Add BasicVSR++ (PaddlePaddle#383)

* add BasicVSR++

Author: wangna11BD

configs/basicvsr++_reds.yaml

@@ -0,0 +1,93 @@
+total_iters: 600000
+output_dir: output_dir
+find_unused_parameters: True
+checkpoints_dir: checkpoints
+use_dataset: True
+# tensor range for function tensor2img
+min_max:
+  (0., 1.)
+
+model:
+  name: BasicVSRModel
+  fix_iter: 5000
+  lr_mult: 0.25
+  generator:
+    name: BasicVSRPlusPlus
+    mid_channels: 64
+    num_blocks: 7
+    is_low_res_input: True
+  pixel_criterion:
+    name: CharbonnierLoss
+    reduction: mean
+
+dataset:
+  train:
+    name: RepeatDataset
+    times: 1000
+    num_workers: 4
+    batch_size: 2  #4 gpus
+    dataset:
+      name: SRREDSMultipleGTDataset
+      mode: train
+      lq_folder: data/REDS/train_sharp_bicubic/X4
+      gt_folder: data/REDS/train_sharp/X4
+      crop_size: 256
+      interval_list: [1]
+      random_reverse: False
+      number_frames: 30
+      use_flip: True
+      use_rot: True
+      scale: 4
+      val_partition: REDS4
+
+  test:
+    name: SRREDSMultipleGTDataset
+    mode: test
+    lq_folder: data/REDS/REDS4_test_sharp_bicubic/X4
+    gt_folder: data/REDS/REDS4_test_sharp/X4
+    interval_list: [1]
+    random_reverse: False
+    number_frames: 100
+    use_flip: False
+    use_rot: False
+    scale: 4
+    val_partition: REDS4
+    num_workers: 0
+    batch_size: 1
+
+lr_scheduler:
+  name: CosineAnnealingRestartLR
+  learning_rate: !!float 1e-4
+  periods: [600000]
+  restart_weights: [1]
+  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/basicvsr_reds.yaml

@@ -10,6 +10,7 @@ min_max:
 model:
   name: BasicVSRModel
   fix_iter: 5000
+  lr_mult: 0.125
   generator:
     name: BasicVSRNet
     mid_channels: 64

configs/iconvsr_reds.yaml

@@ -10,6 +10,7 @@ min_max:
 model:
   name: BasicVSRModel
   fix_iter: 5000
+  lr_mult: 0.125
   generator:
     name: IconVSR
     mid_channels: 64

docs/en_US/tutorials/video_super_resolution.md

@@ -3,7 +3,7 @@
 
 ## 1.1 Principle
 
-  Video super-resolution originates from image super-resolution, which aims to recover high-resolution (HR) images from one or more low resolution (LR) images. The difference between them is that the video is composed of multiple frames, so the video super-resolution usually uses the information between frames to repair. Here we provide the video super-resolution model [EDVR](https://arxiv.org/pdf/1905.02716.pdf).
+  Video super-resolution originates from image super-resolution, which aims to recover high-resolution (HR) images from one or more low resolution (LR) images. The difference between them is that the video is composed of multiple frames, so the video super-resolution usually uses the information between frames to repair. Here we provide the video super-resolution model [EDVR](https://arxiv.org/pdf/1905.02716.pdf).[BasicVSR](https://arxiv.org/pdf/2012.02181.pdf),[IconVSR](https://arxiv.org/pdf/2012.02181.pdf),[BasicVSR++](https://arxiv.org/pdf/2104.13371v1.pdf).
 
   [EDVR](https://arxiv.org/pdf/1905.02716.pdf) wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. The main difficulties of video super-resolution from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. First, to handle large motions, EDVR devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, EDVR propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.
 
@@ -79,6 +79,7 @@ The metrics are PSNR / SSIM.
 | EDVR_L_w_tsa_deblur  | 35.1473 / 0.9526 |
 | BasicVSR_x4  | 31.4325 / 0.8913 |
 | IconVSR_x4  | 31.6882 / 0.8950 |
+| BasicVSR++_x4  | 32.4018 / 0.9071 |
 
 
 ## 1.4 Model Download
@@ -92,6 +93,7 @@ The metrics are PSNR / SSIM.
 | EDVR_L_w_tsa_deblur  | REDS | [EDVR_L_w_tsa_deblur](https://paddlegan.bj.bcebos.com/models/EDVR_L_w_tsa_deblur.pdparams)
 | BasicVSR_x4  | REDS | [BasicVSR_x4](https://paddlegan.bj.bcebos.com/models/BasicVSR_reds_x4.pdparams)
 | IconVSR_x4  | REDS | [IconVSR_x4](https://paddlegan.bj.bcebos.com/models/IconVSR_reds_x4.pdparams)
+| BasicVSR++_x4  | REDS | [BasicVSR++_x4](https://paddlegan.bj.bcebos.com/models/BasicVSR%2B%2B_reds_x4.pdparams)
 
 
 
@@ -120,3 +122,14 @@ The metrics are PSNR / SSIM.
     year = {2021}
     }
 ```
+
+- 3. [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://arxiv.org/pdf/2104.13371v1.pdf)
+
+  ```
+  @article{chan2021basicvsr++,
+    author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
+    title = {BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment},
+    booktitle = {arXiv preprint arXiv:2104.13371},
+    year = {2021}
+    }
+  ```

docs/zh_CN/tutorials/video_super_resolution.md

@@ -3,7 +3,7 @@
 
 ## 1.1 原理介绍
 
-  视频超分源于图像超分，其目的是从一个或多个低分辨率（LR）图像中恢复高分辨率（HR）图像。它们的区别也很明显，由于视频是由多个帧组成的，所以视频超分通常利用帧间的信息来进行修复。这里我们提供视频超分模型[EDVR](https://arxiv.org/pdf/1905.02716.pdf).
+  视频超分源于图像超分，其目的是从一个或多个低分辨率（LR）图像中恢复高分辨率（HR）图像。它们的区别也很明显，由于视频是由多个帧组成的，所以视频超分通常利用帧间的信息来进行修复。这里我们提供视频超分模型[EDVR](https://arxiv.org/pdf/1905.02716.pdf),[BasicVSR](https://arxiv.org/pdf/2012.02181.pdf),[IconVSR](https://arxiv.org/pdf/2012.02181.pdf),[BasicVSR++](https://arxiv.org/pdf/2104.13371v1.pdf).
 
   [EDVR](https://arxiv.org/pdf/1905.02716.pdf)模型在NTIRE19视频恢复和增强挑战赛的四个赛道中都赢得了冠军，并以巨大的优势超过了第二名。视频超分的主要难点在于（1）如何在给定大运动的情况下对齐多个帧；（2）如何有效地融合具有不同运动和模糊的不同帧。首先，为了处理大的运动，EDVR模型设计了一个金字塔级联的可变形（PCD）对齐模块，在该模块中，从粗到精的可变形卷积被使用来进行特征级的帧对齐。其次，EDVR使用了时空注意力（TSA）融合模块，该模块在时间和空间上同时应用注意力机制，以强调后续恢复的重要特征。
 
@@ -75,6 +75,7 @@
 | EDVR_L_w_tsa_deblur  | 35.1473 / 0.9526 |
 | BasicVSR_x4  | 31.4325 / 0.8913 |
 | IconVSR_x4  | 31.6882 / 0.8950 |
+| BasicVSR++_x4  | 32.4018 / 0.9071 |
 
 ## 1.4 模型下载
 | 模型 | 数据集 | 下载地址 |
@@ -87,6 +88,7 @@
 | EDVR_L_w_tsa_deblur  | REDS | [EDVR_L_w_tsa_deblur](https://paddlegan.bj.bcebos.com/models/EDVR_L_w_tsa_deblur.pdparams)
 | BasicVSR_x4  | REDS | [BasicVSR_x4](https://paddlegan.bj.bcebos.com/models/BasicVSR_reds_x4.pdparams)
 | IconVSR_x4  | REDS | [IconVSR_x4](https://paddlegan.bj.bcebos.com/models/IconVSR_reds_x4.pdparams)
+| BasicVSR++_x4  | REDS | [BasicVSR++_x4](https://paddlegan.bj.bcebos.com/models/BasicVSR%2B%2B_reds_x4.pdparams)
 
 
 
@@ -113,3 +115,13 @@
     year = {2021}
     }
   ```
+- 3. [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://arxiv.org/pdf/2104.13371v1.pdf)
+
+  ```
+  @article{chan2021basicvsr++,
+    author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
+    title = {BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment},
+    booktitle = {arXiv preprint arXiv:2104.13371},
+    year = {2021}
+    }
+  ```

ppgan/models/basicvsr_model.py

@@ -29,7 +29,7 @@ class BasicVSRModel(BaseSRModel):
 
     Paper: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR, 2021
     """
-    def __init__(self, generator, fix_iter, pixel_criterion=None):
+    def __init__(self, generator, fix_iter, lr_mult, pixel_criterion=None):
         """Initialize the BasicVSR class.
 
         Args:
@@ -41,6 +41,7 @@ def __init__(self, generator, fix_iter, pixel_criterion=None):
         self.fix_iter = fix_iter
         self.current_iter = 1
         self.flag = True
+        self.lr_mult = lr_mult
         init_basicvsr_weight(self.nets['generator'])
 
     def setup_input(self, input):
@@ -65,7 +66,7 @@ def train_iter(self, optims=None):
                 for name, param in self.nets['generator'].named_parameters():
                     param.trainable = True
                     if 'spynet' in name:
-                        param.optimize_attr['learning_rate'] = 0.125
+                        param.optimize_attr['learning_rate'] = self.lr_mult
                 self.flag = False
                 for net in self.nets.values():
                     net.find_unused_parameters = False

ppgan/models/generators/__init__.py

@@ -35,3 +35,4 @@
 from .iconvsr import IconVSR
 from .gpen import GPEN
 from .pan import PAN
+from .basicvsr_plus_plus import BasicVSRPlusPlus

ppgan/models/generators/basicvsr.py

@@ -1,14 +1,13 @@
 # Copyright (c) MMEditing Authors.
 
-import paddle
-
 import numpy as np
 
+import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
+from paddle.vision.ops import DeformConv2D
 from ...utils.download import get_path_from_url
 from ...modules.init import kaiming_normal_, constant_
-
 from .builder import GENERATORS
 
 
@@ -607,3 +606,69 @@ def forward(self, lrs):
             outputs[i] = out
 
         return paddle.stack(outputs, axis=1)
+
+
+class SecondOrderDeformableAlignment(nn.Layer):
+    """Second-order deformable alignment module.
+    Args:
+        in_channels (int): Same as nn.Conv2d.
+        out_channels (int): Same as nn.Conv2d.
+        kernel_size (int or tuple[int]): Same as nn.Conv2d.
+        stride (int or tuple[int]): Same as nn.Conv2d.
+        padding (int or tuple[int]): Same as nn.Conv2d.
+        dilation (int or tuple[int]): Same as nn.Conv2d.
+        groups (int): Same as nn.Conv2d.
+        deformable_groups (int).
+    """
+    def __init__(self,
+                 in_channels=128,
+                 out_channels=64,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1,
+                 dilation=1,
+                 groups=1,
+                 deformable_groups=16):
+        super(SecondOrderDeformableAlignment, self).__init__()
+
+        self.conv_offset = nn.Sequential(
+            nn.Conv2D(3 * out_channels + 4, out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1),
+            nn.Conv2D(out_channels, out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1),
+            nn.Conv2D(out_channels, out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1),
+            nn.Conv2D(out_channels, 27 * deformable_groups, 3, 1, 1),
+        )
+        self.dcn = DeformConv2D(in_channels,
+                                out_channels,
+                                kernel_size=kernel_size,
+                                stride=stride,
+                                padding=padding,
+                                dilation=dilation,
+                                deformable_groups=deformable_groups)
+        self.init_offset()
+
+    def init_offset(self):
+        constant_(self.conv_offset[-1].weight, 0)
+        constant_(self.conv_offset[-1].bias, 0)
+
+    def forward(self, x, extra_feat, flow_1, flow_2):
+        extra_feat = paddle.concat([extra_feat, flow_1, flow_2], axis=1)
+        out = self.conv_offset(extra_feat)
+        o1, o2, mask = paddle.chunk(out, 3, axis=1)
+
+        # offset
+        offset = 10 * paddle.tanh(paddle.concat((o1, o2), axis=1))
+        offset_1, offset_2 = paddle.chunk(offset, 2, axis=1)
+        offset_1 = offset_1 + flow_1.flip(1).tile(
+            [1, offset_1.shape[1] // 2, 1, 1])
+        offset_2 = offset_2 + flow_2.flip(1).tile(
+            [1, offset_2.shape[1] // 2, 1, 1])
+        offset = paddle.concat([offset_1, offset_2], axis=1)
+
+        # mask
+        mask = F.sigmoid(mask)
+
+        out = self.dcn(x, offset, mask)
+        return out