[Feature] Add TDAN config and models (#347)

* Add TDAN config and models * Add training and test descriptions * Update readme descrptions * Update README
open-mmlab · Jun 1, 2021 · 085a277 · 085a277
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diff --git a/README.md b/README.md
@@ -75,6 +75,7 @@ Supported algorithms:
 - [x] [RDN](configs/restorers/rdn/README.md) (CVPR'2018)
 - [x] [SRCNN](configs/restorers/srcnn/README.md) (TPAMI'2015)
 - [x] [SRResNet&SRGAN](configs/restorers/srresnet_srgan/README.md) (CVPR'2016)
+- [x] [TDAN](configs/restorers/tdan/README.md) (CVPR'2020)
 - [x] [TOF](configs/restorers/tof/README.md) (IJCV'2019)
 - [x] [TTSR](configs/restorers/ttsr/README.md) (CVPR'2020)
 

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -75,6 +75,7 @@ MMEditing 是基于 PyTorch 的图像&视频编辑开源工具箱。是 [OpenMML
 - [x] [RDN](configs/restorers/rdn/README.md) (CVPR'2018)
 - [x] [SRCNN](configs/restorers/srcnn/README.md) (TPAMI'2015)
 - [x] [SRResNet&SRGAN](configs/restorers/srresnet_srgan/README.md) (CVPR'2016)
+- [x] [TDAN](configs/restorers/tdan/README.md) (CVPR'2020)
 - [x] [TOF](configs/restorers/tof/README.md) (IJCV'2019)
 - [x] [TTSR](configs/restorers/ttsr/README.md) (CVPR'2020)
 

diff --git a/configs/restorers/tdan/README.md b/configs/restorers/tdan/README.md
@@ -0,0 +1,67 @@
+# TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```bibtex
+@InProceedings{tian2020tdan,
+  title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},
+  author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
+  booktitle = {Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  year = {2020}
+}
+```
+
+## Results and Models
+
+Evaluated on Y-channel. 8 pixels in each border are cropped before evaluation.
+
+The metrics are `PSNR / SSIM`.
+
+|                                Method                               |   Vid4 (BIx4)   | SPMCS-30 (BIx4) |   Vid4 (BDx4)   | SPMCS-30 (BDx4) |                                                                                                         Download                                                                                                        |
+|:-------------------------------------------------------------------:|:---------------:|:---------------:|:---------------:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| [tdan_vimeo90k_bix4](/configs/restorers/tdan/tdan_vimeo90k_bix4.py) | **26.49/0.792** | **30.42/0.856** |   25.93/0.772   |   29.69/0.842   | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) |
+| [tdan_vimeo90k_bdx4](/configs/restorers/tdan/tdan_vimeo90k_bdx4.py) |   25.80/0.784   |   29.56/0.851   | **26.87/0.815** | **30.77/0.868** | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) |
+
+
+## Train
+
+You can use the following command to train a model.
+
+```shell
+./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
+```
+
+TDAN is trained with two stages.
+
+**Stage 1**: Train with a larger learning rate (1e-4)
+
+
+```shell
+./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_lr1e-4_400k.py 8
+```
+
+**Stage 2**: Fine-tune with a smaller learning rate (5e-5)
+
+```shell
+./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py 8
+```
+
+For more details, you can refer to **Train a model** part in [getting_started](/docs/getting_started.md#train-a-model).
+
+## Test
+
+You can use the following command to test a model.
+
+```shell
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]
+```
+
+Example: Test TDAN on SPMCS-30 using Bicubic downsampling.
+
+```shell
+python tools/test.py configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py  checkpoints/SOME_CHECKPOINT.pth --save_path outputs/
+```
+
+For more details, you can refer to **Inference with pretrained models** part in [getting_started](/docs/getting_started.md#inference-with-pretrained-models).
diff --git a/configs/restorers/tdan/tdan_vimeo90k_bdx4_ft_lr5e-5_800k.py b/configs/restorers/tdan/tdan_vimeo90k_bdx4_ft_lr5e-5_800k.py
@@ -0,0 +1,127 @@
+exp_name = 'tdan_vimeo90k_bdx4_ft_lr5e-5_800k'
+
+# model settings
+model = dict(
+    type='TDAN',
+    generator=dict(type='TDANNet'),
+    pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),
+    lq_pixel_loss=dict(type='MSELoss', loss_weight=0.01, reduction='mean'))
+# model training and testing settings
+train_cfg = None
+test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=8, convert_to='y')
+
+# dataset settings
+train_dataset_type = 'SRVimeo90KDataset'
+val_dataset_type = 'SRVid4Dataset'
+
+train_pipeline = [
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='lq',
+        channel_order='rgb'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='gt',
+        channel_order='rgb'),
+    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),
+    dict(
+        type='Normalize',
+        keys=['lq', 'gt'],
+        mean=[0.5, 0.5, 0.5],
+        std=[1, 1, 1]),
+    dict(type='PairedRandomCrop', gt_patch_size=192),
+    dict(
+        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,
+        direction='horizontal'),
+    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),
+    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),
+    dict(type='FramesToTensor', keys=['lq', 'gt']),
+    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])
+]
+
+val_pipeline = [
+    dict(type='GenerateFrameIndiceswithPadding', padding='reflection'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='lq',
+        channel_order='rgb'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='gt',
+        channel_order='rgb'),
+    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),
+    dict(
+        type='Normalize',
+        keys=['lq', 'gt'],
+        mean=[0.5, 0.5, 0.5],
+        std=[1, 1, 1]),
+    dict(type='FramesToTensor', keys=['lq', 'gt']),
+    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])
+]
+
+data = dict(
+    workers_per_gpu=8,
+    train_dataloader=dict(samples_per_gpu=16, drop_last=True),  # 8 gpus
+    val_dataloader=dict(samples_per_gpu=1),
+    test_dataloader=dict(samples_per_gpu=1),
+    train=dict(
+        type='RepeatDataset',
+        times=1000,
+        dataset=dict(
+            type=train_dataset_type,
+            lq_folder='data/Vimeo-90K/BDx4',
+            gt_folder='data/Vimeo-90K/GT',
+            ann_file='data/Vimeo-90K/meta_info_Vimeo90K_train_GT.txt',
+            num_input_frames=5,
+            pipeline=train_pipeline,
+            scale=4,
+            test_mode=False)),
+    val=dict(
+        type=val_dataset_type,
+        lq_folder='data/Vid4/BDx4',
+        gt_folder='data/Vid4/GT',
+        pipeline=val_pipeline,
+        ann_file='data/Vid4/meta_info_Vid4_GT.txt',
+        scale=4,
+        num_input_frames=5,
+        test_mode=True),
+    test=dict(
+        type=val_dataset_type,
+        lq_folder='data/SPMCS/BDx4',
+        gt_folder='data/SPMCS/GT',
+        pipeline=val_pipeline,
+        ann_file='data/SPMCS/meta_info_SPMCS_GT.txt',
+        scale=4,
+        num_input_frames=5,
+        test_mode=True),
+)
+
+# optimizer
+optimizers = dict(generator=dict(type='Adam', lr=5e-5))
+
+# learning policy
+total_iters = 800000
+lr_config = dict(policy='Step', by_epoch=False, step=[800000], gamma=0.5)
+
+checkpoint_config = dict(interval=50000, save_optimizer=True, by_epoch=False)
+# remove gpu_collect=True in non distributed training
+evaluation = dict(interval=50000, save_image=False, gpu_collect=True)
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        # dict(type='TensorboardLoggerHook'),
+    ])
+visual_config = None
+
+# runtime settings
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = f'./work_dirs/{exp_name}'
+load_from = './experiments/tdan_vimeo90k_bdx4_lr1e-4_400k/iter_400000.pth'
+resume_from = None
+workflow = [('train', 1)]
diff --git a/configs/restorers/tdan/tdan_vimeo90k_bdx4_lr1e-4_400k.py b/configs/restorers/tdan/tdan_vimeo90k_bdx4_lr1e-4_400k.py
@@ -0,0 +1,127 @@
+exp_name = 'tdan_vimeo90k_bdx4_lr1e-4_400k'
+
+# model settings
+model = dict(
+    type='TDAN',
+    generator=dict(type='TDANNet'),
+    pixel_loss=dict(type='MSELoss', loss_weight=1.0, reduction='mean'),
+    lq_pixel_loss=dict(type='MSELoss', loss_weight=0.01, reduction='mean'))
+# model training and testing settings
+train_cfg = None
+test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=8, convert_to='y')
+
+# dataset settings
+train_dataset_type = 'SRVimeo90KDataset'
+val_dataset_type = 'SRVid4Dataset'
+
+train_pipeline = [
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='lq',
+        channel_order='rgb'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='gt',
+        channel_order='rgb'),
+    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),
+    dict(
+        type='Normalize',
+        keys=['lq', 'gt'],
+        mean=[0.5, 0.5, 0.5],
+        std=[1, 1, 1]),
+    dict(type='PairedRandomCrop', gt_patch_size=192),
+    dict(
+        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,
+        direction='horizontal'),
+    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),
+    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),
+    dict(type='FramesToTensor', keys=['lq', 'gt']),
+    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])
+]
+
+val_pipeline = [
+    dict(type='GenerateFrameIndiceswithPadding', padding='reflection'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='lq',
+        channel_order='rgb'),
+    dict(
+        type='LoadImageFromFileList',
+        io_backend='disk',
+        key='gt',
+        channel_order='rgb'),
+    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),
+    dict(
+        type='Normalize',
+        keys=['lq', 'gt'],
+        mean=[0.5, 0.5, 0.5],
+        std=[1, 1, 1]),
+    dict(type='FramesToTensor', keys=['lq', 'gt']),
+    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])
+]
+
+data = dict(
+    workers_per_gpu=8,
+    train_dataloader=dict(samples_per_gpu=16, drop_last=True),  # 8 gpus
+    val_dataloader=dict(samples_per_gpu=1),
+    test_dataloader=dict(samples_per_gpu=1),
+    train=dict(
+        type='RepeatDataset',
+        times=1000,
+        dataset=dict(
+            type=train_dataset_type,
+            lq_folder='data/Vimeo-90K/BDx4',
+            gt_folder='data/Vimeo-90K/GT',
+            ann_file='data/Vimeo-90K/meta_info_Vimeo90K_train_GT.txt',
+            num_input_frames=5,
+            pipeline=train_pipeline,
+            scale=4,
+            test_mode=False)),
+    val=dict(
+        type=val_dataset_type,
+        lq_folder='data/Vid4/BDx4',
+        gt_folder='data/Vid4/GT',
+        pipeline=val_pipeline,
+        ann_file='data/Vid4/meta_info_Vid4_GT.txt',
+        scale=4,
+        num_input_frames=5,
+        test_mode=True),
+    test=dict(
+        type=val_dataset_type,
+        lq_folder='data/SPMCS/BDx4',
+        gt_folder='data/SPMCS/GT',
+        pipeline=val_pipeline,
+        ann_file='data/SPMCS/meta_info_SPMCS_GT.txt',
+        scale=4,
+        num_input_frames=5,
+        test_mode=True),
+)
+
+# optimizer
+optimizers = dict(generator=dict(type='Adam', lr=1e-4, weight_decay=1e-6))
+
+# learning policy
+total_iters = 800000
+lr_config = dict(policy='Step', by_epoch=False, step=[800000], gamma=0.5)
+
+checkpoint_config = dict(interval=50000, save_optimizer=True, by_epoch=False)
+# remove gpu_collect=True in non distributed training
+evaluation = dict(interval=50000, save_image=False, gpu_collect=True)
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        # dict(type='TensorboardLoggerHook'),
+    ])
+visual_config = None
+
+# runtime settings
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = f'./work_dirs/{exp_name}'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]