow[..., 1]
    if norm:
        dx = dx / w  # avoid inplace operations
        dy = dy / h
    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
    flow_comps = [
        quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]
    ]
    return tuple(flow_comps)


def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
    """Recover from quantized flow.

    Args:
        dx (ndarray): Quantized dx.
        dy (ndarray): Quantized dy.
        max_val (float): Maximum value used when quantizing.
        denorm (bool): Whether to multiply flow values with width/height.

    Returns:
        ndarray: Dequantized flow.
    """
    assert dx.shape == dy.shape
    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)

    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]

    if denorm:
        dx *= dx.shape[1]
        dy *= dx.shape[0]
    flow = np.dstack((dx, dy))
    return flow


def quantize(arr, min_val, max_val, levels, dtype=np.int64):
    """Quantize an array of (-inf, inf) to [0, levels-1].

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the quantized array.

    Returns:
        tuple: Quantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    arr = np.clip(arr, min_val, max_val) - min_val
    quantized_arr = np.minimum(
        np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)

    return quantized_arr


def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
    """Dequantize an array.

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the dequantized array.

    Returns:
        tuple: Dequantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(
            f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(
            f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val -
                                                   min_val) / levels + min_val

    return dequantized_arr


================================================
FILE: SRGAN/VmambaIR/utils/img_util.py
================================================
import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    else:
        return _totensor(imgs, bgr2rgb, float32)


def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
    """Convert torch Tensors into image numpy arrays.

    After clamping to [min, max], values will be normalized to [0, 1].

    Args:
        tensor (Tensor or list[Tensor]): Accept shapes:
            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
            2) 3D Tensor of shape (3/1 x H x W);
            3) 2D Tensor of shape (H x W).
            Tensor channel should be in RGB order.
        rgb2bgr (bool): Whether to change rgb to bgr.
        out_type (numpy type): output types. If ``np.uint8``, transform outputs
            to uint8 type with range [0, 255]; otherwise, float type with
            range [0, 1]. Default: ``np.uint8``.
        min_max (tuple[int]): min and max values for clamp.

    Returns:
        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
        shape (H x W). The channel order is BGR.
    """
    if not (torch.is_tensor(tensor) or
            (isinstance(tensor, list)
             and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(
            f'tensor or list of tensors expected, got {type(tensor)}')

    if torch.is_tensor(tensor):
        tensor = [tensor]
    result = []
    for _tensor in tensor:
        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])

        n_dim = _tensor.dim()
        if n_dim == 4:
            img_np = make_grid(
                _tensor, nrow=int(math.sqrt(_tensor.size(0))),
                normalize=False).numpy()
            img_np = img_np.transpose(1, 2, 0)
            if rgb2bgr:
                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 3:
            img_np = _tensor.numpy()
            img_np = img_np.transpose(1, 2, 0)
            if img_np.shape[2] == 1:  # gray image
                img_np = np.squeeze(img_np, axis=2)
            else:
                if rgb2bgr:
                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 2:
            img_np = _tensor.numpy()
        else:
            raise TypeError('Only support 4D, 3D or 2D tensor. '
                            f'But received with dimension: {n_dim}')
        if out_type == np.uint8:
            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
            img_np = (img_np * 255.0).round()
        img_np = img_np.astype(out_type)
        result.append(img_np)
    if len(result) == 1:
        result = result[0]
    return result


def imfrombytes(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    imread_flags = {
        'color': cv2.IMREAD_COLOR,
        'grayscale': cv2.IMREAD_GRAYSCALE,
        'unchanged': cv2.IMREAD_UNCHANGED
    }
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, imread_flags[flag])
    if float32:
        img = img.astype(np.float32) / 255.
    return img

def imfrombytesDP(content, flag='color', float32=False):
    """Read an image from bytes.

    Args:
        content (bytes): Image bytes got from files or other streams.
        flag (str): Flags specifying the color type of a loaded image,
            candidates are `color`, `grayscale` and `unchanged`.
        float32 (bool): Whether to change to float32., If True, will also norm
            to [0, 1]. Default: False.

    Returns:
        ndarray: Loaded image array.
    """
    img_np = np.frombuffer(content, np.uint8)
    if img_np is None:
        raise Exception('None .. !!!')
    img = cv2.imdecode(img_np, cv2.IMREAD_UNCHANGED)
    if float32:
        img = img.astype(np.float32) / 65535.
    return img

def padding(img_lq, img_gt, gt_size):
    h, w, _ = img_lq.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lq, img_gt

    img_lq = cv2.copyMakeBorder(img_lq, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt = cv2.copyMakeBorder(img_gt, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    if img_lq.ndim == 2:
        img_lq = np.expand_dims(img_lq, axis=2)
    if img_gt.ndim == 2:
        img_gt = np.expand_dims(img_gt, axis=2)
    return img_lq, img_gt

def padding_DP(img_lqL, img_lqR, img_gt, gt_size):
    h, w, _ = img_gt.shape

    h_pad = max(0, gt_size - h)
    w_pad = max(0, gt_size - w)
    
    if h_pad == 0 and w_pad == 0:
        return img_lqL, img_lqR, img_gt

    img_lqL = cv2.copyMakeBorder(img_lqL, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_lqR = cv2.copyMakeBorder(img_lqR, 0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    img_gt  = cv2.copyMakeBorder(img_gt,  0, h_pad, 0, w_pad, cv2.BORDER_REFLECT)
    # print('img_lq', img_lq.shape, img_gt.shape)
    return img_lqL, img_lqR, img_gt

def imwrite(img, file_path, params=None, auto_mkdir=True):
    """Write image to file.

    Args:
        img (ndarray): Image array to be written.
        file_path (str): Image file path.
        params (None or list): Same as opencv's :func:`imwrite` interface.
        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
            whether to create it automatically.

    Returns:
        bool: Successful or not.
    """
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(file_path))
        os.makedirs(dir_name, exist_ok=True)
    return cv2.imwrite(file_path, img, params)


def crop_border(imgs, crop_border):
    """Crop borders of images.

    Args:
        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
        crop_border (int): Crop border for each end of height and weight.

    Returns:
        list[ndarray]: Cropped images.
    """
    if crop_border == 0:
        return imgs
    else:
        if isinstance(imgs, list):
            return [
                v[crop_border:-crop_border, crop_border:-crop_border, ...]
                for v in imgs
            ]
        else:
            return imgs[crop_border:-crop_border, crop_border:-crop_border,
                        ...]


================================================
FILE: SRGAN/VmambaIR/utils/lmdb_util.py
================================================
import cv2
import lmdb
import sys
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def make_lmdb_from_imgs(data_path,
                        lmdb_path,
                        img_path_list,
                        keys,
                        batch=5000,
                        compress_level=1,
                        multiprocessing_read=False,
                        n_thread=40,
                        map_size=None):
    """Make lmdb from images.

    Contents of lmdb. The file structure is:
    example.lmdb
    ├── data.mdb
    ├── lock.mdb
    ├── meta_info.txt

    The data.mdb and lock.mdb are standard lmdb files and you can refer to
    https://lmdb.readthedocs.io/en/release/ for more details.

    The meta_info.txt is a specified txt file to record the meta information
    of our datasets. It will be automatically created when preparing
    datasets by our provided dataset tools.
    Each line in the txt file records 1)image name (with extension),
    2)image shape, and 3)compression level, separated by a white space.

    For example, the meta information could be:
    `000_00000000.png (720,1280,3) 1`, which means:
    1) image name (with extension): 000_00000000.png;
    2) image shape: (720,1280,3);
    3) compression level: 1

    We use the image name without extension as the lmdb key.

    If `multiprocessing_read` is True, it will read all the images to memory
    using multiprocessing. Thus, your server needs to have enough memory.

    Args:
        data_path (str): Data path for reading images.
        lmdb_path (str): Lmdb save path.
        img_path_list (str): Image path list.
        keys (str): Used for lmdb keys.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
        multiprocessing_read (bool): Whether use multiprocessing to read all
            the images to memory. Default: False.
        n_thread (int): For multiprocessing.
        map_size (int | None): Map size for lmdb env. If None, use the
            estimated size from images. Default: None
    """

    assert len(img_path_list) == len(keys), (
        'img_path_list and keys should have the same length, '
        f'but got {len(img_path_list)} and {len(keys)}')
    print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
    print(f'Totoal images: {len(img_path_list)}')
    if not lmdb_path.endswith('.lmdb'):
        raise ValueError("lmdb_path must end with '.lmdb'.")
    if osp.exists(lmdb_path):
        print(f'Folder {lmdb_path} already exists. Exit.')
        sys.exit(1)

    if multiprocessing_read:
        # read all the images to memory (multiprocessing)
        dataset = {}  # use dict to keep the order for multiprocessing
        shapes = {}
        print(f'Read images with multiprocessing, #thread: {n_thread} ...')
        pbar = tqdm(total=len(img_path_list), unit='image')

        def callback(arg):
            """get the image data and update pbar."""
            key, dataset[key], shapes[key] = arg
            pbar.update(1)
            pbar.set_description(f'Read {key}')

        pool = Pool(n_thread)
        for path, key in zip(img_path_list, keys):
            pool.apply_async(
                read_img_worker,
                args=(osp.join(data_path, path), key, compress_level),
                callback=callback)
        pool.close()
        pool.join()
        pbar.close()
        print(f'Finish reading {len(img_path_list)} images.')

    # create lmdb environment
    if map_size is None:
        # obtain data size for one image
        img = cv2.imread(
            osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
        _, img_byte = cv2.imencode(
            '.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
        data_size_per_img = img_byte.nbytes
        print('Data size per image is: ', data_size_per_img)
        data_size = data_size_per_img * len(img_path_list)
        map_size = data_size * 10

    env = lmdb.open(lmdb_path, map_size=map_size)

    # write data to lmdb
    pbar = tqdm(total=len(img_path_list), unit='chunk')
    txn = env.begin(write=True)
    txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
    for idx, (path, key) in enumerate(zip(img_path_list, keys)):
        pbar.update(1)
        pbar.set_description(f'Write {key}')
        key_byte = key.encode('ascii')
        if multiprocessing_read:
            img_byte = dataset[key]
            h, w, c = shapes[key]
        else:
            _, img_byte, img_shape = read_img_worker(
                osp.join(data_path, path), key, compress_level)
            h, w, c = img_shape

        txn.put(key_byte, img_byte)
        # write meta information
        txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
        if idx % batch == 0:
            txn.commit()
            txn = env.begin(write=True)
    pbar.close()
    txn.commit()
    env.close()
    txt_file.close()
    print('\nFinish writing lmdb.')


def read_img_worker(path, key, compress_level):
    """Read image worker.

    Args:
        path (str): Image path.
        key (str): Image key.
        compress_level (int): Compress level when encoding images.

    Returns:
        str: Image key.
        byte: Image byte.
        tuple[int]: Image shape.
    """

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    if img.ndim == 2:
        h, w = img.shape
        c = 1
    else:
        h, w, c = img.shape
    _, img_byte = cv2.imencode('.png', img,
                               [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
    return (key, img_byte, (h, w, c))


class LmdbMaker():
    """LMDB Maker.

    Args:
        lmdb_path (str): Lmdb save path.
        map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
        batch (int): After processing batch images, lmdb commits.
            Default: 5000.
        compress_level (int): Compress level when encoding images. Default: 1.
    """

    def __init__(self,
                 lmdb_path,
                 map_size=1024**4,
                 batch=5000,
                 compress_level=1):
        if not lmdb_path.endswith('.lmdb'):
            raise ValueError("lmdb_path must end with '.lmdb'.")
        if osp.exists(lmdb_path):
            print(f'Folder {lmdb_path} already exists. Exit.')
            sys.exit(1)

        self.lmdb_path = lmdb_path
        self.batch = batch
        self.compress_level = compress_level
        self.env = lmdb.open(lmdb_path, map_size=map_size)
        self.txn = self.env.begin(write=True)
        self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
        self.counter = 0

    def put(self, img_byte, key, img_shape):
        self.counter += 1
        key_byte = key.encode('ascii')
        self.txn.put(key_byte, img_byte)
        # write meta information
        h, w, c = img_shape
        self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
        if self.counter % self.batch == 0:
            self.txn.commit()
            self.txn = self.env.begin(write=True)

    def close(self):
        self.txn.commit()
        self.env.close()
        self.txt_file.close()


================================================
FILE: SRGAN/VmambaIR/utils/logger.py
================================================
import datetime
import logging
import time

from .dist_util import get_dist_info, master_only

initialized_logger = {}


class MessageLogger():
    """Message logger for printing.

    Args:
        opt (dict): Config. It contains the following keys:
            name (str): Exp name.
            logger (dict): Contains 'print_freq' (str) for logger interval.
            train (dict): Contains 'total_iter' (int) for total iters.
            use_tb_logger (bool): Use tensorboard logger.
        start_iter (int): Start iter. Default: 1.
        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
    """

    def __init__(self, opt, start_iter=1, tb_logger=None):
        self.exp_name = opt['name']
        self.interval = opt['logger']['print_freq']
        self.start_iter = start_iter
        self.max_iters = opt['train']['total_iter']
        self.use_tb_logger = opt['logger']['use_tb_logger']
        self.tb_logger = tb_logger
        self.start_time = time.time()
        self.logger = get_root_logger()

    @master_only
    def __call__(self, log_vars):
        """Format logging message.

        Args:
            log_vars (dict): It contains the following keys:
                epoch (int): Epoch number.
                iter (int): Current iter.
                lrs (list): List for learning rates.

                time (float): Iter time.
                data_time (float): Data time for each iter.
        """
        # epoch, iter, learning rates
        epoch = log_vars.pop('epoch')
        current_iter = log_vars.pop('iter')
        lrs = log_vars.pop('lrs')

        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(')
        for v in lrs:
            message += f'{v:.3e},'
        message += ')] '

        # time and estimated time
        if 'time' in log_vars.keys():
            iter_time = log_vars.pop('time')
            data_time = log_vars.pop('data_time')

            total_time = time.time() - self.start_time
            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
            message += f'[eta: {eta_str}, '
            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '

        # other items, especially losses
        for k, v in log_vars.items():
            message += f'{k}: {v:.4e} '
            # tensorboard logger
            if self.use_tb_logger and 'debug' not in self.exp_name:
                if k.startswith('l_'):
                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
                else:
                    self.tb_logger.add_scalar(k, v, current_iter)
        self.logger.info(message)


@master_only
def init_tb_logger(log_dir):
    from torch.utils.tensorboard import SummaryWriter
    tb_logger = SummaryWriter(log_dir=log_dir)
    return tb_logger


@master_only
def init_wandb_logger(opt):
    """We now only use wandb to sync tensorboard log."""
    import wandb
    logger = logging.getLogger('basicsr')

    project = opt['logger']['wandb']['project']
    resume_id = opt['logger']['wandb'].get('resume_id')
    if resume_id:
        wandb_id = resume_id
        resume = 'allow'
        logger.warning(f'Resume wandb logger with id={wandb_id}.')
    else:
        wandb_id = wandb.util.generate_id()
        resume = 'never'

    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)

    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')


def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
    """Get the root logger.

    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If `log_file` is specified, a FileHandler will
    also be added.

    Args:
        logger_name (str): root logger name. Default: 'basicsr'.
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.

    Returns:
        logging.Logger: The root logger.
    """
    logger = logging.getLogger(logger_name)
    # if the logger has been initialized, just return it
    if logger_name in initialized_logger:
        return logger

    format_str = '%(asctime)s %(levelname)s: %(message)s'
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(logging.Formatter(format_str))
    logger.addHandler(stream_handler)
    logger.propagate = False
    rank, _ = get_dist_info()
    if rank != 0:
        logger.setLevel('ERROR')
    elif log_file is not None:
        logger.setLevel(log_level)
        # add file handler
        file_handler = logging.FileHandler(log_file, 'w')
        file_handler.setFormatter(logging.Formatter(format_str))
        file_handler.setLevel(log_level)
        logger.addHandler(file_handler)
    initialized_logger[logger_name] = True
    return logger


def get_env_info():
    """Get environment information.

    Currently, only log the software version.
    """
    import torch
    import torchvision

    from basicsr.version import __version__
    msg = r"""
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    """
    msg += ('\nVersion Information: '
            f'\n\tBasicSR: {__version__}'
            f'\n\tPyTorch: {torch.__version__}'
            f'\n\tTorchVision: {torchvision.__version__}')
    return msg

================================================
FILE: SRGAN/VmambaIR/utils/matlab_functions.py
================================================
import math
import numpy as np
import torch


def cubic(x):
    """cubic function used for calculate_weights_indices."""
    absx = torch.abs(x)
    absx2 = absx**2
    absx3 = absx**3
    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx +
                                      2) * (((absx > 1) *
                                             (absx <= 2)).type_as(absx))


def calculate_weights_indices(in_length, out_length, scale, kernel,
                              kernel_width, antialiasing):
    """Calculate weights and indices, used for imresize function.

    Args:
        in_length (int): Input length.
        out_length (int): Output length.
        scale (float): Scale factor.
        kernel_width (int): Kernel width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
    """

    if (scale < 1) and antialiasing:
        # Use a modified kernel (larger kernel width) to simultaneously
        # interpolate and antialias
        kernel_width = kernel_width / scale

    # Output-space coordinates
    x = torch.linspace(1, out_length, out_length)

    # Input-space coordinates. Calculate the inverse mapping such that 0.5
    # in output space maps to 0.5 in input space, and 0.5 + scale in output
    # space maps to 1.5 in input space.
    u = x / scale + 0.5 * (1 - 1 / scale)

    # What is the left-most pixel that can be involved in the computation?
    left = torch.floor(u - kernel_width / 2)

    # What is the maximum number of pixels that can be involved in the
    # computation?  Note: it's OK to use an extra pixel here; if the
    # corresponding weights are all zero, it will be eliminated at the end
    # of this function.
    p = math.ceil(kernel_width) + 2

    # The indices of the input pixels involved in computing the k-th output
    # pixel are in row k of the indices matrix.
    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(
        0, p - 1, p).view(1, p).expand(out_length, p)

    # The weights used to compute the k-th output pixel are in row k of the
    # weights matrix.
    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices

    # apply cubic kernel
    if (scale < 1) and antialiasing:
        weights = scale * cubic(distance_to_center * scale)
    else:
        weights = cubic(distance_to_center)

    # Normalize the weights matrix so that each row sums to 1.
    weights_sum = torch.sum(weights, 1).view(out_length, 1)
    weights = weights / weights_sum.expand(out_length, p)

    # If a column in weights is all zero, get rid of it. only consider the
    # first and last column.
    weights_zero_tmp = torch.sum((weights == 0), 0)
    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 1, p - 2)
        weights = weights.narrow(1, 1, p - 2)
    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 0, p - 2)
        weights = weights.narrow(1, 0, p - 2)
    weights = weights.contiguous()
    indices = indices.contiguous()
    sym_len_s = -indices.min() + 1
    sym_len_e = indices.max() - in_length
    indices = indices + sym_len_s - 1
    return weights, indices, int(sym_len_s), int(sym_len_e)


@torch.no_grad()
def imresize(img, scale, antialiasing=True):
    """imresize function same as MATLAB.

    It now only supports bicubic.
    The same scale applies for both height and width.

    Args:
        img (Tensor | Numpy array):
            Tensor: Input image with shape (c, h, w), [0, 1] range.
            Numpy: Input image with shape (h, w, c), [0, 1] range.
        scale (float): Scale factor. The same scale applies for both height
            and width.
        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
            Default: True.

    Returns:
        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
    """
    if type(img).__module__ == np.__name__:  # numpy type
        numpy_type = True
        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
    else:
        numpy_type = False

    in_c, in_h, in_w = img.size()
    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
    kernel_width = 4
    kernel = 'cubic'

    # get weights and indices
    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(
        in_h, out_h, scale, kernel, kernel_width, antialiasing)
    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(
        in_w, out_w, scale, kernel, kernel_width, antialiasing)
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)

    sym_patch = img[:, :sym_len_hs, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)

    sym_patch = img[:, -sym_len_he:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(in_c, out_h, in_w)
    kernel_width = weights_h.size(1)
    for i in range(out_h):
        idx = int(indices_h[i][0])
        for j in range(in_c):
            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(
                0, 1).mv(weights_h[i])

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)

    sym_patch = out_1[:, :, :sym_len_ws]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, :, -sym_len_we:]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(in_c, out_h, out_w)
    kernel_width = weights_w.size(1)
    for i in range(out_w):
        idx = int(indices_w[i][0])
        for j in range(in_c):
            out_2[j, :, i] = out_1_aug[j, :,
                                       idx:idx + kernel_width].mv(weights_w[i])

    if numpy_type:
        out_2 = out_2.numpy().transpose(1, 2, 0)
    return out_2


def rgb2ycbcr(img, y_only=False):
    """Convert a RGB image to YCbCr image.

    This function produces the same results as Matlab's `rgb2ycbcr` function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
    else:
        out_img = np.matmul(
            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
                  [24.966, 112.0, -18.214]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def bgr2ycbcr(img, y_only=False):
    """Convert a BGR image to YCbCr image.

    The bgr version of rgb2ycbcr.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
    else:
        out_img = np.matmul(
            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2rgb(img):
    """Convert a YCbCr image to RGB image.

    This function produces the same results as Matlab's ycbcr2rgb function.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted RGB image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0, -0.00153632, 0.00791071],
                              [0.00625893, -0.00318811, 0]]) * 255.0 + [
                                  -222.921, 135.576, -276.836
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def ycbcr2bgr(img):
    """Convert a YCbCr image to BGR image.

    The bgr version of ycbcr2rgb.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        ndarray: The converted BGR image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img) * 255
    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
                              [0.00791071, -0.00153632, 0],
                              [0, -0.00318811, 0.00625893]]) * 255.0 + [
                                  -276.836, 135.576, -222.921
                              ]  # noqa: E126
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def _convert_input_type_range(img):
    """Convert the type and range of the input image.

    It converts the input image to np.float32 type and range of [0, 1].
    It is mainly used for pre-processing the input image in colorspace
    convertion functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].

    Returns:
        (ndarray): The converted image with type of np.float32 and range of
            [0, 1].
    """
    img_type = img.dtype
    img = img.astype(np.float32)
    if img_type == np.float32:
        pass
    elif img_type == np.uint8:
        img /= 255.
    else:
        raise TypeError('The img type should be np.float32 or np.uint8, '
                        f'but got {img_type}')
    return img


def _convert_output_type_range(img, dst_type):
    """Convert the type and range of the image according to dst_type.

    It converts the image to desired type and range. If `dst_type` is np.uint8,
    images will be converted to np.uint8 type with range [0, 255]. If
    `dst_type` is np.float32, it converts the image to np.float32 type with
    range [0, 1].
    It is mainly used for post-processing images in colorspace convertion
    functions such as rgb2ycbcr and ycbcr2rgb.

    Args:
        img (ndarray): The image to be converted with np.float32 type and
            range [0, 255].
        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
            converts the image to np.uint8 type with range [0, 255]. If
            dst_type is np.float32, it converts the image to np.float32 type
            with range [0, 1].

    Returns:
        (ndarray): The converted image with desired type and range.
    """
    if dst_type not in (np.uint8, np.float32):
        raise TypeError('The dst_type should be np.float32 or np.uint8, '
                        f'but got {dst_type}')
    if dst_type == np.uint8:
        img = img.round()
    else:
        img /= 255.
    return img.astype(dst_type)


================================================
FILE: SRGAN/VmambaIR/utils/misc.py
================================================
import numpy as np
import os
import random
import time
import torch
from os import path as osp

from .dist_util import master_only
from .logger import get_root_logger


def set_random_seed(seed):
    """Set random seeds."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def get_time_str():
    return time.strftime('%Y%m%d_%H%M%S', time.localtime())


def mkdir_and_rename(path):
    """mkdirs. If path exists, rename it with timestamp and create a new one.

    Args:
        path (str): Folder path.
    """
    if osp.exists(path):
        new_name = path + '_archived_' + get_time_str()
        print(f'Path already exists. Rename it to {new_name}', flush=True)
        os.rename(path, new_name)
    os.makedirs(path, exist_ok=True)


@master_only
def make_exp_dirs(opt):
    """Make dirs for experiments."""
    path_opt = opt['path'].copy()
    if opt['is_train']:
        mkdir_and_rename(path_opt.pop('experiments_root'))
    else:
        mkdir_and_rename(path_opt.pop('results_root'))
    for key, path in path_opt.items():
        if ('strict_load' not in key) and ('pretrain_network'
                                           not in key) and ('resume'
                                                            not in key):
            os.makedirs(path, exist_ok=True)


def scandir(dir_path, suffix=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        suffix (str | tuple(str), optional): File suffix that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
        raise TypeError('"suffix" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, suffix, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if suffix is None:
                    yield return_path
                elif return_path.endswith(suffix):
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, suffix=suffix, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, suffix=suffix, recursive=recursive)

def scandir_SIDD(dir_path, keywords=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        keywords (str | tuple(str), optional): File keywords that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative pathes.
    """

    if (keywords is not None) and not isinstance(keywords, (str, tuple)):
        raise TypeError('"keywords" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, keywords, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if keywords is None:
                    yield return_path
                elif return_path.find(keywords) > 0:
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(
                        entry.path, keywords=keywords, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, keywords=keywords, recursive=recursive)

def check_resume(opt, resume_iter):
    """Check resume states and pretrain_network paths.

    Args:
        opt (dict): Options.
        resume_iter (int): Resume iteration.
    """
    logger = get_root_logger()
    if opt['path']['resume_state']:
        # get all the networks
        networks = [key for key in opt.keys() if key.startswith('network_')]
        flag_pretrain = False
        for network in networks:
            if opt['path'].get(f'pretrain_{network}') is not None:
                flag_pretrain = True
        if flag_pretrain:
            logger.warning(
                'pretrain_network path will be ignored during resuming.')
        # set pretrained model paths
        for network in networks:
            name = f'pretrain_{network}'
            basename = network.replace('network_', '')
            if opt['path'].get('ignore_resume_networks') is None or (
                    basename not in opt['path']['ignore_resume_networks']):
                opt['path'][name] = osp.join(
                    opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
                logger.info(f"Set {name} to {opt['path'][name]}")


def sizeof_fmt(size, suffix='B'):
    """Get human readable file size.

    Args:
        size (int): File size.
        suffix (str): Suffix. Default: 'B'.

    Return:
        str: Formated file siz.
    """
    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
        if abs(size) < 1024.0:
            return f'{size:3.1f} {unit}{suffix}'
        size /= 1024.0
    return f'{size:3.1f} Y{suffix}'


================================================
FILE: SRGAN/VmambaIR/utils/options.py
================================================
import yaml
from collections import OrderedDict
from os import path as osp


def ordered_yaml():
    """Support OrderedDict for yaml.

    Returns:
        yaml Loader and Dumper.
    """
    try:
        from yaml import CDumper as Dumper
        from yaml import CLoader as Loader
    except ImportError:
        from yaml import Dumper, Loader

    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG

    def dict_representer(dumper, data):
        return dumper.represent_dict(data.items())

    def dict_constructor(loader, node):
        return OrderedDict(loader.construct_pairs(node))

    Dumper.add_representer(OrderedDict, dict_representer)
    Loader.add_constructor(_mapping_tag, dict_constructor)
    return Loader, Dumper


def parse(opt_path, is_train=True):
    """Parse option file.

    Args:
        opt_path (str): Option file path.
        is_train (str): Indicate whether in training or not. Default: True.

    Returns:
        (dict): Options.
    """
    with open(opt_path, mode='r') as f:
        Loader, _ = ordered_yaml()
        opt = yaml.load(f, Loader=Loader)

    opt['is_train'] = is_train

    # datasets
    for phase, dataset in opt['datasets'].items():
        # for several datasets, e.g., test_1, test_2
        phase = phase.split('_')[0]
        dataset['phase'] = phase
        if 'scale' in opt:
            dataset['scale'] = opt['scale']
        if dataset.get('dataroot_gt') is not None:
            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
        if dataset.get('dataroot_lq') is not None:
            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])

    # paths
    for key, val in opt['path'].items():
        if (val is not None) and ('resume_state' in key
                                  or 'pretrain_network' in key):
            opt['path'][key] = osp.expanduser(val)
    opt['path']['root'] = osp.abspath(
        osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
    if is_train:
        experiments_root = osp.join(opt['path']['root'], 'experiments',
                                    opt['name'])
        opt['path']['experiments_root'] = experiments_root
        opt['path']['models'] = osp.join(experiments_root, 'models')
        opt['path']['training_states'] = osp.join(experiments_root,
                                                  'training_states')
        opt['path']['log'] = experiments_root
        opt['path']['visualization'] = osp.join(experiments_root,
                                                'visualization')

        # change some options for debug mode
        if 'debug' in opt['name']:
            if 'val' in opt:
                opt['val']['val_freq'] = 8
            opt['logger']['print_freq'] = 1
            opt['logger']['save_checkpoint_freq'] = 8
    else:  # test
        results_root = osp.join(opt['path']['root'], 'results', opt['name'])
        opt['path']['results_root'] = results_root
        opt['path']['log'] = results_root
        opt['path']['visualization'] = osp.join(results_root, 'visualization')

    return opt


def dict2str(opt, indent_level=1):
    """dict to string for printing options.

    Args:
        opt (dict): Option dict.
        indent_level (int): Indent level. Default: 1.

    Return:
        (str): Option string for printing.
    """
    msg = '\n'
    for k, v in opt.items():
        if isinstance(v, dict):
            msg += ' ' * (indent_level * 2) + k + ':['
            msg += dict2str(v, indent_level + 1)
            msg += ' ' * (indent_level * 2) + ']\n'
        else:
            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
    return msg


================================================
FILE: SRGAN/VmambaIR/utils.py
================================================
import cv2
import math
import numpy as np
import os
import queue
import threading
import torch
from basicsr.utils.download_util import load_file_from_url
from torch.nn import functional as F

ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))


class RealESRGANer():
    """A helper class for upsampling images with RealESRGAN.

    Args:
        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
        model (nn.Module): The defined network. Default: None.
        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
            input images into tiles, and then process each of them. Finally, they will be merged into one image.
            0 denotes for do not use tile. Default: 0.
        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
        half (float): Whether to use half precision during inference. Default: False.
    """

    def __init__(self,
                 scale,
                 model_path,
                 model=None,
                 tile=0,
                 tile_pad=10,
                 pre_pad=10,
                 half=False,
                 device=None,
                 gpu_id=None):
        self.scale = scale
        self.tile_size = tile
        self.tile_pad = tile_pad
        self.pre_pad = pre_pad
        self.mod_scale = None
        self.half = half

        # initialize model
        if gpu_id:
            self.device = torch.device(
                f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
        else:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
        # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
        if model_path.startswith('https://'):
            model_path = load_file_from_url(
                url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
        loadnet = torch.load(model_path, map_location=torch.device('cpu'))
        # prefer to use params_ema
        if 'params_ema' in loadnet:
            keyname = 'params_ema'
        else:
            keyname = 'params'
        model.load_state_dict(loadnet[keyname], strict=True)
        model.eval()
        self.model = model.to(self.device)
        if self.half:
            self.model = self.model.half()

    def pre_process(self, img):
        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
            self.img = self.img.half()

        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
            self.mod_scale = 4
        if self.mod_scale is not None:
            self.mod_pad_h, self.mod_pad_w = 0, 0
            _, _, h, w = self.img.size()
            if (h % self.mod_scale != 0):
                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
            if (w % self.mod_scale != 0):
                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')

    def process(self):
        # model inference
        self.output = self.model(self.img)

    def tile_process(self):
        """It will first crop input images to tiles, and then process each tile.
        Finally, all the processed tiles are merged into one images.

        Modified from: https://github.com/ata4/esrgan-launcher
        """
        batch, channel, height, width = self.img.shape
        output_height = height * self.scale
        output_width = width * self.scale
        output_shape = (batch, channel, output_height, output_width)

        # start with black image
        self.output = self.img.new_zeros(output_shape)
        tiles_x = math.ceil(width / self.tile_size)
        tiles_y = math.ceil(height / self.tile_size)

        # loop over all tiles
        for y in range(tiles_y):
            for x in range(tiles_x):
                # extract tile from input image
                ofs_x = x * self.tile_size
                ofs_y = y * self.tile_size
                # input tile area on total image
                input_start_x = ofs_x
                input_end_x = min(ofs_x + self.tile_size, width)
                input_start_y = ofs_y
                input_end_y = min(ofs_y + self.tile_size, height)

                # input tile area on total image with padding
                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
                input_end_x_pad = min(input_end_x + self.tile_pad, width)
                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
                input_end_y_pad = min(input_end_y + self.tile_pad, height)

                # input tile dimensions
                input_tile_width = input_end_x - input_start_x
                input_tile_height = input_end_y - input_start_y
                tile_idx = y * tiles_x + x + 1
                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]

                # upscale tile
                try:
                    with torch.no_grad():
                        output_tile = self.model(input_tile)
                except RuntimeError as error:
                    print('Error', error)
                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')

                # output tile area on total image
                output_start_x = input_start_x * self.scale
                output_end_x = input_end_x * self.scale
                output_start_y = input_start_y * self.scale
                output_end_y = input_end_y * self.scale

                # output tile area without padding
                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale

                # put tile into output image
                self.output[:, :, output_start_y:output_end_y,
                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
                                                                       output_start_x_tile:output_end_x_tile]

    def post_process(self):
        # remove extra pad
        if self.mod_scale is not None:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
        # remove prepad
        if self.pre_pad != 0:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
        return self.output

    @torch.no_grad()
    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
        h_input, w_input = img.shape[0:2]
        # img: numpy
        img = img.astype(np.float32)
        if np.max(img) > 256:  # 16-bit image
            max_range = 65535
            print('\tInput is a 16-bit image')
        else:
            max_range = 255
        img = img / max_range
        if len(img.shape) == 2:  # gray image
            img_mode = 'L'
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
        elif img.shape[2] == 4:  # RGBA image with alpha channel
            img_mode = 'RGBA'
            alpha = img[:, :, 3]
            img = img[:, :, 0:3]
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            if alpha_upsampler == 'realesrgan':
                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
        else:
            img_mode = 'RGB'
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # ------------------- process image (without the alpha channel) ------------------- #
        self.pre_process(img)
        if self.tile_size > 0:
            self.tile_process()
        else:
            self.process()
        output_img = self.post_process()
        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
        if img_mode == 'L':
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)

        # ------------------- process the alpha channel if necessary ------------------- #
        if img_mode == 'RGBA':
            if alpha_upsampler == 'realesrgan':
                self.pre_process(alpha)
                if self.tile_size > 0:
                    self.tile_process()
                else:
                    self.process()
                output_alpha = self.post_process()
                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
            else:  # use the cv2 resize for alpha channel
                h, w = alpha.shape[0:2]
                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)

            # merge the alpha channel
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
            output_img[:, :, 3] = output_alpha

        # ------------------------------ return ------------------------------ #
        if max_range == 65535:  # 16-bit image
            output = (output_img * 65535.0).round().astype(np.uint16)
        else:
            output = (output_img * 255.0).round().astype(np.uint8)

        if outscale is not None and outscale != float(self.scale):
            output = cv2.resize(
                output, (
                    int(w_input * outscale),
                    int(h_input * outscale),
                ), interpolation=cv2.INTER_LANCZOS4)

        return output, img_mode


class PrefetchReader(threading.Thread):
    """Prefetch images.

    Args:
        img_list (list[str]): A image list of image paths to be read.
        num_prefetch_queue (int): Number of prefetch queue.
    """

    def __init__(self, img_list, num_prefetch_queue):
        super().__init__()
        self.que = queue.Queue(num_prefetch_queue)
        self.img_list = img_list

    def run(self):
        for img_path in self.img_list:
            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
            self.que.put(img)

        self.que.put(None)

    def __next__(self):
        next_item = self.que.get()
        if next_item is None:
            raise StopIteration
        return next_item

    def __iter__(self):
        return self


class IOConsumer(threading.Thread):

    def __init__(self, opt, que, qid):
        super().__init__()
        self._queue = que
        self.qid = qid
        self.opt = opt

    def run(self):
        while True:
            msg = self._queue.get()
            if isinstance(msg, str) and msg == 'quit':
                break

            output = msg['output']
            save_path = msg['save_path']
            cv2.imwrite(save_path, output)
        print(f'IO worker {self.qid} is done.')


================================================
FILE: SRGAN/VmambaIR/weights/README.md
================================================
# Weights

Put the downloaded weights to this folder.


================================================
FILE: SRGAN/ldm/classifier.py
================================================
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted

from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config

__models__ = {
    'class_label': EncoderUNetModel,
    'segmentation': UNetModel
}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class NoisyLatentImageClassifier(pl.LightningModule):

    def __init__(self,
                 diffusion_path,
                 num_classes,
                 ckpt_path=None,
                 pool='attention',
                 label_key=None,
                 diffusion_ckpt_path=None,
                 scheduler_config=None,
                 weight_decay=1.e-2,
                 log_steps=10,
                 monitor='val/loss',
                 *args,
                 **kwargs):
        super().__init__(*args, **kwargs)
        self.num_classes = num_classes
        # get latest config of diffusion model
        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
        self.diffusion_config = OmegaConf.load(diffusion_config).model
        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
        self.load_diffusion()

        self.monitor = monitor
        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
        self.log_steps = log_steps

        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
            else self.diffusion_model.cond_stage_key

        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'

        if self.label_key not in __models__:
            raise NotImplementedError()

        self.load_classifier(ckpt_path, pool)

        self.scheduler_config = scheduler_config
        self.use_scheduler = self.scheduler_config is not None
        self.weight_decay = weight_decay

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
            sd, strict=False)
        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def load_diffusion(self):
        model = instantiate_from_config(self.diffusion_config)
        self.diffusion_model = model.eval()
        self.diffusion_model.train = disabled_train
        for param in self.diffusion_model.parameters():
            param.requires_grad = False

    def load_classifier(self, ckpt_path, pool):
        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
        model_config.out_channels = self.num_classes
        if self.label_key == 'class_label':
            model_config.pool = pool

        self.model = __models__[self.label_key](**model_config)
        if ckpt_path is not None:
            print('#####################################################################')
            print(f'load from ckpt "{ckpt_path}"')
            print('#####################################################################')
            self.init_from_ckpt(ckpt_path)

    @torch.no_grad()
    def get_x_noisy(self, x, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x))
        continuous_sqrt_alpha_cumprod = None
        if self.diffusion_model.use_continuous_noise:
            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
            # todo: make sure t+1 is correct here

        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)

    def forward(self, x_noisy, t, *args, **kwargs):
        return self.model(x_noisy, t)

    @torch.no_grad()
    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, 'b h w c -> b c h w')
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    @torch.no_grad()
    def get_conditioning(self, batch, k=None):
        if k is None:
            k = self.label_key
        assert k is not None, 'Needs to provide label key'

        targets = batch[k].to(self.device)

        if self.label_key == 'segmentation':
            targets = rearrange(targets, 'b h w c -> b c h w')
            for down in range(self.numd):
                h, w = targets.shape[-2:]
                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')

            # targets = rearrange(targets,'b c h w -> b h w c')

        return targets

    def compute_top_k(self, logits, labels, k, reduction="mean"):
        _, top_ks = torch.topk(logits, k, dim=1)
        if reduction == "mean":
            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
        elif reduction == "none":
            return (top_ks == labels[:, None]).float().sum(dim=-1)

    def on_train_epoch_start(self):
        # save some memory
        self.diffusion_model.model.to('cpu')

    @torch.no_grad()
    def write_logs(self, loss, logits, targets):
        log_prefix = 'train' if self.training else 'val'
        log = {}
        log[f"{log_prefix}/loss"] = loss.mean()
        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
            logits, targets, k=1, reduction="mean"
        )
        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
            logits, targets, k=5, reduction="mean"
        )

        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
        lr = self.optimizers().param_groups[0]['lr']
        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)

    def shared_step(self, batch, t=None):
        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
        targets = self.get_conditioning(batch)
        if targets.dim() == 4:
            targets = targets.argmax(dim=1)
        if t is None:
            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
        else:
            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
        x_noisy = self.get_x_noisy(x, t)
        logits = self(x_noisy, t)

        loss = F.cross_entropy(logits, targets, reduction='none')

        self.write_logs(loss.detach(), logits.detach(), targets.detach())

        loss = loss.mean()
        return loss, logits, x_noisy, targets

    def training_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)
        return loss

    def reset_noise_accs(self):
        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}

    def on_validation_start(self):
        self.reset_noise_accs()

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)

        for t in self.noisy_acc:
            _, logits, _, targets = self.shared_step(batch, t)
            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))

        return loss

    def configure_optimizers(self):
        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)

        if self.use_scheduler:
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                }]
            return [optimizer], scheduler

        return optimizer

    @torch.no_grad()
    def log_images(self, batch, N=8, *args, **kwargs):
        log = dict()
        x = self.get_input(batch, self.diffusion_model.first_stage_key)
        log['inputs'] = x

        y = self.get_conditioning(batch)

        if self.label_key == 'class_label':
            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
            log['labels'] = y

        if ismap(y):
            log['labels'] = self.diffusion_model.to_rgb(y)

            for step in range(self.log_steps):
                current_time = step * self.log_time_interval

                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)

                log[f'inputs@t{current_time}'] = x_noisy

                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
                pred = rearrange(pred, 'b h w c -> b c h w')

                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)

        for key in log:
            log[key] = log[key][:N]

        return log


================================================
FILE: SRGAN/ldm/lr_scheduler.py
================================================
import numpy as np


class LambdaWarmUpCosineScheduler:
    """
    note: use with a base_lr of 1.0
    """
    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
        self.lr_warm_up_steps = warm_up_steps
        self.lr_start = lr_start
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.lr_max_decay_steps = max_decay_steps
        self.last_lr = 0.
        self.verbosity_interval = verbosity_interval

    def schedule(self, n, **kwargs):
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
        if n < self.lr_warm_up_steps:
            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
            self.last_lr = lr
            return lr
        else:
            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
            t = min(t, 1.0)
            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
                    1 + np.cos(t * np.pi))
            self.last_lr = lr
            return lr

    def __call__(self, n, **kwargs):
        return self.schedule(n,**kwargs)


class LambdaWarmUpCosineScheduler2:
    """
    supports repeated iterations, configurable via lists
    note: use with a base_lr of 1.0.
    """
    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
        self.lr_warm_up_steps = warm_up_steps
        self.f_start = f_start
        self.f_min = f_min
        self.f_max = f_max
        self.cycle_lengths = cycle_lengths
        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
        self.last_f = 0.
        self.verbosity_interval = verbosity_interval

    def find_in_interval(self, n):
        interval = 0
        for cl in self.cum_cycles[1:]:
            if n <= cl:
                return interval
            interval += 1

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                                                       f"current cycle {cycle}")
        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
            t = min(t, 1.0)
            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
                    1 + np.cos(t * np.pi))
            self.last_f = f
            return f

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                                                       f"current cycle {cycle}")

        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
            self.last_f = f
            return f



================================================
FILE: SRGAN/ldm/util.py
================================================
import importlib

import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial

import multiprocessing as mp
from threading import Thread
from queue import Queue

from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont


def log_txt_as_img(wh, xc, size=10):
    # wh a tuple of (width, height)
    # xc a list of captions to plot
    b = len(xc)
    txts = list()
    for bi in range(b):
        txt = Image.new("RGB", wh, color="white")
        draw = ImageDraw.Draw(txt)
        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
        nc = int(40 * (wh[0] / 256))
        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))

        try:
            draw.text((0, 0), lines, fill="black", font=font)
        except UnicodeEncodeError:
            print("Cant encode string for logging. Skipping.")

        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
        txts.append(txt)
    txts = np.stack(txts)
    txts = torch.tensor(txts)
    return txts


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def exists(x):
    return x is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def mean_flat(tensor):
    """
    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
    return total_params


def instantiate_from_config(config):
    if not "target" in config:
        if config == '__is_first_stage__':
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
    # create dummy dataset instance

    # run prefetching
    if idx_to_fn:
        res = func(data, worker_id=idx)
    else:
        res = func(data)
    Q.put([idx, res])
    Q.put("Done")


def parallel_data_prefetch(
        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
):
    # if target_data_type not in ["ndarray", "list"]:
    #     raise ValueError(
    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
    #     )
    if isinstance(data, np.ndarray) and target_data_type == "list":
        raise ValueError("list expected but function got ndarray.")
    elif isinstance(data, abc.Iterable):
        if isinstance(data, dict):
            print(
                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
            )
            data = list(data.values())
        if target_data_type == "ndarray":
            data = np.asarray(data)
        else:
            data = list(data)
    else:
        raise TypeError(
            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
        )

    if cpu_intensive:
        Q = mp.Queue(1000)
        proc = mp.Process
    else:
        Q = Queue(1000)
        proc = Thread
    # spawn processes
    if target_data_type == "ndarray":
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(np.array_split(data, n_proc))
        ]
    else:
        step = (
            int(len(data) / n_proc + 1)
            if len(data) % n_proc != 0
            else int(len(data) / n_proc)
        )
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(
                [data[i: i + step] for i in range(0, len(data), step)]
            )
        ]
    processes = []
    for i in range(n_proc):
        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
        processes += [p]

    # start processes
    print(f"Start prefetching...")
    import time

    start = time.time()
    gather_res = [[] for _ in range(n_proc)]
    try:
        for p in processes:
            p.start()

        k = 0
        while k < n_proc:
            # get result
            res = Q.get()
            if res == "Done":
                k += 1
            else:
                gather_res[res[0]] = res[1]

    except Exception as e:
        print("Exception: ", e)
        for p in processes:
            p.terminate()

        raise e
    finally:
        for p in processes:
            p.join()
        print(f"Prefetching complete. [{time.time() - start} sec.]")

    if target_data_type == 'ndarray':
        if not isinstance(gather_res[0], np.ndarray):
            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)

        # order outputs
        return np.concatenate(gather_res, axis=0)
    elif target_data_type == 'list':
        out = []
        for r in gather_res:
            out.extend(r)
        return out
    else:
        return gather_res


================================================
FILE: SRGAN/ldm/util2.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!


import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat

from ldm.util import instantiate_from_config


def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
    if schedule == "linear":
        betas = (
                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
        )

    elif schedule == "cosine":
        timesteps = (
                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * np.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = np.clip(betas, a_min=0, a_max=0.999)

    elif schedule == "sqrt_linear":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
    elif schedule == "sqrt":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
    else:
        raise ValueError(f"schedule '{schedule}' unknown.")
    return betas.numpy()


def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
    if ddim_discr_method == 'uniform':
        c = num_ddpm_timesteps // num_ddim_timesteps
        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
    elif ddim_discr_method == 'quad':
        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
    else:
        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')

    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
    # add one to get the final alpha values right (the ones from first scale to data during sampling)
    steps_out = ddim_timesteps + 1
    if verbose:
        print(f'Selected timesteps for ddim sampler: {steps_out}')
    return steps_out


def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
    # select alphas for computing the variance schedule
    alphas = alphacums[ddim_timesteps]
    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())

    # according the the formula provided in https://arxiv.org/abs/2010.02502
    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
    if verbose:
        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
        print(f'For the chosen value of eta, which is {eta}, '
              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
    return sigmas, alphas, alphas_prev


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_tensors = list(args[:length])
        ctx.input_params = list(args[length:])

        with torch.no_grad():
            output_tensors = ctx.run_function(*ctx.input_tensors)
        return output_tensors

    @staticmethod
    def backward(ctx, *output_grads):
        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
        with torch.enable_grad():
            # Fixes a bug where the first op in run_function modifies the
            # Tensor storage in place, which is not allowed for detach()'d
            # Tensors.
            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
            output_tensors = ctx.run_function(*shallow_copies)
        input_grads = torch.autograd.grad(
            output_tensors,
            ctx.input_tensors + ctx.input_params,
            output_grads,
            allow_unused=True,
        )
        del ctx.input_tensors
        del ctx.input_params
        del output_tensors
        return (None, None) + input_grads


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=timesteps.device)
        args = timesteps[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    else:
        embedding = repeat(timesteps, 'b -> b d', d=dim)
    return embedding


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def normalization(channels):
    """
    Make a standard normalization layer.
    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNorm32(32, channels)


# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)

def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


class HybridConditioner(nn.Module):

    def __init__(self, c_concat_config, c_crossattn_config):
        super().__init__()
        self.concat_conditioner = instantiate_from_config(c_concat_config)
        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)

    def forward(self, c_concat, c_crossattn):
        c_concat = self.concat_conditioner(c_concat)
        c_crossattn = self.crossattn_conditioner(c_crossattn)
        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}


def noise_like(shape, device, repeat=False):
    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
    noise = lambda: torch.randn(shape, device=device)
    return repeat_noise() if repeat else noise()

================================================
FILE: SRGAN/metric.sh
================================================

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/HR
#folder_restored=/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-SRGAN/results/test_MambaSISR15GAN3/visualization/Set5

folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set14/HR
folder_restored=/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-SRGAN/results/test_MambaSISR15GAN3/visualization/Set14

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/HR
#folder_restored=/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-SRGAN/results/test_MambaSISR15GAN3/visualization/Urban100

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Manga109/HR
#folder_restored=/mnt/bn/shiyuan-arnold/code/Mamber/DiffIR/DiffIR-SRGAN/results/test_MambaSISR15GAN3/visualization/Manga109

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/General100/HR
#folder_restored=/mnt/bn/shiyuan-arnold/code/VmambaIR/SRGAN/results/test_MambaSISR15GAN3/visualization/General100

#folder_gt=/mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/DIV2K100/HR
#folder_restored=/mnt/bn/shiyuan-arnold/code/VmambaIR/SRGAN/results/test_MambaSISR15GAN3/visualization/DIV2K100


python3  Metrics/LPIPS.py \
    --folder_gt $folder_gt  \
    --folder_restored $folder_restored

python3  Metric/PSNR.py \
    --folder_gt $folder_gt  \
    --folder_restored $folder_restored


================================================
FILE: SRGAN/options/MambaSISR15GAN_x4.yml
================================================
# general settings
name: MambaSISR15GAN_x4
model_type: MambaSISRGANModel
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

gt_size: 256
# dataset and data loader settings
datasets:
  train:
    name: DF2K
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub/X4
    meta_info_file: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale_4xpair_sub.txt
    # (for lmdb)
    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
    filename_tmpl: '{}'
    io_backend:
      type: disk
      # (for lmdb)
      # type: lmdb

    gt_size: 256
    use_hflip: true
    use_rot: true

    # data loader
    num_worker_per_gpu: 8
    batch_size_per_gpu: 8
    dataset_enlarge_ratio: 100
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: Urban100
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/HR
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/LR
    # filename_tmpl: '{}x4'
    io_backend:
      type: disk
  
  val_2:
    name: Set5
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/HR
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/LR
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaSISR6
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [15,1,1,1]
  num_refinement_blocks: 15
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/SRGAN/experiments/MambaSISR15_x4_archived_20240618_203930/models/net_g_100000.pth
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999
  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]
  optim_d:
    type: Adam
    lr: !!float 1e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [ 150000]
    gamma: 0.5

  total_iter: 300000
  lr_sr: !!float 2e-4
  gamma_sr: 0.5
  lr_decay_sr: 225000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean
  # perceptual loss (content and style losses)
  perceptual_opt:
    type: PerceptualLoss
    layer_weights:
      # before relu
      'conv1_2': 0.1
      'conv2_2': 0.1
      'conv3_4': 1
      'conv4_4': 1
      'conv5_4': 1
    vgg_type: vgg19
    use_input_norm: true
    perceptual_weight: !!float 1.0
    style_weight: 0
    range_norm: false
    criterion: l1
  # gan loss
  gan_opt:
    type: GANLoss
    gan_type: vanilla
    real_label_val: 1.0
    fake_label_val: 0.0
    loss_weight: !!float 1.0

  net_d_iters: 1
  net_d_init_iters: 0

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 1e4
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 0
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 1e4
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: SRGAN/options/MambaSISR15_x4.yml
================================================
# general settings
name: MambaSISR15_x4
model_type: MambaSISRModel
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0

gt_size: 256
# dataset and data loader settings
datasets:
  train:
    name: DF2K
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub/X4
    meta_info_file: /mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale_4xpair_sub.txt
    # (for lmdb)
    # dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb
    # dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb
    filename_tmpl: '{}'
    io_backend:
      type: disk
      # (for lmdb)
      # type: lmdb

    gt_size: 256
    use_hflip: true
    use_rot: true

    # data loader
    num_worker_per_gpu: 12
    batch_size_per_gpu: 8
    dataset_enlarge_ratio: 100
    prefetch_mode: ~

  # Uncomment these for validation
  val_1:
    name: Urban100
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/HR
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/LR
    # filename_tmpl: '{}x4'
    io_backend:
      type: disk
  
  val_2:
    name: Set5
    type: PairedImageDataset
    dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/HR
    dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/LR
    io_backend:
      type: disk

# network structures
network_g:
  type: MambaSISR6
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [15,1,1,1]
  num_refinement_blocks: 15
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

# path
path:
  pretrain_network_g: ~
  param_key_g: params_ema
  strict_load_g: true
  resume_state: ~

# training settings
train:
  ema_decay: 0.999

  optim_g:
    type: Adam
    lr: !!float 2e-4
    weight_decay: 0
    betas: [0.9, 0.99]

  scheduler:
    type: MultiStepLR
    milestones: [50000,70000]
    gamma: 0.5

  total_iter: 100000
  warmup_iter: -1  # no warm up

  # losses
  pixel_opt:
    type: L1Loss
    loss_weight: 1.0
    reduction: mean

# Uncomment these for validation
# validation settings
val:
  window_size: 8
  val_freq: !!float 1e4
  save_img: False

  metrics:
    psnr: # metric name
      type: calculate_psnr
      crop_border: 4
      test_y_channel: true

# logging settings
logger:
  print_freq: 1000
  save_checkpoint_freq: !!float 1e4
  use_tb_logger: true
  wandb:
    project: ~
    resume_id: ~

# dist training settings
dist_params:
  backend: nccl
  port: 29500

================================================
FILE: SRGAN/options/test_mamba15_x4.yml
================================================
# general settings
name: test_MambaSISR15GAN3
model_type: MambaSISRGANModel
scale: 4
num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
manual_seed: 0



datasets:
  # test_1:  # the 1st test dataset
  #   name: Set5
  #   type: PairedImageDataset
  #   dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/HR
  #   dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Set5/LR
  #   filename_tmpl: '{}'
  #   io_backend:
  #     type: disk

  test_2:  # the 2nd test dataset
    name: Set14
    type: PairedImageDataset
    dataroot_gt: /home/shiy/Data/data/sr/Set14/HR
    dataroot_lq: /home/shiy/Data/data/sr/Set14/LR
    filename_tmpl: '{}'
    io_backend:
      type: disk


  # test_3:  # the 4th test dataset
  #   name: Urban100
  #   type: PairedImageDataset
  #   dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/HR
  #   dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Urban100/LR
  #   filename_tmpl: '{}'
  #   io_backend:
  #     type: disk

  # test_4:  # the 5th test dataset
  #   name: Manga109
  #   type: PairedImageDataset
  #   dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Manga109/HR
  #   dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/Manga109/LR
  #   io_backend:
  #     type: disk

  # test_5:  # the 5th test dataset
  #   name: General100
  #   type: PairedImageDataset
  #   dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/General100/HR
  #   dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/General100/LR
  #   io_backend:
  #     type: disk

  # test_6:  # the 5th test dataset
  #   name: DIV2K100
  #   type: PairedImageDataset
  #   dataroot_gt: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/DIV2K100/HR
  #   dataroot_lq: /mnt/bn/shiyuan-arnold/dataset/DiffIR/SISR/DIV2K100/LR
  #   io_backend:
  #     type: disk


# network structures
network_g:
  type: MambaSISR6
  inp_channels: 3
  out_channels: 3
  dim: 48
  num_blocks: [15,1,1,1]
  num_refinement_blocks: 15
  heads: [1,2,4,8]
  ffn_expansion_factor: 2.66
  bias: False
  LayerNorm_type: WithBias

network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
  num_feat: 64
  skip_connection: True

# path
path:
  # use the pre-trained Real-ESRNet model
  pretrain_network_g: /mnt/bn/shiyuan-arnold/code/VmambaIR/SRGAN/experiments/MambaSISR15GAN_x4/models/net_g_280000.pth
  param_key_g: params_ema
  strict_load_g: True


# validation settings
val:
  window_size: 8
  save_img: True
  suffix: ~  # add suffix to saved images, if None, use exp name

  metrics:
    psnr: # metric name, can be arbitrary
      type: calculate_psnr
      crop_border: 4
      test_y_channel: true
    ssim:
      type: calculate_ssim
      crop_border: 4
      test_y_channel: true






================================================
FILE: SRGAN/pip.sh
================================================
sudo apt-get update
sudo apt install tmux
sudo apt install libgl1-mesa-glx
pip install basicsr --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install -r requirements.txt --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install pandas --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
sudo python3 setup.py develop
#sudo pip uninstall bytedmetrics
pip install einops --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com
pip install lpips --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com

pip install torchsummary --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com

pip install timm --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com

pip install thop --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com

pip install ptflops --index-url http://pypi.douban.com/simple --trusted-host pypi.douban.com

================================================
FILE: SRGAN/requirements.txt
================================================
basicsr>=1.3.3.11
facexlib>=0.2.0.3
gfpgan>=0.2.1
numpy
opencv-python
Pillow
torch>=1.7
torchvision
tqdm


================================================
FILE: SRGAN/scripts/extract_subimages.py
================================================
import argparse
import cv2
import numpy as np
import os
import sys
from basicsr.utils import scandir
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def main(args):
    """A multi-thread tool to crop large images to sub-images for faster IO.

    opt (dict): Configuration dict. It contains:
        n_thread (int): Thread number.
        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
            and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2.
        input_folder (str): Path to the input folder.
        save_folder (str): Path to save folder.
        crop_size (int): Crop size.
        step (int): Step for overlapped sliding window.
        thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.

    Usage:
        For each folder, run this script.
        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
        After process, each sub_folder should have the same number of subimages.
        Remember to modify opt configurations according to your settings.
    """

    opt = {}
    opt['n_thread'] = args.n_thread
    opt['compression_level'] = args.compression_level
    opt['input_folder'] = args.input
    opt['save_folder'] = args.output
    opt['crop_size'] = args.crop_size
    opt['step'] = args.step
    opt['thresh_size'] = args.thresh_size
    extract_subimages(opt)


def extract_subimages(opt):
    """Crop images to subimages.

    Args:
        opt (dict): Configuration dict. It contains:
            input_folder (str): Path to the input folder.
            save_folder (str): Path to save folder.
            n_thread (int): Thread number.
    """
    input_folder = opt['input_folder']
    save_folder = opt['save_folder']
    if not osp.exists(save_folder):
        os.makedirs(save_folder)
        print(f'mkdir {save_folder} ...')
    else:
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)

    # scan all images
    img_list = list(scandir(input_folder, full_path=True))

    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
    pool = Pool(opt['n_thread'])
    for path in img_list:
        pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
    pool.close()
    pool.join()
    pbar.close()
    print('All processes done.')


def worker(path, opt):
    """Worker for each process.

    Args:
        path (str): Image path.
        opt (dict): Configuration dict. It contains:
            crop_size (int): Crop size.
            step (int): Step for overlapped sliding window.
            thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
            save_folder (str): Path to save folder.
            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.

    Returns:
        process_info (str): Process information displayed in progress bar.
    """
    crop_size = opt['crop_size']
    step = opt['step']
    thresh_size = opt['thresh_size']
    img_name, extension = osp.splitext(osp.basename(path))

    # remove the x2, x3, x4 and x8 in the filename for DIV2K
    img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)

    h, w = img.shape[0:2]
    h_space = np.arange(0, h - crop_size + 1, step)
    if h - (h_space[-1] + crop_size) > thresh_size:
        h_space = np.append(h_space, h - crop_size)
    w_space = np.arange(0, w - crop_size + 1, step)
    if w - (w_space[-1] + crop_size) > thresh_size:
        w_space = np.append(w_space, w - crop_size)

    index = 0
    for x in h_space:
        for y in w_space:
            index += 1
            cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
            cropped_img = np.ascontiguousarray(cropped_img)
            cv2.imwrite(
                osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
                [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
    process_info = f'Processing {img_name} ...'
    return process_info


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale_sub', help='Output folder')
    parser.add_argument('--crop_size', type=int, default=400, help='Crop size')
    parser.add_argument('--step', type=int, default=200, help='Step for overlapped sliding window')
    parser.add_argument(
        '--thresh_size',
        type=int,
        default=0,
        help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
    parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
    parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
    args = parser.parse_args()

    main(args)


================================================
FILE: SRGAN/scripts/extract_subimages_DF2K.py
================================================
import argparse
import cv2
import numpy as np
import os
import sys
from basicsr.utils import scandir
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm


def main(args):
    """A multi-thread tool to crop large images to sub-images for faster IO.

    opt (dict): Configuration dict. It contains:
        n_thread (int): Thread number.
        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size
            and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2.
        input_folder (str): Path to the input folder.
        save_folder (str): Path to save folder.
        crop_size (int): Crop size.
        step (int): Step for overlapped sliding window.
        thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.

    Usage:
        For each folder, run this script.
        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
        After process, each sub_folder should have the same number of subimages.
        Remember to modify opt configurations according to your settings.
    """

    opt = {}
    opt['n_thread'] = args.n_thread
    opt['compression_level'] = args.compression_level
    opt['input_folder'] = args.input
    opt['save_folder'] = args.output
    opt['crop_size'] = args.crop_size
    opt['step'] = args.step
    opt['thresh_size'] = args.thresh_size
    extract_subimages(opt)


def extract_subimages(opt):
    """Crop images to subimages.

    Args:
        opt (dict): Configuration dict. It contains:
            input_folder (str): Path to the input folder.
            save_folder (str): Path to save folder.
            n_thread (int): Thread number.
    """
    input_folder = opt['input_folder']
    save_folder = opt['save_folder']
    if not osp.exists(save_folder):
        os.makedirs(save_folder)
        print(f'mkdir {save_folder} ...')
    else:
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)

    # scan all images
    img_list = list(scandir(input_folder, full_path=True))

    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
    pool = Pool(opt['n_thread'])
    for path in img_list:
        pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
    pool.close()
    pool.join()
    pbar.close()
    print('All processes done.')


def worker(path, opt):
    """Worker for each process.

    Args:
        path (str): Image path.
        opt (dict): Configuration dict. It contains:
            crop_size (int): Crop size.
            step (int): Step for overlapped sliding window.
            thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped.
            save_folder (str): Path to save folder.
            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.

    Returns:
        process_info (str): Process information displayed in progress bar.
    """
    crop_size = opt['crop_size']
    step = opt['step']
    thresh_size = opt['thresh_size']
    img_name, extension = osp.splitext(osp.basename(path))

    # remove the x2, x3, x4 and x8 in the filename for DIV2K
    img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')

    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)

    h, w = img.shape[0:2]
    h_space = np.arange(0, h - crop_size + 1, step)
    if h - (h_space[-1] + crop_size) > thresh_size:
        h_space = np.append(h_space, h - crop_size)
    w_space = np.arange(0, w - crop_size + 1, step)
    if w - (w_space[-1] + crop_size) > thresh_size:
        w_space = np.append(w_space, w - crop_size)

    index = 0
    for x in h_space:
        for y in w_space:
            index += 1
            cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
            cropped_img = np.ascontiguousarray(cropped_img)
            cv2.imwrite(
                osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
                [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
    process_info = f'Processing {img_name} ...'
    return process_info


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/HR', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_sub', help='Output folder')
    parser.add_argument('--crop_size', type=int, default=480, help='Crop size')
    parser.add_argument('--step', type=int, default=240, help='Step for overlapped sliding window')
    parser.add_argument(
        '--thresh_size',
        type=int,
        default=0,
        help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
    parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
    parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
    args = parser.parse_args()

    main(args)


================================================
FILE: SRGAN/scripts/generate_meta_info.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['datasets/DF2K', 'datasets/DF2K'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: SRGAN/scripts/generate_meta_info_DF2K.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_sub', '/mnt/bn/xiabinpaint/dataset/DF2K/DF2K_multiscale_sub'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset', '/mnt/bn/xiabinpaint/dataset'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/xiabinpaint/ICCV-SR/KDSR-GAN/datasets/meta_info/meta_info_DF2Kmultiscale_sub.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: SRGAN/scripts/generate_meta_info_OST.py
================================================
import argparse
import cv2
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
            status = True
            if args.check:
                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
                except (IOError, OSError) as error:
                    print(f'Read {img_path} error: {error}')
                    status = False
                if img is None:
                    status = False
                    print(f'Img is None: {img_path}')
            if status:
                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')


if __name__ == '__main__':
    """Generate meta info (txt file) for only Ground-Truth images.

    It can also generate meta info from several folders into one txt file.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset/OST/train/HR'],
        help='Input folder, can be a list')
    parser.add_argument(
        '--root',
        nargs='+',
        default=['/mnt/bn/xiabinpaint/dataset'],
        help='Folder root, should have the length as input folders')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/xiabinpaint/ICCV-SR/KDSR-GAN/datasets/meta_info/meta_info_OST.txt',
        help='txt path for meta info')
    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()

    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)

    main(args)


================================================
FILE: SRGAN/scripts/generate_meta_info_pairdata.py
================================================
import argparse
import glob
import os


def main(args):
    txt_file = open(args.meta_info, 'w')
    # sca images
    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*.png')))
    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*.png')))
    print( len(img_paths_gt) , len(img_paths_lq))
    assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
                                                    f'{len(img_paths_gt)} and {len(img_paths_lq)}.')

    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
        # get the relative paths
        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
        print(f'{img_name_gt} {img_name_lq}')
        txt_file.write(f'{img_name_gt} {img_name_lq}\n')


if __name__ == '__main__':
    """This script is used to generate meta info (txt file) for paired images.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        nargs='+',
        default=['/mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub', '/mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub/X4'],
        help='Input folder, should be [gt_folder, lq_folder]')
    parser.add_argument('--root', nargs='+', default=['/mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub', '/mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/DF2K_multiscale_sub'], help='Folder root, will use the ')
    parser.add_argument(
        '--meta_info',
        type=str,
        default='/mnt/bn/shiyuan-arnold/dataset/DiffIR/realSR/meta_info_DF2Kmultiscale_4xpair_sub.txt',
        help='txt path for meta info')
    args = parser.parse_args()

    assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
    assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
    for i in range(2):
        if args.input[i].endswith('/'):
            args.input[i] = args.input[i][:-1]
        if args.root[i] is None:
            args.root[i] = os.path.dirname(args.input[i])

    main(args)


================================================
FILE: SRGAN/scripts/generate_multiscale_DF2K.py
================================================
import argparse
import glob
import os
from PIL import Image


def main(args):
    # For DF2K, we consider the following three scales,
    # and the smallest image whose shortest edge is 400
    scale_list = [0.75, 0.5, 1 / 3]
    shortest_edge = 400

    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
    for path in path_list:
        print(path)
        basename = os.path.splitext(os.path.basename(path))[0]

        img = Image.open(path)
        width, height = img.size
        for idx, scale in enumerate(scale_list):
            print(f'\t{scale:.2f}')
            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))

        # save the smallest image which the shortest edge is 400
        if width < height:
            ratio = height / width
            width = shortest_edge
            height = int(width * ratio)
        else:
            ratio = width / height
            height = shortest_edge
            width = int(height * ratio)
        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))


if __name__ == '__main__':
    """Generate multi-scale versions for GT images with LANCZOS resampling.
    It is now used for DF2K dataset (DIV2K + Flickr 2K)
    """
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', type=str, default='/mnt/bd/dlspace-hl-256g-0001/datasets/DF2K/HR', help='Input folder')
    parser.add_argument('--output', type=str, default='/mnt/bd/dlspace-hl-256g-0001/datasets/DF2K/DF2K_multiscale', help='Output folder')
    args = parser.parse_args()
    os.makedirs(args.output, exist_ok=True)
    main(args)


================================================
FILE: SRGAN/scripts/pytorch2onnx.py
================================================
import argparse
import torch
import torch.onnx
from DiffIR.archs.S2_arch import DiffIRS2


def main(args):
    # An instance of the model
    model = DiffIRS2( n_encoder_res= 9, dim= 64, scale=args.scale,num_blocks= [13,1,1,1],num_refinement_blocks= 13,heads= [1,2,4,8], ffn_expansion_factor= 2.2,LayerNorm_type= "BiasFree")
    loadnet = torch.load(args.model_path, map_location=torch.device('cpu'))
    model.load_state_dict(loadnet['params_ema'], strict=True)
    # set the train mode to false since we will only run the forward pass.
    model.train(False)
    model.cpu().eval()

    # An example input
    x = torch.rand(1, 3, 64, 64)
    # Export the model
    with torch.no_grad():
        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
    print(torch_out.shape)


if __name__ == '__main__':
    """Convert pytorch model to onnx models"""
    parser = argparse.ArgumentParser()
    parser.add_argument('--scale', type=int, default=4)
    parser.add_argument('--model_path', type=str, default='./experiments/DiffIRS2-GANv2.pth')
    parser.add_argument('--output', type=str, default='DiffIRS2-GANv2-x4.onnx', help='Output onnx path')
    args = parser.parse_args()

    main(args)


================================================
FILE: SRGAN/setup.cfg
================================================
[flake8]
ignore =
    # line break before binary operator (W503)
    W503,
    # line break after binary operator (W504)
    W504,
max-line-length=120

[yapf]
based_on_style = pep8
column_limit = 120
blank_line_before_nested_class_or_def = true
split_before_expression_after_opening_paren = true

[isort]
line_length = 120
multi_line_output = 0
known_standard_library = pkg_resources,setuptools
known_first_party = realesrgan
known_third_party = PIL,basicsr,cv2,numpy,pytest,torch,torchvision,tqdm,yaml
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY

[codespell]
skip = .git,./docs/build
count =
quiet-level = 3

[aliases]
test=pytest

[tool:pytest]
addopts=tests/


================================================
FILE: SRGAN/setup.py
================================================
#!/usr/bin/env python

from setuptools import find_packages, setup

import os
import subprocess
import time

version_file = 'VmambaIR/version.py'


def readme():
    with open('README.md', encoding='utf-8') as f:
        content = f.read()
    return content


def get_git_hash():

    def _minimal_ext_cmd(cmd):
        # construct minimal environment
        env = {}
        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
            v = os.environ.get(k)
            if v is not None:
                env[k] = v
        # LANGUAGE is used on win32
        env['LANGUAGE'] = 'C'
        env['LANG'] = 'C'
        env['LC_ALL'] = 'C'
        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
        return out

    try:
        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
        sha = out.strip().decode('ascii')
    except OSError:
        sha = 'unknown'

    return sha


def get_hash():
    if os.path.exists('.git'):
        sha = get_git_hash()[:7]
    else:
        sha = 'unknown'

    return sha


def write_version_py():
    content = """# GENERATED VERSION FILE
# TIME: {}
__version__ = '{}'
__gitsha__ = '{}'
version_info = ({})
"""
    sha = get_hash()
    with open('VERSION', 'r') as f:
        SHORT_VERSION = f.read().strip()
    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])

    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
    with open(version_file, 'w') as f:
        f.write(version_file_str)


def get_version():
    with open(version_file, 'r') as f:
        exec(compile(f.read(), version_file, 'exec'))
    return locals()['__version__']


def get_requirements(filename='requirements.txt'):
    here = os.path.dirname(os.path.realpath(__file__))
    with open(os.path.join(here, filename), 'r') as f:
        requires = [line.replace('\n', '') for line in f.readlines()]
    return requires


if __name__ == '__main__':
    write_version_py()
    setup(
        name='realesrgan',
        version=get_version(),
        description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
        long_description=readme(),
        long_description_content_type='text/markdown',
        author='Xintao Wang',
        author_email='xintao.wang@outlook.com',
        keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
        url='https://github.com/xinntao/Real-ESRGAN',
        include_package_data=True,
        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
        classifiers=[
            'Development Status :: 4 - Beta',
            'License :: OSI Approved :: Apache Software License',
            'Operating System :: OS Independent',
            'Programming Language :: Python :: 3',
            'Programming Language :: Python :: 3.7',
            'Programming Language :: Python :: 3.8',
        ],
        license='BSD-3-Clause License',
        setup_requires=['cython', 'numpy'],
        install_requires=get_requirements(),
        zip_safe=False)


================================================
FILE: SRGAN/test.sh
================================================

python3  VmambaIR/test.py -opt options/test_mamba15_x4.yml  

================================================
FILE: SRGAN/train_S1.sh
================================================

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 -m torch.distributed.launch --nproc_per_node=8 \
    --master_port=4397 \
    --use_env \
    VmambaIR/train.py \
    -opt options/MambaSISR15_x4.yml \
    --launcher pytorch 


================================================
FILE: SRGAN/train_S2.sh
================================================

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 -m torch.distributed.launch --nproc_per_node=8 \
    --master_port=4397 \
    --use_env \
    VmambaIR/train.py \
    -opt options/MambaSISR15GAN_x4.yml \
    --launcher pytorch 


================================================
FILE: install.md
================================================
# Installation

This repository is built in PyTorch 2.3.0 and tested on Debian 11 environment (Python3.9, CUDA12.1).
Follow these intructions

1. Clone our repository
```
git clone https://github.com/AlphacatPlus/VmambaIR.git
cd VmambaIR
```

2. Make conda environment
```
conda create -n vmambair python=3.9
conda activate vmambair
```

3. Install dependencies
```
cd VmambaIR
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/

```

4. Install mamba
```
cd Mamba
cd kernels/selective_scan && pip install .
```



================================================
FILE: requirements.txt
================================================
numpy
opencv-python
Pillow
torch>=1.7
torchvision
tqdm
einops
lpips
torchsummary
pandas
pyyaml
tb-nightly
basicsr
timm
fvcore