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Repository: ZENGXH/DMM_Net
Branch: master
Commit: a6308688cbcf
Files: 88
Total size: 10.9 MB
Directory structure:
gitextract_fvy9rn0e/
├── .gitignore
├── INSTALL.md
├── MODEL_ZOO.md
├── README.md
├── args.py
├── data/
│ ├── folder_data/
│ │ ├── meta_train3k-CACHE_maskben_folderdata_index_3000.json
│ │ ├── meta_val200-CACHE_maskben_folderdata_index_200.json
│ │ └── meta_val474-CACHE_maskben_folderdata_index_474.json
│ └── ytb_vos/
│ └── splits_813_3k_trainvaltest/
│ ├── meta_test271.json
│ ├── meta_train3k.json
│ ├── meta_val200.json
│ ├── meta_val474.json
│ └── meta_vid_frame_2_predid.json
├── dmm/
│ ├── __init__.py
│ ├── configs/
│ │ ├── default.yaml
│ │ ├── eval.yaml
│ │ └── train.yaml
│ ├── dataloader/
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── base_youtube.py
│ │ ├── collate.py
│ │ ├── dataset.py
│ │ ├── dataset_utils.py
│ │ ├── davis2017.py
│ │ ├── db_info.yaml
│ │ ├── default_collate.py
│ │ ├── evaluation.py
│ │ ├── palette.txt
│ │ ├── transforms/
│ │ │ ├── __init__.py
│ │ │ ├── transforms.py
│ │ │ └── utils.py
│ │ └── youtubeVOS.py
│ ├── measures/
│ │ ├── __init__.py
│ │ ├── f_boundary.py
│ │ ├── jaccard.py
│ │ ├── statistics.py
│ │ └── t_stability.py
│ ├── misc/
│ │ ├── __init__.py
│ │ ├── config.py
│ │ ├── config_youtubeVOS.py
│ │ ├── io_aux.py
│ │ ├── logger.py
│ │ ├── parallel.py
│ │ ├── timer.py
│ │ └── visual.py
│ ├── modules/
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── clstm.py
│ │ ├── dmm_model.py
│ │ ├── evaluator.py
│ │ ├── feature_extractor.py
│ │ ├── match_model.py
│ │ ├── model_encoder.py
│ │ ├── submodules/
│ │ │ └── relax_match.py
│ │ ├── trainer.py
│ │ └── vision.py
│ └── utils/
│ ├── Colorer.py
│ ├── __init__.py
│ ├── boxlist_ops.py
│ ├── checker.py
│ ├── eval_helper.py
│ ├── hungarian.py
│ ├── masker.py
│ ├── match_helper.py
│ ├── metric_logger.py
│ ├── objectives.py
│ ├── pycococreatortools.py
│ └── utils.py
├── eval.py
├── requirements.txt
├── scripts/
│ ├── eval/
│ │ ├── eval.sh
│ │ ├── eval_r50.sh
│ │ └── eval_testdev.sh
│ ├── extract/
│ │ ├── README.md
│ │ ├── extract_proposal_coco81_train.sh
│ │ ├── extract_proposal_ytbbin_trainval_val.sh
│ │ └── process.sh
│ ├── metric/
│ │ ├── __init__.py
│ │ └── full_eval.sh
│ ├── submit.sh
│ └── train/
│ ├── train_101.sh
│ ├── train_online.sh
│ └── train_r50.sh
├── tools/
│ ├── davis_eval.py
│ ├── make_train_online_dataset.py
│ ├── reduce_pth_size_by_videos.py
│ └── submission_check.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*pyc
*pkl
*swp
*nfs*
del/*
*pt
*swo
================================================
FILE: INSTALL.md
================================================
- Clone the repo:
```shell
git clone https://github.com/ZENGXH/DMM_Net.git
```
## Install PyTorch with CUDA
```
conda create --name dmm --file requirements.txt -c pytorch -c conda-forge
pip install cython
pip install pycocotools pyyaml yacs opencv-python scikit-image easydict prettytable lmdb tabulate tqdm munkres tensorboardX
# dont install torchvision with pip, otherwise it may download torch 1.2.0 auto
```
- options:
```
pip install ninja yacs cython matplotlib easydict prettytable tabulate tqdm ipython scipy opencv-python networkx scikit-image tensorboardx cython scipy pillow h5py lmdb PyYAML
conda install -c pytorch pytorch-nightly cudatoolkit=9.0 -y
```
## Install maskrcnn-benchmark
```
cd ..
git clone https://github.com/ZENGXH/maskrcnn-benchmark.git
cd maskrcnn-benchmark
## install pkgs
# git clone https://github.com/pytorch/vision.git
# cd vision
# python setup.py install
# cd ..
#
# git clone https://github.com/cocodataset/cocoapi.git
# cd cocoapi/PythonAPI
# python setup.py build_ext install
# cd ../../
# build maskrcnn-benchmark
python setup.py build develop
```
================================================
FILE: MODEL_ZOO.md
================================================
## MODEL ZOO
## Mask R-CNN model
- trained with binary coco data: [model](https://hkustconnect-my.sharepoint.com/:f:/g/personal/xzengaf_connect_ust_hk/EubFvC7VlrdPrGu5SJgDds4BweDrkRmHlh5RGmIo7gJaig?e=vZDbR1)
- trained with COCO+YTB data: [model](https://hkustconnect-my.sharepoint.com/:f:/g/personal/xzengaf_connect_ust_hk/EgsxOMu7TQFFiqOI5GfyFBAB2BE86dwD8RHryapUTyAf1w?e=LLzawx)
- trained with YTB-testdev-firstframe data: [model](https://hkustconnect-my.sharepoint.com/:f:/g/personal/xzengaf_connect_ust_hk/EqwYsSIjYtNKvAwZZaruSv4Bxgor3HUBTWvT2zunzq_jfA?e=AsSWIG)
================================================
FILE: README.md
================================================
# DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation (ICCV 2019)
[paper.pdf](https://www.cs.toronto.edu/~xiaohui/dmm/paper/dmmnet_iccv19.pdf)
## Overview

### Requirements:
- PyTorch 1.1.0
- matplotlib 3.0.2
- not in requirements.txt:
- cython
- torchvision 0.2.2 or 0.3.0 if cuda10.0
- pycocotools # (2.0-py3.7-linux-x86_64)
- maskrnn-benchmark
- pyyaml yacs
- opencv-python scikit-image
- easydict prettytable lmdb tabulate
## Installation
- Follow [INSTALL.md](INSTALL.md)
## Data
### YouTube-VOS
- Download the YouTube-VOS dataset from their [website](https://youtube-vos.org/dataset/vos/).
Please note that our code is trained and tested only on YouTube-VOS dataset for 2018 version. There is a newer version released 2019 but it is not tested.
- We recommend to symlink the path to the youtube dataset to datasets/ as follows
```
cd datasets
ln -s path/to/youtubeVOS youtubeVOS
```
- The files structure should look like:
```
DMM/datasets
├── youtubeVOS
│ ├── train
│ │ ├── JPEGImages
│ │ │ ├── ...
│ │ ├── Annotations
│ │ │ ├── ...
│ ├── valid
│ │ ├── JPEGImages
│ │ │ ├── ...
│ │ ├── Annotations
│ │ │ ├── ...
│ ├── train_testdev_ot (optional)
│ │ ├── JPEGImages
│ │ │ ├── ...
│ │ ├── Annotations
│ │ │ ├── ...
```
- the `train_testdev_ot` data can be downloaded from [link](https://hkustconnect-my.sharepoint.com/:u:/g/personal/xzengaf_connect_ust_hk/ETsB6vt5U51Npzwcamb67YMBKPjC4tl5ONBilpFPTmsQyA?e=eJNJ6N)
## Prepare proposals
### Option1: Download the extracted file
#### for evaluation
- To eval DMMnet on youtubeVOS with the fine-tuned proposal net, use the propsoals generated by our fine-tuned Mask R-CNN model:
- [proposals-train-val](https://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_train.tar.gz)
```
mkdir -p experiments/proposals/
cd experiments/proposals/
wget https://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_train.tar.gz
tar xzf proposals_ytb_train.tar.gz
```
#### for training
- To train the DMMnet on youtubeVOS train-train split, need to prepare 1. proposals for both train-train and train-val split extracted by coco pretrained X101 Mask R-CNN model
- proposals can be downloaded:
- [proposals_coco81 (train-train, train-val and testdev-online-training)](http://www.cs.toronto.edu/~xiaohui/dmm/proposals/feature_coco81.tar.gz)
```
mkdir -p experiments/proposals/
cd experiments/proposals/
wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/feature_coco81.tar.gz
tar xzf feature_coco81.tar.gz
```
- preprocess the proposals for training DMM:
```
python src/tools/reduce_pth_size_by_videos.py experiments/proposals/coco81/inference/youtubevos_train3k_meta/predictions.pth train 50
python src/tools/reduce_pth_size_by_videos.py experiments/proposals/coco81/inference/youtubevos_val200_meta/predictions.pth trainval 50
python src/tools/reduce_pth_size_by_videos.py experiments/proposals/coco81/inference/youtubevos_testdev_online_meta/predictions.pth train_testdev_ot 90
```
- The files structure should look like:
```
DMM/experiments
├── propnet
│ ├── join_ytb_bin
│ │ ├── model_0172500.pth
│ ├── online_ytb
│ │ ├── model_0225000.pth
├── dmmnet
│ ├── ytb_255_50_matchloss_epo13
│ │ ├── epo13_iter01640
│ ├── ytb_255_50
│ │ ├── epo08_iter01640
│ ├── online_ytb
│ │ ├── epo101
├── proposals
│ ├── coco81
│ │ ├── inference
│ │ │ ├── youtubevos_train3k_meta (optional)
│ │ │ ├── youtubevos_val200_meta
│ │ │ ├── youtubevos_testdev_online_meta (optional)
│ ├── ytb_train
│ │ ├── inference
│ │ │ ├── youtubevos_val200_meta
│ ├── ytb_ot
│ │ ├── inference
│ │ │ ├── youtubevos_testdev_meta
```
### Option2: extract the proposals
- The model trained on youtubeVOS dataset can be found in [MODEL_ZOO.md](./MODEL_ZOO.md)
- The scripts used to extract proposals from the trained model can be found in [scripts/extract/](./scripts/extract)
## Training
- Train DMMnet on youtubeVOS:
```
sh scripts/train/train_101.sh
# or scripts/train/train_50.sh # for resnet 50 mode
```
### Online training
Train DMMnet on the first frame of validation set,
- first download the preprocessed data used for online training from [here](https://hkustconnect-my.sharepoint.com/:u:/g/personal/xzengaf_connect_ust_hk/ETsB6vt5U51Npzwcamb67YMBKPjC4tl5ONBilpFPTmsQyA?e=eJNJ6N), extract the data and put/link the extracted folder as `/PATH/TO/datasets/youtubeVOS/train_testdev_ot`
- prepare proposal, check the Section: Prepare proposals - for training
- get the DMMnet trained on train-train set for 1 epoch from [here](https://hkustconnect-my.sharepoint.com/:u:/g/personal/xzengaf_connect_ust_hk/ESmFqlodogNLrEIMt0ve84IBAOVokxY5HTDG3YMVQ5dzbg?e=6K4VY2) and put it under `experiments/dmmnet/`
- start online training
```
sh scripts/train/train_online.sh # it takes ~0.17h for one epoch
```
## Evaluation
- Evaluate DMMnet on trainval split:
- will need the trained model and the extracted train-val proposal:
```
cd ./experiments/dmmnet/
wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50_matchloss_epo13.tar.gz
tar xzf dmmnet_ytb_255_50_matchloss_epo13.tar.gz
wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50.tar.gz
tar xzf dmmnet_ytb_255_50.tar.gz
cd ../../
cd ./experiments/proposals/
wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_train.tar.gz
tar xzf proposals_ytb_train.tar.gz
cd ../../
```
- run `sh scripts/eval/eval_r50.sh`
- compute the J and F score by `sh scripts/metric/full_eval.sh /PATH/TO/OUTPUT/merged/`
expected results:
| Method | J_mean | J_recall | J_decay | F_mean | F_recall | F_decay |
|-------------|----------|------------|-----------|----------|------------|-----------|
| ytb_R50_w_match_loss_epo13 [model: ytb_255_50_matchloss_epo13](http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50_matchloss_epo13.tar.gz) | 0.611 | 0.702 | 0.104 |0.747 | 0.824 | 0.111 |
| ytb_R50_wo_match_loss_epo08 [model: ytb_255_50](http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50.tar.gz) | 0.6 | 0.684 | 0.104 | 0.742 | 0.819 | 0.109 |
- Evaluate online-trained DMMnet:
- Download proposals extracted by online-trained proposal net:
```
cd ./experiments/proposals/
wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_ot.tar.gz
tar xzf proposals_ytb_ot.tar.gz
cd ../../
```
- Download model:
```
cd experiments/dmmnet/
wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_online_ytb.tar.gz
tar xzf dmmnet_online_ytb.tar.gz
cd ../../
```
- run `scripts/eval/eval_testdev.sh`
- prepare the submission data with `scripts/submit.sh` and submit to [the server](https://competitions.codalab.org/competitions/19544#learn_the_details), expected resules: G mean = 0.579
-----------
part of the code is from https://github.com/imatge-upc/rvos
================================================
FILE: args.py
================================================
import argparse
def get_parser():
parser = argparse.ArgumentParser(description='RIASS')
# DMM related:
# parser.add_argument('-benchmark_cfg', dest='benchmark_cfg', default = 'configs/caffe2/e2e_mask_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml')
parser.add_argument('-config_train', dest='config_train', default = 'configs/default.yaml')
parser.add_argument('-prev_mask_d', dest='prev_mask_d', default=1, \
type=int, help='dimension of prev_mask, original is 1, but if concat with DMM prediction, become 2')
parser.add_argument('-year', dest='year', default = '2017')
parser.add_argument('-imsize',dest='imsize', default=480, type=int)
parser.add_argument('-test_image_w',dest='test_image_w', default=448, type=int)
parser.add_argument('-test_image_h',dest='test_image_h', default=256, type=int, help='shape of image during evaluation ')
parser.add_argument('-batch_size', dest='batch_size', default = 10, type=int)
parser.add_argument('-num_workers', dest='num_workers', default = 0, type=int)
parser.add_argument('-length_clip', dest='length_clip', default = 3, type=int)
parser.add_argument('--single_object',dest='single_object', action='store_true')
parser.set_defaults(single_object=False)
parser.add_argument('--only_temporal',dest='only_temporal', action='store_true')
parser.set_defaults(only_temporal=False)
parser.add_argument('--only_spatial',dest='only_spatial', action='store_true')
parser.set_defaults(only_spatial=False)
parser.add_argument('-load_proposals_dataset',default=0, type=int,help='use offine prop in dataloader')
parser.add_argument('-load_proposals', dest='load_proposals',default=0, type=int,help='use offine prop')
parser.add_argument('-use_refmask', dest='use_refmask',default=0,type=int,help='use reference mask as input')
parser.add_argument('-pred_offline_path_eval',dest='pred_offline_path_eval', nargs='+', type=str, help='offline proposal')
parser.add_argument('-pred_offline_path',dest='pred_offline_path', nargs='+', type=str, help='offline proposal')
parser.add_argument('-pred_offline_meta',dest='pred_offline_meta', default='../data/ytb_vos/splits_813_3k_trainvaltest/meta_vid_frame_2_predid.json', type=str,
help='offline proposal vid+frame to pred id ')
## TRAINING parameters ##
parser.add_argument('--resume', dest='resume',action='store_true', help=('whether to resume training an existing model ' '(the one with name model_name will be used)'))
parser.set_defaults(resume=False)
parser.add_argument('-resume_path', dest='resume_path', default='epoxx_iterxxxx', type=str, help='the epoch name of the resume model; DMM model')
parser.add_argument('-models_root', dest='models_root', default='experiments/', type=str, help='root to save model') #the epoch name of the resume model; DMM model')
parser.add_argument('-epoch_resume', dest='epoch_resume',default= 0,type=int, help=('set epoch_resume if you want flags '
'--finetune_after and --update_encoder to be properly '
'activated (eg if you stop training for whatever reason ' 'at epoch 15, set epoch_resume to 15)'))
parser.add_argument('-overwrite_loadargs', dest='overwrite_loadargs', default=1, type=int, help='use when resume training, use the loaded args or overwrite it')
parser.add_argument('-seed', dest='seed',default = 123, type=int)
parser.add_argument('-gpu_id', dest='gpu_id',default = 0, type=int)
parser.add_argument('--local_rank', dest='local_rank',default = 0, type=int)
parser.add_argument('-lr_decoder', dest='lr_decoder', default = 1e-3,type=float)
parser.add_argument('-lr', dest='lr', default = 1e-3, type=float)
parser.add_argument('-lr_cnn', dest='lr_cnn', default = 1e-4, type=float)
parser.add_argument('-optim_cnn', dest='optim_cnn', default = 'adam', choices=['adam','sgd','rmsprop'])
parser.add_argument('-momentum', dest='momentum', default =0.9,type=float)
parser.add_argument('-weight_decay', dest='weight_decay', default = 1e-6, type=float)
parser.add_argument('-weight_decay_cnn', dest='weight_decay_cnn', default = 1e-6, type=float)
parser.add_argument('-optim', dest='optim', default = 'adam',
choices=['adam','sgd','rmsprop'])
parser.add_argument('-maxseqlen', dest='maxseqlen', default = 5, type=int)
parser.add_argument('-gt_maxseqlen', dest='gt_maxseqlen', default = 5, type=int)
parser.add_argument('-best_val_loss', dest='best_val_loss', default = 0, type=float)
parser.add_argument('-train_h', dest='train_h', type=int, default=255, help='shape of input image, need to be divisible by 32 ')
parser.add_argument('-train_w', dest='train_w', type=int, default=448, help='shape of input image ')
parser.add_argument('-max_eval_iter', dest='max_eval_iter', type=int, default=800, help='early stop for evaluation') # shape of input image ')
parser.add_argument('-sample_inference_mask', dest='sample_inference_mask', type=int, default=0, help='use inference mask as prev mask during training')
parser.add_argument('-cache_data', dest='cache_data', type=int, default=1, help='cache the data in dataloader')
parser.add_argument('-skip_empty_starting_frame', dest='skip_empty_starting_frame', type=int, default=0, help='skip the template which is empty')
parser.add_argument('-random_select_frames', dest='random_select_frames', type=int, default=0, help='step size vary')
# base model fine tuning
parser.add_argument('-finetune_after', dest='finetune_after', default = 0, type=int,
help=('epoch number to start finetuning. set -1 to not finetune.'
'there is a patience term that can allow starting to fine tune '
'earlier (does not apply if value is -1)'))
parser.add_argument('--update_encoder', dest='update_encoder', default=1, help='update weights of encoder or not.')
parser.add_argument('-min_delta', dest='min_delta', default=0.0, type=float)
# stopping criterion
parser.add_argument('-patience', dest='patience', default = 15, type=int,
help=('patience term to activate flags such as '
'use_class_loss, feed_prediction and update_encoder if '
'their matching vars are not -1'))
parser.add_argument('-patience_stop', dest='patience_stop', default = 60, type=int,
help='patience to stop training.')
parser.add_argument('-max_epoch', dest='max_epoch', default = 100, type=int)
# visualization and logging
parser.add_argument('-print_every', dest='print_every', default = 2, type=int)
parser.add_argument('-save_every', dest='save_every', default = 3000, type=int)
parser.add_argument('--log_term', dest='log_term', action='store_true', help='if activated, will show logs in stdout instead of log file.')
parser.set_defaults(log_term=False)
# loss weights
parser.add_argument('-iou_weight',dest='iou_weight',default=1.0, type=float)
parser.add_argument('-loss_weight_iouraw',dest='loss_weight_iouraw',default=18.0, type=float)
parser.add_argument('-loss_weight_match',dest='loss_weight_match',default=1.0, type=float)
# augmentation
parser.add_argument('--augment', dest='augment', action='store_true')
parser.set_defaults(augment=False)
parser.add_argument('--my_augment', dest='my_augment', action='store_true')
parser.set_defaults(my_augment=False)
parser.add_argument('-rotation', dest='rotation', default = 10, type=int)
parser.add_argument('-translation', dest='translation', default = 0.1, type=float)
parser.add_argument('-shear', dest='shear', default = 0.1, type=float)
parser.add_argument('-zoom', dest='zoom', default = 0.7, type=float)
# GPU
parser.add_argument('--cpu', dest='use_gpu', action='store_false')
parser.set_defaults(use_gpu=True)
parser.add_argument('-ngpus', dest='ngpus', default=1,type=int)
parser.add_argument('-distributed', dest='distributed', default=0,type=int)
parser.add_argument('-base_model', dest='base_model', default = 'coco', choices=['resnet101','resnet50','resnet34','vgg16', 'coco'])
parser.add_argument('-skip_mode', dest='skip_mode', default = 'concat',
choices=['sum','concat','mul','none'])
parser.add_argument('-model_name', dest='model_name', default='model')
parser.add_argument('-log_file', dest='log_file', default='train.log')
parser.add_argument('-hidden_size', dest='hidden_size', default = 128, type=int)
parser.add_argument('-kernel_size', dest='kernel_size', default = 3, type=int)
parser.add_argument('-dropout', dest='dropout', default = 0.0, type=float)
# dataset parameters
parser.add_argument('--resize',dest='resize', action='store_true')
parser.set_defaults(resize=False)
parser.add_argument('-num_classes', dest='num_classes', default = 21, type=int)
parser.add_argument('-dataset', dest='dataset', default = 'youtube',choices=['youtube'])
parser.add_argument('-youtube_dir', dest='youtube_dir', default='../../databases/YouTubeVOS/')
# testing
parser.add_argument('-test_model_path', dest='test_model_path', default='')
parser.add_argument('-davis_eval_folder', dest='davis_eval_folder', default='')
parser.add_argument('-eval_split',dest='eval_split', default='trainval')
parser.add_argument('-train_split',dest='train_split', default='train')
parser.add_argument('-mask_th',dest='mask_th', default=0.5, type=float)
parser.add_argument('-test', dest='test', default=0, type=int, help='specify to enter test mode, used in load coco model when num_classes = 2')
parser.add_argument('-eval_flag', default='pred', type=str, help='used to distingush different test setting, mannully')
parser.add_argument('-threshold_mask',dest='threshold_mask', default=0.4, type=float, help='used in plot DMM merged')
parser.add_argument('-max_dets',dest='max_dets', default=100, type=int)
parser.add_argument('-min_size',dest='min_size', default=0.001, type=float)
parser.add_argument('-pad_video', dest='pad_video', type=int, help='use when multi-gpu', default=0)
parser.add_argument('-distributed_manully', dest='distributed_manully', type=int, help='distributed_manully', default=0) #use when multi-gpu', default=0)
parser.add_argument('-distributed_manully_Nrep', dest='distributed_manully_Nrep', type=int, help='N part/device', default=0)
parser.add_argument('-distributed_manully_rank', dest='distributed_manully_rank', type=int, help='manually set local rank', default=0)
return parser
if __name__ =="__main__":
parser = get_parser()
args_dict = parser.parse_args()
================================================
FILE: data/folder_data/meta_train3k-CACHE_maskben_folderdata_index_3000.json
================================================
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gitextract_fvy9rn0e/ ├── .gitignore ├── INSTALL.md ├── MODEL_ZOO.md ├── README.md ├── args.py ├── data/ │ ├── folder_data/ │ │ ├── meta_train3k-CACHE_maskben_folderdata_index_3000.json │ │ ├── meta_val200-CACHE_maskben_folderdata_index_200.json │ │ └── meta_val474-CACHE_maskben_folderdata_index_474.json │ └── ytb_vos/ │ └── splits_813_3k_trainvaltest/ │ ├── meta_test271.json │ ├── meta_train3k.json │ ├── meta_val200.json │ ├── meta_val474.json │ └── meta_vid_frame_2_predid.json ├── dmm/ │ ├── __init__.py │ ├── configs/ │ │ ├── default.yaml │ │ ├── eval.yaml │ │ └── train.yaml │ ├── dataloader/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── base_youtube.py │ │ ├── collate.py │ │ ├── dataset.py │ │ ├── dataset_utils.py │ │ ├── davis2017.py │ │ ├── db_info.yaml │ │ ├── default_collate.py │ │ ├── evaluation.py │ │ ├── palette.txt │ │ ├── transforms/ │ │ │ ├── __init__.py │ │ │ ├── transforms.py │ │ │ └── utils.py │ │ └── youtubeVOS.py │ ├── measures/ │ │ ├── __init__.py │ │ ├── f_boundary.py │ │ ├── jaccard.py │ │ ├── statistics.py │ │ └── t_stability.py │ ├── misc/ │ │ ├── __init__.py │ │ ├── config.py │ │ ├── config_youtubeVOS.py │ │ ├── io_aux.py │ │ ├── logger.py │ │ ├── parallel.py │ │ ├── timer.py │ │ └── visual.py │ ├── modules/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── clstm.py │ │ ├── dmm_model.py │ │ ├── evaluator.py │ │ ├── feature_extractor.py │ │ ├── match_model.py │ │ ├── model_encoder.py │ │ ├── submodules/ │ │ │ └── relax_match.py │ │ ├── trainer.py │ │ └── vision.py │ └── utils/ │ ├── Colorer.py │ ├── __init__.py │ ├── boxlist_ops.py │ ├── checker.py │ ├── eval_helper.py │ ├── hungarian.py │ ├── masker.py │ ├── match_helper.py │ ├── metric_logger.py │ ├── objectives.py │ ├── pycococreatortools.py │ └── utils.py ├── eval.py ├── requirements.txt ├── scripts/ │ ├── eval/ │ │ ├── eval.sh │ │ ├── eval_r50.sh │ │ └── eval_testdev.sh │ ├── extract/ │ │ ├── README.md │ │ ├── extract_proposal_coco81_train.sh │ │ ├── extract_proposal_ytbbin_trainval_val.sh │ │ └── process.sh │ ├── metric/ │ │ ├── __init__.py │ │ └── full_eval.sh │ ├── submit.sh │ └── train/ │ ├── train_101.sh │ ├── train_online.sh │ └── train_r50.sh ├── tools/ │ ├── davis_eval.py │ ├── make_train_online_dataset.py │ ├── reduce_pth_size_by_videos.py │ └── submission_check.py └── train.py
SYMBOL INDEX (367 symbols across 48 files)
FILE: args.py
function get_parser (line 3) | def get_parser():
FILE: dmm/dataloader/base.py
function _load_annotation (line 24) | def _load_annotation(filename,single_object):
function _get_num_objects (line 34) | def _get_num_objects(annotation):
class BaseLoader (line 51) | class BaseLoader(ImageCollection):
method __init__ (line 63) | def __init__(self,split,path,regex,load_func=None, lmdb_env=None, file...
method __str__ (line 80) | def __str__(self):
class Sequence (line 84) | class Sequence(BaseLoader):
method __init__ (line 95) | def __init__(self,split,name,regex="*.jpg", lmdb_env=None, files=None):
class SequenceClip_simple (line 102) | class SequenceClip_simple:
method __init__ (line 112) | def __init__(self, seq, starting_frame):
method __str__ (line 117) | def __str__(self):
class SequenceClip (line 121) | class SequenceClip(BaseLoader):
method __init__ (line 132) | def __init__(self,split,name,starting_frame,regex="*.jpg", lmdb_env=No...
method __str__ (line 139) | def __str__(self):
class Segmentation (line 143) | class Segmentation(BaseLoader):
method __init__ (line 155) | def __init__(self,split,path,single_object,regex="*.png", lmdb_env=None):
method iter_objects_id (line 161) | def iter_objects_id(self):
method iter_objects (line 168) | def iter_objects(self):
class Annotation (line 177) | class Annotation(Segmentation):
method __init__ (line 189) | def __init__(self,split,name,single_object,regex="*.png", lmdb_env=None):
class AnnotationClip_simple (line 195) | class AnnotationClip_simple:
method __init__ (line 206) | def __init__(self, annot, starting_frame):
class AnnotationClip (line 211) | class AnnotationClip(Segmentation):
method __init__ (line 223) | def __init__(self,split,name,starting_frame,single_object,regex="*.png...
FILE: dmm/dataloader/base_youtube.py
function _load_annotation (line 17) | def _load_annotation(filename,single_object):
function _get_num_objects (line 27) | def _get_num_objects(annotation):
function get_seq_path (line 43) | def get_seq_path(split):
function get_seq (line 65) | def get_seq(split):
class BaseLoader (line 86) | class BaseLoader(ImageCollection):
method __init__ (line 96) | def __init__(self, split, path, regex, load_func=None, lmdb_env=None, ...
method __str__ (line 115) | def __str__(self):
class Sequence (line 119) | class Sequence(BaseLoader):
method __init__ (line 127) | def __init__(self,split,name,regex="*.jpg", lmdb_env=None, files=None):
class SequenceClip_simple (line 134) | class SequenceClip_simple:
method __init__ (line 144) | def __init__(self, seq, starting_frame):
method __str__ (line 151) | def __str__(self):
class SequenceClip (line 155) | class SequenceClip(BaseLoader):
method __init__ (line 166) | def __init__(self,split,name,starting_frame,regex="*.jpg", lmdb_env=No...
method __str__ (line 173) | def __str__(self):
class Segmentation (line 177) | class Segmentation(BaseLoader):
method __init__ (line 189) | def __init__(self, split, path, single_object, regex="*.png", lmdb_env...
method iter_objects_id (line 199) | def iter_objects_id(self):
method iter_objects (line 206) | def iter_objects(self):
class Annotation (line 215) | class Annotation(Segmentation):
method __init__ (line 227) | def __init__(self,split,name,single_object,regex="*.png", lmdb_env=None):
class AnnotationClip_simple (line 230) | class AnnotationClip_simple:
method __init__ (line 242) | def __init__(self, annot, starting_frame):
class AnnotationClip (line 246) | class AnnotationClip(Segmentation):
method __init__ (line 257) | def __init__(self,split,name,starting_frame,single_object,regex="*.png...
FILE: dmm/dataloader/collate.py
function eval_collate (line 34) | def eval_collate(batch):
function cocotrain_collate (line 80) | def cocotrain_collate(batch):
FILE: dmm/dataloader/dataset.py
class MyDataset (line 32) | class MyDataset(data.Dataset):
method __init__ (line 37) | def __init__(self,
method init_meta_data (line 80) | def init_meta_data(self):
method get_root_dir (line 86) | def get_root_dir(self):
method process_mask (line 96) | def process_mask(self, proposals):
method get_classes (line 101) | def get_classes(self):
method get_raw_sample (line 104) | def get_raw_sample(self,index):
method init_load_proposals (line 115) | def init_load_proposals(self, vid=None):
method _need_init (line 135) | def _need_init(self):
method __getitem__ (line 146) | def __getitem__(self, index):
method __len__ (line 295) | def __len__(self):
method get_sample_list (line 298) | def get_sample_list(self):
method sequence_from_masks (line 300) | def sequence_from_masks(self, seq_name, annot):
FILE: dmm/dataloader/dataset_utils.py
function get_dataset (line 6) | def get_dataset(args, split, image_transforms = None, target_transforms ...
function sequence_palette (line 20) | def sequence_palette():
FILE: dmm/dataloader/davis2017.py
class DAVISLoader (line 40) | class DAVISLoader(MyDataset):
method __init__ (line 53) | def __init__(self,
method get_raw_sample (line 192) | def get_raw_sample(self, key):
method get_raw_sample_clip (line 206) | def get_raw_sample_clip(self, key):
method sequence_name_to_id (line 220) | def sequence_name_to_id(self,name):
method sequence_name_to_id_clip (line 224) | def sequence_name_to_id_clip(self,name):
method sequence_id_to_name (line 228) | def sequence_id_to_name(self,sid):
method sequence_id_to_name_clip (line 232) | def sequence_id_to_name_clip(self,sid):
method iternames (line 236) | def iternames(self):
method iternames_clips (line 241) | def iternames_clips(self):
method iteritems (line 246) | def iteritems(self):
FILE: dmm/dataloader/default_collate.py
function default_convert (line 15) | def default_convert(data):
function default_collate (line 43) | def default_collate(batch):
FILE: dmm/dataloader/evaluation.py
function db_eval_sequence (line 31) | def db_eval_sequence(segmentations,annotations,measure='J',n_jobs=cfg.N_...
function db_eval (line 60) | def db_eval(db,segmentations,measures,n_jobs=cfg.N_JOBS,verbose=True):
function db_eval_sequence_call (line 114) | def db_eval_sequence_call(input_dict):
function print_results (line 122) | def print_results(evaluation,method_name="-"):
FILE: dmm/dataloader/transforms/transforms.py
function random_crop (line 14) | def random_crop(x_list, random_crop_size, orig_size, **kwargs):
class RandomAffine (line 22) | class RandomAffine(object):
method __init__ (line 24) | def __init__(self,
method __call__ (line 84) | def __call__(self, *inputs):
class Affine (line 99) | class Affine(object):
method __init__ (line 101) | def __init__(self,
method __call__ (line 120) | def __call__(self, *inputs):
class AffineCompose (line 138) | class AffineCompose(object):
method __init__ (line 140) | def __init__(self,
method __call__ (line 166) | def __call__(self, *inputs):
class RandomRotate (line 186) | class RandomRotate(object):
method __init__ (line 188) | def __init__(self,
method __call__ (line 211) | def __call__(self, *inputs):
class RandomChoiceRotate (line 222) | class RandomChoiceRotate(object):
method __init__ (line 224) | def __init__(self,
method __call__ (line 258) | def __call__(self, *inputs):
class Rotate (line 269) | class Rotate(object):
method __init__ (line 271) | def __init__(self,
method __call__ (line 294) | def __call__(self, *inputs):
class RandomTranslate (line 317) | class RandomTranslate(object):
method __init__ (line 319) | def __init__(self,
method __call__ (line 353) | def __call__(self, *inputs):
class RandomChoiceTranslate (line 368) | class RandomChoiceTranslate(object):
method __init__ (line 370) | def __init__(self,
method __call__ (line 406) | def __call__(self, *inputs):
class Translate (line 419) | class Translate(object):
method __init__ (line 421) | def __init__(self,
method __call__ (line 450) | def __call__(self, *inputs):
class RandomShear (line 475) | class RandomShear(object):
method __init__ (line 477) | def __init__(self,
method __call__ (line 499) | def __call__(self, *inputs):
class RandomChoiceShear (line 510) | class RandomChoiceShear(object):
method __init__ (line 512) | def __init__(self,
method __call__ (line 545) | def __call__(self, *inputs):
class Shear (line 557) | class Shear(object):
method __init__ (line 559) | def __init__(self,
method __call__ (line 567) | def __call__(self, *inputs):
class RandomZoom (line 590) | class RandomZoom(object):
method __init__ (line 592) | def __init__(self,
method __call__ (line 621) | def __call__(self, *inputs):
class RandomChoiceZoom (line 633) | class RandomChoiceZoom(object):
method __init__ (line 635) | def __init__(self,
method __call__ (line 669) | def __call__(self, *inputs):
class Zoom (line 681) | class Zoom(object):
method __init__ (line 683) | def __init__(self,
method __call__ (line 709) | def __call__(self, *inputs):
FILE: dmm/dataloader/transforms/utils.py
function th_allclose (line 12) | def th_allclose(x, y):
function th_flatten (line 20) | def th_flatten(x):
function th_c_flatten (line 24) | def th_c_flatten(x):
function th_bc_flatten (line 31) | def th_bc_flatten(x):
function th_zeros_like (line 39) | def th_zeros_like(x):
function th_ones_like (line 42) | def th_ones_like(x):
function th_constant_like (line 45) | def th_constant_like(x, val):
function th_iterproduct (line 49) | def th_iterproduct(*args):
function th_iterproduct_like (line 52) | def th_iterproduct_like(x):
function th_uniform (line 56) | def th_uniform(lower, upper):
function th_gather_nd (line 60) | def th_gather_nd(x, coords):
function th_affine2d (line 67) | def th_affine2d(x, matrix, mode='bilinear', center=True):
function th_nearest_interp2d (line 130) | def th_nearest_interp2d(input, coords):
function th_bilinear_interp2d (line 151) | def th_bilinear_interp2d(input, coords):
function th_affine3d (line 188) | def th_affine3d(x, matrix, mode='trilinear', center=True):
function th_nearest_interp3d (line 226) | def th_nearest_interp3d(input, coords):
function th_trilinear_interp3d (line 245) | def th_trilinear_interp3d(input, coords):
function th_pearsonr (line 300) | def th_pearsonr(x, y):
function th_corrcoef (line 314) | def th_corrcoef(x):
function th_matrixcorr (line 336) | def th_matrixcorr(x, y):
function th_random_choice (line 357) | def th_random_choice(a, n_samples=1, replace=True, p=None):
function save_transform (line 400) | def save_transform(file, transform):
function load_transform (line 409) | def load_transform(file):
FILE: dmm/dataloader/youtubeVOS.py
class YoutubeVOSLoader (line 27) | class YoutubeVOSLoader(MyDataset):
method __init__ (line 39) | def __init__(self,
method get_raw_sample (line 268) | def get_raw_sample(self, key):
method get_raw_sample_clip (line 282) | def get_raw_sample_clip(self, key):
method sequence_name_to_id (line 296) | def sequence_name_to_id(self,name):
method sequence_name_to_id_clip (line 300) | def sequence_name_to_id_clip(self,name):
method sequence_id_to_name (line 304) | def sequence_id_to_name(self,sid):
method sequence_id_to_name_clip (line 308) | def sequence_id_to_name_clip(self,sid):
method iternames (line 312) | def iternames(self):
method iternames_clips (line 317) | def iternames_clips(self):
method iteritems (line 322) | def iteritems(self):
FILE: dmm/measures/f_boundary.py
function db_eval_boundary (line 15) | def db_eval_boundary(foreground_mask,gt_mask,bound_th=0.008):
function seg2bmap (line 80) | def seg2bmap(seg,width=None,height=None):
FILE: dmm/measures/jaccard.py
function db_eval_iou (line 15) | def db_eval_iou(annotation,segmentation):
FILE: dmm/measures/statistics.py
function mean (line 4) | def mean(X):
function recall (line 11) | def recall(X,threshold=0.5):
function decay (line 17) | def decay(X,n_bins=4):
function std (line 32) | def std(X):
function get (line 45) | def get(name):
FILE: dmm/measures/t_stability.py
function get_bijective_pairs (line 15) | def get_bijective_pairs(pairs,costmat):
function bij_pairs_one_dim (line 20) | def bij_pairs_one_dim(pairs, costmat, left_or_right):
function hist_cost_2 (line 33) | def hist_cost_2(BH1,BH2):
function sc_compute (line 48) | def sc_compute(Bsamp,Tsamp,mean_dist,nbins_theta,nbins_r,r_inner,r_outer...
function db_eval_t_stab (line 99) | def db_eval_t_stab(fgmask,ground_truth,timing=True):
FILE: dmm/misc/config.py
class phase (line 24) | class phase(Enum):
function db_read_info (line 82) | def db_read_info():
function db_read_attributes (line 87) | def db_read_attributes():
function db_read_years (line 91) | def db_read_years():
function db_read_sequences (line 95) | def db_read_sequences(year=None,db_phase=None):
FILE: dmm/misc/config_youtubeVOS.py
class phase (line 10) | class phase(Enum):
function db_read_sequences_train_testdev_ot (line 75) | def db_read_sequences_train_testdev_ot():
function db_read_sequences_train (line 84) | def db_read_sequences_train():
function db_read_sequences_val (line 91) | def db_read_sequences_val():
function db_read_sequences_trainval (line 98) | def db_read_sequences_trainval():
function get_img_path (line 109) | def get_img_path(split):
function get_anno_path (line 123) | def get_anno_path(split):
function get_db_path (line 136) | def get_db_path(split):
FILE: dmm/misc/io_aux.py
function imread_indexed (line 6) | def imread_indexed(filename):
function imwrite_indexed (line 14) | def imwrite_indexed(filename,array,color_palette=cfg.palette):
FILE: dmm/misc/parallel.py
function Parallel (line 7) | def Parallel( *args, **kwargs ):
function _pickle_method (line 11) | def _pickle_method(method):
function _unpickle_method (line 16) | def _unpickle_method(func_name, obj):
FILE: dmm/misc/timer.py
class Timer (line 16) | class Timer(object):
method __init__ (line 18) | def __init__(self):
method tic (line 22) | def tic(self):
method toc (line 26) | def toc(self):
FILE: dmm/misc/visual.py
function overlay (line 12) | def overlay(image,mask,colors=[255,0,0],cscale=2,alpha=0.4):
FILE: dmm/modules/base.py
class FeatureExtractorBase (line 18) | class FeatureExtractorBase(nn.Module):
method __init__ (line 22) | def __init__(self, args, pretrained = 1):
method get_backbone_para (line 55) | def get_backbone_para(self):
method get_skip_params (line 61) | def get_skip_params(self):
class RSISMask (line 71) | class RSISMask(nn.Module):
method __init__ (line 76) | def __init__(self, args):
method forward (line 115) | def forward(self, skip_feats, prev_mask_list, prev_state_spatial, prev...
FILE: dmm/modules/clstm.py
class ConvLSTMCell (line 7) | class ConvLSTMCell(nn.Module):
method __init__ (line 9) | def __init__(self, args, input_size, hidden_size, kernel_size, padding):
method forward (line 16) | def forward(self, input_, prev_state_spatial, hidden_state_temporal):
class ConvLSTMCellMask (line 68) | class ConvLSTMCellMask(nn.Module):
method __init__ (line 73) | def __init__(self, args, input_size, hidden_size, kernel_size, padding):
method forward (line 80) | def forward(self, input_, prev_mask, prev_state_spatial, hidden_state_...
FILE: dmm/modules/dmm_model.py
class DMM_Model (line 11) | class DMM_Model(nn.Module):
method __init__ (line 16) | def __init__(self, cfgs, is_test=0):
method fill_template_dict (line 22) | def fill_template_dict(self, args, proposals, features, y_mask, tplt_v...
method inference (line 48) | def inference(self, infos, proposals, backbone_feature, mask_last_occu...
method forward (line 88) | def forward(self, args, proposals, backbone_feature, mask_last_occuren...
method prepare_tplt_feature (line 144) | def prepare_tplt_feature(self, tplt_valid_vid, tplt_dict, bid): #tplt_...
FILE: dmm/modules/evaluator.py
class Evaler (line 20) | class Evaler(nn.Module):
method __init__ (line 25) | def __init__(self, DMM, encoder, decoder, args, dmmcfgs):
method forward (line 63) | def forward(self,batch_idx,inputs,imgs_names,targets,seq_name, args, p...
method inference_timestep (line 151) | def inference_timestep(self, infos, tplt_dict, x, y_mask, prev_thid_li...
method forward_timestep_init (line 215) | def forward_timestep_init(self, args, x, y_mask, predid_cur_frames, pr...
method prepare_frame_names (line 227) | def prepare_frame_names(self, seq_name):
FILE: dmm/modules/feature_extractor.py
class FeatureExtractor (line 5) | class FeatureExtractor(nn.Module):
method __init__ (line 7) | def __init__(self):
method init_pooler (line 11) | def init_pooler(self):
method forward (line 20) | def forward(self, backbone_feature, proposals):
method convert_to_roi_format (line 32) | def convert_to_roi_format(self, boxes):
method roi_pooling (line 39) | def roi_pooling(self, backbone_feature, proposals):
function make_roi_mask_feature_extractor (line 54) | def make_roi_mask_feature_extractor():
function cat (line 57) | def cat(tensors, dim=0):
FILE: dmm/modules/match_model.py
class MatchModel (line 13) | class MatchModel(nn.Module):
method __init__ (line 14) | def __init__(self, cfgs={}, is_test=0):
method forward (line 24) | def forward(self, proposed_feature, proposed_mask, template_feature, m...
method compute_cost_matrix (line 49) | def compute_cost_matrix(self, features, mask, scores, targets=None):
method match_with_first_frame (line 93) | def match_with_first_frame(self, sim_matrix, n_prop, n_tplt, proposed_...
method compute_feature_score (line 150) | def compute_feature_score(self, key_feature, query_feature):
FILE: dmm/modules/model_encoder.py
class FeatureExtractor (line 24) | class FeatureExtractor(FeatureExtractorBase):
method __init__ (line 28) | def __init__(self, args, pretrained = 1):
method init_pred_offline (line 56) | def init_pred_offline(self):
method need_init (line 63) | def need_init(self):
method get_optimizer (line 72) | def get_optimizer(self, optim_name, lr, args, weight_decay = 0, moment...
method forward_base (line 86) | def forward_base(self, coco_input, args):
method forward (line 97) | def forward(self,args,coco_input,predid_cur_frames=None,targets=None,p...
FILE: dmm/modules/submodules/relax_match.py
function project_row (line 9) | def project_row(X):
function project_col (line 21) | def project_col(X):
function relax_matching (line 36) | def relax_matching(C, max_iter=100, proj_iter=100, lr=0.1, return_time=0):
function test (line 108) | def test():
function hungarian_matching (line 120) | def hungarian_matching(cost):
function test_given_cost (line 129) | def test_given_cost(cost):
FILE: dmm/modules/trainer.py
class Trainer (line 29) | class Trainer(nn.Module):
method __init__ (line 34) | def __init__(self, DMM, encoder, decoder, args):
method forward (line 66) | def forward(self,batch_idx,inputs,imgs_names,targets,seq_name,starting...
method forward_timestep (line 132) | def forward_timestep(self, frame_idx, args, tplt_dict, x, y_mask, sw_m...
method refine (line 236) | def refine(self, args, shapes, tplt_dict, feats, prev_thid_list, init_...
method forward_timestep_init (line 308) | def forward_timestep_init(self, args, x, y_mask, mode='train', predid_...
FILE: dmm/modules/vision.py
class ResNet101 (line 6) | class ResNet101(ResNet):
method __init__ (line 8) | def __init__(self):
method forward (line 11) | def forward(self, x):
class ResNet50 (line 23) | class ResNet50(ResNet):
method __init__ (line 25) | def __init__(self):
method forward (line 28) | def forward(self, x):
class ResNet34 (line 40) | class ResNet34(ResNet):
method __init__ (line 42) | def __init__(self):
method forward (line 45) | def forward(self, x):
class VGG16 (line 57) | class VGG16(nn.Module):
method __init__ (line 59) | def __init__(self, num_classes = 1000, features = [64, 64, 'M',
method forward (line 77) | def forward(self, x):
method _initialize_weights (line 117) | def _initialize_weights(self):
FILE: dmm/utils/Colorer.py
function add_coloring_to_emit_windows (line 5) | def add_coloring_to_emit_windows(fn):
function add_coloring_to_emit_ansi (line 74) | def add_coloring_to_emit_ansi(fn):
FILE: dmm/utils/boxlist_ops.py
function _cat (line 5) | def _cat(tensors, dim=0):
function filter_results (line 15) | def filter_results(boxlists, nms_thresh=0.8, max_proposals=0, score_fiel...
function cat_boxlist (line 31) | def cat_boxlist(bboxes):
function boxlist_iou (line 61) | def boxlist_iou(boxlist1, boxlist2):
FILE: dmm/utils/checker.py
function CHECK2D (line 4) | def CHECK2D(tensor):
function CHECK4D (line 8) | def CHECK4D(tensor):
function CHECK5D (line 12) | def CHECK5D(tensor):
function CHECK3D (line 16) | def CHECK3D(tensor):
function CHECKSIZE (line 20) | def CHECKSIZE(tensor, size):
function CHECKEQ (line 26) | def CHECKEQ(a, b,s=None):
function CHECKABSST (line 35) | def CHECKABSST(a, b):
function CHECKEQT (line 41) | def CHECKEQT(a,b):
function CHECKINFO (line 45) | def CHECKINFO(tensor,mode,info):
function CHECKBOX (line 55) | def CHECKBOX(a, b):
function CHECKDEBUG (line 62) | def CHECKDEBUG(dmm_io, check_io, depth=0):
FILE: dmm/utils/eval_helper.py
function compute_iou_binary_mask (line 7) | def compute_iou_binary_mask(annotation,segmentation):
function plot_scores_map (line 22) | def plot_scores_map(mask, fname):
FILE: dmm/utils/hungarian.py
function MaskedNLL (line 10) | def MaskedNLL(target, probs, balance_weights=None):
function StableBalancedMaskedBCE (line 34) | def StableBalancedMaskedBCE(target, out, balance_weight = None):
function softIoU (line 62) | def softIoU(target, out, e=1e-6, need_sigmoid=1):
function match (line 88) | def match(masks, overlaps):
function reorder_mask (line 120) | def reorder_mask(y_mask, permutation):
FILE: dmm/utils/masker.py
class MaskPostProcessor (line 12) | class MaskPostProcessor(nn.Module):
method __init__ (line 23) | def __init__(self, masker=None):
method forward_mask_prop (line 27) | def forward_mask_prop(self, mask_prob, boxes):
method forward (line 53) | def forward(self, x, boxes):
function expand_boxes (line 93) | def expand_boxes(boxes, scale):
function expand_masks (line 110) | def expand_masks(mask, padding):
function paste_mask_in_image (line 120) | def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
function binmask_to_box (line 157) | def binmask_to_box(mask):
class Masker (line 175) | class Masker(object):
method __init__ (line 181) | def __init__(self, threshold, padding=1):
method forward_single_image (line 185) | def forward_single_image(self, masks, boxes):
method __call__ (line 214) | def __call__(self, masks, boxes):
function make_roi_mask_post_processor (line 233) | def make_roi_mask_post_processor(padding=1, mask_threshold=0.5):
FILE: dmm/utils/match_helper.py
function compute_iou_binary_mask_2D (line 9) | def compute_iou_binary_mask_2D(annotation, segmentation):
function compute_matching_loss (line 30) | def compute_matching_loss(proposed_mask, targets, similarity_matrix, cfgs):
function get_cosine_score (line 51) | def get_cosine_score(query_feature, key_feature, cfgs=None):
FILE: dmm/utils/metric_logger.py
class SmoothedValue (line 8) | class SmoothedValue(object):
method __init__ (line 13) | def __init__(self, window_size=20):
method update (line 19) | def update(self, value):
method mean_of_last (line 24) | def mean_of_last(self, window_size):
method median (line 33) | def median(self):
method avg (line 38) | def avg(self):
method global_avg (line 43) | def global_avg(self):
class MetricLogger (line 47) | class MetricLogger(object):
method __init__ (line 48) | def __init__(self, delimiter="\t"):
method fields (line 52) | def fields(self):
method update (line 58) | def update(self, **kwargs):
method load_field (line 64) | def load_field(self, k):
method __getattr__ (line 68) | def __getattr__(self, attr):
method __str__ (line 76) | def __str__(self):
FILE: dmm/utils/objectives.py
class MaskedNLLLoss (line 6) | class MaskedNLLLoss(nn.Module):
method __init__ (line 7) | def __init__(self, balance_weight=None):
method forward (line 10) | def forward(self, y_true, y_pred, sw):
class MaskedBCELoss (line 15) | class MaskedBCELoss(nn.Module):
method __init__ (line 17) | def __init__(self,balance_weight=None):
method forward (line 20) | def forward(self, y_true, y_pred,sw):
class softIoULoss (line 25) | class softIoULoss(nn.Module):
method __init__ (line 27) | def __init__(self):
method forward (line 29) | def forward(self, y_true, y_pred, sw, need_sigmoid=1):
FILE: dmm/utils/pycococreatortools.py
function resize_binary_mask (line 16) | def resize_binary_mask(array, new_size):
function close_contour (line 21) | def close_contour(contour):
function binary_mask_to_rle (line 26) | def binary_mask_to_rle(binary_mask):
function binary_mask_to_polygon (line 36) | def binary_mask_to_polygon(binary_mask, tolerance=0):
function create_image_info (line 63) | def create_image_info(image_id, file_name, image_size,
function create_annotation_info (line 80) | def create_annotation_info(
function create_youtube_json (line 125) | def create_youtube_json(data_path='../../datasets/youtubeVOS/train/'):
FILE: dmm/utils/utils.py
function load_checkpoint (line 11) | def load_checkpoint(model_name,use_gpu=True):
function get_image_transforms (line 28) | def get_image_transforms():
function load_trainer_iter (line 40) | def load_trainer_iter(model_name, current_args):
function load_checkpoint_iter (line 57) | def load_checkpoint_iter(model_name, current_args):
function binmask_to_bbox_xyxy_pt (line 114) | def binmask_to_bbox_xyxy_pt(mask, pad=0, zero_pad=False):
function binmask_to_bbox_xyxy (line 146) | def binmask_to_bbox_xyxy(mask, pad=0, zero_pad=False):
function ohw_mask2boxlist (line 179) | def ohw_mask2boxlist(ohw_mask):
function load_DMM_config (line 212) | def load_DMM_config(config_train, local_rank, default='dmm/configs/defau...
function make_dir (line 235) | def make_dir(dir):
function get_optimizer (line 238) | def get_optimizer(optim_name, lr, parameters, weight_decay = 0, momentum...
function get_skip_dims (line 249) | def get_skip_dims(model_name):
FILE: eval.py
function strip_prefix_if_present (line 25) | def strip_prefix_if_present(state_dict, prefix, replacement):
class Evaluate (line 32) | class Evaluate():
method __init__ (line 33) | def __init__(self,args):
method run_eval (line 68) | def run_eval(self):
FILE: tools/davis_eval.py
function davis_toolbox_evaluation (line 15) | def davis_toolbox_evaluation(output_dir, eval_split, skip_F=1, title=None):
function calc_iou_individual (line 73) | def calc_iou_individual(pred_box, gt_box):
function calculateTpFpFn (line 110) | def calculateTpFpFn(gt_bbox, predict_bbox, IOU_threshold):
function calc_precision_recall (line 149) | def calc_precision_recall(true_pos, false_pos, false_neg):
function evaluate_bbox (line 163) | def evaluate_bbox(prediction_boxes_list, gt_boxes_list, IOU_threshold = ...
FILE: tools/reduce_pth_size_by_videos.py
function save_videos (line 65) | def save_videos(newf, target_dir):
FILE: tools/submission_check.py
function check_seq_results (line 16) | def check_seq_results(seq_name, result_dir, json_data, anno_dir, strict):
FILE: train.py
function save_checkpoint_iter (line 50) | def save_checkpoint_iter(model, args, it, enc_opt, dec_opt):
function average_gradients (line 62) | def average_gradients(model, rank=-1):
function init_dataloaders (line 69) | def init_dataloaders(args):
function build_model (line 107) | def build_model(args):
function trainIters (line 202) | def trainIters(args):
Copy disabled (too large)
Download .json
Condensed preview — 88 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (13,174K chars).
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{
"path": ".gitignore",
"chars": 36,
"preview": "*pyc\n*pkl\n*swp\n*nfs*\ndel/*\n*pt\n*swo\n"
},
{
"path": "INSTALL.md",
"chars": 1103,
"preview": "- Clone the repo: \n```shell\ngit clone https://github.com/ZENGXH/DMM_Net.git\n```\n\n## Install PyTorch with CUDA \n```\nconda"
},
{
"path": "MODEL_ZOO.md",
"chars": 568,
"preview": "## MODEL ZOO\n\n## Mask R-CNN model \n- trained with binary coco data: [model](https://hkustconnect-my.sharepoint.com/:f:/g"
},
{
"path": "README.md",
"chars": 7751,
"preview": "# DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation (ICCV 2019)\n\n[paper.pdf](https://www.cs.to"
},
{
"path": "args.py",
"chars": 10857,
"preview": "import argparse\n\ndef get_parser():\n\n parser = argparse.ArgumentParser(description='RIASS')\n # DMM related: \n # "
},
{
"path": "data/folder_data/meta_train3k-CACHE_maskben_folderdata_index_3000.json",
"chars": 5192657,
"preview": "{\"index2vidfid\": [{\"video\": \"003234408d\", \"frame\": \"00000.jpg\"}, {\"video\": \"003234408d\", \"frame\": \"00005.jpg\"}, {\"video\""
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"preview": "{\"index2vidfid\": [{\"video\": \"df741313c9\", \"frame\": \"00000.jpg\"}, {\"video\": \"df741313c9\", \"frame\": \"00005.jpg\"}, {\"video\""
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"path": "data/folder_data/meta_val474-CACHE_maskben_folderdata_index_474.json",
"chars": 789869,
"preview": "{\"index2vidfid\": [{\"video\": \"0062f687f1\", \"frame\": \"00000.jpg\"}, {\"video\": \"0062f687f1\", \"frame\": \"00005.jpg\"}, {\"video\""
},
{
"path": "data/ytb_vos/splits_813_3k_trainvaltest/meta_test271.json",
"chars": 134575,
"preview": "{\"videos\": {\"ecfff22fd6\": {\"objects\": {\"1\": {\"category\": \"crocodile\", \"frames\": [\"00000\", \"00005\", \"00010\", \"00015\", \"00"
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"path": "data/ytb_vos/splits_813_3k_trainvaltest/meta_train3k.json",
"chars": 1483592,
"preview": "{\"videos\": {\"003234408d\": {\"objects\": {\"1\": {\"category\": \"penguin\", \"frames\": [\"00000\", \"00005\", \"00010\", \"00015\", \"0002"
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"path": "data/ytb_vos/splits_813_3k_trainvaltest/meta_val200.json",
"chars": 92831,
"preview": "{\"videos\": {\"df741313c9\": {\"objects\": {\"1\": {\"category\": \"plant\", \"frames\": [\"00000\", \"00005\", \"00010\", \"00015\", \"00020\""
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"preview": "{\n \"003234408d\": {\n \"00000\": 0,\n \"00005\": 1,\n \"00010\": 2,\n \"00015\": 3,\n \"00020\": 4"
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"chars": 1,
"preview": "\n"
},
{
"path": "dmm/configs/default.yaml",
"chars": 273,
"preview": "sort_max_num: 50 \nmatching_score_thre: 0.0\nscore_weight: 0.3 \n# ----\n# algo\n# ----\nrelax: 1\nrelax_max_iter: 400 \nrelax_p"
},
{
"path": "dmm/configs/eval.yaml",
"chars": 157,
"preview": "matching:\n match_max_score: 1 \n algo: 'relax'\n cost: 'cosine' \nrelax_max_iter: 40 \nrelax_proj_iter: 5 \nrelax_l"
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"path": "dmm/configs/train.yaml",
"chars": 180,
"preview": "matching_score_thre: 0 \nmatching:\n match_max_score: 1 \n algo: 'relax'\n cost: 'cosine' \nrelax_max_iter: 10 \nrela"
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"path": "dmm/dataloader/__init__.py",
"chars": 623,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
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"chars": 6477,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/dataloader/base_youtube.py",
"chars": 8471,
"preview": "import functools\nimport os.path as osp\n\nimport numpy as np\n\nfrom PIL import Image\nfrom skimage.io import ImageCollection"
},
{
"path": "dmm/dataloader/collate.py",
"chars": 4283,
"preview": "r\"\"\"\"Contains definitions of the methods used by the _DataLoaderIter workers to\ncollate samples fetched from dataset int"
},
{
"path": "dmm/dataloader/dataset.py",
"chars": 15412,
"preview": "import os\nimport sys\nimport torch.utils.data as data\nimport torch\nfrom torchvision import transforms\nimport numpy as np\n"
},
{
"path": "dmm/dataloader/dataset_utils.py",
"chars": 1646,
"preview": "import numpy as np\nfrom scipy.ndimage.interpolation import zoom\nimport torch\nimport random\n\ndef get_dataset(args, split,"
},
{
"path": "dmm/dataloader/davis2017.py",
"chars": 9619,
"preview": "# ----------------------------------------------------------------------------\r\n# The 2017 DAVIS Challenge on Video Obje"
},
{
"path": "dmm/dataloader/db_info.yaml",
"chars": 12687,
"preview": "attributes: [AC, BC, CS, DB, DEF, EA, FM, HO, IO, LR, MB, OCC, OV, ROT, SC, SV]\nsets: [train, val, val-dev]\nyears: [201"
},
{
"path": "dmm/dataloader/default_collate.py",
"chars": 3571,
"preview": "r\"\"\"\"Contains definitions of the methods used by the _DataLoaderIter workers to\ncollate samples fetched from dataset int"
},
{
"path": "dmm/dataloader/evaluation.py",
"chars": 4722,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/dataloader/palette.txt",
"chars": 2775,
"preview": "0 0 0\n128 0 0\n0 128 0\n128 128 0\n0 0 128\n128 0 128\n0 128 128\n128 128 128\n64 0 0\n191 0 0\n64 128 0\n191 128 0\n64 0 128\n191 0"
},
{
"path": "dmm/dataloader/transforms/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "dmm/dataloader/transforms/transforms.py",
"chars": 27575,
"preview": "\"\"\"\nAffine transforms implemented on torch tensors, and\nrequiring only one interpolation\nFrom: https://github.com/nculle"
},
{
"path": "dmm/dataloader/transforms/utils.py",
"chars": 11802,
"preview": "\"\"\"\nUtility functions for th.Tensors\n\"\"\"\n\nimport pickle\nimport random\nimport numpy as np\n\nimport torch as th\n\n\ndef th_al"
},
{
"path": "dmm/dataloader/youtubeVOS.py",
"chars": 15835,
"preview": "from collections import namedtuple\n\nfrom torchvision.transforms import transforms as T\nimport numpy as np\nimport pickle\n"
},
{
"path": "dmm/measures/__init__.py",
"chars": 753,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/measures/f_boundary.py",
"chars": 4905,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/measures/jaccard.py",
"chars": 1503,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/measures/statistics.py",
"chars": 857,
"preview": "import numpy as np\nimport warnings\n\ndef mean(X):\n \"\"\"\n Compute average ignoring NaN values.\n \"\"\"\n\n return np.nanmean"
},
{
"path": "dmm/measures/t_stability.py",
"chars": 4882,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/misc/__init__.py",
"chars": 615,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/misc/config.py",
"chars": 3165,
"preview": "#!/usr/bin/env python\n\n\n# ----------------------------------------------------------------------------\n# The 2017 DAVIS "
},
{
"path": "dmm/misc/config_youtubeVOS.py",
"chars": 5044,
"preview": "#!/usr/bin/env python\n\"\"\" Configuration file.\"\"\"\nimport os\nimport json\nimport os.path as osp\nimport numpy as np\nfrom eas"
},
{
"path": "dmm/misc/io_aux.py",
"chars": 572,
"preview": "from PIL import Image\nimport numpy as np\n\nfrom dmm.misc.config import cfg\n\ndef imread_indexed(filename):\n \"\"\" Load imag"
},
{
"path": "dmm/misc/logger.py",
"chars": 653,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/misc/parallel.py",
"chars": 821,
"preview": "try:\n from joblib import Parallel, delayed\nexcept Exception as e:\n print(e) #TODO\n # if joblib does not exist j"
},
{
"path": "dmm/misc/timer.py",
"chars": 894,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/misc/visual.py",
"chars": 1140,
"preview": "# ----------------------------------------------------------------------------\n# The 2017 DAVIS Challenge on Video Objec"
},
{
"path": "dmm/modules/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "dmm/modules/base.py",
"chars": 9045,
"preview": "\"\"\" container for coco model, serve as a API\r\n\"\"\"\r\nimport torch\r\nimport torch.nn as nn\r\nfrom .clstm import ConvLSTMCell,"
},
{
"path": "dmm/modules/clstm.py",
"chars": 5191,
"preview": "import torch\r\nimport logging\r\nfrom torch import nn\r\nimport torch.nn.functional as f\r\nfrom torch.autograd import Variable"
},
{
"path": "dmm/modules/dmm_model.py",
"chars": 8197,
"preview": "\"\"\" DMM model: \n 0. extract feature \n 1. do matching\n\"\"\"\nimport logging\nimport torch\nimport torch.nn as nn\nfrom .f"
},
{
"path": "dmm/modules/evaluator.py",
"chars": 13310,
"preview": "\"\"\" Evaluation engine for rvos + matching model \nsupport only batchsize == n GPU and nGPU > 1\n\"\"\"\nfrom dmm.misc.config_y"
},
{
"path": "dmm/modules/feature_extractor.py",
"chars": 2873,
"preview": "import torch \nimport torch.nn as nn\nfrom maskrcnn_benchmark.modeling.poolers import Pooler\nfrom dmm.utils.checker import"
},
{
"path": "dmm/modules/match_model.py",
"chars": 7589,
"preview": "\"\"\"\nMatchModel: perform matching \n\"\"\"\nimport logging\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional "
},
{
"path": "dmm/modules/model_encoder.py",
"chars": 7472,
"preview": "\"\"\" container for coco model, serve as a API\nwhich can load prediction.pth \n\"\"\"\nimport torch\nimport torch.nn as nn\nimpor"
},
{
"path": "dmm/modules/submodules/relax_match.py",
"chars": 5057,
"preview": "import torch\nimport torch.nn.functional as F\nimport numpy as np\nimport time \nimport logging\nfrom scipy.optimize import l"
},
{
"path": "dmm/modules/trainer.py",
"chars": 17273,
"preview": "\"\"\" the largest container for the training \ns 1. trainer (its decoder and encoder)\n encoder and decoder \n\"\"\"\nimport torc"
},
{
"path": "dmm/modules/vision.py",
"chars": 3965,
"preview": "from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet\nfrom torchvision.models.vgg import VGG, make_layers"
},
{
"path": "dmm/utils/Colorer.py",
"chars": 3948,
"preview": "#!/usr/bin/env python\n# encoding: utf-8\nimport logging\n# now we patch Python code to add color support to logging.Stream"
},
{
"path": "dmm/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "dmm/utils/boxlist_ops.py",
"chars": 2979,
"preview": "import torch \nfrom maskrcnn_benchmark.structures.bounding_box import BoxList\nfrom maskrcnn_benchmark.layers import nms a"
},
{
"path": "dmm/utils/checker.py",
"chars": 3322,
"preview": "import torch\nimport logging\n\ndef CHECK2D(tensor):\n assert(len(tensor.shape) == 2), 'get {} {}'.format(tensor.shape, l"
},
{
"path": "dmm/utils/eval_helper.py",
"chars": 1431,
"preview": "import torch\nimport numpy as np\nimport os\nfrom .checker import *\nfrom PIL import Image\npalette = Image.open('dmm/utils/b"
},
{
"path": "dmm/utils/hungarian.py",
"chars": 4561,
"preview": "import torch\r\nfrom torch.autograd import Variable\r\nimport torch.nn as nn\r\nfrom munkres import Munkres\r\nimport numpy as n"
},
{
"path": "dmm/utils/masker.py",
"chars": 8012,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport numpy as np\nimport torch\nfrom torch impor"
},
{
"path": "dmm/utils/match_helper.py",
"chars": 2634,
"preview": "import logging\nimport os\nimport sys \nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\nfrom .checker "
},
{
"path": "dmm/utils/metric_logger.py",
"chars": 2310,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nfrom collections import defaultdict\nfrom collect"
},
{
"path": "dmm/utils/objectives.py",
"chars": 1273,
"preview": "import torch\r\nfrom .hungarian import softIoU, MaskedNLL, StableBalancedMaskedBCE\r\nimport torch.nn as nn\r\n\r\n\r\nclass Maske"
},
{
"path": "dmm/utils/pycococreatortools.py",
"chars": 7258,
"preview": "#!/usr/bin/env python3\n\nimport os\nimport re\nimport datetime\nimport numpy as np\nimport json\nfrom itertools import groupby"
},
{
"path": "dmm/utils/utils.py",
"chars": 11319,
"preview": "from torch.autograd import Variable\r\nimport torch\r\nimport os\r\nimport numpy as np\r\nimport pickle\r\nimport yaml\r\nimport log"
},
{
"path": "eval.py",
"chars": 5864,
"preview": "import faulthandler\nfaulthandler.enable()\nfrom args import get_parser\nfrom PIL import Image\nimport torch\nimport torch.ut"
},
{
"path": "requirements.txt",
"chars": 1261,
"preview": "# This file may be used to create an environment using:\n# $ conda create --name <env> --file <this file>\n# platform: lin"
},
{
"path": "scripts/eval/eval.sh",
"chars": 1229,
"preview": "path_to_prediction_pth='experiments/proposals/ytb_train/inference/youtubevos_val200_meta/predictions.pth'\neval_flag=ytb\n"
},
{
"path": "scripts/eval/eval_r50.sh",
"chars": 1297,
"preview": "path_to_prediction_pth='experiments/proposals/ytb_train/inference/youtubevos_val200_meta/predictions.pth'\neval_flag=ytb\n"
},
{
"path": "scripts/eval/eval_testdev.sh",
"chars": 1290,
"preview": "#!/bin/bash \n\neval_flag=ytb_testdev_online\nMODEL='experiments/dmmnet/online_ytb/epo90_iter00088/' # epo117_iter00088' \np"
},
{
"path": "scripts/extract/README.md",
"chars": 682,
"preview": "- scripts used to extract prediction of testdev split, online-trained proposal network:\n```\n python \\\n -m torch.di"
},
{
"path": "scripts/extract/extract_proposal_coco81_train.sh",
"chars": 1684,
"preview": "xport NGPUS=1\nsrun -p max12hours --gres=gpu:$NGPUS --mem=26G -c 8 -x ~/.exclude \\\npython \\\n -m torch.distributed.laun"
},
{
"path": "scripts/extract/extract_proposal_ytbbin_trainval_val.sh",
"chars": 1411,
"preview": "export NGPUS=4\n\nsrun -p p100 --gres=gpu:$NGPUS --mem=26G -c 8 -x ~/.exclude \\\npython \\\n -m torch.distributed.launch -"
},
{
"path": "scripts/extract/process.sh",
"chars": 313,
"preview": "srun -p p100 --mem=18G python tools/reduce_pth_size_by_videos.py experiments/proposals/coco81/inference/youtubevos_trai"
},
{
"path": "scripts/metric/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "scripts/metric/full_eval.sh",
"chars": 131,
"preview": "srun -p p100 -x ~/.exclude --mem=7G \\\n python tools/davis_eval.py -dataset youtube -davis_eval_folder $1 -eval_split "
},
{
"path": "scripts/submit.sh",
"chars": 547,
"preview": "model_path=$1\n\nDATE=`date \"+%m%d-%H%M%S\"`\nmodel=${model_path}\nkey=`echo $model| sed 's/\\.//g'`\nkey=`echo $key| sed 's/\\/"
},
{
"path": "scripts/train/train_101.sh",
"chars": 1828,
"preview": "pretrain_path=experiments/dmmnet/pretrained_rvos/\n# if [ ! -d $dir ]\nif [ ! -e $pretrain_path ]; then\n echo \"start do"
},
{
"path": "scripts/train/train_online.sh",
"chars": 1515,
"preview": "BS=4\nNGPU=4\ntrain_h=255 \ntrain_w=448\nlen_clip=3\nNworkers=5\n\nmodel_root='./experiments/models/' # path to saved the model"
},
{
"path": "scripts/train/train_r50.sh",
"chars": 1366,
"preview": "BS=4\nNGPU=4\ntrain_h=255\ntrain_w=448 \nseqlen=5 # max n obj\nlen_clip=3 # N frames \nNworkers=4\n\nmodel_root='../../experim"
},
{
"path": "tools/davis_eval.py",
"chars": 6043,
"preview": "import numpy as np\nimport logging\n\nimport sys\nsys.path.append('.')\n#sys.path.append('dmm')\n\nimport os.path as osp\nimport"
},
{
"path": "tools/make_train_online_dataset.py",
"chars": 2694,
"preview": "import json \nimport os, sys \nfrom PIL import Image \n\n#datapath = '../../datasets/youtubeVOS/train/'\n#targetpath = '../.."
},
{
"path": "tools/reduce_pth_size_by_videos.py",
"chars": 4115,
"preview": "r\"\"\" load the extracted prediction and convert to dict format, \nwith key as vid and imageid \nusage python ./tools/reduce"
},
{
"path": "tools/submission_check.py",
"chars": 3349,
"preview": "\"\"\"\ncheck if the number of file for each video is correct before submission \n\"\"\"\nimport argparse\nimport sys\nfrom PIL imp"
},
{
"path": "train.py",
"chars": 20919,
"preview": "import matplotlib\nmatplotlib.use('Agg')\nimport faulthandler\nfaulthandler.enable()\nimport matplotlib.pyplot as plt\nfrom a"
}
]
About this extraction
This page contains the full source code of the ZENGXH/DMM_Net GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 88 files (10.9 MB), approximately 2.9M tokens, and a symbol index with 367 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.