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 *.zip filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/pic/div2k_comparison.jpg filter=lfs diff=lfs merge=lfs -text
+assets/pic/imgsli1.png filter=lfs diff=lfs merge=lfs -text
+assets/pic/imgsli2.png filter=lfs diff=lfs merge=lfs -text
+assets/pic/imgsli3.png filter=lfs diff=lfs merge=lfs -text
+assets/pic/imgsli4.png filter=lfs diff=lfs merge=lfs -text
+assets/pic/imgsli5.png filter=lfs diff=lfs merge=lfs -text
+assets/pic/london2.jpg filter=lfs diff=lfs merge=lfs -text
+assets/pic/main_framework.jpg filter=lfs diff=lfs merge=lfs -text
+assets/pic/realsr_vis3.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,12 +1,180 @@
----
-title: S3DIFF
-emoji: 🐠
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 5.4.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<h2 align="center">Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors</h2>
+
+<div align="center">
+
+<a href="https://arxiv.org/abs/2409.17058"><img src="https://img.shields.io/badge/ArXiv-2409.17058-red"></a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+<img src="https://visitor-badge.laobi.icu/badge?page_id=ArcticHare105/S3Diff" alt="visitors">
+</p>
+
+[Aiping Zhang]()<sup>1\*</sup>, [Zongsheng Yue]()<sup>2,\*</sup>, [Renjing Pei]()<sup>3</sup>, [Wenqi Ren]()<sup>1</sup>, [Xiaochun Cao]()<sup>1</sup>
+
+<sup>1</sup>School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University<br> <sup>2</sup>S-Lab, Nanyang Technological University<br> <sup>3</sup>Huawei Noah's Ark Lab<br>* Equal contribution.
+</div>
+
+:fire::fire::fire: We have released the code, cheers!
+
+
+:star: If S3Diff is helpful for you, please help star this repo. Thanks! :hugs:
+
+
+## :book: Table Of Contents
+
+- [Update](#update)
+- [TODO](#todo)
+- [Abstract](#abstract)
+- [Framework Overview](#framework_overview)
+- [Visual Comparison](#visual_comparison)
+- [Setup](#setup)
+- [Training](#training)
+- [Inference](#inference)
+
+<!-- - [Installation](#installation)
+- [Inference](#inference) -->
+
+## <a name="update"></a>:new: Update
+
+- **2024.10.07**: Add gradio demo 🚀
+- **2024.09.25**: The code is released :fire:
+- **2024.09.25**: This repo is released :fire:
+<!-- - [**History Updates** >]() -->
+
+## <a name="todo"></a>:hourglass: TODO
+
+- [x] Release Code :computer:
+- [x] Release Checkpoints :link:
+
+## <a name="abstract"></a>:fireworks: Abstract
+
+> Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. However, these methods still face two challenges: the requirement for dozens of sampling steps to achieve satisfactory results, which limits efficiency in real scenarios, and the neglect of degradation models, which are critical auxiliary information in solving the SR problem. In this work, we introduced a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module specifically for SR, which corrects the model parameters based on the pre-estimated degradation information from low-resolution images. This module not only facilitates a powerful data-dependent or degradation-dependent SR model but also preserves the generative prior of the pre-trained diffusion model as much as possible. Furthermore, we tailor a novel training pipeline by introducing an online negative sample generation strategy. Combined with the classifier-free guidance strategy during inference, it largely improves the perceptual quality of the super-resolution results. Extensive experiments have demonstrated the superior efficiency and effectiveness of the proposed model compared to recent state-of-the-art methods.
+
+## <a name="framework_overview"></a>:eyes: Framework Overview
+
+<img src=assets/pic/main_framework.jpg>
+
+:star: Overview of S3Diff. We enhance a pre-trained diffusion model for one-step SR by injecting LoRA layers into the VAE encoder and UNet. Additionally, we employ a pre-trained Degradation Estimation Network to assess image degradation that is used to guide the LoRAs with the introduced block ID embeddings. We tailor a new training pipeline that includes an online negative prompting, reusing generated LR images with negative text prompts. The network is trained with a combination of a reconstruction loss and a GAN loss.
+
+## <a name="visual_comparison"></a>:chart_with_upwards_trend: Visual Comparison
+
+### Image Slide Results
+[<img src="assets/pic/imgsli1.png" height="235px"/>](https://imgsli.com/MzAzNjIy) [<img src="assets/pic/imgsli2.png" height="235px"/>](https://imgsli.com/MzAzNjQ1)  [<img src="assets/pic/imgsli3.png" height="235px"/>](https://imgsli.com/MzAzNjU4)
+[<img src="assets/pic/imgsli4.png" height="272px"/>](https://imgsli.com/MzAzNjU5) [<img src="assets/pic/imgsli5.png" height="272px"/>](https://imgsli.com/MzAzNjI2)
+### Synthesis Dataset
+
+<img src=assets/pic/div2k_comparison.jpg>
+
+### Real-World Dataset
+
+<img src=assets/pic/london2.jpg>
+<img src=assets/pic/realsr_vis3.jpg>
+
+<!-- </details> -->
+
+## <a name="setup"></a> ⚙️ Setup
+```bash
+conda create -n s3diff python=3.10
+conda activate s3diff
+pip install -r requirements.txt
+```
+Or use the conda env file that contains all the required dependencies.
+
+```bash
+conda env create -f environment.yaml
+```
+
+:star: Since we employ peft in our code, we highly recommend following the provided environmental requirements, especially regarding diffusers.
+
+## <a name="training"></a> :wrench: Training
+
+#### Step1: Download the pretrained models
+We enable automatic model download in our code, if you need to conduct offline training, download the pretrained model [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo)
+
+#### Step2: Prepare training data
+We train the S3Diff on [LSDIR](https://github.com/ofsoundof/LSDIR) + 10K samples from [FFHQ](https://github.com/NVlabs/ffhq-dataset), following [SeeSR](https://github.com/cswry/SeeSR) and [OSEDiff](https://github.com/cswry/OSEDiff).
+
+#### Step3: Training for S3Diff
+
+Please modify the paths to training datasets in `configs/sr.yaml`
+Then run:
+
+```bash
+sh run_training.sh
+```
+
+If you need to conduct offline training, modify `run_training.sh` as follows, and fill in `sd_path` with your local path:
+
+```bash
+accelerate launch --num_processes=4 --gpu_ids="0,1,2,3" --main_process_port 29300 src/train_s3diff.py \
+    --sd_path="path_to_checkpoints/sd-turbo" \
+    --de_net_path="assets/mm-realsr/de_net.pth" \
+    --output_dir="./output" \
+    --resolution=512 \
+    --train_batch_size=4 \
+    --enable_xformers_memory_efficient_attention \
+    --viz_freq 25
+```
+
+## <a name="inference"></a> 💫 Inference
+
+#### Step1: Download datasets for inference
+
+#### Step2: Download the pretrained models
+
+We enable automatic model download in our code, if you need to conduct offline inference, download the pretrained model [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) and S3Diff [[HuggingFace](https://huggingface.co/zhangap/S3Diff) | [GoogleDrive](https://drive.google.com/drive/folders/1cWYQYRFpadC4K2GuH8peg_hWEoFddZtj?usp=sharing)]
+
+#### Step3: Inference for S3Diff
+
+Please add the paths to evaluate datasets in `configs/sr_test.yaml` and the path of GT folder in `run_inference.sh`
+Then run:
+
+```bash
+sh run_inference.sh
+```
+
+If you need to conduct offline inference, modify `run_inference.sh` as follows, and fill in with your paths:
+
+```bash
+accelerate launch --num_processes=1 --gpu_ids="0," --main_process_port 29300 src/inference_s3diff.py \
+    --sd_path="path_to_checkpoints/sd-turbo" \
+    --de_net_path="assets/mm-realsr/de_net.pth" \
+    --pretrained_path="path_to_checkpoints/s3diff.pkl" \
+    --output_dir="./output" \
+    --ref_path="path_to_ground_truth_folder" \
+    --align_method="wavelet"
+```
+
+#### Gradio Demo
+
+Please install Gradio first
+```bash
+pip install gradio
+```
+
+Please run the following command to interact with the gradio website, have fun. 🤗
+
+```
+python src/gradio_s3diff.py 
+```
+![s3diff](assets/pic/gradio.png)
+
+## :smiley: Citation
+
+Please cite us if our work is useful for your research.
+
+```
+@article{2024s3diff,
+  author    = {Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, Xiaochun Cao},
+  title     = {Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors},
+  journal   = {arxiv},
+  year      = {2024},
+}
+```
+
+## :notebook: License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+
+## :envelope: Contact
+
+If you have any questions, please feel free to contact [email protected].
+

assets/mm-realsr/de_net.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6e77c1cb0dd51e01ef60bbf7b9d85b3f9399c3c1253889ddb73de1436231b1a
+size 9424338

basicsr/__init__.py

@@ -0,0 +1,12 @@
+# https://github.com/xinntao/BasicSR
+# flake8: noqa
+from .archs import *
+from .data import *
+from .losses import *
+from .metrics import *
+from .models import *
+from .ops import *
+from .test import *
+from .train import *
+from .utils import *
+# from .version import __gitsha__, __version__

basicsr/archs/__init__.py

@@ -0,0 +1,24 @@
+import importlib
+from copy import deepcopy
+from os import path as osp
+
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import ARCH_REGISTRY
+
+__all__ = ['build_network']
+
+# automatically scan and import arch modules for registry
+# scan all the files under the 'archs' folder and collect files ending with '_arch.py'
+arch_folder = osp.dirname(osp.abspath(__file__))
+arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
+# import all the arch modules
+_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
+
+
+def build_network(opt):
+    opt = deepcopy(opt)
+    network_type = opt.pop('type')
+    net = ARCH_REGISTRY.get(network_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Network [{net.__class__.__name__}] is created.')
+    return net

basicsr/archs/arch_util.py

@@ -0,0 +1,355 @@
+import collections.abc
+import math
+import torch
+import torchvision
+import warnings
+from distutils.version import LooseVersion
+from itertools import repeat
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
+from basicsr.utils import get_root_logger
+
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+    """Initialize network weights.
+
+    Args:
+        module_list (list[nn.Module] | nn.Module): Modules 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(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+
+    Args:
+        basic_block (nn.module): nn.module class for basic block.
+        num_basic_block (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+class PixelShufflePack(nn.Module):
+    """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.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 (n, c, h, w).
+        Returns:
+            Tensor: Forward results.
+        """
+        x = self.upsample_conv(x)
+        x = F.pixel_shuffle(x, self.scale_factor)
+        return x
+
+class ResidualBlockNoBN(nn.Module):
+    """Residual block without BN.
+
+    Args:
+        num_feat (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, num_feat=64, res_scale=1, pytorch_init=False):
+        super(ResidualBlockNoBN, self).__init__()
+        self.res_scale = res_scale
+        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.relu = nn.ReLU()
+
+        if not pytorch_init:
+            default_init_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        identity = x
+        x = self.conv1(x)
+        x = self.relu(x)
+        out = self.conv2(x)
+        # out = self.conv2(self.relu(self.conv1(x)))
+        return identity + out * self.res_scale
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
+    """Warp an image or feature map with optical flow.
+
+    Args:
+        x (Tensor): Tensor with size (n, c, h, w).
+        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
+        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
+        padding_mode (str): 'zeros' or 'border' or 'reflection'.
+            Default: 'zeros'.
+        align_corners (bool): Before pytorch 1.3, the default value is
+            align_corners=True. After pytorch 1.3, the default value is
+            align_corners=False. Here, we use the True as default.
+
+    Returns:
+        Tensor: Warped image or feature map.
+    """
+    assert x.size()[-2:] == flow.size()[1:3]
+    _, _, h, w = x.size()
+    # create mesh grid
+    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
+    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
+    grid.requires_grad = False
+
+    vgrid = grid + flow
+    # scale grid to [-1,1]
+    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
+    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
+    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
+    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
+
+    # TODO, what if align_corners=False
+    return output
+
+
+def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
+    """Resize a flow according to ratio or shape.
+
+    Args:
+        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
+        size_type (str): 'ratio' or 'shape'.
+        sizes (list[int | float]): the ratio for resizing or the final output
+            shape.
+            1) The order of ratio should be [ratio_h, ratio_w]. For
+            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
+            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
+            ratio > 1.0).
+            2) The order of output_size should be [out_h, out_w].
+        interp_mode (str): The mode of interpolation for resizing.
+            Default: 'bilinear'.
+        align_corners (bool): Whether align corners. Default: False.
+
+    Returns:
+        Tensor: Resized flow.
+    """
+    _, _, flow_h, flow_w = flow.size()
+    if size_type == 'ratio':
+        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
+    elif size_type == 'shape':
+        output_h, output_w = sizes[0], sizes[1]
+    else:
+        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
+
+    input_flow = flow.clone()
+    ratio_h = output_h / flow_h
+    ratio_w = output_w / flow_w
+    input_flow[:, 0, :, :] *= ratio_w
+    input_flow[:, 1, :, :] *= ratio_h
+    resized_flow = F.interpolate(
+        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
+    return resized_flow
+
+
+# TODO: may write a cpp file
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle.
+
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    b, c, hh, hw = x.size()
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+class DCNv2Pack(ModulatedDeformConvPack):
+    """Modulated deformable conv for deformable alignment.
+
+    Different from the official DCNv2Pack, which generates offsets and masks
+    from the preceding features, this DCNv2Pack takes another different
+    features to generate offsets and masks.
+
+    ``Paper: Delving Deep into Deformable Alignment in Video Super-Resolution``
+    """
+
+    def forward(self, x, feat):
+        out = self.conv_offset(feat)
+        o1, o2, mask = torch.chunk(out, 3, dim=1)
+        offset = torch.cat((o1, o2), dim=1)
+        mask = torch.sigmoid(mask)
+
+        offset_absmean = torch.mean(torch.abs(offset))
+        if offset_absmean > 50:
+            logger = get_root_logger()
+            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
+
+        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
+            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
+                                                 self.dilation, mask)
+        else:
+            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
+                                         self.dilation, self.groups, self.deformable_groups)
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
+            'The distribution of values may be incorrect.',
+            stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        low = norm_cdf((a - mean) / std)
+        up = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [low, up], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * low - 1, 2 * up - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution.
+
+    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
+
+    The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+# From PyTorch
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple

basicsr/archs/basicvsr_arch.py

@@ -0,0 +1,336 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import ResidualBlockNoBN, flow_warp, make_layer
+from .edvr_arch import PCDAlignment, TSAFusion
+from .spynet_arch import SpyNet
+
+
+@ARCH_REGISTRY.register()
+class BasicVSR(nn.Module):
+    """A recurrent network for video SR. Now only x4 is supported.
+
+    Args:
+        num_feat (int): Number of channels. Default: 64.
+        num_block (int): Number of residual blocks for each branch. Default: 15
+        spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
+    """
+
+    def __init__(self, num_feat=64, num_block=15, spynet_path=None):
+        super().__init__()
+        self.num_feat = num_feat
+
+        # alignment
+        self.spynet = SpyNet(spynet_path)
+
+        # propagation
+        self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
+        self.forward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
+
+        # reconstruction
+        self.fusion = nn.Conv2d(num_feat * 2, num_feat, 1, 1, 0, bias=True)
+        self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True)
+        self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
+        self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
+
+        self.pixel_shuffle = nn.PixelShuffle(2)
+
+        # activation functions
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+    def get_flow(self, x):
+        b, n, c, h, w = x.size()
+
+        x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w)
+        x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w)
+
+        flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w)
+        flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w)
+
+        return flows_forward, flows_backward
+
+    def forward(self, x):
+        """Forward function of BasicVSR.
+
+        Args:
+            x: Input frames with shape (b, n, c, h, w). n is the temporal dimension / number of frames.
+        """
+        flows_forward, flows_backward = self.get_flow(x)
+        b, n, _, h, w = x.size()
+
+        # backward branch
+        out_l = []
+        feat_prop = x.new_zeros(b, self.num_feat, h, w)
+        for i in range(n - 1, -1, -1):
+            x_i = x[:, i, :, :, :]
+            if i < n - 1:
+                flow = flows_backward[:, i, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
+            feat_prop = torch.cat([x_i, feat_prop], dim=1)
+            feat_prop = self.backward_trunk(feat_prop)
+            out_l.insert(0, feat_prop)
+
+        # forward branch
+        feat_prop = torch.zeros_like(feat_prop)
+        for i in range(0, n):
+            x_i = x[:, i, :, :, :]
+            if i > 0:
+                flow = flows_forward[:, i - 1, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
+
+            feat_prop = torch.cat([x_i, feat_prop], dim=1)
+            feat_prop = self.forward_trunk(feat_prop)
+
+            # upsample
+            out = torch.cat([out_l[i], feat_prop], dim=1)
+            out = self.lrelu(self.fusion(out))
+            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
+            out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
+            out = self.lrelu(self.conv_hr(out))
+            out = self.conv_last(out)
+            base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)
+            out += base
+            out_l[i] = out
+
+        return torch.stack(out_l, dim=1)
+
+
+class ConvResidualBlocks(nn.Module):
+    """Conv and residual block used in BasicVSR.
+
+    Args:
+        num_in_ch (int): Number of input channels. Default: 3.
+        num_out_ch (int): Number of output channels. Default: 64.
+        num_block (int): Number of residual blocks. Default: 15.
+    """
+
+    def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15):
+        super().__init__()
+        self.main = nn.Sequential(
+            nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch))
+
+    def forward(self, fea):
+        return self.main(fea)
+
+
+@ARCH_REGISTRY.register()
+class IconVSR(nn.Module):
+    """IconVSR, proposed also in the BasicVSR paper.
+
+    Args:
+        num_feat (int): Number of channels. Default: 64.
+        num_block (int): Number of residual blocks for each branch. Default: 15.
+        keyframe_stride (int): Keyframe stride. Default: 5.
+        temporal_padding (int): Temporal padding. Default: 2.
+        spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
+        edvr_path (str): Path to the pretrained EDVR model. Default: None.
+    """
+
+    def __init__(self,
+                 num_feat=64,
+                 num_block=15,
+                 keyframe_stride=5,
+                 temporal_padding=2,
+                 spynet_path=None,
+                 edvr_path=None):
+        super().__init__()
+
+        self.num_feat = num_feat
+        self.temporal_padding = temporal_padding
+        self.keyframe_stride = keyframe_stride
+
+        # keyframe_branch
+        self.edvr = EDVRFeatureExtractor(temporal_padding * 2 + 1, num_feat, edvr_path)
+        # alignment
+        self.spynet = SpyNet(spynet_path)
+
+        # propagation
+        self.backward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True)
+        self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block)
+
+        self.forward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True)
+        self.forward_trunk = ConvResidualBlocks(2 * num_feat + 3, num_feat, num_block)
+
+        # reconstruction
+        self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True)
+        self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
+        self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
+
+        self.pixel_shuffle = nn.PixelShuffle(2)
+
+        # activation functions
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+    def pad_spatial(self, x):
+        """Apply padding spatially.
+
+        Since the PCD module in EDVR requires that the resolution is a multiple
+        of 4, we apply padding to the input LR images if their resolution is
+        not divisible by 4.
+
+        Args:
+            x (Tensor): Input LR sequence with shape (n, t, c, h, w).
+        Returns:
+            Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad).
+        """
+        n, t, c, h, w = x.size()
+
+        pad_h = (4 - h % 4) % 4
+        pad_w = (4 - w % 4) % 4
+
+        # padding
+        x = x.view(-1, c, h, w)
+        x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect')
+
+        return x.view(n, t, c, h + pad_h, w + pad_w)
+
+    def get_flow(self, x):
+        b, n, c, h, w = x.size()
+
+        x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w)
+        x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w)
+
+        flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w)
+        flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w)
+
+        return flows_forward, flows_backward
+
+    def get_keyframe_feature(self, x, keyframe_idx):
+        if self.temporal_padding == 2:
+            x = [x[:, [4, 3]], x, x[:, [-4, -5]]]
+        elif self.temporal_padding == 3:
+            x = [x[:, [6, 5, 4]], x, x[:, [-5, -6, -7]]]
+        x = torch.cat(x, dim=1)
+
+        num_frames = 2 * self.temporal_padding + 1
+        feats_keyframe = {}
+        for i in keyframe_idx:
+            feats_keyframe[i] = self.edvr(x[:, i:i + num_frames].contiguous())
+        return feats_keyframe
+
+    def forward(self, x):
+        b, n, _, h_input, w_input = x.size()
+
+        x = self.pad_spatial(x)
+        h, w = x.shape[3:]
+
+        keyframe_idx = list(range(0, n, self.keyframe_stride))
+        if keyframe_idx[-1] != n - 1:
+            keyframe_idx.append(n - 1)  # last frame is a keyframe
+
+        # compute flow and keyframe features
+        flows_forward, flows_backward = self.get_flow(x)
+        feats_keyframe = self.get_keyframe_feature(x, keyframe_idx)
+
+        # backward branch
+        out_l = []
+        feat_prop = x.new_zeros(b, self.num_feat, h, w)
+        for i in range(n - 1, -1, -1):
+            x_i = x[:, i, :, :, :]
+            if i < n - 1:
+                flow = flows_backward[:, i, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
+            if i in keyframe_idx:
+                feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1)
+                feat_prop = self.backward_fusion(feat_prop)
+            feat_prop = torch.cat([x_i, feat_prop], dim=1)
+            feat_prop = self.backward_trunk(feat_prop)
+            out_l.insert(0, feat_prop)
+
+        # forward branch
+        feat_prop = torch.zeros_like(feat_prop)
+        for i in range(0, n):
+            x_i = x[:, i, :, :, :]
+            if i > 0:
+                flow = flows_forward[:, i - 1, :, :, :]
+                feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))
+            if i in keyframe_idx:
+                feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1)
+                feat_prop = self.forward_fusion(feat_prop)
+
+            feat_prop = torch.cat([x_i, out_l[i], feat_prop], dim=1)
+            feat_prop = self.forward_trunk(feat_prop)
+
+            # upsample
+            out = self.lrelu(self.pixel_shuffle(self.upconv1(feat_prop)))
+            out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
+            out = self.lrelu(self.conv_hr(out))
+            out = self.conv_last(out)
+            base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)
+            out += base
+            out_l[i] = out
+
+        return torch.stack(out_l, dim=1)[..., :4 * h_input, :4 * w_input]
+
+
+class EDVRFeatureExtractor(nn.Module):
+    """EDVR feature extractor used in IconVSR.
+
+    Args:
+        num_input_frame (int): Number of input frames.
+        num_feat (int): Number of feature channels
+        load_path (str): Path to the pretrained weights of EDVR. Default: None.
+    """
+
+    def __init__(self, num_input_frame, num_feat, load_path):
+
+        super(EDVRFeatureExtractor, self).__init__()
+
+        self.center_frame_idx = num_input_frame // 2
+
+        # extract pyramid features
+        self.conv_first = nn.Conv2d(3, num_feat, 3, 1, 1)
+        self.feature_extraction = make_layer(ResidualBlockNoBN, 5, num_feat=num_feat)
+        self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+        self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
+        self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+
+        # pcd and tsa module
+        self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=8)
+        self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_input_frame, center_frame_idx=self.center_frame_idx)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+
+        if load_path:
+            self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
+
+    def forward(self, x):
+        b, n, c, h, w = x.size()
+
+        # extract features for each frame
+        # L1
+        feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w)))
+        feat_l1 = self.feature_extraction(feat_l1)
+        # L2
+        feat_l2 = self.lrelu(self.conv_l2_1(feat_l1))
+        feat_l2 = self.lrelu(self.conv_l2_2(feat_l2))
+        # L3
+        feat_l3 = self.lrelu(self.conv_l3_1(feat_l2))
+        feat_l3 = self.lrelu(self.conv_l3_2(feat_l3))
+
+        feat_l1 = feat_l1.view(b, n, -1, h, w)
+        feat_l2 = feat_l2.view(b, n, -1, h // 2, w // 2)
+        feat_l3 = feat_l3.view(b, n, -1, h // 4, w // 4)
+
+        # PCD alignment
+        ref_feat_l = [  # reference feature list
+            feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(),
+            feat_l3[:, self.center_frame_idx, :, :, :].clone()
+        ]
+        aligned_feat = []
+        for i in range(n):
+            nbr_feat_l = [  # neighboring feature list
+                feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone()
+            ]
+            aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l))
+        aligned_feat = torch.stack(aligned_feat, dim=1)  # (b, t, c, h, w)
+
+        # TSA fusion
+        return self.fusion(aligned_feat)

basicsr/archs/basicvsrpp_arch.py

@@ -0,0 +1,417 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import warnings
+
+from basicsr.archs.arch_util import flow_warp
+from basicsr.archs.basicvsr_arch import ConvResidualBlocks
+from basicsr.archs.spynet_arch import SpyNet
+from basicsr.ops.dcn import ModulatedDeformConvPack
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+@ARCH_REGISTRY.register()
+class BasicVSRPlusPlus(nn.Module):
+    """BasicVSR++ network structure.
+
+    Support either x4 upsampling or same size output. Since DCN is used in this
+    model, it can only be used with CUDA enabled. If CUDA is not enabled,
+    feature alignment will be skipped. Besides, we adopt the official DCN
+    implementation and the version of torch need to be higher than 1.9.
+
+    ``Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment``
+
+    Args:
+        mid_channels (int, optional): Channel number of the intermediate
+            features. Default: 64.
+        num_blocks (int, optional): The number of residual blocks in each
+            propagation branch. Default: 7.
+        max_residue_magnitude (int): The maximum magnitude of the offset
+            residue (Eq. 6 in paper). Default: 10.
+        is_low_res_input (bool, optional): Whether the input is low-resolution
+            or not. If False, the output resolution is equal to the input
+            resolution. Default: True.
+        spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
+        cpu_cache_length (int, optional): When the length of sequence is larger
+            than this value, the intermediate features are sent to CPU. This
+            saves GPU memory, but slows down the inference speed. You can
+            increase this number if you have a GPU with large memory.
+            Default: 100.
+    """
+
+    def __init__(self,
+                 mid_channels=64,
+                 num_blocks=7,
+                 max_residue_magnitude=10,
+                 is_low_res_input=True,
+                 spynet_path=None,
+                 cpu_cache_length=100):
+
+        super().__init__()
+        self.mid_channels = mid_channels
+        self.is_low_res_input = is_low_res_input
+        self.cpu_cache_length = cpu_cache_length
+
+        # optical flow
+        self.spynet = SpyNet(spynet_path)
+
+        # feature extraction module
+        if is_low_res_input:
+            self.feat_extract = ConvResidualBlocks(3, mid_channels, 5)
+        else:
+            self.feat_extract = nn.Sequential(
+                nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
+                nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
+                ConvResidualBlocks(mid_channels, mid_channels, 5))
+
+        # propagation branches
+        self.deform_align = nn.ModuleDict()
+        self.backbone = nn.ModuleDict()
+        modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2']
+        for i, module in enumerate(modules):
+            if torch.cuda.is_available():
+                self.deform_align[module] = SecondOrderDeformableAlignment(
+                    2 * mid_channels,
+                    mid_channels,
+                    3,
+                    padding=1,
+                    deformable_groups=16,
+                    max_residue_magnitude=max_residue_magnitude)
+            self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks)
+
+        # upsampling module
+        self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5)
+
+        self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True)
+
+        self.pixel_shuffle = nn.PixelShuffle(2)
+
+        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, inplace=True)
+
+        # check if the sequence is augmented by flipping
+        self.is_mirror_extended = False
+
+        if len(self.deform_align) > 0:
+            self.is_with_alignment = True
+        else:
+            self.is_with_alignment = False
+            warnings.warn('Deformable alignment module is not added. '
+                          'Probably your CUDA is not configured correctly. DCN can only '
+                          'be used with CUDA enabled. Alignment is skipped now.')
+
+    def check_if_mirror_extended(self, lqs):
+        """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:
+            lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w).
+        """
+
+        if lqs.size(1) % 2 == 0:
+            lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1)
+            if torch.norm(lqs_1 - lqs_2.flip(1)) == 0:
+                self.is_mirror_extended = True
+
+    def compute_flow(self, lqs):
+        """Compute optical flow using SPyNet for feature alignment.
+
+        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:
+            lqs (tensor): Input low quality (LQ) sequence 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 = lqs.size()
+        lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w)
+        lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w)
+
+        flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w)
+
+        if self.is_mirror_extended:  # flows_forward = flows_backward.flip(1)
+            flows_forward = flows_backward.flip(1)
+        else:
+            flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w)
+
+        if self.cpu_cache:
+            flows_backward = flows_backward.cpu()
+            flows_forward = flows_forward.cpu()
+
+        return flows_forward, flows_backward
+
+    def propagate(self, feats, flows, module_name):
+        """Propagate the latent features throughout the sequence.
+
+        Args:
+            feats dict(list[tensor]): Features from previous branches. Each
+                component is a list of tensors with shape (n, c, h, w).
+            flows (tensor): Optical flows with shape (n, t - 1, 2, h, w).
+            module_name (str): The name of the propgation branches. Can either
+                be 'backward_1', 'forward_1', 'backward_2', 'forward_2'.
+
+        Return:
+            dict(list[tensor]): A dictionary containing all the propagated \
+                features. Each key in the dictionary corresponds to a \
+                propagation branch, which is represented by a list of tensors.
+        """
+
+        n, t, _, h, w = flows.size()
+
+        frame_idx = range(0, t + 1)
+        flow_idx = range(-1, t)
+        mapping_idx = list(range(0, len(feats['spatial'])))
+        mapping_idx += mapping_idx[::-1]
+
+        if 'backward' in module_name:
+            frame_idx = frame_idx[::-1]
+            flow_idx = frame_idx
+
+        feat_prop = flows.new_zeros(n, self.mid_channels, h, w)
+        for i, idx in enumerate(frame_idx):
+            feat_current = feats['spatial'][mapping_idx[idx]]
+            if self.cpu_cache:
+                feat_current = feat_current.cuda()
+                feat_prop = feat_prop.cuda()
+            # second-order deformable alignment
+            if i > 0 and self.is_with_alignment:
+                flow_n1 = flows[:, flow_idx[i], :, :, :]
+                if self.cpu_cache:
+                    flow_n1 = flow_n1.cuda()
+
+                cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))
+
+                # initialize second-order features
+                feat_n2 = torch.zeros_like(feat_prop)
+                flow_n2 = torch.zeros_like(flow_n1)
+                cond_n2 = torch.zeros_like(cond_n1)
+
+                if i > 1:  # second-order features
+                    feat_n2 = feats[module_name][-2]
+                    if self.cpu_cache:
+                        feat_n2 = feat_n2.cuda()
+
+                    flow_n2 = flows[:, flow_idx[i - 1], :, :, :]
+                    if self.cpu_cache:
+                        flow_n2 = flow_n2.cuda()
+
+                    flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1))
+                    cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1))
+
+                # flow-guided deformable convolution
+                cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)
+                feat_prop = torch.cat([feat_prop, feat_n2], dim=1)
+                feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2)
+
+            # concatenate and residual blocks
+            feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop]
+            if self.cpu_cache:
+                feat = [f.cuda() for f in feat]
+
+            feat = torch.cat(feat, dim=1)
+            feat_prop = feat_prop + self.backbone[module_name](feat)
+            feats[module_name].append(feat_prop)
+
+            if self.cpu_cache:
+                feats[module_name][-1] = feats[module_name][-1].cpu()
+                torch.cuda.empty_cache()
+
+        if 'backward' in module_name:
+            feats[module_name] = feats[module_name][::-1]
+
+        return feats
+
+    def upsample(self, lqs, feats):
+        """Compute the output image given the features.
+
+        Args:
+            lqs (tensor): Input low quality (LQ) sequence with
+                shape (n, t, c, h, w).
+            feats (dict): The features from the propagation branches.
+
+        Returns:
+            Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
+        """
+
+        outputs = []
+        num_outputs = len(feats['spatial'])
+
+        mapping_idx = list(range(0, num_outputs))
+        mapping_idx += mapping_idx[::-1]
+
+        for i in range(0, lqs.size(1)):
+            hr = [feats[k].pop(0) for k in feats if k != 'spatial']
+            hr.insert(0, feats['spatial'][mapping_idx[i]])
+            hr = torch.cat(hr, dim=1)
+            if self.cpu_cache:
+                hr = hr.cuda()
+
+            hr = self.reconstruction(hr)
+            hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr)))
+            hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr)))
+            hr = self.lrelu(self.conv_hr(hr))
+            hr = self.conv_last(hr)
+            if self.is_low_res_input:
+                hr += self.img_upsample(lqs[:, i, :, :, :])
+            else:
+                hr += lqs[:, i, :, :, :]
+
+            if self.cpu_cache:
+                hr = hr.cpu()
+                torch.cuda.empty_cache()
+
+            outputs.append(hr)
+
+        return torch.stack(outputs, dim=1)
+
+    def forward(self, lqs):
+        """Forward function for BasicVSR++.
+
+        Args:
+            lqs (tensor): Input low quality (LQ) 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 = lqs.size()
+
+        # whether to cache the features in CPU
+        self.cpu_cache = True if t > self.cpu_cache_length else False
+
+        if self.is_low_res_input:
+            lqs_downsample = lqs.clone()
+        else:
+            lqs_downsample = F.interpolate(
+                lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4)
+
+        # check whether the input is an extended sequence
+        self.check_if_mirror_extended(lqs)
+
+        feats = {}
+        # compute spatial features
+        if self.cpu_cache:
+            feats['spatial'] = []
+            for i in range(0, t):
+                feat = self.feat_extract(lqs[:, i, :, :, :]).cpu()
+                feats['spatial'].append(feat)
+                torch.cuda.empty_cache()
+        else:
+            feats_ = self.feat_extract(lqs.view(-1, c, h, w))
+            h, w = feats_.shape[2:]
+            feats_ = feats_.view(n, t, -1, h, w)
+            feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)]
+
+        # compute optical flow using the low-res inputs
+        assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, (
+            'The height and width of low-res inputs must be at least 64, '
+            f'but got {h} and {w}.')
+        flows_forward, flows_backward = self.compute_flow(lqs_downsample)
+
+        # feature propgation
+        for iter_ in [1, 2]:
+            for direction in ['backward', 'forward']:
+                module = f'{direction}_{iter_}'
+
+                feats[module] = []
+
+                if direction == 'backward':
+                    flows = flows_backward
+                elif flows_forward is not None:
+                    flows = flows_forward
+                else:
+                    flows = flows_backward.flip(1)
+
+                feats = self.propagate(feats, flows, module)
+                if self.cpu_cache:
+                    del flows
+                    torch.cuda.empty_cache()
+
+        return self.upsample(lqs, feats)
+
+
+class SecondOrderDeformableAlignment(ModulatedDeformConvPack):
+    """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.
+        bias (bool or str): If specified as `auto`, it will be decided by the
+            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
+            False.
+        max_residue_magnitude (int): The maximum magnitude of the offset
+            residue (Eq. 6 in paper). Default: 10.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)
+
+        super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)
+
+        self.conv_offset = nn.Sequential(
+            nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True),
+            nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1),
+        )
+
+        self.init_offset()
+
+    def init_offset(self):
+
+        def _constant_init(module, val, bias=0):
+            if hasattr(module, 'weight') and module.weight is not None:
+                nn.init.constant_(module.weight, val)
+            if hasattr(module, 'bias') and module.bias is not None:
+                nn.init.constant_(module.bias, bias)
+
+        _constant_init(self.conv_offset[-1], val=0, bias=0)
+
+    def forward(self, x, extra_feat, flow_1, flow_2):
+        extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)
+        out = self.conv_offset(extra_feat)
+        o1, o2, mask = torch.chunk(out, 3, dim=1)
+
+        # offset
+        offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1))
+        offset_1, offset_2 = torch.chunk(offset, 2, dim=1)
+        offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1)
+        offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1)
+        offset = torch.cat([offset_1, offset_2], dim=1)
+
+        # mask
+        mask = torch.sigmoid(mask)
+
+        return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
+                                             self.dilation, mask)
+
+
+# if __name__ == '__main__':
+#     spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth'
+#     model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda()
+#     input = torch.rand(1, 2, 3, 64, 64).cuda()
+#     output = model(input)
+#     print('===================')
+#     print(output.shape)

basicsr/archs/degradat_arch.py

@@ -0,0 +1,90 @@
+from torch import nn as nn
+
+from basicsr.archs.arch_util import ResidualBlockNoBN, default_init_weights
+from basicsr.utils.registry import ARCH_REGISTRY
+
+@ARCH_REGISTRY.register()
+class DEResNet(nn.Module):
+    """Degradation Estimator with ResNetNoBN arch. v2.1, no vector anymore
+    As shown in paper 'Towards Flexible Blind JPEG Artifacts Removal',
+    resnet arch works for image quality estimation.
+    Args:
+        num_in_ch (int): channel number of inputs. Default: 3.
+        num_degradation (int): num of degradation the DE should estimate. Default: 2(blur+noise).
+        degradation_embed_size (int): embedding size of each degradation vector.
+        degradation_degree_actv (int): activation function for degradation degree scalar. Default: sigmoid.
+        num_feats (list): channel number of each stage.
+        num_blocks (list): residual block of each stage.
+        downscales (list): downscales of each stage.
+    """
+
+    def __init__(self,
+                 num_in_ch=3,
+                 num_degradation=2,
+                 degradation_degree_actv='sigmoid',
+                 num_feats=(64, 128, 256, 512),
+                 num_blocks=(2, 2, 2, 2),
+                 downscales=(2, 2, 2, 1)):
+        super(DEResNet, self).__init__()
+
+        assert isinstance(num_feats, list)
+        assert isinstance(num_blocks, list)
+        assert isinstance(downscales, list)
+        assert len(num_feats) == len(num_blocks) and len(num_feats) == len(downscales)
+
+        num_stage = len(num_feats)
+
+        self.conv_first = nn.ModuleList()
+        for _ in range(num_degradation):
+            self.conv_first.append(nn.Conv2d(num_in_ch, num_feats[0], 3, 1, 1))
+        self.body = nn.ModuleList()
+        for _ in range(num_degradation):
+            body = list()
+            for stage in range(num_stage):
+                for _ in range(num_blocks[stage]):
+                    body.append(ResidualBlockNoBN(num_feats[stage]))
+                if downscales[stage] == 1:
+                    if stage < num_stage - 1 and num_feats[stage] != num_feats[stage + 1]:
+                        body.append(nn.Conv2d(num_feats[stage], num_feats[stage + 1], 3, 1, 1))
+                    continue
+                elif downscales[stage] == 2:
+                    body.append(nn.Conv2d(num_feats[stage], num_feats[min(stage + 1, num_stage - 1)], 3, 2, 1))
+                else:
+                    raise NotImplementedError
+            self.body.append(nn.Sequential(*body))
+
+        # self.body = nn.Sequential(*body)
+
+        self.num_degradation = num_degradation
+        self.fc_degree = nn.ModuleList()
+        if degradation_degree_actv == 'sigmoid':
+            actv = nn.Sigmoid
+        elif degradation_degree_actv == 'tanh':
+            actv = nn.Tanh
+        else:
+            raise NotImplementedError(f'only sigmoid and tanh are supported for degradation_degree_actv, '
+                                      f'{degradation_degree_actv} is not supported yet.')
+        for _ in range(num_degradation):
+            self.fc_degree.append(
+                nn.Sequential(
+                    nn.Linear(num_feats[-1], 512),
+                    nn.ReLU(inplace=True),
+                    nn.Linear(512, 1),
+                    actv(),
+                ))
+
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+
+        default_init_weights([self.conv_first, self.body, self.fc_degree], 0.1)
+
+    def forward(self, x):
+        degrees = []
+        for i in range(self.num_degradation):
+            x_out = self.conv_first[i](x)
+            feat = self.body[i](x_out)
+            feat = self.avg_pool(feat)
+            feat = feat.squeeze(-1).squeeze(-1)
+            # for i in range(self.num_degradation):
+            degrees.append(self.fc_degree[i](feat).squeeze(-1))
+
+        return degrees

basicsr/archs/dfdnet_arch.py

@@ -0,0 +1,169 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils.spectral_norm import spectral_norm
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization
+from .vgg_arch import VGGFeatureExtractor
+
+
+class SFTUpBlock(nn.Module):
+    """Spatial feature transform (SFT) with upsampling block.
+
+    Args:
+        in_channel (int): Number of input channels.
+        out_channel (int): Number of output channels.
+        kernel_size (int): Kernel size in convolutions. Default: 3.
+        padding (int): Padding in convolutions. Default: 1.
+    """
+
+    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1):
+        super(SFTUpBlock, self).__init__()
+        self.conv1 = nn.Sequential(
+            Blur(in_channel),
+            spectral_norm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.04, True),
+            # The official codes use two LeakyReLU here, so 0.04 for equivalent
+        )
+        self.convup = nn.Sequential(
+            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+            nn.LeakyReLU(0.2, True),
+        )
+
+        # for SFT scale and shift
+        self.scale_block = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)))
+        self.shift_block = nn.Sequential(
+            spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+            spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid())
+        # The official codes use sigmoid for shift block, do not know why
+
+    def forward(self, x, updated_feat):
+        out = self.conv1(x)
+        # SFT
+        scale = self.scale_block(updated_feat)
+        shift = self.shift_block(updated_feat)
+        out = out * scale + shift
+        # upsample
+        out = self.convup(out)
+        return out
+
+
+@ARCH_REGISTRY.register()
+class DFDNet(nn.Module):
+    """DFDNet: Deep Face Dictionary Network.
+
+    It only processes faces with 512x512 size.
+
+    Args:
+        num_feat (int): Number of feature channels.
+        dict_path (str): Path to the facial component dictionary.
+    """
+
+    def __init__(self, num_feat, dict_path):
+        super().__init__()
+        self.parts = ['left_eye', 'right_eye', 'nose', 'mouth']
+        # part_sizes: [80, 80, 50, 110]
+        channel_sizes = [128, 256, 512, 512]
+        self.feature_sizes = np.array([256, 128, 64, 32])
+        self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4']
+        self.flag_dict_device = False
+
+        # dict
+        self.dict = torch.load(dict_path)
+
+        # vgg face extractor
+        self.vgg_extractor = VGGFeatureExtractor(
+            layer_name_list=self.vgg_layers,
+            vgg_type='vgg19',
+            use_input_norm=True,
+            range_norm=True,
+            requires_grad=False)
+
+        # attention block for fusing dictionary features and input features
+        self.attn_blocks = nn.ModuleDict()
+        for idx, feat_size in enumerate(self.feature_sizes):
+            for name in self.parts:
+                self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx])
+
+        # multi scale dilation block
+        self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1])
+
+        # upsampling and reconstruction
+        self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8)
+        self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4)
+        self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2)
+        self.upsample3 = SFTUpBlock(num_feat * 2, num_feat)
+        self.upsample4 = nn.Sequential(
+            spectral_norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat),
+            UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh())
+
+    def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size):
+        """swap the features from the dictionary."""
+        # get the original vgg features
+        part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone()
+        # resize original vgg features
+        part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False)
+        # use adaptive instance normalization to adjust color and illuminations
+        dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat)
+        # get similarity scores
+        similarity_score = F.conv2d(part_resize_feat, dict_feat)
+        similarity_score = F.softmax(similarity_score.view(-1), dim=0)
+        # select the most similar features in the dict (after norm)
+        select_idx = torch.argmax(similarity_score)
+        swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4])
+        # attention
+        attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat)
+        attn_feat = attn * swap_feat
+        # update features
+        updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat
+        return updated_feat
+
+    def put_dict_to_device(self, x):
+        if self.flag_dict_device is False:
+            for k, v in self.dict.items():
+                for kk, vv in v.items():
+                    self.dict[k][kk] = vv.to(x)
+            self.flag_dict_device = True
+
+    def forward(self, x, part_locations):
+        """
+        Now only support testing with batch size = 0.
+
+        Args:
+            x (Tensor): Input faces with shape (b, c, 512, 512).
+            part_locations (list[Tensor]): Part locations.
+        """
+        self.put_dict_to_device(x)
+        # extract vggface features
+        vgg_features = self.vgg_extractor(x)
+        # update vggface features using the dictionary for each part
+        updated_vgg_features = []
+        batch = 0  # only supports testing with batch size = 0
+        for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes):
+            dict_features = self.dict[f'{f_size}']
+            vgg_feat = vgg_features[vgg_layer]
+            updated_feat = vgg_feat.clone()
+
+            # swap features from dictionary
+            for part_idx, part_name in enumerate(self.parts):
+                location = (part_locations[part_idx][batch] // (512 / f_size)).int()
+                updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name,
+                                              f_size)
+
+            updated_vgg_features.append(updated_feat)
+
+        vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4'])
+        # use updated vgg features to modulate the upsampled features with
+        # SFT (Spatial Feature Transform) scaling and shifting manner.
+        upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3])
+        upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2])
+        upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1])
+        upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0])
+        out = self.upsample4(upsampled_feat)
+
+        return out

basicsr/archs/dfdnet_util.py

@@ -0,0 +1,162 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.nn.utils.spectral_norm import spectral_norm
+
+
+class BlurFunctionBackward(Function):
+
+    @staticmethod
+    def forward(ctx, grad_output, kernel, kernel_flip):
+        ctx.save_for_backward(kernel, kernel_flip)
+        grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1])
+        return grad_input
+
+    @staticmethod
+    def backward(ctx, gradgrad_output):
+        kernel, _ = ctx.saved_tensors
+        grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1])
+        return grad_input, None, None
+
+
+class BlurFunction(Function):
+
+    @staticmethod
+    def forward(ctx, x, kernel, kernel_flip):
+        ctx.save_for_backward(kernel, kernel_flip)
+        output = F.conv2d(x, kernel, padding=1, groups=x.shape[1])
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        kernel, kernel_flip = ctx.saved_tensors
+        grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip)
+        return grad_input, None, None
+
+
+blur = BlurFunction.apply
+
+
+class Blur(nn.Module):
+
+    def __init__(self, channel):
+        super().__init__()
+        kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32)
+        kernel = kernel.view(1, 1, 3, 3)
+        kernel = kernel / kernel.sum()
+        kernel_flip = torch.flip(kernel, [2, 3])
+
+        self.kernel = kernel.repeat(channel, 1, 1, 1)
+        self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1)
+
+    def forward(self, x):
+        return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x))
+
+
+def calc_mean_std(feat, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    n, c = size[:2]
+    feat_var = feat.view(n, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(n, c, 1, 1)
+    feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1)
+    return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+    """Adaptive instance normalization.
+
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+def AttentionBlock(in_channel):
+    return nn.Sequential(
+        spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True),
+        spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)))
+
+
+def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True):
+    """Conv block used in MSDilationBlock."""
+
+    return nn.Sequential(
+        spectral_norm(
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=((kernel_size - 1) // 2) * dilation,
+                bias=bias)),
+        nn.LeakyReLU(0.2),
+        spectral_norm(
+            nn.Conv2d(
+                out_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=((kernel_size - 1) // 2) * dilation,
+                bias=bias)),
+    )
+
+
+class MSDilationBlock(nn.Module):
+    """Multi-scale dilation block."""
+
+    def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True):
+        super(MSDilationBlock, self).__init__()
+
+        self.conv_blocks = nn.ModuleList()
+        for i in range(4):
+            self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias))
+        self.conv_fusion = spectral_norm(
+            nn.Conv2d(
+                in_channels * 4,
+                in_channels,
+                kernel_size=kernel_size,
+                stride=1,
+                padding=(kernel_size - 1) // 2,
+                bias=bias))
+
+    def forward(self, x):
+        out = []
+        for i in range(4):
+            out.append(self.conv_blocks[i](x))
+        out = torch.cat(out, 1)
+        out = self.conv_fusion(out) + x
+        return out
+
+
+class UpResBlock(nn.Module):
+
+    def __init__(self, in_channel):
+        super(UpResBlock, self).__init__()
+        self.body = nn.Sequential(
+            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(in_channel, in_channel, 3, 1, 1),
+        )
+
+    def forward(self, x):
+        out = x + self.body(x)
+        return out

basicsr/archs/discriminator_arch.py

@@ -0,0 +1,150 @@
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn.utils import spectral_norm
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+@ARCH_REGISTRY.register()
+class VGGStyleDiscriminator(nn.Module):
+    """VGG style discriminator with input size 128 x 128 or 256 x 256.
+
+    It is used to train SRGAN, ESRGAN, and VideoGAN.
+
+    Args:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_feat (int): Channel number of base intermediate features.Default: 64.
+    """
+
+    def __init__(self, num_in_ch, num_feat, input_size=128):
+        super(VGGStyleDiscriminator, self).__init__()
+        self.input_size = input_size
+        assert self.input_size == 128 or self.input_size == 256, (
+            f'input size must be 128 or 256, but received {input_size}')
+
+        self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
+        self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False)
+        self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True)
+
+        self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False)
+        self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True)
+        self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False)
+        self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True)
+
+        self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False)
+        self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True)
+        self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False)
+        self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True)
+
+        self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False)
+        self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
+        self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
+        self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
+
+        self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False)
+        self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
+        self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
+        self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
+
+        if self.input_size == 256:
+            self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False)
+            self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
+            self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
+            self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
+
+        self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100)
+        self.linear2 = nn.Linear(100, 1)
+
+        # activation function
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.')
+
+        feat = self.lrelu(self.conv0_0(x))
+        feat = self.lrelu(self.bn0_1(self.conv0_1(feat)))  # output spatial size: /2
+
+        feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))
+        feat = self.lrelu(self.bn1_1(self.conv1_1(feat)))  # output spatial size: /4
+
+        feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))
+        feat = self.lrelu(self.bn2_1(self.conv2_1(feat)))  # output spatial size: /8
+
+        feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))
+        feat = self.lrelu(self.bn3_1(self.conv3_1(feat)))  # output spatial size: /16
+
+        feat = self.lrelu(self.bn4_0(self.conv4_0(feat)))
+        feat = self.lrelu(self.bn4_1(self.conv4_1(feat)))  # output spatial size: /32
+
+        if self.input_size == 256:
+            feat = self.lrelu(self.bn5_0(self.conv5_0(feat)))
+            feat = self.lrelu(self.bn5_1(self.conv5_1(feat)))  # output spatial size: / 64
+
+        # spatial size: (4, 4)
+        feat = feat.view(feat.size(0), -1)
+        feat = self.lrelu(self.linear1(feat))
+        out = self.linear2(feat)
+        return out
+
+
+@ARCH_REGISTRY.register(suffix='basicsr')
+class UNetDiscriminatorSN(nn.Module):
+    """Defines a U-Net discriminator with spectral normalization (SN)
+
+    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
+
+    Arg:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_feat (int): Channel number of base intermediate features. Default: 64.
+        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
+    """
+
+    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
+        super(UNetDiscriminatorSN, self).__init__()
+        self.skip_connection = skip_connection
+        norm = spectral_norm
+        # the first convolution
+        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
+        # downsample
+        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
+        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
+        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
+        # upsample
+        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
+        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
+        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
+        # extra convolutions
+        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
+        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
+        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
+
+    def forward(self, x):
+        # downsample
+        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
+        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
+        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
+        x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
+
+        # upsample
+        x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
+        x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
+
+        if self.skip_connection:
+            x4 = x4 + x2
+        x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
+        x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
+
+        if self.skip_connection:
+            x5 = x5 + x1
+        x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
+        x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
+
+        if self.skip_connection:
+            x6 = x6 + x0
+
+        # extra convolutions
+        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
+        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
+        out = self.conv9(out)
+
+        return out

basicsr/archs/duf_arch.py

@@ -0,0 +1,276 @@
+import numpy as np
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+class DenseBlocksTemporalReduce(nn.Module):
+    """A concatenation of 3 dense blocks with reduction in temporal dimension.
+
+    Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks.
+
+    Args:
+        num_feat (int): Number of channels in the blocks. Default: 64.
+        num_grow_ch (int): Growing factor of the dense blocks. Default: 32
+        adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation.
+            Set to false if you want to train from scratch. Default: False.
+    """
+
+    def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False):
+        super(DenseBlocksTemporalReduce, self).__init__()
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.temporal_reduce1 = nn.Sequential(
+            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True),
+            nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+        self.temporal_reduce2 = nn.Sequential(
+            nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + num_grow_ch,
+                num_feat + num_grow_ch, (1, 1, 1),
+                stride=(1, 1, 1),
+                padding=(0, 0, 0),
+                bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+        self.temporal_reduce3 = nn.Sequential(
+            nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + 2 * num_grow_ch,
+                num_feat + 2 * num_grow_ch, (1, 1, 1),
+                stride=(1, 1, 1),
+                padding=(0, 0, 0),
+                bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(
+                num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True))
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
+
+        Returns:
+            Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w).
+        """
+        x1 = self.temporal_reduce1(x)
+        x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1)
+
+        x2 = self.temporal_reduce2(x1)
+        x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1)
+
+        x3 = self.temporal_reduce3(x2)
+        x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1)
+
+        return x3
+
+
+class DenseBlocks(nn.Module):
+    """ A concatenation of N dense blocks.
+
+    Args:
+        num_feat (int): Number of channels in the blocks. Default: 64.
+        num_grow_ch (int): Growing factor of the dense blocks. Default: 32.
+        num_block (int): Number of dense blocks. The values are:
+            DUF-S (16 layers): 3
+            DUF-M (18 layers): 9
+            DUF-L (52 layers): 21
+        adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation.
+            Set to false if you want to train from scratch. Default: False.
+    """
+
+    def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False):
+        super(DenseBlocks, self).__init__()
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.dense_blocks = nn.ModuleList()
+        for i in range(0, num_block):
+            self.dense_blocks.append(
+                nn.Sequential(
+                    nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True),
+                    nn.Conv3d(
+                        num_feat + i * num_grow_ch,
+                        num_feat + i * num_grow_ch, (1, 1, 1),
+                        stride=(1, 1, 1),
+                        padding=(0, 0, 0),
+                        bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum),
+                    nn.ReLU(inplace=True),
+                    nn.Conv3d(
+                        num_feat + i * num_grow_ch,
+                        num_grow_ch, (3, 3, 3),
+                        stride=(1, 1, 1),
+                        padding=(1, 1, 1),
+                        bias=True)))
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input tensor with shape (b, num_feat, t, h, w).
+
+        Returns:
+            Tensor: Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w).
+        """
+        for i in range(0, len(self.dense_blocks)):
+            y = self.dense_blocks[i](x)
+            x = torch.cat((x, y), 1)
+        return x
+
+
+class DynamicUpsamplingFilter(nn.Module):
+    """Dynamic upsampling filter used in DUF.
+
+    Reference: https://github.com/yhjo09/VSR-DUF
+
+    It only supports input with 3 channels. And it applies the same filters to 3 channels.
+
+    Args:
+        filter_size (tuple): Filter size of generated filters. The shape is (kh, kw). Default: (5, 5).
+    """
+
+    def __init__(self, filter_size=(5, 5)):
+        super(DynamicUpsamplingFilter, self).__init__()
+        if not isinstance(filter_size, tuple):
+            raise TypeError(f'The type of filter_size must be tuple, but got type{filter_size}')
+        if len(filter_size) != 2:
+            raise ValueError(f'The length of filter size must be 2, but got {len(filter_size)}.')
+        # generate a local expansion filter, similar to im2col
+        self.filter_size = filter_size
+        filter_prod = np.prod(filter_size)
+        expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size)  # (kh*kw, 1, kh, kw)
+        self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1)  # repeat for all the 3 channels
+
+    def forward(self, x, filters):
+        """Forward function for DynamicUpsamplingFilter.
+
+        Args:
+            x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w).
+            filters (Tensor): Generated dynamic filters. The shape is (n, filter_prod, upsampling_square, h, w).
+                filter_prod: prod of filter kernel size, e.g., 1*5*5=25.
+                upsampling_square: similar to pixel shuffle, upsampling_square = upsampling * upsampling.
+                e.g., for x 4 upsampling, upsampling_square= 4*4 = 16
+
+        Returns:
+            Tensor: Filtered image with shape (n, 3*upsampling_square, h, w)
+        """
+        n, filter_prod, upsampling_square, h, w = filters.size()
+        kh, kw = self.filter_size
+        expanded_input = F.conv2d(
+            x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3)  # (n, 3*filter_prod, h, w)
+        expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1,
+                                                                              2)  # (n, h, w, 3, filter_prod)
+        filters = filters.permute(0, 3, 4, 1, 2)  # (n, h, w, filter_prod, upsampling_square]
+        out = torch.matmul(expanded_input, filters)  # (n, h, w, 3, upsampling_square)
+        return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w)
+
+
+@ARCH_REGISTRY.register()
+class DUF(nn.Module):
+    """Network architecture for DUF
+
+    ``Paper: Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation``
+
+    Reference: https://github.com/yhjo09/VSR-DUF
+
+    For all the models below, 'adapt_official_weights' is only necessary when
+    loading the weights converted from the official TensorFlow weights.
+    Please set it to False if you are training the model from scratch.
+
+    There are three models with different model size: DUF16Layers, DUF28Layers,
+    and DUF52Layers. This class is the base class for these models.
+
+    Args:
+        scale (int): The upsampling factor. Default: 4.
+        num_layer (int): The number of layers. Default: 52.
+        adapt_official_weights_weights (bool): Whether to adapt the weights
+            translated from the official implementation. Set to false if you
+            want to train from scratch. Default: False.
+    """
+
+    def __init__(self, scale=4, num_layer=52, adapt_official_weights=False):
+        super(DUF, self).__init__()
+        self.scale = scale
+        if adapt_official_weights:
+            eps = 1e-3
+            momentum = 1e-3
+        else:  # pytorch default values
+            eps = 1e-05
+            momentum = 0.1
+
+        self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
+        self.dynamic_filter = DynamicUpsamplingFilter((5, 5))
+
+        if num_layer == 16:
+            num_block = 3
+            num_grow_ch = 32
+        elif num_layer == 28:
+            num_block = 9
+            num_grow_ch = 16
+        elif num_layer == 52:
+            num_block = 21
+            num_grow_ch = 16
+        else:
+            raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.')
+
+        self.dense_block1 = DenseBlocks(
+            num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch,
+            adapt_official_weights=adapt_official_weights)  # T = 7
+        self.dense_block2 = DenseBlocksTemporalReduce(
+            64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights)  # T = 1
+        channels = 64 + num_grow_ch * num_block + num_grow_ch * 3
+        self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum)
+        self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)
+
+        self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+        self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+
+        self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+        self.conv3d_f2 = nn.Conv3d(
+            512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True)
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input with shape (b, 7, c, h, w)
+
+        Returns:
+            Tensor: Output with shape (b, c, h * scale, w * scale)
+        """
+        num_batches, num_imgs, _, h, w = x.size()
+
+        x = x.permute(0, 2, 1, 3, 4)  # (b, c, 7, h, w) for Conv3D
+        x_center = x[:, :, num_imgs // 2, :, :]
+
+        x = self.conv3d1(x)
+        x = self.dense_block1(x)
+        x = self.dense_block2(x)
+        x = F.relu(self.bn3d2(x), inplace=True)
+        x = F.relu(self.conv3d2(x), inplace=True)
+
+        # residual image
+        res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True))
+
+        # filter
+        filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True))
+        filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1)
+
+        # dynamic filter
+        out = self.dynamic_filter(x_center, filter_)
+        out += res.squeeze_(2)
+        out = F.pixel_shuffle(out, self.scale)
+
+        return out

basicsr/archs/ecbsr_arch.py

@@ -0,0 +1,275 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+class SeqConv3x3(nn.Module):
+    """The re-parameterizable block used in the ECBSR architecture.
+
+    ``Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices``
+
+    Reference: https://github.com/xindongzhang/ECBSR
+
+    Args:
+        seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian.
+        in_channels (int): Channel number of input.
+        out_channels (int): Channel number of output.
+        depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
+    """
+
+    def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1):
+        super(SeqConv3x3, self).__init__()
+        self.seq_type = seq_type
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        if self.seq_type == 'conv1x1-conv3x3':
+            self.mid_planes = int(out_channels * depth_multiplier)
+            conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0)
+            self.k0 = conv0.weight
+            self.b0 = conv0.bias
+
+            conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3)
+            self.k1 = conv1.weight
+            self.b1 = conv1.bias
+
+        elif self.seq_type == 'conv1x1-sobelx':
+            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
+            self.k0 = conv0.weight
+            self.b0 = conv0.bias
+
+            # init scale and bias
+            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
+            self.scale = nn.Parameter(scale)
+            bias = torch.randn(self.out_channels) * 1e-3
+            bias = torch.reshape(bias, (self.out_channels, ))
+            self.bias = nn.Parameter(bias)
+            # init mask
+            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
+            for i in range(self.out_channels):
+                self.mask[i, 0, 0, 0] = 1.0
+                self.mask[i, 0, 1, 0] = 2.0
+                self.mask[i, 0, 2, 0] = 1.0
+                self.mask[i, 0, 0, 2] = -1.0
+                self.mask[i, 0, 1, 2] = -2.0
+                self.mask[i, 0, 2, 2] = -1.0
+            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
+
+        elif self.seq_type == 'conv1x1-sobely':
+            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
+            self.k0 = conv0.weight
+            self.b0 = conv0.bias
+
+            # init scale and bias
+            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
+            self.scale = nn.Parameter(torch.FloatTensor(scale))
+            bias = torch.randn(self.out_channels) * 1e-3
+            bias = torch.reshape(bias, (self.out_channels, ))
+            self.bias = nn.Parameter(torch.FloatTensor(bias))
+            # init mask
+            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
+            for i in range(self.out_channels):
+                self.mask[i, 0, 0, 0] = 1.0
+                self.mask[i, 0, 0, 1] = 2.0
+                self.mask[i, 0, 0, 2] = 1.0
+                self.mask[i, 0, 2, 0] = -1.0
+                self.mask[i, 0, 2, 1] = -2.0
+                self.mask[i, 0, 2, 2] = -1.0
+            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
+
+        elif self.seq_type == 'conv1x1-laplacian':
+            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
+            self.k0 = conv0.weight
+            self.b0 = conv0.bias
+
+            # init scale and bias
+            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
+            self.scale = nn.Parameter(torch.FloatTensor(scale))
+            bias = torch.randn(self.out_channels) * 1e-3
+            bias = torch.reshape(bias, (self.out_channels, ))
+            self.bias = nn.Parameter(torch.FloatTensor(bias))
+            # init mask
+            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
+            for i in range(self.out_channels):
+                self.mask[i, 0, 0, 1] = 1.0
+                self.mask[i, 0, 1, 0] = 1.0
+                self.mask[i, 0, 1, 2] = 1.0
+                self.mask[i, 0, 2, 1] = 1.0
+                self.mask[i, 0, 1, 1] = -4.0
+            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
+        else:
+            raise ValueError('The type of seqconv is not supported!')
+
+    def forward(self, x):
+        if self.seq_type == 'conv1x1-conv3x3':
+            # conv-1x1
+            y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
+            # explicitly padding with bias
+            y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
+            b0_pad = self.b0.view(1, -1, 1, 1)
+            y0[:, :, 0:1, :] = b0_pad
+            y0[:, :, -1:, :] = b0_pad
+            y0[:, :, :, 0:1] = b0_pad
+            y0[:, :, :, -1:] = b0_pad
+            # conv-3x3
+            y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1)
+        else:
+            y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
+            # explicitly padding with bias
+            y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
+            b0_pad = self.b0.view(1, -1, 1, 1)
+            y0[:, :, 0:1, :] = b0_pad
+            y0[:, :, -1:, :] = b0_pad
+            y0[:, :, :, 0:1] = b0_pad
+            y0[:, :, :, -1:] = b0_pad
+            # conv-3x3
+            y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels)
+        return y1
+
+    def rep_params(self):
+        device = self.k0.get_device()
+        if device < 0:
+            device = None
+
+        if self.seq_type == 'conv1x1-conv3x3':
+            # re-param conv kernel
+            rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3))
+            # re-param conv bias
+            rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
+            rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1
+        else:
+            tmp = self.scale * self.mask
+            k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device)
+            for i in range(self.out_channels):
+                k1[i, i, :, :] = tmp[i, 0, :, :]
+            b1 = self.bias
+            # re-param conv kernel
+            rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3))
+            # re-param conv bias
+            rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
+            rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1
+        return rep_weight, rep_bias
+
+
+class ECB(nn.Module):
+    """The ECB block used in the ECBSR architecture.
+
+    Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
+    Ref git repo: https://github.com/xindongzhang/ECBSR
+
+    Args:
+        in_channels (int): Channel number of input.
+        out_channels (int): Channel number of output.
+        depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
+        act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu.
+        with_idt (bool): Whether to use identity connection. Default: False.
+    """
+
+    def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False):
+        super(ECB, self).__init__()
+
+        self.depth_multiplier = depth_multiplier
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.act_type = act_type
+
+        if with_idt and (self.in_channels == self.out_channels):
+            self.with_idt = True
+        else:
+            self.with_idt = False
+
+        self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1)
+        self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier)
+        self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels)
+        self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels)
+        self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels)
+
+        if self.act_type == 'prelu':
+            self.act = nn.PReLU(num_parameters=self.out_channels)
+        elif self.act_type == 'relu':
+            self.act = nn.ReLU(inplace=True)
+        elif self.act_type == 'rrelu':
+            self.act = nn.RReLU(lower=-0.05, upper=0.05)
+        elif self.act_type == 'softplus':
+            self.act = nn.Softplus()
+        elif self.act_type == 'linear':
+            pass
+        else:
+            raise ValueError('The type of activation if not support!')
+
+    def forward(self, x):
+        if self.training:
+            y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x)
+            if self.with_idt:
+                y += x
+        else:
+            rep_weight, rep_bias = self.rep_params()
+            y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1)
+        if self.act_type != 'linear':
+            y = self.act(y)
+        return y
+
+    def rep_params(self):
+        weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias
+        weight1, bias1 = self.conv1x1_3x3.rep_params()
+        weight2, bias2 = self.conv1x1_sbx.rep_params()
+        weight3, bias3 = self.conv1x1_sby.rep_params()
+        weight4, bias4 = self.conv1x1_lpl.rep_params()
+        rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), (
+            bias0 + bias1 + bias2 + bias3 + bias4)
+
+        if self.with_idt:
+            device = rep_weight.get_device()
+            if device < 0:
+                device = None
+            weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device)
+            for i in range(self.out_channels):
+                weight_idt[i, i, 1, 1] = 1.0
+            bias_idt = 0.0
+            rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt
+        return rep_weight, rep_bias
+
+
+@ARCH_REGISTRY.register()
+class ECBSR(nn.Module):
+    """ECBSR architecture.
+
+    Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
+    Ref git repo: https://github.com/xindongzhang/ECBSR
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_block (int): Block number in the trunk network.
+        num_channel (int): Channel number.
+        with_idt (bool): Whether use identity in convolution layers.
+        act_type (str): Activation type.
+        scale (int): Upsampling factor.
+    """
+
+    def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale):
+        super(ECBSR, self).__init__()
+        self.num_in_ch = num_in_ch
+        self.scale = scale
+
+        backbone = []
+        backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
+        for _ in range(num_block):
+            backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
+        backbone += [
+            ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt)
+        ]
+
+        self.backbone = nn.Sequential(*backbone)
+        self.upsampler = nn.PixelShuffle(scale)
+
+    def forward(self, x):
+        if self.num_in_ch > 1:
+            shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1)
+        else:
+            shortcut = x  # will repeat the input in the channel dimension (repeat  scale * scale times)
+        y = self.backbone(x) + shortcut
+        y = self.upsampler(y)
+        return y