Implement basicvsr (#356)

* add basicvsr model

configs/basicvsr_reds.yaml

+total_iters: 300000
+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
+  generator:
+    name: BasicVSRNet
+    mid_channels: 64
+    num_blocks: 30
+  pixel_criterion:
+    name: CharbonnierLoss
+    reduction: mean
+
+dataset:
+  train:
+    name: RepeatDataset
+    times: 1000
+    num_workers: 4  # 6
+    batch_size: 2  # 4*2
+    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: 15
+      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 2e-4
+  periods: [300000]
+  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:
+  # FIXME: avoid oom
+  interval: 5000000
+  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: 100
+  visiual_interval: 500
+
+snapshot_config:
+  interval: 5000

ppgan/datasets/__init__.py

@@ -24,4 +24,5 @@ from .starganv2_dataset import StarGANv2Dataset
 from .edvr_dataset import REDSDataset
 from .firstorder_dataset import FirstOrderDataset
 from .lapstyle_dataset import LapStyleDataset
+from .sr_reds_multiple_gt_dataset import SRREDSMultipleGTDataset
 from .mpr_dataset import MPRTrain, MPRVal, MPRTest

ppgan/datasets/builder.py

@@ -19,11 +19,25 @@ import numpy as np
 
 from paddle.distributed import ParallelEnv
 from paddle.io import DistributedBatchSampler
-from ..utils.registry import Registry
+
+from .repeat_dataset import RepeatDataset
+from ..utils.registry import Registry, build_from_config
 
 DATASETS = Registry("DATASETS")
 
 
+def build_dataset(cfg):
+    name = cfg.pop('name')
+
+    if name == 'RepeatDataset':
+        dataset_ = build_from_config(cfg['dataset'], DATASETS)
+        dataset = RepeatDataset(dataset_, cfg['times'])
+    else:
+        dataset = dataset = DATASETS.get(name)(**cfg)
+
+    return dataset
+
+
 def build_dataloader(cfg, is_train=True, distributed=True):
     cfg_ = cfg.copy()
 
@@ -31,9 +45,7 @@ def build_dataloader(cfg, is_train=True, distributed=True):
     num_workers = cfg_.pop('num_workers', 0)
     use_shared_memory = cfg_.pop('use_shared_memory', True)
 
-    name = cfg_.pop('name')
-
-    dataset = DATASETS.get(name)(**cfg_)
+    dataset = build_dataset(cfg_)
 
     if distributed:
         sampler = DistributedBatchSampler(dataset,

ppgan/datasets/repeat_dataset.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+
+
+class RepeatDataset(paddle.io.Dataset):
+    """A wrapper of repeated dataset.
+
+    The length of repeated dataset will be `times` larger than the original
+    dataset. This is useful when the data loading time is long but the dataset
+    is small. Using RepeatDataset can reduce the data loading time between
+    epochs.
+
+    Args:
+        dataset (:obj:`Dataset`): The dataset to be repeated.
+        times (int): Repeat times.
+    """
+
+    def __init__(self, dataset, times):
+        self.dataset = dataset
+        self.times = times
+
+        self._ori_len = len(self.dataset)
+
+    def __getitem__(self, idx):
+        """Get item at each call.
+
+        Args:
+            idx (int): Index for getting each item.
+        """
+        return self.dataset[idx % self._ori_len]
+
+    def __len__(self):
+        """Length of the dataset.
+
+        Returns:
+            int: Length of the dataset.
+        """
+        return self.times * self._ori_len

ppgan/datasets/sr_reds_multiple_gt_dataset.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import random
+import numpy as np
+import cv2
+from paddle.io import Dataset
+
+from .builder import DATASETS
+
+logger = logging.getLogger(__name__)
+
+
+@DATASETS.register()
+class SRREDSMultipleGTDataset(Dataset):
+    """REDS dataset for video super resolution for recurrent networks.
+
+    The dataset loads several LQ (Low-Quality) frames and GT (Ground-Truth)
+    frames. Then it applies specified transforms and finally returns a dict
+    containing paired data and other information.
+
+    Args:
+        lq_folder (str | :obj:`Path`): Path to a lq folder.
+        gt_folder (str | :obj:`Path`): Path to a gt folder.
+        num_input_frames (int): Number of input frames.
+        pipeline (list[dict | callable]): A sequence of data transformations.
+        scale (int): Upsampling scale ratio.
+        val_partition (str): Validation partition mode. Choices ['official' or
+        'REDS4']. Default: 'official'.
+        test_mode (bool): Store `True` when building test dataset.
+            Default: `False`.
+    """
+    def __init__(self,
+                 mode,
+                 lq_folder,
+                 gt_folder,
+                 crop_size=256,
+                 interval_list=[1],
+                 random_reverse=False,
+                 number_frames=15,
+                 use_flip=False,
+                 use_rot=False,
+                 scale=4,
+                 val_partition='REDS4',
+                 batch_size=4):
+        super(SRREDSMultipleGTDataset, self).__init__()
+        self.mode = mode
+        self.fileroot = str(lq_folder)
+        self.gtroot = str(gt_folder)
+        self.crop_size = crop_size
+        self.interval_list = interval_list
+        self.random_reverse = random_reverse
+        self.number_frames = number_frames
+        self.use_flip = use_flip
+        self.use_rot = use_rot
+        self.scale = scale
+        self.val_partition = val_partition
+        self.batch_size = batch_size
+        self.data_infos = self.load_annotations()
+
+    def __getitem__(self, idx):
+        """Get item at each call.
+
+        Args:
+            idx (int): Index for getting each item.
+        """
+        item = self.data_infos[idx]
+        idt = random.randint(0, 100 - self.number_frames)
+        item = item + '_' + f'{idt:03d}'
+        img_LQs, img_GTs = self.get_sample_data(
+            item, self.number_frames, self.interval_list, self.random_reverse,
+            self.gtroot, self.fileroot, self.crop_size, self.scale,
+            self.use_flip, self.use_rot, self.mode)
+        return {'lq': img_LQs, 'gt': img_GTs, 'lq_path': self.data_infos[idx]}
+
+    def load_annotations(self):
+        """Load annoations for REDS dataset.
+
+        Returns:
+            dict: Returned dict for LQ and GT pairs.
+        """
+        # generate keys
+        keys = [f'{i:03d}' for i in range(0, 270)]
+
+        if self.val_partition == 'REDS4':
+            val_partition = ['000', '011', '015', '020']
+        elif self.val_partition == 'official':
+            val_partition = [f'{i:03d}' for i in range(240, 270)]
+        else:
+            raise ValueError(f'Wrong validation partition {self.val_partition}.'
+                             f'Supported ones are ["official", "REDS4"]')
+
+        if self.mode == 'train':
+            keys = [v for v in keys if v not in val_partition]
+        else:
+            keys = [v for v in keys if v in val_partition]
+
+        data_infos = []
+        for key in keys:
+            data_infos.append(key)
+
+        return data_infos
+
+    def get_sample_data(self,
+                        item,
+                        number_frames,
+                        interval_list,
+                        random_reverse,
+                        gtroot,
+                        fileroot,
+                        crop_size,
+                        scale,
+                        use_flip,
+                        use_rot,
+                        mode='train'):
+        video_name = item.split('_')[0]
+        frame_name = item.split('_')[1]
+        frame_idxs = self.get_neighbor_frames(frame_name,
+                                              number_frames=number_frames,
+                                              interval_list=interval_list,
+                                              random_reverse=random_reverse)
+
+        frame_list = []
+        gt_list = []
+        for frame_idx in frame_idxs:
+            frame_idx_name = "%08d" % frame_idx
+            img = self.read_img(
+                os.path.join(fileroot, video_name, frame_idx_name + '.png'))
+            frame_list.append(img)
+            gt_img = self.read_img(
+                os.path.join(gtroot, video_name, frame_idx_name + '.png'))
+            gt_list.append(gt_img)
+        H, W, C = frame_list[0].shape
+        # add random crop
+        if (mode == 'train') or (mode == 'valid'):
+            LQ_size = crop_size // scale
+            rnd_h = random.randint(0, max(0, H - LQ_size))
+            rnd_w = random.randint(0, max(0, W - LQ_size))
+            frame_list = [
+                v[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
+                for v in frame_list
+            ]
+            rnd_h_HR, rnd_w_HR = int(rnd_h * scale), int(rnd_w * scale)
+            gt_list = [
+                v[rnd_h_HR:rnd_h_HR + crop_size,
+                  rnd_w_HR:rnd_w_HR + crop_size, :] for v in gt_list
+            ]
+
+        # add random flip and rotation
+        for v in gt_list:
+            frame_list.append(v)
+        if (mode == 'train') or (mode == 'valid'):
+            rlt = self.img_augment(frame_list, use_flip, use_rot)
+        else:
+            rlt = frame_list
+        frame_list = rlt[0:number_frames]
+        gt_list = rlt[number_frames:]
+
+        # stack LQ images to NHWC, N is the frame number
+        frame_list = [v.transpose(2, 0, 1).astype('float32') for v in frame_list]
+        gt_list = [v.transpose(2, 0, 1).astype('float32') for v in gt_list]
+
+        img_LQs = np.stack(frame_list, axis=0)
+        img_GTs = np.stack(gt_list, axis=0)
+
+        return img_LQs, img_GTs
+
+    def get_neighbor_frames(self, frame_name, number_frames, interval_list,
+                            random_reverse):
+        frame_idx = int(frame_name)
+        interval = random.choice(interval_list)
+        neighbor_list = list(
+            range(frame_idx, frame_idx + number_frames, interval))
+        if random_reverse and random.random() < 0.5:
+            neighbor_list.reverse()
+
+        assert len(neighbor_list) == number_frames, \
+            "frames slected have length({}), but it should be ({})".format(len(neighbor_list), number_frames)
+
+        return neighbor_list
+
+    def read_img(self, path, size=None):
+        """read image by cv2
+
+        return: Numpy float32, HWC, BGR, [0,1]
+        """
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        img = img.astype(np.float32) / 255.
+        if img.ndim == 2:
+            img = np.expand_dims(img, axis=2)
+        # some images have 4 channels
+        if img.shape[2] > 3:
+            img = img[:, :, :3]
+        return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    def img_augment(self, img_list, hflip=True, rot=True):
+        """horizontal flip OR rotate (0, 90, 180, 270 degrees)
+        """
+        hflip = hflip and random.random() < 0.5
+        vflip = rot and random.random() < 0.5
+        rot90 = rot and random.random() < 0.5
+
+        def _augment(img):
+            if hflip:
+                img = img[:, ::-1, :]
+            if vflip:
+                img = img[::-1, :, :]
+            if rot90:
+                img = img.transpose(1, 0, 2)
+            return img
+
+        return [_augment(img) for img in img_list]
+
+    def __len__(self):
+        """Length of the dataset.
+
+        Returns:
+            int: Length of the dataset.
+        """
+        return len(self.data_infos)

ppgan/engine/trainer.py

@@ -323,7 +323,6 @@ class Trainer:
                is_save_image=False):
         """
         visual the images, use visualdl or directly write to the directory
-
         Parameters:
             results_dir (str)     --  directory name which contains saved images
             visual_results (dict) --  the results images dict
@@ -440,7 +439,6 @@ class Trainer:
     def close(self):
         """
         when finish the training need close file handler or other.
-
         """
         if self.enable_visualdl:
-            self.vdl_logger.close()
+            self.vdl_logger.close()
\ No newline at end of file

ppgan/models/__init__.py

@@ -30,4 +30,5 @@ from .starganv2_model import StarGANv2Model
 from .edvr_model import EDVRModel
 from .firstorder_model import FirstOrderModel
 from .lapstyle_model import LapStyleDraModel, LapStyleRevFirstModel, LapStyleRevSecondModel
+from .basicvsr_model import BasicVSRModel
 from .mpr_model import MPRModel

ppgan/models/basicvsr_model.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn as nn
+
+from .builder import MODELS
+from .sr_model import BaseSRModel
+from .generators.basicvsr import ResidualBlockNoBN, PixelShufflePack, SPyNet
+from ..modules.init import reset_parameters
+from ..utils.visual import tensor2img
+
+
+@MODELS.register()
+class BasicVSRModel(BaseSRModel):
+    """BasicVSR Model.
+
+    Paper: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR, 2021
+    """
+    def __init__(self, generator, fix_iter, pixel_criterion=None):
+        """Initialize the BasicVSR class.
+
+        Args:
+            generator (dict): config of generator.
+            fix_iter (dict): config of fix_iter.
+            pixel_criterion (dict): config of pixel criterion.
+        """
+        super(BasicVSRModel, self).__init__(generator, pixel_criterion)
+        self.fix_iter = fix_iter
+        self.current_iter = 1
+        self.flag = True
+        init_basicvsr_weight(self.nets['generator'])
+
+    def setup_input(self, input):
+        self.lq = paddle.to_tensor(input['lq'])
+        self.visual_items['lq'] = self.lq[:, 0, :, :, :]
+        if 'gt' in input:
+            self.gt = paddle.to_tensor(input['gt'])
+            self.visual_items['gt'] = self.gt[:, 0, :, :, :]
+        self.image_paths = input['lq_path']
+
+    def train_iter(self, optims=None):
+        optims['optim'].clear_grad()
+        if self.fix_iter:
+            if self.current_iter == 1:
+                print('Train BasicVSR with fixed spynet for', self.fix_iter,
+                      'iters.')
+                for name, param in self.nets['generator'].named_parameters():
+                    if 'spynet' in name:
+                        param.trainable = False
+            elif self.current_iter >= self.fix_iter + 1 and self.flag:
+                print('Train all the parameters.')
+                for name, param in self.nets['generator'].named_parameters():
+                    param.trainable = True
+                    if 'spynet' in name:
+                        param.optimize_attr['learning_rate'] = 0.125
+                self.flag = False
+                for net in self.nets.values():
+                    net.find_unused_parameters = False
+
+        self.output = self.nets['generator'](self.lq)
+        self.visual_items['output'] = self.output[:, 0, :, :, :]
+        # pixel loss
+        loss_pixel = self.pixel_criterion(self.output, self.gt)
+        
+        loss_pixel.backward()
+        optims['optim'].step()
+        
+        self.losses['loss_pixel'] = loss_pixel
+
+        self.current_iter += 1
+
+    def test_iter(self, metrics=None):
+        self.nets['generator'].eval()
+        with paddle.no_grad():
+            self.output = self.nets['generator'](self.lq)
+            self.visual_items['output'] = self.output[:, 0, :, :, :]
+        self.nets['generator'].train()
+
+        out_img = []
+        gt_img = []
+        for out_tensor, gt_tensor in zip(self.output[0], self.gt[0]):
+            # print(out_tensor.shape, gt_tensor.shape)
+            out_img.append(tensor2img(out_tensor, (0., 1.)))
+            gt_img.append(tensor2img(gt_tensor, (0., 1.)))
+
+        if metrics is not None:
+            for metric in metrics.values():
+                metric.update(out_img, gt_img)
+
+
+def init_basicvsr_weight(net):
+    for m in net.children():
+        if hasattr(m, 'weight') and not isinstance(m, (nn.BatchNorm, nn.BatchNorm2D)):
+            reset_parameters(m)
+            continue
+
+        if (not isinstance(
+                    m, (ResidualBlockNoBN, PixelShufflePack, SPyNet))):
+            init_basicvsr_weight(m)

ppgan/models/criterions/pixel_loss.py

@@ -59,8 +59,9 @@ class CharbonnierLoss():
         eps (float): Default: 1e-12.
 
     """
-    def __init__(self, eps=1e-12):
+    def __init__(self, eps=1e-12, reduction='sum'):
         self.eps = eps
+        self.reduction = reduction
 
     def __call__(self, pred, target, **kwargs):
         """Forward Function.
@@ -69,7 +70,14 @@ class CharbonnierLoss():
             pred (Tensor): of shape (N, C, H, W). Predicted tensor.
             target (Tensor): of shape (N, C, H, W). Ground truth tensor.
         """
-        return paddle.sum(paddle.sqrt((pred - target)**2 + self.eps))
+        if self.reduction == 'sum':
+            out = paddle.sum(paddle.sqrt((pred - target)**2 + self.eps))
+        elif self.reduction == 'mean':
+            out = paddle.mean(paddle.sqrt((pred - target)**2 + self.eps))
+        else:
+            raise NotImplementedError('CharbonnierLoss %s not implemented' %
+                                      self.reduction)
+        return out
 
 
 @CRITERIONS.register()

ppgan/models/generators/__init__.py

@@ -30,4 +30,5 @@ from .generator_starganv2 import StarGANv2Generator, StarGANv2Style, StarGANv2Ma
 from .edvr import EDVRNet
 from .generator_firstorder import FirstOrderGenerator
 from .generater_lapstyle import DecoderNet, Encoder, RevisionNet
+from .basicvsr import BasicVSRNet
 from .mpr import MPRNet

ppgan/models/generators/basicvsr.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+
+import numpy as np
+import scipy.io as scio
+
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import initializer
+from ...utils.download import get_path_from_url
+from ...modules.init import kaiming_normal_, constant_
+
+from .builder import GENERATORS
+
+
+@paddle.no_grad()
+def default_init_weights(layer_list, scale=1, bias_fill=0, **kwargs):
+    """Initialize network weights.
+
+    Args:
+        layer_list (list[nn.Layer] | nn.Layer): Layers to be initialized.
+        scale (float): Scale initialized weights, especially for residual
+            blocks. Default: 1.
+        bias_fill (float): The value to fill bias. Default: 0
+        kwargs (dict): Other arguments for initialization function.
+    """
+    if not isinstance(layer_list, list):
+        layer_list = [layer_list]
+    for m in layer_list:
+        if isinstance(m, nn.Conv2D):
+            kaiming_normal_(m.weight, **kwargs)
+            scale_weight = scale * m.weight
+            m.weight.set_value(scale_weight)
+            if m.bias is not None:
+                constant_(m.bias, bias_fill)
+        elif isinstance(m, nn.Linear):
+            kaiming_normal_(m.weight, **kwargs)
+            scale_weight = scale * m.weight
+            m.weight.set_value(scale_weight)
+            if m.bias is not None:
+                constant_(m.bias, bias_fill)
+        elif isinstance(m, nn.BatchNorm):
+            constant_(m.weight, 1)
+
+
+class PixelShufflePack(nn.Layer):
+    """ Pixel Shuffle upsample layer.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        scale_factor (int): Upsample ratio.
+        upsample_kernel (int): Kernel size of Conv layer to expand channels.
+
+    Returns:
+        Upsampled feature map.
+    """
+    def __init__(self, in_channels, out_channels, scale_factor,
+                 upsample_kernel):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.scale_factor = scale_factor
+        self.upsample_kernel = upsample_kernel
+        self.upsample_conv = nn.Conv2D(self.in_channels,
+                                       self.out_channels * scale_factor *
+                                       scale_factor,
+                                       self.upsample_kernel,
+                                       padding=(self.upsample_kernel - 1) // 2)
+        self.pixel_shuffle = nn.PixelShuffle(self.scale_factor)
+        self.init_weights()
+
+    def init_weights(self):
+        """Initialize weights for PixelShufflePack.
+        """
+        default_init_weights(self, 1)
+
+    def forward(self, x):
+        """Forward function for PixelShufflePack.
+
+        Args:
+            x (Tensor): Input tensor with shape (in_channels, c, h, w).
+
+        Returns:
+            Tensor with shape (out_channels, c, scale_factor*h, scale_factor*w).
+        """
+        x = self.upsample_conv(x)
+        x = self.pixel_shuffle(x)
+        return x
+
+
+def MakeMultiBlocks(func, num_layers, nf=64):
+    """Make layers by stacking the same blocks.
+
+    Args:
+        func (nn.Layer): nn.Layer class for basic block.
+        num_layers (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    Blocks = nn.Sequential()
+    for i in range(num_layers):
+        Blocks.add_sublayer('block%d' % i, func(nf))
+    return Blocks
+
+
+class ResidualBlockNoBN(nn.Layer):
+    """Residual block without BN.
+
+    It has a style of:
+        ---Conv-ReLU-Conv-+-
+         |________________|
+
+    Args:
+        nf (int): Channel number of intermediate features.
+            Default: 64.
+        res_scale (float): Residual scale. Default: 1.0.
+    """
+    def __init__(self, nf=64, res_scale=1.0):
+        super(ResidualBlockNoBN, self).__init__()
+        self.nf = nf
+        self.res_scale = res_scale
+        self.conv1 = nn.Conv2D(self.nf, self.nf, 3, 1, 1)
+        self.conv2 = nn.Conv2D(self.nf, self.nf, 3, 1, 1)
+        self.relu = nn.ReLU()
+        if self.res_scale == 1.0:
+            default_init_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor with shape (n, c, h, w).
+        """
+        identity = x
+        out = self.conv2(self.relu(self.conv1(x)))
+        return identity + out * self.res_scale
+
+
+def flow_warp(x,
+              flow,
+              interpolation='bilinear',
+              padding_mode='zeros',
+              align_corners=True):
+    """Warp an image or a feature map with optical flow.
+
+    Args:
+        x (Tensor): Tensor with size (n, c, h, w).
+        flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is
+            a two-channel, denoting the width and height relative offsets.
+            Note that the values are not normalized to [-1, 1].
+        interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.
+            Default: 'bilinear'.
+        padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.
+            Default: 'zeros'.
+        align_corners (bool): Whether align corners. Default: True.
+
+    Returns:
+        Tensor: Warped image or feature map.
+    """
+    if x.shape[-2:] != flow.shape[1:3]:
+        raise ValueError(f'The spatial sizes of input ({x.shape[-2:]}) and '
+                         f'flow ({flow.shape[1:3]}) are not the same.')
+    _, _, h, w = x.shape
+    # create mesh grid
+    grid_y, grid_x = paddle.meshgrid(paddle.arange(0, h), paddle.arange(0, w))
+    grid = paddle.stack((grid_x, grid_y), axis=2)  # (w, h, 2)
+    grid = paddle.cast(grid, 'float32')
+    grid.stop_gradient = True
+
+    grid_flow = grid + flow
+    # scale grid_flow to [-1,1]
+    grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0
+    grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0
+    grid_flow = paddle.stack((grid_flow_x, grid_flow_y), axis=3)
+    output = F.grid_sample(x,
+                           grid_flow,
+                           mode=interpolation,
+                           padding_mode=padding_mode,
+                           align_corners=align_corners)
+    return output
+
+
+class SPyNetBasicModule(nn.Layer):
+    """Basic Module for SPyNet.
+
+    Paper:
+        Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017
+    """
+    def __init__(self):
+        super().__init__()
+
+        self.conv1 = nn.Conv2D(in_channels=8,
+                               out_channels=32,
+                               kernel_size=7,
+                               stride=1,
+                               padding=3)
+        self.conv2 = nn.Conv2D(in_channels=32,
+                               out_channels=64,
+                               kernel_size=7,
+                               stride=1,
+                               padding=3)
+        self.conv3 = nn.Conv2D(in_channels=64,
+                               out_channels=32,
+                               kernel_size=7,
+                               stride=1,
+                               padding=3)
+        self.conv4 = nn.Conv2D(in_channels=32,
+                               out_channels=16,
+                               kernel_size=7,
+                               stride=1,
+                               padding=3)
+        self.conv5 = nn.Conv2D(in_channels=16,
+                               out_channels=2,
+                               kernel_size=7,
+                               stride=1,
+                               padding=3)
+        self.relu = nn.ReLU()
+
+    def forward(self, tensor_input):
+        """
+        Args:
+            tensor_input (Tensor): Input tensor with shape (b, 8, h, w).
+                8 channels contain:
+                [reference image (3), neighbor image (3), initial flow (2)].
+
+        Returns:
+            Tensor: Refined flow with shape (b, 2, h, w)
+        """
+        out = self.relu(self.conv1(tensor_input))
+        out = self.relu(self.conv2(out))
+        out = self.relu(self.conv3(out))
+        out = self.relu(self.conv4(out))
+        out = self.conv5(out)
+        return out
+
+
+class SPyNet(nn.Layer):
+    """SPyNet network structure.
+
+    The difference to the SPyNet in paper is that
+        1. more SPyNetBasicModule is used in this version, and
+        2. no batch normalization is used in this version.
+
+    Paper:
+        Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017
+
+    """
+    def __init__(self):
+        super().__init__()
+
+        self.basic_module0 = SPyNetBasicModule()
+        self.basic_module1 = SPyNetBasicModule()
+        self.basic_module2 = SPyNetBasicModule()
+        self.basic_module3 = SPyNetBasicModule()
+        self.basic_module4 = SPyNetBasicModule()
+        self.basic_module5 = SPyNetBasicModule()
+
+        self.register_buffer(
+            'mean',
+            paddle.to_tensor([0.485, 0.456, 0.406]).reshape([1, 3, 1, 1]))
+        self.register_buffer(
+            'std',
+            paddle.to_tensor([0.229, 0.224, 0.225]).reshape([1, 3, 1, 1]))
+
+    def compute_flow(self, ref, supp):
+        """Compute flow from ref to supp.
+
+        Note that in this function, the images are already resized to a
+        multiple of 32.
+
+        Args:
+            ref (Tensor): Reference image with shape of (n, 3, h, w).
+            supp (Tensor): Supporting image with shape of (n, 3, h, w).
+
+        Returns:
+            Tensor: Estimated optical flow: (n, 2, h, w).
+        """
+
+        n, _, h, w = ref.shape
+
+        # normalize the input images
+        ref = [(ref - self.mean) / self.std]
+        supp = [(supp - self.mean) / self.std]
+
+        # generate downsampled frames
+        for level in range(5):
+            ref.append(F.avg_pool2d(ref[-1], kernel_size=2, stride=2))
+            supp.append(F.avg_pool2d(supp[-1], kernel_size=2, stride=2))
+        ref = ref[::-1]
+        supp = supp[::-1]
+
+        # flow computation
+        flow = paddle.to_tensor(np.zeros([n, 2, h // 32, w // 32], 'float32'))
+
+        # level=0
+        flow_up = flow
+        flow = flow_up + self.basic_module0(
+            paddle.concat([
+                ref[0],
+                flow_warp(supp[0],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        # level=1
+        flow_up = F.interpolate(
+            flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+        flow = flow_up + self.basic_module1(
+            paddle.concat([
+                ref[1],
+                flow_warp(supp[1],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        # level=2
+        flow_up = F.interpolate(
+            flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+        flow = flow_up + self.basic_module2(
+            paddle.concat([
+                ref[2],
+                flow_warp(supp[2],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        # level=3
+        flow_up = F.interpolate(
+            flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+        flow = flow_up + self.basic_module3(
+            paddle.concat([
+                ref[3],
+                flow_warp(supp[3],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        # level=4
+        flow_up = F.interpolate(
+            flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+        flow = flow_up + self.basic_module4(
+            paddle.concat([
+                ref[4],
+                flow_warp(supp[4],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        # level=5
+        flow_up = F.interpolate(
+            flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
+        flow = flow_up + self.basic_module5(
+            paddle.concat([
+                ref[5],
+                flow_warp(supp[5],
+                          flow_up.transpose([0, 2, 3, 1]),
+                          padding_mode='border'), flow_up
+            ], 1))
+
+        return flow
+
+    def forward(self, ref, supp):
+        """Forward function of SPyNet.
+
+        This function computes the optical flow from ref to supp.
+
+        Args:
+            ref (Tensor): Reference image with shape of (n, 3, h, w).
+            supp (Tensor): Supporting image with shape of (n, 3, h, w).
+
+        Returns:
+            Tensor: Estimated optical flow: (n, 2, h, w).
+        """
+
+        # upsize to a multiple of 32
+        h, w = ref.shape[2:4]
+        w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)
+        h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)
+        ref = F.interpolate(ref,
+                            size=(h_up, w_up),
+                            mode='bilinear',
+                            align_corners=False)
+        supp = F.interpolate(supp,
+                             size=(h_up, w_up),
+                             mode='bilinear',
+                             align_corners=False)
+        ref.stop_gradient = False
+        supp.stop_gradient = False
+        # compute flow, and resize back to the original resolution
+        flow_up = self.compute_flow(ref, supp)
+        flow = F.interpolate(flow_up,
+                             size=(h, w),
+                             mode='bilinear',
+                             align_corners=False)
+
+        # adjust the flow values
+        # todo: grad bug
+        # flow[:, 0, :, :] *= (float(w) / float(w_up))
+        # flow[:, 1, :, :] *= (float(h) / float(h_up))
+
+        flow_x = flow[:, 0:1, :, :] * (float(w) / float(w_up))
+        flow_y = flow[:, 1:2, :, :] * (float(h) / float(h_up))
+        flow = paddle.concat([flow_x, flow_y], 1)
+
+        return flow
+
+
+class ResidualBlocksWithInputConv(nn.Layer):
+    """Residual blocks with a convolution in front.
+
+    Args:
+        in_channels (int): Number of input channels of the first conv.
+        out_channels (int): Number of channels of the residual blocks.
+            Default: 64.
+        num_blocks (int): Number of residual blocks. Default: 30.
+    """
+    def __init__(self, in_channels, out_channels=64, num_blocks=30):
+        super().__init__()
+
+        # a convolution used to match the channels of the residual blocks
+        self.covn1 = nn.Conv2D(in_channels, out_channels, 3, 1, 1)
+        self.Leaky_relu = nn.LeakyReLU(negative_slope=0.1)
+
+        # residual blocks
+        self.ResidualBlocks = MakeMultiBlocks(ResidualBlockNoBN,
+                                              num_blocks,
+                                              nf=out_channels)
+
+    def forward(self, feat):
+        """
+        Forward function for ResidualBlocksWithInputConv.
+
+        Args:
+            feat (Tensor): Input feature with shape (n, in_channels, h, w)
+
+        Returns:
+            Tensor: Output feature with shape (n, out_channels, h, w)
+        """
+        out = self.Leaky_relu(self.covn1(feat))
+        out = self.ResidualBlocks(out)
+        return out
+
+
+@GENERATORS.register()
+class BasicVSRNet(nn.Layer):
+    """BasicVSR network structure for video super-resolution.
+
+    Support only x4 upsampling.
+    Paper:
+        BasicVSR: The Search for Essential Components in Video Super-Resolution
+        and Beyond, CVPR, 2021
+
+    Args:
+        mid_channels (int): Channel number of the intermediate features.
+            Default: 64.
+        num_blocks (int): Number of residual blocks in each propagation branch.
+            Default: 30.
+    """
+    def __init__(self, mid_channels=64, num_blocks=30):
+
+        super().__init__()
+
+        self.mid_channels = mid_channels
+
+        # optical flow network for feature alignment
+        self.spynet = SPyNet()
+        weight_path = get_path_from_url(
+            'https://paddlegan.bj.bcebos.com/models/spynet.pdparams')
+        self.spynet.set_state_dict(paddle.load(weight_path))
+
+        # propagation branches
+        self.backward_resblocks = ResidualBlocksWithInputConv(
+            mid_channels + 3, mid_channels, num_blocks)
+        self.forward_resblocks = ResidualBlocksWithInputConv(
+            mid_channels + 3, mid_channels, num_blocks)
+
+        # upsample
+        self.fusion = nn.Conv2D(mid_channels * 2, mid_channels, 1, 1, 0)
+        self.upsample1 = PixelShufflePack(mid_channels,
+                                          mid_channels,
+                                          2,
+                                          upsample_kernel=3)
+        self.upsample2 = PixelShufflePack(mid_channels,
+                                          64,
+                                          2,
+                                          upsample_kernel=3)
+        self.conv_hr = nn.Conv2D(64, 64, 3, 1, 1)
+        self.conv_last = nn.Conv2D(64, 3, 3, 1, 1)
+        self.img_upsample = nn.Upsample(scale_factor=4,
+                                        mode='bilinear',
+                                        align_corners=False)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1)
+
+    def check_if_mirror_extended(self, lrs):
+        """Check whether the input is a mirror-extended sequence.
+
+        If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the
+        (t-1-i)-th frame.
+
+        Args:
+            lrs (tensor): Input LR images with shape (n, t, c, h, w)
+        """
+
+        self.is_mirror_extended = False
+        if lrs.shape[1] % 2 == 0:
+            lrs_1, lrs_2 = paddle.chunk(lrs, 2, axis=1)
+            lrs_2 = paddle.flip(lrs_2, [1])
+            if paddle.norm(lrs_1 - lrs_2) == 0:
+                self.is_mirror_extended = True
+
+    def compute_flow(self, lrs):
+        """Compute optical flow using SPyNet for feature warping.
+
+        Note that if the input is an mirror-extended sequence, 'flows_forward'
+        is not needed, since it is equal to 'flows_backward.flip(1)'.
+
+        Args:
+            lrs (tensor): Input LR images with shape (n, t, c, h, w)
+
+        Return:
+            tuple(Tensor): Optical flow. 'flows_forward' corresponds to the
+                flows used for forward-time propagation (current to previous).
+                'flows_backward' corresponds to the flows used for
+                backward-time propagation (current to next).
+        """
+
+        n, t, c, h, w = lrs.shape
+
+        lrs_1 = lrs[:, :-1, :, :, :].reshape([-1, c, h, w])
+        lrs_2 = lrs[:, 1:, :, :, :].reshape([-1, c, h, w])
+
+        flows_backward = self.spynet(lrs_1, lrs_2).reshape([n, t - 1, 2, h, w])
+
+        if self.is_mirror_extended:  # flows_forward = flows_backward.flip(1)
+            flows_forward = None
+        else:
+            flows_forward = self.spynet(lrs_2,
+                                        lrs_1).reshape([n, t - 1, 2, h, w])
+
+        return flows_forward, flows_backward
+
+    def forward(self, lrs):
+        """Forward function for BasicVSR.
+
+        Args:
+            lrs (Tensor): Input LR sequence with shape (n, t, c, h, w).
+
+        Returns:
+            Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
+        """
+
+        n, t, c, h, w = lrs.shape
+        assert h >= 64 and w >= 64, (
+            'The height and width of inputs should be at least 64, '
+            f'but got {h} and {w}.')
+
+        # check whether the input is an extended sequence
+        self.check_if_mirror_extended(lrs)
+
+        # compute optical flow
+        flows_forward, flows_backward = self.compute_flow(lrs)
+
+        # backward-time propgation
+        outputs = []
+        feat_prop = paddle.to_tensor(
+            np.zeros([n, self.mid_channels, h, w], 'float32'))
+        for i in range(t - 1, -1, -1):
+            if i < t - 1:  # no warping required for the last timestep
+                flow = flows_backward[:, i, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.transpose([0, 2, 3, 1]))
+
+            feat_prop = paddle.concat([lrs[:, i, :, :, :], feat_prop], axis=1)
+            feat_prop = self.backward_resblocks(feat_prop)
+
+            outputs.append(feat_prop)
+        outputs = outputs[::-1]
+
+        # forward-time propagation and upsampling
+        feat_prop = paddle.zeros_like(feat_prop)
+        for i in range(0, t):
+            lr_curr = lrs[:, i, :, :, :]
+            if i > 0:  # no warping required for the first timestep
+                if flows_forward is not None:
+                    flow = flows_forward[:, i - 1, :, :, :]
+                else:
+                    flow = flows_backward[:, -i, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.transpose([0, 2, 3, 1]))
+
+            feat_prop = paddle.concat([lr_curr, feat_prop], axis=1)
+            feat_prop = self.forward_resblocks(feat_prop)
+
+            # upsampling given the backward and forward features
+            out = paddle.concat([outputs[i], feat_prop], axis=1)
+            out = self.lrelu(self.fusion(out))
+            out = self.lrelu(self.upsample1(out))
+            out = self.lrelu(self.upsample2(out))
+            out = self.lrelu(self.conv_hr(out))
+            out = self.conv_last(out)
+            base = self.img_upsample(lr_curr)
+            out += base
+            outputs[i] = out
+
+        return paddle.stack(outputs, axis=1)