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/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/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
+
+