Repository: xiaom233/BSRN Branch: main Commit: 2d63d5239c9f Files: 182 Total size: 3.5 MB Directory structure: gitextract_k1iv7nsr/ ├── .gitignore ├── .pre-commit-config.yaml ├── INSTALL.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── VERSION ├── basicsr/ │ ├── __init__.py │ ├── archs/ │ │ ├── BSRN_arch.py │ │ ├── Blocks.py │ │ ├── RFDN_arch.py │ │ ├── Upsamplers.py │ │ ├── __init__.py │ │ ├── arch_util.py │ │ ├── rfdnfinalB5_arch.py │ │ └── vgg_arch.py │ ├── data/ │ │ ├── __init__.py │ │ ├── data_sampler.py │ │ ├── data_util.py │ │ ├── degradations.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 │ ├── losses/ │ │ ├── __init__.py │ │ ├── loss_util.py │ │ └── losses.py │ ├── metrics/ │ │ ├── __init__.py │ │ ├── fid.py │ │ ├── metric_util.py │ │ ├── niqe.py │ │ ├── niqe_pris_params.npz │ │ └── psnr_ssim.py │ ├── models/ │ │ ├── __init__.py │ │ ├── base_model.py │ │ ├── edvr_model.py │ │ ├── esrgan_model.py │ │ ├── hifacegan_model.py │ │ ├── lr_scheduler.py │ │ ├── sr_model.py │ │ ├── srgan_model.py │ │ ├── stylegan2_model.py │ │ ├── swinir_model.py │ │ ├── video_base_model.py │ │ ├── video_gan_model.py │ │ ├── video_recurrent_gan_model.py │ │ └── video_recurrent_model.py │ ├── ops/ │ │ ├── __init__.py │ │ ├── dcn/ │ │ │ ├── __init__.py │ │ │ ├── deform_conv.py │ │ │ └── src/ │ │ │ ├── deform_conv_cuda.cpp │ │ │ ├── deform_conv_cuda_kernel.cu │ │ │ └── deform_conv_ext.cpp │ │ ├── fused_act/ │ │ │ ├── __init__.py │ │ │ ├── fused_act.py │ │ │ └── src/ │ │ │ ├── fused_bias_act.cpp │ │ │ └── fused_bias_act_kernel.cu │ │ └── upfirdn2d/ │ │ ├── __init__.py │ │ ├── src/ │ │ │ ├── upfirdn2d.cpp │ │ │ └── upfirdn2d_kernel.cu │ │ └── upfirdn2d.py │ ├── summary.py │ ├── test.py │ ├── train.py │ └── utils/ │ ├── __init__.py │ ├── diffjpeg.py │ ├── dist_util.py │ ├── download_util.py │ ├── face_util.py │ ├── file_client.py │ ├── flow_util.py │ ├── img_process_util.py │ ├── img_util.py │ ├── lmdb_util.py │ ├── logger.py │ ├── matlab_functions.py │ ├── misc.py │ ├── options.py │ └── registry.py ├── colab/ │ └── README.md ├── datasets/ │ └── README.md ├── docs/ │ ├── Config.md │ ├── Config_CN.md │ ├── DatasetPreparation.md │ ├── DatasetPreparation_CN.md │ ├── Datasets.md │ ├── Datasets_CN.md │ ├── DesignConvention.md │ ├── DesignConvention_CN.md │ ├── HOWTOs.md │ ├── HOWTOs_CN.md │ ├── Logging.md │ ├── Logging_CN.md │ ├── Makefile │ ├── Metrics.md │ ├── Metrics_CN.md │ ├── ModelZoo.md │ ├── ModelZoo_CN.md │ ├── Models.md │ ├── Models_CN.md │ ├── TrainTest.md │ ├── TrainTest_CN.md │ ├── api.rst │ ├── conf.py │ ├── history_updates.md │ ├── index.rst │ └── make.bat ├── experiments/ │ └── pretrained_models/ │ ├── net_g_BSRN-S.pth │ ├── net_g_BSRN_x2.pth │ ├── net_g_BSRN_x3.pth │ └── net_g_BSRN_x4.pth ├── options/ │ ├── test/ │ │ ├── benchmark_BSRN-S_x4.yml │ │ ├── benchmark_BSRN_x2.yml │ │ ├── benchmark_BSRN_x3.yml │ │ └── benchmark_BSRN_x4.yml │ └── train/ │ ├── train_BSRN-S_x4.yml │ ├── train_BSRN_x2.yml │ ├── train_BSRN_x3.yml │ └── train_BSRN_x4.yml ├── requirements.txt ├── scripts/ │ ├── data_preparation/ │ │ ├── create_lmdb.py │ │ ├── download_datasets.py │ │ ├── extract_images_from_tfrecords.py │ │ ├── extract_subimages.py │ │ ├── generate_meta_info.py │ │ ├── prepare_hifacegan_dataset.py │ │ └── regroup_reds_dataset.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── download_gdrive.py │ ├── download_pretrained_models.py │ ├── matlab_scripts/ │ │ ├── back_projection/ │ │ │ ├── backprojection.m │ │ │ ├── main_bp.m │ │ │ └── main_reverse_filter.m │ │ ├── generate_LR_Vimeo90K.m │ │ └── generate_bicubic_img.m │ ├── metrics/ │ │ ├── calculate_fid_folder.py │ │ ├── calculate_fid_stats_from_datasets.py │ │ ├── calculate_lpips.py │ │ ├── calculate_niqe.py │ │ ├── calculate_psnr_ssim.py │ │ └── calculate_stylegan2_fid.py │ ├── model_conversion/ │ │ ├── convert_dfdnet.py │ │ ├── convert_models.py │ │ ├── convert_ridnet.py │ │ └── convert_stylegan.py │ └── publish_models.py ├── setup.cfg ├── setup.py └── tests/ ├── README.md ├── 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_archs/ │ ├── test_basicvsr_arch.py │ ├── test_discriminator_arch.py │ ├── test_duf_arch.py │ ├── test_ecbsr_arch.py │ └── test_srresnet_arch.py ├── test_data/ │ ├── test_paired_image_dataset.py │ └── test_single_image_dataset.py ├── test_losses/ │ └── test_losses.py ├── test_metrics/ │ └── test_psnr_ssim.py └── test_models/ └── test_sr_model.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # ignored folders results/* tb_logger/* wandb/* tmp/* *.DS_Store .idea # ignored files version.py # ignored files with suffix *.html *.png *.jpeg *.jpg *.gif *.zip # template # 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/ *.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/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # 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/ ================================================ FILE: .pre-commit-config.yaml ================================================ repos: # flake8 - repo: https://github.com/PyCQA/flake8 rev: 3.8.3 hooks: - id: flake8 args: ["--config=setup.cfg", "--ignore=W504, W503"] # modify known_third_party - repo: https://github.com/asottile/seed-isort-config rev: v2.2.0 hooks: - id: seed-isort-config # isort - repo: https://github.com/timothycrosley/isort rev: 5.2.2 hooks: - id: isort # yapf - repo: https://github.com/pre-commit/mirrors-yapf rev: v0.30.0 hooks: - id: yapf # codespell - repo: https://github.com/codespell-project/codespell rev: v2.1.0 hooks: - id: codespell # pre-commit-hooks - repo: https://github.com/pre-commit/pre-commit-hooks rev: v3.2.0 hooks: - id: trailing-whitespace # Trim trailing whitespace - id: check-yaml # Attempt to load all yaml files to verify syntax - id: check-merge-conflict # Check for files that contain merge conflict strings - id: double-quote-string-fixer # Replace double quoted strings with single quoted strings - id: end-of-file-fixer # Make sure files end in a newline and only a newline - id: requirements-txt-fixer # Sort entries in requirements.txt and remove incorrect entry for pkg-resources==0.0.0 - id: fix-encoding-pragma # Remove the coding pragma: # -*- coding: utf-8 -*- args: ["--remove"] - id: mixed-line-ending # Replace or check mixed line ending args: ["--fix=lf"] ================================================ FILE: INSTALL.md ================================================ # Installation ## Contents - [Requirements](#requirements) - [BASICSR_EXT and BASICSR_JIT environment variables](#basicsr_ext-and-basicsr_jit-environment-variables) - [Installation Options](#installation-options) - [Install from PyPI](#install-from-pypi) - [Install from a local clone](#Install-from-a-local-clone) - [FAQ](#faq) ## Requirements - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) - [PyTorch >= 1.7](https://pytorch.org/) - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) - Linux (We have not tested on Windows) ## BASICSR_EXT and BASICSR_JIT Environment Variables If you want to use PyTorch C++ extensions:
 deformable convolution: [*dcn* for EDVR](basicsr/ops) (For torchvision>=0.9.0, we use the official `torchvision.ops.deform_conv2d` instead)
 StyleGAN customized operators: [*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/ops)
you also need to: 1. **compile** the PyTorch C++ extensions during installation 2. OR **load** the PyTorch C++ extensions just-in-time (JIT) You may choose one of the options according to your needs. | Option | Pros| Cons | Cases | Env Variable| | :--- | :--- | :--- | :--- |:--- | | **Compile** PyTorch C++ extensions during installation | **Quickly load** the compiled extensions during running | May have more stringent requirements for the environment, and you may encounter annoying issues | If you need to train/inference those models for many times, it will save your time| Set `BASICSR_EXT=True` during **installation**| | **Load** PyTorch C++ extensions just-in-time (JIT) | Have less requirements, may have less issues | Each time you run the model, it will takes several minutes to load extensions again | If you just want to do simple inferences, it is more convenient| Set `BASICSR_JIT=True` during **running** (not **installation**) | For those who need to compile the PyTorch C++ extensions during installation, remember: - Make sure that your gcc and g++ version: gcc & g++ >= 5 Note that: - The `BASICSR_JIT` has higher priority, that is, even you have successfully compiled PyTorch C++ extensions during installation, it will still load the extensions just-in-time if you set `BASICSR_JIT=True` in your running commands. - :x: Do not set `BASICSR_JIT` during installation. Installation commands are in [Installation Options](#installation-options). - :heavy_check_mark: If you want to load PyTorch C++ extensions just-in-time (JIT), just set `BASICSR_JIT=True` before your **running** commands. For example, `BASICSR_JIT=True python inference/inference_stylegan2.py`. If you do not need those PyTorch C++ extensions, just skip it. There is no need to set `BASICSR_EXT` or `BASICSR_JIT` environment variables. ## Installation Options There are two options to install BASICSR, according to your needs. - If you just want to use BASICSR as a **package** (just like [GFPGAN](https://github.com/TencentARC/GFPGAN) and []()), it is recommended to install from PyPI. - If you want to **investigate** the details of BASICSR OR **develop** it OR **modify** it to fulfill your needs, it is better to install from a local clone. ### Install from PyPI - If you do not need C++ extensions (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)): ```bash pip install basicsr ``` - If you want to use C++ extensions in **JIT mode** without compiling them during installatoin (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)): ```bash pip install basicsr ``` - If you want to **compile C++ extensions during installation**, please set the environment variable `BASICSR_EXT=True`: ```bash BASICSR_EXT=True pip install basicsr ``` The compilation may fail without any error prints. If you encounter running errors, such as `ImportError: cannot import name 'deform_conv_ext' | 'fused_act_ext' | 'upfirdn2d_ext'`, you may check the compilation process by re-installation. The following command will print detailed log: ```bash BASICSR_EXT=True pip install basicsr -vvv ``` You may also want to specify the CUDA paths: ```bash CUDA_HOME=/usr/local/cuda \ CUDNN_INCLUDE_DIR=/usr/local/cuda \ CUDNN_LIB_DIR=/usr/local/cuda \ BASICSR_EXT=True pip install basicsr ``` ### Install from a local clone 1. Clone the repo ```bash git clone https://github.com/xinntao/BasicSR.git ``` 1. Install dependent packages ```bash cd BasicSR pip install -r requirements.txt ``` 1. Install BasicSR
Please run the following commands in the **BasicSR root path** to install BasicSR:
- If you do not need C++ extensions (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)): ```bash python setup.py develop ``` - If you want to use C++ extensions in **JIT mode** without compiling them during installatoin (more details are [here](#basicsr_ext-and-basicsr_jit-environment-variables)): ```bash python setup.py develop ``` - If you want to **compile C++ extensions during installation**, please set the environment variable `BASICSR_EXT=True`: ```bash BASICSR_EXT=True python setup.py develop ``` You may also want to specify the CUDA paths: ```bash CUDA_HOME=/usr/local/cuda \ CUDNN_INCLUDE_DIR=/usr/local/cuda \ CUDNN_LIB_DIR=/usr/local/cuda \ BASICSR_EXT=True python setup.py develop ``` ## FAQ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2022 Hoven Li 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: MANIFEST.in ================================================ include basicsr/ops/dcn/src/*.cu basicsr/ops/dcn/src/*.cpp include basicsr/ops/fused_act/src/*.cu basicsr/ops/fused_act/src/*.cpp include basicsr/ops/upfirdn2d/src/*.cu basicsr/ops/upfirdn2d/src/*.cpp include basicsr/metrics/niqe_pris_params.npz include VERSION include requirements.txt ================================================ FILE: README.md ================================================ # BSRN [Blueprint Separable Residual Network for Efficient Image Super-Resolution](https://arxiv.org/abs/2205.05996) [Zheyuan Li](https://xiaom233.github.io/), Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, Chao Dong BibTex ``` @InProceedings{Li_2022_CVPR, author = {Li, Zheyuan and Liu, Yingqi and Chen, Xiangyu and Cai, Haoming and Gu, Jinjin and Qiao, Yu and Dong, Chao}, title = {Blueprint Separable Residual Network for Efficient Image Super-Resolution}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {833-843} } ``` ## Environment [PyTorch >= 1.7](https://pytorch.org/) [BasicSR >= 1.3.4.9](https://github.com/XPixelGroup/BasicSR) ### Installation ``` pip install -r requirements.txt python setup.py develop ``` ## How To Test · Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model. · The pretrained models are available in ./experiments/pretrained_models/ · Then run the follwing codes (taking net_g_BSRN_x4.pth as an example): ``` python basicsr/test.py -opt options/test/benchmark_BSRN_x4.yml ``` The testing results will be saved in the ./results folder. ## How To Train · Refer to ./options/train for the configuration file of the model to train. · Preparation of training data can refer to this page. All datasets can be downloaded at the official website. · Note that the default training dataset is based on lmdb, refer to [docs in BasicSR](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md) to learn how to generate the training datasets. · The training command is like ``` CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/train_BSRN_x4.yml CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/train_BSRN-S_x4.yml --launcher pytorch ``` For more training commands and details, please check the docs in [BasicSR](https://github.com/XPixelGroup/BasicSR) ## Results The inference results on benchmark datasets are available at [Google Drive](https://drive.google.com/drive/folders/18uRxyAWwpAfKuxgDneacJkF4-rAyR7XR?usp=sharing) or [Baidu Netdisk](https://pan.baidu.com/s/1N9zLwsOBM8MxqpfK5zpZXw) (access code: VISU). ## Contact If you have any question, please email zheyuanli884886@gmail.com or join in the Wechat group of BasicSR to discuss with the authors. ================================================ FILE: VERSION ================================================ 1.3.4.9 ================================================ FILE: basicsr/__init__.py ================================================ # https://github.com/xinntao/BasicSR # flake8: noqa from .archs import * from .data import * from .losses import * from .metrics import * from .models import * from .ops import * from .test import * from .train import * from .utils import * from .version import __gitsha__, __version__ ================================================ FILE: basicsr/archs/BSRN_arch.py ================================================ ''' This repository is used to implement all upsamplers(only x4) and tools for Efficient SR @author LI Zehyuan from SIAT LIU yingqi from SIAT ''' from functools import partial import torch import torch.nn as nn import torch.nn.functional as F import math import basicsr.archs.Upsamplers as Upsamplers from basicsr.utils.registry import ARCH_REGISTRY class DepthWiseConv(nn.Module): def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", with_norm=False, bn_kwargs=None): super(DepthWiseConv, self).__init__() self.dw = torch.nn.Conv2d( in_channels=in_ch, out_channels=in_ch, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_ch, bias=bias, padding_mode=padding_mode, ) self.pw = torch.nn.Conv2d( in_channels=in_ch, out_channels=out_ch, kernel_size=(1, 1), stride=1, padding=0, dilation=1, groups=1, bias=False, ) def forward(self, input): out = self.dw(input) out = self.pw(out) return out class BSConvU(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", with_ln=False, bn_kwargs=None): super().__init__() self.with_ln = with_ln # check arguments if bn_kwargs is None: bn_kwargs = {} # pointwise self.pw=torch.nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=(1, 1), stride=1, padding=0, dilation=1, groups=1, bias=False, ) # depthwise self.dw = torch.nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=out_channels, bias=bias, padding_mode=padding_mode, ) def forward(self, fea): fea = self.pw(fea) fea = self.dw(fea) return fea class BSConvS(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", p=0.25, min_mid_channels=4, with_ln=False, bn_kwargs=None): super().__init__() self.with_ln = with_ln # check arguments assert 0.0 <= p <= 1.0 mid_channels = min(in_channels, max(min_mid_channels, math.ceil(p * in_channels))) if bn_kwargs is None: bn_kwargs = {} # pointwise 1 self.pw1 = torch.nn.Conv2d( in_channels=in_channels, out_channels=mid_channels, kernel_size=(1, 1), stride=1, padding=0, dilation=1, groups=1, bias=False, ) # pointwise 2 self.add_module("pw2", torch.nn.Conv2d( in_channels=mid_channels, out_channels=out_channels, kernel_size=(1, 1), stride=1, padding=0, dilation=1, groups=1, bias=False, )) # depthwise self.dw = torch.nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=out_channels, bias=bias, padding_mode=padding_mode, ) def forward(self, x): fea = self.pw1(x) fea = self.pw2(fea) fea = self.dw(fea) return fea def _reg_loss(self): W = self[0].weight[:, :, 0, 0] WWt = torch.mm(W, torch.transpose(W, 0, 1)) I = torch.eye(WWt.shape[0], device=WWt.device) return torch.norm(WWt - I, p="fro") def stdv_channels(F): assert (F.dim() == 4) F_mean = mean_channels(F) F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True) / (F.size(2) * F.size(3)) return F_variance.pow(0.5) def mean_channels(F): assert(F.dim() == 4) spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True) return spatial_sum / (F.size(2) * F.size(3)) class CCALayer(nn.Module): def __init__(self, channel, reduction=16): super(CCALayer, self).__init__() self.contrast = stdv_channels self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv_du = nn.Sequential( nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True), nn.ReLU(inplace=True), nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True), nn.Sigmoid() ) def forward(self, x): y = self.contrast(x) + self.avg_pool(x) y = self.conv_du(y) return x * y class ChannelAttention(nn.Module): """Channel attention used in RCAN. Args: num_feat (int): Channel number of intermediate features. squeeze_factor (int): Channel squeeze factor. Default: 16. """ def __init__(self, num_feat, squeeze_factor=16): super(ChannelAttention, self).__init__() self.attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid()) def forward(self, x): y = self.attention(x) return x * y class ESA(nn.Module): def __init__(self, num_feat=50, conv=nn.Conv2d, p=0.25): super(ESA, self).__init__() f = num_feat // 4 BSConvS_kwargs = {} if conv.__name__ == 'BSConvS': BSConvS_kwargs = {'p': p} self.conv1 = nn.Conv2d(num_feat, f, 1) self.conv_f = nn.Conv2d(f, f, 1) self.maxPooling = nn.MaxPool2d(kernel_size=7, stride=3) self.conv_max = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv2 = conv(f, f, 3, 2, 0) self.conv3 = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv3_ = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv4 = nn.Conv2d(f, num_feat, 1) self.sigmoid = nn.Sigmoid() self.GELU = nn.GELU() def forward(self, input): c1_ = (self.conv1(input)) c1 = self.conv2(c1_) v_max = self.maxPooling(c1) v_range = self.GELU(self.conv_max(v_max)) c3 = self.GELU(self.conv3(v_range)) c3 = self.conv3_(c3) c3 = F.interpolate(c3, (input.size(2), input.size(3)), mode='bilinear', align_corners=False) cf = self.conv_f(c1_) c4 = self.conv4((c3 + cf)) m = self.sigmoid(c4) return input * m class ESDB(nn.Module): def __init__(self, in_channels, out_channels, conv=nn.Conv2d, p=0.25): super(ESDB, self).__init__() kwargs = {'padding': 1} if conv.__name__ == 'BSConvS': kwargs = {'p': p} self.dc = self.distilled_channels = in_channels // 2 self.rc = self.remaining_channels = in_channels self.c1_d = nn.Conv2d(in_channels, self.dc, 1) self.c1_r = conv(in_channels, self.rc, kernel_size=3, **kwargs) self.c2_d = nn.Conv2d(self.remaining_channels, self.dc, 1) self.c2_r = conv(self.remaining_channels, self.rc, kernel_size=3, **kwargs) self.c3_d = nn.Conv2d(self.remaining_channels, self.dc, 1) self.c3_r = conv(self.remaining_channels, self.rc, kernel_size=3, **kwargs) self.c4 = conv(self.remaining_channels, self.dc, kernel_size=3, **kwargs) self.act = nn.GELU() self.c5 = nn.Conv2d(self.dc * 4, in_channels, 1) self.esa = ESA(in_channels, conv) self.cca = CCALayer(in_channels) def forward(self, input): distilled_c1 = self.act(self.c1_d(input)) r_c1 = (self.c1_r(input)) r_c1 = self.act(r_c1 + input) distilled_c2 = self.act(self.c2_d(r_c1)) r_c2 = (self.c2_r(r_c1)) r_c2 = self.act(r_c2 + r_c1) distilled_c3 = self.act(self.c3_d(r_c2)) r_c3 = (self.c3_r(r_c2)) r_c3 = self.act(r_c3 + r_c2) r_c4 = self.act(self.c4(r_c3)) out = torch.cat([distilled_c1, distilled_c2, distilled_c3, r_c4], dim=1) out = self.c5(out) out_fused = self.esa(out) out_fused = self.cca(out_fused) return out_fused + input def make_layer(block, n_layers): layers = [] for _ in range(n_layers): layers.append(block()) return nn.Sequential(*layers) @ARCH_REGISTRY.register() class BSRN(nn.Module): def __init__(self, num_in_ch=3, num_feat=64, num_block=8, num_out_ch=3, upscale=4, conv='BSConvU', upsampler='pixelshuffledirect', p=0.25): super(BSRN, self).__init__() kwargs = {'padding': 1} if conv == 'BSConvS': kwargs = {'p': p} print(conv) if conv == 'DepthWiseConv': self.conv = DepthWiseConv elif conv == 'BSConvU': self.conv = BSConvU elif conv == 'BSConvS': self.conv = BSConvS else: self.conv = nn.Conv2d self.fea_conv = self.conv(num_in_ch * 4, num_feat, kernel_size=3, **kwargs) self.B1 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B2 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B3 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B4 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B5 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B6 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B7 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B8 = ESDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.c1 = nn.Conv2d(num_feat * num_block, num_feat, 1) self.GELU = nn.GELU() self.c2 = self.conv(num_feat, num_feat, kernel_size=3, **kwargs) if upsampler == 'pixelshuffledirect': self.upsampler = Upsamplers.PixelShuffleDirect(scale=upscale, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'pixelshuffleblock': self.upsampler = Upsamplers.PixelShuffleBlcok(in_feat=num_feat, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'nearestconv': self.upsampler = Upsamplers.NearestConv(in_ch=num_feat, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'pa': self.upsampler = Upsamplers.PA_UP(nf=num_feat, unf=24, out_nc=num_out_ch) else: raise NotImplementedError(("Check the Upsampeler. None or not support yet")) def forward(self, input): input = torch.cat([input, input, input, input], dim=1) out_fea = self.fea_conv(input) out_B1 = self.B1(out_fea) out_B2 = self.B2(out_B1) out_B3 = self.B3(out_B2) out_B4 = self.B4(out_B3) out_B5 = self.B5(out_B4) out_B6 = self.B6(out_B5) out_B7 = self.B7(out_B6) out_B8 = self.B8(out_B7) trunk = torch.cat([out_B1, out_B2, out_B3, out_B4, out_B5, out_B6, out_B7, out_B8], dim=1) out_B = self.c1(trunk) out_B = self.GELU(out_B) out_lr = self.c2(out_B) + out_fea output = self.upsampler(out_lr) return output ================================================ FILE: basicsr/archs/Blocks.py ================================================ ''' This repository is used to implement all blocks and tools for Efficient SR @author LI Zehyuan from SIAT LIU yingqi from SIAT ''' from functools import partial import torch import torch.nn as nn import math import torch.nn.functional as F from distutils.version import LooseVersion # 1*1卷积使用nn.Linear实现 class DepthWiseConv(nn.Module): def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", with_norm=True, bn_kwargs=None): super(DepthWiseConv, self).__init__() # 也相当于分组为1的分组卷积 self.depth_conv = nn.Conv2d(in_channels=in_ch, out_channels=in_ch, kernel_size=3, stride=1, padding=1, groups=in_ch) self.point_conv = nn.Linear(in_ch, out_ch) # self.point_conv = nn.Conv2d(in_channels=in_ch, # out_channels=out_ch, # kernel_size=1, # stride=1, # padding=0, # groups=1) def forward(self, input): out = self.depth_conv(input) out = out.permute(0, 2, 3, 1) out = self.point_conv(out) out = out.permute(0, 3, 1, 2) return out class BSConvU(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", with_ln=False, bn_kwargs=None): super().__init__() self.with_ln = with_ln # check arguments if bn_kwargs is None: bn_kwargs = {} # pointwise self.pw = nn.Linear(in_channels, out_channels) # self.pw=torch.nn.Conv2d( # in_channels=in_channels, # out_channels=out_channels, # kernel_size=(1, 1), # stride=1, # padding=0, # dilation=1, # groups=1, # bias=False, # ) # batchnorm if with_ln: self.ln = torch.nn.LayerNorm(out_channels, **bn_kwargs) # depthwise self.dw = torch.nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=out_channels, bias=bias, padding_mode=padding_mode, ) def forward(self, fea): fea = fea.permute(0, 2, 3, 1) fea = self.pw(fea) if self.with_ln: fea = self.ln(fea) fea = self.dw(fea.permute(0, 3, 1, 2)) return fea class BSConvS(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=True, padding_mode="zeros", p=0.25, min_mid_channels=4, with_ln=True, bn_kwargs=None): super().__init__() self.with_ln = with_ln # check arguments assert 0.0 <= p <= 1.0 mid_channels = min(in_channels, max(min_mid_channels, math.ceil(p * in_channels))) if bn_kwargs is None: bn_kwargs = {} # pointwise 1 self.pw1 = nn.Linear(in_channels, mid_channels) # self.pw1 = torch.nn.Conv2d( # in_channels=in_channels, # out_channels=mid_channels, # kernel_size=(1, 1), # stride=1, # padding=0, # dilation=1, # groups=1, # bias=False, # ) # batchnorm if with_ln: self.ln1 = torch.nn.LayerNorm(mid_channels, **bn_kwargs) # pointwise 2 self.pw2 = nn.Linear(mid_channels, out_channels) # self.add_module("pw2", torch.nn.Conv2d( # in_channels=mid_channels, # out_channels=out_channels, # kernel_size=(1, 1), # stride=1, # padding=0, # dilation=1, # groups=1, # bias=False, # )) # batchnorm if with_ln: self.ln2 = torch.nn.LayerNorm(out_channels, **bn_kwargs) # depthwise self.dw = torch.nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=out_channels, bias=bias, padding_mode=padding_mode, ) def forward(self, x): fea = x.permute(0, 2, 3, 1) fea = self.pw1(fea) if self.with_ln: fea = self.ln1(fea) fea = self.pw2(fea) if self.with_ln: fea = self.ln2(fea) fea = self.dw(fea.permute(0, 3, 1, 2)) return fea def _reg_loss(self): W = self[0].weight[:, :, 0, 0] WWt = torch.mm(W, torch.transpose(W, 0, 1)) I = torch.eye(WWt.shape[0], device=WWt.device) return torch.norm(WWt - I, p="fro") # replicate the input RGB channels at the head of the net class ChannelReplicate(nn.Module): def __init__(self, factor=3): super(ChannelReplicate, self).__init__() self.factor = factor def forward(self, input): template = input for i in range(0, self.factor-1): input = torch.cat((template, input), 1) return input # shuffleNetv1 # shufflblock combine group_conv with channle shuffle class ShuffleV1Block(nn.Module): def __init__(self, inp, oup, *, group, first_group, mid_channels, ksize, stride): super(ShuffleV1Block, self).__init__() self.stride = stride assert stride in [1, 2] self.mid_channels = mid_channels self.ksize = ksize pad = ksize // 2 self.pad = pad self.inp = inp self.group = group if stride == 2: outputs = oup - inp else: outputs = oup branch_main_1 = [ # pw nn.Conv2d(inp, mid_channels, 1, 1, 0, groups=1 if first_group else group, bias=False), nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True), # dw nn.Conv2d(mid_channels, mid_channels, ksize, stride, pad, groups=mid_channels, bias=False), nn.BatchNorm2d(mid_channels), ] branch_main_2 = [ # pw-linear nn.Conv2d(mid_channels, outputs, 1, 1, 0, groups=group, bias=False), nn.BatchNorm2d(outputs), ] self.branch_main_1 = nn.Sequential(*branch_main_1) self.branch_main_2 = nn.Sequential(*branch_main_2) if stride == 2: self.branch_proj = nn.AvgPool2d(kernel_size=3, stride=2, padding=1) def forward(self, old_x): x = old_x x_proj = old_x x = self.branch_main_1(x) if self.group > 1: x = self.channel_shuffle_v1(x) # 此处我觉得代码有问题，channle shuffle应该在branch_main_1中的pw之后 x = self.branch_main_2(x) if self.stride == 1: return F.relu(x + x_proj) elif self.stride == 2: return torch.cat((self.branch_proj(x_proj), F.relu(x)), 1) def channel_shuffle_v1(self, x): batchsize, num_channels, height, width = x.data.size() assert num_channels % self.group == 0 group_channels = num_channels // self.group x = x.reshape(batchsize, group_channels, self.group, height, width) x = x.permute(0, 2, 1, 3, 4) #将x换位，相当于x = (batchsize, self.group, group_channles, height, width) x = x.reshape(batchsize, num_channels, height, width) return x # shufflev2block # Gconv-->conv , remove channle shuffle in the branch class ShuffleV2Block(nn.Module): def __init__(self, inp, oup, mid_channels, *, ksize, stride): super(ShuffleV2Block, self).__init__() self.stride = stride assert stride in [1, 2] self.mid_channels = mid_channels self.ksize = ksize pad = ksize // 2 self.pad = pad self.inp = inp outputs = oup - inp branch_main = [ # pw nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False), nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True), # dw nn.Conv2d(mid_channels, mid_channels, ksize, stride, pad, groups=mid_channels, bias=False), nn.BatchNorm2d(mid_channels), # pw-linear nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False), nn.BatchNorm2d(outputs), nn.ReLU(inplace=True), ] self.branch_main = nn.Sequential(*branch_main) if stride == 2: branch_proj = [ # dw nn.Conv2d(inp, inp, ksize, stride, pad, groups=inp, bias=False), nn.BatchNorm2d(inp), # pw-linear nn.Conv2d(inp, inp, 1, 1, 0, bias=False), nn.BatchNorm2d(inp), nn.ReLU(inplace=True), ] self.branch_proj = nn.Sequential(*branch_proj) else: self.branch_proj = None def forward(self, old_x): if self.stride==1: x_proj, x = self.channel_shuffle_v2(old_x) return torch.cat((x_proj, self.branch_main(x)), 1) # 将上一个block得到的old_x经过两个不同分支，注意这里的恒等体现了channle spilt. 然后横着concat elif self.stride==2: x_proj = old_x x = old_x return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1) # 对应paper中第二幅图 def channel_shuffle_v2(self, x): batchsize, num_channels, height, width = x.data.size() assert (num_channels % 4 == 0) x = x.reshape(batchsize * num_channels // 2, 2, height * width) x = x.permute(1, 0, 2) x = x.reshape(2, -1, num_channels // 2, height, width) return x[0], x[1] # Performer # for simpler you can just import and use like below # from performer_pytorch import FastAttention # https://github.com/lucidrains/performer-pytorch for more details # Linear Transformer # https://github.com/idiap/fast-transformers # Linear Transformer class FeatureMap(nn.Module): """Define the FeatureMap interface.""" def __init__(self, query_dims): super().__init__() self.query_dims = query_dims def new_feature_map(self, device): """Create a new instance of this feature map. In particular, if it is a random feature map sample new parameters.""" raise NotImplementedError() def forward_queries(self, x): """Encode the queries `x` using this feature map.""" return self(x) def forward_keys(self, x): """Encode the keys `x` using this feature map.""" return self(x) def forward(self, x): """Encode x using this feature map. For symmetric feature maps it suffices to define this function, but for asymmetric feature maps one needs to define the `forward_queries` and `forward_keys` functions.""" raise NotImplementedError() @classmethod def factory(cls, *args, **kwargs): """Return a function that when called with the query dimensions returns an instance of this feature map. It is inherited by the subclasses so it is available in all feature maps. """ def inner(query_dims): return cls(query_dims, *args, **kwargs) return inner class ActivationFunctionFeatureMap(FeatureMap): """Define a feature map that is simply an element-wise activation function.""" def __init__(self, query_dims, activation_function): super().__init__(query_dims) self.activation_function = activation_function def new_feature_map(self, device): return def forward(self, x): return self.activation_function(x) class LinearAttention(nn.Module): """Implement unmasked attention using dot product of feature maps in O(N D^2) complexity. Given the queries, keys and values as Q, K, V instead of computing V' = softmax(Q.mm(K.t()), dim=-1).mm(V), we make use of a feature map function Φ(.) and perform the following computation V' = normalize(Φ(Q).mm(Φ(K).t())).mm(V). The above can be computed in O(N D^2) complexity where D is the dimensionality of Q, K and V and N is the sequence length. Depending on the feature map, however, the complexity of the attention might be limited. Arguments --------- feature_map: callable, a callable that applies the feature map to the last dimension of a tensor (default: elu(x)+1) eps: float, a small number to ensure the numerical stability of the denominator (default: 1e-6) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, query_dimensions, feature_map=None, eps=1e-6, event_dispatcher=""): super(LinearAttention, self).__init__() elu_feature_map = ActivationFunctionFeatureMap.factory( lambda x: torch.nn.functional.elu(x) + 1 ) self.feature_map = ( feature_map(query_dimensions) if feature_map else elu_feature_map(query_dimensions) ) self.eps = eps # self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, queries, keys, values, attn_mask, query_lengths, key_lengths): # Apply the feature map to the queries and keys self.feature_map.new_feature_map(queries.device) Q = self.feature_map.forward_queries(queries) K = self.feature_map.forward_keys(keys) # Apply the key padding mask and make sure that the attn_mask is # all_ones if not attn_mask.all_ones: raise RuntimeError(("LinearAttention does not support arbitrary " "attention masks")) K = K * key_lengths.float_matrix[:, :, None, None] # Compute the KV matrix, namely the dot product of keys and values so # that we never explicitly compute the attention matrix and thus # decrease the complexity KV = torch.einsum("nshd,nshm->nhmd", K, values) # Compute the normalizer Z = 1/(torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1))+self.eps) # Finally compute and return the new values V = torch.einsum("nlhd,nhmd,nlh->nlhm", Q, KV, Z) return V.contiguous() ================================================ FILE: basicsr/archs/RFDN_arch.py ================================================ from collections import OrderedDict from basicsr.utils.registry import ARCH_REGISTRY import torch.nn as nn import torch import torch.nn.functional as F def conv_layer(in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1): padding = int((kernel_size - 1) / 2) * dilation return nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, bias=True, dilation=dilation, groups=groups) def norm(norm_type, nc): norm_type = norm_type.lower() if norm_type == 'batch': layer = nn.BatchNorm2d(nc, affine=True) elif norm_type == 'instance': layer = nn.InstanceNorm2d(nc, affine=False) else: raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type)) return layer def pad(pad_type, padding): pad_type = pad_type.lower() if padding == 0: return None if pad_type == 'reflect': layer = nn.ReflectionPad2d(padding) elif pad_type == 'replicate': layer = nn.ReplicationPad2d(padding) else: raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type)) return layer def get_valid_padding(kernel_size, dilation): kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) padding = (kernel_size - 1) // 2 return padding def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True, pad_type='zero', norm_type=None, act_type='relu'): padding = get_valid_padding(kernel_size, dilation) p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None padding = padding if pad_type == 'zero' else 0 c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, groups=groups) a = activation(act_type) if act_type else None n = norm(norm_type, out_nc) if norm_type else None return sequential(p, c, n, a) def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1): act_type = act_type.lower() if act_type == 'relu': layer = nn.ReLU(inplace) elif act_type == 'lrelu': layer = nn.LeakyReLU(neg_slope, inplace) elif act_type == 'prelu': layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) else: raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type)) return layer class ShortcutBlock(nn.Module): def __init__(self, submodule): super(ShortcutBlock, self).__init__() self.sub = submodule def forward(self, x): output = x + self.sub(x) return output def mean_channels(F): assert (F.dim() == 4) spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True) return spatial_sum / (F.size(2) * F.size(3)) def stdv_channels(F): assert (F.dim() == 4) F_mean = mean_channels(F) F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True) / (F.size(2) * F.size(3)) return F_variance.pow(0.5) def sequential(*args): if len(args) == 1: if isinstance(args[0], OrderedDict): raise NotImplementedError('sequential does not support OrderedDict input.') return args[0] modules = [] for module in args: if isinstance(module, nn.Sequential): for submodule in module.children(): modules.append(submodule) elif isinstance(module, nn.Module): modules.append(module) return nn.Sequential(*modules) class ESA(nn.Module): def __init__(self, n_feats, conv): super(ESA, self).__init__() f = n_feats // 4 self.conv1 = conv(n_feats, f, kernel_size=1) self.conv_f = conv(f, f, kernel_size=1) self.conv_max = conv(f, f, kernel_size=3, padding=1) self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) self.conv3 = conv(f, f, kernel_size=3, padding=1) self.conv3_ = conv(f, f, kernel_size=3, padding=1) self.conv4 = conv(f, n_feats, kernel_size=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x): c1_ = (self.conv1(x)) c1 = self.conv2(c1_) v_max = F.max_pool2d(c1, kernel_size=7, stride=3) v_range = self.relu(self.conv_max(v_max)) c3 = self.relu(self.conv3(v_range)) c3 = self.conv3_(c3) c3 = F.interpolate(c3, (x.size(2), x.size(3)), mode='bilinear', align_corners=False) cf = self.conv_f(c1_) c4 = self.conv4(c3 + cf) m = self.sigmoid(c4) return x * m class RFDB(nn.Module): def __init__(self, in_channels, distillation_rate=0.25): super(RFDB, self).__init__() self.dc = self.distilled_channels = in_channels // 2 self.rc = self.remaining_channels = in_channels self.c1_d = conv_layer(in_channels, self.dc, 1) self.c1_r = conv_layer(in_channels, self.rc, 3) self.c2_d = conv_layer(self.remaining_channels, self.dc, 1) self.c2_r = conv_layer(self.remaining_channels, self.rc, 3) self.c3_d = conv_layer(self.remaining_channels, self.dc, 1) self.c3_r = conv_layer(self.remaining_channels, self.rc, 3) self.c4 = conv_layer(self.remaining_channels, self.dc, 3) self.act = activation('lrelu', neg_slope=0.05) self.c5 = conv_layer(self.dc * 4, in_channels, 1) self.esa = ESA(in_channels, nn.Conv2d) def forward(self, input): distilled_c1 = self.act(self.c1_d(input)) r_c1 = (self.c1_r(input)) r_c1 = self.act(r_c1 + input) distilled_c2 = self.act(self.c2_d(r_c1)) r_c2 = (self.c2_r(r_c1)) r_c2 = self.act(r_c2 + r_c1) distilled_c3 = self.act(self.c3_d(r_c2)) r_c3 = (self.c3_r(r_c2)) r_c3 = self.act(r_c3 + r_c2) r_c4 = self.act(self.c4(r_c3)) out = torch.cat([distilled_c1, distilled_c2, distilled_c3, r_c4], dim=1) out_fused = self.esa(self.c5(out)) return out_fused def pixelshuffle_block(in_channels, out_channels, upscale_factor=2, kernel_size=3, stride=1): conv = conv_layer(in_channels, out_channels * (upscale_factor ** 2), kernel_size, stride) pixel_shuffle = nn.PixelShuffle(upscale_factor) return sequential(conv, pixel_shuffle) def make_model(args, parent=False): model = RFDN() return model @ARCH_REGISTRY.register() class RFDN(nn.Module): def __init__(self, in_nc=3, nf=50, num_modules=4, out_nc=3, upscale=4): super(RFDN, self).__init__() self.fea_conv = conv_layer(in_nc, nf, kernel_size=3) self.B1 = RFDB(in_channels=nf) self.B2 = RFDB(in_channels=nf) self.B3 = RFDB(in_channels=nf) self.B4 = RFDB(in_channels=nf) self.c = conv_block(nf * num_modules, nf, kernel_size=1, act_type='lrelu') self.LR_conv = conv_layer(nf, nf, kernel_size=3) upsample_block = pixelshuffle_block self.upsampler = upsample_block(nf, out_nc, upscale_factor=4) self.scale_idx = 0 def forward(self, input): input = input * 255. out_fea = self.fea_conv(input) out_B1 = self.B1(out_fea) out_B2 = self.B2(out_B1) out_B3 = self.B3(out_B2) out_B4 = self.B4(out_B3) out_B = self.c(torch.cat([out_B1, out_B2, out_B3, out_B4], dim=1)) out_lr = self.LR_conv(out_B) + out_fea output = self.upsampler(out_lr) return output/255. def set_scale(self, scale_idx): self.scale_idx = scale_idx ================================================ FILE: basicsr/archs/Upsamplers.py ================================================ ''' This repository is used to implement all upsamplers(only x4) and tools for Efficient SR @author LI Zehyuan from SIAT LIU yingqi from SIAT ''' from functools import partial import torch import torch.nn as nn import torch.nn.functional as F import math class PA(nn.Module): '''PA is pixel attention''' def __init__(self, nf): super(PA, self).__init__() self.conv = nn.Conv2d(nf, nf, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): y = self.conv(x) y = self.sigmoid(y) out = torch.mul(x, y) return out class PA_UP(nn.Module): def __init__(self, nf, unf, out_nc, scale=4, conv=nn.Conv2d): super(PA_UP, self).__init__() self.upconv1 = conv(nf, unf, 3, 1, 1, bias=True) self.att1 = PA(unf) self.HRconv1 = conv(unf, unf, 3, 1, 1, bias=True) if scale == 4: self.upconv2 = conv(unf, unf, 3, 1, 1, bias=True) self.att2 = PA(unf) self.HRconv2 = conv(unf, unf, 3, 1, 1, bias=True) self.conv_last = conv(unf, out_nc, 3, 1, 1, bias=True) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, fea): fea = self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(self.att1(fea)) fea = self.lrelu(self.HRconv1(fea)) fea = self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(self.att2(fea)) fea = self.lrelu(self.HRconv2(fea)) fea = self.conv_last(fea) return fea class PA_UP_Dropout(nn.Module): def __init__(self, nf, unf, out_nc, scale=4, conv=nn.Conv2d, P=0.7): super(PA_UP_Dropout, self).__init__() self.upconv1 = conv(nf, unf, 3, 1, 1, bias=True) self.att1 = PA(unf) self.HRconv1 = conv(unf, unf, 3, 1, 1, bias=True) if scale == 4: self.upconv2 = conv(unf, unf, 3, 1, 1, bias=True) self.att2 = PA(unf) self.HRconv2 = conv(unf, unf, 3, 1, 1, bias=True) self.dropout = nn.Dropout(p=P) self.conv_last = conv(unf, out_nc, 3, 1, 1, bias=True) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, fea): fea = self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(self.att1(fea)) fea = self.lrelu(self.HRconv1(fea)) fea = self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(self.att2(fea)) fea = self.lrelu(self.HRconv2(fea)) fea = self.dropout(fea) fea = self.conv_last(fea) return fea class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. 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, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): h, w = self.input_resolution flops = h * w * self.num_feat * 3 * 9 return flops class PixelShuffleDirect(nn.Module): def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): super(PixelShuffleDirect, self).__init__() self.upsampleOneStep = UpsampleOneStep(scale, num_feat, num_out_ch, input_resolution=None) def forward(self, x): return self.upsampleOneStep(x) class PixelShuffleBlcok(nn.Module): def __init__(self, in_feat, num_feat, num_out_ch): super(PixelShuffleBlcok, self).__init__() self.conv_before_upsample = nn.Sequential( nn.Conv2d(in_feat, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = nn.Sequential( nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1), nn.PixelShuffle(2), nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1), nn.PixelShuffle(2) ) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) def forward(self, x): x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) return x class NearestConv(nn.Module): def __init__(self, in_ch, num_feat, num_out_ch, conv=nn.Conv2d): super(NearestConv, self).__init__() self.conv_before_upsample = nn.Sequential( conv(in_ch, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_up1 = conv(num_feat, num_feat, 3, 1, 1) self.conv_up2 = conv(num_feat, num_feat, 3, 1, 1) self.conv_hr = conv(num_feat, num_feat, 3, 1, 1) self.conv_last = conv(num_feat, num_out_ch, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): x = self.conv_before_upsample(x) x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.conv_last(self.lrelu(self.conv_hr(x))) return x class NearestConvDropout(nn.Module): def __init__(self, in_ch, num_feat, num_out_ch, P=0.7): super(NearestConvDropout, self).__init__() self.conv_before_upsample = nn.Sequential( nn.Conv2d(in_ch, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.dropout = nn.Dropout(p=P) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.act = nn.GELU() def forward(self, x): x = self.conv_before_upsample(x) x = self.act(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.act(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.dropout(x) x = self.conv_last(self.act(self.conv_hr(x))) return x ================================================ FILE: basicsr/archs/__init__.py ================================================ import importlib from copy import deepcopy from os import path as osp from basicsr.utils import get_root_logger, scandir from basicsr.utils.registry import ARCH_REGISTRY __all__ = ['build_network'] # automatically scan and import arch modules for registry # 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'basicsr.archs.{file_name}') for file_name in arch_filenames] def build_network(opt): opt = deepcopy(opt) network_type = opt.pop('type') net = ARCH_REGISTRY.get(network_type)(**opt) logger = get_root_logger() logger.info(f'Network [{net.__class__.__name__}] is created.') return net ================================================ FILE: basicsr/archs/arch_util.py ================================================ import collections.abc import math import torch import torchvision import warnings from distutils.version import LooseVersion from itertools import repeat 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 basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv from basicsr.utils import get_root_logger @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.') if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'): return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, mask) else: return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups) def _no_grad_trunc_normal_(tensor, mean, std, a, b): # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1. + math.erf(x / math.sqrt(2.))) / 2. if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn( 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' 'The distribution of values may be incorrect.', stacklevel=2) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values low = norm_cdf((a - mean) / std) up = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [low, up], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * low - 1, 2 * up - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): r"""Fills the input Tensor with values drawn from a truncated normal distribution. From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) # From PyTorch def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple ================================================ FILE: basicsr/archs/rfdnfinalB5_arch.py ================================================ ''' This repository is used to implement all upsamplers(only x4) and tools for Efficient SR @author LI Zehyuan from SIAT LIU yingqi from SIAT ''' from functools import partial import torch import torch.nn as nn import torch.nn.functional as F import math import basicsr.archs.Blocks as Blocks import basicsr.archs.Upsamplers as Upsamplers from basicsr.utils.registry import ARCH_REGISTRY class ESA(nn.Module): def __init__(self, num_feat=50, conv=nn.Conv2d, p=0.25): super(ESA, self).__init__() f = num_feat // 4 BSConvS_kwargs = {} if conv.__name__ == 'BSConvS': BSConvS_kwargs = {'p': p} self.conv1 = nn.Linear(num_feat, f) self.conv_f = nn.Linear(f, f) self.maxPooling = nn.MaxPool2d(kernel_size=7, stride=3) self.conv_max = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv2 = nn.Conv2d(f, f, 3, 2, 0) self.conv3 = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv3_ = conv(f, f, kernel_size=3, **BSConvS_kwargs) self.conv4 = nn.Linear(f, num_feat) self.sigmoid = nn.Sigmoid() self.GELU = nn.GELU() def forward(self, input): x = input.permute(0, 2, 3, 1) c1_ = (self.conv1(x)) c1 = self.conv2(c1_.permute(0, 3, 1, 2)) v_max = self.maxPooling(c1) v_range = self.GELU(self.conv_max(v_max)) c3 = self.GELU(self.conv3(v_range)) c3 = self.conv3_(c3) c3 = F.interpolate(c3, (input.size(2), input.size(3)), mode='bilinear', align_corners=False) cf = self.conv_f(c1_) c4 = self.conv4((c3.permute(0, 2, 3, 1) + cf)) m = self.sigmoid(c4.permute(0, 3, 1, 2)) return input * m class RFDB(nn.Module): def __init__(self, in_channels, out_channels, distillation_rate=0.25, conv=nn.Conv2d, p=0.25): super(RFDB, self).__init__() kwargs = {'padding': 1} if conv.__name__ == 'BSConvS': kwargs = {'p': p} self.dc = self.distilled_channels = in_channels // 2 self.rc = self.remaining_channels = in_channels self.c1_d = nn.Linear(in_channels, self.dc) self.c1_r = conv(in_channels, self.rc, kernel_size=3, **kwargs) self.c2_d = nn.Linear(self.remaining_channels, self.dc) self.c2_r = conv(self.remaining_channels, self.rc, kernel_size=3, **kwargs) self.c3_d = nn.Linear(self.remaining_channels, self.dc) self.c3_r = conv(self.remaining_channels, self.rc, kernel_size=3, **kwargs) self.c4 = conv(self.remaining_channels, self.dc, kernel_size=3, **kwargs) self.act = nn.GELU() self.c5 = nn.Linear(self.dc * 4, in_channels) self.esa = ESA(in_channels, conv) self.cw = nn.Parameter(torch.normal(mean=1, std=0.2, size=(1, in_channels))) self.conv_out = nn.Linear(in_channels, out_channels) def forward(self, input): distilled_c1 = self.act(self.c1_d(input.permute(0, 2, 3, 1))) r_c1 = (self.c1_r(input)) r_c1 = self.act(r_c1 + input) distilled_c2 = self.act(self.c2_d(r_c1.permute(0, 2, 3, 1))) r_c2 = (self.c2_r(r_c1)) r_c2 = self.act(r_c2 + r_c1) distilled_c3 = self.act(self.c3_d(r_c2.permute(0, 2, 3, 1))) r_c3 = (self.c3_r(r_c2)) r_c3 = self.act(r_c3 + r_c2) r_c4 = self.act(self.c4(r_c3)) out = torch.cat([distilled_c1, distilled_c2, distilled_c3, r_c4.permute(0, 2, 3, 1)], dim=3) out = self.c5(out).permute(0, 3, 1, 2) out_fused = self.esa(out) out_fused = out_fused.permute(0, 2, 3, 1) * self.cw out_fused = self.conv_out(out_fused) return out_fused.permute(0, 3, 1, 2) + input def make_layer(block, n_layers): layers = [] for _ in range(n_layers): layers.append(block()) return nn.Sequential(*layers) @ARCH_REGISTRY.register() class RFDNFINALB5(nn.Module): def __init__(self, num_in_ch=3, num_feat=50, num_block=4, num_out_ch=3, upscale=4, conv='BSConvU', upsampler='pixelshuffledirect', p=0.25): super(RFDNFINALB5, self).__init__() kwargs = {'padding': 1} if conv == 'BSConvS': kwargs = {'p': p} print(conv) if conv == 'DepthWiseConv': self.conv = Blocks.DepthWiseConv elif conv == 'BSConvU': self.conv = Blocks.BSConvU elif conv == 'BSConvS': self.conv = Blocks.BSConvS else: self.conv = nn.Conv2d self.fea_conv = self.conv(num_in_ch * 4, num_feat, kernel_size=3, **kwargs) # RFDB_block_f = functools.partial(RFDB, in_channels=num_feat, conv=self.conv, p=p) # RFDB_trunk = make_layer(RFDB_block_f, num_block) self.B1 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B2 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B3 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B4 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) self.B5 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) # self.B6 = RFDB(in_channels=num_feat, out_channels=num_feat, conv=self.conv, p=p) # self.B7 = RFDB(in_channels=num_feat, conv=self.conv, p=p) self.c1 = nn.Linear(num_feat * num_block, num_feat) self.GELU = nn.GELU() self.c2 = self.conv(num_feat, num_feat, kernel_size=3, **kwargs) if upsampler == 'pixelshuffledirect': self.upsampler = Upsamplers.PixelShuffleDirect(scale=upscale, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'pixelshuffleblock': self.upsampler = Upsamplers.PixelShuffleBlcok(in_feat=num_feat, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'nearestconv': self.upsampler = Upsamplers.NearestConv(in_ch=num_feat, num_feat=num_feat, num_out_ch=num_out_ch) elif upsampler == 'pa': self.upsampler = Upsamplers.PA_UP(nf=num_feat, unf=24, out_nc=num_out_ch) else: raise NotImplementedError(("Check the Upsampeler. None or not support yet")) def forward(self, input): input = torch.cat([input, input, input, input], dim=1) out_fea = self.fea_conv(input) out_B1 = self.B1(out_fea) out_B2 = self.B2(out_B1) out_B3 = self.B3(out_B2) out_B4 = self.B4(out_B3) out_B5 = self.B5(out_B4) # out_B6 = self.B6(out_B5) # out_B7 = self.B7(out_B6) trunk = torch.cat([out_B1, out_B2, out_B3, out_B4, out_B5], dim=1) out_B = self.c1(trunk.permute(0, 2, 3, 1)) out_B = self.GELU(out_B.permute(0, 3, 1, 2)) # print(out_B.shape) out_lr = self.c2(out_B) + out_fea # output = self.c3(out_lr) output = self.upsampler(out_lr) return output ================================================ FILE: basicsr/archs/vgg_arch.py ================================================ import os import torch from collections import OrderedDict from torch import nn as nn from torchvision.models import vgg as vgg from basicsr.utils.registry import ARCH_REGISTRY VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth' NAMES = { 'vgg11': [ 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' ], 'vgg13': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' ], 'vgg16': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'pool5' ], 'vgg19': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5' ] } def insert_bn(names): """Insert bn layer after each conv. Args: names (list): The list of layer names. Returns: list: The list of layer names with bn layers. """ names_bn = [] for name in names: names_bn.append(name) if 'conv' in name: position = name.replace('conv', '') names_bn.append('bn' + position) return names_bn @ARCH_REGISTRY.register() class VGGFeatureExtractor(nn.Module): """VGG network for feature extraction. In this implementation, we allow users to choose whether use normalization in the input feature and the type of vgg network. Note that the pretrained path must fit the vgg type. Args: layer_name_list (list[str]): Forward function returns the corresponding features according to the layer_name_list. Example: {'relu1_1', 'relu2_1', 'relu3_1'}. vgg_type (str): Set the type of vgg network. Default: 'vgg19'. use_input_norm (bool): If True, normalize the input image. Importantly, the input feature must in the range [0, 1]. Default: True. range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. Default: False. requires_grad (bool): If true, the parameters of VGG network will be optimized. Default: False. remove_pooling (bool): If true, the max pooling operations in VGG net will be removed. Default: False. pooling_stride (int): The stride of max pooling operation. Default: 2. """ def __init__(self, layer_name_list, vgg_type='vgg19', use_input_norm=True, range_norm=False, requires_grad=False, remove_pooling=False, pooling_stride=2): super(VGGFeatureExtractor, self).__init__() self.layer_name_list = layer_name_list self.use_input_norm = use_input_norm self.range_norm = range_norm self.names = NAMES[vgg_type.replace('_bn', '')] if 'bn' in vgg_type: self.names = insert_bn(self.names) # only borrow layers that will be used to avoid unused params max_idx = 0 for v in layer_name_list: idx = self.names.index(v) if idx > max_idx: max_idx = idx if os.path.exists(VGG_PRETRAIN_PATH): vgg_net = getattr(vgg, vgg_type)(pretrained=False) state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage) vgg_net.load_state_dict(state_dict) else: vgg_net = getattr(vgg, vgg_type)(pretrained=True) features = vgg_net.features[:max_idx + 1] modified_net = OrderedDict() for k, v in zip(self.names, features): if 'pool' in k: # if remove_pooling is true, pooling operation will be removed if remove_pooling: continue else: # in some cases, we may want to change the default stride modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride) else: modified_net[k] = v self.vgg_net = nn.Sequential(modified_net) if not requires_grad: self.vgg_net.eval() for param in self.parameters(): param.requires_grad = False else: self.vgg_net.train() for param in self.parameters(): param.requires_grad = True if self.use_input_norm: # the mean is for image with range [0, 1] self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) # the std is for image with range [0, 1] self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ if self.range_norm: x = (x + 1) / 2 if self.use_input_norm: x = (x - self.mean) / self.std output = {} for key, layer in self.vgg_net._modules.items(): x = layer(x) if key in self.layer_name_list: output[key] = x.clone() return output ================================================ FILE: basicsr/data/__init__.py ================================================ import importlib import numpy as np import random import torch import torch.utils.data from copy import deepcopy from functools import partial from os import path as osp from basicsr.data.prefetch_dataloader import PrefetchDataLoader from basicsr.utils import get_root_logger, scandir from basicsr.utils.dist_util import get_dist_info from basicsr.utils.registry import DATASET_REGISTRY __all__ = ['build_dataset', 'build_dataloader'] # automatically scan and import dataset modules for registry # 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'basicsr.data.{file_name}') for file_name in dataset_filenames] def build_dataset(dataset_opt): """Build dataset from options. Args: dataset_opt (dict): Configuration for dataset. It must contain: name (str): Dataset name. type (str): Dataset type. """ dataset_opt = deepcopy(dataset_opt) dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) logger = get_root_logger() logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} ' 'is built.') return dataset def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): """Build 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) dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', 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: 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: 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: basicsr/data/data_util.py ================================================ import cv2 import numpy as np import torch from os import path as osp from torch.nn import functional as F from basicsr.data.transforms import mod_crop from basicsr.utils import img2tensor, scandir def read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False): """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. return_imgname(bool): Whether return image names. Default False. Returns: Tensor: size (t, c, h, w), RGB, [0, 1]. list[str]: Returned image name list. """ 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) if return_imgname: imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths] return imgs, imgnames else: 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, f'The len of keys should be 2 with [input_key, gt_key]. 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, f'The len of keys should be 2 with [input_key, gt_key]. 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.strip().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, f'The len of keys should be 2 with [input_key, gt_key]. 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 gt_path in gt_paths: basename, ext = osp.splitext(osp.basename(gt_path)) input_name = f'{filename_tmpl.format(basename)}{ext}' input_path = osp.join(input_folder, input_name) assert input_name in input_paths, f'{input_name} is not in {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 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: basicsr/data/degradations.py ================================================ import cv2 import math import numpy as np import random import torch from scipy import special from scipy.stats import multivariate_normal from torchvision.transforms.functional_tensor import rgb_to_grayscale # -------------------------------------------------------------------- # # --------------------------- blur kernels --------------------------- # # -------------------------------------------------------------------- # # --------------------------- util functions --------------------------- # def sigma_matrix2(sig_x, sig_y, theta): """Calculate the rotated sigma matrix (two dimensional matrix). Args: sig_x (float): sig_y (float): theta (float): Radian measurement. Returns: ndarray: Rotated sigma matrix. """ d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]]) u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T)) def mesh_grid(kernel_size): """Generate the mesh grid, centering at zero. Args: kernel_size (int): Returns: xy (ndarray): with the shape (kernel_size, kernel_size, 2) xx (ndarray): with the shape (kernel_size, kernel_size) yy (ndarray): with the shape (kernel_size, kernel_size) """ ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.) xx, yy = np.meshgrid(ax, ax) xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size, 1))).reshape(kernel_size, kernel_size, 2) return xy, xx, yy def pdf2(sigma_matrix, grid): """Calculate PDF of the bivariate Gaussian distribution. Args: sigma_matrix (ndarray): with the shape (2, 2) grid (ndarray): generated by :func:`mesh_grid`, with the shape (K, K, 2), K is the kernel size. Returns: kernel (ndarrray): un-normalized kernel. """ inverse_sigma = np.linalg.inv(sigma_matrix) kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2)) return kernel def cdf2(d_matrix, grid): """Calculate the CDF of the standard bivariate Gaussian distribution. Used in skewed Gaussian distribution. Args: d_matrix (ndarrasy): skew matrix. grid (ndarray): generated by :func:`mesh_grid`, with the shape (K, K, 2), K is the kernel size. Returns: cdf (ndarray): skewed cdf. """ rv = multivariate_normal([0, 0], [[1, 0], [0, 1]]) grid = np.dot(grid, d_matrix) cdf = rv.cdf(grid) return cdf def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True): """Generate a bivariate isotropic or anisotropic Gaussian kernel. In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. Args: kernel_size (int): sig_x (float): sig_y (float): theta (float): Radian measurement. grid (ndarray, optional): generated by :func:`mesh_grid`, with the shape (K, K, 2), K is the kernel size. Default: None isotropic (bool): Returns: kernel (ndarray): normalized kernel. """ if grid is None: grid, _, _ = mesh_grid(kernel_size) if isotropic: sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) else: sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) kernel = pdf2(sigma_matrix, grid) kernel = kernel / np.sum(kernel) return kernel def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): """Generate a bivariate generalized Gaussian kernel. Described in `Parameter Estimation For Multivariate Generalized Gaussian Distributions`_ by Pascal et. al (2013). In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. Args: kernel_size (int): sig_x (float): sig_y (float): theta (float): Radian measurement. beta (float): shape parameter, beta = 1 is the normal distribution. grid (ndarray, optional): generated by :func:`mesh_grid`, with the shape (K, K, 2), K is the kernel size. Default: None Returns: kernel (ndarray): normalized kernel. .. _Parameter Estimation For Multivariate Generalized Gaussian Distributions: https://arxiv.org/abs/1302.6498 """ if grid is None: grid, _, _ = mesh_grid(kernel_size) if isotropic: sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) else: sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) inverse_sigma = np.linalg.inv(sigma_matrix) kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta)) kernel = kernel / np.sum(kernel) return kernel def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): """Generate a plateau-like anisotropic kernel. 1 / (1+x^(beta)) Ref: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. Args: kernel_size (int): sig_x (float): sig_y (float): theta (float): Radian measurement. beta (float): shape parameter, beta = 1 is the normal distribution. grid (ndarray, optional): generated by :func:`mesh_grid`, with the shape (K, K, 2), K is the kernel size. Default: None Returns: kernel (ndarray): normalized kernel. """ if grid is None: grid, _, _ = mesh_grid(kernel_size) if isotropic: sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) else: sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) inverse_sigma = np.linalg.inv(sigma_matrix) kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1) kernel = kernel / np.sum(kernel) return kernel def random_bivariate_Gaussian(kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=None, isotropic=True): """Randomly generate bivariate isotropic or anisotropic Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. Args: kernel_size (int): sigma_x_range (tuple): [0.6, 5] sigma_y_range (tuple): [0.6, 5] rotation range (tuple): [-math.pi, math.pi] noise_range(tuple, optional): multiplicative kernel noise, [0.75, 1.25]. Default: None Returns: kernel (ndarray): """ assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) if isotropic is False: assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) rotation = np.random.uniform(rotation_range[0], rotation_range[1]) else: sigma_y = sigma_x rotation = 0 kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic) # add multiplicative noise if noise_range is not None: assert noise_range[0] < noise_range[1], 'Wrong noise range.' noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) kernel = kernel * noise kernel = kernel / np.sum(kernel) return kernel def random_bivariate_generalized_Gaussian(kernel_size, sigma_x_range, sigma_y_range, rotation_range, beta_range, noise_range=None, isotropic=True): """Randomly generate bivariate generalized Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. Args: kernel_size (int): sigma_x_range (tuple): [0.6, 5] sigma_y_range (tuple): [0.6, 5] rotation range (tuple): [-math.pi, math.pi] beta_range (tuple): [0.5, 8] noise_range(tuple, optional): multiplicative kernel noise, [0.75, 1.25]. Default: None Returns: kernel (ndarray): """ assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) if isotropic is False: assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) rotation = np.random.uniform(rotation_range[0], rotation_range[1]) else: sigma_y = sigma_x rotation = 0 # assume beta_range[0] < 1 < beta_range[1] if np.random.uniform() < 0.5: beta = np.random.uniform(beta_range[0], 1) else: beta = np.random.uniform(1, beta_range[1]) kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) # add multiplicative noise if noise_range is not None: assert noise_range[0] < noise_range[1], 'Wrong noise range.' noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) kernel = kernel * noise kernel = kernel / np.sum(kernel) return kernel def random_bivariate_plateau(kernel_size, sigma_x_range, sigma_y_range, rotation_range, beta_range, noise_range=None, isotropic=True): """Randomly generate bivariate plateau kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. Args: kernel_size (int): sigma_x_range (tuple): [0.6, 5] sigma_y_range (tuple): [0.6, 5] rotation range (tuple): [-math.pi/2, math.pi/2] beta_range (tuple): [1, 4] noise_range(tuple, optional): multiplicative kernel noise, [0.75, 1.25]. Default: None Returns: kernel (ndarray): """ assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) if isotropic is False: assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) rotation = np.random.uniform(rotation_range[0], rotation_range[1]) else: sigma_y = sigma_x rotation = 0 # TODO: this may be not proper if np.random.uniform() < 0.5: beta = np.random.uniform(beta_range[0], 1) else: beta = np.random.uniform(1, beta_range[1]) kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) # add multiplicative noise if noise_range is not None: assert noise_range[0] < noise_range[1], 'Wrong noise range.' noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) kernel = kernel * noise kernel = kernel / np.sum(kernel) return kernel def random_mixed_kernels(kernel_list, kernel_prob, kernel_size=21, sigma_x_range=(0.6, 5), sigma_y_range=(0.6, 5), rotation_range=(-math.pi, math.pi), betag_range=(0.5, 8), betap_range=(0.5, 8), noise_range=None): """Randomly generate mixed kernels. Args: kernel_list (tuple): a list name of kernel types, support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso', 'plateau_aniso'] kernel_prob (tuple): corresponding kernel probability for each kernel type kernel_size (int): sigma_x_range (tuple): [0.6, 5] sigma_y_range (tuple): [0.6, 5] rotation range (tuple): [-math.pi, math.pi] beta_range (tuple): [0.5, 8] noise_range(tuple, optional): multiplicative kernel noise, [0.75, 1.25]. Default: None Returns: kernel (ndarray): """ kernel_type = random.choices(kernel_list, kernel_prob)[0] if kernel_type == 'iso': kernel = random_bivariate_Gaussian( kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True) elif kernel_type == 'aniso': kernel = random_bivariate_Gaussian( kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False) elif kernel_type == 'generalized_iso': kernel = random_bivariate_generalized_Gaussian( kernel_size, sigma_x_range, sigma_y_range, rotation_range, betag_range, noise_range=noise_range, isotropic=True) elif kernel_type == 'generalized_aniso': kernel = random_bivariate_generalized_Gaussian( kernel_size, sigma_x_range, sigma_y_range, rotation_range, betag_range, noise_range=noise_range, isotropic=False) elif kernel_type == 'plateau_iso': kernel = random_bivariate_plateau( kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True) elif kernel_type == 'plateau_aniso': kernel = random_bivariate_plateau( kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False) return kernel np.seterr(divide='ignore', invalid='ignore') def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): """2D sinc filter, ref: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter Args: cutoff (float): cutoff frequency in radians (pi is max) kernel_size (int): horizontal and vertical size, must be odd. pad_to (int): pad kernel size to desired size, must be odd or zero. """ assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' kernel = np.fromfunction( lambda x, y: cutoff * special.j1(cutoff * np.sqrt( (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) kernel = kernel / np.sum(kernel) if pad_to > kernel_size: pad_size = (pad_to - kernel_size) // 2 kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) return kernel # ------------------------------------------------------------- # # --------------------------- noise --------------------------- # # ------------------------------------------------------------- # # ----------------------- Gaussian Noise ----------------------- # def generate_gaussian_noise(img, sigma=10, gray_noise=False): """Generate Gaussian noise. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. sigma (float): Noise scale (measured in range 255). Default: 10. Returns: (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], float32. """ if gray_noise: noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255. noise = np.expand_dims(noise, axis=2).repeat(3, axis=2) else: noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255. return noise def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False): """Add Gaussian noise. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. sigma (float): Noise scale (measured in range 255). Default: 10. Returns: (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], float32. """ noise = generate_gaussian_noise(img, sigma, gray_noise) out = img + noise if clip and rounds: out = np.clip((out * 255.0).round(), 0, 255) / 255. elif clip: out = np.clip(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0): """Add Gaussian noise (PyTorch version). Args: img (Tensor): Shape (b, c, h, w), range[0, 1], float32. scale (float | Tensor): Noise scale. Default: 1.0. Returns: (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], float32. """ b, _, h, w = img.size() if not isinstance(sigma, (float, int)): sigma = sigma.view(img.size(0), 1, 1, 1) if isinstance(gray_noise, (float, int)): cal_gray_noise = gray_noise > 0 else: gray_noise = gray_noise.view(b, 1, 1, 1) cal_gray_noise = torch.sum(gray_noise) > 0 if cal_gray_noise: noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255. noise_gray = noise_gray.view(b, 1, h, w) # always calculate color noise noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255. if cal_gray_noise: noise = noise * (1 - gray_noise) + noise_gray * gray_noise return noise def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False): """Add Gaussian noise (PyTorch version). Args: img (Tensor): Shape (b, c, h, w), range[0, 1], float32. scale (float | Tensor): Noise scale. Default: 1.0. Returns: (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], float32. """ noise = generate_gaussian_noise_pt(img, sigma, gray_noise) out = img + noise if clip and rounds: out = torch.clamp((out * 255.0).round(), 0, 255) / 255. elif clip: out = torch.clamp(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out # ----------------------- Random Gaussian Noise ----------------------- # def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0): sigma = np.random.uniform(sigma_range[0], sigma_range[1]) if np.random.uniform() < gray_prob: gray_noise = True else: gray_noise = False return generate_gaussian_noise(img, sigma, gray_noise) def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): noise = random_generate_gaussian_noise(img, sigma_range, gray_prob) out = img + noise if clip and rounds: out = np.clip((out * 255.0).round(), 0, 255) / 255. elif clip: out = np.clip(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0): sigma = torch.rand( img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0] gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) gray_noise = (gray_noise < gray_prob).float() return generate_gaussian_noise_pt(img, sigma, gray_noise) def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob) out = img + noise if clip and rounds: out = torch.clamp((out * 255.0).round(), 0, 255) / 255. elif clip: out = torch.clamp(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out # ----------------------- Poisson (Shot) Noise ----------------------- # def generate_poisson_noise(img, scale=1.0, gray_noise=False): """Generate poisson noise. Ref: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. scale (float): Noise scale. Default: 1.0. gray_noise (bool): Whether generate gray noise. Default: False. Returns: (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], float32. """ if gray_noise: img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # round and clip image for counting vals correctly img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = len(np.unique(img)) vals = 2**np.ceil(np.log2(vals)) out = np.float32(np.random.poisson(img * vals) / float(vals)) noise = out - img if gray_noise: noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2) return noise * scale def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False): """Add poisson noise. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. scale (float): Noise scale. Default: 1.0. gray_noise (bool): Whether generate gray noise. Default: False. Returns: (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], float32. """ noise = generate_poisson_noise(img, scale, gray_noise) out = img + noise if clip and rounds: out = np.clip((out * 255.0).round(), 0, 255) / 255. elif clip: out = np.clip(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0): """Generate a batch of poisson noise (PyTorch version) Args: img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. scale (float | Tensor): Noise scale. Number or Tensor with shape (b). Default: 1.0. gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). 0 for False, 1 for True. Default: 0. Returns: (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], float32. """ b, _, h, w = img.size() if isinstance(gray_noise, (float, int)): cal_gray_noise = gray_noise > 0 else: gray_noise = gray_noise.view(b, 1, 1, 1) cal_gray_noise = torch.sum(gray_noise) > 0 if cal_gray_noise: img_gray = rgb_to_grayscale(img, num_output_channels=1) # round and clip image for counting vals correctly img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255. # use for-loop to get the unique values for each sample vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)] vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1) out = torch.poisson(img_gray * vals) / vals noise_gray = out - img_gray noise_gray = noise_gray.expand(b, 3, h, w) # always calculate color noise # round and clip image for counting vals correctly img = torch.clamp((img * 255.0).round(), 0, 255) / 255. # use for-loop to get the unique values for each sample vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)] vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] vals = img.new_tensor(vals_list).view(b, 1, 1, 1) out = torch.poisson(img * vals) / vals noise = out - img if cal_gray_noise: noise = noise * (1 - gray_noise) + noise_gray * gray_noise if not isinstance(scale, (float, int)): scale = scale.view(b, 1, 1, 1) return noise * scale def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0): """Add poisson noise to a batch of images (PyTorch version). Args: img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. scale (float | Tensor): Noise scale. Number or Tensor with shape (b). Default: 1.0. gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). 0 for False, 1 for True. Default: 0. Returns: (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], float32. """ noise = generate_poisson_noise_pt(img, scale, gray_noise) out = img + noise if clip and rounds: out = torch.clamp((out * 255.0).round(), 0, 255) / 255. elif clip: out = torch.clamp(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out # ----------------------- Random Poisson (Shot) Noise ----------------------- # def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0): scale = np.random.uniform(scale_range[0], scale_range[1]) if np.random.uniform() < gray_prob: gray_noise = True else: gray_noise = False return generate_poisson_noise(img, scale, gray_noise) def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): noise = random_generate_poisson_noise(img, scale_range, gray_prob) out = img + noise if clip and rounds: out = np.clip((out * 255.0).round(), 0, 255) / 255. elif clip: out = np.clip(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0): scale = torch.rand( img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0] gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) gray_noise = (gray_noise < gray_prob).float() return generate_poisson_noise_pt(img, scale, gray_noise) def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob) out = img + noise if clip and rounds: out = torch.clamp((out * 255.0).round(), 0, 255) / 255. elif clip: out = torch.clamp(out, 0, 1) elif rounds: out = (out * 255.0).round() / 255. return out # ------------------------------------------------------------------------ # # --------------------------- JPEG compression --------------------------- # # ------------------------------------------------------------------------ # def add_jpg_compression(img, quality=90): """Add JPG compression artifacts. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. quality (float): JPG compression quality. 0 for lowest quality, 100 for best quality. Default: 90. Returns: (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], float32. """ img = np.clip(img, 0, 1) encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality] _, encimg = cv2.imencode('.jpg', img * 255., encode_param) img = np.float32(cv2.imdecode(encimg, 1)) / 255. return img def random_add_jpg_compression(img, quality_range=(90, 100)): """Randomly add JPG compression artifacts. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. quality_range (tuple[float] | list[float]): JPG compression quality range. 0 for lowest quality, 100 for best quality. Default: (90, 100). Returns: (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], float32. """ quality = np.random.uniform(quality_range[0], quality_range[1]) return add_jpg_compression(img, quality) ================================================ FILE: basicsr/data/ffhq_dataset.py ================================================ import random import time from os import path as osp from torch.utils import data as data from torchvision.transforms.functional import normalize from basicsr.data.transforms import augment from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() 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] # avoid errors caused by high latency in reading files retry = 3 while retry > 0: try: img_bytes = self.file_client.get(gt_path) except Exception as e: logger = get_root_logger() logger.warning(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) # 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: 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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0799_s021.png (480,480,3) 0799_s022.png (480,480,3) 0799_s023.png (480,480,3) 0799_s024.png (480,480,3) 0799_s025.png (480,480,3) 0799_s026.png (480,480,3) 0799_s027.png (480,480,3) 0799_s028.png (480,480,3) 0799_s029.png (480,480,3) 0799_s030.png (480,480,3) 0799_s031.png (480,480,3) 0799_s032.png (480,480,3) 0799_s033.png (480,480,3) 0799_s034.png (480,480,3) 0799_s035.png (480,480,3) 0799_s036.png (480,480,3) 0799_s037.png (480,480,3) 0799_s038.png (480,480,3) 0799_s039.png (480,480,3) 0799_s040.png (480,480,3) 0800_s001.png (480,480,3) 0800_s002.png (480,480,3) 0800_s003.png (480,480,3) 0800_s004.png (480,480,3) 0800_s005.png (480,480,3) 0800_s006.png (480,480,3) 0800_s007.png (480,480,3) 0800_s008.png (480,480,3) 0800_s009.png (480,480,3) 0800_s010.png (480,480,3) 0800_s011.png (480,480,3) 0800_s012.png (480,480,3) 0800_s013.png (480,480,3) 0800_s014.png (480,480,3) 0800_s015.png (480,480,3) 0800_s016.png (480,480,3) 0800_s017.png (480,480,3) 0800_s018.png (480,480,3) 0800_s019.png (480,480,3) 0800_s020.png (480,480,3) 0800_s021.png (480,480,3) 0800_s022.png (480,480,3) 0800_s023.png (480,480,3) 0800_s024.png (480,480,3) 0800_s025.png (480,480,3) 0800_s026.png (480,480,3) 0800_s027.png (480,480,3) 0800_s028.png (480,480,3) 0800_s029.png (480,480,3) 0800_s030.png (480,480,3) 0800_s031.png (480,480,3) 0800_s032.png (480,480,3) 0800_s033.png (480,480,3) 0800_s034.png (480,480,3) 0800_s035.png (480,480,3) 0800_s036.png (480,480,3) 0800_s037.png (480,480,3) 0800_s038.png (480,480,3) 0800_s039.png (480,480,3) 0800_s040.png (480,480,3) ================================================ FILE: 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: basicsr/data/meta_info/meta_info_REDS_GT.txt ================================================ 000 100 (720,1280,3) 001 100 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(720,1280,3) 192 100 (720,1280,3) 193 100 (720,1280,3) 194 100 (720,1280,3) 195 100 (720,1280,3) 196 100 (720,1280,3) 197 100 (720,1280,3) 198 100 (720,1280,3) 199 100 (720,1280,3) 200 100 (720,1280,3) 201 100 (720,1280,3) 202 100 (720,1280,3) 203 100 (720,1280,3) 204 100 (720,1280,3) 205 100 (720,1280,3) 206 100 (720,1280,3) 207 100 (720,1280,3) 208 100 (720,1280,3) 209 100 (720,1280,3) 210 100 (720,1280,3) 211 100 (720,1280,3) 212 100 (720,1280,3) 213 100 (720,1280,3) 214 100 (720,1280,3) 215 100 (720,1280,3) 216 100 (720,1280,3) 217 100 (720,1280,3) 218 100 (720,1280,3) 219 100 (720,1280,3) 220 100 (720,1280,3) 221 100 (720,1280,3) 222 100 (720,1280,3) 223 100 (720,1280,3) 224 100 (720,1280,3) 225 100 (720,1280,3) 226 100 (720,1280,3) 227 100 (720,1280,3) 228 100 (720,1280,3) 229 100 (720,1280,3) 230 100 (720,1280,3) 231 100 (720,1280,3) 232 100 (720,1280,3) 233 100 (720,1280,3) 234 100 (720,1280,3) 235 100 (720,1280,3) 236 100 (720,1280,3) 237 100 (720,1280,3) 238 100 (720,1280,3) 239 100 (720,1280,3) 240 100 (720,1280,3) 241 100 (720,1280,3) 242 100 (720,1280,3) 243 100 (720,1280,3) 244 100 (720,1280,3) 245 100 (720,1280,3) 246 100 (720,1280,3) 247 100 (720,1280,3) 248 100 (720,1280,3) 249 100 (720,1280,3) 250 100 (720,1280,3) 251 100 (720,1280,3) 252 100 (720,1280,3) 253 100 (720,1280,3) 254 100 (720,1280,3) 255 100 (720,1280,3) 256 100 (720,1280,3) 257 100 (720,1280,3) 258 100 (720,1280,3) 259 100 (720,1280,3) 260 100 (720,1280,3) 261 100 (720,1280,3) 262 100 (720,1280,3) 263 100 (720,1280,3) 264 100 (720,1280,3) 265 100 (720,1280,3) 266 100 (720,1280,3) 267 100 (720,1280,3) 268 100 (720,1280,3) 269 100 (720,1280,3) ================================================ FILE: 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: basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt ================================================ 240 100 (720,1280,3) 241 100 (720,1280,3) 242 100 (720,1280,3) 243 100 (720,1280,3) 244 100 (720,1280,3) 245 100 (720,1280,3) 246 100 (720,1280,3) 247 100 (720,1280,3) 248 100 (720,1280,3) 249 100 (720,1280,3) 250 100 (720,1280,3) 251 100 (720,1280,3) 252 100 (720,1280,3) 253 100 (720,1280,3) 254 100 (720,1280,3) 255 100 (720,1280,3) 256 100 (720,1280,3) 257 100 (720,1280,3) 258 100 (720,1280,3) 259 100 (720,1280,3) 260 100 (720,1280,3) 261 100 (720,1280,3) 262 100 (720,1280,3) 263 100 (720,1280,3) 264 100 (720,1280,3) 265 100 (720,1280,3) 266 100 (720,1280,3) 267 100 (720,1280,3) 268 100 (720,1280,3) 269 100 (720,1280,3) ================================================ FILE: basicsr/data/meta_info/meta_info_Vimeo90K_test_GT.txt ================================================ 00001/0266 7 (256,448,3) 00001/0268 7 (256,448,3) 00001/0275 7 (256,448,3) 00001/0278 7 (256,448,3) 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(256,448,3) 00096/0904 7 (256,448,3) 00096/0905 7 (256,448,3) 00096/0906 7 (256,448,3) 00096/0907 7 (256,448,3) 00096/0908 7 (256,448,3) 00096/0909 7 (256,448,3) 00096/0910 7 (256,448,3) 00096/0911 7 (256,448,3) 00096/0912 7 (256,448,3) 00096/0913 7 (256,448,3) 00096/0914 7 (256,448,3) 00096/0915 7 (256,448,3) 00096/0916 7 (256,448,3) 00096/0917 7 (256,448,3) 00096/0918 7 (256,448,3) 00096/0919 7 (256,448,3) 00096/0920 7 (256,448,3) 00096/0921 7 (256,448,3) 00096/0922 7 (256,448,3) 00096/0923 7 (256,448,3) 00096/0924 7 (256,448,3) 00096/0925 7 (256,448,3) 00096/0926 7 (256,448,3) 00096/0927 7 (256,448,3) 00096/0928 7 (256,448,3) 00096/0929 7 (256,448,3) 00096/0930 7 (256,448,3) 00096/0931 7 (256,448,3) 00096/0932 7 (256,448,3) 00096/0933 7 (256,448,3) 00096/0934 7 (256,448,3) 00096/0935 7 (256,448,3) 00096/0936 7 (256,448,3) ================================================ FILE: basicsr/data/paired_image_dataset.py ================================================ from torch.utils import data as data from torchvision.transforms.functional import normalize from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, imfrombytes, img2tensor from basicsr.utils.matlab_functions import rgb2ycbcr from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() class PairedImageDataset(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. 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(PairedImageDataset, 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) 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']) # color space transform if 'color' in self.opt and self.opt['color'] == 'y': img_gt = rgb2ycbcr(img_gt, y_only=True)[..., None] img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] # crop the unmatched GT images during validation or testing, especially for SR benchmark datasets # TODO: It is better to update the datasets, rather than force to crop if self.opt['phase'] != 'train': img_gt = img_gt[0:img_lq.shape[0] * scale, 0:img_lq.shape[1] * scale, :] # 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: 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: 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 basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.flow_util import dequantize_flow from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() 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, separated 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_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. """ 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(start_frame_idx, end_frame_idx + 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_hflip'], self.opt['use_rot'], img_flows) else: img_results = augment(img_lqs, self.opt['use_hflip'], 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) @DATASET_REGISTRY.register() class REDSRecurrentDataset(data.Dataset): """REDS dataset for training recurrent networks. 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, separated 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_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. """ def __init__(self, opt): super(REDSRecurrentDataset, self).__init__() self.opt = opt self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) self.num_frame = opt['num_frame'] 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'].") if opt['test_mode']: self.keys = [v for v in self.keys if v.split('/')[0] in val_partition] else: 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 hasattr(self, 'flow_root') and 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.get('interval_list', [1]) self.random_reverse = opt.get('random_reverse', False) interval_str = ','.join(str(x) for x in self.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 # determine the neighboring frames interval = random.choice(self.interval_list) # ensure not exceeding the borders start_frame_idx = int(frame_name) if start_frame_idx > 100 - self.num_frame * interval: start_frame_idx = random.randint(0, 100 - self.num_frame * interval) end_frame_idx = start_frame_idx + self.num_frame * interval neighbor_list = list(range(start_frame_idx, end_frame_idx, interval)) # random reverse if self.random_reverse and random.random() < 0.5: neighbor_list.reverse() # get the neighboring LQ and GT frames img_lqs = [] img_gts = [] for neighbor in neighbor_list: if self.is_lmdb: img_lq_path = f'{clip_name}/{neighbor:08d}' img_gt_path = f'{clip_name}/{neighbor:08d}' else: img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png' # get LQ img_bytes = self.file_client.get(img_lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) img_lqs.append(img_lq) # get GT img_bytes = self.file_client.get(img_gt_path, 'gt') img_gt = imfrombytes(img_bytes, float32=True) img_gts.append(img_gt) # randomly crop img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) # augmentation - flip, rotate img_lqs.extend(img_gts) img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) img_results = img2tensor(img_results) img_gts = torch.stack(img_results[len(img_lqs) // 2:], dim=0) img_lqs = torch.stack(img_results[:len(img_lqs) // 2], dim=0) # img_lqs: (t, c, h, w) # img_gts: (t, c, h, w) # key: str return {'lq': img_lqs, 'gt': img_gts, 'key': key} def __len__(self): return len(self.keys) ================================================ FILE: 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 basicsr.data.data_util import paths_from_lmdb from basicsr.utils import FileClient, imfrombytes, img2tensor, scandir from basicsr.utils.matlab_functions import rgb2ycbcr from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() 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.rstrip().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) # color space transform if 'color' in self.opt and self.opt['color'] == 'y': img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] # 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: basicsr/data/transforms.py ================================================ import cv2 import random import torch 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, gt_patch_size, scale, gt_path=None): """Paired random crop. Support Numpy array and Tensor inputs. It crops lists of lq and gt images with corresponding locations. Args: img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): 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. gt_patch_size (int): GT patch size. scale (int): Scale factor. gt_path (str): Path to ground-truth. Default: None. 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] # determine input type: Numpy array or Tensor input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy' if input_type == 'Tensor': h_lq, w_lq = img_lqs[0].size()[-2:] h_gt, w_gt = img_gts[0].size()[-2:] else: h_lq, w_lq = img_lqs[0].shape[0:2] h_gt, w_gt = img_gts[0].shape[0:2] 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 if input_type == 'Tensor': img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs] else: 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) if input_type == 'Tensor': img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts] else: 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 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 ================================================ FILE: basicsr/data/video_test_dataset.py ================================================ import glob import torch from os import path as osp from torch.utils import data as data from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq from basicsr.utils import get_root_logger, scandir from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() 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']) @DATASET_REGISTRY.register() 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']) @DATASET_REGISTRY.register() 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 following 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 } @DATASET_REGISTRY.register() 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: basicsr/data/vimeo90k_dataset.py ================================================ import random import torch from pathlib import Path from torch.utils import data as data from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.registry import DATASET_REGISTRY @DATASET_REGISTRY.register() 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, separated 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_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. """ 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_hflip'], 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) @DATASET_REGISTRY.register() class Vimeo90KRecurrentDataset(Vimeo90KDataset): def __init__(self, opt): super(Vimeo90KRecurrentDataset, self).__init__(opt) self.flip_sequence = opt['flip_sequence'] self.neighbor_list = [1, 2, 3, 4, 5, 6, 7] 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 neighboring LQ and GT frames img_lqs = [] img_gts = [] for neighbor in self.neighbor_list: if self.is_lmdb: img_lq_path = f'{clip}/{seq}/im{neighbor}' img_gt_path = f'{clip}/{seq}/im{neighbor}' else: img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png' # LQ img_bytes = self.file_client.get(img_lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) # GT img_bytes = self.file_client.get(img_gt_path, 'gt') img_gt = imfrombytes(img_bytes, float32=True) img_lqs.append(img_lq) img_gts.append(img_gt) # randomly crop img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) # augmentation - flip, rotate img_lqs.extend(img_gts) img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) img_results = img2tensor(img_results) img_lqs = torch.stack(img_results[:7], dim=0) img_gts = torch.stack(img_results[7:], dim=0) if self.flip_sequence: # flip the sequence: 7 frames to 14 frames img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0) img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0) # img_lqs: (t, c, h, w) # img_gt: (c, h, w) # key: str return {'lq': img_lqs, 'gt': img_gts, 'key': key} def __len__(self): return len(self.keys) ================================================ FILE: basicsr/losses/__init__.py ================================================ from copy import deepcopy from basicsr.utils import get_root_logger from basicsr.utils.registry import LOSS_REGISTRY from .losses import (CharbonnierLoss, GANLoss, L1Loss, MSELoss, PerceptualLoss, WeightedTVLoss, g_path_regularize, gradient_penalty_loss, r1_penalty) __all__ = [ 'L1Loss', 'MSELoss', 'CharbonnierLoss', 'WeightedTVLoss', 'PerceptualLoss', 'GANLoss', 'gradient_penalty_loss', 'r1_penalty', 'g_path_regularize' ] def build_loss(opt): """Build loss from options. Args: opt (dict): Configuration. It must contain: type (str): Model type. """ opt = deepcopy(opt) loss_type = opt.pop('type') loss = LOSS_REGISTRY.get(loss_type)(**opt) logger = get_root_logger() logger.info(f'Loss [{loss.__class__.__name__}] is created.') return loss ================================================ FILE: basicsr/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: basicsr/losses/losses.py ================================================ import math import torch from torch import autograd as autograd from torch import nn as nn from torch.nn import functional as F from basicsr.archs.vgg_arch import VGGFeatureExtractor from basicsr.utils.registry import LOSS_REGISTRY from .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) @LOSS_REGISTRY.register() 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}. 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) @LOSS_REGISTRY.register() 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}. 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) @LOSS_REGISTRY.register() class CharbonnierLoss(nn.Module): """Charbonnier loss (one variant of Robust L1Loss, a differentiable variant of L1Loss). Described in "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution". 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'. eps (float): A value used to control the curvature near zero. Default: 1e-12. """ def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12): super(CharbonnierLoss, self).__init__() if reduction not in ['none', 'mean', 'sum']: raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') self.loss_weight = loss_weight self.reduction = reduction self.eps = eps 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 * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction) @LOSS_REGISTRY.register() class WeightedTVLoss(L1Loss): """Weighted TV loss. Args: loss_weight (float): Loss weight. Default: 1.0. """ def __init__(self, loss_weight=1.0, reduction='mean'): if reduction not in ['mean', 'sum']: raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: mean | sum') super(WeightedTVLoss, self).__init__(loss_weight=loss_weight, reduction=reduction) def forward(self, pred, weight=None): if weight is None: y_weight = None x_weight = None else: y_weight = weight[:, :, :-1, :] x_weight = weight[:, :, :, :-1] y_diff = super().forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=y_weight) x_diff = super().forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=x_weight) loss = x_diff + y_diff return loss @LOSS_REGISTRY.register() class PerceptualLoss(nn.Module): """Perceptual loss with commonly used style loss. Args: layer_weights (dict): The weight for each layer of vgg feature. Here is an example: {'conv5_4': 1.}, which means the conv5_4 feature layer (before relu5_4) will be extracted with weight 1.0 in calculating losses. vgg_type (str): The type of vgg network used as feature extractor. Default: 'vgg19'. use_input_norm (bool): If True, normalize the input image in vgg. Default: True. range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. Default: False. perceptual_weight (float): If `perceptual_weight > 0`, the perceptual loss will be calculated and the loss will multiplied by the weight. Default: 1.0. style_weight (float): If `style_weight > 0`, the style loss will be calculated and the loss will multiplied by the weight. Default: 0. criterion (str): Criterion used for perceptual loss. Default: 'l1'. """ def __init__(self, layer_weights, vgg_type='vgg19', use_input_norm=True, range_norm=False, perceptual_weight=1.0, style_weight=0., criterion='l1'): super(PerceptualLoss, self).__init__() self.perceptual_weight = perceptual_weight self.style_weight = style_weight self.layer_weights = layer_weights self.vgg = VGGFeatureExtractor( layer_name_list=list(layer_weights.keys()), vgg_type=vgg_type, use_input_norm=use_input_norm, range_norm=range_norm) self.criterion_type = criterion if self.criterion_type == 'l1': self.criterion = torch.nn.L1Loss() elif self.criterion_type == 'l2': self.criterion = torch.nn.L2loss() elif self.criterion_type == 'fro': self.criterion = None else: raise NotImplementedError(f'{criterion} criterion has not been supported.') def forward(self, x, gt): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). gt (Tensor): Ground-truth tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ # extract vgg features x_features = self.vgg(x) gt_features = self.vgg(gt.detach()) # calculate perceptual loss if self.perceptual_weight > 0: percep_loss = 0 for k in x_features.keys(): if self.criterion_type == 'fro': percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] else: percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] percep_loss *= self.perceptual_weight else: percep_loss = None # calculate style loss if self.style_weight > 0: style_loss = 0 for k in x_features.keys(): if self.criterion_type == 'fro': style_loss += torch.norm( self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] else: style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( gt_features[k])) * self.layer_weights[k] style_loss *= self.style_weight else: style_loss = None return percep_loss, style_loss def _gram_mat(self, x): """Calculate Gram matrix. Args: x (torch.Tensor): Tensor with shape of (n, c, h, w). Returns: torch.Tensor: Gram matrix. """ n, c, h, w = x.size() features = x.view(n, c, w * h) features_t = features.transpose(1, 2) gram = features.bmm(features_t) / (c * h * w) return gram @LOSS_REGISTRY.register() class GANLoss(nn.Module): """Define GAN loss. Args: gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. real_label_val (float): The value for real label. Default: 1.0. fake_label_val (float): The value for fake label. Default: 0.0. loss_weight (float): Loss weight. Default: 1.0. Note that loss_weight is only for generators; and it is always 1.0 for discriminators. """ def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): super(GANLoss, self).__init__() self.gan_type = gan_type self.loss_weight = loss_weight self.real_label_val = real_label_val self.fake_label_val = fake_label_val if self.gan_type == 'vanilla': self.loss = nn.BCEWithLogitsLoss() elif self.gan_type == 'lsgan': self.loss = nn.MSELoss() elif self.gan_type == 'wgan': self.loss = self._wgan_loss elif self.gan_type == 'wgan_softplus': self.loss = self._wgan_softplus_loss elif self.gan_type == 'hinge': self.loss = nn.ReLU() else: raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.') def _wgan_loss(self, input, target): """wgan loss. Args: input (Tensor): Input tensor. target (bool): Target label. Returns: Tensor: wgan loss. """ return -input.mean() if target else input.mean() def _wgan_softplus_loss(self, input, target): """wgan loss with soft plus. softplus is a smooth approximation to the ReLU function. In StyleGAN2, it is called: Logistic loss for discriminator; Non-saturating loss for generator. Args: input (Tensor): Input tensor. target (bool): Target label. Returns: Tensor: wgan loss. """ return F.softplus(-input).mean() if target else F.softplus(input).mean() def get_target_label(self, input, target_is_real): """Get target label. Args: input (Tensor): Input tensor. target_is_real (bool): Whether the target is real or fake. Returns: (bool | Tensor): Target tensor. Return bool for wgan, otherwise, return Tensor. """ if self.gan_type in ['wgan', 'wgan_softplus']: return target_is_real target_val = (self.real_label_val if target_is_real else self.fake_label_val) return input.new_ones(input.size()) * target_val def forward(self, input, target_is_real, is_disc=False): """ Args: input (Tensor): The input for the loss module, i.e., the network prediction. target_is_real (bool): Whether the targe is real or fake. is_disc (bool): Whether the loss for discriminators or not. Default: False. Returns: Tensor: GAN loss value. """ target_label = self.get_target_label(input, target_is_real) if self.gan_type == 'hinge': if is_disc: # for discriminators in hinge-gan input = -input if target_is_real else input loss = self.loss(1 + input).mean() else: # for generators in hinge-gan loss = -input.mean() else: # other gan types loss = self.loss(input, target_label) # loss_weight is always 1.0 for discriminators return loss if is_disc else loss * self.loss_weight @LOSS_REGISTRY.register() class MultiScaleGANLoss(GANLoss): """ MultiScaleGANLoss accepts a list of predictions """ def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): super(MultiScaleGANLoss, self).__init__(gan_type, real_label_val, fake_label_val, loss_weight) def forward(self, input, target_is_real, is_disc=False): """ The input is a list of tensors, or a list of (a list of tensors) """ if isinstance(input, list): loss = 0 for pred_i in input: if isinstance(pred_i, list): # Only compute GAN loss for the last layer # in case of multiscale feature matching pred_i = pred_i[-1] # Safe operation: 0-dim tensor calling self.mean() does nothing loss_tensor = super().forward(pred_i, target_is_real, is_disc).mean() loss += loss_tensor return loss / len(input) else: return super().forward(input, target_is_real, is_disc) def r1_penalty(real_pred, real_img): """R1 regularization for discriminator. The core idea is to penalize the gradient on real data alone: when the generator distribution produces the true data distribution and the discriminator is equal to 0 on the data manifold, the gradient penalty ensures that the discriminator cannot create a non-zero gradient orthogonal to the data manifold without suffering a loss in the GAN game. Ref: Eq. 9 in Which training methods for GANs do actually converge. """ grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0] grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() return grad_penalty def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3]) grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0] path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) path_penalty = (path_lengths - path_mean).pow(2).mean() return path_penalty, path_lengths.detach().mean(), path_mean.detach() def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None): """Calculate gradient penalty for wgan-gp. Args: discriminator (nn.Module): Network for the discriminator. real_data (Tensor): Real input data. fake_data (Tensor): Fake input data. weight (Tensor): Weight tensor. Default: None. Returns: Tensor: A tensor for gradient penalty. """ batch_size = real_data.size(0) alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1)) # interpolate between real_data and fake_data interpolates = alpha * real_data + (1. - alpha) * fake_data interpolates = autograd.Variable(interpolates, requires_grad=True) disc_interpolates = discriminator(interpolates) gradients = autograd.grad( outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones_like(disc_interpolates), create_graph=True, retain_graph=True, only_inputs=True)[0] if weight is not None: gradients = gradients * weight gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() if weight is not None: gradients_penalty /= torch.mean(weight) return gradients_penalty @LOSS_REGISTRY.register() class GANFeatLoss(nn.Module): """Define feature matching loss for gans Args: criterion (str): Support 'l1', 'l2', 'charbonnier'. loss_weight (float): Loss weight. Default: 1.0. reduction (str): Specifies the reduction to apply to the output. Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. """ def __init__(self, criterion='l1', loss_weight=1.0, reduction='mean'): super(GANFeatLoss, self).__init__() if criterion == 'l1': self.loss_op = L1Loss(loss_weight, reduction) elif criterion == 'l2': self.loss_op = MSELoss(loss_weight, reduction) elif criterion == 'charbonnier': self.loss_op = CharbonnierLoss(loss_weight, reduction) else: raise ValueError(f'Unsupported loss mode: {criterion}. Supported ones are: l1|l2|charbonnier') self.loss_weight = loss_weight def forward(self, pred_fake, pred_real): num_d = len(pred_fake) loss = 0 for i in range(num_d): # for each discriminator # last output is the final prediction, exclude it num_intermediate_outputs = len(pred_fake[i]) - 1 for j in range(num_intermediate_outputs): # for each layer output unweighted_loss = self.loss_op(pred_fake[i][j], pred_real[i][j].detach()) loss += unweighted_loss / num_d return loss * self.loss_weight ================================================ FILE: basicsr/metrics/__init__.py ================================================ from copy import deepcopy from basicsr.utils.registry import METRIC_REGISTRY from .niqe import calculate_niqe from .psnr_ssim import calculate_psnr, calculate_ssim __all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe'] def calculate_metric(data, opt): """Calculate metric from data and options. Args: opt (dict): Configuration. It must contain: type (str): Model type. """ opt = deepcopy(opt) metric_type = opt.pop('type') metric = METRIC_REGISTRY.get(metric_type)(**data, **opt) return metric ================================================ FILE: basicsr/metrics/fid.py ================================================ import numpy as np import torch import torch.nn as nn from scipy import linalg from tqdm import tqdm from basicsr.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: basicsr/metrics/metric_util.py ================================================ import numpy as np from basicsr.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: basicsr/metrics/niqe.py ================================================ import cv2 import math import numpy as np import os from scipy.ndimage.filters import convolve from scipy.special import gamma from basicsr.metrics.metric_util import reorder_image, to_y_channel from basicsr.utils.matlab_functions import imresize from basicsr.utils.registry import METRIC_REGISTRY def estimate_aggd_param(block): """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters. 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 implementation 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)) if scale == 1: img = imresize(img / 255., scale=0.5, antialiasing=True) 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) quality = float(np.squeeze(quality)) return quality @METRIC_REGISTRY.register() def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs): """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 > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296) 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 converted to 'y' (of MATLAB YCbCr) or 'gray'. Default: 'y'. Returns: float: NIQE result. """ ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) # we use the official params estimated from the pristine dataset. niqe_pris_params = np.load(os.path.join(ROOT_DIR, '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] # round is necessary for being consistent with MATLAB's result img = img.round() niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window) return niqe_result ================================================ FILE: basicsr/metrics/psnr_ssim.py ================================================ import cv2 import numpy as np from basicsr.metrics.metric_util import reorder_image, to_y_channel from basicsr.utils.registry import METRIC_REGISTRY @METRIC_REGISTRY.register() def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): """Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img (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 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 img.shape == img2.shape, (f'Image shapes are different: {img.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"') img = reorder_image(img, input_order=input_order) img2 = reorder_image(img2, input_order=input_order) img = img.astype(np.float64) img2 = img2.astype(np.float64) if crop_border != 0: img = img[crop_border:-crop_border, crop_border:-crop_border, ...] img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] if test_y_channel: img = to_y_channel(img) img2 = to_y_channel(img2) mse = np.mean((img - img2)**2) if mse == 0: return float('inf') return 20. * np.log10(255. / np.sqrt(mse)) def _ssim(img, img2): """Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img (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 img = img.astype(np.float64) img2 = img2.astype(np.float64) kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img, -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(img**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(img * 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() @METRIC_REGISTRY.register() def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): """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: img (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 img.shape == img2.shape, (f'Image shapes are different: {img.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"') img = reorder_image(img, input_order=input_order) img2 = reorder_image(img2, input_order=input_order) img = img.astype(np.float64) img2 = img2.astype(np.float64) if crop_border != 0: img = img[crop_border:-crop_border, crop_border:-crop_border, ...] img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] if test_y_channel: img = to_y_channel(img) img2 = to_y_channel(img2) ssims = [] for i in range(img.shape[2]): ssims.append(_ssim(img[..., i], img2[..., i])) return np.array(ssims).mean() ================================================ FILE: basicsr/models/__init__.py ================================================ import importlib from copy import deepcopy from os import path as osp from basicsr.utils import get_root_logger, scandir from basicsr.utils.registry import MODEL_REGISTRY __all__ = ['build_model'] # automatically scan and import model modules for registry # 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'basicsr.models.{file_name}') for file_name in model_filenames] def build_model(opt): """Build model from options. Args: opt (dict): Configuration. It must contain: model_type (str): Model type. """ opt = deepcopy(opt) model = MODEL_REGISTRY.get(opt['model_type'])(opt) logger = get_root_logger() logger.info(f'Model [{model.__class__.__name__}] is created.') return model ================================================ FILE: basicsr/models/base_model.py ================================================ import os import time import torch from collections import OrderedDict from copy import deepcopy from torch.nn.parallel import DataParallel, DistributedDataParallel from basicsr.models import lr_scheduler as lr_scheduler from basicsr.utils import get_root_logger from basicsr.utils.dist_util import master_only 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): """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. """ if self.opt['dist']: self.dist_validation(dataloader, current_iter, tb_logger, save_img) else: self.nondist_validation(dataloader, current_iter, tb_logger, save_img) def _initialize_best_metric_results(self, dataset_name): """Initialize the best metric results dict for recording the best metric value and iteration.""" if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: return elif not hasattr(self, 'best_metric_results'): self.best_metric_results = dict() # add a dataset record record = dict() for metric, content in self.opt['val']['metrics'].items(): better = content.get('better', 'higher') init_val = float('-inf') if better == 'higher' else float('inf') record[metric] = dict(better=better, val=init_val, iter=-1) self.best_metric_results[dataset_name] = record def _update_best_metric_result(self, dataset_name, metric, val, current_iter): if self.best_metric_results[dataset_name][metric]['better'] == 'higher': if val >= self.best_metric_results[dataset_name][metric]['val']: self.best_metric_results[dataset_name][metric]['val'] = val self.best_metric_results[dataset_name][metric]['iter'] = current_iter else: if val <= self.best_metric_results[dataset_name][metric]['val']: self.best_metric_results[dataset_name][metric]['val'] = val self.best_metric_results[dataset_name][metric]['iter'] = current_iter 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 get_optimizer(self, optim_type, params, lr, **kwargs): if optim_type == 'Adam': optimizer = torch.optim.Adam(params, lr, **kwargs) elif optim_type == 'SGD': optimizer = torch.optim.SGD(params, lr, **kwargs) else: raise NotImplementedError(f'optimizer {optim_type} is not supperted yet.') return optimizer 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'])) 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__} - {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 = get_root_logger() 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 # avoid occasional writing errors retry = 3 while retry > 0: try: torch.save(save_dict, save_path) except Exception as e: logger = get_root_logger() logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') time.sleep(1) else: break finally: retry -= 1 if retry == 0: logger.warning(f'Still cannot save {save_path}. Just ignore it.') # raise IOError(f'Cannot save {save_path}.') def _print_different_keys_loading(self, crt_net, load_net, strict=True): """Print keys with different name or different size when loading models. 1. Print keys with different 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()) logger = get_root_logger() 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'. """ logger = get_root_logger() net = self.get_bare_model(net) 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] logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') # 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) # avoid occasional writing errors retry = 3 while retry > 0: try: torch.save(state, save_path) except Exception as e: logger = get_root_logger() logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') time.sleep(1) else: break finally: retry -= 1 if retry == 0: logger.warning(f'Still cannot save {save_path}. Just ignore it.') # raise IOError(f'Cannot save {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: basicsr/models/edvr_model.py ================================================ from basicsr.utils import get_root_logger from basicsr.utils.registry import MODEL_REGISTRY from .video_base_model import VideoBaseModel @MODEL_REGISTRY.register() class EDVRModel(VideoBaseModel): """EDVR Model. Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. # noqa: E501 """ def __init__(self, opt): super(EDVRModel, self).__init__(opt) if self.is_train: self.train_tsa_iter = opt['train'].get('tsa_iter') def setup_optimizers(self): train_opt = self.opt['train'] dcn_lr_mul = train_opt.get('dcn_lr_mul', 1) logger = get_root_logger() logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.') if dcn_lr_mul == 1: optim_params = self.net_g.parameters() else: # separate dcn params and normal params for different lr normal_params = [] dcn_params = [] for name, param in self.net_g.named_parameters(): if 'dcn' in name: dcn_params.append(param) else: normal_params.append(param) optim_params = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }, { 'params': dcn_params, 'lr': train_opt['optim_g']['lr'] * dcn_lr_mul }, ] optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) self.optimizers.append(self.optimizer_g) def optimize_parameters(self, current_iter): if self.train_tsa_iter: if current_iter == 1: logger = get_root_logger() logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.') for name, param in self.net_g.named_parameters(): if 'fusion' not in name: param.requires_grad = False elif current_iter == self.train_tsa_iter: logger = get_root_logger() logger.warning('Train all the parameters.') for param in self.net_g.parameters(): param.requires_grad = True super(EDVRModel, self).optimize_parameters(current_iter) ================================================ FILE: basicsr/models/esrgan_model.py ================================================ import torch from collections import OrderedDict from basicsr.utils.registry import MODEL_REGISTRY from .srgan_model import SRGANModel @MODEL_REGISTRY.register() class ESRGANModel(SRGANModel): """ESRGAN model for single image super-resolution.""" def optimize_parameters(self, current_iter): # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() 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 (relativistic gan) real_d_pred = self.net_d(self.gt).detach() fake_g_pred = self.net_d(self.output) l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False) l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False) l_g_gan = (l_g_real + l_g_fake) / 2 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() # gan loss (relativistic gan) # In order to avoid the error in distributed training: # "Error detected in CudnnBatchNormBackward: RuntimeError: one of # the variables needed for gradient computation has been modified by # an inplace operation", # we separate the backwards for real and fake, and also detach the # tensor for calculating mean. # real fake_d_pred = self.net_d(self.output).detach() real_d_pred = self.net_d(self.gt) l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5 l_d_real.backward() # fake fake_d_pred = self.net_d(self.output.detach()) l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 l_d_fake.backward() self.optimizer_d.step() loss_dict['l_d_real'] = l_d_real loss_dict['l_d_fake'] = l_d_fake loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) self.log_dict = self.reduce_loss_dict(loss_dict) if self.ema_decay > 0: self.model_ema(decay=self.ema_decay) ================================================ FILE: basicsr/models/hifacegan_model.py ================================================ import torch from collections import OrderedDict from os import path as osp from tqdm import tqdm from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.metrics import calculate_metric from basicsr.utils import imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY from .sr_model import SRModel @MODEL_REGISTRY.register() class HiFaceGANModel(SRModel): """HiFaceGAN model for generic-purpose face restoration. No prior modeling required, works for any degradations. Currently doesn't support EMA for inference. """ def init_training_settings(self): train_opt = self.opt['train'] self.ema_decay = train_opt.get('ema_decay', 0) if self.ema_decay > 0: raise (NotImplementedError('HiFaceGAN does not support EMA now. Pass')) self.net_g.train() self.net_d = build_network(self.opt['network_d']) self.net_d = self.model_to_device(self.net_d) self.print_network(self.net_d) # define losses # HiFaceGAN does not use pixel loss by default if train_opt.get('pixel_opt'): self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) else: self.cri_pix = None if train_opt.get('perceptual_opt'): self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) else: self.cri_perceptual = None if train_opt.get('feature_matching_opt'): self.cri_feat = build_loss(train_opt['feature_matching_opt']).to(self.device) else: self.cri_feat = None if self.cri_pix is None and self.cri_perceptual is None: raise ValueError('Both pixel and perceptual losses are None.') if train_opt.get('gan_opt'): self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) self.net_d_iters = train_opt.get('net_d_iters', 1) self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] # optimizer g optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **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) def discriminate(self, input_lq, output, ground_truth): """ This is a conditional (on the input) discriminator In Batch Normalization, the fake and real images are recommended to be in the same batch to avoid disparate statistics in fake and real images. So both fake and real images are fed to D all at once. """ h, w = output.shape[-2:] if output.shape[-2:] != input_lq.shape[-2:]: lq = torch.nn.functional.interpolate(input_lq, (h, w)) real = torch.nn.functional.interpolate(ground_truth, (h, w)) fake_concat = torch.cat([lq, output], dim=1) real_concat = torch.cat([lq, real], dim=1) else: fake_concat = torch.cat([input_lq, output], dim=1) real_concat = torch.cat([input_lq, ground_truth], dim=1) fake_and_real = torch.cat([fake_concat, real_concat], dim=0) discriminator_out = self.net_d(fake_and_real) pred_fake, pred_real = self._divide_pred(discriminator_out) return pred_fake, pred_real @staticmethod def _divide_pred(pred): """ Take the prediction of fake and real images from the combined batch. The prediction contains the intermediate outputs of multiscale GAN, so it's usually a list """ if type(pred) == list: fake = [] real = [] for p in pred: fake.append([tensor[:tensor.size(0) // 2] for tensor in p]) real.append([tensor[tensor.size(0) // 2:] for tensor in p]) else: fake = pred[:pred.size(0) // 2] real = pred[pred.size(0) // 2:] return fake, real def optimize_parameters(self, current_iter): # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() 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 # Requires real prediction for feature matching loss pred_fake, pred_real = self.discriminate(self.lq, self.output, self.gt) l_g_gan = self.cri_gan(pred_fake, True, is_disc=False) l_g_total += l_g_gan loss_dict['l_g_gan'] = l_g_gan # feature matching loss if self.cri_feat: l_g_feat = self.cri_feat(pred_fake, pred_real) l_g_total += l_g_feat loss_dict['l_g_feat'] = l_g_feat 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() # TODO: Benchmark test between HiFaceGAN and SRGAN implementation: # SRGAN use the same fake output for discriminator update # while HiFaceGAN regenerate a new output using updated net_g # This should not make too much difference though. Stick to SRGAN now. # ------------------------------------------------------------------- # ---------- Below are original HiFaceGAN code snippet -------------- # ------------------------------------------------------------------- # with torch.no_grad(): # fake_image = self.net_g(self.lq) # fake_image = fake_image.detach() # fake_image.requires_grad_() # pred_fake, pred_real = self.discriminate(self.lq, fake_image, self.gt) # real pred_fake, pred_real = self.discriminate(self.lq, self.output.detach(), self.gt) l_d_real = self.cri_gan(pred_real, True, is_disc=True) loss_dict['l_d_real'] = l_d_real # fake l_d_fake = self.cri_gan(pred_fake, False, is_disc=True) loss_dict['l_d_fake'] = l_d_fake l_d_total = (l_d_real + l_d_fake) / 2 l_d_total.backward() self.optimizer_d.step() self.log_dict = self.reduce_loss_dict(loss_dict) if self.ema_decay > 0: print('HiFaceGAN does not support EMA now. pass') def validation(self, dataloader, current_iter, tb_logger, save_img=False): """ Warning: HiFaceGAN requires train() mode even for validation For more info, see https://github.com/Lotayou/Face-Renovation/issues/31 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. """ if self.opt['network_g']['type'] in ('HiFaceGAN', 'SPADEGenerator'): self.net_g.train() if self.opt['dist']: self.dist_validation(dataloader, current_iter, tb_logger, save_img) else: print('In HiFaceGANModel: The new metrics package is under development.' + 'Using super method now (Only PSNR & SSIM are supported)') super().nondist_validation(dataloader, current_iter, tb_logger, save_img) def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): """ TODO: Validation using updated metric system The metrics are now evaluated after all images have been tested This allows batch processing, and also allows evaluation of distributional metrics, such as: @ Frechet Inception Distance: FID @ Maximum Mean Discrepancy: MMD Warning: Need careful batch management for different inference settings. """ dataset_name = dataloader.dataset.opt['name'] with_metrics = self.opt['val'].get('metrics') is not None if with_metrics: self.metric_results = dict() # {metric: 0 for metric in self.opt['val']['metrics'].keys()} sr_tensors = [] gt_tensors = [] pbar = tqdm(total=len(dataloader), unit='image') for val_data in dataloader: img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] self.feed_data(val_data) self.test() visuals = self.get_current_visuals() # detached cpu tensor, non-squeeze sr_tensors.append(visuals['result']) if 'gt' in visuals: gt_tensors.append(visuals['gt']) 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') else: if self.opt['val']['suffix']: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["val"]["suffix"]}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["name"]}.png') imwrite(tensor2img(visuals['result']), save_img_path) pbar.update(1) pbar.set_description(f'Test {img_name}') pbar.close() if with_metrics: sr_pack = torch.cat(sr_tensors, dim=0) gt_pack = torch.cat(gt_tensors, dim=0) # calculate metrics for name, opt_ in self.opt['val']['metrics'].items(): # The new metric caller automatically returns mean value # FIXME: ERROR: calculate_metric only supports two arguments. Now the codes cannot be successfully run self.metric_results[name] = calculate_metric(dict(sr_pack=sr_pack, gt_pack=gt_pack), opt_) self._log_validation_metric_values(current_iter, dataset_name, tb_logger) def save(self, epoch, current_iter): if hasattr(self, 'net_g_ema'): print('HiFaceGAN does not support EMA now. Fallback to normal mode.') self.save_network(self.net_g, 'net_g', current_iter) self.save_network(self.net_d, 'net_d', current_iter) self.save_training_state(epoch, current_iter) ================================================ FILE: basicsr/models/lr_scheduler.py ================================================ import math from collections import Counter from torch.optim.lr_scheduler import _LRScheduler 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] 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 minimum 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 ] ================================================ FILE: basicsr/models/sr_model.py ================================================ import torch from collections import OrderedDict from os import path as osp from tqdm import tqdm from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.metrics import calculate_metric from basicsr.utils import get_root_logger, imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY from .base_model import BaseModel @MODEL_REGISTRY.register() class SRModel(BaseModel): """Base SR model for single image super-resolution.""" def __init__(self, opt): super(SRModel, self).__init__(opt) # define network self.net_g = build_network(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: param_key = self.opt['path'].get('param_key_g', 'params') self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) 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 = build_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'): self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) else: self.cri_pix = None if train_opt.get('perceptual_opt'): self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) else: self.cri_perceptual = None if self.cri_pix is None and self.cri_perceptual is None: raise ValueError('Both pixel and perceptual losses 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') self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) self.optimizers.append(self.optimizer_g) 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() 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) def test(self): if hasattr(self, 'net_g_ema'): self.net_g_ema.eval() with torch.no_grad(): self.output = self.net_g_ema(self.lq) else: self.net_g.eval() with torch.no_grad(): self.output = self.net_g(self.lq) self.net_g.train() def dist_validation(self, dataloader, current_iter, tb_logger, save_img): if self.opt['rank'] == 0: self.nondist_validation(dataloader, current_iter, tb_logger, save_img) def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): dataset_name = dataloader.dataset.opt['name'] with_metrics = self.opt['val'].get('metrics') is not None use_pbar = self.opt['val'].get('pbar', False) if with_metrics: if not hasattr(self, 'metric_results'): # only execute in the first run self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} # initialize the best metric results for each dataset_name (supporting multiple validation datasets) self._initialize_best_metric_results(dataset_name) # zero self.metric_results if with_metrics: self.metric_results = {metric: 0 for metric in self.metric_results} metric_data = dict() if use_pbar: pbar = tqdm(total=len(dataloader), unit='image') for idx, val_data in enumerate(dataloader): img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] self.feed_data(val_data) self.test() visuals = self.get_current_visuals() sr_img = tensor2img([visuals['result']]) metric_data['img'] = sr_img if 'gt' in visuals: gt_img = tensor2img([visuals['gt']]) metric_data['img2'] = gt_img 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') else: if self.opt['val']['suffix']: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["val"]["suffix"]}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["name"]}.png') imwrite(sr_img, save_img_path) if with_metrics: # calculate metrics for name, opt_ in self.opt['val']['metrics'].items(): self.metric_results[name] += calculate_metric(metric_data, opt_) if use_pbar: pbar.update(1) pbar.set_description(f'Test {img_name}') if use_pbar: pbar.close() if with_metrics: for metric in self.metric_results.keys(): self.metric_results[metric] /= (idx + 1) # update the best metric result self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) self._log_validation_metric_values(current_iter, dataset_name, tb_logger) def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): log_str = f'Validation {dataset_name}\n' for metric, value in self.metric_results.items(): log_str += f'\t # {metric}: {value:.4f}' if hasattr(self, 'best_metric_results'): log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') log_str += '\n' 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/{dataset_name}/{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 hasattr(self, 'net_g_ema'): 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: basicsr/models/srgan_model.py ================================================ import torch from collections import OrderedDict from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.utils import get_root_logger from basicsr.utils.registry import MODEL_REGISTRY from .sr_model import SRModel @MODEL_REGISTRY.register() class SRGANModel(SRModel): """SRGAN model for single image super-resolution.""" def init_training_settings(self): 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 = build_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 network net_d self.net_d = build_network(self.opt['network_d']) self.net_d = self.model_to_device(self.net_d) self.print_network(self.net_d) # load pretrained models load_path = self.opt['path'].get('pretrain_network_d', None) if load_path is not None: param_key = self.opt['path'].get('param_key_d', 'params') self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) self.net_g.train() self.net_d.train() # define losses if train_opt.get('pixel_opt'): self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) else: self.cri_pix = None if train_opt.get('perceptual_opt'): self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) else: self.cri_perceptual = None if train_opt.get('gan_opt'): self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) self.net_d_iters = train_opt.get('net_d_iters', 1) self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] # optimizer g optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **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) def optimize_parameters(self, current_iter): # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() 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()) 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() self.log_dict = self.reduce_loss_dict(loss_dict) if self.ema_decay > 0: self.model_ema(decay=self.ema_decay) def save(self, epoch, current_iter): if hasattr(self, 'net_g_ema'): 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_network(self.net_d, 'net_d', current_iter) self.save_training_state(epoch, current_iter) ================================================ FILE: basicsr/models/stylegan2_model.py ================================================ import cv2 import math import numpy as np import random import torch from collections import OrderedDict from os import path as osp from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.losses.losses import g_path_regularize, r1_penalty from basicsr.utils import imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY from .base_model import BaseModel @MODEL_REGISTRY.register() class StyleGAN2Model(BaseModel): """StyleGAN2 model.""" def __init__(self, opt): super(StyleGAN2Model, self).__init__(opt) # define network net_g self.net_g = build_network(opt['network_g']) self.net_g = self.model_to_device(self.net_g) self.print_network(self.net_g) # load pretrained model load_path = self.opt['path'].get('pretrain_network_g', None) if load_path is not None: param_key = self.opt['path'].get('param_key_g', 'params') self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) # latent dimension: self.num_style_feat self.num_style_feat = opt['network_g']['num_style_feat'] num_val_samples = self.opt['val'].get('num_val_samples', 16) self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device) if self.is_train: self.init_training_settings() def init_training_settings(self): train_opt = self.opt['train'] # define network net_d self.net_d = build_network(self.opt['network_d']) self.net_d = self.model_to_device(self.net_d) self.print_network(self.net_d) # load pretrained model load_path = self.opt['path'].get('pretrain_network_d', None) if load_path is not None: param_key = self.opt['path'].get('param_key_d', 'params') self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) # define network net_g with Exponential Moving Average (EMA) # net_g_ema only used for testing on one GPU and saving, do not need to # wrap with DistributedDataParallel self.net_g_ema = build_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.train() self.net_d.train() self.net_g_ema.eval() # define losses # gan loss (wgan) self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) # regularization weights self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator self.path_reg_weight = train_opt['path_reg_weight'] # for generator self.net_g_reg_every = train_opt['net_g_reg_every'] self.net_d_reg_every = train_opt['net_d_reg_every'] self.mixing_prob = train_opt['mixing_prob'] self.mean_path_length = 0 # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] # optimizer g net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1) if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC': normal_params = [] style_mlp_params = [] modulation_conv_params = [] for name, param in self.net_g.named_parameters(): if 'modulation' in name: normal_params.append(param) elif 'style_mlp' in name: style_mlp_params.append(param) elif 'modulated_conv' in name: modulation_conv_params.append(param) else: normal_params.append(param) optim_params_g = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }, { 'params': style_mlp_params, 'lr': train_opt['optim_g']['lr'] * 0.01 }, { 'params': modulation_conv_params, 'lr': train_opt['optim_g']['lr'] / 3 } ] else: normal_params = [] for name, param in self.net_g.named_parameters(): normal_params.append(param) optim_params_g = [{ # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }] optim_type = train_opt['optim_g'].pop('type') lr = train_opt['optim_g']['lr'] * net_g_reg_ratio betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) self.optimizers.append(self.optimizer_g) # optimizer d net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC': normal_params = [] linear_params = [] for name, param in self.net_d.named_parameters(): if 'final_linear' in name: linear_params.append(param) else: normal_params.append(param) optim_params_d = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_d']['lr'] }, { 'params': linear_params, 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512)) } ] else: normal_params = [] for name, param in self.net_d.named_parameters(): normal_params.append(param) optim_params_d = [{ # add normal params first 'params': normal_params, 'lr': train_opt['optim_d']['lr'] }] optim_type = train_opt['optim_d'].pop('type') lr = train_opt['optim_d']['lr'] * net_d_reg_ratio betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) self.optimizers.append(self.optimizer_d) def feed_data(self, data): self.real_img = data['gt'].to(self.device) def make_noise(self, batch, num_noise): if num_noise == 1: noises = torch.randn(batch, self.num_style_feat, device=self.device) else: noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0) return noises def mixing_noise(self, batch, prob): if random.random() < prob: return self.make_noise(batch, 2) else: return [self.make_noise(batch, 1)] def optimize_parameters(self, current_iter): loss_dict = OrderedDict() # optimize net_d for p in self.net_d.parameters(): p.requires_grad = True self.optimizer_d.zero_grad() batch = self.real_img.size(0) noise = self.mixing_noise(batch, self.mixing_prob) fake_img, _ = self.net_g(noise) fake_pred = self.net_d(fake_img.detach()) real_pred = self.net_d(self.real_img) # wgan loss with softplus (logistic loss) for discriminator l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True) loss_dict['l_d'] = l_d # In wgan, real_score should be positive and fake_score should be # negative loss_dict['real_score'] = real_pred.detach().mean() loss_dict['fake_score'] = fake_pred.detach().mean() l_d.backward() if current_iter % self.net_d_reg_every == 0: self.real_img.requires_grad = True real_pred = self.net_d(self.real_img) l_d_r1 = r1_penalty(real_pred, self.real_img) l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) # TODO: why do we need to add 0 * real_pred, otherwise, a runtime # error will arise: RuntimeError: Expected to have finished # reduction in the prior iteration before starting a new one. # This error indicates that your module has parameters that were # not used in producing loss. loss_dict['l_d_r1'] = l_d_r1.detach().mean() l_d_r1.backward() self.optimizer_d.step() # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() noise = self.mixing_noise(batch, self.mixing_prob) fake_img, _ = self.net_g(noise) fake_pred = self.net_d(fake_img) # wgan loss with softplus (non-saturating loss) for generator l_g = self.cri_gan(fake_pred, True, is_disc=False) loss_dict['l_g'] = l_g l_g.backward() if current_iter % self.net_g_reg_every == 0: path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink']) noise = self.mixing_noise(path_batch_size, self.mixing_prob) fake_img, latents = self.net_g(noise, return_latents=True) l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length) l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0]) # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0] l_g_path.backward() loss_dict['l_g_path'] = l_g_path.detach().mean() loss_dict['path_length'] = path_lengths self.optimizer_g.step() self.log_dict = self.reduce_loss_dict(loss_dict) # EMA self.model_ema(decay=0.5**(32 / (10 * 1000))) def test(self): with torch.no_grad(): self.net_g_ema.eval() self.output, _ = self.net_g_ema([self.fixed_sample]) def dist_validation(self, dataloader, current_iter, tb_logger, save_img): if self.opt['rank'] == 0: self.nondist_validation(dataloader, current_iter, tb_logger, save_img) def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): assert dataloader is None, 'Validation dataloader should be None.' self.test() result = tensor2img(self.output, min_max=(-1, 1)) if self.opt['is_train']: save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png') imwrite(result, save_img_path) # add sample images to tb_logger result = (result / 255.).astype(np.float32) result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if tb_logger is not None: tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC') def save(self, epoch, current_iter): self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) self.save_network(self.net_d, 'net_d', current_iter) self.save_training_state(epoch, current_iter) ================================================ FILE: basicsr/models/swinir_model.py ================================================ import torch from torch.nn import functional as F from basicsr.utils.registry import MODEL_REGISTRY from .sr_model import SRModel @MODEL_REGISTRY.register() class SwinIRModel(SRModel): def test(self): # pad to multiplication of window_size window_size = self.opt['network_g']['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') if hasattr(self, 'net_g_ema'): self.net_g_ema.eval() with torch.no_grad(): self.output = self.net_g_ema(img) else: self.net_g.eval() with torch.no_grad(): self.output = self.net_g(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] ================================================ FILE: basicsr/models/video_base_model.py ================================================ import torch from collections import Counter from os import path as osp from torch import distributed as dist from tqdm import tqdm from basicsr.metrics import calculate_metric from basicsr.utils import get_root_logger, imwrite, tensor2img from basicsr.utils.dist_util import get_dist_info from basicsr.utils.registry import MODEL_REGISTRY from .sr_model import SRModel @MODEL_REGISTRY.register() class VideoBaseModel(SRModel): """Base video SR model.""" def dist_validation(self, dataloader, current_iter, tb_logger, save_img): dataset = dataloader.dataset dataset_name = dataset.opt['name'] with_metrics = self.opt['val']['metrics'] is not None # initialize self.metric_results # It is a dict: { # 'folder1': tensor (num_frame x len(metrics)), # 'folder2': tensor (num_frame x len(metrics)) # } if with_metrics: if not hasattr(self, 'metric_results'): # only execute in the first run self.metric_results = {} num_frame_each_folder = Counter(dataset.data_info['folder']) for folder, num_frame in num_frame_each_folder.items(): self.metric_results[folder] = torch.zeros( num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') # initialize the best metric results self._initialize_best_metric_results(dataset_name) # zero self.metric_results rank, world_size = get_dist_info() if with_metrics: for _, tensor in self.metric_results.items(): tensor.zero_() metric_data = dict() # record all frames (border and center frames) if rank == 0: pbar = tqdm(total=len(dataset), unit='frame') for idx in range(rank, len(dataset), world_size): val_data = dataset[idx] val_data['lq'].unsqueeze_(0) val_data['gt'].unsqueeze_(0) folder = val_data['folder'] frame_idx, max_idx = val_data['idx'].split('/') lq_path = val_data['lq_path'] self.feed_data(val_data) self.test() visuals = self.get_current_visuals() result_img = tensor2img([visuals['result']]) metric_data['img'] = result_img if 'gt' in visuals: gt_img = tensor2img([visuals['gt']]) metric_data['img2'] = gt_img 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']: raise NotImplementedError('saving image is not supported during training.') else: if 'vimeo' in dataset_name.lower(): # vimeo90k dataset split_result = lq_path.split('/') img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}' else: # other datasets, e.g., REDS, Vid4 img_name = osp.splitext(osp.basename(lq_path))[0] if self.opt['val']['suffix']: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, f'{img_name}_{self.opt["val"]["suffix"]}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, f'{img_name}_{self.opt["name"]}.png') imwrite(result_img, save_img_path) if with_metrics: # calculate metrics for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): result = calculate_metric(metric_data, opt_) self.metric_results[folder][int(frame_idx), metric_idx] += result # progress bar if rank == 0: for _ in range(world_size): pbar.update(1) pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}') if rank == 0: pbar.close() if with_metrics: if self.opt['dist']: # collect data among GPUs for _, tensor in self.metric_results.items(): dist.reduce(tensor, 0) dist.barrier() else: pass # assume use one gpu in non-dist testing if rank == 0: self._log_validation_metric_values(current_iter, dataset_name, tb_logger) def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): logger = get_root_logger() logger.warning('nondist_validation is not implemented. Run dist_validation.') self.dist_validation(dataloader, current_iter, tb_logger, save_img) def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): # ----------------- calculate the average values for each folder, and for each metric ----------------- # # average all frames for each sub-folder # metric_results_avg is a dict:{ # 'folder1': tensor (len(metrics)), # 'folder2': tensor (len(metrics)) # } metric_results_avg = { folder: torch.mean(tensor, dim=0).cpu() for (folder, tensor) in self.metric_results.items() } # total_avg_results is a dict: { # 'metric1': float, # 'metric2': float # } total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} for folder, tensor in metric_results_avg.items(): for idx, metric in enumerate(total_avg_results.keys()): total_avg_results[metric] += metric_results_avg[folder][idx].item() # average among folders for metric in total_avg_results.keys(): total_avg_results[metric] /= len(metric_results_avg) # update the best metric result self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter) # ------------------------------------------ log the metric ------------------------------------------ # log_str = f'Validation {dataset_name}\n' for metric_idx, (metric, value) in enumerate(total_avg_results.items()): log_str += f'\t # {metric}: {value:.4f}' for folder, tensor in metric_results_avg.items(): log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}' if hasattr(self, 'best_metric_results'): log_str += (f'\n\t Best: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') log_str += '\n' logger = get_root_logger() logger.info(log_str) if tb_logger: for metric_idx, (metric, value) in enumerate(total_avg_results.items()): tb_logger.add_scalar(f'metrics/{metric}', value, current_iter) for folder, tensor in metric_results_avg.items(): tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter) ================================================ FILE: basicsr/models/video_gan_model.py ================================================ from basicsr.utils.registry import MODEL_REGISTRY from .srgan_model import SRGANModel from .video_base_model import VideoBaseModel @MODEL_REGISTRY.register() class VideoGANModel(SRGANModel, VideoBaseModel): """Video GAN model. Use multiple inheritance. It will first use the functions of SRGANModel: init_training_settings setup_optimizers optimize_parameters save Then find functions in VideoBaseModel. """ ================================================ FILE: basicsr/models/video_recurrent_gan_model.py ================================================ import torch from collections import OrderedDict from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.utils import get_root_logger from basicsr.utils.registry import MODEL_REGISTRY from .video_recurrent_model import VideoRecurrentModel @MODEL_REGISTRY.register() class VideoRecurrentGANModel(VideoRecurrentModel): def init_training_settings(self): 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}') # build network net_g with Exponential Moving Average (EMA) # net_g_ema only used for testing on one GPU and saving. # There is no need to wrap with DistributedDataParallel self.net_g_ema = build_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 network net_d self.net_d = build_network(self.opt['network_d']) self.net_d = self.model_to_device(self.net_d) self.print_network(self.net_d) # load pretrained models load_path = self.opt['path'].get('pretrain_network_d', None) if load_path is not None: param_key = self.opt['path'].get('param_key_d', 'params') self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) self.net_g.train() self.net_d.train() # define losses if train_opt.get('pixel_opt'): self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) else: self.cri_pix = None if train_opt.get('perceptual_opt'): self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) else: self.cri_perceptual = None if train_opt.get('gan_opt'): self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) self.net_d_iters = train_opt.get('net_d_iters', 1) self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] if train_opt['fix_flow']: normal_params = [] flow_params = [] for name, param in self.net_g.named_parameters(): if 'spynet' in name: # The fix_flow now only works for spynet. flow_params.append(param) else: normal_params.append(param) optim_params = [ { # add flow params first 'params': flow_params, 'lr': train_opt['lr_flow'] }, { 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }, ] else: optim_params = self.net_g.parameters() # optimizer g optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, 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) def optimize_parameters(self, current_iter): logger = get_root_logger() # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False if self.fix_flow_iter: if current_iter == 1: logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') for name, param in self.net_g.named_parameters(): if 'spynet' in name or 'edvr' in name: param.requires_grad_(False) elif current_iter == self.fix_flow_iter: logger.warning('Train all the parameters.') self.net_g.requires_grad_(True) self.optimizer_g.zero_grad() self.output = self.net_g(self.lq) _, _, c, h, w = self.output.size() 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.view(-1, c, h, w), self.gt.view(-1, c, h, w)) 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.view(-1, c, h, w)) 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 # reshape to (b*n, c, h, w) real_d_pred = self.net_d(self.gt.view(-1, c, h, w)) 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 # reshape to (b*n, c, h, w) fake_d_pred = self.net_d(self.output.view(-1, c, h, w).detach()) 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() self.log_dict = self.reduce_loss_dict(loss_dict) if self.ema_decay > 0: self.model_ema(decay=self.ema_decay) 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_network(self.net_d, 'net_d', current_iter) self.save_training_state(epoch, current_iter) ================================================ FILE: basicsr/models/video_recurrent_model.py ================================================ import torch from collections import Counter from os import path as osp from torch import distributed as dist from tqdm import tqdm from basicsr.metrics import calculate_metric from basicsr.utils import get_root_logger, imwrite, tensor2img from basicsr.utils.dist_util import get_dist_info from basicsr.utils.registry import MODEL_REGISTRY from .video_base_model import VideoBaseModel @MODEL_REGISTRY.register() class VideoRecurrentModel(VideoBaseModel): def __init__(self, opt): super(VideoRecurrentModel, self).__init__(opt) if self.is_train: self.fix_flow_iter = opt['train'].get('fix_flow') def setup_optimizers(self): train_opt = self.opt['train'] flow_lr_mul = train_opt.get('flow_lr_mul', 1) logger = get_root_logger() logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.') if flow_lr_mul == 1: optim_params = self.net_g.parameters() else: # separate flow params and normal params for different lr normal_params = [] flow_params = [] for name, param in self.net_g.named_parameters(): if 'spynet' in name: flow_params.append(param) else: normal_params.append(param) optim_params = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }, { 'params': flow_params, 'lr': train_opt['optim_g']['lr'] * flow_lr_mul }, ] optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) self.optimizers.append(self.optimizer_g) def optimize_parameters(self, current_iter): if self.fix_flow_iter: logger = get_root_logger() if current_iter == 1: logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') for name, param in self.net_g.named_parameters(): if 'spynet' in name or 'edvr' in name: param.requires_grad_(False) elif current_iter == self.fix_flow_iter: logger.warning('Train all the parameters.') self.net_g.requires_grad_(True) super(VideoRecurrentModel, self).optimize_parameters(current_iter) def dist_validation(self, dataloader, current_iter, tb_logger, save_img): dataset = dataloader.dataset dataset_name = dataset.opt['name'] with_metrics = self.opt['val']['metrics'] is not None # initialize self.metric_results # It is a dict: { # 'folder1': tensor (num_frame x len(metrics)), # 'folder2': tensor (num_frame x len(metrics)) # } if with_metrics: if not hasattr(self, 'metric_results'): # only execute in the first run self.metric_results = {} num_frame_each_folder = Counter(dataset.data_info['folder']) for folder, num_frame in num_frame_each_folder.items(): self.metric_results[folder] = torch.zeros( num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') # initialize the best metric results self._initialize_best_metric_results(dataset_name) # zero self.metric_results rank, world_size = get_dist_info() if with_metrics: for _, tensor in self.metric_results.items(): tensor.zero_() metric_data = dict() num_folders = len(dataset) num_pad = (world_size - (num_folders % world_size)) % world_size if rank == 0: pbar = tqdm(total=len(dataset), unit='folder') # Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded. # (To avoid wait-dead) for i in range(rank, num_folders + num_pad, world_size): idx = min(i, num_folders - 1) val_data = dataset[idx] folder = val_data['folder'] # compute outputs val_data['lq'].unsqueeze_(0) val_data['gt'].unsqueeze_(0) self.feed_data(val_data) val_data['lq'].squeeze_(0) val_data['gt'].squeeze_(0) self.test() visuals = self.get_current_visuals() # tentative for out of GPU memory del self.lq del self.output if 'gt' in visuals: del self.gt torch.cuda.empty_cache() if self.center_frame_only: visuals['result'] = visuals['result'].unsqueeze(1) if 'gt' in visuals: visuals['gt'] = visuals['gt'].unsqueeze(1) # evaluate if i < num_folders: for idx in range(visuals['result'].size(1)): result = visuals['result'][0, idx, :, :, :] result_img = tensor2img([result]) # uint8, bgr metric_data['img'] = result_img if 'gt' in visuals: gt = visuals['gt'][0, idx, :, :, :] gt_img = tensor2img([gt]) # uint8, bgr metric_data['img2'] = gt_img if save_img: if self.opt['is_train']: raise NotImplementedError('saving image is not supported during training.') else: if self.center_frame_only: # vimeo-90k clip_ = val_data['lq_path'].split('/')[-3] seq_ = val_data['lq_path'].split('/')[-2] name_ = f'{clip_}_{seq_}' img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, f"{name_}_{self.opt['name']}.png") else: # others img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, f"{idx:08d}_{self.opt['name']}.png") # image name only for REDS dataset imwrite(result_img, img_path) # calculate metrics if with_metrics: for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): result = calculate_metric(metric_data, opt_) self.metric_results[folder][idx, metric_idx] += result # progress bar if rank == 0: for _ in range(world_size): pbar.update(1) pbar.set_description(f'Folder: {folder}') if rank == 0: pbar.close() if with_metrics: if self.opt['dist']: # collect data among GPUs for _, tensor in self.metric_results.items(): dist.reduce(tensor, 0) dist.barrier() if rank == 0: self._log_validation_metric_values(current_iter, dataset_name, tb_logger) def test(self): n = self.lq.size(1) self.net_g.eval() flip_seq = self.opt['val'].get('flip_seq', False) self.center_frame_only = self.opt['val'].get('center_frame_only', False) if flip_seq: self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1) with torch.no_grad(): self.output = self.net_g(self.lq) if flip_seq: output_1 = self.output[:, :n, :, :, :] output_2 = self.output[:, n:, :, :, :].flip(1) self.output = 0.5 * (output_1 + output_2) if self.center_frame_only: self.output = self.output[:, n // 2, :, :, :] self.net_g.train() ================================================ FILE: basicsr/ops/__init__.py ================================================ ================================================ FILE: basicsr/ops/dcn/__init__.py ================================================ from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv, modulated_deform_conv) __all__ = [ 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv', 'modulated_deform_conv' ] ================================================ FILE: basicsr/ops/dcn/deform_conv.py ================================================ import math import os import torch from torch import nn as nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.nn import functional as F from torch.nn.modules.utils import _pair, _single BASICSR_JIT = os.getenv('BASICSR_JIT') if BASICSR_JIT == 'True': from torch.utils.cpp_extension import load module_path = os.path.dirname(__file__) deform_conv_ext = load( 'deform_conv', sources=[ os.path.join(module_path, 'src', 'deform_conv_ext.cpp'), os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'), os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'), ], ) else: try: from . import deform_conv_ext except ImportError: pass # avoid annoying print output # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' # '1. compile with BASICSR_EXT=True. or\n ' # '2. set BASICSR_JIT=True during running') class DeformConvFunction(Function): @staticmethod def forward(ctx, input, offset, weight, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, im2col_step=64): if input is not None and input.dim() != 4: raise ValueError(f'Expected 4D tensor as input, got {input.dim()}' 'D tensor instead.') ctx.stride = _pair(stride) ctx.padding = _pair(padding) ctx.dilation = _pair(dilation) ctx.groups = groups ctx.deformable_groups = deformable_groups ctx.im2col_step = im2col_step ctx.save_for_backward(input, offset, weight) output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones if not input.is_cuda: raise NotImplementedError else: cur_im2col_step = min(ctx.im2col_step, input.shape[0]) assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' deform_conv_ext.deform_conv_forward(input, weight, offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, ctx.deformable_groups, cur_im2col_step) return output @staticmethod @once_differentiable def backward(ctx, grad_output): input, offset, weight = ctx.saved_tensors grad_input = grad_offset = grad_weight = None if not grad_output.is_cuda: raise NotImplementedError else: cur_im2col_step = min(ctx.im2col_step, input.shape[0]) assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: grad_input = torch.zeros_like(input) grad_offset = torch.zeros_like(offset) deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input, grad_offset, weight, ctx.bufs_[0], weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, ctx.deformable_groups, cur_im2col_step) if ctx.needs_input_grad[2]: grad_weight = torch.zeros_like(weight) deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, cur_im2col_step) return (grad_input, grad_offset, grad_weight, None, None, None, None, None) @staticmethod def _output_size(input, weight, padding, dilation, stride): channels = weight.size(0) output_size = (input.size(0), channels) for d in range(input.dim() - 2): in_size = input.size(d + 2) pad = padding[d] kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 stride_ = stride[d] output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) if not all(map(lambda s: s > 0, output_size)): raise ValueError(f'convolution input is too small (output would be {"x".join(map(str, output_size))})') return output_size class ModulatedDeformConvFunction(Function): @staticmethod def forward(ctx, input, offset, mask, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1): ctx.stride = stride ctx.padding = padding ctx.dilation = dilation ctx.groups = groups ctx.deformable_groups = deformable_groups ctx.with_bias = bias is not None if not ctx.with_bias: bias = input.new_empty(1) # fake tensor if not input.is_cuda: raise NotImplementedError if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: ctx.save_for_backward(input, offset, mask, weight, bias) output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) ctx._bufs = [input.new_empty(0), input.new_empty(0)] deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output, ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, ctx.groups, ctx.deformable_groups, ctx.with_bias) return output @staticmethod @once_differentiable def backward(ctx, grad_output): if not grad_output.is_cuda: raise NotImplementedError input, offset, mask, weight, bias = ctx.saved_tensors grad_input = torch.zeros_like(input) grad_offset = torch.zeros_like(offset) grad_mask = torch.zeros_like(mask) grad_weight = torch.zeros_like(weight) grad_bias = torch.zeros_like(bias) deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], grad_input, grad_weight, grad_bias, grad_offset, grad_mask, grad_output, weight.shape[2], weight.shape[3], ctx.stride, ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, ctx.groups, ctx.deformable_groups, ctx.with_bias) if not ctx.with_bias: grad_bias = None return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) @staticmethod def _infer_shape(ctx, input, weight): n = input.size(0) channels_out = weight.size(0) height, width = input.shape[2:4] kernel_h, kernel_w = weight.shape[2:4] height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 return n, channels_out, height_out, width_out deform_conv = DeformConvFunction.apply modulated_deform_conv = ModulatedDeformConvFunction.apply class DeformConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, bias=False): super(DeformConv, self).__init__() assert not bias assert in_channels % groups == 0, f'in_channels {in_channels} is not divisible by groups {groups}' assert out_channels % groups == 0, f'out_channels {out_channels} is not divisible by groups {groups}' self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = _pair(stride) self.padding = _pair(padding) self.dilation = _pair(dilation) self.groups = groups self.deformable_groups = deformable_groups # enable compatibility with nn.Conv2d self.transposed = False self.output_padding = _single(0) self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size)) self.reset_parameters() def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) def forward(self, x, offset): # To fix an assert error in deform_conv_cuda.cpp:128 # input image is smaller than kernel input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1]) if input_pad: pad_h = max(self.kernel_size[0] - x.size(2), 0) pad_w = max(self.kernel_size[1] - x.size(3), 0) x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups) if input_pad: out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous() return out class DeformConvPack(DeformConv): """A Deformable Conv Encapsulation that acts as normal Conv layers. Args: in_channels (int): Same as nn.Conv2d. out_channels (int): Same as nn.Conv2d. kernel_size (int or tuple[int]): Same as nn.Conv2d. stride (int or tuple[int]): Same as nn.Conv2d. padding (int or tuple[int]): Same as nn.Conv2d. dilation (int or tuple[int]): Same as nn.Conv2d. groups (int): Same as nn.Conv2d. bias (bool or str): If specified as `auto`, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False. """ _version = 2 def __init__(self, *args, **kwargs): super(DeformConvPack, self).__init__(*args, **kwargs) self.conv_offset = nn.Conv2d( self.in_channels, self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1], kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding), dilation=_pair(self.dilation), bias=True) self.init_offset() def init_offset(self): self.conv_offset.weight.data.zero_() self.conv_offset.bias.data.zero_() def forward(self, x): offset = self.conv_offset(x) return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups) class ModulatedDeformConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, bias=True): super(ModulatedDeformConv, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = stride self.padding = padding self.dilation = dilation self.groups = groups self.deformable_groups = deformable_groups self.with_bias = bias # enable compatibility with nn.Conv2d self.transposed = False self.output_padding = _single(0) self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) if bias: self.bias = nn.Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.init_weights() def init_weights(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) if self.bias is not None: self.bias.data.zero_() def forward(self, x, offset, mask): return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups) class ModulatedDeformConvPack(ModulatedDeformConv): """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers. Args: in_channels (int): Same as nn.Conv2d. out_channels (int): Same as nn.Conv2d. kernel_size (int or tuple[int]): Same as nn.Conv2d. stride (int or tuple[int]): Same as nn.Conv2d. padding (int or tuple[int]): Same as nn.Conv2d. dilation (int or tuple[int]): Same as nn.Conv2d. groups (int): Same as nn.Conv2d. bias (bool or str): If specified as `auto`, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False. """ _version = 2 def __init__(self, *args, **kwargs): super(ModulatedDeformConvPack, self).__init__(*args, **kwargs) self.conv_offset = nn.Conv2d( self.in_channels, self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding), dilation=_pair(self.dilation), bias=True) self.init_weights() def init_weights(self): super(ModulatedDeformConvPack, self).init_weights() if hasattr(self, 'conv_offset'): self.conv_offset.weight.data.zero_() self.conv_offset.bias.data.zero_() def forward(self, x): out = self.conv_offset(x) o1, o2, mask = torch.chunk(out, 3, dim=1) offset = torch.cat((o1, o2), dim=1) mask = torch.sigmoid(mask) return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups) ================================================ FILE: basicsr/ops/dcn/src/deform_conv_cuda.cpp ================================================ // modify from // https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c #include #include #include #include void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor data_col); void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor grad_im); void deformable_col2im_coord( const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor grad_offset); void modulated_deformable_im2col_cuda( const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor data_col); void modulated_deformable_col2im_cuda( const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor grad_im); void modulated_deformable_col2im_coord_cuda( const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor grad_offset, at::Tensor grad_mask); void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, at::Tensor weight, int kH, int kW, int dH, int dW, int padH, int padW, int dilationH, int dilationW, int group, int deformable_group) { TORCH_CHECK(weight.ndimension() == 4, "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " "but got: %s", weight.ndimension()); TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); TORCH_CHECK(kW > 0 && kH > 0, "kernel size should be greater than zero, but got kH: %d kW: %d", kH, kW); TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), "kernel size should be consistent with weight, ", "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, kW, weight.size(2), weight.size(3)); TORCH_CHECK(dW > 0 && dH > 0, "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); TORCH_CHECK( dilationW > 0 && dilationH > 0, "dilation should be greater than 0, but got dilationH: %d dilationW: %d", dilationH, dilationW); int ndim = input.ndimension(); int dimf = 0; int dimh = 1; int dimw = 2; if (ndim == 4) { dimf++; dimh++; dimw++; } TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", ndim); long nInputPlane = weight.size(1) * group; long inputHeight = input.size(dimh); long inputWidth = input.size(dimw); long nOutputPlane = weight.size(0); long outputHeight = (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; long outputWidth = (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; TORCH_CHECK(nInputPlane % deformable_group == 0, "input channels must divide deformable group size"); if (outputWidth < 1 || outputHeight < 1) AT_ERROR( "Given input size: (%ld x %ld x %ld). " "Calculated output size: (%ld x %ld x %ld). Output size is too small", nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, outputWidth); TORCH_CHECK(input.size(1) == nInputPlane, "invalid number of input planes, expected: %d, but got: %d", nInputPlane, input.size(1)); TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), "input image is smaller than kernel"); TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), "invalid spatial size of offset, expected height: %d width: %d, but " "got height: %d width: %d", outputHeight, outputWidth, offset.size(2), offset.size(3)); TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), "invalid number of channels of offset"); if (gradOutput != NULL) { TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, "invalid number of gradOutput planes, expected: %d, but got: %d", nOutputPlane, gradOutput->size(dimf)); TORCH_CHECK((gradOutput->size(dimh) == outputHeight && gradOutput->size(dimw) == outputWidth), "invalid size of gradOutput, expected height: %d width: %d , but " "got height: %d width: %d", outputHeight, outputWidth, gradOutput->size(dimh), gradOutput->size(dimw)); } } int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, at::Tensor offset, at::Tensor output, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step) { // todo: resize columns to include im2col: done // todo: add im2col_step as input // todo: add new output buffer and transpose it to output (or directly // transpose output) todo: possibly change data indexing because of // parallel_imgs shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, dilationH, dilationW, group, deformable_group); at::DeviceGuard guard(input.device()); input = input.contiguous(); offset = offset.contiguous(); weight = weight.contiguous(); int batch = 1; if (input.ndimension() == 3) { // Force batch batch = 0; input.unsqueeze_(0); offset.unsqueeze_(0); } // todo: assert batchsize dividable by im2col_step long batchSize = input.size(0); long nInputPlane = input.size(1); long inputHeight = input.size(2); long inputWidth = input.size(3); long nOutputPlane = weight.size(0); long outputWidth = (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; long outputHeight = (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, outputHeight, outputWidth}); columns = at::zeros( {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, input.options()); if (ones.ndimension() != 2 || ones.size(0) * ones.size(1) < outputHeight * outputWidth) { ones = at::ones({outputHeight, outputWidth}, input.options()); } input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, inputHeight, inputWidth}); offset = offset.view({batchSize / im2col_step, im2col_step, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); at::Tensor output_buffer = at::zeros({batchSize / im2col_step, nOutputPlane, im2col_step * outputHeight, outputWidth}, output.options()); output_buffer = output_buffer.view( {output_buffer.size(0), group, output_buffer.size(1) / group, output_buffer.size(2), output_buffer.size(3)}); for (int elt = 0; elt < batchSize / im2col_step; elt++) { deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, dilationH, dilationW, im2col_step, deformable_group, columns); columns = columns.view({group, columns.size(0) / group, columns.size(1)}); weight = weight.view({group, weight.size(0) / group, weight.size(1), weight.size(2), weight.size(3)}); for (int g = 0; g < group; g++) { output_buffer[elt][g] = output_buffer[elt][g] .flatten(1) .addmm_(weight[g].flatten(1), columns[g]) .view_as(output_buffer[elt][g]); } } output_buffer = output_buffer.view( {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), output_buffer.size(3), output_buffer.size(4)}); output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, outputHeight, outputWidth}); output_buffer.transpose_(1, 2); output.copy_(output_buffer); output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); offset = offset.view( {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); if (batch == 0) { output = output.view({nOutputPlane, outputHeight, outputWidth}); input = input.view({nInputPlane, inputHeight, inputWidth}); offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); } return 1; } int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradInput, at::Tensor gradOffset, at::Tensor weight, at::Tensor columns, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step) { shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, dilationH, dilationW, group, deformable_group); at::DeviceGuard guard(input.device()); input = input.contiguous(); offset = offset.contiguous(); gradOutput = gradOutput.contiguous(); weight = weight.contiguous(); int batch = 1; if (input.ndimension() == 3) { // Force batch batch = 0; input = input.view({1, input.size(0), input.size(1), input.size(2)}); offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); gradOutput = gradOutput.view( {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); } long batchSize = input.size(0); long nInputPlane = input.size(1); long inputHeight = input.size(2); long inputWidth = input.size(3); long nOutputPlane = weight.size(0); long outputWidth = (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; long outputHeight = (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); columns = at::zeros( {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, input.options()); // change order of grad output gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, nOutputPlane, outputHeight, outputWidth}); gradOutput.transpose_(1, 2); gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, inputHeight, inputWidth}); input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, inputHeight, inputWidth}); gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); offset = offset.view({batchSize / im2col_step, im2col_step, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); for (int elt = 0; elt < batchSize / im2col_step; elt++) { // divide into groups columns = columns.view({group, columns.size(0) / group, columns.size(1)}); weight = weight.view({group, weight.size(0) / group, weight.size(1), weight.size(2), weight.size(3)}); gradOutput = gradOutput.view( {gradOutput.size(0), group, gradOutput.size(1) / group, gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); for (int g = 0; g < group; g++) { columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), gradOutput[elt][g].flatten(1), 0.0f, 1.0f); } columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)}); gradOutput = gradOutput.view( {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, dilationH, dilationW, im2col_step, deformable_group, gradOffset[elt]); deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, dilationH, dilationW, im2col_step, deformable_group, gradInput[elt]); } gradOutput.transpose_(1, 2); gradOutput = gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); gradOffset = gradOffset.view( {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); offset = offset.view( {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); if (batch == 0) { gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); input = input.view({nInputPlane, inputHeight, inputWidth}); gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); gradOffset = gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); } return 1; } int deform_conv_backward_parameters_cuda( at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradWeight, // at::Tensor gradBias, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, float scale, int im2col_step) { // todo: transpose and reshape outGrad // todo: reshape columns // todo: add im2col_step as input shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, padW, dilationH, dilationW, group, deformable_group); at::DeviceGuard guard(input.device()); input = input.contiguous(); offset = offset.contiguous(); gradOutput = gradOutput.contiguous(); int batch = 1; if (input.ndimension() == 3) { // Force batch batch = 0; input = input.view( at::IntList({1, input.size(0), input.size(1), input.size(2)})); gradOutput = gradOutput.view( {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); } long batchSize = input.size(0); long nInputPlane = input.size(1); long inputHeight = input.size(2); long inputWidth = input.size(3); long nOutputPlane = gradWeight.size(0); long outputWidth = (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; long outputHeight = (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); columns = at::zeros( {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, input.options()); gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, nOutputPlane, outputHeight, outputWidth}); gradOutput.transpose_(1, 2); at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); gradOutputBuffer = gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, outputHeight, outputWidth}); gradOutputBuffer.copy_(gradOutput); gradOutputBuffer = gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step * outputHeight, outputWidth}); gradOutput.transpose_(1, 2); gradOutput = gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, inputHeight, inputWidth}); offset = offset.view({batchSize / im2col_step, im2col_step, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); for (int elt = 0; elt < batchSize / im2col_step; elt++) { deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, dilationH, dilationW, im2col_step, deformable_group, columns); // divide into group gradOutputBuffer = gradOutputBuffer.view( {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); columns = columns.view({group, columns.size(0) / group, columns.size(1)}); gradWeight = gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), gradWeight.size(2), gradWeight.size(3)}); for (int g = 0; g < group; g++) { gradWeight[g] = gradWeight[g] .flatten(1) .addmm_(gradOutputBuffer[elt][g].flatten(1), columns[g].transpose(1, 0), 1.0, scale) .view_as(gradWeight[g]); } gradOutputBuffer = gradOutputBuffer.view( {gradOutputBuffer.size(0), gradOutputBuffer.size(1) * gradOutputBuffer.size(2), gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)}); gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), gradWeight.size(2), gradWeight.size(3), gradWeight.size(4)}); } input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); offset = offset.view( {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); if (batch == 0) { gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); input = input.view({nInputPlane, inputHeight, inputWidth}); } return 1; } void modulated_deform_conv_cuda_forward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, int kernel_h, int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int group, const int deformable_group, const bool with_bias) { TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); at::DeviceGuard guard(input.device()); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_out = weight.size(0); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); if (channels != channels_kernel * group) AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", channels, channels_kernel * group); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; if (ones.ndimension() != 2 || ones.size(0) * ones.size(1) < height_out * width_out) { // Resize plane and fill with ones... ones = at::ones({height_out, width_out}, input.options()); } // resize output output = output.view({batch, channels_out, height_out, width_out}).zero_(); // resize temporary columns columns = at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); output = output.view({output.size(0), group, output.size(1) / group, output.size(2), output.size(3)}); for (int b = 0; b < batch; b++) { modulated_deformable_im2col_cuda( input[b], offset[b], mask[b], 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns); // divide into group weight = weight.view({group, weight.size(0) / group, weight.size(1), weight.size(2), weight.size(3)}); columns = columns.view({group, columns.size(0) / group, columns.size(1)}); for (int g = 0; g < group; g++) { output[b][g] = output[b][g] .flatten(1) .addmm_(weight[g].flatten(1), columns[g]) .view_as(output[b][g]); } weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), weight.size(3), weight.size(4)}); columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)}); } output = output.view({output.size(0), output.size(1) * output.size(2), output.size(3), output.size(4)}); if (with_bias) { output += bias.view({1, bias.size(0), 1, 1}); } } void modulated_deform_conv_cuda_backward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor columns, at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, const bool with_bias) { TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); at::DeviceGuard guard(input.device()); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); if (channels != channels_kernel * group) AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", channels, channels_kernel * group); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; if (ones.ndimension() != 2 || ones.size(0) * ones.size(1) < height_out * width_out) { // Resize plane and fill with ones... ones = at::ones({height_out, width_out}, input.options()); } grad_input = grad_input.view({batch, channels, height, width}); columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, input.options()); grad_output = grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, grad_output.size(2), grad_output.size(3)}); for (int b = 0; b < batch; b++) { // divide int group columns = columns.view({group, columns.size(0) / group, columns.size(1)}); weight = weight.view({group, weight.size(0) / group, weight.size(1), weight.size(2), weight.size(3)}); for (int g = 0; g < group; g++) { columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), grad_output[b][g].flatten(1), 0.0f, 1.0f); } columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)}); weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), weight.size(3), weight.size(4)}); // gradient w.r.t. input coordinate data modulated_deformable_col2im_coord_cuda( columns, input[b], offset[b], mask[b], 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], grad_mask[b]); // gradient w.r.t. input data modulated_deformable_col2im_cuda( columns, offset[b], mask[b], 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_input[b]); // gradient w.r.t. weight, dWeight should accumulate across the batch and // group modulated_deformable_im2col_cuda( input[b], offset[b], mask[b], 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns); columns = columns.view({group, columns.size(0) / group, columns.size(1)}); grad_weight = grad_weight.view({group, grad_weight.size(0) / group, grad_weight.size(1), grad_weight.size(2), grad_weight.size(3)}); if (with_bias) grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); for (int g = 0; g < group; g++) { grad_weight[g] = grad_weight[g] .flatten(1) .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) .view_as(grad_weight[g]); if (with_bias) { grad_bias[g] = grad_bias[g] .view({-1, 1}) .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) .view(-1); } } columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)}); grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), grad_weight.size(2), grad_weight.size(3), grad_weight.size(4)}); if (with_bias) grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); } grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), grad_output.size(2), grad_output.size(3), grad_output.size(4)}); } ================================================ FILE: basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu ================================================ /*! ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** * * COPYRIGHT * * All contributions by the University of California: * Copyright (c) 2014-2017 The Regents of the University of California (Regents) * All rights reserved. * * All other contributions: * Copyright (c) 2014-2017, the respective contributors * All rights reserved. * * Caffe uses a shared copyright model: each contributor holds copyright over * their contributions to Caffe. The project versioning records all such * contribution and copyright details. If a contributor wants to further mark * their specific copyright on a particular contribution, they should indicate * their copyright solely in the commit message of the change when it is * committed. * * LICENSE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * 2. 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. * * 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 THE COPYRIGHT OWNER OR CONTRIBUTORS 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. * * CONTRIBUTION AGREEMENT * * By contributing to the BVLC/caffe repository through pull-request, comment, * or otherwise, the contributor releases their content to the * license and copyright terms herein. * ***************** END Caffe Copyright Notice and Disclaimer ******************** * * Copyright (c) 2018 Microsoft * Licensed under The MIT License [see LICENSE for details] * \file modulated_deformable_im2col.cuh * \brief Function definitions of converting an image to * column matrix based on kernel, padding, dilation, and offset. * These functions are mainly used in deformable convolution operators. * \ref: https://arxiv.org/abs/1703.06211 * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng */ // modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu #include #include #include #include #include #include using namespace at; #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ i += blockDim.x * gridDim.x) const int CUDA_NUM_THREADS = 1024; const int kMaxGridNum = 65535; inline int GET_BLOCKS(const int N) { return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); } template __device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, const int height, const int width, scalar_t h, scalar_t w) { int h_low = floor(h); int w_low = floor(w); int h_high = h_low + 1; int w_high = w_low + 1; scalar_t lh = h - h_low; scalar_t lw = w - w_low; scalar_t hh = 1 - lh, hw = 1 - lw; scalar_t v1 = 0; if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; scalar_t v2 = 0; if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; scalar_t v3 = 0; if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; scalar_t v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); return val; } template __device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, const int h, const int w, const int height, const int width) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; scalar_t weight = 0; if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); return weight; } template __device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, const int height, const int width, const scalar_t *im_data, const int data_width, const int bp_dir) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; scalar_t weight = 0; if (bp_dir == 0) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } else if (bp_dir == 1) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } return weight; } template __global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int num_channels, const int deformable_group, const int height_col, const int width_col, scalar_t *data_col) { CUDA_KERNEL_LOOP(index, n) { // index index of output matrix const int w_col = index % width_col; const int h_col = (index / width_col) % height_col; const int b_col = (index / width_col / height_col) % batch_size; const int c_im = (index / width_col / height_col) / batch_size; const int c_col = c_im * kernel_h * kernel_w; // compute deformable group index const int deformable_group_index = c_im / channel_per_deformable_group; const int h_in = h_col * stride_h - pad_h; const int w_in = w_col * stride_w - pad_w; scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; for (int i = 0; i < kernel_h; ++i) { for (int j = 0; j < kernel_w; ++j) { const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; scalar_t val = static_cast(0); const scalar_t h_im = h_in + i * dilation_h + offset_h; const scalar_t w_im = w_in + j * dilation_w + offset_w; if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { //const scalar_t map_h = i * dilation_h + offset_h; //const scalar_t map_w = j * dilation_w + offset_w; //const int cur_height = height - h_in; //const int cur_width = width - w_in; //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); } *data_col_ptr = val; data_col_ptr += batch_size * height_col * width_col; } } } } void deformable_im2col( const at::Tensor data_im, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor data_col) { // num_axes should be smaller than block size // todo: check parallel_imgs is correctly passed in int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; int num_kernels = channels * height_col * width_col * parallel_imgs; int channel_per_deformable_group = channels / deformable_group; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_im.scalar_type(), "deformable_im2col_gpu", ([&] { const scalar_t *data_im_ = data_im.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); scalar_t *data_col_ = data_col.data_ptr(); deformable_im2col_gpu_kernel<<>>( num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, parallel_imgs, channels, deformable_group, height_col, width_col, data_col_); })); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); } } template __global__ void deformable_col2im_gpu_kernel( const int n, const scalar_t *data_col, const scalar_t *data_offset, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int deformable_group, const int height_col, const int width_col, scalar_t *grad_im) { CUDA_KERNEL_LOOP(index, n) { const int j = (index / width_col / height_col / batch_size) % kernel_w; const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; // compute the start and end of the output const int deformable_group_index = c / channel_per_deformable_group; int w_out = index % width_col; int h_out = (index / width_col) % height_col; int b = (index / width_col / height_col) % batch_size; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; const scalar_t cur_top_grad = data_col[index]; const int cur_h = (int)cur_inv_h_data; const int cur_w = (int)cur_inv_w_data; for (int dy = -2; dy <= 2; dy++) { for (int dx = -2; dx <= 2; dx++) { if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && abs(cur_inv_w_data - (cur_w + dx)) < 1) { int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); } } } } } void deformable_col2im( const at::Tensor data_col, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor grad_im) { // todo: make sure parallel_imgs is passed in correctly int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; int channel_per_deformable_group = channels / deformable_group; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_col.scalar_type(), "deformable_col2im_gpu", ([&] { const scalar_t *data_col_ = data_col.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); scalar_t *grad_im_ = grad_im.data_ptr(); deformable_col2im_gpu_kernel<<>>( num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, parallel_imgs, deformable_group, height_col, width_col, grad_im_); })); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); } } template __global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, const scalar_t *data_im, const scalar_t *data_offset, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int offset_channels, const int deformable_group, const int height_col, const int width_col, scalar_t *grad_offset) { CUDA_KERNEL_LOOP(index, n) { scalar_t val = 0; int w = index % width_col; int h = (index / width_col) % height_col; int c = (index / width_col / height_col) % offset_channels; int b = (index / width_col / height_col) / offset_channels; // compute the start and end of the output const int deformable_group_index = c / (2 * kernel_h * kernel_w); const int col_step = kernel_h * kernel_w; int cnt = 0; const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) { const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; const int bp_dir = offset_c % 2; int j = (col_pos / width_col / height_col / batch_size) % kernel_w; int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; int w_out = col_pos % width_col; int h_out = (col_pos / width_col) % height_col; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; scalar_t inv_h = h_in + i * dilation_h + offset_h; scalar_t inv_w = w_in + j * dilation_w + offset_w; if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { inv_h = inv_w = -2; } const scalar_t weight = get_coordinate_weight( inv_h, inv_w, height, width, data_im_ptr + cnt * height * width, width, bp_dir); val += weight * data_col_ptr[col_pos]; cnt += 1; } grad_offset[index] = val; } } void deformable_col2im_coord( const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const int channels, const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) { int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { const scalar_t *data_col_ = data_col.data_ptr(); const scalar_t *data_im_ = data_im.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); scalar_t *grad_offset_ = grad_offset.data_ptr(); deformable_col2im_coord_gpu_kernel<<>>( num_kernels, data_col_, data_im_, data_offset_, channels, height, width, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, height_col, width_col, grad_offset_); })); } template __device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, const int height, const int width, scalar_t h, scalar_t w) { int h_low = floor(h); int w_low = floor(w); int h_high = h_low + 1; int w_high = w_low + 1; scalar_t lh = h - h_low; scalar_t lw = w - w_low; scalar_t hh = 1 - lh, hw = 1 - lw; scalar_t v1 = 0; if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; scalar_t v2 = 0; if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; scalar_t v3 = 0; if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; scalar_t v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); return val; } template __device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, const int h, const int w, const int height, const int width) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; scalar_t weight = 0; if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); return weight; } template __device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, const int height, const int width, const scalar_t *im_data, const int data_width, const int bp_dir) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; scalar_t weight = 0; if (bp_dir == 0) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } else if (bp_dir == 1) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } return weight; } template __global__ void modulated_deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int num_channels, const int deformable_group, const int height_col, const int width_col, scalar_t *data_col) { CUDA_KERNEL_LOOP(index, n) { // index index of output matrix const int w_col = index % width_col; const int h_col = (index / width_col) % height_col; const int b_col = (index / width_col / height_col) % batch_size; const int c_im = (index / width_col / height_col) / batch_size; const int c_col = c_im * kernel_h * kernel_w; // compute deformable group index const int deformable_group_index = c_im / channel_per_deformable_group; const int h_in = h_col * stride_h - pad_h; const int w_in = w_col * stride_w - pad_w; scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; for (int i = 0; i < kernel_h; ++i) { for (int j = 0; j < kernel_w; ++j) { const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; scalar_t val = static_cast(0); const scalar_t h_im = h_in + i * dilation_h + offset_h; const scalar_t w_im = w_in + j * dilation_w + offset_w; //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { //const float map_h = i * dilation_h + offset_h; //const float map_w = j * dilation_w + offset_w; //const int cur_height = height - h_in; //const int cur_width = width - w_in; //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); } *data_col_ptr = val * mask; data_col_ptr += batch_size * height_col * width_col; //data_col_ptr += height_col * width_col; } } } } template __global__ void modulated_deformable_col2im_gpu_kernel(const int n, const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int deformable_group, const int height_col, const int width_col, scalar_t *grad_im) { CUDA_KERNEL_LOOP(index, n) { const int j = (index / width_col / height_col / batch_size) % kernel_w; const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; // compute the start and end of the output const int deformable_group_index = c / channel_per_deformable_group; int w_out = index % width_col; int h_out = (index / width_col) % height_col; int b = (index / width_col / height_col) % batch_size; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; const scalar_t cur_top_grad = data_col[index] * mask; const int cur_h = (int)cur_inv_h_data; const int cur_w = (int)cur_inv_w_data; for (int dy = -2; dy <= 2; dy++) { for (int dx = -2; dx <= 2; dx++) { if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && abs(cur_inv_w_data - (cur_w + dx)) < 1) { int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); } } } } } template __global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int offset_channels, const int deformable_group, const int height_col, const int width_col, scalar_t *grad_offset, scalar_t *grad_mask) { CUDA_KERNEL_LOOP(index, n) { scalar_t val = 0, mval = 0; int w = index % width_col; int h = (index / width_col) % height_col; int c = (index / width_col / height_col) % offset_channels; int b = (index / width_col / height_col) / offset_channels; // compute the start and end of the output const int deformable_group_index = c / (2 * kernel_h * kernel_w); const int col_step = kernel_h * kernel_w; int cnt = 0; const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) { const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; const int bp_dir = offset_c % 2; int j = (col_pos / width_col / height_col / batch_size) % kernel_w; int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; int w_out = col_pos % width_col; int h_out = (col_pos / width_col) % height_col; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; scalar_t inv_h = h_in + i * dilation_h + offset_h; scalar_t inv_w = w_in + j * dilation_w + offset_w; if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { inv_h = inv_w = -2; } else { mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); } const scalar_t weight = dmcn_get_coordinate_weight( inv_h, inv_w, height, width, data_im_ptr + cnt * height * width, width, bp_dir); val += weight * data_col_ptr[col_pos] * mask; cnt += 1; } // KERNEL_ASSIGN(grad_offset[index], offset_req, val); grad_offset[index] = val; if (offset_c % 2 == 0) // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; } } void modulated_deformable_im2col_cuda( const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor data_col) { // num_axes should be smaller than block size const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * batch_size * height_col * width_col; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { const scalar_t *data_im_ = data_im.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); const scalar_t *data_mask_ = data_mask.data_ptr(); scalar_t *data_col_ = data_col.data_ptr(); modulated_deformable_im2col_gpu_kernel<<>>( num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, channels, deformable_group, height_col, width_col, data_col_); })); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); } } void modulated_deformable_col2im_cuda( const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor grad_im) { const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { const scalar_t *data_col_ = data_col.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); const scalar_t *data_mask_ = data_mask.data_ptr(); scalar_t *grad_im_ = grad_im.data_ptr(); modulated_deformable_col2im_gpu_kernel<<>>( num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, deformable_group, height_col, width_col, grad_im_); })); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); } } void modulated_deformable_col2im_coord_cuda( const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, at::Tensor grad_offset, at::Tensor grad_mask) { const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; AT_DISPATCH_FLOATING_TYPES_AND_HALF( data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { const scalar_t *data_col_ = data_col.data_ptr(); const scalar_t *data_im_ = data_im.data_ptr(); const scalar_t *data_offset_ = data_offset.data_ptr(); const scalar_t *data_mask_ = data_mask.data_ptr(); scalar_t *grad_offset_ = grad_offset.data_ptr(); scalar_t *grad_mask_ = grad_mask.data_ptr(); modulated_deformable_col2im_coord_gpu_kernel<<>>( num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, grad_offset_, grad_mask_); })); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); } } ================================================ FILE: basicsr/ops/dcn/src/deform_conv_ext.cpp ================================================ // modify from // https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c #include #include #include #include #define WITH_CUDA // always use cuda #ifdef WITH_CUDA int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, at::Tensor offset, at::Tensor output, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step); int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradInput, at::Tensor gradOffset, at::Tensor weight, at::Tensor columns, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step); int deform_conv_backward_parameters_cuda( at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradWeight, // at::Tensor gradBias, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, float scale, int im2col_step); void modulated_deform_conv_cuda_forward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, int kernel_h, int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int group, const int deformable_group, const bool with_bias); void modulated_deform_conv_cuda_backward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor columns, at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, const bool with_bias); #endif int deform_conv_forward(at::Tensor input, at::Tensor weight, at::Tensor offset, at::Tensor output, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step) { if (input.device().is_cuda()) { #ifdef WITH_CUDA return deform_conv_forward_cuda(input, weight, offset, output, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, im2col_step); #else AT_ERROR("deform conv is not compiled with GPU support"); #endif } AT_ERROR("deform conv is not implemented on CPU"); } int deform_conv_backward_input(at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradInput, at::Tensor gradOffset, at::Tensor weight, at::Tensor columns, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, int im2col_step) { if (input.device().is_cuda()) { #ifdef WITH_CUDA return deform_conv_backward_input_cuda(input, offset, gradOutput, gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, im2col_step); #else AT_ERROR("deform conv is not compiled with GPU support"); #endif } AT_ERROR("deform conv is not implemented on CPU"); } int deform_conv_backward_parameters( at::Tensor input, at::Tensor offset, at::Tensor gradOutput, at::Tensor gradWeight, // at::Tensor gradBias, at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, int deformable_group, float scale, int im2col_step) { if (input.device().is_cuda()) { #ifdef WITH_CUDA return deform_conv_backward_parameters_cuda(input, offset, gradOutput, gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, scale, im2col_step); #else AT_ERROR("deform conv is not compiled with GPU support"); #endif } AT_ERROR("deform conv is not implemented on CPU"); } void modulated_deform_conv_forward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, int kernel_h, int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int group, const int deformable_group, const bool with_bias) { if (input.device().is_cuda()) { #ifdef WITH_CUDA return modulated_deform_conv_cuda_forward(input, weight, bias, ones, offset, mask, output, columns, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, with_bias); #else AT_ERROR("modulated deform conv is not compiled with GPU support"); #endif } AT_ERROR("modulated deform conv is not implemented on CPU"); } void modulated_deform_conv_backward( at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, at::Tensor offset, at::Tensor mask, at::Tensor columns, at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, const bool with_bias) { if (input.device().is_cuda()) { #ifdef WITH_CUDA return modulated_deform_conv_cuda_backward(input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, with_bias); #else AT_ERROR("modulated deform conv is not compiled with GPU support"); #endif } AT_ERROR("modulated deform conv is not implemented on CPU"); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("deform_conv_forward", &deform_conv_forward, "deform forward"); m.def("deform_conv_backward_input", &deform_conv_backward_input, "deform_conv_backward_input"); m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, "deform_conv_backward_parameters"); m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, "modulated deform conv forward"); m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, "modulated deform conv backward"); } ================================================ FILE: basicsr/ops/fused_act/__init__.py ================================================ from .fused_act import FusedLeakyReLU, fused_leaky_relu __all__ = ['FusedLeakyReLU', 'fused_leaky_relu'] ================================================ FILE: basicsr/ops/fused_act/fused_act.py ================================================ # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 import os import torch from torch import nn from torch.autograd import Function BASICSR_JIT = os.getenv('BASICSR_JIT') if BASICSR_JIT == 'True': from torch.utils.cpp_extension import load module_path = os.path.dirname(__file__) fused_act_ext = load( 'fused', sources=[ os.path.join(module_path, 'src', 'fused_bias_act.cpp'), os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'), ], ) else: try: from . import fused_act_ext except ImportError: pass # avoid annoying print output # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' # '1. compile with BASICSR_EXT=True. or\n ' # '2. set BASICSR_JIT=True during running') class FusedLeakyReLUFunctionBackward(Function): @staticmethod def forward(ctx, grad_output, out, negative_slope, scale): ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale empty = grad_output.new_empty(0) grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale) dim = [0] if grad_input.ndim > 2: dim += list(range(2, grad_input.ndim)) grad_bias = grad_input.sum(dim).detach() return grad_input, grad_bias @staticmethod def backward(ctx, gradgrad_input, gradgrad_bias): out, = ctx.saved_tensors gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale) return gradgrad_out, None, None, None class FusedLeakyReLUFunction(Function): @staticmethod def forward(ctx, input, bias, negative_slope, scale): empty = input.new_empty(0) out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale return out @staticmethod def backward(ctx, grad_output): out, = ctx.saved_tensors grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale) return grad_input, grad_bias, None, None class FusedLeakyReLU(nn.Module): def __init__(self, channel, negative_slope=0.2, scale=2**0.5): super().__init__() self.bias = nn.Parameter(torch.zeros(channel)) self.negative_slope = negative_slope self.scale = scale def forward(self, input): return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) ================================================ FILE: basicsr/ops/fused_act/src/fused_bias_act.cpp ================================================ // from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp #include torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, int act, int grad, float alpha, float scale); #define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, int act, int grad, float alpha, float scale) { CHECK_CUDA(input); CHECK_CUDA(bias); return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); } ================================================ FILE: basicsr/ops/fused_act/src/fused_bias_act_kernel.cu ================================================ // from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu // Copyright (c) 2019, NVIDIA Corporation. All rights reserved. // // This work is made available under the Nvidia Source Code License-NC. // To view a copy of this license, visit // https://nvlabs.github.io/stylegan2/license.html #include #include #include #include #include #include #include template static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; scalar_t zero = 0.0; for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { scalar_t x = p_x[xi]; if (use_bias) { x += p_b[(xi / step_b) % size_b]; } scalar_t ref = use_ref ? p_ref[xi] : zero; scalar_t y; switch (act * 10 + grad) { default: case 10: y = x; break; case 11: y = x; break; case 12: y = 0.0; break; case 30: y = (x > 0.0) ? x : x * alpha; break; case 31: y = (ref > 0.0) ? x : x * alpha; break; case 32: y = 0.0; break; } out[xi] = y * scale; } } torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, int act, int grad, float alpha, float scale) { int curDevice = -1; cudaGetDevice(&curDevice); cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); auto x = input.contiguous(); auto b = bias.contiguous(); auto ref = refer.contiguous(); int use_bias = b.numel() ? 1 : 0; int use_ref = ref.numel() ? 1 : 0; int size_x = x.numel(); int size_b = b.numel(); int step_b = 1; for (int i = 1 + 1; i < x.dim(); i++) { step_b *= x.size(i); } int loop_x = 4; int block_size = 4 * 32; int grid_size = (size_x - 1) / (loop_x * block_size) + 1; auto y = torch::empty_like(x); AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { fused_bias_act_kernel<<>>( y.data_ptr(), x.data_ptr(), b.data_ptr(), ref.data_ptr(), act, grad, alpha, scale, loop_x, size_x, step_b, size_b, use_bias, use_ref ); }); return y; } ================================================ FILE: basicsr/ops/upfirdn2d/__init__.py ================================================ from .upfirdn2d import upfirdn2d __all__ = ['upfirdn2d'] ================================================ FILE: basicsr/ops/upfirdn2d/src/upfirdn2d.cpp ================================================ // from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp #include torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, int up_x, int up_y, int down_x, int down_y, int pad_x0, int pad_x1, int pad_y0, int pad_y1); #define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, int up_x, int up_y, int down_x, int down_y, int pad_x0, int pad_x1, int pad_y0, int pad_y1) { CHECK_CUDA(input); CHECK_CUDA(kernel); return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); } ================================================ FILE: basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu ================================================ // from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu // Copyright (c) 2019, NVIDIA Corporation. All rights reserved. // // This work is made available under the Nvidia Source Code License-NC. // To view a copy of this license, visit // https://nvlabs.github.io/stylegan2/license.html #include #include #include #include #include #include #include static __host__ __device__ __forceinline__ int floor_div(int a, int b) { int c = a / b; if (c * b > a) { c--; } return c; } struct UpFirDn2DKernelParams { int up_x; int up_y; int down_x; int down_y; int pad_x0; int pad_x1; int pad_y0; int pad_y1; int major_dim; int in_h; int in_w; int minor_dim; int kernel_h; int kernel_w; int out_h; int out_w; int loop_major; int loop_x; }; template __global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, const scalar_t *kernel, const UpFirDn2DKernelParams p) { int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; int out_y = minor_idx / p.minor_dim; minor_idx -= out_y * p.minor_dim; int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; int major_idx_base = blockIdx.z * p.loop_major; if (out_x_base >= p.out_w || out_y >= p.out_h || major_idx_base >= p.major_dim) { return; } int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major && major_idx < p.major_dim; loop_major++, major_idx++) { for (int loop_x = 0, out_x = out_x_base; loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; const scalar_t *x_p = &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx]; const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; int x_px = p.minor_dim; int k_px = -p.up_x; int x_py = p.in_w * p.minor_dim; int k_py = -p.up_y * p.kernel_w; scalar_t v = 0.0f; for (int y = 0; y < h; y++) { for (int x = 0; x < w; x++) { v += static_cast(*x_p) * static_cast(*k_p); x_p += x_px; k_p += k_px; } x_p += x_py - w * x_px; k_p += k_py - w * k_px; } out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v; } } } template __global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, const scalar_t *kernel, const UpFirDn2DKernelParams p) { const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; __shared__ volatile float sk[kernel_h][kernel_w]; __shared__ volatile float sx[tile_in_h][tile_in_w]; int minor_idx = blockIdx.x; int tile_out_y = minor_idx / p.minor_dim; minor_idx -= tile_out_y * p.minor_dim; tile_out_y *= tile_out_h; int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; int major_idx_base = blockIdx.z * p.loop_major; if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) { return; } for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) { int ky = tap_idx / kernel_w; int kx = tap_idx - ky * kernel_w; scalar_t v = 0.0; if (kx < p.kernel_w & ky < p.kernel_h) { v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; } sk[ky][kx] = v; } for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) { for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) { int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; int tile_in_x = floor_div(tile_mid_x, up_x); int tile_in_y = floor_div(tile_mid_y, up_y); __syncthreads(); for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) { int rel_in_y = in_idx / tile_in_w; int rel_in_x = in_idx - rel_in_y * tile_in_w; int in_x = rel_in_x + tile_in_x; int in_y = rel_in_y + tile_in_y; scalar_t v = 0.0; if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx]; } sx[rel_in_y][rel_in_x] = v; } __syncthreads(); for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) { int rel_out_y = out_idx / tile_out_w; int rel_out_x = out_idx - rel_out_y * tile_out_w; int out_x = rel_out_x + tile_out_x; int out_y = rel_out_y + tile_out_y; int mid_x = tile_mid_x + rel_out_x * down_x; int mid_y = tile_mid_y + rel_out_y * down_y; int in_x = floor_div(mid_x, up_x); int in_y = floor_div(mid_y, up_y); int rel_in_x = in_x - tile_in_x; int rel_in_y = in_y - tile_in_y; int kernel_x = (in_x + 1) * up_x - mid_x - 1; int kernel_y = (in_y + 1) * up_y - mid_y - 1; scalar_t v = 0.0; #pragma unroll for (int y = 0; y < kernel_h / up_y; y++) #pragma unroll for (int x = 0; x < kernel_w / up_x; x++) v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x]; if (out_x < p.out_w & out_y < p.out_h) { out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v; } } } } } torch::Tensor upfirdn2d_op(const torch::Tensor &input, const torch::Tensor &kernel, int up_x, int up_y, int down_x, int down_y, int pad_x0, int pad_x1, int pad_y0, int pad_y1) { int curDevice = -1; cudaGetDevice(&curDevice); cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); UpFirDn2DKernelParams p; auto x = input.contiguous(); auto k = kernel.contiguous(); p.major_dim = x.size(0); p.in_h = x.size(1); p.in_w = x.size(2); p.minor_dim = x.size(3); p.kernel_h = k.size(0); p.kernel_w = k.size(1); p.up_x = up_x; p.up_y = up_y; p.down_x = down_x; p.down_y = down_y; p.pad_x0 = pad_x0; p.pad_x1 = pad_x1; p.pad_y0 = pad_y0; p.pad_y1 = pad_y1; p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y; p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x; auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); int mode = -1; int tile_out_h = -1; int tile_out_w = -1; if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { mode = 1; tile_out_h = 16; tile_out_w = 64; } if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) { mode = 2; tile_out_h = 16; tile_out_w = 64; } if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { mode = 3; tile_out_h = 16; tile_out_w = 64; } if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) { mode = 4; tile_out_h = 16; tile_out_w = 64; } if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) { mode = 5; tile_out_h = 8; tile_out_w = 32; } if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) { mode = 6; tile_out_h = 8; tile_out_w = 32; } dim3 block_size; dim3 grid_size; if (tile_out_h > 0 && tile_out_w > 0) { p.loop_major = (p.major_dim - 1) / 16384 + 1; p.loop_x = 1; block_size = dim3(32 * 8, 1, 1); grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, (p.major_dim - 1) / p.loop_major + 1); } else { p.loop_major = (p.major_dim - 1) / 16384 + 1; p.loop_x = 4; block_size = dim3(4, 32, 1); grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, (p.out_w - 1) / (p.loop_x * block_size.y) + 1, (p.major_dim - 1) / p.loop_major + 1); } AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { switch (mode) { case 1: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; case 2: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; case 3: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; case 4: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; case 5: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; case 6: upfirdn2d_kernel <<>>(out.data_ptr(), x.data_ptr(), k.data_ptr(), p); break; default: upfirdn2d_kernel_large<<>>( out.data_ptr(), x.data_ptr(), k.data_ptr(), p); } }); return out; } ================================================ FILE: basicsr/ops/upfirdn2d/upfirdn2d.py ================================================ # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 import os import torch from torch.autograd import Function from torch.nn import functional as F BASICSR_JIT = os.getenv('BASICSR_JIT') if BASICSR_JIT == 'True': from torch.utils.cpp_extension import load module_path = os.path.dirname(__file__) upfirdn2d_ext = load( 'upfirdn2d', sources=[ os.path.join(module_path, 'src', 'upfirdn2d.cpp'), os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'), ], ) else: try: from . import upfirdn2d_ext except ImportError: pass # avoid annoying print output # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' # '1. compile with BASICSR_EXT=True. or\n ' # '2. set BASICSR_JIT=True during running') class UpFirDn2dBackward(Function): @staticmethod def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size): up_x, up_y = up down_x, down_y = down g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) grad_input = upfirdn2d_ext.upfirdn2d( grad_output, grad_kernel, down_x, down_y, up_x, up_y, g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1, ) grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) ctx.save_for_backward(kernel) pad_x0, pad_x1, pad_y0, pad_y1 = pad ctx.up_x = up_x ctx.up_y = up_y ctx.down_x = down_x ctx.down_y = down_y ctx.pad_x0 = pad_x0 ctx.pad_x1 = pad_x1 ctx.pad_y0 = pad_y0 ctx.pad_y1 = pad_y1 ctx.in_size = in_size ctx.out_size = out_size return grad_input @staticmethod def backward(ctx, gradgrad_input): kernel, = ctx.saved_tensors gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) gradgrad_out = upfirdn2d_ext.upfirdn2d( gradgrad_input, kernel, ctx.up_x, ctx.up_y, ctx.down_x, ctx.down_y, ctx.pad_x0, ctx.pad_x1, ctx.pad_y0, ctx.pad_y1, ) # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], # ctx.out_size[1], ctx.in_size[3]) gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]) return gradgrad_out, None, None, None, None, None, None, None, None class UpFirDn2d(Function): @staticmethod def forward(ctx, input, kernel, up, down, pad): up_x, up_y = up down_x, down_y = down pad_x0, pad_x1, pad_y0, pad_y1 = pad kernel_h, kernel_w = kernel.shape _, channel, in_h, in_w = input.shape ctx.in_size = input.shape input = input.reshape(-1, in_h, in_w, 1) ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 ctx.out_size = (out_h, out_w) ctx.up = (up_x, up_y) ctx.down = (down_x, down_y) ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) g_pad_x0 = kernel_w - pad_x0 - 1 g_pad_y0 = kernel_h - pad_y0 - 1 g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1) # out = out.view(major, out_h, out_w, minor) out = out.view(-1, channel, out_h, out_w) return out @staticmethod def backward(ctx, grad_output): kernel, grad_kernel = ctx.saved_tensors grad_input = UpFirDn2dBackward.apply( grad_output, kernel, grad_kernel, ctx.up, ctx.down, ctx.pad, ctx.g_pad, ctx.in_size, ctx.out_size, ) return grad_input, None, None, None, None def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): if input.device.type == 'cpu': out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) else: out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])) return out def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): _, channel, in_h, in_w = input.shape input = input.reshape(-1, in_h, in_w, 1) _, in_h, in_w, minor = input.shape kernel_h, kernel_w = kernel.shape out = input.view(-1, in_h, 1, in_w, 1, minor) out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] out = out.permute(0, 3, 1, 2) out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) out = F.conv2d(out, w) out = out.reshape( -1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, ) out = out.permute(0, 2, 3, 1) out = out[:, ::down_y, ::down_x, :] out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 return out.view(-1, channel, out_h, out_w) ================================================ FILE: basicsr/summary.py ================================================ from thop import profile import torch from archs.BSRN_arch import BSRN as net model = net() input = torch.randn(1, 3, 320, 180) macs, params = profile(model, inputs=(input, )) print("Multi-adds[G] ") print(macs/1e9) print("Parameters [K]") print(params/1e3) ================================================ FILE: basicsr/test.py ================================================ import logging import torch from os import path as osp from basicsr.data import build_dataloader, build_dataset from basicsr.models import build_model from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs from basicsr.utils.options import dict2str, parse_options def test_pipeline(root_path): # parse options, set distributed setting, set ramdom seed opt, _ = parse_options(root_path, 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 _, dataset_opt in sorted(opt['datasets'].items()): test_set = build_dataset(dataset_opt) test_loader = build_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 = build_model(opt) for test_loader in test_loaders: test_set_name = test_loader.dataset.opt['name'] logger.info(f'Testing {test_set_name}...') model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img']) if __name__ == '__main__': root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) test_pipeline(root_path) ================================================ FILE: basicsr/train.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 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 ramdom 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']}, " f"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'.") # training logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') 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(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: basicsr/utils/__init__.py ================================================ from .diffjpeg import DiffJPEG from .file_client import FileClient from .img_process_util import USMSharp, usm_sharp from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img from .logger import AvgTimer, 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, set_random_seed, sizeof_fmt __all__ = [ # file_client.py 'FileClient', # img_util.py 'img2tensor', 'tensor2img', 'imfrombytes', 'imwrite', 'crop_border', # logger.py 'MessageLogger', 'AvgTimer', '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', # diffjpeg 'DiffJPEG', # img_process_util 'USMSharp', 'usm_sharp' ] ================================================ FILE: basicsr/utils/diffjpeg.py ================================================ """ Modified from https://github.com/mlomnitz/DiffJPEG For images not divisible by 8 https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 """ import itertools import numpy as np import torch import torch.nn as nn from torch.nn import functional as F # ------------------------ utils ------------------------# y_table = np.array( [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], dtype=np.float32).T y_table = nn.Parameter(torch.from_numpy(y_table)) c_table = np.empty((8, 8), dtype=np.float32) c_table.fill(99) c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T c_table = nn.Parameter(torch.from_numpy(c_table)) def diff_round(x): """ Differentiable rounding function """ return torch.round(x) + (x - torch.round(x))**3 def quality_to_factor(quality): """ Calculate factor corresponding to quality Args: quality(float): Quality for jpeg compression. Returns: float: Compression factor. """ if quality < 50: quality = 5000. / quality else: quality = 200. - quality * 2 return quality / 100. # ------------------------ compression ------------------------# class RGB2YCbCrJpeg(nn.Module): """ Converts RGB image to YCbCr """ def __init__(self): super(RGB2YCbCrJpeg, self).__init__() matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], dtype=np.float32).T self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) self.matrix = nn.Parameter(torch.from_numpy(matrix)) def forward(self, image): """ Args: image(Tensor): batch x 3 x height x width Returns: Tensor: batch x height x width x 3 """ image = image.permute(0, 2, 3, 1) result = torch.tensordot(image, self.matrix, dims=1) + self.shift return result.view(image.shape) class ChromaSubsampling(nn.Module): """ Chroma subsampling on CbCr channels """ def __init__(self): super(ChromaSubsampling, self).__init__() def forward(self, image): """ Args: image(tensor): batch x height x width x 3 Returns: y(tensor): batch x height x width cb(tensor): batch x height/2 x width/2 cr(tensor): batch x height/2 x width/2 """ image_2 = image.permute(0, 3, 1, 2).clone() cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) cb = cb.permute(0, 2, 3, 1) cr = cr.permute(0, 2, 3, 1) return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) class BlockSplitting(nn.Module): """ Splitting image into patches """ def __init__(self): super(BlockSplitting, self).__init__() self.k = 8 def forward(self, image): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x h*w/64 x h x w """ height, _ = image.shape[1:3] batch_size = image.shape[0] image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) class DCT8x8(nn.Module): """ Discrete Cosine Transformation """ def __init__(self): super(DCT8x8, self).__init__() tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) for x, y, u, v in itertools.product(range(8), repeat=4): tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) alpha = np.array([1. / np.sqrt(2)] + [1] * 7) self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) def forward(self, image): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ image = image - 128 result = self.scale * torch.tensordot(image, self.tensor, dims=2) result.view(image.shape) return result class YQuantize(nn.Module): """ JPEG Quantization for Y channel Args: rounding(function): rounding function to use """ def __init__(self, rounding): super(YQuantize, self).__init__() self.rounding = rounding self.y_table = y_table def forward(self, image, factor=1): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ if isinstance(factor, (int, float)): image = image.float() / (self.y_table * factor) else: b = factor.size(0) table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) image = image.float() / table image = self.rounding(image) return image class CQuantize(nn.Module): """ JPEG Quantization for CbCr channels Args: rounding(function): rounding function to use """ def __init__(self, rounding): super(CQuantize, self).__init__() self.rounding = rounding self.c_table = c_table def forward(self, image, factor=1): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ if isinstance(factor, (int, float)): image = image.float() / (self.c_table * factor) else: b = factor.size(0) table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) image = image.float() / table image = self.rounding(image) return image class CompressJpeg(nn.Module): """Full JPEG compression algorithm Args: rounding(function): rounding function to use """ def __init__(self, rounding=torch.round): super(CompressJpeg, self).__init__() self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) self.c_quantize = CQuantize(rounding=rounding) self.y_quantize = YQuantize(rounding=rounding) def forward(self, image, factor=1): """ Args: image(tensor): batch x 3 x height x width Returns: dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. """ y, cb, cr = self.l1(image * 255) components = {'y': y, 'cb': cb, 'cr': cr} for k in components.keys(): comp = self.l2(components[k]) if k in ('cb', 'cr'): comp = self.c_quantize(comp, factor=factor) else: comp = self.y_quantize(comp, factor=factor) components[k] = comp return components['y'], components['cb'], components['cr'] # ------------------------ decompression ------------------------# class YDequantize(nn.Module): """Dequantize Y channel """ def __init__(self): super(YDequantize, self).__init__() self.y_table = y_table def forward(self, image, factor=1): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ if isinstance(factor, (int, float)): out = image * (self.y_table * factor) else: b = factor.size(0) table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) out = image * table return out class CDequantize(nn.Module): """Dequantize CbCr channel """ def __init__(self): super(CDequantize, self).__init__() self.c_table = c_table def forward(self, image, factor=1): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ if isinstance(factor, (int, float)): out = image * (self.c_table * factor) else: b = factor.size(0) table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) out = image * table return out class iDCT8x8(nn.Module): """Inverse discrete Cosine Transformation """ def __init__(self): super(iDCT8x8, self).__init__() alpha = np.array([1. / np.sqrt(2)] + [1] * 7) self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) for x, y, u, v in itertools.product(range(8), repeat=4): tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) def forward(self, image): """ Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width """ image = image * self.alpha result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 result.view(image.shape) return result class BlockMerging(nn.Module): """Merge patches into image """ def __init__(self): super(BlockMerging, self).__init__() def forward(self, patches, height, width): """ Args: patches(tensor) batch x height*width/64, height x width height(int) width(int) Returns: Tensor: batch x height x width """ k = 8 batch_size = patches.shape[0] image_reshaped = patches.view(batch_size, height // k, width // k, k, k) image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) return image_transposed.contiguous().view(batch_size, height, width) class ChromaUpsampling(nn.Module): """Upsample chroma layers """ def __init__(self): super(ChromaUpsampling, self).__init__() def forward(self, y, cb, cr): """ Args: y(tensor): y channel image cb(tensor): cb channel cr(tensor): cr channel Returns: Tensor: batch x height x width x 3 """ def repeat(x, k=2): height, width = x.shape[1:3] x = x.unsqueeze(-1) x = x.repeat(1, 1, k, k) x = x.view(-1, height * k, width * k) return x cb = repeat(cb) cr = repeat(cr) return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) class YCbCr2RGBJpeg(nn.Module): """Converts YCbCr image to RGB JPEG """ def __init__(self): super(YCbCr2RGBJpeg, self).__init__() matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) self.matrix = nn.Parameter(torch.from_numpy(matrix)) def forward(self, image): """ Args: image(tensor): batch x height x width x 3 Returns: Tensor: batch x 3 x height x width """ result = torch.tensordot(image + self.shift, self.matrix, dims=1) return result.view(image.shape).permute(0, 3, 1, 2) class DeCompressJpeg(nn.Module): """Full JPEG decompression algorithm Args: rounding(function): rounding function to use """ def __init__(self, rounding=torch.round): super(DeCompressJpeg, self).__init__() self.c_dequantize = CDequantize() self.y_dequantize = YDequantize() self.idct = iDCT8x8() self.merging = BlockMerging() self.chroma = ChromaUpsampling() self.colors = YCbCr2RGBJpeg() def forward(self, y, cb, cr, imgh, imgw, factor=1): """ Args: compressed(dict(tensor)): batch x h*w/64 x 8 x 8 imgh(int) imgw(int) factor(float) Returns: Tensor: batch x 3 x height x width """ components = {'y': y, 'cb': cb, 'cr': cr} for k in components.keys(): if k in ('cb', 'cr'): comp = self.c_dequantize(components[k], factor=factor) height, width = int(imgh / 2), int(imgw / 2) else: comp = self.y_dequantize(components[k], factor=factor) height, width = imgh, imgw comp = self.idct(comp) components[k] = self.merging(comp, height, width) # image = self.chroma(components['y'], components['cb'], components['cr']) image = self.colors(image) image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) return image / 255 # ------------------------ main DiffJPEG ------------------------ # class DiffJPEG(nn.Module): """This JPEG algorithm result is slightly different from cv2. DiffJPEG supports batch processing. Args: differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round """ def __init__(self, differentiable=True): super(DiffJPEG, self).__init__() if differentiable: rounding = diff_round else: rounding = torch.round self.compress = CompressJpeg(rounding=rounding) self.decompress = DeCompressJpeg(rounding=rounding) def forward(self, x, quality): """ Args: x (Tensor): Input image, bchw, rgb, [0, 1] quality(float): Quality factor for jpeg compression scheme. """ factor = quality if isinstance(factor, (int, float)): factor = quality_to_factor(factor) else: for i in range(factor.size(0)): factor[i] = quality_to_factor(factor[i]) h, w = x.size()[-2:] h_pad, w_pad = 0, 0 # why should use 16 if h % 16 != 0: h_pad = 16 - h % 16 if w % 16 != 0: w_pad = 16 - w % 16 x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) y, cb, cr = self.compress(x, factor=factor) recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) recovered = recovered[:, :, 0:h, 0:w] return recovered if __name__ == '__main__': import cv2 from basicsr.utils import img2tensor, tensor2img img_gt = cv2.imread('test.png') / 255. # -------------- cv2 -------------- # encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) img_lq = np.float32(cv2.imdecode(encimg, 1)) cv2.imwrite('cv2_JPEG_20.png', img_lq) # -------------- DiffJPEG -------------- # jpeger = DiffJPEG(differentiable=False).cuda() img_gt = img2tensor(img_gt) img_gt = torch.stack([img_gt, img_gt]).cuda() quality = img_gt.new_tensor([20, 40]) out = jpeger(img_gt, quality=quality) cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) ================================================ FILE: 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: basicsr/utils/download_util.py ================================================ import math import os import requests from torch.hub import download_url_to_file, get_dir from tqdm import tqdm from urllib.parse import urlparse 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)} / {readable_file_size}') if chunk: # filter out keep-alive new chunks f.write(chunk) if pbar is not None: pbar.close() def load_file_from_url(url, model_dir=None, progress=True, file_name=None): """Load file form http url, will download models if necessary. Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py Args: url (str): URL to be downloaded. model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. Default: None. progress (bool): Whether to show the download progress. Default: True. file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. Returns: str: The path to the downloaded file. """ if model_dir is None: # use the pytorch hub_dir hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') os.makedirs(model_dir, exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(model_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file ================================================ FILE: basicsr/utils/face_util.py ================================================ import cv2 import numpy as np import os import torch from skimage import transform as trans from basicsr.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: 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, **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 different 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: 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, 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(os.path.dirname(filename), exist_ok=True) cv2.imwrite(filename, dxdy) 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: basicsr/utils/img_process_util.py ================================================ import cv2 import numpy as np import torch from torch.nn import functional as F def filter2D(img, kernel): """PyTorch version of cv2.filter2D Args: img (Tensor): (b, c, h, w) kernel (Tensor): (b, k, k) """ k = kernel.size(-1) b, c, h, w = img.size() if k % 2 == 1: img = F.pad(img, (k // 2, k // 2, k // 2, k // 2), mode='reflect') else: raise ValueError('Wrong kernel size') ph, pw = img.size()[-2:] if kernel.size(0) == 1: # apply the same kernel to all batch images img = img.view(b * c, 1, ph, pw) kernel = kernel.view(1, 1, k, k) return F.conv2d(img, kernel, padding=0).view(b, c, h, w) else: img = img.view(1, b * c, ph, pw) kernel = kernel.view(b, 1, k, k).repeat(1, c, 1, 1).view(b * c, 1, k, k) return F.conv2d(img, kernel, groups=b * c).view(b, c, h, w) def usm_sharp(img, weight=0.5, radius=50, threshold=10): """USM sharpening. Input image: I; Blurry image: B. 1. sharp = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * sharp + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) sharp = img + weight * residual sharp = np.clip(sharp, 0, 1) return soft_mask * sharp + (1 - soft_mask) * img class USMSharp(torch.nn.Module): def __init__(self, radius=50, sigma=0): super(USMSharp, self).__init__() if radius % 2 == 0: radius += 1 self.radius = radius kernel = cv2.getGaussianKernel(radius, sigma) kernel = torch.FloatTensor(np.dot(kernel, kernel.transpose())).unsqueeze_(0) self.register_buffer('kernel', kernel) def forward(self, img, weight=0.5, threshold=10): blur = filter2D(img, self.kernel) residual = img - blur mask = torch.abs(residual) * 255 > threshold mask = mask.float() soft_mask = filter2D(mask, self.kernel) sharp = img + weight * residual sharp = torch.clip(sharp, 0, 1) return soft_mask * sharp + (1 - soft_mask) * img ================================================ FILE: 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: if img.dtype == 'float64': img = img.astype('float32') 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(f'Only support 4D, 3D or 2D tensor. 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 tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)): """This implementation is slightly faster than tensor2img. It now only supports torch tensor with shape (1, c, h, w). Args: tensor (Tensor): Now only support torch tensor with (1, c, h, w). rgb2bgr (bool): Whether to change rgb to bgr. Default: True. min_max (tuple[int]): min and max values for clamp. """ output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0) output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255 output = output.type(torch.uint8).cpu().numpy() if rgb2bgr: output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output 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} img = cv2.imdecode(img_np, imread_flags[flag]) if float32: img = img.astype(np.float32) / 255. return img 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) ok = cv2.imwrite(file_path, img, params) if not ok: raise IOError('Failed in writing images.') 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: 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: basicsr/utils/logger.py ================================================ import datetime import logging import time from .dist_util import get_dist_info, master_only initialized_logger = {} class AvgTimer(): def __init__(self, window=200): self.window = window # average window self.current_time = 0 self.total_time = 0 self.count = 0 self.avg_time = 0 self.start() def start(self): self.start_time = self.tic = time.time() def record(self): self.count += 1 self.toc = time.time() self.current_time = self.toc - self.tic self.total_time += self.current_time # calculate average time self.avg_time = self.total_time / self.count # reset if self.count > self.window: self.count = 0 self.total_time = 0 self.tic = time.time() def get_current_time(self): return self.current_time def get_avg_time(self): return self.avg_time 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() def reset_start_time(self): self.start_time = time.time() @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}, 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 = get_root_logger() project = opt['logger']['wandb']['project'] resume_id = opt['logger']['wandb'].get('resume_id') entity = opt['logger']['wandb'].get('entity') 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' if entity: wandb.init(entity=entity, id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True) else: 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}, entity={entity}.') 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: 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. """ squeeze_flag = False if type(img).__module__ == np.__name__: # numpy type numpy_type = True if img.ndim == 2: img = img[:, :, None] squeeze_flag = True img = torch.from_numpy(img.transpose(2, 0, 1)).float() else: numpy_type = False if img.ndim == 2: img = img.unsqueeze(0) squeeze_flag = True 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 squeeze_flag: out_2 = out_2.squeeze(0) if numpy_type: out_2 = out_2.numpy() if not squeeze_flag: out_2 = out_2.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 conversion 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(f'The img type should be np.float32 or np.uint8, 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 conversion 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(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}') if dst_type == np.uint8: img = img.round() else: img /= 255. return img.astype(dst_type) ================================================ FILE: 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 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' in key) or ('pretrain_network' in key) or ('resume' in key) or ('param_key' in key): continue else: 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 paths. """ 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 check_resume(opt, resume_iter): """Check resume states and pretrain_network paths. Args: opt (dict): Options. resume_iter (int): Resume iteration. """ 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: print('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 (network not in opt['path']['ignore_resume_networks']): opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth') print(f"Set {name} to {opt['path'][name]}") # change param_key to params in resume param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')] for param_key in param_keys: if opt['path'][param_key] == 'params_ema': opt['path'][param_key] = 'params' print(f'Set {param_key} to params') def sizeof_fmt(size, suffix='B'): """Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Formatted 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: basicsr/utils/options.py ================================================ import argparse import random import torch import yaml from collections import OrderedDict from os import path as osp from basicsr.utils import set_random_seed from basicsr.utils.dist_util import get_dist_info, init_dist, master_only 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 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 def _postprocess_yml_value(value): # None if value == '~' or value.lower() == 'none': return None # bool if value.lower() == 'true': return True elif value.lower() == 'false': return False # !!float number if value.startswith('!!float'): return float(value.replace('!!float', '')) # number if value.isdigit(): return int(value) elif value.replace('.', '', 1).isdigit() and value.count('.') < 2: return float(value) # list if value.startswith('['): return eval(value) # str return value def parse_options(root_path, 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('--auto_resume', action='store_true') parser.add_argument('--debug', action='store_true') parser.add_argument('--local_rank', type=int, default=0) parser.add_argument( '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999') args = parser.parse_args() # parse yml to dict with open(args.opt, mode='r') as f: opt = yaml.load(f, Loader=ordered_yaml()[0]) # 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) 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']) # force to update yml options if args.force_yml is not None: for entry in args.force_yml: # now do not support creating new keys keys, value = entry.split('=') keys, value = keys.strip(), value.strip() value = _postprocess_yml_value(value) eval_str = 'opt' for key in keys.split(':'): eval_str += f'["{key}"]' eval_str += '=value' # using exec function exec(eval_str) opt['auto_resume'] = args.auto_resume opt['is_train'] = is_train # debug setting if args.debug and not opt['name'].startswith('debug'): opt['name'] = 'debug_' + opt['name'] if opt['num_gpu'] == 'auto': opt['num_gpu'] = torch.cuda.device_count() # datasets for phase, dataset in opt['datasets'].items(): # for multiple datasets, e.g., val_1, val_2; 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) if is_train: experiments_root = osp.join(root_path, '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(root_path, 'results', opt['name']) opt['path']['results_root'] = results_root opt['path']['log'] = results_root opt['path']['visualization'] = osp.join(results_root, 'visualization') return opt, args @master_only def copy_opt_file(opt_file, experiments_root): # copy the yml file to the experiment root import sys import time from shutil import copyfile cmd = ' '.join(sys.argv) filename = osp.join(experiments_root, osp.basename(opt_file)) copyfile(opt_file, filename) with open(filename, 'r+') as f: lines = f.readlines() lines.insert(0, f'# GENERATE TIME: {time.asctime()}\n# CMD:\n# {cmd}\n\n') f.seek(0) f.writelines(lines) ================================================ FILE: basicsr/utils/registry.py ================================================ # Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py # noqa: E501 class Registry(): """ The registry that provides name -> object mapping, to support third-party users' custom modules. To create a registry (e.g. a backbone registry): .. code-block:: python BACKBONE_REGISTRY = Registry('BACKBONE') To register an object: .. code-block:: python @BACKBONE_REGISTRY.register() class MyBackbone(): ... Or: .. code-block:: python BACKBONE_REGISTRY.register(MyBackbone) """ def __init__(self, name): """ Args: name (str): the name of this registry """ self._name = name self._obj_map = {} def _do_register(self, name, obj): assert (name not in self._obj_map), (f"An object named '{name}' was already registered " f"in '{self._name}' registry!") self._obj_map[name] = obj def register(self, obj=None): """ Register the given object under the the name `obj.__name__`. Can be used as either a decorator or not. See docstring of this class for usage. """ if obj is None: # used as a decorator def deco(func_or_class): name = func_or_class.__name__ self._do_register(name, func_or_class) return func_or_class return deco # used as a function call name = obj.__name__ self._do_register(name, obj) def get(self, name): ret = self._obj_map.get(name) if ret is None: raise KeyError(f"No object named '{name}' found in '{self._name}' registry!") return ret def __contains__(self, name): return name in self._obj_map def __iter__(self): return iter(self._obj_map.items()) def keys(self): return self._obj_map.keys() DATASET_REGISTRY = Registry('dataset') ARCH_REGISTRY = Registry('arch') MODEL_REGISTRY = Registry('model') LOSS_REGISTRY = Registry('loss') METRIC_REGISTRY = Registry('metric') ================================================ FILE: colab/README.md ================================================ # Colab google colab logo To maintain a small size of BasicSR repo, we do not include the original colab notebooks in this repo, but provide links to the google colab. | Face Restoration| | | :--- | :---: | |DFDNet | [BasicSR_inference_DFDNet.ipynb](https://colab.research.google.com/drive/1RoNDeipp9yPjI3EbpEbUhn66k5Uzg4n8?usp=sharing)| | **Super-Resolution**| | |ESRGAN |[BasicSR_inference_ESRGAN.ipynb](https://colab.research.google.com/drive/1JQScYICvEC3VqaabLu-lxvq9h7kSV1ML?usp=sharing)| | **Deblurring**| | | **Denoise**| | ================================================ FILE: datasets/README.md ================================================ # Symlink/Put all the datasets here It is recommended to symlink your dataset root to this folder - `datasets` with the command `ln -s xxx yyy`. Please refer to [DatasetPreparation.md](../docs/DatasetPreparation.md) for more details about data preparation. --- 推荐把数据通过 `ln -s xxx yyy` 软链到当前目录 `datasets` 下. 更多数据准备的细节参见 [DatasetPreparation_CN.md](../docs/DatasetPreparation_CN.md). ================================================ FILE: docs/Config.md ================================================ # Configuration [English](Config.md) **|** [简体中文](Config_CN.md) #### Contents 1. [Experiment Name Convention](#Experiment-Name-Convention) 1. [Configuration Explanation](#Configuration-Explanation) 1. [Training Configuration](#Training-Configuration) 1. [Testing Configuration](#Testing-Configuration) ## Experiment Name Convention Taking `001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb` as an example: - `001`: We usually use index for managing experiments - `MSRResNet`: Model name, here is Modified SRResNet - `x4_f64b16`: Import configuration parameters. It means the upsampling ratio is 4; the channel number of middle features is 64; and it uses 16 residual block - `DIV2K`: Training data is DIV2K - `1000k`: Total training iteration is 1000k - `B16G1`: Batch size is 16; one GPU is used for training - `wandb`: Use wandb logger; the training process has beed uploaded to wandb server **Note**: If `debug` is in the experiment name, it will enter the debug mode. That is, the program will log and validate more intensively and will not use `tensorboard logger` and `wandb logger`. ## Configuration Explanation We use yaml files for configuration. ### Training Configuration Taking [train_MSRResNet_x4.yml](../options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml) as an example: ```yml #################################### # The following are general settings #################################### # Experiment name, more details are in [Experiment Name Convention]. If debug in the experiment name, it will enter debug mode name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb # Model type. Usually the class name defined in the `models` folder model_type: SRModel # The scale of the output over the input. In SR, it is the upsampling ratio. If not defined, use 1 scale: 4 # The number of GPUs for training num_gpu: 1 # set num_gpu: 0 for cpu mode # Random seed manual_seed: 0 ######################################################## # The following are the dataset and data loader settings ######################################################## datasets: # Training dataset settings train: # Dataset name name: DIV2K # Dataset type. Usually the class name defined in the `data` folder type: PairedImageDataset #### The following arguments are flexible and can be obtained in the corresponding doc # GT (Ground-Truth) folder path dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub # LQ (Low-Quality) folder path dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # template for file name. Usually, LQ files have suffix like `_x4`. It is used for file name mismatching filename_tmpl: '{}' # IO backend, more details are in [docs/DatasetPreparation.md] io_backend: # directly read from disk type: disk # Ground-Truth training patch size gt_size: 128 # Whether to use horizontal flip. Here, flip is for horizontal flip use_hflip: true # Whether to rotate. Here for rotations with every 90 degree use_rot: true #### The following are data loader settings # Whether to shuffle use_shuffle: true # Number of workers of reading data for each GPU num_worker_per_gpu: 6 # Total training batch size batch_size_per_gpu: 16 # THe ratio of enlarging dataset. For example, it will repeat 100 times for a dataset with 15 images # So that after one epoch, it will read 1500 times. It is used for accelerating data loader # since it costs too much time at the start of a new epoch dataset_enlarge_ratio: 100 # validation dataset settings val: # Dataset name name: Set5 # Dataset type. Usually the class name defined in the `data` folder type: PairedImageDataset #### The following arguments are flexible and can be obtained in the corresponding doc # GT (Ground-Truth) folder path dataroot_gt: datasets/Set5/GTmod12 # LQ (Low-Quality) folder path dataroot_lq: datasets/Set5/LRbicx4 # IO backend, more details are in [docs/DatasetPreparation.md] io_backend: # directly read from disk type: disk ################################################## # The following are the network structure settings ################################################## # network g settings network_g: # Architecture type. Usually the class name defined in the `basicsr/archs` folder type: MSRResNet #### The following arguments are flexible and can be obtained in the corresponding doc # Channel number of inputs num_in_ch: 3 # Channel number of outputs num_out_ch: 3 # Channel number of middle features num_feat: 64 # block number num_block: 16 # upsampling ratio upscale: 4 ######################################################### # The following are path, pretraining and resume settings ######################################################### path: # Path for pretrained models, usually end with pth pretrain_network_g: ~ # Whether to load pretrained models strictly, that is the corresponding parameter names should be the same strict_load_g: true # Path for resume state. Usually in the `experiments/exp_name/training_states` folder # This argument will over-write the `pretrain_network_g` resume_state: ~ ##################################### # The following are training settings ##################################### train: # Optimizer settings optim_g: # Optimizer type type: Adam #### The following arguments are flexible and can be obtained in the corresponding doc # Learning rate lr: !!float 2e-4 weight_decay: 0 # beta1 and beta2 for the Adam betas: [0.9, 0.99] # Learning rate scheduler settings scheduler: # Scheduler type type: CosineAnnealingRestartLR #### The following arguments are flexible and can be obtained in the corresponding doc # Cosine Annealing periods periods: [250000, 250000, 250000, 250000] # Cosine Annealing restart weights restart_weights: [1, 1, 1, 1] # Cosine Annealing minimum learning rate eta_min: !!float 1e-7 # Total iterations for training total_iter: 1000000 # Warm up iterations. -1 indicates no warm up warmup_iter: -1 #### The following are loss settings # Pixel-wise loss options pixel_opt: # Loss type. Usually the class name defined in the `basicsr/models/losses` folder type: L1Loss # Loss weight loss_weight: 1.0 # Loss reduction mode reduction: mean ####################################### # The following are validation settings ####################################### val: # validation frequency. Validate every 5000 iterations val_freq: !!float 5e3 # Whether to save images during validation save_img: false # Metrics in validation metrics: # Metric name. It can be arbitrary psnr: # Metric type. Usually the function name defined in the`basicsr/metrics` folder type: calculate_psnr #### The following arguments are flexible and can be obtained in the corresponding doc # Whether to crop border during validation crop_border: 4 # Whether to convert to Y(CbCr) for validation test_y_channel: false ######################################## # The following are the logging settings ######################################## logger: # Logger frequency print_freq: 100 # The frequency for saving checkpoints save_checkpoint_freq: !!float 5e3 # Whether to tensorboard logger use_tb_logger: true # Whether to use wandb logger. Currently, wandb only sync the tensorboard log. So we should also turn on tensorboard when using wandb wandb: # wandb project name. Default is None, that is not using wandb. # Here, we use the basicsr wandb project: https://app.wandb.ai/xintao/basicsr project: basicsr # If resuming, wandb id could automatically link previous logs resume_id: ~ ################################################ # The following are distributed training setting # Only require for slurm training ################################################ dist_params: backend: nccl port: 29500 ``` ### Testing Configuration Taking [test_MSRResNet_x4.yml](../options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml) as an example: ```yml # Experiment name name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb # Model type. Usually the class name defined in the `models` folder model_type: SRModel # The scale of the output over the input. In SR, it is the upsampling ratio. If not defined, use 1 scale: 4 # The number of GPUs for testing num_gpu: 1 # set num_gpu: 0 for cpu mode ######################################################## # The following are the dataset and data loader settings ######################################################## datasets: # Testing dataset settings. The first testing dataset test_1: # Dataset name name: Set5 # Dataset type. Usually the class name defined in the `data` folder type: PairedImageDataset #### The following arguments are flexible and can be obtained in the corresponding doc # GT (Ground-Truth) folder path dataroot_gt: datasets/Set5/GTmod12 # LQ (Low-Quality) folder path dataroot_lq: datasets/Set5/LRbicx4 # IO backend, more details are in [docs/DatasetPreparation.md] io_backend: # directly read from disk type: disk # Testing dataset settings. The second testing dataset test_2: name: Set14 type: PairedImageDataset dataroot_gt: datasets/Set14/GTmod12 dataroot_lq: datasets/Set14/LRbicx4 io_backend: type: disk # Testing dataset settings. The third testing dataset test_3: name: DIV2K100 type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_valid_HR dataroot_lq: datasets/DIV2K/DIV2K_valid_LR_bicubic/X4 filename_tmpl: '{}x4' io_backend: type: disk ################################################## # The following are the network structure settings ################################################## # network g settings network_g: # Architecture type. Usually the class name defined in the `basicsr/archs` folder type: MSRResNet #### The following arguments are flexible and can be obtained in the corresponding doc # Channel number of inputs num_in_ch: 3 # Channel number of outputs num_out_ch: 3 # Channel number of middle features num_feat: 64 # block number num_block: 16 # upsampling ratio upscale: 4 upscale: 4 ################################################# # The following are path and pretraining settings ################################################# path: ## Path for pretrained models, usually end with pth pretrain_network_g: experiments/001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb/models/net_g_1000000.pth # Whether to load pretrained models strictly, that is the corresponding parameter names should be the same strict_load_g: true ########################################################## # The following are validation settings (Also for testing) ########################################################## val: # Whether to save images during validation save_img: true # Suffix for saved images. If None, use exp name suffix: ~ # Metrics in validation metrics: # Metric name. It can be arbitrary psnr: # Metric type. Usually the function name defined in the`basicsr/metrics` folder type: calculate_psnr #### The following arguments are flexible and can be obtained in the corresponding doc # Whether to crop border during validation crop_border: 4 # Whether to convert to Y(CbCr) for validation test_y_channel: false # Another metric ssim: type: calculate_ssim crop_border: 4 test_y_channel: false ``` ================================================ FILE: docs/Config_CN.md ================================================ # 配置文件 [English](Config.md) **|** [简体中文](Config_CN.md) #### 目录 1. [实验命名](#实验命名) 1. [配置文件说明](#配置文件说明) 1. [训练配置文件](#训练配置文件) 1. [测试配置文件](#测试配置文件) ## 实验命名 以`001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb`为例: - `001`: 我们一般给实验标号, 方便实验管理 - `MSRResNet`: 模型名称, 这里是Modified SRResNet - `x4_f64b16`: 重要配置参数, 这里表示放大4倍; 中间feature通道数是64, 使用了16个Residual Block - `DIV2K`: 训练数据集是DIV2K - `1000k`: 训练了1000k iterations - `B16G1`: Batch size为16, 使用一个GPU训练 - `wandb`: 使用了wandb, 训练过程上传到了wandb云服务器 **注意**: 如果在实验名字中有`debug`字样, 则会进入debug模式, 即程序会更密集地log和validate, 并且不会使用`tensorboard logger`和`wandb logger`. ## 配置文件说明 我们使用了 yaml 格式来做配置文件. ### 训练配置文件 我们以[train_MSRResNet_x4.yml](../options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml)为例, 说明训练配置文件的含义: ```yml ################# # 以下为通用的设置 ################# # 实验名称, 具体可参见 [实验名称命名], 若实验名字中有debug字样, 则会进入debug模式 name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb # 使用的model类型, 一般为在`models`目录下定义的模型的类名 model_type: SRModel # 输出相比输入的放大比率, 在SR中是放大倍数; 若有些任务没有这个配置, 则写1 scale: 4 # 训练卡数 num_gpu: 1 # set num_gpu: 0 for cpu mode # 随机种子设定 manual_seed: 0 ################################# # 以下为dataset和data loader的设置 ################################# datasets: # 训练数据集的设置 train: # 数据集的名称 name: DIV2K # 数据集的类型, 一般为在`data`目录下定义的dataset的类名 type: PairedImageDataset #### 以下属性是灵活的, 可以在相应类的说明文档中获得; 若新加数据集, 则可以根据需要添加 # GT (Ground-Truth) 图像的文件夹路径 dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub # LQ (Low-Quality) 图像的文件夹路径 dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # 文件名字模板, 一般LQ文件会有类似`_x4`这样的文件后缀, 这个就是来处理GT和LQ文件后缀不匹配的问题的 filename_tmpl: '{}' # IO 读取的backend, 详细可以参见 [docs/DatasetPreparation_CN.md] io_backend: # disk 表示直接从硬盘读取 type: disk # 训练中Ground-Truth的Training patch的大小 gt_size: 128 # 是否使用horizontal flip, 这里的flip特指 horizontal flip use_hflip: true # 是否使用rotation, 这里指的是每隔90°旋转 use_rot: true #### 下面是data loader的设置 # data loader是否使用shuffle use_shuffle: true # 每一个GPU的data loader读取进程数目 num_worker_per_gpu: 6 # 总共的训练batch size batch_size_per_gpu: 16 # 扩大dataset的倍率. 比如数据集有15张图, 则会重复这些图片100次, 这样一个epoch下来, 能够读取1500张图 # (事实上是重复读的). 它经常用来加速data loader, 因为在有的机器上, 一个epoch结束, 会重启进程, 往往会很慢 dataset_enlarge_ratio: 100 # validation 数据集的设置 val: # 数据集名称 name: Set5 # 数据集的类型, 一般为在`data`目录下定义的dataset的类名 type: PairedImageDataset #### 以下属性是灵活的, 可以在相应类的说明文档中获得; 若新加数据集, 则可以根据需要添加 # GT (Ground-Truth) 图像的文件夹路径 dataroot_gt: datasets/Set5/GTmod12 # LQ (Low-Quality) 图像的文件夹路径 dataroot_lq: datasets/Set5/LRbicx4 # IO 读取的backend, 详细可以参见 [docs/DatasetPreparation_CN.md] io_backend: # disk 表示直接从硬盘读取 type: disk ##################### # 以下为网络结构的设置 ##################### # 网络g的设置 network_g: # 网络结构 (Architecture)的类型, 一般为在`basicsr/archs`目录下定义的dataset的类名 type: MSRResNet #### 以下属性是灵活的, 可以在相应类的说明文档中获得 # 输入通道数目 num_in_ch: 3 # 输出通道数目 num_out_ch: 3 # 中间特征通道数目 num_feat: 64 # 使用block的数目 num_block: 16 # SR的放大倍数 upscale: 4 ###################################### # 以下为路径和与训练模型、重启训练的设置 ###################################### path: # 预训练模型的路径, 需要以pth结尾的模型 pretrain_network_g: ~ # 加载预训练模型的时候, 是否需要网络参数的名称严格对应 strict_load_g: true # 重启训练的状态路径, 一般在`experiments/exp_name/training_states`目录下 # 这个设置了, 会覆盖 pretrain_network_g 的设定 resume_state: ~ ################# # 以下为训练的设置 ################# train: # 优化器设置 optim_g: # 优化器类型 type: Adam ##### 以下属性是灵活的, 根据不同优化器有不同的设置 # 学习率 lr: !!float 2e-4 weight_decay: 0 # Adam优化器的 beta1 和 beta2 betas: [0.9, 0.99] # 学习率的设定 scheduler: # 学习率Scheduler的类型 type: CosineAnnealingRestartLR #### 以下属性是灵活的, 根据学习率Scheduler有不同的设置 # Cosine Annealing的周期 periods: [250000, 250000, 250000, 250000] # Cosine Annealing每次Restart的权重 restart_weights: [1, 1, 1, 1] # Cosine Annealing的学习率最小值 eta_min: !!float 1e-7 # 总共的训练迭代次数 total_iter: 1000000 # warm up的iteration数目, 如是-1, 表示没有warm up warmup_iter: -1 # no warm up #### 以下是loss的设置 # pixel-wise loss的options pixel_opt: # loss类型, 一般为在`basicsr/models/losses`目录下定义的loss类名 type: L1Loss # loss 权重 loss_weight: 1.0 # loss reduction方式 reduction: mean ####################### # 以下为Validation的设置 ####################### val: # validation的频率, 每隔 5000 iterations 做一次validation val_freq: !!float 5e3 # 是否需要在validation的时候保存图片 save_img: false # Validation时候使用的metric metrics: # metric的名字, 这个名字可以是任意的 psnr: # metric的类型, 一般为在`basicsr/metrics`目录下定义的metric函数名 type: calculate_psnr #### 以下属性是灵活的, 根据metric有不同的设置 # 计算metric时, 是否需要crop border crop_border: 4 # 是否转成在Y(CbCr)空间上计算metric test_y_channel: false #################### # 以下为Logging的设置 #################### logger: # 屏幕上打印的logger频率 print_freq: 100 # 保存checkpoint的频率 save_checkpoint_freq: !!float 5e3 # 是否使用tensorboard logger use_tb_logger: true # 是否使用wandb logger, 目前wandb只是同步tensorboard的内容, 因此要使用wandb, 必须也同时使用tensorboard wandb: # wandb的project. 默认是 None, 即不使用wandb. # 这里使用了 basicsr wandb project: https://app.wandb.ai/xintao/basicsr project: basicsr # 如果是resume, 可以输入上次的wandb id, 则log可以接起来 resume_id: ~ ############################################################# # 以下为distributed training的设置, 目前只有在Slurm训练下才需要 ############################################################# dist_params: backend: nccl port: 29500 ``` ### 测试配置文件 我们以[test_MSRResNet_x4.yml](../options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml)为例, 说明测试配置文件的含义: ```yml # 实验名称 name: 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb # 使用的model类型, 一般为在`models`目录下定义的模型的类名 model_type: SRModel # 输出相比输入的放大比率, 在SR中是放大倍数; 若有些任务没有这个配置, 则写1 scale: 4 # 测试卡数 num_gpu: 1 # set num_gpu: 0 for cpu mode ################################# # 以下为dataset和data loader的设置 ################################# datasets: # 测试数据集的设置, 后缀1表示第一个测试集 test_1: # 数据集的名称 name: Set5 # 数据集的类型, 一般为在`data`目录下定义的dataset的类名 type: PairedImageDataset #### 以下属性是灵活的, 可以在相应类的说明文档中获得; 若新加数据集, 则可以根据需要添加 # GT (Ground-Truth) 图像的文件夹路径 dataroot_gt: datasets/Set5/GTmod12 # LQ (Low-Quality) 图像的文件夹路径 dataroot_lq: datasets/Set5/LRbicx4 # IO 读取的backend, 详细可以参见 [docs/DatasetPreparation_CN.md] io_backend: # disk 表示直接从硬盘读取 type: disk # 测试数据集的设置, 后缀2表示第二个测试集 test_2: name: Set14 type: PairedImageDataset dataroot_gt: datasets/Set14/GTmod12 dataroot_lq: datasets/Set14/LRbicx4 io_backend: type: disk # 测试数据集的设置, 后缀3表示第三个测试集 test_3: name: DIV2K100 type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_valid_HR dataroot_lq: datasets/DIV2K/DIV2K_valid_LR_bicubic/X4 filename_tmpl: '{}x4' io_backend: type: disk ##################### # 以下为网络结构的设置 ##################### # 网络g的设置 network_g: # 网络结构 (Architecture)的类型, 一般为在`basicsr/archs`目录下定义的dataset的类名 type: MSRResNet #### 以下属性是灵活的, 可以在相应类的说明文档中获得 # 输入通道数目 num_in_ch: 3 # 输出通道数目 num_out_ch: 3 # 中间特征通道数目 num_feat: 64 # 使用block的数目 num_block: 16 # SR的放大倍数 upscale: 4 ############################# # 以下为路径和与训练模型的设置 ############################# path: # 预训练模型的路径, 需要以pth结尾的模型 pretrain_network_g: experiments/001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb/models/net_g_1000000.pth # 加载预训练模型的时候, 是否需要网络参数的名称严格对应 strict_load_g: true ################################## # 以下为Validation (也是测试)的设置 ################################## val: # 是否需要在测试的时候保存图片 save_img: true # 对保存的图片添加后缀，如果是None, 则使用exp name suffix: ~ # 测试时候使用的metric metrics: # metric的名字, 这个名字可以是任意的 psnr: # metric的类型, 一般为在`basicsr/metrics`目录下定义的metric函数名 type: calculate_psnr #### 以下属性是灵活的, 根据metric有不同的设置 # 计算metric时, 是否需要crop border crop_border: 4 # 是否转成在Y(CbCr)空间上计算metric test_y_channel: false # 另外一个metric ssim: type: calculate_ssim crop_border: 4 test_y_channel: false ``` ================================================ FILE: docs/DatasetPreparation.md ================================================ # Dataset Preparation [English](DatasetPreparation.md) **|** [简体中文](DatasetPreparation_CN.md) #### Contents 1. [Data Storage Format](#Data-Storage-Format) 1. [How to Use](#How-to-Use) 1. [How to Implement](#How-to-Implement) 1. [LMDB Description](#LMDB-Description) 1. [Data Pre-fetcher](#Data-Pre-fetcher) 1. [Image Super-Resolution](#Image-Super-Resolution) 1. [DIV2K](#DIV2K) 1. [Common Image SR Datasets](#Common-Image-SR-Datasets) 1. [Video Super-Resolution](#Video-Super-Resolution) 1. [REDS](#REDS) 1. [Vimeo90K](#Vimeo90K) 1. [StylgeGAN2](#StyleGAN2) 1. [FFHQ](#FFHQ) ## Data Storage Format At present, there are three types of data storage formats supported: 1. Store in `hard disk` directly in the format of images / video frames. 1. Make [LMDB](https://lmdb.readthedocs.io/en/release/), which could accelerate the IO and decompression speed during training. 1. [memcached](https://memcached.org/) is also supported, if they are installed (usually on clusters). #### How to Use At present, we can modify the configuration yaml file to support different data storage formats. Taking [PairedImageDataset](../basicsr/data/paired_image_dataset.py) as an example, we can modify the yaml file according to different requirements. 1. Directly read disk data. ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub io_backend: type: disk ``` 1. Use LMDB. We need to make LMDB before using it. Please refer to [LMDB description](#LMDB-Description). Note that we add meta information to the original LMDB, and the specific binary contents are also different. Therefore, LMDB from other sources can not be used directly. ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb io_backend: type: lmdb ``` 1. Use Memcached Your machine/clusters mush support memcached before using it. The configuration file should be modified accordingly. ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K_train_HR_sub dataroot_lq: datasets/DIV2K_train_LR_bicubicX4_sub io_backend: type: memcached server_list_cfg: /mnt/lustre/share/memcached_client/server_list.conf client_cfg: /mnt/lustre/share/memcached_client/client.conf sys_path: /mnt/lustre/share/pymc/py3 ``` #### How to Implement The implementation is to call the elegant fileclient design in [mmcv](https://github.com/open-mmlab/mmcv). In order to be compatible with BasicSR, we have made some changes to the interface (mainly to adapt to LMDB). See [file_client.py](../basicsr/utils/file_client.py) for details. When we implement our own dataloader, we can easily call the interfaces to support different data storage forms. Please refer to [PairedImageDataset](../basicsr/data/paired_image_dataset.py) for more details. #### LMDB Description During training, we use LMDB to speed up the IO and CPU decompression. (During testing, usually the data is limited and it is generally not necessary to use LMDB). The acceleration depends on the configurations of the machine, and the following factors will affect the speed: 1. Some machines will clean cache regularly, and LMDB depends on the cache mechanism. Therefore, if the data fails to be cached, you need to check it. After the command `free -h`, the cache occupied by LMDB will be recorded under the `buff/cache` entry. 1. Whether the memory of the machine is large enough to put the whole LMDB data in. If not, it will affect the speed due to the need to constantly update the cache. 1. If you cache the LMDB dataset for the first time, it may affect the training speed. So before training, you can enter the LMDB dataset directory and cache the data by: ` cat data.mdb > /dev/nul`. In addition to the standard LMDB file (data.mdb and lock.mdb), we also add `meta_info.txt` to record additional information. Here is an example: **Folder Structure** ```txt DIV2K_train_HR_sub.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt ``` **meta information** `meta_info.txt`, We use txt file to record for readability. The contents are: ```txt 0001_s001.png (480,480,3) 1 0001_s002.png (480,480,3) 1 0001_s003.png (480,480,3) 1 0001_s004.png (480,480,3) 1 ... ``` Each line records an image with three fields, which indicate: - Image name (with suffix): 0001_s001.png - Image size: (480, 480,3) represents a 480x480x3 image - Other parameters (BasicSR uses cv2 compression level for PNG): In restoration tasks, we usually use PNG format, so `1` represents the PNG compression level `CV_IMWRITE_PNG_COMPRESSION` is 1. It can be an integer in [0, 9]. A larger value indicates stronger compression, that is, smaller storage space and longer compression time. **Binary Content** For convenience, the binary content stored in LMDB dataset is encoded image by cv2: `cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]`. You can control the compression level by `compress_level`, balancing storage space and the speed of reading (including decompression). **How to Make LMDB** We provide a script to make LMDB. Before running the script, we need to modify the corresponding parameters accordingly. At present, we support DIV2K, REDS and Vimeo90K datasets; other datasets can also be made in a similar way.
`python scripts/data_preparation/create_lmdb.py` #### Data Pre-fetcher Apar from using LMDB for speed up, we could use data per-fetcher. Please refer to [prefetch_dataloader](../basicsr/data/prefetch_dataloader.py) for implementation.
It can be achieved by setting `prefetch_mode` in the configuration file. Currently, it provided three modes: 1. None. It does not use data pre-fetcher by default. If you have already use LMDB or the IO is OK, you can set it to None. ```yml prefetch_mode: ~ ``` 1. `prefetch_mode: cuda`. Use CUDA prefetcher. Please see [NVIDIA/apex](https://github.com/NVIDIA/apex/issues/304#) for more details. It will occupy more GPU memory. Note that in the mode. you must also set `pin_memory=True`. ```yml prefetch_mode: cuda pin_memory: true ``` 1. `prefetch_mode: cpu`. Use CPU prefetcher, please see [IgorSusmelj/pytorch-styleguide](https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#) for more details. (In my tests, this mode does not accelerate) ```yml prefetch_mode: cpu num_prefetch_queue: 1 # 1 by default ``` ## Image Super-Resolution It is recommended to symlink the dataset root to `datasets` with the command `ln -s xxx yyy`. If your folder structure is different, you may need to change the corresponding paths in config files. ### DIV2K [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) is a widely-used dataset in image super-resolution. In many research works, a MATLAB bicubic downsampling kernel is assumed. It may not be practical because the MATLAB bicubic downsampling kernel is not a good approximation for the implicit degradation kernels in real-world scenarios. And there is another topic named *blind restoration* that deals with this gap. **Preparation Steps** 1. Download the datasets from the [official DIV2K website](https://data.vision.ee.ethz.ch/cvl/DIV2K/).
1. Crop to sub-images: DIV2K has 2K resolution (e.g., 2048 × 1080) images but the training patches are usually small (e.g., 128x128 or 192x192). So there is a waste if reading the whole image but only using a very small part of it. In order to accelerate the IO speed during training, we crop the 2K resolution images to sub-images (here, we crop to 480x480 sub-images).
Note that the size of sub-images is different from the training patch size (`gt_size`) defined in the config file. Specifically, the cropped sub-images with 480x480 are stored. The dataloader will further randomly crop the sub-images to `GT_size x GT_size` patches for training.
Run the script [extract_subimages.py](../scripts/data_preparation/extract_subimages.py): ```python python scripts/data_preparation/extract_subimages.py ``` Remember to modify the paths and configurations if you have different settings. 1. [Optional] Create LMDB files. Please refer to [LMDB Description](#LMDB-Description). `python scripts/data_preparation/create_lmdb.py`. Use the `create_lmdb_for_div2k` function and remember to modify the paths and configurations accordingly. 1. Test the dataloader with the script `tests/test_paired_image_dataset.py`. Remember to modify the paths and configurations accordingly. 1. [Optional] If you want to use meta_info_file, you may need to run `python scripts/data_preparation/generate_meta_info.py` to generate the meta_info_file. ### Common Image SR Datasets We provide a list of common image super-resolution datasets.
Name Datasets Short Description Download
Classical SR Training T91 91 images for training Google Drive / Baidu Drive
BSDS200 A subset (train) of BSD500 for training
General100 100 images for training
Classical SR Testing Set5 Set5 test dataset
Set14 Set14 test dataset
BSDS100 A subset (test) of BSD500 for testing
urban100 100 building images for testing (regular structures)
manga109 109 images of Japanese manga for testing
historical 10 gray low-resolution images without the ground-truth
2K Resolution DIV2K proposed in NTIRE17 (800 train and 100 validation) official website
Flickr2K 2650 2K images from Flickr for training official website
DF2K A merged training dataset of DIV2K and Flickr2K -
OST (Outdoor Scenes) OST Training 7 categories images with rich textures Google Drive / Baidu Drive
OST300 300 test images of outdoor scenes
PIRM PIRM PIRM self-val, val, test datasets Google Drive / Baidu Drive
## Video Super-Resolution It is recommended to symlink the dataset root to `datasets` with the command `ln -s xxx yyy`. If your folder structure is different, you may need to change the corresponding paths in config files. ### REDS [Official website](https://seungjunnah.github.io/Datasets/reds.html).
We regroup the training and validation dataset into one folder. The original training dataset has 240 clips from 000 to 239. And we rename the validation clips from 240 to 269. **Validation Partition** The official validation partition and that used in EDVR for competition are different: | name | clips | total number | |:----------:|:----------:|:----------:| | REDSOfficial | [240, 269] | 30 clips | | REDS4 | 000, 011, 015, 020 clips from the *original training set* | 4 clips | All the left clips are used for training. Note that it it not required to explicitly separate the training and validation datasets; and the dataloader does that. **Preparation Steps** 1. Download the datasets from the [official website](https://seungjunnah.github.io/Datasets/reds.html). 1. Regroup the training and validation datasets: `python scripts/data_preparation/regroup_reds_dataset.py` 1. [Optional] Make LMDB files when necessary. Please refer to [LMDB Description](#LMDB-Description). `python scripts/data_preparation/create_lmdb.py`. Use the `create_lmdb_for_reds` function and remember to modify the paths and configurations accordingly. 1. Test the dataloader with the script `tests/test_reds_dataset.py`. Remember to modify the paths and configurations accordingly. ### Vimeo90K [Official webpage](http://toflow.csail.mit.edu/) 1. Download the dataset: [`Septuplets dataset --> The original training + test set (82GB)`](http://data.csail.mit.edu/tofu/dataset/vimeo_septuplet.zip).This is the Ground-Truth (GT). There is a `sep_trainlist.txt` file listing the training samples in the download zip file. 1. Generate the low-resolution images (TODO) The low-resolution images in the Vimeo90K test dataset are generated with the MATLAB bicubic downsampling kernel. Use the script `data_scripts/generate_LR_Vimeo90K.m` (run in MATLAB) to generate the low-resolution images. 1. [Optional] Make LMDB files when necessary. Please refer to [LMDB Description](#LMDB-Description). `python scripts/data_preparation/create_lmdb.py`. Use the `create_lmdb_for_vimeo90k` function and remember to modify the paths and configurations accordingly. 1. Test the dataloader with the script `tests/test_vimeo90k_dataset.py`. Remember to modify the paths and configurations accordingly. ## StyleGAN2 ### FFHQ Training dataset: [FFHQ](https://github.com/NVlabs/ffhq-dataset). 1. Download FFHQ dataset. Recommend to download the tfrecords files from [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset). 1. Extract tfrecords to images or LMDBs. (TensorFlow is required to read tfrecords). For each resolution, we will create images folder or LMDB files separately. ```bash python scripts/data_preparation/extract_images_from_tfrecords.py ``` ================================================ FILE: docs/DatasetPreparation_CN.md ================================================ # 数据准备 [English](DatasetPreparation.md) **|** [简体中文](DatasetPreparation_CN.md) #### 目录 1. [数据存储形式](#数据存储形式) 1. [如何使用](#如何使用) 1. [如何实现](#如何实现) 1. [LMDB具体说明](#LMDB具体说明) 1. [预读取数据](#预读取数据) 1. [图像数据](#图像数据) 1. [DIV2K](#DIV2K) 1. [其他常见图像超分数据集](#其他常见图像超分数据集) 1. [视频帧数据](#视频帧数据) 1. [REDS](#REDS) 1. [Vimeo90K](#Vimeo90K) 1. [StylgeGAN2](#StyleGAN2) 1. [FFHQ](#FFHQ) ## 数据存储形式 目前支持的数据存储形式有以下三种: 1. 直接以图像/视频帧的格式存放在硬盘 2. 制作成 [LMDB](https://lmdb.readthedocs.io/en/release/). 训练数据使用这种形式, 一般会加快读取速度. 3. 若是支持 [Memcached](https://memcached.org/), 则可以使用. 它们一般应用在集群上. #### 如何使用 目前, 我们可以通过 configuration yaml 文件方便的修改. 以支持DIV2K的 [PairedImageDataset](../basicsr/data/paired_image_dataset.py) 为例, 根据不同的要求修改yaml文件: 1. 直接读取硬盘数据 ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub io_backend: type: disk ``` 1. 使用LMDB. 在使用前需要先制作LMDB, 参见 [LMDB具体说明](#LMDB具体说明), 注意我们在原有的 LMDB 上, 新增加了 meta 信息, 而且具体保存二进制内容也不同, 因此其他来源的LMDB并不能直接拿过来使用. ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K/DIV2K_train_HR_sub.lmdb dataroot_lq: datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb io_backend: type: lmdb ``` 1. 使用Memcached 机器/集群需要支持 Memcached. 具体的配置文件根据实际的 Memcached 需要进行修改: ```yaml type: PairedImageDataset dataroot_gt: datasets/DIV2K_train_HR_sub dataroot_lq: datasets/DIV2K_train_LR_bicubicX4_sub io_backend: type: memcached server_list_cfg: /mnt/lustre/share/memcached_client/server_list.conf client_cfg: /mnt/lustre/share/memcached_client/client.conf sys_path: /mnt/lustre/share/pymc/py3 ``` #### 如何实现 实现是调用了[MMCV](https://github.com/open-mmlab/mmcv) 优雅的 FileClient 设计. 为了兼容 BasicSR, 我们对接口做了一些改动 (主要是为了适应LMDB), 参见 [file_client.py](../basicsr/utils/file_client.py). 在实现我们自己的 dataloader 的时候, 可以方便地调用接口, 以实现对不同数据存储形式的支持, 具体可以参考 [PairedImageDataset](../basicsr/data/paired_image_dataset.py) 的写法. #### LMDB具体说明 我们在训练的时候使用 LMDB 存储形式可以加快IO和CPU解压缩的速度 (测试的时候数据较少, 一般就没有太必要使用 LMDB). 其具体的加速要根据机器的配置来, 以下几个因素会影响: 1. 有的机器设置了定时清理缓存, 而 LMDB 依赖于缓存. 因此若一直缓存不进去, 则需要检查一下. 一般 `free -h` 命令下, LMDB 占用的缓存会记录在 `buff/cache` 条目下面 1. 机器的内存是否足够大, 能够把整个 LMDB 数据都放进去. 如果不是, 则它由于需要不断更换缓存, 会影响速度 1. 若是第一次缓存 LMDB 数据集, 可能会影响训练速度. 可以在训练前, 进入 LMDB 数据集目录, 把数据先缓存进去: `cat data.mdb > /dev/nul` 除了标准的 LMDB 文件 (data.mdb 和 lock.mdb) 外, 我们还增加了 `meta_info.txt` 来记录额外的信息. 下面用一个例子来说明: **文件结构** ```txt DIV2K_train_HR_sub.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt ``` **meta信息** `meta_info.txt`, 我们采用txt来记录, 是为了可读性. 其里面的内容为: ```txt 0001_s001.png (480,480,3) 1 0001_s002.png (480,480,3) 1 0001_s003.png (480,480,3) 1 0001_s004.png (480,480,3) 1 ... ``` 每一行记录了一张图片, 有三个字段, 分别表示: - 图像名称 (带后缀): 0001_s001.png - 图像大小: (480,480,3) 表示是480x480x3的图像 - 其他参数 (BasicSR里面使用了 cv2 压缩 png 程度): 因为在复原任务中, 我们通常使用 png 来存储, 所以这个 1 表示 png 的压缩程度 `CV_IMWRITE_PNG_COMPRESSION` 是 1. 它可以取值为[0, 9]的整数, 更大的值表示更强的压缩, 即更小的储存空间和更长的压缩时间. **二进制内容** 为了方便, 我们在 LMDB 数据集中存储的二进制内容是 cv2 encode过的 image: `cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]`. 可以通过 `compress_level` 控制压缩程度, 平衡存储空间和读取(包括解压缩)的速度. **如何制作** 我们提供了脚本来制作. 在运行脚本前, 需要根据需求修改相应的参数. 目前支持 DIV2K, REDS 和 Vimeo90K 数据集; 其他数据集可仿照进行制作.
`python scripts/data_preparation/create_lmdb.py` #### 预读取数据 除了使用LMDB来加速外, 还可以采用预读取数据来加速, 实现参见 [prefetch_dataloader](../basicsr/data/prefetch_dataloader.py).
这个可以通过配置文件中的 `prefetch_mode` 来指定. 目前提供了三种模式: 1. None. 默认不使用. 如果使用了 LMDB 或者 IO 不成问题, 则可不使用 ```yml prefetch_mode: ~ ``` 1. `prefetch_mode: cuda`. 使用 CUDA prefetcher, 具体介绍参见 [NVIDIA/apex](https://github.com/NVIDIA/apex/issues/304#). 它会多占用一些GPU显存. 注意: 这个模式下, 一定要设置 `pin_memory=True` ```yml prefetch_mode: cuda pin_memory: true ``` 1. `prefetch_mode: cpu`. 使用 CPU prefetcher, 具体介绍参见 [IgorSusmelj/pytorch-styleguide](https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#). (目前测试，这个加速不明显) ```yml prefetch_mode: cpu num_prefetch_queue: 1 # 1 by default ``` ## 图像数据 推荐把数据通过 `ln -s xxx yyy` 软链到`BasicSR/datasets`下. 如果你的文件结构不同, 需要相应地修改configuration yaml文件的路径. ### DIV2K DIV2K 数据集被广泛使用在图像复原的任务中. **数据准备步骤** 1. 从[官网](https://data.vision.ee.ethz.ch/cvl/DIV2K)下载数据. 1. Crop to sub-images: 因为 DIV2K 数据集是 2K 分辨率的 (比如: 2048x1080), 而我们在训练的时候往往并不要那么大 (常见的是 128x128 或者 192x192 的训练patch). 因此我们可以先把2K的图片裁剪成有overlap的 480x480 的子图像块. 然后再由 dataloader 从这个 480x480 的子图像块中随机crop出 128x128 或者 192x192 的训练patch.
运行脚本 [extract_subimages.py](../scripts/data_preparation/extract_subimages.py): ```python python scripts/data_preparation/extract_subimages.py ``` 使用之前可能需要修改文件里面的路径和配置参数. **注意**: sub-image 的尺寸和训练patch的尺寸 (`gt_size`) 是不同的. 我们先把2K分辨率的图像 crop 成 sub-images (往往是 480x480), 然后存储起来. 在训练的时候, dataloader会读取这些sub-images, 然后进一步随机裁剪成 `gt_size` x `gt_size`的大小. 1. [可选] 若需要使用 LMDB, 则需要制作 LMDB, 参考 [LMDB具体说明](#LMDB具体说明). `python scripts/data_preparation/create_lmdb.py`, 注意选择`create_lmdb_for_div2k`函数, 并需要修改函数相应的配置和路径. 1. 测试: `tests/test_paired_image_dataset.py`, 注意修改函数相应的配置和路径. 1. [可选] 若需要使用 meta_info_file, 运行 `python scripts/data_preparation/generate_meta_info.py` 来生成 meta_info_file. ### 其他常见图像超分数据集 我们提供了常见图像超分数据集的列表.
Name Datasets Short Description Download
Classical SR Training T91 91 images for training Google Drive / Baidu Drive
BSDS200 A subset (train) of BSD500 for training
General100 100 images for training
Classical SR Testing Set5 Set5 test dataset
Set14 Set14 test dataset
BSDS100 A subset (test) of BSD500 for testing
urban100 100 building images for testing (regular structures)
manga109 109 images of Japanese manga for testing
historical 10 gray low-resolution images without the ground-truth
2K Resolution DIV2K proposed in NTIRE17 (800 train and 100 validation) official website
Flickr2K 2650 2K images from Flickr for training official website
DF2K A merged training dataset of DIV2K and Flickr2K -
OST (Outdoor Scenes) OST Training 7 categories images with rich textures Google Drive / Baidu Drive
OST300 300 test images of outdoor scenes
PIRM PIRM PIRM self-val, val, test datasets Google Drive / Baidu Drive
## 视频帧数据 推荐把数据通过 `ln -s xxx yyy` 软链到`BasicSR/datasets`下. 如果你的文件结构不同, 需要相应地修改configuration yaml文件的路径. ### REDS [官网](https://seungjunnah.github.io/Datasets/reds.html)
我们重新整合了 training 和 validation 数据到一个文件夹中: 训练集合原来有240个clip (序号从000到239), 我们把validation clips重命名, 从240到269. **Validation的划分** 官方的validation划分和EDVR的划分不同 (当时为了比赛的设置): | name | clips | total number | |:----------:|:----------:|:----------:| | REDSOfficial | [240, 269] | 30 clips | | REDS4 | 000, 011, 015, 020 clips from the *original training set* | 4 clips | 余下的clips拿来做训练集合. 注意: 我们不需要显式地分开训练和验证集合, dataloader会做这件事. **数据准备步骤** 1. 从[官网](https://seungjunnah.github.io/Datasets/reds.html)下载数据 1. 整合 training 和 validation 数据: `python scripts/data_preparation/regroup_reds_dataset.py` 1. [可选] 若需要使用 LMDB, 则需要制作 LMDB, 参考 [LMDB具体说明](#LMDB具体说明). `python scripts/data_preparation/create_lmdb.py`, 注意选择`create_lmdb_for_reds`函数, 并需要修改函数相应的配置和路径. 1. 测试: `python tests/test_reds_dataset.py`, 注意修改函数相应的配置和路径. ### Vimeo90K [官网](http://toflow.csail.mit.edu/) **数据准备步骤** 1. 下载数据: [`Septuplets dataset --> The original training + test set (82GB)`](http://data.csail.mit.edu/tofu/dataset/vimeo_septuplet.zip). 这些是Ground-Truth. 里面有`sep_trainlist.txt`文件来区分训练数据. 1. 生成低分辨率图片. (TODO) The low-resolution images in the Vimeo90K test dataset are generated with the MATLAB bicubic downsampling kernel. Use the script `data_scripts/generate_LR_Vimeo90K.m` (run in MATLAB) to generate the low-resolution images. 1. [可选] 若需要使用 LMDB, 则需要制作 LMDB, 参考 [LMDB具体说明](#LMDB具体说明). `python scripts/data_preparation/create_lmdb.py`, 注意选择`create_lmdb_for_vimeo90k`函数, 并需要修改函数相应的配置和路径. 1. 测试: `python tests/test_vimeo90k_dataset.py`, 注意修改函数相应的配置和路径. ## StyleGAN2 ### FFHQ 训练数据集: [FFHQ](https://github.com/NVlabs/ffhq-dataset). 1. 下载 FFHQ 数据集. 推荐从 [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset) 下载 tfrecords 文件. 1. 从 tfrecords 提取到*图片*或者*LMDB*. (需要安装 TensorFlow 来读取 tfrecords). 我们对每一个分辨率的人脸都单独创建文件夹或者LMDB文件. ```bash python scripts/data_preparation/extract_images_from_tfrecords.py ``` ================================================ FILE: docs/Datasets.md ================================================ # Datasets [English](Datasets.md) **|** [简体中文](Datasets_CN.md) ## Supported Datasets | Class | Task |Train/Test | Description | | :------------- | :----------:| :----------: | :----------: | | [PairedImageDataset](../basicsr/data/paired_image_dataset.py) | Image Super-Resolution | Train|Support paired data | | [SingleImageDataset](../basicsr/data/single_image_dataset.py) | Image Super-Resolution | Test|Only read low quality images, used in tests without Ground-Truth| | [REDSDataset](../basicsr/data/reds_dataset.py) | Video Super-Resolution | Train|REDS training dataset | | [Vimeo90KDataset](../basicsr/data/vimeo90k_dataset.py) | Video Super-Resolution |Train| Vimeo90K training dataset| | [VideoTestDataset](../basicsr/data/video_test_dataset.py) | Video Super-Resolution | Test|Base video test dataset, supporting Vid4, REDS testing datasets| | [VideoTestVimeo90KDataset](../basicsr/data/video_test_dataset.py) | Video Super-Resolution |Test| Inherit `VideoTestDataset`, Vimeo90K testing dataset| | [VideoTestDUFDataset](../basicsr/data/video_test_dataset.py) | Video Super-Resolution |Test| Inherit `VideoTestDataset`, testing dataset for method DUF, supporting Vid4 dataset| | [FFHQDataset](../basicsr/data/ffhq_dataset.py) | Face Generation |Train| FFHQ training dataset| 1. Common transformations and functions are in [transforms.py](../basicsr/data/transforms.py) and [util.py](../basicsr/data/util.py), respectively ================================================ FILE: docs/Datasets_CN.md ================================================ # 数据处理 [English](Datasets.md) **|** [简体中文](Datasets_CN.md) ## 支持的数据处理 | 类 | 任务 |训练/测试 | 描述 | | :------------- | :----------:| :----------: | :----------: | | [PairedImageDataset](../basicsr/data/paired_image_dataset.py) | 图像超分 | 训练|支持读取成对的训练数据 | | [SingleImageDataset](../basicsr/data/single_image_dataset.py) | 图像超分 | 测试|只读取low quality的图像, 用在没有Ground-Truth的测试中 | | [REDSDataset](../basicsr/data/reds_dataset.py) | 视频超分 | 训练|REDS的训练数据集 | | [Vimeo90KDataset](../basicsr/data/vimeo90k_dataset.py) | 视频超分 |训练| Vimeo90K的训练数据集| | [VideoTestDataset](../basicsr/data/video_test_dataset.py) | 视频超分 | 测试|基础的视频超分测试集, 支持Vid4, REDS测试集| | [VideoTestVimeo90KDataset](../basicsr/data/video_test_dataset.py) | 视频超分 |测试| 继承`VideoTestDataset`, Vimeo90K的测试数据集| | [VideoTestDUFDataset](../basicsr/data/video_test_dataset.py) | 视频超分 |测试| 继承`VideoTestDataset`, 方法DUF的测试数据集, 支持Vid4| | [FFHQDataset](../basicsr/data/ffhq_dataset.py) | 人脸生成 |训练| FFHQ的训练数据集| 1. 共用的变换和函数分别在 [transforms.py](../basicsr/data/transforms.py) 和 [util.py](../basicsr/data/util.py) 中 ================================================ FILE: docs/DesignConvention.md ================================================ # Codebase Designs and Conventions [English](DesignConvention.md) **|** [简体中文](DesignConvention_CN.md) #### Contents 1. [Overall Framework](#Overall-Framework) 1. [Features](#Features) 1. [Dynamic Instantiation](#Dynamic-Instantiation) 1. [Conventions](#Conventions) ## Overall Framework The `BasicSR` framework can be divided into the following parts: data, model, options/configs and training process.
When we modify or add a new method, we often modify/add it from the above aspects.
The figure below shows the overall framework. ![overall_structure](../assets/overall_structure.png) ## Features ### Dynamic Instantiation When we add a new class or function, it can be used directly in the configuration file. The program will automatically scan, find and instantiate according to the class name or function name in the configuration file. This process is called dynamic instantiation. Specifically, we implement it through `importlib` and `getattr`. Taking the data module as example, we follow the below steps in [`data/__init__.py`](../basicsr/data/__init__.py): 1. Scan all the files under the data folder with '_dataset' in file names 1. Import the classes or functions in these files through `importlib` 1. Instantiate through `getattr` according to the name in the configuration file ```python # 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'basicsr.data.{file_name}') for file_name in dataset_filenames ] ... # dynamic instantiation for module in _dataset_modules: dataset_cls = getattr(module, dataset_type, None) if dataset_cls is not None: break ``` We use the similar techniques for the following modules. Pay attention to the conventions of file suffix when using them: | Module | File Suffix | Example | | :------------- | :----------: | :----------: | | Data | `_dataset.py` | `data/paired_image_dataset.py` | | Model | `_model.py` | `basicsr/models/sr_model.py` | | Archs | `_arch.py` | `basicsr/archs/srresnet_arch.py`| Note: 1. The above file suffixes are only used when necessary. Other file names should avoid using the above suffixes. 1. Note that the class name or function name cannot be repeated. In addition, we also use `importlib` and `getattr` for `losses` and `metrics`. However, for losses and metrics, the number of files is smaller and the changes are less. So, we do not use the strategy of scanning files. For these two modules, after adding new classes or functions, we need to add the corresponding class or function names to `__init__.py`. | Module | Path | Modify `__init__.py` | | :------------- | :----------: | :----------: | | Losses | `basicsr/models/losses` | [`basicsr/models/losses/__init__.py`](../basicsr/models/losses/__init__.py) | | Metrics | `basicsr/metrics` | [`basicsr/metrics/__init__.py`](../basicsr/metrics/__init__.py)| ## Conventions 1. In dynamic instantiation, there are requirements to the file suffix in the following module. Otherwise, automatic instantiation cannot be achieved. | Module | File Suffix | Example | | :------------- | :----------: | :----------: | | Data | `_dataset.py` | `data/paired_image_dataset.py` | | Model | `_model.py` | `basicsr/models/sr_model.py` | | Archs | `_arch.py` | `basicsr/archs/srresnet_arch.py`| 1. When logging, the loss items are recommended to start with `l_`, so that all these loss items will be grouped together in tensorboard. For example, in [basicsr/models/srgan_model.py](../basicsr/models/srgan_model.py), we use `l_g_pix`, `l_g_percep`, `l_g_gan`, etc for loss items. In [basicsr/utils/logger.py](../basicsr/utils/logger.py), these items will be grouped together: ```python 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) ``` ================================================ FILE: docs/DesignConvention_CN.md ================================================ # 代码库的设计和约定 [English](DesignConvention.md) **|** [简体中文](DesignConvention_CN.md) #### 目录 1. [整体框架](#整体框架) 1. [特性](#特性) 1. [动态实例化](#动态实例化) 1. [约定](#约定) ## 整体框架 整个 `BasicSR` 框架可以分为以下几个部分 —— 数据 (Data), 模型 (Model), 配置文件 (Options/Configs) 和训练过程.
当我们修改或定义新的方法时, 也往往是从以上几个方面进行修改/添加的.
下图概括了整体的框架. ![overall_structure](../assets/overall_structure.png) ## 特性 ### 动态实例化 (Dynamic Instantiation)
当我们新写了类 (Class) 或 函数 时, 可直接在配置文件中使用. 程序会根据配置文件的类名 或 函数名, 自动查找并实例化. 这个过程称为 动态实例化 (Dynamic Instantiation). 具体而言, 我们是通过 `importlib` 和 `getattr` 来实现的. 以data为例, 我们在[`data/__init__.py`](../basicsr/data/__init__.py) 中是如下做的: 1. 扫描所有以`_dataset.py`为结尾的文件 (这是约定) 1. 把这些文件中的 类 或 函数 通过 importlib 都 import 进来 1. 根据配置文件中的名称, 通过`getattr`实例化 ```python # 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'basicsr.data.{file_name}') for file_name in dataset_filenames ] ... # dynamic instantiation for module in _dataset_modules: dataset_cls = getattr(module, dataset_type, None) if dataset_cls is not None: break ``` 我们对以下模块使用了类似的技巧, 在使用的时候需要注意文件后缀名称的约定: | Module | File Suffix | Example | | :------------- | :----------: | :----------: | | Data | `_dataset.py` | `data/paired_image_dataset.py` | | Model | `_model.py` | `basicsr/models/sr_model.py` | | Archs | `_arch.py` | `basicsr/archs/srresnet_arch.py`| 注意: 1. 上面的文件后缀只用在需要的文件中, 其他文件命名尽量避免使用以上的后缀 1. 注意 类名 或 函数名 不能重复 另外对 `losses` 和 `metrics`, 我们也使用了 `importlib` 和 `getattr`, 但是和上面不一样的是, 对于losses和metrics, 由于文件数量比较少, 改动也少, 因此我们不采用扫描文件的方式, 而是在新增加类/函数后, 需要在相应的 `__init__.py` 中增加类/函数名称. | Module | Path | Modify `__init__.py` | | :------------- | :----------: | :----------: | | Losses | `basicsr/models/losses` | [`basicsr/models/losses/__init__.py`](../basicsr/models/losses/__init__.py) | | Metrics | `basicsr/metrics` | [`basicsr/metrics/__init__.py`](../basicsr/metrics/__init__.py)| ## 约定 1. 动态实例化, 以下模块文件后缀名有要求, 否则不能做到自动实例化. | Module | File Suffix | Example | | :------------- | :----------: | :----------: | | Data | `_dataset.py` | `data/paired_image_dataset.py` | | Model | `_model.py` | `basicsr/models/sr_model.py` | | Archs | `_arch.py` | `basicsr/archs/srresnet_arch.py`| 1. 在Log的时候, loss项使用`l_`开头, 这样在 tensorboard 显示的时候, 所有loss会被组织到一起. 比如在 [basicsr/models/srgan_model.py](../basicsr/models/srgan_model.py)中, 使用了`l_g_pix`, `l_g_percep`, `l_g_gan`等. 在[basicsr/utils/logger.py](../basicsr/utils/logger.py), 他们会被组织到一起: ```python 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) ``` ================================================ FILE: docs/HOWTOs.md ================================================ # HOWTOs [English](HOWTOs.md) **|** [简体中文](HOWTOs_CN.md) ## How to train StyleGAN2 1. Prepare training dataset: [FFHQ](https://github.com/NVlabs/ffhq-dataset). More details are in [DatasetPreparation.md](DatasetPreparation.md#StyleGAN2) 1. Download FFHQ dataset. Recommend to download the tfrecords files from [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset). 1. Extract tfrecords to images or LMDBs (TensorFlow is required to read tfrecords): > python scripts/data_preparation/extract_images_from_tfrecords.py 1. Modify the config file in `options/train/StyleGAN/train_StyleGAN2_256_Cmul2_FFHQ.yml` 1. Train with distributed training. More training commands are in [TrainTest.md](TrainTest.md). > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/StyleGAN/train_StyleGAN2_256_Cmul2_FFHQ_800k.yml --launcher pytorch ## How to inference StyleGAN2 1. Download pre-trained models from **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) to the `experiments/pretrained_models` folder. 1. Test. > python inference/inference_stylegan2.py 1. The results are in the `samples` folder. ## How to inference DFDNet 1. Install [dlib](http://dlib.net/), because DFDNet uses dlib to do face recognition and landmark detection. [Installation reference](https://github.com/davisking/dlib). 1. Clone dlib repo: `git clone git@github.com:davisking/dlib.git` 1. `cd dlib` 1. Install: `python setup.py install` 2. Download the dlib pretrained models from **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) to the `experiments/pretrained_models/dlib` folder.
You can download by run the following command OR manually download the pretrained models. > python scripts/download_pretrained_models.py dlib 3. Download pretrained DFDNet models, dictionary and face template from **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) to the `experiments/pretrained_models/DFDNet` folder.
You can download by run the the following command OR manually download the pretrained models. > python scripts/download_pretrained_models.py DFDNet 4. Prepare the testing dataset in the `datasets`, for example, we put images in the `datasets/TestWhole` folder. 5. Test. > python inference/inference_dfdnet.py --upscale_factor=2 --test_path datasets/TestWhole 6. The results are in the `results/DFDNet` folder. ## How to train SwinIR (SR) We take the classical SR X4 with DIV2K for example. 1. Prepare the training dataset: [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/). More details are in [DatasetPreparation.md](DatasetPreparation.md#image-super-resolution) 1. Prepare the validation dataset: Set5. You can download with [this guidance](DatasetPreparation.md#common-image-sr-datasets) 1. Modify the config file in [`options/train/SwinIR/train_SwinIR_SRx4_scratch.yml`](../options/train/SwinIR/train_SwinIR_SRx4_scratch.yml) accordingly. 1. Train with distributed training. More training commands are in [TrainTest.md](TrainTest.md). > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4331 basicsr/train.py -opt options/train/SwinIR/train_SwinIR_SRx4_scratch.yml --launcher pytorch --auto_resume Note that: 1. Different from the original setting in the paper where the X4 model is finetuned from the X2 model, we directly train it from scratch. 1. We also use `EMA (Exponential Moving Average)`. Note that all model trainings in BasicSR supports EMA. 1. In the **250K iteration** of training X4 model, it can achieve comparable performance to the official model. | ClassicalSR DIV2KX4 | PSNR (RGB) | PSNR (Y) | SSIM (RGB) | SSIM (Y) | | :--- | :---: | :---: | :---: | :---: | | Official | 30.803 | 32.728 | 0.8738|0.9028 | | Reproduce |30.832 | 32.756 | 0.8739| 0.9025 | ## How to inference SwinIR (SR) 1. Download pre-trained models from the [**official SwinIR repo**](https://github.com/JingyunLiang/SwinIR/releases/tag/v0.0) to the `experiments/pretrained_models/SwinIR` folder. 1. Inference. > python inference/inference_swinir.py --input datasets/Set5/LRbicx4 --patch_size 48 --model_path experiments/pretrained_models/SwinIR/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth --output results/SwinIR_SRX4_DIV2K/Set5 1. The results are in the `results/SwinIR_SRX4_DIV2K/Set5` folder. 1. You may want to calculate the PSNR/SSIM values. > python scripts/metrics/calculate_psnr_ssim.py --gt datasets/Set5/GTmod12/ --restored results/SwinIR_SRX4_DIV2K/Set5 --crop_border 4 or test with the Y channel with the `--test_y_channel` argument. > python scripts/metrics/calculate_psnr_ssim.py --gt datasets/Set5/GTmod12/ --restored results/SwinIR_SRX4_DIV2K/Set5 --crop_border 4 --test_y_channel ================================================ FILE: docs/HOWTOs_CN.md ================================================ # HOWTOs [English](HOWTOs.md) **|** [简体中文](HOWTOs_CN.md) ## 如何训练 StyleGAN2 1. 准备训练数据集: [FFHQ](https://github.com/NVlabs/ffhq-dataset). 更多细节: [DatasetPreparation_CN.md](DatasetPreparation_CN.md#StyleGAN2) 1. 下载 FFHQ 数据集. 推荐从 [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset) 下载 tfrecords 文件. 1. 从tfrecords 提取到*图片*或者*LMDB*. (需要安装 TensorFlow 来读取 tfrecords). > python scripts/data_preparation/extract_images_from_tfrecords.py 1. 修改配置文件 `options/train/StyleGAN/train_StyleGAN2_256_Cmul2_FFHQ.yml` 1. 使用分布式训练. 更多训练命令: [TrainTest_CN.md](TrainTest_CN.md) > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/StyleGAN/train_StyleGAN2_256_Cmul2_FFHQ.yml --launcher pytorch ## 如何测试 StyleGAN2 1. 从 **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) 下载预训练模型到 `experiments/pretrained_models` 文件夹. 1. 测试. > python inference/inference_stylegan2.py 1. 结果在 `samples` 文件夹 ## 如何测试 DFDNet 1. 安装 [dlib](http://dlib.net/). 因为 DFDNet 使用 dlib 做人脸检测和关键点检测. [安装参考](https://github.com/davisking/dlib). 1. 克隆 dlib repo: `git clone git@github.com:davisking/dlib.git` 1. `cd dlib` 1. 安装: `python setup.py install` 2. 从 **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) 下载预训练的 dlib 模型到 `experiments/pretrained_models/dlib` 文件夹.
你可以通过运行下面的命令下载 或 手动下载. > python scripts/download_pretrained_models.py dlib 3. 从 **ModelZoo** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) 下载 DFDNet 模型, 字典和人脸关键点模板到 `experiments/pretrained_models/DFDNet` 文件夹.
你可以通过运行下面的命令下载 或 手动下载. > python scripts/download_pretrained_models.py DFDNet 4. 准备测试图片到 `datasets`, 比如说我们把测试图片放在 `datasets/TestWhole` 文件夹. 5. 测试. > python inference/inference_dfdnet.py --upscale_factor=2 --test_path datasets/TestWhole 6. 结果在 `results/DFDNet` 文件夹. ## How to train SwinIR (SR) We take the classical SR X4 with DIV2K for example. 1. Prepare the training dataset: [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/). More details are in [DatasetPreparation.md](DatasetPreparation.md#image-super-resolution) 1. Prepare the validation dataset: Set5. You can download with [this guidance](DatasetPreparation.md#common-image-sr-datasets) 1. Modify the config file in [`options/train/SwinIR/train_SwinIR_SRx4_scratch.yml`](../options/train/SwinIR/train_SwinIR_SRx4_scratch.yml) accordingly. 1. Train with distributed training. More training commands are in [TrainTest.md](TrainTest.md). > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4331 basicsr/train.py -opt options/train/SwinIR/train_SwinIR_SRx4_scratch.yml --launcher pytorch --auto_resume Note that: 1. Different from the original setting in the paper where the X4 model is finetuned from the X2 model, we directly train it from scratch. 1. We also use `EMA (Exponential Moving Average)`. Note that all model trainings in BasicSR supports EMA. 1. In the **250K iteration** of training X4 model, it can achieve comparable performance to the official model. | ClassicalSR DIV2KX4 | PSNR (RGB) | PSNR (Y) | SSIM (RGB) | SSIM (Y) | | :--- | :---: | :---: | :---: | :---: | | Official | 30.803 | 32.728 | 0.8738|0.9028 | | Reproduce |30.832 | 32.756 | 0.8739| 0.9025 | ## How to inference SwinIR (SR) 1. Download pre-trained models from the [**official SwinIR repo**](https://github.com/JingyunLiang/SwinIR/releases/tag/v0.0) to the `experiments/pretrained_models/SwinIR` folder. 1. Inference. > python inference/inference_swinir.py --input datasets/Set5/LRbicx4 --patch_size 48 --model_path experiments/pretrained_models/SwinIR/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth --output results/SwinIR_SRX4_DIV2K/Set5 1. The results are in the `results/SwinIR_SRX4_DIV2K/Set5` folder. 1. You may want to calculate the PSNR/SSIM values. > python scripts/metrics/calculate_psnr_ssim.py --gt datasets/Set5/GTmod12/ --restored results/SwinIR_SRX4_DIV2K/Set5 --crop_border 4 or test with the Y channel with the `--test_y_channel` argument. > python scripts/metrics/calculate_psnr_ssim.py --gt datasets/Set5/GTmod12/ --restored results/SwinIR_SRX4_DIV2K/Set5 --crop_border 4 --test_y_channel ================================================ FILE: docs/Logging.md ================================================ # Logging [English](Logging.md) **|** [简体中文](Logging_CN.md) #### Contents 1. [Text Logger](#Text-Logger) 1. [Tensorboard Logger](#Tensorboard-Logger) 1. [Wandb Logger](#Wandb-Logger) ## Text Logger Print the log to both the text file and screen. The text file usually locates in `experiments/exp_name/train_exp_name_timestamp.txt`. ## Tensorboard Logger - Use Tensorboard logger. Set `use_tb_logger: true` in the yml configuration file: ```yml logger: use_tb_logger: true ``` - File location: `tb_logger/exp_name` - View in the browser: ```bash tensorboard --logdir tb_logger --port 5500 --bind_all ``` ## Wandb Logger [wandb](https://www.wandb.com/) can be viewed as a cloud version of tensorboard. One can easily view training processes and curves in wandb. Currently, we only sync the tensorboard log to wandb. So we should also turn on tensorboard when using wandb. Configuration file: ```yml ogger: # Whether to tensorboard logger use_tb_logger: true # Whether to use wandb logger. Currently, wandb only sync the tensorboard log. So we should also turn on tensorboard when using wandb wandb: # wandb project name. Default is None, that is not using wandb. # Here, we use the basicsr wandb project: https://app.wandb.ai/xintao/basicsr project: basicsr # If resuming, wandb id could automatically link previous logs resume_id: ~ ``` **[Examples of training curves in wandb](https://app.wandb.ai/xintao/basicsr)**

================================================ FILE: docs/Logging_CN.md ================================================ # Logging日志 [English](Logging.md) **|** [简体中文](Logging_CN.md) #### 目录 1. [文本屏幕日志](#文本屏幕日志) 1. [Tensorboard日志](#Tensorboard日志) 1. [Wandb日志](#Wandb日志) ## 文本屏幕日志 将日志信息同时输出到文件和屏幕. 文件位置一般为`experiments/exp_name/train_exp_name_timestamp.txt`. ## Tensorboard日志 - 开启. 在 yml 配置文件中设置 `use_tb_logger: true`: ```yml logger: use_tb_logger: true ``` - 文件位置: `tb_logger/exp_name` - 在浏览器中查看: ```bash tensorboard --logdir tb_logger --port 5500 --bind_all ``` ## Wandb日志 [wandb](https://www.wandb.com/) 类似tensorboard的云端版本, 可以在浏览器方便地查看模型训练的过程和曲线. 我们目前只是把tensorboard的内容同步到wandb上, 因此要使用wandb, 必须打开tensorboard logger. 配置文件如下: ```yml logger: # 是否使用tensorboard logger use_tb_logger: true # 是否使用wandb logger, 目前wandb只是同步tensorboard的内容, 因此要使用wandb, 必须也同时使用tensorboard wandb: # wandb的project. 默认是 None, 即不使用wandb. # 这里使用了 basicsr wandb project: https://app.wandb.ai/xintao/basicsr project: basicsr # 如果是resume, 可以输入上次的wandb id, 则log可以接起来 resume_id: ~ ``` **[wandb训练曲线样例](https://app.wandb.ai/xintao/basicsr)**

================================================ FILE: docs/Makefile ================================================ # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/Metrics.md ================================================ # Metrics [English](Metrics.md) **|** [简体中文](Metrics_CN.md) ## PSNR and SSIM ## NIQE ## FID > FID measures the similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. > FID is calculated by computing the [Fréchet distance](https://en.wikipedia.org/wiki/Fr%C3%A9chet_distance) between two Gaussians fitted to feature representations of the Inception network. References - https://github.com/mseitzer/pytorch-fid - [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) - [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337) ### Pre-calculated FFHQ inception feature statistics Usually, we put the downloaded inception feature statistics in `basicsr/metrics`. :arrow_double_down: Google Drive: [metrics data](https://drive.google.com/drive/folders/13cWIQyHX3iNmZRJ5v8v3kdyrT9RBTAi9?usp=sharing) :arrow_double_down: 百度网盘: [评价指标数据](https://pan.baidu.com/s/10mMKXSEgrC5y7m63W5vbMQ)
| File Name | Dataset | Image Shape | Sample Numbers| | :------------- | :----------:|:----------:|:----------:| | inception_FFHQ_256-0948f50d.pth | FFHQ | 256 x 256 | 50,000 | | inception_FFHQ_512-f7b384ab.pth | FFHQ | 512 x 512 | 50,000 | | inception_FFHQ_1024-75f195dc.pth | FFHQ | 1024 x 1024 | 50,000 | | inception_FFHQ_256_stylegan2_pytorch-abba9d31.pth | FFHQ | 256 x 256 | 50,000 | - All the FFHQ inception feature statistics calculated on the resized 299 x 299 size. - `inception_FFHQ_256_stylegan2_pytorch-abba9d31.pth` is converted from the statistics in [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch). ================================================ FILE: docs/Metrics_CN.md ================================================ # 评价指标 [English](Metrics.md) **|** [简体中文](Metrics_CN.md) ## PSNR and SSIM ## NIQE ## FID > FID measures the similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. > FID is calculated by computing the [Fréchet distance](https://en.wikipedia.org/wiki/Fr%C3%A9chet_distance) between two Gaussians fitted to feature representations of the Inception network. 参考 - https://github.com/mseitzer/pytorch-fid - [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) - [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337) ### Pre-calculated FFHQ inception feature statistics 通常, 我们把下载的 inception 网络的特征统计数据 (用于计算FID) 放在 `basicsr/metrics`. :arrow_double_down: 百度网盘: [评价指标数据](https://pan.baidu.com/s/10mMKXSEgrC5y7m63W5vbMQ) :arrow_double_down: Google Drive: [metrics data](https://drive.google.com/drive/folders/13cWIQyHX3iNmZRJ5v8v3kdyrT9RBTAi9?usp=sharing)
| File Name | Dataset | Image Shape | Sample Numbers| | :------------- | :----------:|:----------:|:----------:| | inception_FFHQ_256-0948f50d.pth | FFHQ | 256 x 256 | 50,000 | | inception_FFHQ_512-f7b384ab.pth | FFHQ | 512 x 512 | 50,000 | | inception_FFHQ_1024-75f195dc.pth | FFHQ | 1024 x 1024 | 50,000 | | inception_FFHQ_256_stylegan2_pytorch-abba9d31.pth | FFHQ | 256 x 256 | 50,000 | - All the FFHQ inception feature statistics calculated on the resized 299 x 299 size. - `inception_FFHQ_256_stylegan2_pytorch-abba9d31.pth` is converted from the statistics in [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch). ================================================ FILE: docs/ModelZoo.md ================================================ # Model Zoo and Baselines [English](ModelZoo.md) **|** [简体中文](ModelZoo_CN.md) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) --- We provide: 1. Official models converted directly from official released models 1. Reproduced models with `BasicSR`. Pre-trained models and log examples are provided You can put the downloaded models in the `experiments/pretrained_models` folder. **[Download official pre-trained models]** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) You can use the scrip to download pre-trained models from Google Drive. ```python python scripts/download_pretrained_models.py ESRGAN # method can be ESRGAN, EDVR, StyleGAN, EDSR, DUF, DFDNet, dlib ``` **[Download reproduced models and logs]** ([Google Drive](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing), [百度网盘](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)) In addition, we upload the training process and curves in [wandb](https://www.wandb.com/). **[Training curves in wandb](https://app.wandb.ai/xintao/basicsr)**

#### Contents 1. [Image Super-Resolution](#Image-Super-Resolution) 1. [Image SR Official Models](#Image-SR-Official-Models) 1. [Image SR Reproduced Models](#Image-SR-Reproduced-Models) 1. [Video Super-Resolution](#Video-Super-Resolution) ## Image Super-Resolution When evaluation: - We crop `scale` border pixels in each border - Evaluated on RGB channels ### Image SR Official Models |Exp Name | Set5 (PSNR/SSIM) | Set14 (PSNR/SSIM) |DIV2K100 (PSNR/SSIM) | | :------------- | :----------: | :----------: |:----------: | | EDSR_Mx2_f64b16_DIV2K_official-3ba7b086 | 35.7768 / 0.9442 | 31.4966 / 0.8939 | 34.6291 / 0.9373 | | EDSR_Mx3_f64b16_DIV2K_official-6908f88a | 32.3597 / 0.903 | 28.3932 / 0.8096 | 30.9438 / 0.8737 | | EDSR_Mx4_f64b16_DIV2K_official-0c287733 | 30.1821 / 0.8641 | 26.7528 / 0.7432 | 28.9679 / 0.8183 | | EDSR_Lx2_f256b32_DIV2K_official-be38e77d | 35.9979 / 0.9454 | 31.8583 / 0.8971 | 35.0495 / 0.9407 | | EDSR_Lx3_f256b32_DIV2K_official-3660f70d | 32.643 / 0.906 | 28.644 / 0.8152 | 31.28 / 0.8798 | | EDSR_Lx4_f256b32_DIV2K_official-76ee1c8f | 30.5499 / 0.8701 | 27.0011 / 0.7509 | 29.277 / 0.8266 | ### Image SR Reproduced Models Experiment name conventions are in [Config.md](Config.md). |Exp Name | Set5 (PSNR/SSIM) | Set14 (PSNR/SSIM) |DIV2K100 (PSNR/SSIM) | | :------------- | :----------: | :----------: |:----------: | | 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb | 30.2468 / 0.8651 | 26.7817 / 0.7451 | 28.9967 / 0.8195 | | 002_MSRResNet_x2_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb | 35.7483 / 0.9442 | 31.5403 / 0.8937 |34.6699 / 0.9377| | 003_MSRResNet_x3_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb | 32.4038 / 0.9032| 28.4418 / 0.8106|30.9726 / 0.8743 | | 004_MSRGAN_x4_f64b16_DIV2K_400k_B16G1_wandb | 28.0158 / 0.8087|24.7474 / 0.6623 | 26.6504 / 0.7462| | | | | | | 201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb | 35.7395 / 0.944|31.4348 / 0.8934 |34.5798 / 0.937 | | 202_EDSR_Mx3_f64b16_DIV2K_300k_B16G1_201pretrain_wandb|32.315 / 0.9026 |28.3866 / 0.8088 |30.9095 / 0.8731| | 203_EDSR_Mx4_f64b16_DIV2K_300k_B16G1_201pretrain_wandb|30.1726 / 0.8641 |26.721 / 0.743 |28.9506 / 0.818| | 204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb | 35.9792 / 0.9453 | 31.7284 / 0.8959 | 34.9544 / 0.9399 | | 205_EDSR_Lx3_f256b32_DIV2K_300k_B16G1_204pretrain_wandb | 32.6467 / 0.9057 | 28.6859 / 0.8152 | 31.2664 / 0.8793 | | 206_EDSR_Lx4_f256b32_DIV2K_300k_B16G1_204pretrain_wandb | 30.4718 / 0.8695 | 26.9616 / 0.7502 | 29.2621 / 0.8265 | ## Video Super-Resolution #### Evaluation In the evaluation, we include all the input frames and do not crop any border pixels unless otherwise stated.
We do not use the self-ensemble (flip testing) strategy and any other post-processing methods. ## EDVR **Name convention**
EDVR\_(training dataset)\_(track name)\_(model complexity) - track name. There are four tracks in the NTIRE 2019 Challenges on Video Restoration and Enhancement: - **SR**: super-resolution with a fixed downsampling kernel (MATLAB bicubic downsampling kernel is frequently used). Most of the previous video SR methods focus on this setting. - **SRblur**: the inputs are also degraded with motion blur. - **deblur**: standard deblurring (motion blur). - **deblurcomp**: motion blur + video compression artifacts. - model complexity - **L** (Large): # of channels = 128, # of back residual blocks = 40. This setting is used in our competition submission. - **M** (Moderate): # of channels = 64, # of back residual blocks = 10. | Model name |[Test Set] PSNR/SSIM | |:----------:|:----------:| | EDVR_Vimeo90K_SR_L | [Vid4] (Y1) 27.35/0.8264 [[↓Results]](https://drive.google.com/open?id=14nozpSfe9kC12dVuJ9mspQH5ZqE4mT9K)
(RGB) 25.83/0.8077| | EDVR_REDS_SR_M | [REDS] (RGB) 30.53/0.8699 [[↓Results]](https://drive.google.com/open?id=1Mek3JIxkjJWjhZhH4qVwTXnRZutKUtC-)| | EDVR_REDS_SR_L | [REDS] (RGB) 31.09/0.8800 [[↓Results]](https://drive.google.com/open?id=1h6E0QVZyJ5SBkcnYaT1puxYYPVbPsTLt)| | EDVR_REDS_SRblur_L | [REDS] (RGB) 28.88/0.8361 [[↓Results]](https://drive.google.com/open?id=1-8MNkQuMVMz30UilB9m_d0SXicwFEPZH)| | EDVR_REDS_deblur_L | [REDS] (RGB) 34.80/0.9487 [[↓Results]](https://drive.google.com/open?id=133wCHTwiiRzenOEoStNbFuZlCX8Jn2at)| | EDVR_REDS_deblurcomp_L | [REDS] (RGB) 30.24/0.8567 [[↓Results]](https://drive.google.com/open?id=1VjC4fXBXy0uxI8Kwxh-ijj4PZkfsLuTX) | 1 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. #### Stage 2 models for the NTIRE19 Competition | Model name |[Test Set] PSNR/SSIM | |:----------:|:----------:| | EDVR_REDS_SR_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_SRblur_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_deblur_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_deblurcomp_Stage2 | [REDS] (RGB) / [[↓Results]]() | ## DUF The models are converted from the [officially released models](https://github.com/yhjo09/VSR-DUF).
| Model name | [Test Set] PSNR/SSIM1 | Official Results2 | |:----------:|:----------:|:----------:| | DUF_x4_52L_official3 | [Vid4] (Y4) 27.33/0.8319 [[↓Results]](https://drive.google.com/open?id=1U9xGhlDSpPPQvKN0BAzXfjUCvaFxwsQf)
(RGB) 25.80/0.8138 | (Y) 27.33/0.8318 [[↓Results]](https://drive.google.com/open?id=1HUmf__cSL7td7J4cXo2wvbVb14Y8YG2j)
(RGB) 25.79/0.8136 | | DUF_x4_28L_official | [Vid4] | | | DUF_x4_16L_official | [Vid4] | | | DUF_x3_16L_official | [Vid4] | | | DUF_x2_16L_official | [Vid4] | | 1 We crop eight pixels near image boundary for DUF due to its severe boundary effects.
2 The official results are obtained by running the official codes and models.
3 Different from the official codes, where `zero padding` is used for border frames, we use `new_info` strategy.
4 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. ## TOF The models are converted from the [officially released models](https://github.com/anchen1011/toflow).
| Model name | [Test Set] PSNR/SSIM | Official Results1 | |:----------:|:----------:|:----------:| | TOF_official2 | [Vid4] (Y3) 25.86/0.7626 [[↓Results]](https://drive.google.com/open?id=1Xp5U6uZeM44ShzawfuW_E-NmQ30hk-Be)
(RGB) 24.38/0.7403 | (Y) 25.89/0.7651 [[↓Results]](https://drive.google.com/open?id=1WY3CcdzbRhpvDi3aGc1jAhIbeC6GUrM8)
(RGB) 24.41/0.7428 | 1 The official results are obtained by running the official codes and models. Note that TOFlow does not provide a strategy for border frame recovery and we simply use a `replicate` strategy for border frames.
2 The converted model has slightly different results, due to different implementation. And we use `new_info` strategy for border frames.
3 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. ================================================ FILE: docs/ModelZoo_CN.md ================================================ # 模型库和基准 [English](ModelZoo.md) **|** [简体中文](ModelZoo_CN.md) :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) --- 我们提供了: 1. 官方的模型, 它们是从官方release的models直接转化过来的 1. 复现的模型, 使用`BasicSR`的框架复现的, 提供模型和log的例子 下载的模型可以放在 `experiments/pretrained_models` 文件夹. **[下载官方提供的预训练模型]** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) 你可以使用以下脚本从Google Drive下载预训练模型. ```python python scripts/download_pretrained_models.py ESRGAN # method can be ESRGAN, EDVR, StyleGAN, EDSR, DUF, DFDNet, dlib ``` **[下载复现的模型和log]** ([Google Drive](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing), [百度网盘](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)) 此外, 我们在 [wandb](https://www.wandb.com/) 上更新了模型训练的过程和曲线. 大家可以方便的比较: **[wandb训练曲线](https://app.wandb.ai/xintao/basicsr)**

#### 目录 1. [图像超分辨率](#图像超分辨率) 1. [图像超分官方模型](#图像超分官方模型) 1. [图像超分复现模型](#图像超分复现模型) 1. [视频超分辨率](#视频超分辨率) ## 图像超分辨率 在计算指标时: - 所有的图像各条边crop了scale的像素 - 都在RGB通道上测试 ### 图像超分官方模型 |Exp Name | Set5 (PSNR/SSIM) | Set14 (PSNR/SSIM) |DIV2K100 (PSNR/SSIM) | | :------------- | :----------: | :----------: |:----------: | | EDSR_Mx2_f64b16_DIV2K_official-3ba7b086 | 35.7768 / 0.9442 | 31.4966 / 0.8939 | 34.6291 / 0.9373 | | EDSR_Mx3_f64b16_DIV2K_official-6908f88a | 32.3597 / 0.903 | 28.3932 / 0.8096 | 30.9438 / 0.8737 | | EDSR_Mx4_f64b16_DIV2K_official-0c287733 | 30.1821 / 0.8641 | 26.7528 / 0.7432 | 28.9679 / 0.8183 | | EDSR_Lx2_f256b32_DIV2K_official-be38e77d | 35.9979 / 0.9454 | 31.8583 / 0.8971 | 35.0495 / 0.9407 | | EDSR_Lx3_f256b32_DIV2K_official-3660f70d | 32.643 / 0.906 | 28.644 / 0.8152 | 31.28 / 0.8798 | | EDSR_Lx4_f256b32_DIV2K_official-76ee1c8f | 30.5499 / 0.8701 | 27.0011 / 0.7509 | 29.277 / 0.8266 | ### 图像超分复现模型 实验名称的命名规则参见 [Config_CN.md](Config_CN.md). |Exp Name | Set5 (PSNR/SSIM) | Set14 (PSNR/SSIM) |DIV2K100 (PSNR/SSIM) | | :------------- | :----------: | :----------: |:----------: | | 001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb | 30.2468 / 0.8651 | 26.7817 / 0.7451 | 28.9967 / 0.8195 | | 002_MSRResNet_x2_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb | 35.7483 / 0.9442 | 31.5403 / 0.8937 |34.6699 / 0.9377| | 003_MSRResNet_x3_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb | 32.4038 / 0.9032| 28.4418 / 0.8106|30.9726 / 0.8743 | | 004_MSRGAN_x4_f64b16_DIV2K_400k_B16G1_wandb | 28.0158 / 0.8087|24.7474 / 0.6623 | 26.6504 / 0.7462| | | | | | | 201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb | 35.7395 / 0.944|31.4348 / 0.8934 |34.5798 / 0.937 | | 202_EDSR_Mx3_f64b16_DIV2K_300k_B16G1_201pretrain_wandb|32.315 / 0.9026 |28.3866 / 0.8088 |30.9095 / 0.8731| | 203_EDSR_Mx4_f64b16_DIV2K_300k_B16G1_201pretrain_wandb|30.1726 / 0.8641 |26.721 / 0.743 |28.9506 / 0.818| | 204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb | 35.9792 / 0.9453 | 31.7284 / 0.8959 | 34.9544 / 0.9399 | | 205_EDSR_Lx3_f256b32_DIV2K_300k_B16G1_204pretrain_wandb | 32.6467 / 0.9057 | 28.6859 / 0.8152 | 31.2664 / 0.8793 | | 206_EDSR_Lx4_f256b32_DIV2K_300k_B16G1_204pretrain_wandb | 30.4718 / 0.8695 | 26.9616 / 0.7502 | 29.2621 / 0.8265 | ## 视频超分辨率 #### Evaluation In the evaluation, we include all the input frames and do not crop any border pixels unless otherwise stated.
We do not use the self-ensemble (flip testing) strategy and any other post-processing methods. ## EDVR **Name convention**
EDVR\_(training dataset)\_(track name)\_(model complexity) - track name. There are four tracks in the NTIRE 2019 Challenges on Video Restoration and Enhancement: - **SR**: super-resolution with a fixed downsampling kernel (MATLAB bicubic downsampling kernel is frequently used). Most of the previous video SR methods focus on this setting. - **SRblur**: the inputs are also degraded with motion blur. - **deblur**: standard deblurring (motion blur). - **deblurcomp**: motion blur + video compression artifacts. - model complexity - **L** (Large): # of channels = 128, # of back residual blocks = 40. This setting is used in our competition submission. - **M** (Moderate): # of channels = 64, # of back residual blocks = 10. | Model name |[Test Set] PSNR/SSIM | |:----------:|:----------:| | EDVR_Vimeo90K_SR_L | [Vid4] (Y1) 27.35/0.8264 [[↓Results]](https://drive.google.com/open?id=14nozpSfe9kC12dVuJ9mspQH5ZqE4mT9K)
(RGB) 25.83/0.8077| | EDVR_REDS_SR_M | [REDS] (RGB) 30.53/0.8699 [[↓Results]](https://drive.google.com/open?id=1Mek3JIxkjJWjhZhH4qVwTXnRZutKUtC-)| | EDVR_REDS_SR_L | [REDS] (RGB) 31.09/0.8800 [[↓Results]](https://drive.google.com/open?id=1h6E0QVZyJ5SBkcnYaT1puxYYPVbPsTLt)| | EDVR_REDS_SRblur_L | [REDS] (RGB) 28.88/0.8361 [[↓Results]](https://drive.google.com/open?id=1-8MNkQuMVMz30UilB9m_d0SXicwFEPZH)| | EDVR_REDS_deblur_L | [REDS] (RGB) 34.80/0.9487 [[↓Results]](https://drive.google.com/open?id=133wCHTwiiRzenOEoStNbFuZlCX8Jn2at)| | EDVR_REDS_deblurcomp_L | [REDS] (RGB) 30.24/0.8567 [[↓Results]](https://drive.google.com/open?id=1VjC4fXBXy0uxI8Kwxh-ijj4PZkfsLuTX) | 1 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. #### Stage 2 models for the NTIRE19 Competition | Model name |[Test Set] PSNR/SSIM | |:----------:|:----------:| | EDVR_REDS_SR_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_SRblur_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_deblur_Stage2 | [REDS] (RGB) / [[↓Results]]()| | EDVR_REDS_deblurcomp_Stage2 | [REDS] (RGB) / [[↓Results]]() | ## DUF The models are converted from the [officially released models](https://github.com/yhjo09/VSR-DUF).
| Model name | [Test Set] PSNR/SSIM1 | Official Results2 | |:----------:|:----------:|:----------:| | DUF_x4_52L_official3 | [Vid4] (Y4) 27.33/0.8319 [[↓Results]](https://drive.google.com/open?id=1U9xGhlDSpPPQvKN0BAzXfjUCvaFxwsQf)
(RGB) 25.80/0.8138 | (Y) 27.33/0.8318 [[↓Results]](https://drive.google.com/open?id=1HUmf__cSL7td7J4cXo2wvbVb14Y8YG2j)
(RGB) 25.79/0.8136 | | DUF_x4_28L_official | [Vid4] | | | DUF_x4_16L_official | [Vid4] | | | DUF_x3_16L_official | [Vid4] | | | DUF_x2_16L_official | [Vid4] | | 1 We crop eight pixels near image boundary for DUF due to its severe boundary effects.
2 The official results are obtained by running the official codes and models.
3 Different from the official codes, where `zero padding` is used for border frames, we use `new_info` strategy.
4 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. ## TOF The models are converted from the [officially released models](https://github.com/anchen1011/toflow).
| Model name | [Test Set] PSNR/SSIM | Official Results1 | |:----------:|:----------:|:----------:| | TOF_official2 | [Vid4] (Y3) 25.86/0.7626 [[↓Results]](https://drive.google.com/open?id=1Xp5U6uZeM44ShzawfuW_E-NmQ30hk-Be)
(RGB) 24.38/0.7403 | (Y) 25.89/0.7651 [[↓Results]](https://drive.google.com/open?id=1WY3CcdzbRhpvDi3aGc1jAhIbeC6GUrM8)
(RGB) 24.41/0.7428 | 1 The official results are obtained by running the official codes and models. Note that TOFlow does not provide a strategy for border frame recovery and we simply use a `replicate` strategy for border frames.
2 The converted model has slightly different results, due to different implementation. And we use `new_info` strategy for border frames.
3 Y or RGB denotes the evaluation on Y (luminance) or RGB channels. ================================================ FILE: docs/Models.md ================================================ # Models [English](Models.md) **|** [简体中文](Models_CN.md) #### Contents 1. [Supported Models](#Supported-Models) 1. [Inheritance Relationship](#Inheritance-Relationship) ## Supported Models | Class | Description |Supported Algorithms | | :------------- | :----------:| :----------: | | [BaseModel](../basicsr/models/base_model.py) | Abstract base class, define common functions|| | [SRModel](../basicsr/models/sr_model.py) | Base image SR class | SRCNN, EDSR, SRResNet, RCAN, RRDBNet, etc | | [SRGANModel](../basicsr/models/srgan_model.py) | SRGAN image SR class | SRGAN | | [ESRGANModel](../basicsr/models/esrgan_model.py) | ESRGAN image SR class|ESRGAN| | [VideoBaseModel](../basicsr/models/video_base_model.py) | Base video SR class | | | [EDVRModel](../basicsr/models/edvr_model.py) | EDVR video SR class |EDVR| | [StyleGAN2Model](../basicsr/models/stylegan2_model.py) | StyleGAN2 generation class |StyleGAN2| ## Inheritance Relationship In order to reuse components among models, we use a lot of inheritance. The following is the inheritance relationship: ```txt BaseModel ├── SRModel │ ├── SRGANModel │ │ └── ESRGANModel │ └── VideoBaseModel │ ├── VideoGANModel │ └── EDVRModel └── StyleGAN2Model ``` ================================================ FILE: docs/Models_CN.md ================================================ # 模型 [English](Models.md) **|** [简体中文](Models_CN.md) #### 目录 1. [支持的模型](#支持的模型) 1. [继承关系](#继承关系) ## 支持的模型 | 类 | 描述 |支持的算法 | | :------------- | :----------:| :----------: | | [BaseModel](../basicsr/models/base_model.py) | 抽象基类, 定义了共有的函数|| | [SRModel](../basicsr/models/sr_model.py) | 基础图像超分类 | SRCNN, EDSR, SRResNet, RCAN, RRDBNet, etc | | [SRGANModel](../basicsr/models/srgan_model.py) | SRGAN图像超分类 | SRGAN | | [ESRGANModel](../basicsr/models/esrgan_model.py) | ESRGAN图像超分类 |ESRGAN| | [VideoBaseModel](../basicsr/models/video_base_model.py) | 基础视频超分类 | | | [EDVRModel](../basicsr/models/edvr_model.py) | EDVR视频超分类 |EDVR| | [StyleGAN2Model](../basicsr/models/stylegan2_model.py) | StyleGAN2图像生成类 |StyleGAN2| ## 继承关系 为增加模型间的复用, 我们大量使用了继承, 以下为各个模型之间的继承关系: ```txt BaseModel ├── SRModel │ ├── SRGANModel │ │ ├── ESRGANModel │ ├── VideoBaseModel │ ├── EDVRModel ├── StyleGAN2Model ``` ================================================ FILE: docs/TrainTest.md ================================================ # Training and Testing [English](TrainTest.md) **|** [简体中文](TrainTest_CN.md) Please run the commands in the root path of `BasicSR`.
In general, both the training and testing include the following steps: 1. Prepare datasets. Please refer to [DatasetPreparation.md](DatasetPreparation.md) 1. Modify config files. The config files are under the `options` folder. For more specific configuration information, please refer to [Config](Config.md) 1. [Optional] You may need to download pre-trained models if you are testing or using pre-trained models. Please see [ModelZoo](ModelZoo.md) 1. Run commands. Use [Training Commands](#Training-Commands) or [Testing Commands](#Testing-Commands) accordingly. #### 目录 1. [Training Commands](#Training-Commands) 1. [Single GPU Training](#Single-GPU-Training) 1. [Distributed (Multi-GPUs) Training](#Distributed-Training) 1. [Slurm Training](#Slurm-Training) 1. [Testing Commands](#Testing-Commands) 1. [Single GPU Testing](#Single-GPU-Testing) 1. [Distributed (Multi-GPUs) Testing](#Distributed-Testing) 1. [Slurm Testing](#Slurm-Testing) ## Training Commands ### Single GPU Training > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0 \\\ > python basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml ### Distributed Training **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher pytorch or > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > ./scripts/dist_train.sh 8 options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher pytorch or > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > ./scripts/dist_train.sh 4 options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml ### Slurm Training [Introduction to Slurm](https://slurm.schedmd.com/quickstart.html) **1 GPU** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=MSRResNetx4 --gres=gpu:1 --ntasks=1 --ntasks-per-node=1 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml --launcher="slurm" **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=EDVRMwoTSA --gres=gpu:4 --ntasks=4 --ntasks-per-node=4 --cpus-per-task=4 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher="slurm" **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=EDVRMwoTSA --gres=gpu:8 --ntasks=8 --ntasks-per-node=8 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher="slurm" ## Testing Commands ### Single GPU Testing > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0 \\\ > python basicsr/test.py -opt options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml ### Distributed Testing **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher pytorch or > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > ./scripts/dist_test.sh 8 options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher pytorch or > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > ./scripts/dist_test.sh 4 options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml ### Slurm Testing [Introduction to Slurm](https://slurm.schedmd.com/quickstart.html) **1 GPU** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:1 --ntasks=1 --ntasks-per-node=1 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml --launcher="slurm" **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:4 --ntasks=4 --ntasks-per-node=4 --cpus-per-task=4 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher="slurm" **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:8 --ntasks=8 --ntasks-per-node=8 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher="slurm" ================================================ FILE: docs/TrainTest_CN.md ================================================ # 训练和测试 [English](TrainTest.md) **|** [简体中文](TrainTest_CN.md) 所有的命令都在 `BasicSR` 的根目录下运行.
一般来说, 训练和测试都有以下的步骤: 1. 准备数据. 参见 [DatasetPreparation_CN.md](DatasetPreparation_CN.md) 1. 修改Config文件. Config文件在 `options` 目录下面. 具体的Config配置含义, 可参考 [Config说明](Config_CN.md) 1. [Optional] 如果是测试或需要预训练, 则需下载预训练模型, 参见 [模型库](ModelZoo_CN.md) 1. 运行命令. 根据需要，使用 [训练命令](#训练命令) 或 [测试命令](#测试命令) #### 目录 1. [训练命令](#训练命令) 1. [单GPU训练](#单GPU训练) 1. [分布式(多卡)训练](#分布式训练) 1. [Slurm训练](#Slurm训练) 1. [测试命令](#测试命令) 1. [单GPU测试](#单GPU测试) 1. [分布式(多卡)测试](#分布式测试) 1. [Slurm测试](#Slurm测试) ## 训练命令 ### 单GPU训练 > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0 \\\ > python basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml ### 分布式训练 **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher pytorch 或者 > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > ./scripts/dist_train.sh 8 options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher pytorch 或者 > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > ./scripts/dist_train.sh 4 options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml ### Slurm训练 [Slurm介绍](https://slurm.schedmd.com/quickstart.html) **1 GPU** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=MSRResNetx4 --gres=gpu:1 --ntasks=1 --ntasks-per-node=1 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/SRResNet_SRGAN/train_MSRResNet_x4.yml --launcher="slurm" **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=EDVRMwoTSA --gres=gpu:4 --ntasks=4 --ntasks-per-node=4 --cpus-per-task=4 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher="slurm" **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=EDVRMwoTSA --gres=gpu:8 --ntasks=8 --ntasks-per-node=8 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/train.py -opt options/train/EDVR/train_EDVR_M_x4_SR_REDS_woTSA.yml --launcher="slurm" ## 测试命令 ### 单GPU测试 > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0 \\\ > python basicsr/test.py -opt options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml ### 分布式测试 **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher pytorch 或者 > CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \\\ > ./scripts/dist_test.sh 8 options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher pytorch 或者 > CUDA_VISIBLE_DEVICES=0,1,2,3 \\\ > ./scripts/dist_test.sh 4 options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml ### Slurm测试 [Slurm介绍](https://slurm.schedmd.com/quickstart.html) **1 GPU** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:1 --ntasks=1 --ntasks-per-node=1 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/SRResNet_SRGAN/test_MSRResNet_x4.yml --launcher="slurm" **4 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:4 --ntasks=4 --ntasks-per-node=4 --cpus-per-task=4 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher="slurm" **8 GPUs** > PYTHONPATH="./:${PYTHONPATH}" \\\ > GLOG_vmodule=MemcachedClient=-1 \\\ > srun -p [partition] --mpi=pmi2 --job-name=test --gres=gpu:8 --ntasks=8 --ntasks-per-node=8 --cpus-per-task=6 --kill-on-bad-exit=1 \\\ > python -u basicsr/test.py -opt options/test/EDVR/test_EDVR_M_x4_SR_REDS.yml --launcher="slurm" ================================================ FILE: docs/api.rst ================================================ API ================= basicsr.data -------------- .. automodule:: basicsr.data :members: basicsr.models --------------- .. automodule:: basicsr.models :members: basicsr.archs -------------- .. automodule:: basicsr.archs :members: basicsr.ops -------------- .. automodule:: basicsr.ops :members: basicsr.losses -------------- .. automodule:: basicsr.losses :members: basicsr.utils -------------- .. automodule:: basicsr.utils :members: basicsr.metrics -------------- .. automodule:: basicsr.metrics :members: basicsr.train & test -------------- .. automodule:: basicsr.train :members: .. automodule:: basicsr.test :members: ================================================ FILE: docs/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os # import subprocess import sys sys.path.insert(0, os.path.abspath('..')) # -- Project information ----------------------------------------------------- project = 'BasicSR' copyright = '2018-2021, BasicSR Authors' author = 'BasicSR Authors' # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', 'recommonmark', 'sphinx_markdown_tables' ] source_suffix = { '.rst': 'restructuredtext', '.md': 'markdown', } # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # The master toctree document. master_doc = 'index' ================================================ FILE: docs/history_updates.md ================================================ # History of New Features/Updates :triangular_flag_on_post: **New Features/Updates** - :white_check_mark: Oct 5, 2021. Add **ECBSR training and testing** codes: [ECBSR](https://github.com/xindongzhang/ECBSR). > ACMMM21: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
> Xindong Zhang, Hui Zeng, Lei Zhang - :white_check_mark: Sep 2, 2021. Add **SwinIR training and testing** codes: [SwinIR](https://github.com/JingyunLiang/SwinIR) by [Jingyun Liang](https://github.com/JingyunLiang). More details are in [HOWTOs.md](docs/HOWTOs.md#how-to-train-swinir-sr) > ICCVW21: SwinIR: Image Restoration Using Swin Transformer
> Jingyun Liang, Jiezhang Cao, Sun, Guolei Sun, Kai Zhang, Luc Van Gool and Radu Timofte - :white_check_mark: Aug 5, 2021. Add NIQE, which produces the same results as MATLAB (both are 5.7296 for tests/data/baboon.png). - :white_check_mark: July 31, 2021. Add **bi-directional video super-resolution** codes: [**BasicVSR** and IconVSR](https://arxiv.org/abs/2012.02181). > CVPR21: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
> Kelvin C.K., Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy - :white_check_mark: July 20, 2021. Add **dual-blind face restoration** codes: [HiFaceGAN](https://github.com/Lotayou/Face-Renovation) codes by [Lotayou](https://lotayou.github.io/). - :white_check_mark: Nov 29, 2020. Add **ESRGAN** and **DFDNet** [colab demo](../colab) - :white_check_mark: Sep 8, 2020. Add **blind face restoration** inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet). > ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries
> Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang - :white_check_mark: Aug 27, 2020. Add **StyleGAN2 training and testing** codes: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch). > CVPR20: Analyzing and Improving the Image Quality of StyleGAN
> Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila - :white_check_mark: Aug 19, 2020. A **brand-new** BasicSR v1.0.0 online. ================================================ FILE: docs/index.rst ================================================ Welcome to BasicSR's documentation! =================================== .. toctree:: :maxdepth: 3 :caption: API api.rst Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: options/test/benchmark_BSRN-S_x4.yml ================================================ name: BSRN-S_C48B5_L1_1500k suffix: ~ # add suffix to saved images model_type: SRModel scale: 4 crop_border: ~ # crop border when evaluation. If None, crop the scale pixels num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 datasets: test_1: # the 1st test dataset name: Set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod4 dataroot_lq: ./datasets/Set5/LRbicx4 io_backend: type: disk test_2: # the 2st test dataset name: Set14 type: PairedImageDataset dataroot_gt: ./datasets/Set14/GTmod4 dataroot_lq: ./datasets/Set14/LRbicx4 io_backend: type: disk test_3: name: BSD100 type: PairedImageDataset dataroot_gt: ./datasets/BSD100/GTmod4 dataroot_lq: ./datasets/BSD100/LRbicx4 io_backend: type: disk test_4: name: Urban100 type: PairedImageDataset dataroot_gt: ./datasets/Urban100/GTmod4 dataroot_lq: ./datasets/Urban100/LRbicx4 io_backend: type: disk test_5: name: Manga109 type: PairedImageDataset dataroot_gt: ./datasets/Manga109/GTmod4 dataroot_lq: ./datasets/Manga109/LRbicx4 io_backend: type: disk # network structures network_g: type: RFDNFINALB5 num_in_ch: 3 num_feat: 48 num_block: 5 num_out_ch: 3 upscale: 4 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: experiments/pretrained_models/net_g_BSRN-S.pth strict_load_g: true # validation settings val: save_img: true suffix: ~ # add suffix to saved images, if None, use exp name pbar: true 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: options/test/benchmark_BSRN_x2.yml ================================================ name: test_BSRN_x2_C64B8_L1_1000k suffix: ~ # add suffix to saved images model_type: SRModel scale: 2 crop_border: ~ # crop border when evaluation. If None, crop the scale pixels num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 datasets: test_1: # the 1st test dataset name: val_set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod2 dataroot_lq: ./datasets/Set5/LRbicx2 io_backend: type: disk test_2: # the 2st test dataset name: val_set14 type: PairedImageDataset dataroot_gt: ./datasets/Set14/GTmod2 dataroot_lq: ./datasets/Set14/LRbicx2 io_backend: type: disk test_3: name: BSD100 type: PairedImageDataset dataroot_gt: ./datasets/BSD100/GTmod2 dataroot_lq: ./datasets/BSD100/LRbicx2 io_backend: type: disk test_4: name: Urban100 type: PairedImageDataset dataroot_gt: ./datasets/Urban100/GTmod2 dataroot_lq: ./datasets/Urban100/LRbicx2 io_backend: type: disk test_5: name: Manga109 type: PairedImageDataset dataroot_gt: ./datasets/Manga109/GTmod2 dataroot_lq: ./datasets/Manga109/LRbicx2 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 2 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: experiments/pretrained_models/net_g_BSRN_x2.pth strict_load_g: true # validation settings val: save_img: true suffix: ~ # add suffix to saved images, if None, use exp name pbar: true metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 2 test_y_channel: true ssim: type: calculate_ssim crop_border: 2 test_y_channel: true ================================================ FILE: options/test/benchmark_BSRN_x3.yml ================================================ name: test_BSRN_x3_C64B8_L1_1000k suffix: ~ # add suffix to saved images model_type: SRModel scale: 3 crop_border: ~ # crop border when evaluation. If None, crop the scale pixels num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 datasets: test_1: # the 1st test dataset name: val_set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod3 dataroot_lq: ./datasets/Set5/LRbicx3 io_backend: type: disk test_2: # the 2st test dataset name: val_set14 type: PairedImageDataset dataroot_gt: ./datasets/Set14/GTmod3 dataroot_lq: ./datasets/Set14/LRbicx3 io_backend: type: disk test_3: name: BSD100 type: PairedImageDataset dataroot_gt: ./datasets/BSD100/GTmod3 dataroot_lq: ./datasets/BSD100/LRbicx3 io_backend: type: disk test_4: name: Urban100 type: PairedImageDataset dataroot_gt: ./datasets/Urban100/GTmod3 dataroot_lq: ./datasets/Urban100/LRbicx3 io_backend: type: disk test_5: name: Manga109 type: PairedImageDataset dataroot_gt: ./datasets/Manga109/GTmod3 dataroot_lq: ./datasets/Manga109/LRbicx3 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 3 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: experiments/pretrained_models/net_g_BSRN_x3.pth strict_load_g: true # validation settings val: save_img: true suffix: ~ # add suffix to saved images, if None, use exp name pbar: true metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 3 test_y_channel: true ssim: type: calculate_ssim crop_border: 3 test_y_channel: true ================================================ FILE: options/test/benchmark_BSRN_x4.yml ================================================ name: test_BSRN_x4_C64B8_L1_1000k suffix: ~ # add suffix to saved images model_type: SRModel scale: 4 crop_border: ~ # crop border when evaluation. If None, crop the scale pixels num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 datasets: test_1: # the 1st test dataset name: Set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod4 dataroot_lq: ./datasets/Set5/LRbicx4 io_backend: type: disk test_2: # the 2st test dataset name: Set14 type: PairedImageDataset dataroot_gt: ./datasets/Set14/GTmod4 dataroot_lq: ./datasets/Set14/LRbicx4 io_backend: type: disk test_3: name: BSD100 type: PairedImageDataset dataroot_gt: ./datasets/BSD100/GTmod4 dataroot_lq: ./datasets/BSD100/LRbicx4 io_backend: type: disk test_4: name: Urban100 type: PairedImageDataset dataroot_gt: ./datasets/Urban100/GTmod4 dataroot_lq: ./datasets/Urban100/LRbicx4 io_backend: type: disk test_5: name: Manga109 type: PairedImageDataset dataroot_gt: ./datasets/Manga109/GTmod4 dataroot_lq: ./datasets/Manga109/LRbicx4 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 4 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: experiments/pretrained_models/net_g_BSRN_x4.pth strict_load_g: true # validation settings val: save_img: true suffix: ~ # add suffix to saved images, if None, use exp name pbar: true 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: options/train/train_BSRN-S_x4.yml ================================================ # general settings name: BSRN-S_x4_C48B5_G4L1_1500k model_type: SRModel scale: 4 num_gpu: 4 # set num_gpu: 0 for cpu mode manual_seed: 0 # dataset and data loader settings datasets: train: name: DF2K type: PairedImageDataset # dataroot_gt: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_HR_sub # dataroot_lq: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # (for lmdb) dataroot_gt: ./datasets/DF2K/DF2K_HR_sub.lmdb dataroot_lq: ./datasets/DF2K/DF2K_train_LR_bicubic_X4_sub.lmdb filename_tmpl: '{}' io_backend: type: lmdb # (for lmdb) # type: lmdb gt_size: 256 use_hflip: true use_rot: true # data loader use_shuffle: true num_worker_per_gpu: 3 batch_size_per_gpu: 64 dataset_enlarge_ratio: 100 prefetch_mode: ~ val: name: DIV2K_val type: PairedImageDataset dataroot_gt: ./datasets/DIV2K/DIV2K_valid_HR/ dataroot_lq: ./datasets/DIV2K/DIV2K_valid_LR_bicubic/X4/ io_backend: type: disk # network structures network_g: type: RFDNFINALB5 num_in_ch: 3 num_feat: 48 num_block: 5 num_out_ch: 3 upscale: 4 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: ~ strict_load_g: true resume_state: ~ # training settings train: optim_g: type: Adam lr: !!float 2e-3 weight_decay: 0 betas: [0.9, 0.999] scheduler: type: CosineAnnealingRestartLR periods: [1500000] restart_weights: [1] eta_min: !!float 1e-7 total_iter: 1500000 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 metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 4 test_y_channel: false # logging settings logger: print_freq: 100 save_checkpoint_freq: !!float 5e3 use_tb_logger: true wandb: project: ~ resume_id: ~ entity: ~ # dist training settings dist_params: backend: nccl port: 29500 ================================================ FILE: options/train/train_BSRN_x2.yml ================================================ # general settings name: test_BSRN_x2_C64B8_L1_1000k model_type: SRModel scale: 2 num_gpu: 2 # set num_gpu: 0 for cpu mode manual_seed: 0 # dataset and data loader settings datasets: train: name: DF2K type: PairedImageDataset # dataroot_gt: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_HR_sub # dataroot_lq: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # (for lmdb) dataroot_gt: ./datasets/DF2K/DF2K_HR_sub.lmdb dataroot_lq: ./datasets/DF2K/DF2K_train_LR_bicubic_X2_sub.lmdb filename_tmpl: '{}' io_backend: type: lmdb # (for lmdb) # type: lmdb gt_size: 96 use_hflip: true use_rot: true # data loader use_shuffle: true num_worker_per_gpu: 3 batch_size_per_gpu: 32 dataset_enlarge_ratio: 100 prefetch_mode: ~ val: name: Set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod2 dataroot_lq: ./datasets/Set5/LRbicx2 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 2 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: ~ strict_load_g: true resume_state: # training settings train: optim_g: type: Adam lr: !!float 1e-3 weight_decay: 0 betas: [0.9, 0.999] scheduler: type: CosineAnnealingRestartLR periods: [1000000] restart_weights: [1] eta_min: !!float 1e-7 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 metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 2 test_y_channel: true # logging settings logger: print_freq: 100 save_checkpoint_freq: !!float 5e3 use_tb_logger: true wandb: project: ~ resume_id: ~ entity: ~ # dist training settings dist_params: backend: nccl port: 29500 ================================================ FILE: options/train/train_BSRN_x3.yml ================================================ # general settings name: test_BSRN_x3_C64B8_L1_1000k model_type: SRModel scale: 3 num_gpu: 2 # set num_gpu: 0 for cpu mode manual_seed: 0 # dataset and data loader settings datasets: train: name: DF2K type: PairedImageDataset # dataroot_gt: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_HR_sub # dataroot_lq: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # (for lmdb) dataroot_gt: ./datasets/DF2K/DF2K_HR_sub.lmdb dataroot_lq: ./datasets/DF2K/DF2K_train_LR_bicubic_X3_sub.lmdb filename_tmpl: '{}' io_backend: type: lmdb # (for lmdb) # type: lmdb gt_size: 144 use_hflip: true use_rot: true # data loader use_shuffle: true num_worker_per_gpu: 3 batch_size_per_gpu: 32 dataset_enlarge_ratio: 100 prefetch_mode: ~ val: name: Set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod3 dataroot_lq: ./datasets/Set5/LRbicx3 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 3 # change_c: 15 conv: BSConvU # path path: pretrain_network_g: ~ strict_load_g: true resume_state: ~ # training settings train: optim_g: type: Adam lr: !!float 1e-3 weight_decay: 0 betas: [0.9, 0.999] scheduler: type: CosineAnnealingRestartLR periods: [1000000] restart_weights: [1] eta_min: !!float 1e-7 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 metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 3 test_y_channel: true # logging settings logger: print_freq: 100 save_checkpoint_freq: !!float 5e3 use_tb_logger: true wandb: project: ~ resume_id: ~ entity: ~ # dist training settings dist_params: backend: nccl port: 29500 ================================================ FILE: options/train/train_BSRN_x4.yml ================================================ # general settings name: BSRN_x4_C64B8_L1_1000k model_type: SRModel scale: 4 num_gpu: 2 # set num_gpu: 0 for cpu mode manual_seed: 0 # dataset and data loader settings datasets: train: name: DF2K type: PairedImageDataset # dataroot_gt: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_HR_sub # dataroot_lq: /mnt/yxsun/Project/Data/datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub # (for lmdb) dataroot_gt: ./datasets/DF2K/DF2K_HR_sub.lmdb dataroot_lq: ./datasets/DF2K/DF2K_train_LR_bicubic_X4_sub.lmdb filename_tmpl: '{}' io_backend: type: lmdb # (for lmdb) # type: lmdb gt_size: 192 use_hflip: true use_rot: true # data loader use_shuffle: true num_worker_per_gpu: 3 batch_size_per_gpu: 32 dataset_enlarge_ratio: 100 prefetch_mode: ~ val: name: Set5 type: PairedImageDataset dataroot_gt: ./datasets/Set5/GTmod4 dataroot_lq: ./datasets/Set5/LRbicx4 io_backend: type: disk # network structures network_g: type: BSRN num_in_ch: 3 num_feat: 64 num_block: 8 num_out_ch: 3 upscale: 4 conv: BSConvU # path path: pretrain_network_g: ~ strict_load_g: true resume_state: ~ # training settings train: optim_g: type: Adam lr: !!float 1e-3 weight_decay: 0 betas: [0.9, 0.999] scheduler: type: CosineAnnealingRestartLR periods: [1000000] restart_weights: [1] eta_min: !!float 1e-7 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 1e5 save_img: false metrics: psnr: # metric name, can be arbitrary type: calculate_psnr crop_border: 4 test_y_channel: false # logging settings logger: print_freq: 100 save_checkpoint_freq: !!float 5e3 use_tb_logger: true wandb: project: ~ resume_id: ~ entity: ~ # dist training settings dist_params: backend: nccl port: 29500 ================================================ FILE: requirements.txt ================================================ addict future lmdb numpy opencv-python Pillow pyyaml requests scikit-image scipy tb-nightly torch>=1.7 torchvision tqdm yapf ================================================ FILE: scripts/data_preparation/create_lmdb.py ================================================ import argparse from os import path as osp from basicsr.utils import scandir from basicsr.utils.lmdb_util import make_lmdb_from_imgs def create_lmdb_for_div2k(): """Create lmdb files for DIV2K dataset. Usage: Before run this script, please run `extract_subimages.py`. Typically, there are four folders to be processed for DIV2K dataset. DIV2K_train_HR_sub DIV2K_train_LR_bicubic/X2_sub DIV2K_train_LR_bicubic/X3_sub DIV2K_train_LR_bicubic/X4_sub Remember to modify opt configurations according to your settings. """ # HR images folder_path = 'datasets/DIV2K/DIV2K_train_HR_sub' lmdb_path = 'datasets/DIV2K/DIV2K_train_HR_sub.lmdb' img_path_list, keys = prepare_keys_div2k(folder_path) make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys) # LRx2 images # folder_path = 'datasets/DF2K/DF2K_train_LR_bicubic_X2_sub' # lmdb_path = 'datasets/DF2K/DF2K_train_LR_bicubic_X2_sub.lmdb' # img_path_list, keys = prepare_keys_div2k(folder_path) # make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys) # LRx3 images #folder_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X3_sub' #lmdb_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic_X3_sub.lmdb' #img_path_list, keys = prepare_keys_div2k(folder_path) #make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys) # LRx4 images folder_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub' lmdb_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb' img_path_list, keys = prepare_keys_div2k(folder_path) make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys) def prepare_keys_div2k(folder_path): """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='png', recursive=False))) keys = [img_path.split('.png')[0] for img_path in sorted(img_path_list)] return img_path_list, keys def create_lmdb_for_reds(): """Create lmdb files for REDS dataset. Usage: Before run this script, please run `merge_reds_train_val.py`. We take two folders for example: train_sharp train_sharp_bicubic Remember to modify opt configurations according to your settings. """ # train_sharp folder_path = 'datasets/REDS/train_sharp' lmdb_path = 'datasets/REDS/train_sharp_with_val.lmdb' img_path_list, keys = prepare_keys_reds(folder_path) make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, multiprocessing_read=True) # train_sharp_bicubic folder_path = 'datasets/REDS/train_sharp_bicubic' lmdb_path = 'datasets/REDS/train_sharp_bicubic_with_val.lmdb' img_path_list, keys = prepare_keys_reds(folder_path) make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, multiprocessing_read=True) def prepare_keys_reds(folder_path): """Prepare image path list and keys for REDS 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='png', recursive=True))) keys = [v.split('.png')[0] for v in img_path_list] # example: 000/00000000 return img_path_list, keys def create_lmdb_for_vimeo90k(): """Create lmdb files for Vimeo90K dataset. Usage: Remember to modify opt configurations according to your settings. """ # GT folder_path = 'datasets/vimeo90k/vimeo_septuplet/sequences' lmdb_path = 'datasets/vimeo90k/vimeo90k_train_GT_only4th.lmdb' train_list_path = 'datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt' img_path_list, keys = prepare_keys_vimeo90k(folder_path, train_list_path, 'gt') make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, multiprocessing_read=True) # LQ folder_path = 'datasets/vimeo90k/vimeo_septuplet_matlabLRx4/sequences' lmdb_path = 'datasets/vimeo90k/vimeo90k_train_LR7frames.lmdb' train_list_path = 'datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt' img_path_list, keys = prepare_keys_vimeo90k(folder_path, train_list_path, 'lq') make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, multiprocessing_read=True) def prepare_keys_vimeo90k(folder_path, train_list_path, mode): """Prepare image path list and keys for Vimeo90K dataset. Args: folder_path (str): Folder path. train_list_path (str): Path to the official train list. mode (str): One of 'gt' or 'lq'. Returns: list[str]: Image path list. list[str]: Key list. """ print('Reading image path list ...') with open(train_list_path, 'r') as fin: train_list = [line.strip() for line in fin] img_path_list = [] keys = [] for line in train_list: folder, sub_folder = line.split('/') img_path_list.extend([osp.join(folder, sub_folder, f'im{j + 1}.png') for j in range(7)]) keys.extend([f'{folder}/{sub_folder}/im{j + 1}' for j in range(7)]) if mode == 'gt': print('Only keep the 4th frame for the gt mode.') img_path_list = [v for v in img_path_list if v.endswith('im4.png')] keys = [v for v in keys if v.endswith('/im4')] return img_path_list, keys if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--dataset', type=str, help=("Options: 'DIV2K', 'REDS', 'Vimeo90K' " 'You may need to modify the corresponding configurations in codes.')) args = parser.parse_args() dataset = args.dataset.lower() if dataset == 'div2k': create_lmdb_for_div2k() elif dataset == 'reds': create_lmdb_for_reds() elif dataset == 'vimeo90k': create_lmdb_for_vimeo90k() else: raise ValueError('Wrong dataset.') ================================================ FILE: scripts/data_preparation/download_datasets.py ================================================ import argparse import glob import os from os import path as osp from basicsr.utils.download_util import download_file_from_google_drive def download_dataset(dataset, file_ids): save_path_root = './datasets/' os.makedirs(save_path_root, exist_ok=True) for file_name, file_id in file_ids.items(): save_path = osp.abspath(osp.join(save_path_root, file_name)) if osp.exists(save_path): user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n') if user_response.lower() == 'y': print(f'Covering {file_name} to {save_path}') download_file_from_google_drive(file_id, save_path) elif user_response.lower() == 'n': print(f'Skipping {file_name}') else: raise ValueError('Wrong input. Only accepts Y/N.') else: print(f'Downloading {file_name} to {save_path}') download_file_from_google_drive(file_id, save_path) # unzip if save_path.endswith('.zip'): extracted_path = save_path.replace('.zip', '') print(f'Extract {save_path} to {extracted_path}') import zipfile with zipfile.ZipFile(save_path, 'r') as zip_ref: zip_ref.extractall(extracted_path) file_name = file_name.replace('.zip', '') subfolder = osp.join(extracted_path, file_name) if osp.isdir(subfolder): print(f'Move {subfolder} to {extracted_path}') import shutil for path in glob.glob(osp.join(subfolder, '*')): shutil.move(path, extracted_path) shutil.rmtree(subfolder) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( 'dataset', type=str, help=("Options: 'Set5', 'Set14'. " "Set to 'all' if you want to download all the dataset.")) args = parser.parse_args() file_ids = { 'Set5': { 'Set5.zip': # file name '1RtyIeUFTyW8u7oa4z7a0lSzT3T1FwZE9', # file id }, 'Set14': { 'Set14.zip': '1vsw07sV8wGrRQ8UARe2fO5jjgy9QJy_E', } } if args.dataset == 'all': for dataset in file_ids.keys(): download_dataset(dataset, file_ids[dataset]) else: download_dataset(args.dataset, file_ids[args.dataset]) ================================================ FILE: scripts/data_preparation/extract_images_from_tfrecords.py ================================================ import argparse import cv2 import glob import numpy as np import os from basicsr.utils.lmdb_util import LmdbMaker def convert_celeba_tfrecords(tf_file, log_resolution, save_root, save_type='img', compress_level=1): """Convert CelebA tfrecords to images or lmdb files. Args: tf_file (str): Input tfrecords file in glob pattern. Example: 'datasets/celeba/celeba_tfrecords/validation/validation-r08-s-*-of-*.tfrecords' # noqa:E501 log_resolution (int): Log scale of resolution. save_root (str): Path root to save. save_type (str): Save type. Options: img | lmdb. Default: img. compress_level (int): Compress level when encoding images. Default: 1. """ if 'validation' in tf_file: phase = 'validation' else: phase = 'train' if save_type == 'lmdb': save_path = os.path.join(save_root, f'celeba_{2**log_resolution}_{phase}.lmdb') lmdb_maker = LmdbMaker(save_path) elif save_type == 'img': save_path = os.path.join(save_root, f'celeba_{2**log_resolution}_{phase}') else: raise ValueError('Wrong save type.') os.makedirs(save_path, exist_ok=True) idx = 0 for record in sorted(glob.glob(tf_file)): print('Processing record: ', record) record_iterator = tf.python_io.tf_record_iterator(record) for string_record in record_iterator: example = tf.train.Example() example.ParseFromString(string_record) # label = example.features.feature['label'].int64_list.value[0] # attr = example.features.feature['attr'].int64_list.value # male = attr[20] # young = attr[39] shape = example.features.feature['shape'].int64_list.value h, w, c = shape img_str = example.features.feature['data'].bytes_list.value[0] img = np.fromstring(img_str, dtype=np.uint8).reshape((h, w, c)) img = img[:, :, [2, 1, 0]] if save_type == 'img': cv2.imwrite(os.path.join(save_path, f'{idx:08d}.png'), img) elif save_type == 'lmdb': _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) key = f'{idx:08d}/r{log_resolution:02d}' lmdb_maker.put(img_byte, key, (h, w, c)) idx += 1 print(idx) if save_type == 'lmdb': lmdb_maker.close() def convert_ffhq_tfrecords(tf_file, log_resolution, save_root, save_type='img', compress_level=1): """Convert FFHQ tfrecords to images or lmdb files. Args: tf_file (str): Input tfrecords file. log_resolution (int): Log scale of resolution. save_root (str): Path root to save. save_type (str): Save type. Options: img | lmdb. Default: img. compress_level (int): Compress level when encoding images. Default: 1. """ if save_type == 'lmdb': save_path = os.path.join(save_root, f'ffhq_{2**log_resolution}.lmdb') lmdb_maker = LmdbMaker(save_path) elif save_type == 'img': save_path = os.path.join(save_root, f'ffhq_{2**log_resolution}') else: raise ValueError('Wrong save type.') os.makedirs(save_path, exist_ok=True) idx = 0 for record in sorted(glob.glob(tf_file)): print('Processing record: ', record) record_iterator = tf.python_io.tf_record_iterator(record) for string_record in record_iterator: example = tf.train.Example() example.ParseFromString(string_record) shape = example.features.feature['shape'].int64_list.value c, h, w = shape img_str = example.features.feature['data'].bytes_list.value[0] img = np.fromstring(img_str, dtype=np.uint8).reshape((c, h, w)) img = img.transpose(1, 2, 0) img = img[:, :, [2, 1, 0]] if save_type == 'img': cv2.imwrite(os.path.join(save_path, f'{idx:08d}.png'), img) elif save_type == 'lmdb': _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) key = f'{idx:08d}/r{log_resolution:02d}' lmdb_maker.put(img_byte, key, (h, w, c)) idx += 1 print(idx) if save_type == 'lmdb': lmdb_maker.close() def make_ffhq_lmdb_from_imgs(folder_path, log_resolution, save_root, save_type='lmdb', compress_level=1): """Make FFHQ lmdb from images. Args: folder_path (str): Folder path. log_resolution (int): Log scale of resolution. save_root (str): Path root to save. save_type (str): Save type. Options: img | lmdb. Default: img. compress_level (int): Compress level when encoding images. Default: 1. """ if save_type == 'lmdb': save_path = os.path.join(save_root, f'ffhq_{2**log_resolution}_crop1.2.lmdb') lmdb_maker = LmdbMaker(save_path) else: raise ValueError('Wrong save type.') os.makedirs(save_path, exist_ok=True) img_list = sorted(glob.glob(os.path.join(folder_path, '*'))) for idx, img_path in enumerate(img_list): print(f'Processing {idx}: ', img_path) img = cv2.imread(img_path) h, w, c = img.shape if save_type == 'lmdb': _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) key = f'{idx:08d}/r{log_resolution:02d}' lmdb_maker.put(img_byte, key, (h, w, c)) if save_type == 'lmdb': lmdb_maker.close() if __name__ == '__main__': """Read tfrecords w/o define a graph. We have tested it on TensorFlow 1.15 Ref: http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ """ parser = argparse.ArgumentParser() parser.add_argument( '--dataset', type=str, default='ffhq', help="Dataset name. Options: 'ffhq' | 'celeba'. Default: 'ffhq'.") parser.add_argument( '--tf_file', type=str, default='datasets/ffhq/ffhq-r10.tfrecords', help=( 'Input tfrecords file. For celeba, it should be glob pattern. ' 'Put quotes around the wildcard argument to prevent the shell ' 'from expanding it.' "Example: 'datasets/celeba/celeba_tfrecords/validation/validation-r08-s-*-of-*.tfrecords'" # noqa:E501 )) parser.add_argument('--log_resolution', type=int, default=10, help='Log scale of resolution.') parser.add_argument('--save_root', type=str, default='datasets/ffhq/', help='Save root path.') parser.add_argument( '--save_type', type=str, default='img', help="Save type. Options: 'img' | 'lmdb'. Default: 'img'.") parser.add_argument( '--compress_level', type=int, default=1, help='Compress level when encoding images. Default: 1.') args = parser.parse_args() try: import tensorflow as tf except Exception: raise ImportError('You need to install tensorflow to read tfrecords.') if args.dataset == 'ffhq': convert_ffhq_tfrecords( args.tf_file, args.log_resolution, args.save_root, save_type=args.save_type, compress_level=args.compress_level) else: convert_celeba_tfrecords( args.tf_file, args.log_resolution, args.save_root, save_type=args.save_type, compress_level=args.compress_level) ================================================ FILE: scripts/data_preparation/extract_subimages.py ================================================ import cv2 import numpy as np import os import sys from multiprocessing import Pool from os import path as osp from tqdm import tqdm from basicsr.utils import scandir def main(): """A multi-thread tool to crop large images to sub-images for faster IO. It is used for DIV2K dataset. 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 four folders to be processed for DIV2K dataset. DIV2K_train_HR DIV2K_train_LR_bicubic/X2 DIV2K_train_LR_bicubic/X3 DIV2K_train_LR_bicubic/X4 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'] = 20 opt['compression_level'] = 3 # # HR images # opt['input_folder'] = '../../datasets/DIV2K/DIV2K_train_HR' # opt['save_folder'] = '../../datasets/DIV2K/DIV2K_train_HR_sub' # opt['crop_size'] = 480 # opt['step'] = 240 # opt['thresh_size'] = 0 # extract_subimages(opt) # # LRx2 images # opt['input_folder'] = 'datasets/DIV2K/DF2K_train_LR_bicubic_X2' # opt['save_folder'] = 'datasets/DF2K/DF2K_train_LR_bicubic_X2_sub' # opt['crop_size'] = 240 # opt['step'] = 120 # opt['thresh_size'] = 0 # extract_subimages(opt) # LRx3 images #opt['input_folder'] = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X3' #opt['save_folder'] = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X3_sub' #opt['crop_size'] = 160 #opt['step'] = 80 #opt['thresh_size'] = 0 #extract_subimages(opt) # LRx4 images opt['input_folder'] = '../../datasets/DIV2K/DIV2K_train_LR_bicubic/X4' opt['save_folder'] = '../../datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub' opt['crop_size'] = 120 opt['step'] = 60 opt['thresh_size'] = 0 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) 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__': main() ================================================ FILE: scripts/data_preparation/generate_meta_info.py ================================================ from os import path as osp from PIL import Image from basicsr.utils import scandir def generate_meta_info_div2k(): """Generate meta info for DIV2K dataset. """ gt_folder = 'datasets/DIV2K/DIV2K_train_HR_sub/' meta_info_txt = 'basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt' img_list = sorted(list(scandir(gt_folder))) with open(meta_info_txt, 'w') as f: for idx, img_path in enumerate(img_list): img = Image.open(osp.join(gt_folder, img_path)) # lazy load width, height = img.size mode = img.mode if mode == 'RGB': n_channel = 3 elif mode == 'L': n_channel = 1 else: raise ValueError(f'Unsupported mode {mode}.') info = f'{img_path} ({height},{width},{n_channel})' print(idx + 1, info) f.write(f'{info}\n') if __name__ == '__main__': generate_meta_info_div2k() ================================================ FILE: scripts/data_preparation/prepare_hifacegan_dataset.py ================================================ import cv2 import os from tqdm import tqdm class Mosaic16x: """ Mosaic16x: A customized image augmentor for 16-pixel mosaic By default it replaces each pixel value with the mean value of its 16x16 neighborhood """ def augment_image(self, x): h, w = x.shape[:2] x = x.astype('float') # avoid overflow for uint8 irange, jrange = (h + 15) // 16, (w + 15) // 16 for i in range(irange): for j in range(jrange): mean = x[i * 16:(i + 1) * 16, j * 16:(j + 1) * 16].mean(axis=(0, 1)) x[i * 16:(i + 1) * 16, j * 16:(j + 1) * 16] = mean return x.astype('uint8') class DegradationSimulator: """ Generating training/testing data pairs on the fly. The degradation script is aligned with HiFaceGAN paper settings. Args: opt(str | op): Config for degradation script, with degradation type and parameters Custom degradation is possible by passing an inherited class from ia.augmentors """ def __init__(self, ): import imgaug.augmenters as ia self.default_deg_templates = { 'sr4x': ia.Sequential([ # It's almost like a 4x bicubic downsampling ia.Resize((0.25000, 0.25001), cv2.INTER_AREA), ia.Resize({ 'height': 512, 'width': 512 }, cv2.INTER_CUBIC), ]), 'sr4x8x': ia.Sequential([ ia.Resize((0.125, 0.25), cv2.INTER_AREA), ia.Resize({ 'height': 512, 'width': 512 }, cv2.INTER_CUBIC), ]), 'denoise': ia.OneOf([ ia.AdditiveGaussianNoise(scale=(20, 40), per_channel=True), ia.AdditiveLaplaceNoise(scale=(20, 40), per_channel=True), ia.AdditivePoissonNoise(lam=(15, 30), per_channel=True), ]), 'deblur': ia.OneOf([ ia.MotionBlur(k=(10, 20)), ia.GaussianBlur((3.0, 8.0)), ]), 'jpeg': ia.JpegCompression(compression=(50, 85)), '16x': Mosaic16x(), } rand_deg_list = [ self.default_deg_templates['deblur'], self.default_deg_templates['denoise'], self.default_deg_templates['jpeg'], self.default_deg_templates['sr4x8x'], ] self.default_deg_templates['face_renov'] = ia.Sequential(rand_deg_list, random_order=True) def create_training_dataset(self, deg, gt_folder, lq_folder=None): from imgaug.augmenters.meta import Augmenter # baseclass """ Create a degradation simulator and apply it to GT images on the fly Save the degraded result in the lq_folder (if None, name it as GT_deg) """ if not lq_folder: suffix = deg if isinstance(deg, str) else 'custom' lq_folder = '_'.join([gt_folder.replace('gt', 'lq'), suffix]) print(lq_folder) os.makedirs(lq_folder, exist_ok=True) if isinstance(deg, str): assert deg in self.default_deg_templates, ( f'Degration type {deg} not recognized: {"|".join(list(self.default_deg_templates.keys()))}') deg = self.default_deg_templates[deg] else: assert isinstance(deg, Augmenter), f'Deg must be either str|Augmenter, got {deg}' names = os.listdir(gt_folder) for name in tqdm(names): gt = cv2.imread(os.path.join(gt_folder, name)) lq = deg.augment_image(gt) # pack = np.concatenate([lq, gt], axis=0) cv2.imwrite(os.path.join(lq_folder, name), lq) print('Dataset prepared.') if __name__ == '__main__': simuator = DegradationSimulator() gt_folder = 'datasets/FFHQ_512_gt' deg = 'sr4x' simuator.create_training_dataset(deg, gt_folder) ================================================ FILE: scripts/data_preparation/regroup_reds_dataset.py ================================================ import glob import os def regroup_reds_dataset(train_path, val_path): """Regroup original REDS datasets. We merge train and validation data into one folder, and separate the validation clips in reds_dataset.py. There are 240 training clips (starting from 0 to 239), so we name the validation clip index starting from 240 to 269 (total 30 validation clips). Args: train_path (str): Path to the train folder. val_path (str): Path to the validation folder. """ # move the validation data to the train folder val_folders = glob.glob(os.path.join(val_path, '*')) for folder in val_folders: new_folder_idx = int(folder.split('/')[-1]) + 240 os.system(f'cp -r {folder} {os.path.join(train_path, str(new_folder_idx))}') if __name__ == '__main__': # train_sharp train_path = 'datasets/REDS/train_sharp' val_path = 'datasets/REDS/val_sharp' regroup_reds_dataset(train_path, val_path) # train_sharp_bicubic train_path = 'datasets/REDS/train_sharp_bicubic/X4' val_path = 'datasets/REDS/val_sharp_bicubic/X4' regroup_reds_dataset(train_path, val_path) ================================================ FILE: scripts/dist_test.sh ================================================ #!/usr/bin/env bash GPUS=$1 CONFIG=$2 PORT=${PORT:-4321} # usage if [ $# -ne 2 ] ;then echo "usage:" echo "./scripts/dist_test.sh [number of gpu] [path to option file]" exit fi PYTHONPATH="$(dirname $0)/..:${PYTHONPATH}" \ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ basicsr/test.py -opt $CONFIG --launcher pytorch ================================================ FILE: scripts/dist_train.sh ================================================ #!/usr/bin/env bash GPUS=$1 CONFIG=$2 PORT=${PORT:-4321} # usage if [ $# -lt 2 ] ;then echo "usage:" echo "./scripts/dist_train.sh [number of gpu] [path to option file]" exit fi PYTHONPATH="$(dirname $0)/..:${PYTHONPATH}" \ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ basicsr/train.py -opt $CONFIG --launcher pytorch ${@:3} ================================================ FILE: scripts/download_gdrive.py ================================================ import argparse from basicsr.utils.download_util import download_file_from_google_drive if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--id', type=str, help='File id') parser.add_argument('--output', type=str, help='Save path') args = parser.parse_args() download_file_from_google_drive(args.id, args.save_path) ================================================ FILE: scripts/download_pretrained_models.py ================================================ import argparse import os from os import path as osp from basicsr.utils.download_util import download_file_from_google_drive def download_pretrained_models(method, file_ids): save_path_root = f'./experiments/pretrained_models/{method}' os.makedirs(save_path_root, exist_ok=True) for file_name, file_id in file_ids.items(): save_path = osp.abspath(osp.join(save_path_root, file_name)) if osp.exists(save_path): user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n') if user_response.lower() == 'y': print(f'Covering {file_name} to {save_path}') download_file_from_google_drive(file_id, save_path) elif user_response.lower() == 'n': print(f'Skipping {file_name}') else: raise ValueError('Wrong input. Only accepts Y/N.') else: print(f'Downloading {file_name} to {save_path}') download_file_from_google_drive(file_id, save_path) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( 'method', type=str, help=("Options: 'ESRGAN', 'EDVR', 'StyleGAN', 'EDSR', 'DUF', 'DFDNet', 'dlib', 'TOF', 'flownet', 'BasicVSR'. " "Set to 'all' to download all the models.")) args = parser.parse_args() file_ids = { 'ESRGAN': { 'ESRGAN_SRx4_DF2KOST_official-ff704c30.pth': # file name '1b3_bWZTjNO3iL2js1yWkJfjZykcQgvzT', # file id 'ESRGAN_PSNR_SRx4_DF2K_official-150ff491.pth': '1swaV5iBMFfg-DL6ZyiARztbhutDCWXMM' }, 'EDVR': { 'EDVR_L_x4_SR_REDS_official-9f5f5039.pth': '127KXEjlCwfoPC1aXyDkluNwr9elwyHNb', 'EDVR_L_x4_SR_Vimeo90K_official-162b54e4.pth': '1aVR3lkX6ItCphNLcT7F5bbbC484h4Qqy', 'EDVR_M_woTSA_x4_SR_REDS_official-1edf645c.pth': '1C_WdN-NyNj-P7SOB5xIVuHl4EBOwd-Ny', 'EDVR_M_x4_SR_REDS_official-32075921.pth': '1dd6aFj-5w2v08VJTq5mS9OFsD-wALYD6', 'EDVR_L_x4_SRblur_REDS_official-983d7b8e.pth': '1GZz_87ybR8eAAY3X2HWwI3L6ny7-5Yvl', 'EDVR_L_deblur_REDS_official-ca46bd8c.pth': '1_ma2tgHscZtkIY2tEJkVdU-UP8bnqBRE', 'EDVR_L_deblurcomp_REDS_official-0e988e5c.pth': '1fEoSeLFnHSBbIs95Au2W197p8e4ws4DW' }, 'StyleGAN': { 'stylegan2_ffhq_config_f_1024_official-3ab41b38.pth': '1qtdsT1FrvKQsFiW3OqOcIb-VS55TVy1g', 'stylegan2_ffhq_config_f_1024_discriminator_official-a386354a.pth': '1nPqCxm8TkDU3IvXdHCzPUxlBwR5Pd78G', 'stylegan2_cat_config_f_256_official-0a9173ad.pth': '1gfJkX6XO5pJ2J8LyMdvUgGldz7xwWpBJ', 'stylegan2_cat_config_f_256_discriminator_official-2c97fd08.pth': '1hy5FEQQl28XvfqpiWvSBd8YnIzsyDRb7', 'stylegan2_church_config_f_256_official-44ba63bf.pth': '1FCQMZXeOKZyl-xYKbl1Y_x2--rFl-1N_', 'stylegan2_church_config_f_256_discriminator_official-20cd675b.pth': # noqa: E501 '1BS9ODHkUkhfTGFVfR6alCMGtr9nGm9ox', 'stylegan2_car_config_f_512_official-e8fcab4f.pth': '14jS-nWNTguDSd1kTIX-tBHp2WdvK7hva', 'stylegan2_car_config_f_512_discriminator_official-5008e3d1.pth': '1UxkAzZ0zvw4KzBVOUpShCivsdXBS8Zi2', 'stylegan2_horse_config_f_256_official-26d57fee.pth': '12QsZ-mrO8_4gC0UrO15Jb3ykcQ88HxFx', 'stylegan2_horse_config_f_256_discriminator_official-be6c4c33.pth': '1me4ybSib72xA9ZxmzKsHDtP-eNCKw_X4' }, 'EDSR': { 'EDSR_Mx2_f64b16_DIV2K_official-3ba7b086.pth': '1mREMGVDymId3NzIc2u90sl_X4-pb4ZcV', 'EDSR_Mx3_f64b16_DIV2K_official-6908f88a.pth': '1EriqQqlIiRyPbrYGBbwr_FZzvb3iwqz5', 'EDSR_Mx4_f64b16_DIV2K_official-0c287733.pth': '1bCK6cFYU01uJudLgUUe-jgx-tZ3ikOWn', 'EDSR_Lx2_f256b32_DIV2K_official-be38e77d.pth': '15257lZCRZ0V6F9LzTyZFYbbPrqNjKyMU', 'EDSR_Lx3_f256b32_DIV2K_official-3660f70d.pth': '18q_D434sLG_rAZeHGonAX8dkqjoyZ2su', 'EDSR_Lx4_f256b32_DIV2K_official-76ee1c8f.pth': '1GCi30YYCzgMCcgheGWGusP9aWKOAy5vl' }, 'DUF': { 'DUF_x2_16L_official-39537cb9.pth': '1e91cEZOlUUk35keK9EnuK0F54QegnUKo', 'DUF_x3_16L_official-34ce53ec.pth': '1XN6aQj20esM7i0hxTbfiZr_SL8i4PZ76', 'DUF_x4_16L_official-bf8f0cfa.pth': '1V_h9U1CZgLSHTv1ky2M3lvuH-hK5hw_J', 'DUF_x4_28L_official-cbada450.pth': '1M8w0AMBJW65MYYD-_8_be0cSH_SHhDQ4', 'DUF_x4_52L_official-483d2c78.pth': '1GcmEWNr7mjTygi-QCOVgQWOo5OCNbh_T' }, 'TOF': { 'tof_x4_vimeo90k_official-32c9e01f.pth': '1TgQiXXsvkTBFrQ1D0eKPgL10tQGu0gKb' }, 'DFDNet': { 'DFDNet_dict_512-f79685f0.pth': '1iH00oMsoN_1OJaEQw3zP7_wqiAYMnY79', 'DFDNet_official-d1fa5650.pth': '1u6Sgcp8gVoy4uVTrOJKD3y9RuqH2JBAe' }, 'dlib': { 'mmod_human_face_detector-4cb19393.dat': '1FUM-hcoxNzFCOpCWbAUStBBMiU4uIGIL', 'shape_predictor_5_face_landmarks-c4b1e980.dat': '1PNPSmFjmbuuUDd5Mg5LDxyk7tu7TQv2F', 'shape_predictor_68_face_landmarks-fbdc2cb8.dat': '1IneH-O-gNkG0SQpNCplwxtOAtRCkG2ni' }, 'flownet': { 'spynet_sintel_final-3d2a1287.pth': '1VZz1cikwTRVX7zXoD247DB7n5Tj_LQpF' }, 'BasicVSR': { 'BasicVSR_REDS4-543c8261.pth': '1wLWdz18lWf9Z7lomHPkdySZ-_GV2920p', 'BasicVSR_Vimeo90K_BDx4-e9bf46eb.pth': '1baaf4RSpzs_zcDAF_s2CyArrGvLgmXxW', 'BasicVSR_Vimeo90K_BIx4-2a29695a.pth': '1ykIu1jv5wo95Kca2TjlieJFxeV4VVfHP', 'EDVR_REDS_pretrained_for_IconVSR-f62a2f1e.pth': '1ShfwddugTmT3_kB8VL6KpCMrIpEO5sBi', 'EDVR_Vimeo90K_pretrained_for_IconVSR-ee48ee92.pth': '16vR262NDVyVv5Q49xp2Sb-Llu05f63tt', 'IconVSR_REDS-aaa5367f.pth': '1b8ir754uIAFUSJ8YW_cmPzqer19AR7Hz', 'IconVSR_Vimeo90K_BDx4-cfcb7e00.pth': '13lp55s-YTd-fApx8tTy24bbHsNIGXdAH', 'IconVSR_Vimeo90K_BIx4-35fec07c.pth': '1lWUB36ERjFbAspr-8UsopJ6xwOuWjh2g' } } if args.method == 'all': for method in file_ids.keys(): download_pretrained_models(method, file_ids[method]) else: download_pretrained_models(args.method, file_ids[args.method]) ================================================ FILE: scripts/matlab_scripts/back_projection/backprojection.m ================================================ function [im_h] = backprojection(im_h, im_l, maxIter) [row_l, col_l,~] = size(im_l); [row_h, col_h,~] = size(im_h); p = fspecial('gaussian', 5, 1); p = p.^2; p = p./sum(p(:)); im_l = double(im_l); im_h = double(im_h); for ii = 1:maxIter im_l_s = imresize(im_h, [row_l, col_l], 'bicubic'); im_diff = im_l - im_l_s; im_diff = imresize(im_diff, [row_h, col_h], 'bicubic'); im_h(:,:,1) = im_h(:,:,1) + conv2(im_diff(:,:,1), p, 'same'); im_h(:,:,2) = im_h(:,:,2) + conv2(im_diff(:,:,2), p, 'same'); im_h(:,:,3) = im_h(:,:,3) + conv2(im_diff(:,:,3), p, 'same'); end ================================================ FILE: scripts/matlab_scripts/back_projection/main_bp.m ================================================ clear; close all; clc; LR_folder = './LR'; % LR preout_folder = './results'; % pre output save_folder = './results_20bp'; filepaths = dir(fullfile(preout_folder, '*.png')); max_iter = 20; if ~ exist(save_folder, 'dir') mkdir(save_folder); end for idx_im = 1:length(filepaths) fprintf([num2str(idx_im) '\n']); im_name = filepaths(idx_im).name; im_LR = im2double(imread(fullfile(LR_folder, im_name))); im_out = im2double(imread(fullfile(preout_folder, im_name))); %tic im_out = backprojection(im_out, im_LR, max_iter); %toc imwrite(im_out, fullfile(save_folder, im_name)); end ================================================ FILE: scripts/matlab_scripts/back_projection/main_reverse_filter.m ================================================ clear; close all; clc; LR_folder = './LR'; % LR preout_folder = './results'; % pre output save_folder = './results_20if'; filepaths = dir(fullfile(preout_folder, '*.png')); max_iter = 20; if ~ exist(save_folder, 'dir') mkdir(save_folder); end for idx_im = 1:length(filepaths) fprintf([num2str(idx_im) '\n']); im_name = filepaths(idx_im).name; im_LR = im2double(imread(fullfile(LR_folder, im_name))); im_out = im2double(imread(fullfile(preout_folder, im_name))); J = imresize(im_LR,4,'bicubic'); %tic for m = 1:max_iter im_out = im_out + (J - imresize(imresize(im_out,1/4,'bicubic'),4,'bicubic')); end %toc imwrite(im_out, fullfile(save_folder, im_name)); end ================================================ FILE: scripts/matlab_scripts/generate_LR_Vimeo90K.m ================================================ function generate_LR_Vimeo90K() %% matlab code to genetate bicubic-downsampled for Vimeo90K dataset up_scale = 4; mod_scale = 4; idx = 0; filepaths = dir('/home/xtwang/datasets/vimeo90k/vimeo_septuplet/sequences/*/*/*.png'); for i = 1 : length(filepaths) [~,imname,ext] = fileparts(filepaths(i).name); folder_path = filepaths(i).folder; save_LR_folder = strrep(folder_path,'vimeo_septuplet','vimeo_septuplet_matlabLRx4'); if ~exist(save_LR_folder, 'dir') mkdir(save_LR_folder); end if isempty(imname) disp('Ignore . folder.'); elseif strcmp(imname, '.') disp('Ignore .. folder.'); else idx = idx + 1; str_result = sprintf('%d\t%s.\n', idx, imname); fprintf(str_result); % read image img = imread(fullfile(folder_path, [imname, ext])); img = im2double(img); % modcrop img = modcrop(img, mod_scale); % LR im_LR = imresize(img, 1/up_scale, 'bicubic'); if exist('save_LR_folder', 'var') imwrite(im_LR, fullfile(save_LR_folder, [imname, '.png'])); end end end end %% modcrop function img = modcrop(img, modulo) if size(img,3) == 1 sz = size(img); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2)); else tmpsz = size(img); sz = tmpsz(1:2); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2),:); end end ================================================ FILE: scripts/matlab_scripts/generate_bicubic_img.m ================================================ function generate_bicubic_img() %% matlab code to genetate mod images, bicubic-downsampled images and %% bicubic_upsampled images %% set configurations % comment the unnecessary lines input_folder = '../../datasets/Set5/original'; save_mod_folder = '../../datasets/Set5/GTmod12'; save_lr_folder = '../../datasets/Set5/LRbicx2'; % save_bic_folder = ''; mod_scale = 12; up_scale = 2; if exist('save_mod_folder', 'var') if exist(save_mod_folder, 'dir') disp(['It will cover ', save_mod_folder]); else mkdir(save_mod_folder); end end if exist('save_lr_folder', 'var') if exist(save_lr_folder, 'dir') disp(['It will cover ', save_lr_folder]); else mkdir(save_lr_folder); end end if exist('save_bic_folder', 'var') if exist(save_bic_folder, 'dir') disp(['It will cover ', save_bic_folder]); else mkdir(save_bic_folder); end end idx = 0; filepaths = dir(fullfile(input_folder,'*.*')); for i = 1 : length(filepaths) [paths, img_name, ext] = fileparts(filepaths(i).name); if isempty(img_name) disp('Ignore . folder.'); elseif strcmp(img_name, '.') disp('Ignore .. folder.'); else idx = idx + 1; str_result = sprintf('%d\t%s.\n', idx, img_name); fprintf(str_result); % read image img = imread(fullfile(input_folder, [img_name, ext])); img = im2double(img); % modcrop img = modcrop(img, mod_scale); if exist('save_mod_folder', 'var') imwrite(img, fullfile(save_mod_folder, [img_name, '.png'])); end % LR im_lr = imresize(img, 1/up_scale, 'bicubic'); if exist('save_lr_folder', 'var') imwrite(im_lr, fullfile(save_lr_folder, [img_name, '.png'])); end % Bicubic if exist('save_bic_folder', 'var') im_bicubic = imresize(im_lr, up_scale, 'bicubic'); imwrite(im_bicubic, fullfile(save_bic_folder, [img_name, '.png'])); end end end end %% modcrop function img = modcrop(img, modulo) if size(img,3) == 1 sz = size(img); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2)); else tmpsz = size(img); sz = tmpsz(1:2); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2),:); end end ================================================ FILE: scripts/metrics/calculate_fid_folder.py ================================================ import argparse import math import numpy as np import torch from torch.utils.data import DataLoader from basicsr.data import build_dataset from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_patched_inception_v3 def calculate_fid_folder(): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') parser = argparse.ArgumentParser() parser.add_argument('folder', type=str, help='Path to the folder.') parser.add_argument('--fid_stats', type=str, help='Path to the dataset fid statistics.') parser.add_argument('--batch_size', type=int, default=64) parser.add_argument('--num_sample', type=int, default=50000) parser.add_argument('--num_workers', type=int, default=4) parser.add_argument('--backend', type=str, default='disk', help='io backend for dataset. Option: disk, lmdb') args = parser.parse_args() # inception model inception = load_patched_inception_v3(device) # create dataset opt = {} opt['name'] = 'SingleImageDataset' opt['type'] = 'SingleImageDataset' opt['dataroot_lq'] = args.folder opt['io_backend'] = dict(type=args.backend) opt['mean'] = [0.5, 0.5, 0.5] opt['std'] = [0.5, 0.5, 0.5] dataset = build_dataset(opt) # create dataloader data_loader = DataLoader( dataset=dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, sampler=None, drop_last=False) args.num_sample = min(args.num_sample, len(dataset)) total_batch = math.ceil(args.num_sample / args.batch_size) def data_generator(data_loader, total_batch): for idx, data in enumerate(data_loader): if idx >= total_batch: break else: yield data['lq'] features = extract_inception_features(data_generator(data_loader, total_batch), inception, total_batch, device) features = features.numpy() total_len = features.shape[0] features = features[:args.num_sample] print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.') sample_mean = np.mean(features, 0) sample_cov = np.cov(features, rowvar=False) # load the dataset stats stats = torch.load(args.fid_stats) real_mean = stats['mean'] real_cov = stats['cov'] # calculate FID metric fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov) print('fid:', fid) if __name__ == '__main__': calculate_fid_folder() ================================================ FILE: scripts/metrics/calculate_fid_stats_from_datasets.py ================================================ import argparse import math import numpy as np import torch from torch.utils.data import DataLoader from basicsr.data import build_dataset from basicsr.metrics.fid import extract_inception_features, load_patched_inception_v3 def calculate_stats_from_dataset(): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') parser = argparse.ArgumentParser() parser.add_argument('--num_sample', type=int, default=50000) parser.add_argument('--batch_size', type=int, default=64) parser.add_argument('--size', type=int, default=512) parser.add_argument('--dataroot', type=str, default='datasets/ffhq') args = parser.parse_args() # inception model inception = load_patched_inception_v3(device) # create dataset opt = {} opt['name'] = 'FFHQ' opt['type'] = 'FFHQDataset' opt['dataroot_gt'] = f'datasets/ffhq/ffhq_{args.size}.lmdb' opt['io_backend'] = dict(type='lmdb') opt['use_hflip'] = False opt['mean'] = [0.5, 0.5, 0.5] opt['std'] = [0.5, 0.5, 0.5] dataset = build_dataset(opt) # create dataloader data_loader = DataLoader( dataset=dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, sampler=None, drop_last=False) total_batch = math.ceil(args.num_sample / args.batch_size) def data_generator(data_loader, total_batch): for idx, data in enumerate(data_loader): if idx >= total_batch: break else: yield data['gt'] features = extract_inception_features(data_generator(data_loader, total_batch), inception, total_batch, device) features = features.numpy() total_len = features.shape[0] features = features[:args.num_sample] print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.') mean = np.mean(features, 0) cov = np.cov(features, rowvar=False) save_path = f'inception_{opt["name"]}_{args.size}.pth' torch.save( dict(name=opt['name'], size=args.size, mean=mean, cov=cov), save_path, _use_new_zipfile_serialization=False) if __name__ == '__main__': calculate_stats_from_dataset() ================================================ FILE: scripts/metrics/calculate_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 try: import lpips except ImportError: print('Please install lpips: pip install lpips') def main(): # Configurations # ------------------------------------------------------------------------- folder_gt = 'datasets/celeba/celeba_512_validation' folder_restored = 'datasets/celeba/celeba_512_validation_lq' # crop_border = 4 suffix = '' # ------------------------------------------------------------------------- loss_fn_vgg = lpips.LPIPS(net='vgg').cuda() # RGB, normalized to [-1,1] lpips_all = [] img_list = sorted(glob.glob(osp.join(folder_gt, '*'))) mean = [0.5, 0.5, 0.5] std = [0.5, 0.5, 0.5] for i, img_path in enumerate(img_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(folder_restored, basename + suffix + ext), 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(), img_gt.unsqueeze(0).cuda()) print(f'{i+1:3d}: {basename:25}. \tLPIPS: {lpips_val:.6f}.') lpips_all.append(lpips_val) print(f'Average: LPIPS: {sum(lpips_all) / len(lpips_all):.6f}') if __name__ == '__main__': main() ================================================ FILE: scripts/metrics/calculate_niqe.py ================================================ import argparse import cv2 import os import warnings from basicsr.metrics import calculate_niqe from basicsr.utils import scandir def main(args): niqe_all = [] img_list = sorted(scandir(args.input, recursive=True, full_path=True)) for i, img_path in enumerate(img_list): basename, _ = os.path.splitext(os.path.basename(img_path)) img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) with warnings.catch_warnings(): warnings.simplefilter('ignore', category=RuntimeWarning) niqe_score = calculate_niqe(img, args.crop_border, input_order='HWC', convert_to='y') print(f'{i+1:3d}: {basename:25}. \tNIQE: {niqe_score:.6f}') niqe_all.append(niqe_score) print(args.input) print(f'Average: NIQE: {sum(niqe_all) / len(niqe_all):.6f}') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--input', type=str, default='datasets/val_set14/Set14', help='Input path') parser.add_argument('--crop_border', type=int, default=0, help='Crop border for each side') args = parser.parse_args() main(args) ================================================ FILE: scripts/metrics/calculate_psnr_ssim.py ================================================ import argparse import cv2 import numpy as np from os import path as osp from basicsr.metrics import calculate_psnr, calculate_ssim from basicsr.utils import scandir from basicsr.utils.matlab_functions import bgr2ycbcr def main(args): """Calculate PSNR and SSIM for images. """ psnr_all = [] ssim_all = [] img_list_gt = sorted(list(scandir(args.gt, recursive=True, full_path=True))) img_list_restored = sorted(list(scandir(args.restored, recursive=True, full_path=True))) if args.test_y_channel: print('Testing Y channel.') else: print('Testing RGB channels.') for i, img_path in enumerate(img_list_gt): basename, ext = osp.splitext(osp.basename(img_path)) img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255. if args.suffix == '': img_path_restored = img_list_restored[i] else: img_path_restored = osp.join(args.restored, basename + args.suffix + ext) img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255. if args.correct_mean_var: mean_l = [] std_l = [] for j in range(3): mean_l.append(np.mean(img_gt[:, :, j])) std_l.append(np.std(img_gt[:, :, j])) for j in range(3): # correct twice mean = np.mean(img_restored[:, :, j]) img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j] std = np.std(img_restored[:, :, j]) img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j] mean = np.mean(img_restored[:, :, j]) img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j] std = np.std(img_restored[:, :, j]) img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j] if args.test_y_channel and img_gt.ndim == 3 and img_gt.shape[2] == 3: img_gt = bgr2ycbcr(img_gt, y_only=True) img_restored = bgr2ycbcr(img_restored, y_only=True) # calculate PSNR and SSIM psnr = calculate_psnr(img_gt * 255, img_restored * 255, crop_border=args.crop_border, input_order='HWC') ssim = calculate_ssim(img_gt * 255, img_restored * 255, crop_border=args.crop_border, input_order='HWC') print(f'{i+1:3d}: {basename:25}. \tPSNR: {psnr:.6f} dB, \tSSIM: {ssim:.6f}') psnr_all.append(psnr) ssim_all.append(ssim) print(args.gt) print(args.restored) print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f} dB, SSIM: {sum(ssim_all) / len(ssim_all):.6f}') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--gt', type=str, default='datasets/val_set14/Set14', help='Path to gt (Ground-Truth)') parser.add_argument('--restored', type=str, default='results/Set14', help='Path to restored images') parser.add_argument('--crop_border', type=int, default=0, help='Crop border for each side') parser.add_argument('--suffix', type=str, default='', help='Suffix for restored images') parser.add_argument( '--test_y_channel', action='store_true', help='If True, test Y channel (In MatLab YCbCr format). If False, test RGB channels.') parser.add_argument('--correct_mean_var', action='store_true', help='Correct the mean and var of restored images.') args = parser.parse_args() main(args) ================================================ FILE: scripts/metrics/calculate_stylegan2_fid.py ================================================ import argparse import math import numpy as np import torch from torch import nn from basicsr.archs.stylegan2_arch import StyleGAN2Generator from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_patched_inception_v3 def calculate_stylegan2_fid(): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') parser = argparse.ArgumentParser() parser.add_argument('ckpt', type=str, help='Path to the stylegan2 checkpoint.') parser.add_argument('fid_stats', type=str, help='Path to the dataset fid statistics.') parser.add_argument('--size', type=int, default=256) parser.add_argument('--channel_multiplier', type=int, default=2) parser.add_argument('--batch_size', type=int, default=64) parser.add_argument('--num_sample', type=int, default=50000) parser.add_argument('--truncation', type=float, default=1) parser.add_argument('--truncation_mean', type=int, default=4096) args = parser.parse_args() # create stylegan2 model generator = StyleGAN2Generator( out_size=args.size, num_style_feat=512, num_mlp=8, channel_multiplier=args.channel_multiplier, resample_kernel=(1, 3, 3, 1)) generator.load_state_dict(torch.load(args.ckpt)['params_ema']) generator = nn.DataParallel(generator).eval().to(device) if args.truncation < 1: with torch.no_grad(): truncation_latent = generator.mean_latent(args.truncation_mean) else: truncation_latent = None # inception model inception = load_patched_inception_v3(device) total_batch = math.ceil(args.num_sample / args.batch_size) def sample_generator(total_batch): for _ in range(total_batch): with torch.no_grad(): latent = torch.randn(args.batch_size, 512, device=device) samples, _ = generator([latent], truncation=args.truncation, truncation_latent=truncation_latent) yield samples features = extract_inception_features(sample_generator(total_batch), inception, total_batch, device) features = features.numpy() total_len = features.shape[0] features = features[:args.num_sample] print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.') sample_mean = np.mean(features, 0) sample_cov = np.cov(features, rowvar=False) # load the dataset stats stats = torch.load(args.fid_stats) real_mean = stats['mean'] real_cov = stats['cov'] # calculate FID metric fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov) print('fid:', fid) if __name__ == '__main__': calculate_stylegan2_fid() ================================================ FILE: scripts/model_conversion/convert_dfdnet.py ================================================ import torch from basicsr.archs.dfdnet_arch import DFDNet from basicsr.archs.vgg_arch import NAMES def convert_net(ori_net, crt_net): for crt_k, _ in crt_net.items(): # vgg feature extractor if 'vgg_extractor' in crt_k: ori_k = crt_k.replace('vgg_extractor', 'VggExtract').replace('vgg_net', 'model') if 'mean' in crt_k: ori_k = ori_k.replace('mean', 'RGB_mean') elif 'std' in crt_k: ori_k = ori_k.replace('std', 'RGB_std') else: idx = NAMES['vgg19'].index(crt_k.split('.')[2]) if 'weight' in crt_k: ori_k = f'VggExtract.model.features.{idx}.weight' else: ori_k = f'VggExtract.model.features.{idx}.bias' elif 'attn_blocks' in crt_k: if 'left_eye' in crt_k: ori_k = crt_k.replace('attn_blocks.left_eye', 'le') elif 'right_eye' in crt_k: ori_k = crt_k.replace('attn_blocks.right_eye', 're') elif 'mouth' in crt_k: ori_k = crt_k.replace('attn_blocks.mouth', 'mo') elif 'nose' in crt_k: ori_k = crt_k.replace('attn_blocks.nose', 'no') else: raise ValueError('Wrong!') elif 'multi_scale_dilation' in crt_k: if 'conv_blocks' in crt_k: _, _, c, d, e = crt_k.split('.') ori_k = f'MSDilate.conv{int(c)+1}.{d}.{e}' else: ori_k = crt_k.replace('multi_scale_dilation.conv_fusion', 'MSDilate.convi') elif crt_k.startswith('upsample'): ori_k = crt_k.replace('upsample', 'up') if 'scale_block' in crt_k: ori_k = ori_k.replace('scale_block', 'ScaleModel1') elif 'shift_block' in crt_k: ori_k = ori_k.replace('shift_block', 'ShiftModel1') elif 'upsample4' in crt_k and 'body' in crt_k: ori_k = ori_k.replace('body', 'Model') else: print('unprocess key: ', crt_k) # replace if crt_net[crt_k].size() != ori_net[ori_k].size(): raise ValueError('Wrong tensor size: \n' f'crt_net: {crt_net[crt_k].size()}\n' f'ori_net: {ori_net[ori_k].size()}') else: crt_net[crt_k] = ori_net[ori_k] return crt_net if __name__ == '__main__': ori_net = torch.load('experiments/pretrained_models/DFDNet/DFDNet_official_original.pth') dfd_net = DFDNet(64, dict_path='experiments/pretrained_models/DFDNet/DFDNet_dict_512.pth') crt_net = dfd_net.state_dict() crt_net_params = convert_net(ori_net, crt_net) torch.save( dict(params=crt_net_params), 'experiments/pretrained_models/DFDNet/DFDNet_official.pth', _use_new_zipfile_serialization=False) ================================================ FILE: scripts/model_conversion/convert_models.py ================================================ import torch def convert_edvr(): ori_net = torch.load('experiments/pretrained_models/EDVR_REDS_SR_M.pth') crt_net = torch.load('xxx/net_g_8.pth') save_path = './edvr_medium_x4_reds_sr_official.pth' # for k, v in ori_net.items(): # print(k) # print('*****') # for k, v in crt_net.items(): # print(k) for crt_k, _ in crt_net.items(): # deblur hr in if 'predeblur.stride_conv_hr1' in crt_k: ori_k = crt_k.replace('predeblur.stride_conv_hr1', 'pre_deblur.conv_first_2') elif 'predeblur.stride_conv_hr2' in crt_k: ori_k = crt_k.replace('predeblur.stride_conv_hr2', 'pre_deblur.conv_first_3') elif 'predeblur.conv_first' in crt_k: ori_k = crt_k.replace('predeblur.conv_first', 'pre_deblur.conv_first_1') # predeblur module # elif 'predeblur.conv_first' in crt_k: # ori_k = crt_k.replace('predeblur.conv_first', # 'pre_deblur.conv_first') elif 'predeblur.stride_conv_l2' in crt_k: ori_k = crt_k.replace('predeblur.stride_conv_l2', 'pre_deblur.deblur_L2_conv') elif 'predeblur.stride_conv_l3' in crt_k: ori_k = crt_k.replace('predeblur.stride_conv_l3', 'pre_deblur.deblur_L3_conv') elif 'predeblur.resblock_l3' in crt_k: ori_k = crt_k.replace('predeblur.resblock_l3', 'pre_deblur.RB_L3_1') elif 'predeblur.resblock_l2' in crt_k: ori_k = crt_k.replace('predeblur.resblock_l', 'pre_deblur.RB_L') elif 'predeblur.resblock_l1' in crt_k: a, b, c, d, e = crt_k.split('.') ori_k = f'pre_deblur.RB_L1_{int(c)+1}.{d}.{e}' elif 'conv_l2' in crt_k: ori_k = crt_k.replace('conv_l2_', 'fea_L2_conv') elif 'conv_l3' in crt_k: ori_k = crt_k.replace('conv_l3_', 'fea_L3_conv') elif 'pcd_align.dcn_pack' in crt_k: idx = crt_k.split('.l')[1].split('.')[0] name = crt_k.split('.l')[1].split('.')[1] ori_k = f'pcd_align.L{idx}_dcnpack.{name}' if 'conv_offset' in crt_k: name = name.replace('conv_offset', 'conv_offset_mask') weight_bias = crt_k.split('.l')[1].split('.')[2] ori_k = f'pcd_align.L{idx}_dcnpack.{name}.{weight_bias}' elif 'pcd_align.offset_conv' in crt_k: _, b, c, d = crt_k.split('.') idx = b.split('conv')[1] level = c.split('l')[1] ori_k = f'pcd_align.L{level}_offset_conv{idx}.{d}' elif 'pcd_align.feat_conv' in crt_k: a, b, c, d = crt_k.split('.') level = c.split('l')[1] ori_k = f'pcd_align.L{level}_fea_conv.{d}' elif 'pcd_align.cas_dcnpack' in crt_k: ori_k = crt_k.replace('conv_offset', 'conv_offset_mask') elif ('conv_first' in crt_k or 'feature_extraction' in crt_k or 'pcd_align.cas_offset' in crt_k or 'upconv' in crt_k or 'conv_last' in crt_k or 'conv_1x1' in crt_k): ori_k = crt_k elif 'temporal_attn1' in crt_k: ori_k = crt_k.replace('fusion.temporal_attn1', 'tsa_fusion.tAtt_2') elif 'temporal_attn2' in crt_k: ori_k = crt_k.replace('fusion.temporal_attn2', 'tsa_fusion.tAtt_1') elif 'fusion.feat_fusion' in crt_k: ori_k = crt_k.replace('fusion.feat_fusion', 'tsa_fusion.fea_fusion') elif 'fusion.spatial_attn_add' in crt_k: ori_k = crt_k.replace('fusion.spatial_attn_add', 'tsa_fusion.sAtt_add_') elif 'fusion.spatial_attn_l' in crt_k: ori_k = crt_k.replace('fusion.spatial_attn_l', 'tsa_fusion.sAtt_L') elif 'fusion.spatial_attn' in crt_k: ori_k = crt_k.replace('fusion.spatial_attn', 'tsa_fusion.sAtt_') elif 'reconstruction' in crt_k: ori_k = crt_k.replace('reconstruction', 'recon_trunk') elif 'conv_hr' in crt_k: ori_k = crt_k.replace('conv_hr', 'HRconv') # for model woTSA elif 'fusion' in crt_k: ori_k = crt_k.replace('fusion', 'tsa_fusion') else: print('unprocess key', crt_k) # print(ori_k) crt_net[crt_k] = ori_net[ori_k] ori_k = None torch.save(crt_net, save_path) def convert_edsr(ori_net_path, crt_net_path, save_path, num_block=32): """Convert EDSR models in https://github.com/thstkdgus35/EDSR-PyTorch. It supports converting x2, x3 and x4 models. Args: ori_net_path (str): Original network path. crt_net_path (str): Current network path. save_path (str): The path to save the converted model. num_block (int): Number of blocks. Default: 16. """ ori_net = torch.load(ori_net_path) crt_net = torch.load(crt_net_path) for crt_k, _ in crt_net.items(): if 'conv_first' in crt_k: ori_k = crt_k.replace('conv_first', 'head.0') crt_net[crt_k] = ori_net[ori_k] elif 'conv_after_body' in crt_k: ori_k = crt_k.replace('conv_after_body', f'body.{num_block}') elif 'body' in crt_k: ori_k = crt_k.replace('conv1', 'body.0').replace('conv2', 'body.2') elif 'upsample.0' in crt_k: ori_k = crt_k.replace('upsample.0', 'tail.0.0') elif 'upsample.2' in crt_k: ori_k = crt_k.replace('upsample.2', 'tail.0.2') elif 'conv_last' in crt_k: ori_k = crt_k.replace('conv_last', 'tail.1') else: print('unprocess key', crt_k) crt_net[crt_k] = ori_net[ori_k] torch.save(crt_net, save_path) def convert_rcan_model(): ori_net = torch.load('RCAN_model_best.pt') crt_net = torch.load('experiments/201_RCANx4_scratch_DIV2K_rand0/models/net_g_5000.pth') # for ori_k, ori_v in ori_net.items(): # print(ori_k) for crt_k, _ in crt_net.items(): # print(crt_k) if 'conv_first' in crt_k: ori_k = crt_k.replace('conv_first', 'head.0') crt_net[crt_k] = ori_net[ori_k] elif 'conv_after_body' in crt_k: ori_k = crt_k.replace('conv_after_body', 'body.10') elif 'upsample.0' in crt_k: ori_k = crt_k.replace('upsample.0', 'tail.0.0') elif 'upsample.2' in crt_k: ori_k = crt_k.replace('upsample.2', 'tail.0.2') elif 'conv_last' in crt_k: ori_k = crt_k.replace('conv_last', 'tail.1') elif 'attention' in crt_k: _, ai, _, bi, _, ci, d, di, e = crt_k.split('.') ori_k = f'body.{ai}.body.{bi}.body.{ci}.conv_du.{int(di)-1}.{e}' elif 'rcab' in crt_k: a, ai, b, bi, c, ci, d = crt_k.split('.') ori_k = f'body.{ai}.body.{bi}.body.{ci}.{d}' elif 'body' in crt_k: ori_k = crt_k.replace('conv.', 'body.20.') else: print('unprocess key', crt_k) crt_net[crt_k] = ori_net[ori_k] torch.save(crt_net, 'RCAN_model_best.pth') def convert_esrgan_model(): from basicsr.archs.rrdbnet_arch import RRDBNet rrdb = RRDBNet(3, 3, num_feat=64, num_block=23, num_grow_ch=32) crt_net = rrdb.state_dict() # for k, v in crt_net.items(): # print(k) ori_net = torch.load('experiments/pretrained_models/RRDB_ESRGAN_x4.pth') # for k, v in ori_net.items(): # print(k) for crt_k, _ in crt_net.items(): if 'rdb' in crt_k: ori_k = crt_k.replace('rdb', 'RDB').replace('body', 'RRDB_trunk') elif 'conv_body' in crt_k: ori_k = crt_k.replace('conv_body', 'trunk_conv') elif 'conv_up' in crt_k: ori_k = crt_k.replace('conv_up', 'upconv') elif 'conv_hr' in crt_k: ori_k = crt_k.replace('conv_hr', 'HRconv') else: ori_k = crt_k print(crt_k) crt_net[crt_k] = ori_net[ori_k] torch.save(crt_net, 'experiments/pretrained_models/ESRGAN_x4_SR_DF2KOST_official.pth') def convert_duf_model(): from basicsr.archs.duf_arch import DUF scale = 2 duf = DUF(scale=scale, num_layer=16, adapt_official_weights=True) crt_net = duf.state_dict() # for k, v in crt_net.items(): # print(k) ori_net = torch.load('experiments/pretrained_models/old_DUF_x2_16L_official.pth') # print('******') # for k, v in ori_net.items(): # print(k) ''' for crt_k, crt_v in crt_net.items(): if 'conv3d1' in crt_k: ori_k = crt_k.replace('conv3d1', 'conv3d_1') elif 'conv3d2' in crt_k: ori_k = crt_k.replace('conv3d2', 'conv3d_2') elif 'dense_block1.dense_blocks' in crt_k: # dense_block1.dense_blocks.0.0.weight a, b, c, d, e = crt_k.split('.') # dense_block_1.dense_blocks.0.weight ori_k = f'dense_block_1.dense_blocks.{int(c) * 6 + int(d)}.{e}' elif 'dense_block2.temporal_reduce1.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.0', 'dense_block_2.bn3d_1') elif 'dense_block2.temporal_reduce1.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.2', 'dense_block_2.conv3d_1') elif 'dense_block2.temporal_reduce1.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.3', 'dense_block_2.bn3d_2') elif 'dense_block2.temporal_reduce1.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.5', 'dense_block_2.conv3d_2') elif 'dense_block2.temporal_reduce2.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.0', 'dense_block_2.bn3d_3') elif 'dense_block2.temporal_reduce2.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.2', 'dense_block_2.conv3d_3') elif 'dense_block2.temporal_reduce2.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.3', 'dense_block_2.bn3d_4') elif 'dense_block2.temporal_reduce2.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.5', 'dense_block_2.conv3d_4') elif 'dense_block2.temporal_reduce3.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.0', 'dense_block_2.bn3d_5') elif 'dense_block2.temporal_reduce3.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.2', 'dense_block_2.conv3d_5') elif 'dense_block2.temporal_reduce3.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.3', 'dense_block_2.bn3d_6') elif 'dense_block2.temporal_reduce3.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.5', 'dense_block_2.conv3d_6') elif 'bn3d2' in crt_k: ori_k = crt_k.replace('bn3d2', 'bn3d_2') else: ori_k = crt_k print(crt_k) crt_net[crt_k] = ori_net[ori_k] ''' # for 16 layers for crt_k, _ in crt_net.items(): if 'conv3d1' in crt_k: ori_k = crt_k.replace('conv3d1', 'conv3d_1') elif 'conv3d2' in crt_k: ori_k = crt_k.replace('conv3d2', 'conv3d_2') elif 'dense_block1.dense_blocks.0.0' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.0.0', 'dense_block_1.bn3d_1') elif 'dense_block1.dense_blocks.0.2' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.0.2', 'dense_block_1.conv3d_1') elif 'dense_block1.dense_blocks.0.3' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.0.3', 'dense_block_1.bn3d_2') elif 'dense_block1.dense_blocks.0.5' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.0.5', 'dense_block_1.conv3d_2') elif 'dense_block1.dense_blocks.1.0' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.1.0', 'dense_block_1.bn3d_3') elif 'dense_block1.dense_blocks.1.2' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.1.2', 'dense_block_1.conv3d_3') elif 'dense_block1.dense_blocks.1.3' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.1.3', 'dense_block_1.bn3d_4') elif 'dense_block1.dense_blocks.1.5' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.1.5', 'dense_block_1.conv3d_4') elif 'dense_block1.dense_blocks.2.0' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.2.0', 'dense_block_1.bn3d_5') elif 'dense_block1.dense_blocks.2.2' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.2.2', 'dense_block_1.conv3d_5') elif 'dense_block1.dense_blocks.2.3' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.2.3', 'dense_block_1.bn3d_6') elif 'dense_block1.dense_blocks.2.5' in crt_k: ori_k = crt_k.replace('dense_block1.dense_blocks.2.5', 'dense_block_1.conv3d_6') elif 'dense_block2.temporal_reduce1.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.0', 'dense_block_2.bn3d_1') elif 'dense_block2.temporal_reduce1.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.2', 'dense_block_2.conv3d_1') elif 'dense_block2.temporal_reduce1.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.3', 'dense_block_2.bn3d_2') elif 'dense_block2.temporal_reduce1.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce1.5', 'dense_block_2.conv3d_2') elif 'dense_block2.temporal_reduce2.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.0', 'dense_block_2.bn3d_3') elif 'dense_block2.temporal_reduce2.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.2', 'dense_block_2.conv3d_3') elif 'dense_block2.temporal_reduce2.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.3', 'dense_block_2.bn3d_4') elif 'dense_block2.temporal_reduce2.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce2.5', 'dense_block_2.conv3d_4') elif 'dense_block2.temporal_reduce3.0' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.0', 'dense_block_2.bn3d_5') elif 'dense_block2.temporal_reduce3.2' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.2', 'dense_block_2.conv3d_5') elif 'dense_block2.temporal_reduce3.3' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.3', 'dense_block_2.bn3d_6') elif 'dense_block2.temporal_reduce3.5' in crt_k: ori_k = crt_k.replace('dense_block2.temporal_reduce3.5', 'dense_block_2.conv3d_6') elif 'bn3d2' in crt_k: ori_k = crt_k.replace('bn3d2', 'bn3d_2') else: ori_k = crt_k print(crt_k) crt_net[crt_k] = ori_net[ori_k] x = crt_net['conv3d_r2.weight'].clone() x1 = x[::3, ...] x2 = x[1::3, ...] x3 = x[2::3, ...] crt_net['conv3d_r2.weight'][:scale**2, ...] = x1 crt_net['conv3d_r2.weight'][scale**2:2 * (scale**2), ...] = x2 crt_net['conv3d_r2.weight'][2 * (scale**2):, ...] = x3 x = crt_net['conv3d_r2.bias'].clone() x1 = x[::3, ...] x2 = x[1::3, ...] x3 = x[2::3, ...] crt_net['conv3d_r2.bias'][:scale**2, ...] = x1 crt_net['conv3d_r2.bias'][scale**2:2 * (scale**2), ...] = x2 crt_net['conv3d_r2.bias'][2 * (scale**2):, ...] = x3 torch.save(crt_net, 'experiments/pretrained_models/DUF_x2_16L_official.pth') if __name__ == '__main__': # convert EDSR models # ori_net_path = 'path to original model' # crt_net_path = 'path to current model' # save_path = 'save path' # convert_edsr(ori_net_path, crt_net_path, save_path, num_block=32) convert_duf_model() ================================================ FILE: scripts/model_conversion/convert_ridnet.py ================================================ import torch from collections import OrderedDict from basicsr.archs.ridnet_arch import RIDNet if __name__ == '__main__': ori_net_checkpoint = torch.load( 'experiments/pretrained_models/RIDNet/RIDNet_official_original.pt', map_location=lambda storage, loc: storage) rid_net = RIDNet(3, 64, 3) new_ridnet_dict = OrderedDict() rid_net_namelist = [] for name, param in rid_net.named_parameters(): rid_net_namelist.append(name) count = 0 for name, param in ori_net_checkpoint.items(): new_ridnet_dict[rid_net_namelist[count]] = param count += 1 rid_net.load_state_dict(new_ridnet_dict) torch.save(rid_net.state_dict(), 'experiments/pretrained_models/RIDNet/RIDNet.pth') ================================================ FILE: scripts/model_conversion/convert_stylegan.py ================================================ import torch from basicsr.archs.stylegan2_arch import StyleGAN2Discriminator, StyleGAN2Generator def convert_net_g(ori_net, crt_net): """Convert network generator.""" for crt_k, crt_v in crt_net.items(): if 'style_mlp' in crt_k: ori_k = crt_k.replace('style_mlp', 'style') elif 'constant_input.weight' in crt_k: ori_k = crt_k.replace('constant_input.weight', 'input.input') # style conv1 elif 'style_conv1.modulated_conv' in crt_k: ori_k = crt_k.replace('style_conv1.modulated_conv', 'conv1.conv') elif 'style_conv1' in crt_k: if crt_v.shape == torch.Size([1]): ori_k = crt_k.replace('style_conv1', 'conv1.noise') else: ori_k = crt_k.replace('style_conv1', 'conv1') # style conv elif 'style_convs' in crt_k: ori_k = crt_k.replace('style_convs', 'convs').replace('modulated_conv', 'conv') if crt_v.shape == torch.Size([1]): ori_k = ori_k.replace('.weight', '.noise.weight') # to_rgb1 elif 'to_rgb1.modulated_conv' in crt_k: ori_k = crt_k.replace('to_rgb1.modulated_conv', 'to_rgb1.conv') # to_rgbs elif 'to_rgbs' in crt_k: ori_k = crt_k.replace('modulated_conv', 'conv') elif 'noises' in crt_k: ori_k = crt_k.replace('.noise', '.noise_') else: ori_k = crt_k # replace if crt_net[crt_k].size() != ori_net[ori_k].size(): raise ValueError('Wrong tensor size: \n' f'crt_net: {crt_net[crt_k].size()}\n' f'ori_net: {ori_net[ori_k].size()}') else: crt_net[crt_k] = ori_net[ori_k] return crt_net def convert_net_d(ori_net, crt_net): """Convert network discriminator.""" for crt_k, _ in crt_net.items(): if 'conv_body' in crt_k: ori_k = crt_k.replace('conv_body', 'convs') else: ori_k = crt_k # replace if crt_net[crt_k].size() != ori_net[ori_k].size(): raise ValueError('Wrong tensor size: \n' f'crt_net: {crt_net[crt_k].size()}\n' f'ori_net: {ori_net[ori_k].size()}') else: crt_net[crt_k] = ori_net[ori_k] return crt_net if __name__ == '__main__': """Convert official stylegan2 weights from stylegan2-pytorch.""" # configuration ori_net = torch.load('experiments/pretrained_models/stylegan2-ffhq.pth') save_path_g = 'experiments/pretrained_models/stylegan2_ffhq_config_f_1024_official.pth' # noqa: E501 save_path_d = 'experiments/pretrained_models/stylegan2_ffhq_config_f_1024_discriminator_official.pth' # noqa: E501 out_size = 1024 channel_multiplier = 1 # convert generator crt_net = StyleGAN2Generator(out_size, num_style_feat=512, num_mlp=8, channel_multiplier=channel_multiplier) crt_net = crt_net.state_dict() crt_net_params_ema = convert_net_g(ori_net['g_ema'], crt_net) torch.save(dict(params_ema=crt_net_params_ema, latent_avg=ori_net['latent_avg']), save_path_g) # convert discriminator crt_net = StyleGAN2Discriminator(out_size, channel_multiplier=channel_multiplier) crt_net = crt_net.state_dict() crt_net_params = convert_net_d(ori_net['d'], crt_net) torch.save(dict(params=crt_net_params), save_path_d) ================================================ FILE: scripts/publish_models.py ================================================ import glob import subprocess import torch from os import path as osp from torch.serialization import _is_zipfile, _open_file_like def update_sha(paths): print('# Update sha ...') for idx, path in enumerate(paths): print(f'{idx+1:03d}: Processing {path}') net = torch.load(path, map_location=torch.device('cpu')) basename = osp.basename(path) if 'params' not in net and 'params_ema' not in net: user_response = input(f'WARN: Model {basename} does not have "params"/"params_ema" key. ' 'Do you still want to continue? Y/N\n') if user_response.lower() == 'y': pass elif user_response.lower() == 'n': raise ValueError('Please modify..') else: raise ValueError('Wrong input. Only accepts Y/N.') if '-' in basename: # check whether the sha is the latest old_sha = basename.split('-')[1].split('.')[0] new_sha = subprocess.check_output(['sha256sum', path]).decode()[:8] if old_sha != new_sha: final_file = path.split('-')[0] + f'-{new_sha}.pth' print(f'\tSave from {path} to {final_file}') subprocess.Popen(['mv', path, final_file]) else: sha = subprocess.check_output(['sha256sum', path]).decode()[:8] final_file = path.split('.pth')[0] + f'-{sha}.pth' print(f'\tSave from {path} to {final_file}') subprocess.Popen(['mv', path, final_file]) def convert_to_backward_compatible_models(paths): """Convert to backward compatible pth files. PyTorch 1.6 uses a updated version of torch.save. In order to be compatible with previous PyTorch version, save it with _use_new_zipfile_serialization=False. """ print('# Convert to backward compatible pth files ...') for idx, path in enumerate(paths): print(f'{idx+1:03d}: Processing {path}') flag_need_conversion = False with _open_file_like(path, 'rb') as opened_file: if _is_zipfile(opened_file): flag_need_conversion = True if flag_need_conversion: net = torch.load(path, map_location=torch.device('cpu')) print('\tConverting to compatible pth file...') torch.save(net, path, _use_new_zipfile_serialization=False) if __name__ == '__main__': paths = glob.glob('experiments/pretrained_models/*.pth') + glob.glob('experiments/pretrained_models/**/*.pth') convert_to_backward_compatible_models(paths) update_sha(paths) ================================================ FILE: 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 = basicsr known_third_party = PIL,cv2,lmdb,numpy,pytest,requests,scipy,skimage,torch,torchvision,tqdm,yaml no_lines_before = STDLIB,LOCALFOLDER default_section = THIRDPARTY [codespell] skip = .git,./docs/build,*.cfg count = quiet-level = 3 ignore-words-list = gool [aliases] test=pytest [tool:pytest] addopts=tests/ ================================================ FILE: setup.py ================================================ #!/usr/bin/env python from setuptools import find_packages, setup import os import subprocess import time import torch from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension version_file = 'basicsr/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] # currently ignore this # 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__ = '{}' __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 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'): 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__': cuda_ext = os.getenv('BASICSR_EXT') # whether compile cuda ext if cuda_ext == 'True': ext_modules = [ make_cuda_ext( name='deform_conv_ext', module='basicsr.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.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.ops.upfirdn2d', sources=['src/upfirdn2d.cpp'], sources_cuda=['src/upfirdn2d_kernel.cu']), ] else: ext_modules = [] write_version_py() setup( name='basicsr', version=get_version(), description='Open Source Image and Video Super-Resolution Toolbox', long_description=readme(), long_description_content_type='text/markdown', author='Xintao Wang', author_email='xintao.wang@outlook.com', keywords='computer vision, restoration, super resolution', url='https://github.com/xinntao/BasicSR', 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='Apache License 2.0', setup_requires=['cython', 'numpy'], install_requires=get_requirements(), ext_modules=ext_modules, cmdclass={'build_ext': BuildExtension}, zip_safe=False) ================================================ FILE: tests/README.md ================================================ # UnitTest - It requires GPU CUDA environment ================================================ FILE: tests/data/gt.lmdb/meta_info.txt ================================================ baboon.png (480,492,3) 1 comic.png (360,240,3) 1 ================================================ FILE: tests/data/lq.lmdb/meta_info.txt ================================================ baboon.png (120,123,3) 1 comic.png (80,60,3) 1 ================================================ FILE: tests/data/meta_info_gt.txt ================================================ baboon.png comic.png ================================================ FILE: tests/data/meta_info_pair.txt ================================================ gt/baboon.png, lq/baboon.png gt/comic.png, lq/comic.png ================================================ FILE: tests/test_archs/test_basicvsr_arch.py ================================================ import torch from basicsr.archs.basicvsr_arch import BasicVSR, ConvResidualBlocks, IconVSR def test_basicvsr(): """Test arch: BasicVSR.""" # model init and forward net = BasicVSR(num_feat=12, num_block=2, spynet_path=None).cuda() img = torch.rand((1, 2, 3, 64, 64), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 2, 3, 256, 256) def test_convresidualblocks(): """Test block: ConvResidualBlocks.""" # model init and forward net = ConvResidualBlocks(num_in_ch=3, num_out_ch=8, num_block=2).cuda() img = torch.rand((1, 3, 16, 16), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 8, 16, 16) def test_iconvsr(): """Test arch: IconVSR.""" # model init and forward net = IconVSR( num_feat=8, num_block=1, keyframe_stride=2, temporal_padding=2, spynet_path=None, edvr_path=None).cuda() img = torch.rand((1, 6, 3, 64, 64), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 6, 3, 256, 256) # --------------------------- temporal padding 3 ------------------------- # net = IconVSR( num_feat=8, num_block=1, keyframe_stride=2, temporal_padding=3, spynet_path=None, edvr_path=None).cuda() img = torch.rand((1, 8, 3, 64, 64), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 8, 3, 256, 256) ================================================ FILE: tests/test_archs/test_discriminator_arch.py ================================================ import pytest import torch from basicsr.archs.discriminator_arch import VGGStyleDiscriminator def test_vggstylediscriminator(): """Test arch: VGGStyleDiscriminator.""" # model init and forward net = VGGStyleDiscriminator(num_in_ch=3, num_feat=4).cuda() img = torch.rand((1, 3, 128, 128), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 1) # ----------------------- input_size is 256 x 256------------------------ # net = VGGStyleDiscriminator(num_in_ch=3, num_feat=4, input_size=256).cuda() img = torch.rand((1, 3, 256, 256), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 1) # ----------------------- input feature size is not identical to input_size------------------------- # with pytest.raises(AssertionError): img = torch.rand((1, 3, 128, 128), dtype=torch.float32).cuda() output = net(img) # ----------------------- input_size is not 128 or 256------------------------- # with pytest.raises(AssertionError): net = VGGStyleDiscriminator(num_in_ch=3, num_feat=4, input_size=64) ================================================ FILE: tests/test_archs/test_duf_arch.py ================================================ import pytest import torch from basicsr.archs.duf_arch import DUF, DynamicUpsamplingFilter def test_duf(): """Test arch: DUF.""" # model init and forward net = DUF(scale=4, num_layer=16, adapt_official_weights=False).cuda() img = torch.rand((1, 7, 3, 48, 48), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 3, 192, 192) # ----------------- test scale x3, num_layer=28 ---------------------- # net = DUF(scale=3, num_layer=28, adapt_official_weights=True).cuda() output = net(img) assert output.shape == (1, 3, 144, 144) # ----------------- test scale x2, num_layer=52 ---------------------- # net = DUF(scale=2, num_layer=52, adapt_official_weights=True).cuda() output = net(img) assert output.shape == (1, 3, 96, 96) # ----------------- unsupported num_layers ---------------------- # with pytest.raises(ValueError): net = DUF(scale=2, num_layer=4, adapt_official_weights=True) def test_dynamicupsamplingfilter(): """Test block: DynamicUpsamplingFilter""" net = DynamicUpsamplingFilter(filter_size=(3, 3)).cuda() img = torch.rand((2, 3, 12, 12), dtype=torch.float32).cuda() filters = torch.rand((2, 9, 2, 12, 12), dtype=torch.float32).cuda() output = net(img, filters) assert output.shape == (2, 6, 12, 12) # ----------------- wrong filter_size type ---------------------- # with pytest.raises(TypeError): DynamicUpsamplingFilter(filter_size=4) # ----------------- wrong filter_size shape ---------------------- # with pytest.raises(ValueError): DynamicUpsamplingFilter(filter_size=(3, 3, 3)) ================================================ FILE: tests/test_archs/test_ecbsr_arch.py ================================================ import pytest import torch from basicsr.archs.ecbsr_arch import ECB, ECBSR, SeqConv3x3 def test_ecbsr(): """Test arch: ECBSR.""" # model init and forward net = ECBSR(num_in_ch=1, num_out_ch=1, num_block=1, num_channel=4, with_idt=False, act_type='prelu', scale=4).cuda() img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 1, 48, 48) # ----------------- test 3 channels ---------------------- # net = ECBSR(num_in_ch=3, num_out_ch=3, num_block=1, num_channel=4, with_idt=True, act_type='rrelu', scale=2).cuda() img = torch.rand((1, 3, 12, 12), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 3, 24, 24) def test_seqconv3x3(): """Test block: SeqConv3x3.""" # model init and forward net = SeqConv3x3(seq_type='conv1x1-conv3x3', in_channels=2, out_channels=2, depth_multiplier=2).cuda() img = torch.rand((1, 2, 12, 12), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 2, 12, 12) # test rep_params conv = torch.nn.Conv2d(2, 2, 3, 1, 1).cuda() conv.weight.data, conv.bias.data = net.rep_params() output_rep = conv(img) assert output_rep.shape == (1, 2, 12, 12) # whether the two results are close assert torch.allclose(output, output_rep, rtol=1e-5, atol=1e-5) # ----------------- test rep_params with conv1x1-laplacian seq ---------------------- # net = SeqConv3x3(seq_type='conv1x1-laplacian', in_channels=4, out_channels=4, depth_multiplier=3).cuda() img = torch.rand((1, 4, 12, 12), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 4, 12, 12) # test rep_params conv = torch.nn.Conv2d(4, 4, 3, 1, 1).cuda() conv.weight.data, conv.bias.data = net.rep_params() output_rep = conv(img) assert output_rep.shape == (1, 4, 12, 12) # whether the two results are close assert torch.allclose(output, output_rep, rtol=1e-5, atol=1e-5) # ----------------- unsupported type ---------------------- # with pytest.raises(ValueError): SeqConv3x3(seq_type='noseq', in_channels=1, out_channels=1) def test_ecb(): """Test block: ECB.""" # model init and forward net = ECB(in_channels=2, out_channels=2, depth_multiplier=1, act_type='softplus', with_idt=False).cuda() img = torch.rand((1, 2, 12, 12), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 2, 12, 12) # test rep_params net = net.eval() output_rep = net(img) assert output_rep.shape == (1, 2, 12, 12) # whether the two results are close assert torch.allclose(output, output_rep, rtol=1e-5, atol=1e-5) # ----------------- linear activation function and identity---------------------- # net = ECB(in_channels=2, out_channels=2, depth_multiplier=1, act_type='linear', with_idt=True).cuda() output = net(img) assert output.shape == (1, 2, 12, 12) # test rep_params net = net.eval() output_rep = net(img) assert output_rep.shape == (1, 2, 12, 12) # whether the two results are close assert torch.allclose(output, output_rep, rtol=1e-5, atol=1e-5) # ----------------- relu activation function---------------------- # net = ECB(in_channels=2, out_channels=2, depth_multiplier=1, act_type='relu', with_idt=False).cuda() output = net(img) assert output.shape == (1, 2, 12, 12) # ----------------- unsupported type ---------------------- # with pytest.raises(ValueError): ECB(in_channels=2, out_channels=2, depth_multiplier=1, act_type='unknown', with_idt=False) ================================================ FILE: tests/test_archs/test_srresnet_arch.py ================================================ import torch from basicsr.archs.srresnet_arch import MSRResNet def test_msrresnet(): """Test arch: MSRResNet.""" # model init and forward net = MSRResNet(num_in_ch=3, num_out_ch=3, num_feat=12, num_block=2, upscale=4).cuda() img = torch.rand((1, 3, 16, 16), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 3, 64, 64) # ----------------- the x3 case ---------------------- # net = MSRResNet(num_in_ch=1, num_out_ch=1, num_feat=4, num_block=1, upscale=3).cuda() img = torch.rand((1, 1, 16, 16), dtype=torch.float32).cuda() output = net(img) assert output.shape == (1, 1, 48, 48) ================================================ FILE: tests/test_data/test_paired_image_dataset.py ================================================ import yaml from basicsr.data.paired_image_dataset import PairedImageDataset def test_pairedimagedataset(): """Test dataset: PairedImageDataset""" opt_str = r""" name: Test type: PairedImageDataset dataroot_gt: tests/data/gt dataroot_lq: tests/data/lq meta_info_file: tests/data/meta_info_gt.txt filename_tmpl: '{}' io_backend: type: disk scale: 4 mean: [0.5, 0.5, 0.5] std: [0.5, 0.5, 0.5] gt_size: 128 use_hflip: true use_rot: true phase: train """ opt = yaml.safe_load(opt_str) dataset = PairedImageDataset(opt) assert dataset.io_backend_opt['type'] == 'disk' # io backend assert len(dataset) == 2 # whether to read correct meta info assert dataset.mean == [0.5, 0.5, 0.5] # test __getitem__ result = dataset.__getitem__(0) # check returned keys expected_keys = ['lq', 'gt', 'lq_path', 'gt_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['lq_path'] == 'tests/data/lq/baboon.png' assert result['gt_path'] == 'tests/data/gt/baboon.png' # ------------------ test filename_tmpl -------------------- # opt.pop('filename_tmpl') opt['io_backend'] = dict(type='disk') dataset = PairedImageDataset(opt) assert dataset.filename_tmpl == '{}' # ------------------ test scan folder mode -------------------- # opt.pop('meta_info_file') opt['io_backend'] = dict(type='disk') dataset = PairedImageDataset(opt) assert dataset.io_backend_opt['type'] == 'disk' # io backend assert len(dataset) == 2 # whether to correctly scan folders # ------------------ test lmdb backend and with y channel-------------------- # opt['dataroot_gt'] = 'tests/data/gt.lmdb' opt['dataroot_lq'] = 'tests/data/lq.lmdb' opt['io_backend'] = dict(type='lmdb') opt['color'] = 'y' opt['mean'] = [0.5] opt['std'] = [0.5] dataset = PairedImageDataset(opt) assert dataset.io_backend_opt['type'] == 'lmdb' # io backend assert len(dataset) == 2 # whether to read correct meta info assert dataset.std == [0.5] # test __getitem__ result = dataset.__getitem__(1) # check returned keys expected_keys = ['lq', 'gt', 'lq_path', 'gt_path'] assert set(expected_keys).issubset(set(result.keys())) # check shape and contents assert result['gt'].shape == (1, 128, 128) assert result['lq'].shape == (1, 32, 32) assert result['lq_path'] == 'comic' assert result['gt_path'] == 'comic' # ------------------ test case: val/test mode -------------------- # opt['phase'] = 'test' opt['io_backend'] = dict(type='lmdb') dataset = PairedImageDataset(opt) # test __getitem__ result = dataset.__getitem__(0) # check returned keys expected_keys = ['lq', 'gt', 'lq_path', 'gt_path'] assert set(expected_keys).issubset(set(result.keys())) # check shape and contents assert result['gt'].shape == (1, 480, 492) assert result['lq'].shape == (1, 120, 123) assert result['lq_path'] == 'baboon' assert result['gt_path'] == 'baboon' ================================================ FILE: tests/test_data/test_single_image_dataset.py ================================================ import yaml from basicsr.data.single_image_dataset import SingleImageDataset def test_singleimagedataset(): """Test dataset: SingleImageDataset""" opt_str = r""" name: Test type: SingleImageDataset dataroot_lq: tests/data/lq meta_info_file: tests/data/meta_info_gt.txt io_backend: type: disk mean: [0.5, 0.5, 0.5] std: [0.5, 0.5, 0.5] """ opt = yaml.safe_load(opt_str) dataset = SingleImageDataset(opt) assert dataset.io_backend_opt['type'] == 'disk' # io backend assert len(dataset) == 2 # whether to read correct meta info assert dataset.mean == [0.5, 0.5, 0.5] # test __getitem__ result = dataset.__getitem__(0) # check returned keys expected_keys = ['lq', 'lq_path'] assert set(expected_keys).issubset(set(result.keys())) # check shape and contents assert result['lq'].shape == (3, 120, 123) assert result['lq_path'] == 'tests/data/lq/baboon.png' # ------------------ test scan folder mode -------------------- # opt.pop('meta_info_file') opt['io_backend'] = dict(type='disk') dataset = SingleImageDataset(opt) assert dataset.io_backend_opt['type'] == 'disk' # io backend assert len(dataset) == 2 # whether to correctly scan folders # ------------------ test lmdb backend and with y channel-------------------- # opt['dataroot_lq'] = 'tests/data/lq.lmdb' opt['io_backend'] = dict(type='lmdb') opt['color'] = 'y' opt['mean'] = [0.5] opt['std'] = [0.5] dataset = SingleImageDataset(opt) assert dataset.io_backend_opt['type'] == 'lmdb' # io backend assert len(dataset) == 2 # whether to read correct meta info assert dataset.std == [0.5] # test __getitem__ result = dataset.__getitem__(1) # check returned keys expected_keys = ['lq', 'lq_path'] assert set(expected_keys).issubset(set(result.keys())) # check shape and contents assert result['lq'].shape == (1, 90, 60) assert result['lq_path'] == 'comic' ================================================ FILE: tests/test_losses/test_losses.py ================================================ import pytest import torch from basicsr.losses.losses import CharbonnierLoss, L1Loss, MSELoss, WeightedTVLoss @pytest.mark.parametrize('loss_class', [L1Loss, MSELoss, CharbonnierLoss]) def test_pixellosses(loss_class): """Test loss: pixel losses""" pred = torch.rand((1, 3, 4, 4), dtype=torch.float32) target = torch.rand((1, 3, 4, 4), dtype=torch.float32) loss = loss_class(loss_weight=1.0, reduction='mean') out = loss(pred, target, weight=None) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # -------------------- test with other reduction -------------------- # # reduction = none loss = loss_class(loss_weight=1.0, reduction='none') out = loss(pred, target, weight=None) assert isinstance(out, torch.Tensor) assert out.shape == (1, 3, 4, 4) # test with spatial weights weight = torch.rand((1, 3, 4, 4), dtype=torch.float32) out = loss(pred, target, weight=weight) assert isinstance(out, torch.Tensor) assert out.shape == (1, 3, 4, 4) # reduction = sum loss = loss_class(loss_weight=1.0, reduction='sum') out = loss(pred, target, weight=None) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # -------------------- test unsupported loss reduction -------------------- # with pytest.raises(ValueError): loss_class(loss_weight=1.0, reduction='unknown') def test_weightedtvloss(): """Test loss: WeightedTVLoss""" pred = torch.rand((1, 3, 4, 4), dtype=torch.float32) loss = WeightedTVLoss(loss_weight=1.0, reduction='mean') out = loss(pred, weight=None) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # test with spatial weights weight = torch.rand((1, 3, 4, 4), dtype=torch.float32) out = loss(pred, weight=weight) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # -------------------- test reduction = sum-------------------- # loss = WeightedTVLoss(loss_weight=1.0, reduction='sum') out = loss(pred, weight=None) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # test with spatial weights weight = torch.rand((1, 3, 4, 4), dtype=torch.float32) out = loss(pred, weight=weight) assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([]) # -------------------- test unsupported loss reduction -------------------- # with pytest.raises(ValueError): WeightedTVLoss(loss_weight=1.0, reduction='unknown') with pytest.raises(ValueError): WeightedTVLoss(loss_weight=1.0, reduction='none') ================================================ FILE: tests/test_metrics/test_psnr_ssim.py ================================================ import numpy as np import pytest from basicsr.metrics.psnr_ssim import calculate_psnr, calculate_ssim def test_calculate_psnr(): """Test metric: calculate_psnr""" # mismatched image shapes with pytest.raises(AssertionError): calculate_psnr(np.ones((16, 16)), np.ones((10, 10)), crop_border=0) # wrong input order with pytest.raises(ValueError): calculate_psnr(np.ones((16, 16)), np.ones((16, 16)), crop_border=1, input_order='WRONG') out = calculate_psnr(np.ones((10, 10, 3)), np.ones((10, 10, 3)) * 2, crop_border=1, test_y_channel=True) assert isinstance(out, float) # test float inf out = calculate_psnr(np.ones((10, 10, 3)), np.ones((10, 10, 3)), crop_border=0) assert out == float('inf') def test_calculate_ssim(): """Test metric: calculate_ssim""" # mismatched image shapes with pytest.raises(AssertionError): calculate_ssim(np.ones((16, 16)), np.ones((10, 10)), crop_border=0) # wrong input order with pytest.raises(ValueError): calculate_ssim(np.ones((16, 16)), np.ones((16, 16)), crop_border=1, input_order='WRONG') out = calculate_ssim(np.ones((10, 10, 3)), np.ones((10, 10, 3)) * 2, crop_border=1, test_y_channel=True) assert isinstance(out, float) ================================================ FILE: tests/test_models/test_sr_model.py ================================================ import tempfile import torch import yaml from basicsr.archs.srresnet_arch import MSRResNet from basicsr.data.paired_image_dataset import PairedImageDataset from basicsr.losses.losses import L1Loss, PerceptualLoss from basicsr.models.sr_model import SRModel def test_srmodel(): """Test model: SRModel""" opt_str = r""" scale: 4 num_gpu: 1 manual_seed: 0 is_train: True dist: False # network structures network_g: type: MSRResNet num_in_ch: 3 num_out_ch: 3 num_feat: 4 num_block: 1 upscale: 4 # path path: pretrain_network_g: ~ 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: CosineAnnealingRestartLR periods: [250000, 250000, 250000, 250000] restart_weights: [1, 1, 1, 1] eta_min: !!float 1e-7 total_iter: 1000000 warmup_iter: -1 # no warm up # losses pixel_opt: type: L1Loss loss_weight: 1.0 reduction: mean perceptual_opt: type: PerceptualLoss layer_weights: 'conv5_4': 1 # before relu vgg_type: vgg19 use_input_norm: true range_norm: false perceptual_weight: 1.0 style_weight: 1.0 criterion: l1 # validation settings val: val_freq: !!float 5e3 save_img: True metrics: psnr: # metric name type: calculate_psnr crop_border: 4 test_y_channel: false better: higher # the higher, the better. Default: higher """ opt = yaml.safe_load(opt_str) # build model model = SRModel(opt) # test attributes assert model.__class__.__name__ == 'SRModel' assert isinstance(model.net_g, MSRResNet) assert isinstance(model.cri_pix, L1Loss) assert isinstance(model.cri_perceptual, PerceptualLoss) assert isinstance(model.optimizers[0], torch.optim.Adam) assert model.ema_decay == 0.999 # prepare data gt = torch.rand((1, 3, 32, 32), dtype=torch.float32) lq = torch.rand((1, 3, 8, 8), dtype=torch.float32) data = dict(gt=gt, lq=lq) model.feed_data(data) # check data shape assert model.lq.shape == (1, 3, 8, 8) assert model.gt.shape == (1, 3, 32, 32) # ----------------- test optimize_parameters -------------------- # model.optimize_parameters(1) assert model.output.shape == (1, 3, 32, 32) assert isinstance(model.log_dict, dict) # check returned keys expected_keys = ['l_pix', 'l_percep', 'l_style'] assert set(expected_keys).issubset(set(model.log_dict.keys())) # ----------------- test save -------------------- # with tempfile.TemporaryDirectory() as tmpdir: model.opt['path']['models'] = tmpdir model.opt['path']['training_states'] = tmpdir model.save(0, 1) # ----------------- test the test function -------------------- # model.test() assert model.output.shape == (1, 3, 32, 32) # delete net_g_ema model.__delattr__('net_g_ema') model.test() assert model.output.shape == (1, 3, 32, 32) assert model.net_g.training is True # should back to training mode after testing # ----------------- test nondist_validation -------------------- # # construct dataloader dataset_opt = dict( name='Test', 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 with tempfile.TemporaryDirectory() as tmpdir: model.opt['path']['visualization'] = tmpdir model.nondist_validation(dataloader, 1, None, save_img=True) assert model.is_train is True # check metric_results assert 'psnr' in model.metric_results assert isinstance(model.metric_results['psnr'], float) # in validation mode with tempfile.TemporaryDirectory() as tmpdir: model.opt['is_train'] = False model.opt['val']['suffix'] = 'test' model.opt['path']['visualization'] = tmpdir model.opt['val']['pbar'] = True model.nondist_validation(dataloader, 1, None, save_img=True) # check metric_results assert 'psnr' in model.metric_results assert isinstance(model.metric_results['psnr'], float) # if opt['val']['suffix'] is None model.opt['val']['suffix'] = None model.opt['name'] = 'demo' model.opt['path']['visualization'] = tmpdir model.nondist_validation(dataloader, 1, None, save_img=True) # check metric_results assert 'psnr' in model.metric_results assert isinstance(model.metric_results['psnr'], float)