add EDVR128 (#308)

configs/edvr_l_w_tsa.yaml

+total_iters: 600000
+output_dir: output_dir
+checkpoints_dir: checkpoints
+# tensor range for function tensor2img
+min_max:
+  (0., 1.)
+
+model:
+  name: EDVRModel
+  tsa_iter: 50000
+  generator:
+    name: EDVRNet
+    in_nf: 3
+    out_nf: 3
+    scale_factor: 4
+    nf: 128
+    nframes: 5
+    groups: 8
+    front_RBs: 5
+    back_RBs: 40
+    center: 2
+    predeblur: False
+    HR_in: False
+    w_TSA: True
+  pixel_criterion:
+    name: CharbonnierLoss
+
+dataset:
+  train:
+    name: REDSDataset
+    mode: train
+    gt_folder: data/REDS/train_sharp/X4
+    lq_folder: data/REDS/train_sharp_bicubic/X4
+    img_format: png
+    crop_size: 256
+    interval_list: [1]
+    random_reverse: False
+    number_frames: 5
+    use_flip: True
+    use_rot: True
+    buf_size: 1024
+    scale: 4
+    fix_random_seed: 10
+    num_workers: 3
+    batch_size: 4 # 8GUPs
+
+
+  test:
+    name: REDSDataset
+    mode: test
+    gt_folder: data/REDS/REDS4_test_sharp/X4
+    lq_folder: data/REDS/REDS4_test_sharp_bicubic/X4
+    img_format: png
+    interval_list: [1]
+    random_reverse: False
+    number_frames: 5
+    batch_size: 1
+    use_flip: False
+    use_rot: False
+    buf_size: 1024
+    scale: 4
+    fix_random_seed: 10
+
+lr_scheduler:
+  name: CosineAnnealingRestartLR
+  learning_rate: !!float 4e-4
+  periods: [50000, 100000, 150000, 150000, 150000]
+  restart_weights: [1, 0.5, 0.5, 0.5, 0.5]
+  eta_min: !!float 1e-7
+
+optimizer:
+  name: Adam
+  # add parameters of net_name to optim
+  # name should in self.nets
+  net_names:
+    - generator
+  beta1: 0.9
+  beta2: 0.99
+
+validate:
+  interval: 5000
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      name: PSNR
+      crop_border: 0
+      test_y_channel: False
+    ssim:
+      name: SSIM
+      crop_border: 0
+      test_y_channel: False
+
+log_config:
+  interval: 10
+  visiual_interval: 5000
+
+snapshot_config:
+  interval: 5000

configs/edvr_l_wo_tsa.yaml

+total_iters: 600000
+output_dir: output_dir
+checkpoints_dir: checkpoints
+# tensor range for function tensor2img
+min_max:
+  (0., 1.)
+
+model:
+  name: EDVRModel
+  tsa_iter: 0
+  generator:
+    name: EDVRNet
+    in_nf: 3
+    out_nf: 3
+    scale_factor: 4
+    nf: 128
+    nframes: 5
+    groups: 8
+    front_RBs: 5
+    back_RBs: 40
+    center: 2
+    predeblur: False
+    HR_in: False
+    w_TSA: False
+  pixel_criterion:
+    name: CharbonnierLoss
+
+dataset:
+  train:
+    name: REDSDataset
+    mode: train
+    gt_folder: data/REDS/train_sharp/X4
+    lq_folder: data/REDS/train_sharp_bicubic/X4
+    img_format: png
+    crop_size: 256
+    interval_list: [1]
+    random_reverse: False
+    number_frames: 5
+    use_flip: True
+    use_rot: True
+    buf_size: 1024
+    scale: 4
+    fix_random_seed: 10
+    num_workers: 3
+    batch_size: 4  # 8GUPs
+
+
+  test:
+    name: REDSDataset
+    mode: test
+    gt_folder: data/REDS/REDS4_test_sharp/X4
+    lq_folder: data/REDS/REDS4_test_sharp_bicubic/X4
+    img_format: png
+    interval_list: [1]
+    random_reverse: False
+    number_frames: 5
+    batch_size: 1
+    use_flip: False
+    use_rot: False
+    buf_size: 1024
+    scale: 4
+    fix_random_seed: 10
+
+lr_scheduler:
+  name: CosineAnnealingRestartLR
+  learning_rate: !!float 4e-4
+  periods: [150000, 150000, 150000, 150000]
+  restart_weights: [1, 0.5, 0.5, 0.5]
+  eta_min: !!float 1e-7
+
+optimizer:
+  name: Adam
+  # add parameters of net_name to optim
+  # name should in self.nets
+  net_names:
+    - generator
+  beta1: 0.9
+  beta2: 0.99
+
+validate:
+  interval: 5000
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      name: PSNR
+      crop_border: 0
+      test_y_channel: False
+    ssim:
+      name: SSIM
+      crop_border: 0
+      test_y_channel: False
+
+log_config:
+  interval: 10
+  visiual_interval: 500
+
+snapshot_config:
+  interval: 5000

configs/edvr.yaml → configs/edvr_m_w_tsa.yaml

@@ -22,7 +22,6 @@ model:
     predeblur: False
     HR_in: False
     w_TSA: True
-    TSA_only: False
   pixel_criterion:
     name: CharbonnierLoss
 
@@ -43,7 +42,7 @@ dataset:
     scale: 4
     fix_random_seed: 10
     num_workers: 3
-    batch_size: 4
+    batch_size: 4 # 8GUPs
 
 
   test:

configs/edvr_wo_tsa.yaml → configs/edvr_m_wo_tsa.yaml

@@ -22,7 +22,6 @@ model:
     predeblur: False
     HR_in: False
     w_TSA: False
-    TSA_only: False
   pixel_criterion:
     name: CharbonnierLoss
 
@@ -43,7 +42,7 @@ dataset:
     scale: 4
     fix_random_seed: 10
     num_workers: 3
-    batch_size: 4
+    batch_size: 4 # 8GUPs
 
 
   test:

docs/en_US/tutorials/video_super_resolution.md

@@ -33,18 +33,37 @@
 
 ### 1.2.2 Train/Test
 
-  The command to train and test edvr model with the processed EDVR is as follows:
+  According to the number of channels, EDVR are divided into EDVR_L(128 channels) and EDVR_M (64 channels). Then, taking EDVR_M as an example, the model training and testing are introduced.
+
+  The train of EDVR is generally divided into two stages. First, train EDVR without TSA module.
+
+  The command to train and test edvr without TSA module is as follows:
+
+  Train a model:
+  ```
+     python -u tools/main.py --config-file configs/edvr_m_wo_tsa.yaml
+  ```
+
+  Test the model:
+  ```
+     python tools/main.py --config-file configs/edvr_m_wo_tsa.yaml --evaluate-only --load ${PATH_OF_WEIGHT_WITHOUT_TSA}
+  ```
+
+  Then the weight of EDVR without TSA module is used as the initialization of edvr model to train the complete edvr model.
+
+  The command to train and test edvr is as follows:
 
   Train a model:
   ```
-     python -u tools/main.py --config-file configs/edvr.yaml
+     python -u tools/main.py --config-file configs/edvr_m_w_tsa.yaml --load ${PATH_OF_WEIGHT_WITHOUT_TSA}
   ```
 
   Test the model:
   ```
-     python tools/main.py --config-file configs/edvr.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
+     python tools/main.py --config-file configs/edvr_m_w_tsa.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
   ```
 
+
 ## 1.3 Results
 The experimental results are evaluated on RGB channel.
 
@@ -55,6 +74,7 @@ The metrics are PSNR / SSIM.
 | EDVR_M_wo_tsa_SRx4  | 30.4429 / 0.8684 |
 | EDVR_M_w_tsa_SRx4  | 30.5169 / 0.8699 |
 | EDVR_L_wo_tsa_SRx4  | 30.8649 / 0.8761 |
+| EDVR_L_w_tsa_SRx4  | 30.9336 / 0.8773 |
 
 
 ## 1.4 Model Download
@@ -63,6 +83,7 @@ The metrics are PSNR / SSIM.
 | EDVR_M_wo_tsa_SRx4  | REDS | [EDVR_M_wo_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_M_wo_tsa_SRx4.pdparams)
 | EDVR_M_w_tsa_SRx4  | REDS | [EDVR_M_w_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_M_w_tsa_SRx4.pdparams)
 | EDVR_L_wo_tsa_SRx4  | REDS | [EDVR_L_wo_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_L_wo_tsa_SRx4.pdparams)
+| EDVR_L_w_tsa_SRx4  | REDS | [EDVR_L_w_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_L_w_tsa_SRx4.pdparams)
 
 
 
@@ -81,4 +102,3 @@ The metrics are PSNR / SSIM.
     year = {2019}
     }
   ```
-

docs/zh_CN/tutorials/video_super_resolution.md

@@ -33,18 +33,33 @@
 
 ### 1.2.2 训练/测试
 
-  使用处理后的REDS数据集训练与测试EDVR模型命令如下:
+  EDVR模型根据模型中间通道数分为EDVR_L(128通道)和EDVR_M(64通道)两种模型。下面以EDVR_M模型为例介绍模型训练与测试。
+
+  EDVR模型训练一般分两个阶段训练，先不带TSA模块训练，训练与测试命令如下:
+
+  训练模型:
+  ```
+     python -u tools/main.py --config-file configs/edvr_m_wo_tsa.yaml
+  ```
+
+  测试模型:
+  ```
+     python tools/main.py --config-file configs/edvr_m_wo_tsa.yaml --evaluate-only --load ${PATH_OF_WEIGHT_WITHOUT_TSA}
+  ```
+
+  然后用保存的不带TSA模块的EDVR权重作为EDVR模型的初始化，训练完整的EDVR模型，训练与测试命令如下:
 
   训练模型:
   ```
-     python -u tools/main.py --config-file configs/edvr.yaml
+     python -u tools/main.py --config-file configs/edvr_m_w_tsa.yaml --load ${PATH_OF_WEIGHT_WITHOUT_TSA}
   ```
 
   测试模型:
   ```
-     python tools/main.py --config-file configs/edvr.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
+     python tools/main.py --config-file configs/edvr_m_w_tsa.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
   ```
 
+
 ## 1.3 实验结果展示
 实验数值结果是在 RGB 通道上进行评估。
 
@@ -55,6 +70,7 @@
 | EDVR_M_wo_tsa_SRx4  | 30.4429 / 0.8684 |
 | EDVR_M_w_tsa_SRx4  | 30.5169 / 0.8699 |
 | EDVR_L_wo_tsa_SRx4  | 30.8649 / 0.8761 |
+| EDVR_L_w_tsa_SRx4  | 30.9336 / 0.8773 |
 
 
 ## 1.4 模型下载
@@ -63,6 +79,7 @@
 | EDVR_M_wo_tsa_SRx4  | REDS | [EDVR_M_wo_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_M_wo_tsa_SRx4.pdparams)
 | EDVR_M_w_tsa_SRx4  | REDS | [EDVR_M_w_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_M_w_tsa_SRx4.pdparams)
 | EDVR_L_wo_tsa_SRx4  | REDS | [EDVR_L_wo_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_L_wo_tsa_SRx4.pdparams)
+| EDVR_L_w_tsa_SRx4  | REDS | [EDVR_L_w_tsa_SRx4](https://paddlegan.bj.bcebos.com/models/EDVR_L_w_tsa_SRx4.pdparams)
 
 
 
@@ -81,4 +98,3 @@
     year = {2019}
     }
   ```
-

ppgan/models/generators/edvr.py

@@ -15,10 +15,8 @@
 import paddle
 
 import numpy as np
-import scipy.io as scio
 
 import paddle.nn as nn
-from paddle.nn import initializer
 from ...modules.init import kaiming_normal_, constant_
 
 from ...modules.dcn import DeformableConv_dygraph
@@ -63,11 +61,8 @@ class ResidualBlockNoBN(nn.Layer):
          |________________|
 
     Args:
-        num_feat (int): Channel number of intermediate features.
+        nf (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, nf=64):
         super(ResidualBlockNoBN, self).__init__()
@@ -612,8 +607,7 @@ class EDVRNet(nn.Layer):
                  center=None,
                  predeblur=False,
                  HR_in=False,
-                 w_TSA=True,
-                 TSA_only=False):
+                 w_TSA=True):
         super(EDVRNet, self).__init__()
         self.in_nf = in_nf
         self.out_nf = out_nf
@@ -637,23 +631,6 @@ class EDVRNet(nn.Layer):
                                    out_channels=self.nf,
                                    kernel_size=1,
                                    stride=1)
-        else:
-            if self.HR_in:
-                self.conv_first_1 = nn.Conv2D(in_channels=self.in_nf,
-                                              out_channels=self.nf,
-                                              kernel_size=3,
-                                              stride=1,
-                                              padding=1)
-                self.conv_first_2 = nn.Conv2D(in_channels=self.nf,
-                                              out_channels=self.nf,
-                                              kernel_size=3,
-                                              stride=2,
-                                              padding=1)
-                self.conv_first_3 = nn.Conv2D(in_channels=self.nf,
-                                              out_channels=self.nf,
-                                              kernel_size=3,
-                                              stride=2,
-                                              padding=1)
         else:
             self.conv_first = nn.Conv2D(in_channels=self.in_nf,
                                         out_channels=self.nf,
@@ -711,16 +688,16 @@ class EDVRNet(nn.Layer):
                                  padding=1)
         self.pixel_shuffle = nn.PixelShuffle(2)
         self.upconv2 = nn.Conv2D(in_channels=self.nf,
-                                 out_channels=4 * self.nf,
+                                 out_channels=4 * 64,
                                  kernel_size=3,
                                  stride=1,
                                  padding=1)
-        self.HRconv = nn.Conv2D(in_channels=self.nf,
-                                out_channels=self.nf,
+        self.HRconv = nn.Conv2D(in_channels=64,
+                                out_channels=64,
                                 kernel_size=3,
                                 stride=1,
                                 padding=1)
-        self.conv_last = nn.Conv2D(in_channels=self.nf,
+        self.conv_last = nn.Conv2D(in_channels=64,
                                    out_channels=self.out_nf,
                                    kernel_size=3,
                                    stride=1,
@@ -746,16 +723,6 @@ class EDVRNet(nn.Layer):
             L1_fea = self.cov_1(L1_fea)
             if self.HR_in:
                 H, W = H // self.scale_factor, W // self.scale_factor
-        else:
-            if self.HR_in:
-                L1_fea = self.conv_first_1(L1_fea)
-                L1_fea = self.Leaky_relu(L1_fea)
-                L1_fea = self.conv_first_2(L1_fea)
-                L1_fea = self.Leaky_relu(L1_fea)
-                L1_fea = self.conv_first_3(L1_fea)
-                L1_fea = self.Leaky_relu(L1_fea)
-                H = H // self.scale_factor
-                W = W // self.scale_factor
         else:
             L1_fea = self.conv_first(L1_fea)
             L1_fea = self.Leaky_relu(L1_fea)