ridcp

Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest
+
+COPY . /tmp/
+
+WORKDIR /tmp
+
+# RUN mkdir -p /root/.cache/torch/hub/checkpoints/
+
+RUN pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
+
+# RUN cp -r ./extra_models/* /root/.cache/torch/hub/checkpoints/
+

LICENSE

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\ No newline at end of file

README.md

+# RIDCP
+
+## 论文
+
+**RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors**
+
+* https://arxiv.org/pdf/2304.03994.pdf
+
+## 模型结构
+
+
+ridcp先使用Encoder对输入进行特征提取得到Z，特征Z流向$`G_{vq}`$以及G，流向$`G_{vq}`$的Z被替换(CHM方法)为码本中的特征$`Z_{vq}`$，然后$`Z_{vq}`$通过$`G_{vq}`$获取各个层的特征$`F^i_{vq}`$，通过G获取到的特征$`F^i`$通过与$`F^i_{vq}`$的特征进行融合(NFA方法)得到新的特征$`F^i`$，直至G的最后一层，得到目标输出。
+
+![img.png](imgs/img.png)
+![img_2.png](imgs/img_2.png)
+
+
+## 算法原理
+
+
+用途：该算法通过有效利用先验知识（码本）来完成图像去雾功能。
+
+原理：
+
+1. 融合特征(NFA)，消除仅使用码本特征导致的图像失真；
+2. 使用CHM（距离计算方式）匹配更好的码本特征。
+
+
+**NFA**
+
+![Alt text](imgs/nfa1.png)
+
+![Alt text](imgs/nfa2.png)
+
+**CHM**
+
+![Alt text](imgs/chm1.png)
+
+![Alt text](imgs/chm2.png)
+
+![Alt text](imgs/chm3.png)
+
+
+这里 $`\hat{z}`$代表通过Encoder获取到的特征, $`z_k`$代表码本中的第k个特征，$`\alpha`$ 有最佳值21.25，$`f^k_h`$与$`f^k_c`$为统计值。
+
+## 环境配置
+
+
+### Docker (方法一)
+
+    docker pull FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest
+    docker run --shm-size 10g --network=host --name=ridcp --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -it 2bb84d403fac bash   <your IMAGE ID>
+    pip install -r requirements.txt
+    BASICSR_EXT=True python setup.py develop
+
+### Dockerfile (方法二)
+    
+    # 需要在对应的目录下
+    docker build -t <IMAGE_NAME>:<TAG> .
+    # <your IMAGE ID>用以上拉取的docker的镜像ID替换
+    docker run -it --shm-size 10g --network=host --name=ridcp --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined 
+    <your IMAGE ID> bash
+    BASICSR_EXT=True python setup.py develop
+    
+
+### Anaconda (方法三)
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+https://developer.hpccube.com/tool/
+
+    DTK驱动：dtk23.04
+    python：python3.8
+    torch:1.13.1
+    torchvision:0.14.1
+    torchaudio:0.13.1
+    deepspeed:0.9.2
+    apex:0.1
+
+Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应
+
+2、其它非特殊库参照requirements.txt安装
+
+    pip install -r requirements.txt
+
+3、其他
+
+    BASICSR_EXT=True python setup.py develop
+
+Tips: 使用阿里的pip源
+
+
+## 数据集
+
+下载地址（训练集）：https://pan.baidu.com/s/1oX3AZkVlEa7S1sSO12r47Q （提取码：qqqo）
+
+
+    datasets
+        |- clear_images_no_haze_no_dark_500
+            |- xxx.jpg
+            |- ...
+        |- depth_500
+            |- xxx.npy
+            |- ...
+
+下载地址（测试集）：https://sites.google.com/view/reside-dehaze-datasets/reside-%CE%B2
+
+
+    RTTS
+        |- Annotations
+            |- xxx.xml
+            |- ...
+        |- ImageSets
+            |- Main
+                |- xxx.txt
+                |- ...
+        |- JPEGImages
+            |- xxx.png
+            |- ...
+        
+
+## 训练
+
+模型下载：https://pan.baidu.com/s/1ps9dPmerWyXILxb6lkHihQ  （HQPs，CHM，提取码：huea）
+
+    # 这里的X表示设备编号，如0，1，2，3
+    CUDA_VISIBLE_DEVICES=X,X,X,X python basicsr/train.py -opt options/RIDCP.yml
+
+## 推理
+
+模型下载：https://pan.baidu.com/s/1ps9dPmerWyXILxb6lkHihQ  （RIDCP，提取码：huea）
+
+    python inference_ridcp.py -i examples -w pretrained_models/pretrained_RIDCP.pth -o results --use_weight --alpha -21.25
+    
+参数说明：
+1. -i <路径>: 输入的图片或者文件夹
+2. -w <路径>: 模型参数
+3. -o <路径>: 处理完后的结果路径
+
+
+## result
+
+
+上图是输入图像，下图是输出图像
+
+![img_6.png](imgs/img_6.png)
+
+
+### 精度
+
+|platform|FADE|BRISQUE|NIMA|
+|:---|:---:|:---:|:---:|
+|DCU|0.845|17.295|5.0|
+|GPU|0.944|18.782|4.4267|
+
+注意：FADE与BRISQUE的值越小越好，NIMA的值越大越好。
+
+
+## 应用场景
+
+
+### 算法类别
+
+`图像去雾`
+
+### 热点应用行业
+`交通,医疗,环保,气象`
+
+## 源码仓库及问题反馈
+
+https://developer.hpccube.com/codes/modelzoo/ridcp_pytorch
+
+## 参考资料
+
+* https://github.com/RQ-Wu/RIDCP_dehazing

basicsr/__init__.py

+# https://github.com/xinntao/BasicSR
+# flake8: noqa
+from .archs import *
+from .data import *
+from .losses import *
+from .models import *
+from .test import *
+from .train import *
+from .utils import *

basicsr/active_codebook.py

+from basicsr.archs.femasr_arch import FeMaSRNet
+from basicsr.archs.dehaze_vq_warp_arch import VQWarpDehazeNet
+from basicsr.archs import build_network
+from collections import Counter
+import torch
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+haze_path = '../dataset/RTTS/JPEGImages/'
+clear_path = '../dataset/OTS/clear_images_no_haze_no_dark/'
+
+hq_opt = {
+    'gt_resolution': 256,
+    'norm_type': 'gn',
+    'act_type': 'silu',
+    'scale_factor': 1,
+    'codebook_params': [[32, 1024, 512]],
+    'LQ_stage': True,
+    'use_quantilize': True,
+    'use_semantic_loss': False
+}
+
+dict_total = []
+if __name__ == '__main__':
+    ckpt_path = 'experiments/vq_warp_dehaze_residual/models/net_g_19000.pth'
+    # print(hq_opt['codebook_params'].size())
+    net_vq = VQWarpDehazeNet(**hq_opt).cuda()
+    net_vq.load_state_dict(torch.load(ckpt_path)['params'])
+
+    i = 0
+    for filename in os.listdir(haze_path):
+        filepath = os.path.join(haze_path, filename)
+        image = cv2.imread(filepath)[:, :, ::-1] / 255.0
+        image = torch.FloatTensor(image).unsqueeze(0).cuda().permute(0, 3, 1, 2)
+
+        _, index_list = net_vq.test(image)
+        dict_total += list(index_list[0].flatten(0).cpu().numpy())
+
+        i = i +1
+        print(i)
+        if i > 100:
+            break
+
+result = Counter(dict_total)
+result_image = np.zeros((32, 32))
+print(len(result))
+for k in result.keys():
+    k_index = int(k)
+    result_image[k // 32, k % 32] = result[k]
+
+# result_image = (result_image - result_image.min()) / (result_image.max() - result_image.min())
+# result_image = np.log(result_image + 1)
+plt.imshow(result_image)
+plt.savefig('visual_code_dehaze_exp.png')
\ No newline at end of file

basicsr/archs/__init__.py

+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

+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.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 ResidualBlockNoBN(nn.Module):
+    """Residual block without BN.
+
+    It has a style of:
+        ---Conv-ReLU-Conv-+-
+         |________________|
+
+    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(inplace=True)
+
+        if not pytorch_init:
+            default_init_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        identity = 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)
+
+
+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/dehaze_vq_weight_arch.py

+import torch
+import torch.nn.functional as F
+from torch import nn as nn
+import numpy as np
+import math
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
+
+from .network_swinir import RSTB
+from .ridcp_utils import ResBlock, CombineQuantBlock 
+from .vgg_arch import VGGFeatureExtractor
+
+
+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.
+    Ref:
+        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.')
+        
+        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
+                                         self.dilation, self.groups, self.deformable_groups)
+
+class VectorQuantizer(nn.Module):
+    """
+    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
+    ____________________________________________
+    Discretization bottleneck part of the VQ-VAE.
+    Inputs:
+    - n_e : number of embeddings
+    - e_dim : dimension of embedding
+    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
+    _____________________________________________
+    """
+
+    def __init__(self, n_e, e_dim, weight_path='pretrained_models/weight_for_matching_dehazing_Flickr.pth', beta=0.25, LQ_stage=False, use_weight=True, weight_alpha=1.0):
+        super().__init__()
+        self.n_e = int(n_e)
+        self.e_dim = int(e_dim)
+        self.LQ_stage = LQ_stage
+        self.beta = beta
+        self.use_weight = use_weight
+        self.weight_alpha = weight_alpha
+        if self.use_weight:
+            self.weight = nn.Parameter(torch.load(weight_path))
+            self.weight.requires_grad = False 
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+    
+    def dist(self, x, y):
+        if x.shape == y.shape:
+            return (x - y) ** 2
+        else:
+            return torch.sum(x ** 2, dim=1, keepdim=True) + \
+                    torch.sum(y**2, dim=1) - 2 * \
+                    torch.matmul(x, y.t())
+    
+    def gram_loss(self, x, y):
+        b, h, w, c = x.shape
+        x = x.reshape(b, h*w, c)
+        y = y.reshape(b, h*w, c)
+
+        gmx = x.transpose(1, 2) @ x / (h*w)
+        gmy = y.transpose(1, 2) @ y / (h*w)
+    
+        return (gmx - gmy).square().mean()
+
+    def forward(self, z, gt_indices=None, current_iter=None, weight_alpha=None):
+        """
+        Args:
+            z: input features to be quantized, z (continuous) -> z_q (discrete)
+               z.shape = (batch, channel, height, width)
+            gt_indices: feature map of given indices, used for visualization. 
+        """
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = z.permute(0, 2, 3, 1).contiguous()
+        z_flattened = z.view(-1, self.e_dim)
+
+        codebook = self.embedding.weight
+
+        d = self.dist(z_flattened, codebook)
+        if self.use_weight and self.LQ_stage:
+            if weight_alpha is not None:
+                self.weight_alpha = weight_alpha
+            d = d * torch.exp(self.weight_alpha * self.weight)
+        
+        # find closest encodings
+        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
+        min_encodings = torch.zeros(min_encoding_indices.shape[0], codebook.shape[0]).to(z)
+        min_encodings.scatter_(1, min_encoding_indices, 1)
+
+        if gt_indices is not None:
+            gt_indices = gt_indices.reshape(-1)
+
+            gt_min_indices = gt_indices.reshape_as(min_encoding_indices)
+            gt_min_onehot = torch.zeros(gt_min_indices.shape[0], codebook.shape[0]).to(z)
+            gt_min_onehot.scatter_(1, gt_min_indices, 1)
+
+            z_q_gt = torch.matmul(gt_min_onehot, codebook)
+            z_q_gt = z_q_gt.view(z.shape)
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings, codebook)
+        z_q = z_q.view(z.shape)
+
+        e_latent_loss = torch.mean((z_q.detach() - z)**2)
+        q_latent_loss = torch.mean((z_q - z.detach())**2)
+
+        if self.LQ_stage and gt_indices is not None:
+            # codebook_loss = self.dist(z_q, z_q_gt.detach()).mean() \
+                            # + self.beta * self.dist(z_q_gt.detach(), z) 
+            codebook_loss = self.beta * self.dist(z_q_gt.detach(), z) 
+            texture_loss = self.gram_loss(z, z_q_gt.detach()) 
+            # print("codebook loss:", codebook_loss.mean(), "\ntexture_loss: ", texture_loss.mean())
+            codebook_loss = codebook_loss + texture_loss 
+        else:
+            codebook_loss = q_latent_loss + e_latent_loss * self.beta
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q, codebook_loss, min_encoding_indices.reshape(z_q.shape[0], 1, z_q.shape[2], z_q.shape[3])
+    
+    def get_codebook_entry(self, indices):
+        b, _, h, w = indices.shape
+
+        indices = indices.flatten().to(self.embedding.weight.device)
+        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
+        min_encodings.scatter_(1, indices[:,None], 1)
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)        
+        z_q = z_q.view(b, h, w, -1).permute(0, 3, 1, 2).contiguous()
+        return z_q
+
+class SwinLayers(nn.Module):
+    def __init__(self, input_resolution=(32, 32), embed_dim=256, 
+                blk_depth=6,
+                num_heads=8,
+                window_size=8,
+                **kwargs):
+        super().__init__()
+        self.swin_blks = nn.ModuleList()
+        for i in range(4):
+            layer = RSTB(embed_dim, input_resolution, blk_depth, num_heads, window_size, patch_size=1, **kwargs)
+            self.swin_blks.append(layer)
+    
+    def forward(self, x):
+        b, c, h, w = x.shape
+        x = x.reshape(b, c, h*w).transpose(1, 2)
+        for m in self.swin_blks:
+            x = m(x, (h, w))
+        x = x.transpose(1, 2).reshape(b, c, h, w) 
+        return x
+
+
+class MultiScaleEncoder(nn.Module):
+    def __init__(self,
+                 in_channel,
+                 max_depth,
+                 input_res=256,
+                 channel_query_dict=None,
+                 norm_type='gn',
+                 act_type='leakyrelu',
+                 LQ_stage=True,
+                 **swin_opts,
+                 ):
+        super().__init__()
+        self.LQ_stage = LQ_stage
+        ksz = 3
+
+        self.in_conv = nn.Conv2d(in_channel, channel_query_dict[input_res], 4, padding=1)
+
+        self.blocks = nn.ModuleList()
+        self.up_blocks = nn.ModuleList()
+        self.max_depth = max_depth
+        res = input_res
+        for i in range(max_depth):
+            in_ch, out_ch = channel_query_dict[res], channel_query_dict[res // 2]
+            tmp_down_block = [
+                nn.Conv2d(in_ch, out_ch, ksz, stride=2, padding=1),
+                ResBlock(out_ch, out_ch, norm_type, act_type),
+                ResBlock(out_ch, out_ch, norm_type, act_type),
+            ]
+            self.blocks.append(nn.Sequential(*tmp_down_block))
+            res = res // 2
+
+        if LQ_stage: 
+            self.blocks.append(SwinLayers(**swin_opts))
+
+    def forward(self, input):
+        # input.requires_grad = True
+        x = self.in_conv(input)
+        # if self.LQ_stage:
+        #     print('input: ', input.requires_grad)
+        #     for p in self.in_conv.parameters():
+        #         print('conv: ', p.requires_grad)
+        #     print('first output:', x.requires_grad)
+
+        for idx, m in enumerate(self.blocks):
+            with torch.backends.cudnn.flags(enabled=False):
+                x = m(x)
+
+        return x
+
+
+class DecoderBlock(nn.Module):
+
+    def __init__(self, in_channel, out_channel, norm_type='gn', act_type='leakyrelu'):
+        super().__init__()
+
+        self.block = []
+        self.block += [
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(in_channel, out_channel, 3, stride=1, padding=1),
+            ResBlock(out_channel, out_channel, norm_type, act_type),
+            ResBlock(out_channel, out_channel, norm_type, act_type),
+        ]
+
+        self.block = nn.Sequential(*self.block)
+
+    def forward(self, input):
+        return self.block(input)
+
+class WarpBlock(nn.Module):
+    def __init__(self, in_channel):
+        super().__init__()
+        self.offset = nn.Conv2d(in_channel * 2, in_channel, 3, stride=1, padding=1)
+        self.dcn = DCNv2Pack(in_channel, in_channel, 3, padding=1, deformable_groups=4)
+
+    def forward(self, x_vq, x_residual):
+        x_residual = self.offset(torch.cat([x_vq, x_residual], dim=1))
+        feat_after_warp = self.dcn(x_vq, x_residual)
+
+        return feat_after_warp
+
+
+class MultiScaleDecoder(nn.Module):
+    def __init__(self,
+                 in_channel,
+                 max_depth,
+                 input_res=256,
+                 channel_query_dict=None,
+                 norm_type='gn',
+                 act_type='leakyrelu',
+                 only_residual=False,
+                 use_warp=True
+                 ):
+        super().__init__()
+        self.only_residual = only_residual
+        self.use_warp = use_warp
+        self.upsampler = nn.ModuleList()
+        self.warp = nn.ModuleList()
+        res =  input_res // (2 ** max_depth)
+        for i in range(max_depth):
+            in_channel, out_channel = channel_query_dict[res], channel_query_dict[res * 2]
+            self.upsampler.append(nn.Sequential(
+                nn.Upsample(scale_factor=2),
+                nn.Conv2d(in_channel, out_channel, 3, stride=1, padding=1),
+                ResBlock(out_channel, out_channel, norm_type, act_type),
+                ResBlock(out_channel, out_channel, norm_type, act_type),
+                )
+            )
+            self.warp.append(WarpBlock(out_channel))
+            res = res * 2
+
+    def forward(self, input, code_decoder_output):
+        x = input
+        for idx, m in enumerate(self.upsampler):
+            with torch.backends.cudnn.flags(enabled=False):
+                if not self.only_residual:
+                    x = m(x)
+                    if self.use_warp:
+                        x_vq = self.warp[idx](code_decoder_output[idx], x)
+                        # print(idx, x.mean(), x_vq.mean())
+                        x = x + x_vq * (x.mean() / x_vq.mean())
+                    else:
+                        x = x + code_decoder_output[idx]
+                else:
+                    x = m(x)
+        # print()
+        return x
+
+@ARCH_REGISTRY.register()
+class VQWeightDehazeNet(nn.Module):
+    def __init__(self,
+                 *,
+                 in_channel=3,
+                 codebook_params=None,
+                 gt_resolution=256,
+                 LQ_stage=False,
+                 norm_type='gn',
+                 act_type='silu',
+                 use_quantize=True,
+                 use_semantic_loss=False,
+                 use_residual=True,
+                 only_residual=False,
+                 use_weight=False,
+                 use_warp=True,
+                 weight_alpha=1.0,
+                 **ignore_kwargs):
+        super().__init__()
+
+        codebook_params = np.array(codebook_params)
+
+        self.codebook_scale = codebook_params[:, 0]
+        codebook_emb_num = codebook_params[:, 1].astype(int)
+        codebook_emb_dim = codebook_params[:, 2].astype(int)
+
+        self.use_quantize = use_quantize
+        self.in_channel = in_channel
+        self.gt_res = gt_resolution
+        self.LQ_stage = LQ_stage
+        self.use_residual = use_residual
+        self.only_residual = only_residual
+        self.use_weight = use_weight
+        self.use_warp = use_warp
+        self.weight_alpha = weight_alpha
+
+        channel_query_dict = {
+            8: 256,
+            16: 256,
+            32: 256,
+            64: 256,
+            128: 128,
+            256: 64,
+            512: 32,
+        }
+
+        # build encoder 
+        self.max_depth = int(np.log2(gt_resolution // self.codebook_scale[0]))
+        self.multiscale_encoder = MultiScaleEncoder(
+                                in_channel,     
+                                self.max_depth,  
+                                self.gt_res, 
+                                channel_query_dict,
+                                norm_type, act_type, LQ_stage
+                            )
+        if self.LQ_stage and self.use_residual:
+            self.multiscale_decoder = MultiScaleDecoder(
+                                in_channel,     
+                                self.max_depth,  
+                                self.gt_res, 
+                                channel_query_dict,
+                                norm_type, act_type, only_residual, use_warp=self.use_warp
+            )
+
+
+        # build decoder
+        self.decoder_group = nn.ModuleList()
+        for i in range(self.max_depth):
+            res = gt_resolution // 2**self.max_depth * 2**i
+            in_ch, out_ch = channel_query_dict[res], channel_query_dict[res * 2]
+            self.decoder_group.append(DecoderBlock(in_ch, out_ch, norm_type, act_type))
+
+        self.out_conv = nn.Conv2d(out_ch, 3, 3, 1, 1)
+        self.residual_conv = nn.Conv2d(out_ch, 3, 3, 1, 1)
+
+        # build multi-scale vector quantizers 
+        self.quantize_group = nn.ModuleList()
+        self.before_quant_group = nn.ModuleList()
+        self.after_quant_group = nn.ModuleList()
+
+        for scale in range(0, codebook_params.shape[0]):
+            quantize = VectorQuantizer(
+                codebook_emb_num[scale],
+                codebook_emb_dim[scale],
+                LQ_stage=self.LQ_stage,
+                use_weight=self.use_weight,
+                weight_alpha=self.weight_alpha
+            )
+            self.quantize_group.append(quantize)
+
+            scale_in_ch = channel_query_dict[self.codebook_scale[scale]]
+            if scale == 0:
+                quant_conv_in_ch = scale_in_ch
+                comb_quant_in_ch1 = codebook_emb_dim[scale]
+                comb_quant_in_ch2 = 0
+            else:
+                quant_conv_in_ch = scale_in_ch * 2
+                comb_quant_in_ch1 = codebook_emb_dim[scale - 1]
+                comb_quant_in_ch2 = codebook_emb_dim[scale]
+
+            self.before_quant_group.append(nn.Conv2d(quant_conv_in_ch, codebook_emb_dim[scale], 1))
+            self.after_quant_group.append(CombineQuantBlock(comb_quant_in_ch1, comb_quant_in_ch2, scale_in_ch))
+
+        # semantic loss for HQ pretrain stage
+        self.use_semantic_loss = use_semantic_loss
+        if use_semantic_loss:
+            self.conv_semantic = nn.Sequential(
+                nn.Conv2d(512, 512, 1, 1, 0),
+                nn.ReLU(),
+                )
+            self.vgg_feat_layer = 'relu4_4'
+            self.vgg_feat_extractor = VGGFeatureExtractor([self.vgg_feat_layer]) 
+
+    def encode_and_decode(self, input, gt_indices=None, current_iter=None, weight_alpha=None):
+        # if self.training:
+        #     for p in self.multiscale_encoder.parameters():
+        #         p.requires_grad = True
+        enc_feats = self.multiscale_encoder(input)
+
+        if self.use_semantic_loss:
+            with torch.no_grad():
+                vgg_feat = self.vgg_feat_extractor(input)[self.vgg_feat_layer]
+
+        codebook_loss_list = []
+        indices_list = []
+        semantic_loss_list = []
+        code_decoder_output = []
+
+        quant_idx = 0
+        prev_dec_feat = None
+        prev_quant_feat = None
+        out_img = None
+        out_img_residual = None
+
+        x = enc_feats
+        for i in range(self.max_depth):
+            cur_res = self.gt_res // 2**self.max_depth * 2**i
+            if cur_res in self.codebook_scale:  # needs to perform quantize
+                if prev_dec_feat is not None:
+                    before_quant_feat = torch.cat((x, prev_dec_feat), dim=1)
+                else:
+                    before_quant_feat = x
+                feat_to_quant = self.before_quant_group[quant_idx](before_quant_feat)
+
+                if weight_alpha is not None:
+                    self.weight_alpha = weight_alpha
+                if gt_indices is not None:
+                    z_quant, codebook_loss, indices = self.quantize_group[quant_idx](feat_to_quant, gt_indices[quant_idx], weight_alpha=self.weight_alpha)
+                else:
+                    z_quant, codebook_loss, indices = self.quantize_group[quant_idx](feat_to_quant, weight_alpha=self.weight_alpha)
+
+                if self.use_semantic_loss:
+                    semantic_z_quant = self.conv_semantic(z_quant)
+                    semantic_loss = F.mse_loss(semantic_z_quant, vgg_feat)
+                    semantic_loss_list.append(semantic_loss)
+                
+                if not self.use_quantize:
+                    z_quant = feat_to_quant
+
+                after_quant_feat = self.after_quant_group[quant_idx](z_quant, prev_quant_feat)
+
+                codebook_loss_list.append(codebook_loss)
+                indices_list.append(indices)
+
+                quant_idx += 1
+                prev_quant_feat = z_quant
+                x = after_quant_feat
+
+            x = self.decoder_group[i](x)
+            code_decoder_output.append(x)
+            prev_dec_feat = x
+        
+        out_img = self.out_conv(x)
+
+        if self.LQ_stage and self.use_residual:
+            if self.only_residual:
+                residual_feature = self.multiscale_decoder(enc_feats, code_decoder_output)
+            else:
+                residual_feature = self.multiscale_decoder(enc_feats.detach(), code_decoder_output)
+            out_img_residual = self.residual_conv(residual_feature)
+
+        if len(codebook_loss_list) > 0:
+            codebook_loss = sum(codebook_loss_list) 
+        else:
+            codebook_loss = 0
+        semantic_loss = sum(semantic_loss_list) if len(semantic_loss_list) else codebook_loss * 0
+
+        return out_img, out_img_residual, codebook_loss, semantic_loss, feat_to_quant, z_quant, indices_list
+    
+    def decode_indices(self, indices):
+        assert len(indices.shape) == 4, f'shape of indices must be (b, 1, h, w), but got {indices.shape}'
+
+        z_quant = self.quantize_group[0].get_codebook_entry(indices)
+        x = self.after_quant_group[0](z_quant)
+
+        for m in self.decoder_group:
+            x = m(x)
+        out_img = self.out_conv(x)
+        return out_img
+
+    @torch.no_grad()
+    def test_tile(self, input, tile_size=240, tile_pad=16):
+        # return self.test(input)
+        """It will first crop input images to tiles, and then process each tile.
+        Finally, all the processed tiles are merged into one images.
+        Modified from: https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
+        """
+        batch, channel, height, width = input.shape
+        output_height = height
+        output_width = width
+        output_shape = (batch, channel, output_height, output_width)
+
+        # start with black image
+        output = input.new_zeros(output_shape)
+        tiles_x = math.ceil(width / tile_size)
+        tiles_y = math.ceil(height / tile_size)
+
+        # loop over all tiles
+        for y in range(tiles_y):
+            for x in range(tiles_x):
+                # extract tile from input image
+                ofs_x = x * tile_size
+                ofs_y = y * tile_size
+                # input tile area on total image
+                input_start_x = ofs_x
+                input_end_x = min(ofs_x + tile_size, width)
+                input_start_y = ofs_y
+                input_end_y = min(ofs_y + tile_size, height)
+
+                # input tile area on total image with padding
+                input_start_x_pad = max(input_start_x - tile_pad, 0)
+                input_end_x_pad = min(input_end_x + tile_pad, width)
+                input_start_y_pad = max(input_start_y - tile_pad, 0)
+                input_end_y_pad = min(input_end_y + tile_pad, height)
+
+                # input tile dimensions
+                input_tile_width = input_end_x - input_start_x
+                input_tile_height = input_end_y - input_start_y
+                tile_idx = y * tiles_x + x + 1
+                input_tile = input[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
+
+                # upscale tile
+                output_tile = self.test(input_tile)
+
+                # output tile area on total image
+                output_start_x = input_start_x
+                output_end_x = input_end_x
+                output_start_y = input_start_y
+                output_end_y = input_end_y
+
+                # output tile area without padding
+                output_start_x_tile = (input_start_x - input_start_x_pad)
+                output_end_x_tile = output_start_x_tile + input_tile_width
+                output_start_y_tile = (input_start_y - input_start_y_pad)
+                output_end_y_tile = output_start_y_tile + input_tile_height
+
+                # put tile into output image
+                output[:, :, output_start_y:output_end_y,
+                       output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
+                                                                  output_start_x_tile:output_end_x_tile]
+        return output
+
+    @torch.no_grad()
+    def test(self, input, weight_alpha=None):
+        org_use_semantic_loss = self.use_semantic_loss
+        self.use_semantic_loss = False
+
+        # padding to multiple of window_size * 8
+        wsz = 32
+        _, _, h_old, w_old = input.shape
+        h_pad = (h_old // wsz + 1) * wsz - h_old
+        w_pad = (w_old // wsz + 1) * wsz - w_old
+        input = torch.cat([input, torch.flip(input, [2])], 2)[:, :, :h_old + h_pad, :]
+        input = torch.cat([input, torch.flip(input, [3])], 3)[:, :, :, :w_old + w_pad]
+
+        output_vq, output, _, _, _, after_quant, index = self.encode_and_decode(input, None, None, weight_alpha=weight_alpha)
+
+        if output is not None:
+            output = output[..., :h_old, :w_old]
+        if output_vq is not None:
+            output_vq = output_vq[..., :h_old, :w_old]
+
+        self.use_semantic_loss = org_use_semantic_loss 
+        return output, index
+
+    def forward(self, input, gt_indices=None, weight_alpha=None):
+
+        if gt_indices is not None:
+            # in LQ training stage, need to pass GT indices for supervise.
+            dec, dec_residual, codebook_loss, semantic_loss, quant_before_feature, quant_after_feature, indices = self.encode_and_decode(input, gt_indices, weight_alpha=weight_alpha)
+        else:
+            # in HQ stage, or LQ test stage, no GT indices needed.
+            dec, dec_residual, codebook_loss, semantic_loss, quant_before_feature, quant_after_feature, indices = self.encode_and_decode(input, weight_alpha=weight_alpha)
+
+        return dec, dec_residual, codebook_loss, semantic_loss, quant_before_feature, quant_after_feature, indices

basicsr/archs/discriminator_arch.py

+from basicsr.utils.registry import ARCH_REGISTRY
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn.utils import spectral_norm
+
+
+@ARCH_REGISTRY.register()
+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
\ No newline at end of file

basicsr/archs/network_swinir.py

+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        # with torch.backends.cudnn.flags(enabled=False):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.img_size
+        if self.norm is not None:
+            flops += H * W * self.embed_dim
+        return flops
+
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+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)
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    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, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+
+
+class SwinIR(nn.Module):
+    r""" SwinIR
+        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(SwinIR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+        
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+
+        x = x / self.img_range + self.mean
+
+        return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = SwinIR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)

basicsr/archs/ridcp_utils.py

+import torch
+from torch.nn import functional as F
+from torch import nn as nn
+
+class NormLayer(nn.Module):
+    """Normalization Layers.
+    ------------
+    # Arguments
+        - channels: input channels, for batch norm and instance norm.
+        - input_size: input shape without batch size, for layer norm.
+    """
+    def __init__(self, channels, norm_type='bn'):
+        super(NormLayer, self).__init__()
+        norm_type = norm_type.lower()
+        self.norm_type = norm_type
+        self.channels = channels
+        if norm_type == 'bn':
+            self.norm = nn.BatchNorm2d(channels, affine=True)
+        elif norm_type == 'in':
+            self.norm = nn.InstanceNorm2d(channels, affine=False)
+        elif norm_type == 'gn':
+            self.norm = nn.GroupNorm(num_groups=32, num_channels=channels, eps=1e-6, affine=True)
+        elif norm_type == 'none':
+            self.norm = lambda x: x*1.0
+        else:
+            assert 1==0, 'Norm type {} not support.'.format(norm_type)
+
+    def forward(self, x):
+        return self.norm(x)
+
+
+class ActLayer(nn.Module):
+    """activation layer.
+    ------------
+    # Arguments
+        - relu type: type of relu layer, candidates are
+            - ReLU
+            - LeakyReLU: default relu slope 0.2
+            - PRelu 
+            - SELU
+            - none: direct pass
+    """
+    def __init__(self, channels, relu_type='leakyrelu'):
+        super(ActLayer, self).__init__()
+        relu_type = relu_type.lower()
+        if relu_type == 'relu':
+            self.func = nn.ReLU(True)
+        elif relu_type == 'leakyrelu':
+            self.func = nn.LeakyReLU(0.2, inplace=True)
+        elif relu_type == 'prelu':
+            self.func = nn.PReLU(channels)
+        elif relu_type == 'none':
+            self.func = lambda x: x*1.0
+        elif relu_type == 'silu':
+            self.func = nn.SiLU(True)
+        elif relu_type == 'gelu':
+            self.func = nn.GELU()
+        else:
+            assert 1==0, 'activation type {} not support.'.format(relu_type)
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class ResBlock(nn.Module):
+    """
+    Use preactivation version of residual block, the same as taming
+    """
+    def __init__(self, in_channel, out_channel, norm_type='gn', act_type='leakyrelu'):
+        super(ResBlock, self).__init__()
+
+        self.conv = nn.Sequential(
+            NormLayer(in_channel, norm_type),
+            ActLayer(in_channel, act_type),
+            nn.Conv2d(in_channel, out_channel, 3, stride=1, padding=1),
+            NormLayer(out_channel, norm_type),
+            ActLayer(out_channel, act_type),
+            nn.Conv2d(out_channel, out_channel, 3, stride=1, padding=1),
+        )
+
+    def forward(self, input):
+        with torch.backends.cudnn.flags(enabled=True, deterministic=True, benchmark=False):
+            res = self.conv(input)
+        out = res + input
+        return out
+
+
+class CombineQuantBlock(nn.Module):
+    def __init__(self, in_ch1, in_ch2, out_channel):
+        super().__init__()
+        self.conv = nn.Conv2d(in_ch1 + in_ch2, out_channel, 3, 1, 1)
+
+    def forward(self, input1, input2=None):
+        if input2 is not None:
+            input2 = F.interpolate(input2, input1.shape[2:])
+            input = torch.cat((input1, input2), dim=1)
+        else:
+            input = input1
+        out = self.conv(input)
+        return out
+
+