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CodeFormer/.gitignore

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+.vscode
+
+# ignored files
+version.py
+
+# ignored files with suffix
+*.html
+# *.png
+# *.jpeg
+# *.jpg
+*.pt
+*.gif
+*.pth
+*.dat
+*.zip
+
+# template
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# project
+results/
+dlib/
+*.pth
+*_old*
+

CodeFormer/README.md

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+<p align="center">
+  <img src="assets/CodeFormer_logo.png" height=110>
+</p>
+
+## Towards Robust Blind Face Restoration with Codebook Lookup Transformer
+
+[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI)
+
+
+<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) ![visitors](https://visitor-badge.glitch.me/badge?page_id=sczhou/CodeFormer)
+
+[Shangchen Zhou](https://shangchenzhou.com/), [Kelvin C.K. Chan](https://ckkelvinchan.github.io/), [Chongyi Li](https://li-chongyi.github.io/), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/) 
+
+S-Lab, Nanyang Technological University
+
+<img src="assets/network.jpg" width="800px"/>
+
+
+:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs: 
+
+### Update
+
+- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/). Try out online demo! [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer)
+- **2022.09.04**: Add face upsampling `--face_upsample` for high-resolution AI-created face enhancement.
+- **2022.08.23**: Some modifications on face detection and fusion for better AI-created face enhancement.
+- **2022.08.07**: Integrate [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement.
+- **2022.07.29**: Integrate new face detectors of `['RetinaFace'(default), 'YOLOv5']`. 
+- **2022.07.17**: Add Colab demo of CodeFormer. <a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
+- **2022.07.16**: Release inference code for face restoration. :blush:
+- **2022.06.21**: This repo is created.
+
+### TODO
+- [ ] Add checkpoint for face inpainting
+- [ ] Add training code and config files
+- [x] ~~Add background image enhancement~~
+
+#### Face Restoration
+
+<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/>
+<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>
+
+#### Face Color Enhancement and Restoration
+
+<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>
+
+#### Face Inpainting
+
+<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>
+
+
+
+### Dependencies and Installation
+
+- Pytorch >= 1.7.1
+- CUDA >= 10.1
+- Other required packages in `requirements.txt`
+```
+# git clone this repository
+git clone https://github.com/sczhou/CodeFormer
+cd CodeFormer
+
+# create new anaconda env
+conda create -n codeformer python=3.8 -y
+conda activate codeformer
+
+# install python dependencies
+pip3 install -r requirements.txt
+python basicsr/setup.py develop
+```
+<!-- conda install -c conda-forge dlib -->
+
+### Quick Inference
+
+##### Download Pre-trained Models:
+Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by runing the following command.
+```
+python scripts/download_pretrained_models.py facelib
+```
+
+Download the CodeFormer pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by runing the following command.
+```
+python scripts/download_pretrained_models.py CodeFormer
+```
+
+##### Prepare Testing Data:
+You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder.
+
+
+##### Testing on Face Restoration:
+```
+# For cropped and aligned faces
+python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
+
+# For the whole images
+# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
+# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
+python inference_codeformer.py --w 0.7 --test_path [input folder]
+```
+
+NOTE that *w* is in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result. 
+
+The results will be saved in the `results` folder.
+
+### Citation
+If our work is useful for your research, please consider citing:
+
+    @article{zhou2022codeformer,
+        author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
+        title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
+        journal = {arXiv preprint arXiv:2206.11253},
+        year = {2022}
+    }
+
+### License
+
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+
+### Acknowledgement
+
+This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). We also borrow some codes from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). Thanks for their awesome works.
+
+### Contact
+If you have any question, please feel free to reach me out at `shangchenzhou@gmail.com`.

CodeFormer/basicsr/VERSION

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+1.3.2

CodeFormer/basicsr/__init__.py

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

CodeFormer/basicsr/archs/__init__.py

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+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

CodeFormer/basicsr/archs/arcface_arch.py

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+import torch.nn as nn
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+def conv3x3(inplanes, outplanes, stride=1):
+    """A simple wrapper for 3x3 convolution with padding.
+
+    Args:
+        inplanes (int): Channel number of inputs.
+        outplanes (int): Channel number of outputs.
+        stride (int): Stride in convolution. Default: 1.
+    """
+    return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    """Basic residual block used in the ResNetArcFace architecture.
+
+    Args:
+        inplanes (int): Channel number of inputs.
+        planes (int): Channel number of outputs.
+        stride (int): Stride in convolution. Default: 1.
+        downsample (nn.Module): The downsample module. Default: None.
+    """
+    expansion = 1  # output channel expansion ratio
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class IRBlock(nn.Module):
+    """Improved residual block (IR Block) used in the ResNetArcFace architecture.
+
+    Args:
+        inplanes (int): Channel number of inputs.
+        planes (int): Channel number of outputs.
+        stride (int): Stride in convolution. Default: 1.
+        downsample (nn.Module): The downsample module. Default: None.
+        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
+    """
+    expansion = 1  # output channel expansion ratio
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    """Bottleneck block used in the ResNetArcFace architecture.
+
+    Args:
+        inplanes (int): Channel number of inputs.
+        planes (int): Channel number of outputs.
+        stride (int): Stride in convolution. Default: 1.
+        downsample (nn.Module): The downsample module. Default: None.
+    """
+    expansion = 4  # output channel expansion ratio
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class SEBlock(nn.Module):
+    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
+
+    Args:
+        channel (int): Channel number of inputs.
+        reduction (int): Channel reduction ration. Default: 16.
+    """
+
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # pool to 1x1 without spatial information
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
+            nn.Sigmoid())
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+@ARCH_REGISTRY.register()
+class ResNetArcFace(nn.Module):
+    """ArcFace with ResNet architectures.
+
+    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
+
+    Args:
+        block (str): Block used in the ArcFace architecture.
+        layers (tuple(int)): Block numbers in each layer.
+        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
+    """
+
+    def __init__(self, block, layers, use_se=True):
+        if block == 'IRBlock':
+            block = IRBlock
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNetArcFace, self).__init__()
+
+        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.bn4 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc5 = nn.Linear(512 * 8 * 8, 512)
+        self.bn5 = nn.BatchNorm1d(512)
+
+        # initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, num_blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
+        self.inplanes = planes
+        for _ in range(1, num_blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.bn4(x)
+        x = self.dropout(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc5(x)
+        x = self.bn5(x)
+
+        return x

CodeFormer/basicsr/archs/arch_util.py

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+import collections.abc
+import math
+import torch
+import torchvision
+import warnings
+from distutils.version import LooseVersion
+from itertools import repeat
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
+from basicsr.utils import get_root_logger
+
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+    """Initialize network weights.
+
+    Args:
+        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
+        scale (float): Scale initialized weights, especially for residual
+            blocks. Default: 1.
+        bias_fill (float): The value to fill bias. Default: 0
+        kwargs (dict): Other arguments for initialization function.
+    """
+    if not isinstance(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+
+    Args:
+        basic_block (nn.module): nn.module class for basic block.
+        num_basic_block (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+class 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)
+
+
+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.')
+
+        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
+            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
+                                                 self.dilation, mask)
+        else:
+            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
+                                         self.dilation, self.groups, self.deformable_groups)
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
+            'The distribution of values may be incorrect.',
+            stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        low = norm_cdf((a - mean) / std)
+        up = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [low, up], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * low - 1, 2 * up - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution.
+
+    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
+
+    The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+# From PyTorch
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple

CodeFormer/basicsr/archs/codeformer_arch.py

@@ -0,0 +1,276 @@
+import math
+import numpy as np
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+from typing import Optional, List
+
+from basicsr.archs.vqgan_arch import *
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def calc_mean_std(feat, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.view(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(b, c, 1, 1)
+    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+    return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+    """Adaptive instance normalization.
+
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, x, mask=None):
+        if mask is None:
+            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class TransformerSALayer(nn.Module):
+    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
+        # Implementation of Feedforward model - MLP
+        self.linear1 = nn.Linear(embed_dim, dim_mlp)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_mlp, embed_dim)
+
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.norm2 = nn.LayerNorm(embed_dim)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(self, tgt,
+                tgt_mask: Optional[Tensor] = None,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        
+        # self attention
+        tgt2 = self.norm1(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout1(tgt2)
+
+        # ffn
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+class Fuse_sft_block(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super().__init__()
+        self.encode_enc = ResBlock(2*in_ch, out_ch)
+
+        self.scale = nn.Sequential(
+                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+                    nn.LeakyReLU(0.2, True),
+                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+        self.shift = nn.Sequential(
+                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+                    nn.LeakyReLU(0.2, True),
+                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+    def forward(self, enc_feat, dec_feat, w=1):
+        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
+        scale = self.scale(enc_feat)
+        shift = self.shift(enc_feat)
+        residual = w * (dec_feat * scale + shift)
+        out = dec_feat + residual
+        return out
+
+
+@ARCH_REGISTRY.register()
+class CodeFormer(VQAutoEncoder):
+    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
+                codebook_size=1024, latent_size=256,
+                connect_list=['32', '64', '128', '256'],
+                fix_modules=['quantize','generator']):
+        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
+
+        if fix_modules is not None:
+            for module in fix_modules:
+                for param in getattr(self, module).parameters():
+                    param.requires_grad = False
+
+        self.connect_list = connect_list
+        self.n_layers = n_layers
+        self.dim_embd = dim_embd
+        self.dim_mlp = dim_embd*2
+
+        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
+        self.feat_emb = nn.Linear(256, self.dim_embd)
+
+        # transformer
+        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) 
+                                    for _ in range(self.n_layers)])
+
+        # logits_predict head
+        self.idx_pred_layer = nn.Sequential(
+            nn.LayerNorm(dim_embd),
+            nn.Linear(dim_embd, codebook_size, bias=False))
+        
+        self.channels = {
+            '16': 512,
+            '32': 256,
+            '64': 256,
+            '128': 128,
+            '256': 128,
+            '512': 64,
+        }
+
+        # after second residual block for > 16, before attn layer for ==16
+        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
+        # after first residual block for > 16, before attn layer for ==16
+        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
+
+        # fuse_convs_dict
+        self.fuse_convs_dict = nn.ModuleDict()
+        for f_size in self.connect_list:
+            in_ch = self.channels[f_size]
+            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
+        # ################### Encoder #####################
+        enc_feat_dict = {}
+        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
+        for i, block in enumerate(self.encoder.blocks):
+            x = block(x) 
+            if i in out_list:
+                enc_feat_dict[str(x.shape[-1])] = x.clone()
+
+        lq_feat = x
+        # ################# Transformer ###################
+        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
+        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
+        # BCHW -> BC(HW) -> (HW)BC
+        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
+        query_emb = feat_emb
+        # Transformer encoder
+        for layer in self.ft_layers:
+            query_emb = layer(query_emb, query_pos=pos_emb)
+
+        # output logits
+        logits = self.idx_pred_layer(query_emb) # (hw)bn
+        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
+
+        if code_only: # for training stage II
+          # logits doesn't need softmax before cross_entropy loss
+            return logits, lq_feat
+
+        # ################# Quantization ###################
+        # if self.training:
+        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
+        #     # b(hw)c -> bc(hw) -> bchw
+        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
+        # ------------
+        soft_one_hot = F.softmax(logits, dim=2)
+        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
+        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
+        # preserve gradients
+        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
+
+        if detach_16:
+            quant_feat = quant_feat.detach() # for training stage III
+        if adain:
+            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
+
+        # ################## Generator ####################
+        x = quant_feat
+        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
+
+        for i, block in enumerate(self.generator.blocks):
+            x = block(x) 
+            if i in fuse_list: # fuse after i-th block
+                f_size = str(x.shape[-1])
+                if w>0:
+                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
+        out = x
+        # logits doesn't need softmax before cross_entropy loss
+        return out, logits, lq_feat

CodeFormer/basicsr/archs/rrdbnet_arch.py

@@ -0,0 +1,119 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.utils.registry import ARCH_REGISTRY
+from .arch_util import default_init_weights, make_layer, pixel_unshuffle
+
+
+class ResidualDenseBlock(nn.Module):
+    """Residual Dense Block.
+
+    Used in RRDB block in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block.
+
+    Used in RRDB-Net in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat, num_grow_ch=32):
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x):
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return out * 0.2 + x
+
+
+@ARCH_REGISTRY.register()
+class RRDBNet(nn.Module):
+    """Networks consisting of Residual in Residual Dense Block, which is used
+    in ESRGAN.
+
+    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
+
+    We extend ESRGAN for scale x2 and scale x1.
+    Note: This is one option for scale 1, scale 2 in RRDBNet.
+    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
+    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64
+        num_block (int): Block number in the trunk network. Defaults: 23
+        num_grow_ch (int): Channels for each growth. Default: 32.
+    """
+
+    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
+        super(RRDBNet, self).__init__()
+        self.scale = scale
+        if scale == 2:
+            num_in_ch = num_in_ch * 4
+        elif scale == 1:
+            num_in_ch = num_in_ch * 16
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
+        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        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)
+
+    def forward(self, x):
+        if self.scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        elif self.scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        else:
+            feat = x
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out

CodeFormer/basicsr/archs/vgg_arch.py

@@ -0,0 +1,161 @@
+import os
+import torch
+from collections import OrderedDict
+from torch import nn as nn
+from torchvision.models import vgg as vgg
+
+from basicsr.utils.registry import ARCH_REGISTRY
+
+VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth'
+NAMES = {
+    'vgg11': [
+        'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2',
+        'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',
+        'pool5'
+    ],
+    'vgg13': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4',
+        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
+    ],
+    'vgg16': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
+        'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
+        'pool5'
+    ],
+    'vgg19': [
+        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
+        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1',
+        'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
+        'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5'
+    ]
+}
+
+
+def insert_bn(names):
+    """Insert bn layer after each conv.
+
+    Args:
+        names (list): The list of layer names.
+
+    Returns:
+        list: The list of layer names with bn layers.
+    """
+    names_bn = []
+    for name in names:
+        names_bn.append(name)
+        if 'conv' in name:
+            position = name.replace('conv', '')
+            names_bn.append('bn' + position)
+    return names_bn
+
+
+@ARCH_REGISTRY.register()
+class VGGFeatureExtractor(nn.Module):
+    """VGG network for feature extraction.
+
+    In this implementation, we allow users to choose whether use normalization
+    in the input feature and the type of vgg network. Note that the pretrained
+    path must fit the vgg type.
+
+    Args:
+        layer_name_list (list[str]): Forward function returns the corresponding
+            features according to the layer_name_list.
+            Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
+        vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
+        use_input_norm (bool): If True, normalize the input image. Importantly,
+            the input feature must in the range [0, 1]. Default: True.
+        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
+            Default: False.
+        requires_grad (bool): If true, the parameters of VGG network will be
+            optimized. Default: False.
+        remove_pooling (bool): If true, the max pooling operations in VGG net
+            will be removed. Default: False.
+        pooling_stride (int): The stride of max pooling operation. Default: 2.
+    """
+
+    def __init__(self,
+                 layer_name_list,
+                 vgg_type='vgg19',
+                 use_input_norm=True,
+                 range_norm=False,
+                 requires_grad=False,
+                 remove_pooling=False,
+                 pooling_stride=2):
+        super(VGGFeatureExtractor, self).__init__()
+
+        self.layer_name_list = layer_name_list
+        self.use_input_norm = use_input_norm
+        self.range_norm = range_norm
+
+        self.names = NAMES[vgg_type.replace('_bn', '')]
+        if 'bn' in vgg_type:
+            self.names = insert_bn(self.names)
+
+        # only borrow layers that will be used to avoid unused params
+        max_idx = 0
+        for v in layer_name_list:
+            idx = self.names.index(v)
+            if idx > max_idx:
+                max_idx = idx
+
+        if os.path.exists(VGG_PRETRAIN_PATH):
+            vgg_net = getattr(vgg, vgg_type)(pretrained=False)
+            state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage)
+            vgg_net.load_state_dict(state_dict)
+        else:
+            vgg_net = getattr(vgg, vgg_type)(pretrained=True)
+
+        features = vgg_net.features[:max_idx + 1]
+
+        modified_net = OrderedDict()
+        for k, v in zip(self.names, features):
+            if 'pool' in k:
+                # if remove_pooling is true, pooling operation will be removed
+                if remove_pooling:
+                    continue
+                else:
+                    # in some cases, we may want to change the default stride
+                    modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride)
+            else:
+                modified_net[k] = v
+
+        self.vgg_net = nn.Sequential(modified_net)
+
+        if not requires_grad:
+            self.vgg_net.eval()
+            for param in self.parameters():
+                param.requires_grad = False
+        else:
+            self.vgg_net.train()
+            for param in self.parameters():
+                param.requires_grad = True
+
+        if self.use_input_norm:
+            # the mean is for image with range [0, 1]
+            self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+            # the std is for image with range [0, 1]
+            self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+    def forward(self, x):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor: Forward results.
+        """
+        if self.range_norm:
+            x = (x + 1) / 2
+        if self.use_input_norm:
+            x = (x - self.mean) / self.std
+        output = {}
+
+        for key, layer in self.vgg_net._modules.items():
+            x = layer(x)
+            if key in self.layer_name_list:
+                output[key] = x.clone()
+
+        return output

CodeFormer/basicsr/archs/vqgan_arch.py

@@ -0,0 +1,435 @@
+'''
+VQGAN code, adapted from the original created by the Unleashing Transformers authors:
+https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
+
+'''
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import copy
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    
+
+@torch.jit.script
+def swish(x):
+    return x*torch.sigmoid(x)
+
+
+#  Define VQVAE classes
+class VectorQuantizer(nn.Module):
+    def __init__(self, codebook_size, emb_dim, beta):
+        super(VectorQuantizer, self).__init__()
+        self.codebook_size = codebook_size  # number of embeddings
+        self.emb_dim = emb_dim  # dimension of embedding
+        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
+        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
+
+    def forward(self, z):
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = z.permute(0, 2, 3, 1).contiguous()
+        z_flattened = z.view(-1, self.emb_dim)
+
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
+            2 * torch.matmul(z_flattened, self.embedding.weight.t())
+
+        mean_distance = torch.mean(d)
+        # find closest encodings
+        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
+        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
+        # [0-1], higher score, higher confidence
+        min_encoding_scores = torch.exp(-min_encoding_scores/10)
+
+        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
+        min_encodings.scatter_(1, min_encoding_indices, 1)
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
+        # compute loss for embedding
+        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # perplexity
+        e_mean = torch.mean(min_encodings, dim=0)
+        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
+        # reshape back to match original input shape
+        z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q, loss, {
+            "perplexity": perplexity,
+            "min_encodings": min_encodings,
+            "min_encoding_indices": min_encoding_indices,
+            "min_encoding_scores": min_encoding_scores,
+            "mean_distance": mean_distance
+            }
+
+    def get_codebook_feat(self, indices, shape):
+        # input indices: batch*token_num -> (batch*token_num)*1
+        # shape: batch, height, width, channel
+        indices = indices.view(-1,1)
+        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
+        min_encodings.scatter_(1, indices, 1)
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
+
+        if shape is not None:  # reshape back to match original input shape
+            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class GumbelQuantizer(nn.Module):
+    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
+        super().__init__()
+        self.codebook_size = codebook_size  # number of embeddings
+        self.emb_dim = emb_dim  # dimension of embedding
+        self.straight_through = straight_through
+        self.temperature = temp_init
+        self.kl_weight = kl_weight
+        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
+        self.embed = nn.Embedding(codebook_size, emb_dim)
+
+    def forward(self, z):
+        hard = self.straight_through if self.training else True
+
+        logits = self.proj(z)
+
+        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
+
+        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
+
+        # + kl divergence to the prior loss
+        qy = F.softmax(logits, dim=1)
+        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
+        min_encoding_indices = soft_one_hot.argmax(dim=1)
+
+        return z_q, diff, {
+            "min_encoding_indices": min_encoding_indices
+        }
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x):
+        pad = (0, 1, 0, 1)
+        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+
+        return x
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, out_channels=None):
+        super(ResBlock, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels if out_channels is None else out_channels
+        self.norm1 = normalize(in_channels)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = normalize(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x_in):
+        x = x_in
+        x = self.norm1(x)
+        x = swish(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = swish(x)
+        x = self.conv2(x)
+        if self.in_channels != self.out_channels:
+            x_in = self.conv_out(x_in)
+
+        return x + x_in
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0
+        )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h*w)
+        q = q.permute(0, 2, 1)   
+        k = k.reshape(b, c, h*w)
+        w_ = torch.bmm(q, k) 
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = F.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h*w)
+        w_ = w_.permute(0, 2, 1) 
+        h_ = torch.bmm(v, w_)
+        h_ = h_.reshape(b, c, h, w)
+
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+
+class Encoder(nn.Module):
+    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
+        super().__init__()
+        self.nf = nf
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.attn_resolutions = attn_resolutions
+
+        curr_res = self.resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+
+        blocks = []
+        # initial convultion
+        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
+
+        # residual and downsampling blocks, with attention on smaller res (16x16)
+        for i in range(self.num_resolutions):
+            block_in_ch = nf * in_ch_mult[i]
+            block_out_ch = nf * ch_mult[i]
+            for _ in range(self.num_res_blocks):
+                blocks.append(ResBlock(block_in_ch, block_out_ch))
+                block_in_ch = block_out_ch
+                if curr_res in attn_resolutions:
+                    blocks.append(AttnBlock(block_in_ch))
+
+            if i != self.num_resolutions - 1:
+                blocks.append(Downsample(block_in_ch))
+                curr_res = curr_res // 2
+
+        # non-local attention block
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+        blocks.append(AttnBlock(block_in_ch))
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+        # normalise and convert to latent size
+        blocks.append(normalize(block_in_ch))
+        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+            
+        return x
+
+
+class Generator(nn.Module):
+    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
+        super().__init__()
+        self.nf = nf 
+        self.ch_mult = ch_mult 
+        self.num_resolutions = len(self.ch_mult)
+        self.num_res_blocks = res_blocks
+        self.resolution = img_size 
+        self.attn_resolutions = attn_resolutions
+        self.in_channels = emb_dim
+        self.out_channels = 3
+        block_in_ch = self.nf * self.ch_mult[-1]
+        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
+
+        blocks = []
+        # initial conv
+        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
+
+        # non-local attention block
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+        blocks.append(AttnBlock(block_in_ch))
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+        for i in reversed(range(self.num_resolutions)):
+            block_out_ch = self.nf * self.ch_mult[i]
+
+            for _ in range(self.num_res_blocks):
+                blocks.append(ResBlock(block_in_ch, block_out_ch))
+                block_in_ch = block_out_ch
+
+                if curr_res in self.attn_resolutions:
+                    blocks.append(AttnBlock(block_in_ch))
+
+            if i != 0:
+                blocks.append(Upsample(block_in_ch))
+                curr_res = curr_res * 2
+
+        blocks.append(normalize(block_in_ch))
+        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
+
+        self.blocks = nn.ModuleList(blocks)
+   
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+            
+        return x
+
+  
+@ARCH_REGISTRY.register()
+class VQAutoEncoder(nn.Module):
+    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
+                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
+        super().__init__()
+        logger = get_root_logger()
+        self.in_channels = 3 
+        self.nf = nf 
+        self.n_blocks = res_blocks 
+        self.codebook_size = codebook_size
+        self.embed_dim = emb_dim
+        self.ch_mult = ch_mult
+        self.resolution = img_size
+        self.attn_resolutions = attn_resolutions
+        self.quantizer_type = quantizer
+        self.encoder = Encoder(
+            self.in_channels,
+            self.nf,
+            self.embed_dim,
+            self.ch_mult,
+            self.n_blocks,
+            self.resolution,
+            self.attn_resolutions
+        )
+        if self.quantizer_type == "nearest":
+            self.beta = beta #0.25
+            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
+        elif self.quantizer_type == "gumbel":
+            self.gumbel_num_hiddens = emb_dim
+            self.straight_through = gumbel_straight_through
+            self.kl_weight = gumbel_kl_weight
+            self.quantize = GumbelQuantizer(
+                self.codebook_size,
+                self.embed_dim,
+                self.gumbel_num_hiddens,
+                self.straight_through,
+                self.kl_weight
+            )
+        self.generator = Generator(
+            self.nf, 
+            self.embed_dim,
+            self.ch_mult, 
+            self.n_blocks, 
+            self.resolution, 
+            self.attn_resolutions
+        )
+
+        if model_path is not None:
+            chkpt = torch.load(model_path, map_location='cpu')
+            if 'params_ema' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
+                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
+            elif 'params' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+                logger.info(f'vqgan is loaded from: {model_path} [params]')
+            else:
+                raise ValueError(f'Wrong params!')
+
+
+    def forward(self, x):
+        x = self.encoder(x)
+        quant, codebook_loss, quant_stats = self.quantize(x)
+        x = self.generator(quant)
+        return x, codebook_loss, quant_stats
+
+
+
+# patch based discriminator
+@ARCH_REGISTRY.register()
+class VQGANDiscriminator(nn.Module):
+    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
+        super().__init__()
+
+        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
+        ndf_mult = 1
+        ndf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            ndf_mult_prev = ndf_mult
+            ndf_mult = min(2 ** n, 8)
+            layers += [
+                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(ndf * ndf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        ndf_mult_prev = ndf_mult
+        ndf_mult = min(2 ** n_layers, 8)
+
+        layers += [
+            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(ndf * ndf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+
+        layers += [
+            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
+        self.main = nn.Sequential(*layers)
+
+        if model_path is not None:
+            chkpt = torch.load(model_path, map_location='cpu')
+            if 'params_d' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
+            elif 'params' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+            else:
+                raise ValueError(f'Wrong params!')
+
+    def forward(self, x):
+        return self.main(x)

CodeFormer/basicsr/data/__init__.py

@@ -0,0 +1,100 @@
+import importlib
+import numpy as np
+import random
+import torch
+import torch.utils.data
+from copy import deepcopy
+from functools import partial
+from os import path as osp
+
+from basicsr.data.prefetch_dataloader import PrefetchDataLoader
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.dist_util import get_dist_info
+from basicsr.utils.registry import DATASET_REGISTRY
+
+__all__ = ['build_dataset', 'build_dataloader']
+
+# automatically scan and import dataset modules for registry
+# scan all the files under the data folder with '_dataset' in file names
+data_folder = osp.dirname(osp.abspath(__file__))
+dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
+# import all the dataset modules
+_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames]
+
+
+def build_dataset(dataset_opt):
+    """Build dataset from options.
+
+    Args:
+        dataset_opt (dict): Configuration for dataset. It must constain:
+            name (str): Dataset name.
+            type (str): Dataset type.
+    """
+    dataset_opt = deepcopy(dataset_opt)
+    dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt)
+    logger = get_root_logger()
+    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} ' 'is built.')
+    return dataset
+
+
+def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None):
+    """Build dataloader.
+
+    Args:
+        dataset (torch.utils.data.Dataset): Dataset.
+        dataset_opt (dict): Dataset options. It contains the following keys:
+            phase (str): 'train' or 'val'.
+            num_worker_per_gpu (int): Number of workers for each GPU.
+            batch_size_per_gpu (int): Training batch size for each GPU.
+        num_gpu (int): Number of GPUs. Used only in the train phase.
+            Default: 1.
+        dist (bool): Whether in distributed training. Used only in the train
+            phase. Default: False.
+        sampler (torch.utils.data.sampler): Data sampler. Default: None.
+        seed (int | None): Seed. Default: None
+    """
+    phase = dataset_opt['phase']
+    rank, _ = get_dist_info()
+    if phase == 'train':
+        if dist:  # distributed training
+            batch_size = dataset_opt['batch_size_per_gpu']
+            num_workers = dataset_opt['num_worker_per_gpu']
+        else:  # non-distributed training
+            multiplier = 1 if num_gpu == 0 else num_gpu
+            batch_size = dataset_opt['batch_size_per_gpu'] * multiplier
+            num_workers = dataset_opt['num_worker_per_gpu'] * multiplier
+        dataloader_args = dict(
+            dataset=dataset,
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=num_workers,
+            sampler=sampler,
+            drop_last=True)
+        if sampler is None:
+            dataloader_args['shuffle'] = True
+        dataloader_args['worker_init_fn'] = partial(
+            worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None
+    elif phase in ['val', 'test']:  # validation
+        dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
+    else:
+        raise ValueError(f'Wrong dataset phase: {phase}. ' "Supported ones are 'train', 'val' and 'test'.")
+
+    dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)
+
+    prefetch_mode = dataset_opt.get('prefetch_mode')
+    if prefetch_mode == 'cpu':  # CPUPrefetcher
+        num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1)
+        logger = get_root_logger()
+        logger.info(f'Use {prefetch_mode} prefetch dataloader: ' f'num_prefetch_queue = {num_prefetch_queue}')
+        return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args)
+    else:
+        # prefetch_mode=None: Normal dataloader
+        # prefetch_mode='cuda': dataloader for CUDAPrefetcher
+        return torch.utils.data.DataLoader(**dataloader_args)
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+    # Set the worker seed to num_workers * rank + worker_id + seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)

CodeFormer/basicsr/data/data_sampler.py

@@ -0,0 +1,48 @@
+import math
+import torch
+from torch.utils.data.sampler import Sampler
+
+
+class EnlargedSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+
+    Modified from torch.utils.data.distributed.DistributedSampler
+    Support enlarging the dataset for iteration-based training, for saving
+    time when restart the dataloader after each epoch
+
+    Args:
+        dataset (torch.utils.data.Dataset): Dataset used for sampling.
+        num_replicas (int | None): Number of processes participating in
+            the training. It is usually the world_size.
+        rank (int | None): Rank of the current process within num_replicas.
+        ratio (int): Enlarging ratio. Default: 1.
+    """
+
+    def __init__(self, dataset, num_replicas, rank, ratio=1):
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas)
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        indices = torch.randperm(self.total_size, generator=g).tolist()
+
+        dataset_size = len(self.dataset)
+        indices = [v % dataset_size for v in indices]
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch

CodeFormer/basicsr/data/data_util.py

@@ -0,0 +1,305 @@
+import cv2
+import numpy as np
+import torch
+from os import path as osp
+from torch.nn import functional as F
+
+from basicsr.data.transforms import mod_crop
+from basicsr.utils import img2tensor, scandir
+
+
+def read_img_seq(path, require_mod_crop=False, scale=1):
+    """Read a sequence of images from a given folder path.
+
+    Args:
+        path (list[str] | str): List of image paths or image folder path.
+        require_mod_crop (bool): Require mod crop for each image.
+            Default: False.
+        scale (int): Scale factor for mod_crop. Default: 1.
+
+    Returns:
+        Tensor: size (t, c, h, w), RGB, [0, 1].
+    """
+    if isinstance(path, list):
+        img_paths = path
+    else:
+        img_paths = sorted(list(scandir(path, full_path=True)))
+    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
+    if require_mod_crop:
+        imgs = [mod_crop(img, scale) for img in imgs]
+    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
+    imgs = torch.stack(imgs, dim=0)
+    return imgs
+
+
+def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
+    """Generate an index list for reading `num_frames` frames from a sequence
+    of images.
+
+    Args:
+        crt_idx (int): Current center index.
+        max_frame_num (int): Max number of the sequence of images (from 1).
+        num_frames (int): Reading num_frames frames.
+        padding (str): Padding mode, one of
+            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
+            Examples: current_idx = 0, num_frames = 5
+            The generated frame indices under different padding mode:
+            replicate: [0, 0, 0, 1, 2]
+            reflection: [2, 1, 0, 1, 2]
+            reflection_circle: [4, 3, 0, 1, 2]
+            circle: [3, 4, 0, 1, 2]
+
+    Returns:
+        list[int]: A list of indices.
+    """
+    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
+    assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
+
+    max_frame_num = max_frame_num - 1  # start from 0
+    num_pad = num_frames // 2
+
+    indices = []
+    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
+        if i < 0:
+            if padding == 'replicate':
+                pad_idx = 0
+            elif padding == 'reflection':
+                pad_idx = -i
+            elif padding == 'reflection_circle':
+                pad_idx = crt_idx + num_pad - i
+            else:
+                pad_idx = num_frames + i
+        elif i > max_frame_num:
+            if padding == 'replicate':
+                pad_idx = max_frame_num
+            elif padding == 'reflection':
+                pad_idx = max_frame_num * 2 - i
+            elif padding == 'reflection_circle':
+                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
+            else:
+                pad_idx = i - num_frames
+        else:
+            pad_idx = i
+        indices.append(pad_idx)
+    return indices
+
+
+def paired_paths_from_lmdb(folders, keys):
+    """Generate paired paths from lmdb files.
+
+    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
+
+    lq.lmdb
+    ├── data.mdb
+    ├── lock.mdb
+    ├── meta_info.txt
+
+    The data.mdb and lock.mdb are standard lmdb files and you can refer to
+    https://lmdb.readthedocs.io/en/release/ for more details.
+
+    The meta_info.txt is a specified txt file to record the meta information
+    of our datasets. It will be automatically created when preparing
+    datasets by our provided dataset tools.
+    Each line in the txt file records
+    1)image name (with extension),
+    2)image shape,
+    3)compression level, separated by a white space.
+    Example: `baboon.png (120,125,3) 1`
+
+    We use the image name without extension as the lmdb key.
+    Note that we use the same key for the corresponding lq and gt images.
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+            Note that this key is different from lmdb keys.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
+        raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
+                         f'formats. But received {input_key}: {input_folder}; '
+                         f'{gt_key}: {gt_folder}')
+    # ensure that the two meta_info files are the same
+    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
+        input_lmdb_keys = [line.split('.')[0] for line in fin]
+    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
+        gt_lmdb_keys = [line.split('.')[0] for line in fin]
+    if set(input_lmdb_keys) != set(gt_lmdb_keys):
+        raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
+    else:
+        paths = []
+        for lmdb_key in sorted(input_lmdb_keys):
+            paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
+        return paths
+
+
+def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
+    """Generate paired paths from an meta information file.
+
+    Each line in the meta information file contains the image names and
+    image shape (usually for gt), separated by a white space.
+
+    Example of an meta information file:
+    ```
+    0001_s001.png (480,480,3)
+    0001_s002.png (480,480,3)
+    ```
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+        meta_info_file (str): Path to the meta information file.
+        filename_tmpl (str): Template for each filename. Note that the
+            template excludes the file extension. Usually the filename_tmpl is
+            for files in the input folder.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    with open(meta_info_file, 'r') as fin:
+        gt_names = [line.split(' ')[0] for line in fin]
+
+    paths = []
+    for gt_name in gt_names:
+        basename, ext = osp.splitext(osp.basename(gt_name))
+        input_name = f'{filename_tmpl.format(basename)}{ext}'
+        input_path = osp.join(input_folder, input_name)
+        gt_path = osp.join(gt_folder, gt_name)
+        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
+    return paths
+
+
+def paired_paths_from_folder(folders, keys, filename_tmpl):
+    """Generate paired paths from folders.
+
+    Args:
+        folders (list[str]): A list of folder path. The order of list should
+            be [input_folder, gt_folder].
+        keys (list[str]): A list of keys identifying folders. The order should
+            be in consistent with folders, e.g., ['lq', 'gt'].
+        filename_tmpl (str): Template for each filename. Note that the
+            template excludes the file extension. Usually the filename_tmpl is
+            for files in the input folder.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
+                               f'But got {len(folders)}')
+    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
+    input_folder, gt_folder = folders
+    input_key, gt_key = keys
+
+    input_paths = list(scandir(input_folder))
+    gt_paths = list(scandir(gt_folder))
+    assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
+                                               f'{len(input_paths)}, {len(gt_paths)}.')
+    paths = []
+    for gt_path in gt_paths:
+        basename, ext = osp.splitext(osp.basename(gt_path))
+        input_name = f'{filename_tmpl.format(basename)}{ext}'
+        input_path = osp.join(input_folder, input_name)
+        assert input_name in input_paths, (f'{input_name} is not in ' f'{input_key}_paths.')
+        gt_path = osp.join(gt_folder, gt_path)
+        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
+    return paths
+
+
+def paths_from_folder(folder):
+    """Generate paths from folder.
+
+    Args:
+        folder (str): Folder path.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+
+    paths = list(scandir(folder))
+    paths = [osp.join(folder, path) for path in paths]
+    return paths
+
+
+def paths_from_lmdb(folder):
+    """Generate paths from lmdb.
+
+    Args:
+        folder (str): Folder path.
+
+    Returns:
+        list[str]: Returned path list.
+    """
+    if not folder.endswith('.lmdb'):
+        raise ValueError(f'Folder {folder}folder should in lmdb format.')
+    with open(osp.join(folder, 'meta_info.txt')) as fin:
+        paths = [line.split('.')[0] for line in fin]
+    return paths
+
+
+def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
+    """Generate Gaussian kernel used in `duf_downsample`.
+
+    Args:
+        kernel_size (int): Kernel size. Default: 13.
+        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
+
+    Returns:
+        np.array: The Gaussian kernel.
+    """
+    from scipy.ndimage import filters as filters
+    kernel = np.zeros((kernel_size, kernel_size))
+    # set element at the middle to one, a dirac delta
+    kernel[kernel_size // 2, kernel_size // 2] = 1
+    # gaussian-smooth the dirac, resulting in a gaussian filter
+    return filters.gaussian_filter(kernel, sigma)
+
+
+def duf_downsample(x, kernel_size=13, scale=4):
+    """Downsamping with Gaussian kernel used in the DUF official code.
+
+    Args:
+        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
+        kernel_size (int): Kernel size. Default: 13.
+        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
+            Default: 4.
+
+    Returns:
+        Tensor: DUF downsampled frames.
+    """
+    assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
+
+    squeeze_flag = False
+    if x.ndim == 4:
+        squeeze_flag = True
+        x = x.unsqueeze(0)
+    b, t, c, h, w = x.size()
+    x = x.view(-1, 1, h, w)
+    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
+    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
+
+    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
+    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
+    x = F.conv2d(x, gaussian_filter, stride=scale)
+    x = x[:, :, 2:-2, 2:-2]
+    x = x.view(b, t, c, x.size(2), x.size(3))
+    if squeeze_flag:
+        x = x.squeeze(0)
+    return x

CodeFormer/basicsr/data/prefetch_dataloader.py

@@ -0,0 +1,125 @@
+import queue as Queue
+import threading
+import torch
+from torch.utils.data import DataLoader
+
+
+class PrefetchGenerator(threading.Thread):
+    """A general prefetch generator.
+
+    Ref:
+    https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+    Args:
+        generator: Python generator.
+        num_prefetch_queue (int): Number of prefetch queue.
+    """
+
+    def __init__(self, generator, num_prefetch_queue):
+        threading.Thread.__init__(self)
+        self.queue = Queue.Queue(num_prefetch_queue)
+        self.generator = generator
+        self.daemon = True
+        self.start()
+
+    def run(self):
+        for item in self.generator:
+            self.queue.put(item)
+        self.queue.put(None)
+
+    def __next__(self):
+        next_item = self.queue.get()
+        if next_item is None:
+            raise StopIteration
+        return next_item
+
+    def __iter__(self):
+        return self
+
+
+class PrefetchDataLoader(DataLoader):
+    """Prefetch version of dataloader.
+
+    Ref:
+    https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
+
+    TODO:
+    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
+    ddp.
+
+    Args:
+        num_prefetch_queue (int): Number of prefetch queue.
+        kwargs (dict): Other arguments for dataloader.
+    """
+
+    def __init__(self, num_prefetch_queue, **kwargs):
+        self.num_prefetch_queue = num_prefetch_queue
+        super(PrefetchDataLoader, self).__init__(**kwargs)
+
+    def __iter__(self):
+        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
+
+
+class CPUPrefetcher():
+    """CPU prefetcher.
+
+    Args:
+        loader: Dataloader.
+    """
+
+    def __init__(self, loader):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+
+    def next(self):
+        try:
+            return next(self.loader)
+        except StopIteration:
+            return None
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+
+
+class CUDAPrefetcher():
+    """CUDA prefetcher.
+
+    Ref:
+    https://github.com/NVIDIA/apex/issues/304#
+
+    It may consums more GPU memory.
+
+    Args:
+        loader: Dataloader.
+        opt (dict): Options.
+    """
+
+    def __init__(self, loader, opt):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+        self.opt = opt
+        self.stream = torch.cuda.Stream()
+        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
+        self.preload()
+
+    def preload(self):
+        try:
+            self.batch = next(self.loader)  # self.batch is a dict
+        except StopIteration:
+            self.batch = None
+            return None
+        # put tensors to gpu
+        with torch.cuda.stream(self.stream):
+            for k, v in self.batch.items():
+                if torch.is_tensor(v):
+                    self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
+
+    def next(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        batch = self.batch
+        self.preload()
+        return batch
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+        self.preload()

CodeFormer/basicsr/data/transforms.py

@@ -0,0 +1,165 @@
+import cv2
+import random
+
+
+def mod_crop(img, scale):
+    """Mod crop images, used during testing.
+
+    Args:
+        img (ndarray): Input image.
+        scale (int): Scale factor.
+
+    Returns:
+        ndarray: Result image.
+    """
+    img = img.copy()
+    if img.ndim in (2, 3):
+        h, w = img.shape[0], img.shape[1]
+        h_remainder, w_remainder = h % scale, w % scale
+        img = img[:h - h_remainder, :w - w_remainder, ...]
+    else:
+        raise ValueError(f'Wrong img ndim: {img.ndim}.')
+    return img
+
+
+def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path):
+    """Paired random crop.
+
+    It crops lists of lq and gt images with corresponding locations.
+
+    Args:
+        img_gts (list[ndarray] | ndarray): GT images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        gt_patch_size (int): GT patch size.
+        scale (int): Scale factor.
+        gt_path (str): Path to ground-truth.
+
+    Returns:
+        list[ndarray] | ndarray: GT images and LQ images. If returned results
+            only have one element, just return ndarray.
+    """
+
+    if not isinstance(img_gts, list):
+        img_gts = [img_gts]
+    if not isinstance(img_lqs, list):
+        img_lqs = [img_lqs]
+
+    h_lq, w_lq, _ = img_lqs[0].shape
+    h_gt, w_gt, _ = img_gts[0].shape
+    lq_patch_size = gt_patch_size // scale
+
+    if h_gt != h_lq * scale or w_gt != w_lq * scale:
+        raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
+                         f'multiplication of LQ ({h_lq}, {w_lq}).')
+    if h_lq < lq_patch_size or w_lq < lq_patch_size:
+        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
+                         f'({lq_patch_size}, {lq_patch_size}). '
+                         f'Please remove {gt_path}.')
+
+    # randomly choose top and left coordinates for lq patch
+    top = random.randint(0, h_lq - lq_patch_size)
+    left = random.randint(0, w_lq - lq_patch_size)
+
+    # crop lq patch
+    img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
+
+    # crop corresponding gt patch
+    top_gt, left_gt = int(top * scale), int(left * scale)
+    img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
+    if len(img_gts) == 1:
+        img_gts = img_gts[0]
+    if len(img_lqs) == 1:
+        img_lqs = img_lqs[0]
+    return img_gts, img_lqs
+
+
+def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
+    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
+
+    We use vertical flip and transpose for rotation implementation.
+    All the images in the list use the same augmentation.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
+            is an ndarray, it will be transformed to a list.
+        hflip (bool): Horizontal flip. Default: True.
+        rotation (bool): Ratotation. Default: True.
+        flows (list[ndarray]: Flows to be augmented. If the input is an
+            ndarray, it will be transformed to a list.
+            Dimension is (h, w, 2). Default: None.
+        return_status (bool): Return the status of flip and rotation.
+            Default: False.
+
+    Returns:
+        list[ndarray] | ndarray: Augmented images and flows. If returned
+            results only have one element, just return ndarray.
+
+    """
+    hflip = hflip and random.random() < 0.5
+    vflip = rotation and random.random() < 0.5
+    rot90 = rotation and random.random() < 0.5
+
+    def _augment(img):
+        if hflip:  # horizontal
+            cv2.flip(img, 1, img)
+        if vflip:  # vertical
+            cv2.flip(img, 0, img)
+        if rot90:
+            img = img.transpose(1, 0, 2)
+        return img
+
+    def _augment_flow(flow):
+        if hflip:  # horizontal
+            cv2.flip(flow, 1, flow)
+            flow[:, :, 0] *= -1
+        if vflip:  # vertical
+            cv2.flip(flow, 0, flow)
+            flow[:, :, 1] *= -1
+        if rot90:
+            flow = flow.transpose(1, 0, 2)
+            flow = flow[:, :, [1, 0]]
+        return flow
+
+    if not isinstance(imgs, list):
+        imgs = [imgs]
+    imgs = [_augment(img) for img in imgs]
+    if len(imgs) == 1:
+        imgs = imgs[0]
+
+    if flows is not None:
+        if not isinstance(flows, list):
+            flows = [flows]
+        flows = [_augment_flow(flow) for flow in flows]
+        if len(flows) == 1:
+            flows = flows[0]
+        return imgs, flows
+    else:
+        if return_status:
+            return imgs, (hflip, vflip, rot90)
+        else:
+            return imgs
+
+
+def img_rotate(img, angle, center=None, scale=1.0):
+    """Rotate image.
+
+    Args:
+        img (ndarray): Image to be rotated.
+        angle (float): Rotation angle in degrees. Positive values mean
+            counter-clockwise rotation.
+        center (tuple[int]): Rotation center. If the center is None,
+            initialize it as the center of the image. Default: None.
+        scale (float): Isotropic scale factor. Default: 1.0.
+    """
+    (h, w) = img.shape[:2]
+
+    if center is None:
+        center = (w // 2, h // 2)
+
+    matrix = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated_img = cv2.warpAffine(img, matrix, (w, h))
+    return rotated_img

CodeFormer/basicsr/losses/__init__.py

@@ -0,0 +1,26 @@
+from copy import deepcopy
+
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import LOSS_REGISTRY
+from .losses import (CharbonnierLoss, GANLoss, L1Loss, MSELoss, PerceptualLoss, WeightedTVLoss, g_path_regularize,
+                     gradient_penalty_loss, r1_penalty)
+
+__all__ = [
+    'L1Loss', 'MSELoss', 'CharbonnierLoss', 'WeightedTVLoss', 'PerceptualLoss', 'GANLoss', 'gradient_penalty_loss',
+    'r1_penalty', 'g_path_regularize'
+]
+
+
+def build_loss(opt):
+    """Build loss from options.
+
+    Args:
+        opt (dict): Configuration. It must constain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    loss_type = opt.pop('type')
+    loss = LOSS_REGISTRY.get(loss_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Loss [{loss.__class__.__name__}] is created.')
+    return loss

CodeFormer/basicsr/losses/loss_util.py

@@ -0,0 +1,95 @@
+import functools
+from torch.nn import functional as F
+
+
+def reduce_loss(loss, reduction):
+    """Reduce loss as specified.
+
+    Args:
+        loss (Tensor): Elementwise loss tensor.
+        reduction (str): Options are 'none', 'mean' and 'sum'.
+
+    Returns:
+        Tensor: Reduced loss tensor.
+    """
+    reduction_enum = F._Reduction.get_enum(reduction)
+    # none: 0, elementwise_mean:1, sum: 2
+    if reduction_enum == 0:
+        return loss
+    elif reduction_enum == 1:
+        return loss.mean()
+    else:
+        return loss.sum()
+
+
+def weight_reduce_loss(loss, weight=None, reduction='mean'):
+    """Apply element-wise weight and reduce loss.
+
+    Args:
+        loss (Tensor): Element-wise loss.
+        weight (Tensor): Element-wise weights. Default: None.
+        reduction (str): Same as built-in losses of PyTorch. Options are
+            'none', 'mean' and 'sum'. Default: 'mean'.
+
+    Returns:
+        Tensor: Loss values.
+    """
+    # if weight is specified, apply element-wise weight
+    if weight is not None:
+        assert weight.dim() == loss.dim()
+        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
+        loss = loss * weight
+
+    # if weight is not specified or reduction is sum, just reduce the loss
+    if weight is None or reduction == 'sum':
+        loss = reduce_loss(loss, reduction)
+    # if reduction is mean, then compute mean over weight region
+    elif reduction == 'mean':
+        if weight.size(1) > 1:
+            weight = weight.sum()
+        else:
+            weight = weight.sum() * loss.size(1)
+        loss = loss.sum() / weight
+
+    return loss
+
+
+def weighted_loss(loss_func):
+    """Create a weighted version of a given loss function.
+
+    To use this decorator, the loss function must have the signature like
+    `loss_func(pred, target, **kwargs)`. The function only needs to compute
+    element-wise loss without any reduction. This decorator will add weight
+    and reduction arguments to the function. The decorated function will have
+    the signature like `loss_func(pred, target, weight=None, reduction='mean',
+    **kwargs)`.
+
+    :Example:
+
+    >>> import torch
+    >>> @weighted_loss
+    >>> def l1_loss(pred, target):
+    >>>     return (pred - target).abs()
+
+    >>> pred = torch.Tensor([0, 2, 3])
+    >>> target = torch.Tensor([1, 1, 1])
+    >>> weight = torch.Tensor([1, 0, 1])
+
+    >>> l1_loss(pred, target)
+    tensor(1.3333)
+    >>> l1_loss(pred, target, weight)
+    tensor(1.5000)
+    >>> l1_loss(pred, target, reduction='none')
+    tensor([1., 1., 2.])
+    >>> l1_loss(pred, target, weight, reduction='sum')
+    tensor(3.)
+    """
+
+    @functools.wraps(loss_func)
+    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
+        # get element-wise loss
+        loss = loss_func(pred, target, **kwargs)
+        loss = weight_reduce_loss(loss, weight, reduction)
+        return loss
+
+    return wrapper

CodeFormer/basicsr/losses/losses.py

@@ -0,0 +1,455 @@
+import math
+import lpips
+import torch
+from torch import autograd as autograd
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.archs.vgg_arch import VGGFeatureExtractor
+from basicsr.utils.registry import LOSS_REGISTRY
+from .loss_util import weighted_loss
+
+_reduction_modes = ['none', 'mean', 'sum']
+
+
+@weighted_loss
+def l1_loss(pred, target):
+    return F.l1_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def mse_loss(pred, target):
+    return F.mse_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def charbonnier_loss(pred, target, eps=1e-12):
+    return torch.sqrt((pred - target)**2 + eps)
+
+
+@LOSS_REGISTRY.register()
+class L1Loss(nn.Module):
+    """L1 (mean absolute error, MAE) loss.
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(L1Loss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class MSELoss(nn.Module):
+    """MSE (L2) loss.
+
+    Args:
+        loss_weight (float): Loss weight for MSE loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(MSELoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class CharbonnierLoss(nn.Module):
+    """Charbonnier loss (one variant of Robust L1Loss, a differentiable
+    variant of L1Loss).
+
+    Described in "Deep Laplacian Pyramid Networks for Fast and Accurate
+        Super-Resolution".
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+        eps (float): A value used to control the curvature near zero.
+            Default: 1e-12.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12):
+        super(CharbonnierLoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+        self.eps = eps
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
+                weights. Default: None.
+        """
+        return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class WeightedTVLoss(L1Loss):
+    """Weighted TV loss.
+
+        Args:
+            loss_weight (float): Loss weight. Default: 1.0.
+    """
+
+    def __init__(self, loss_weight=1.0):
+        super(WeightedTVLoss, self).__init__(loss_weight=loss_weight)
+
+    def forward(self, pred, weight=None):
+        y_diff = super(WeightedTVLoss, self).forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=weight[:, :, :-1, :])
+        x_diff = super(WeightedTVLoss, self).forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=weight[:, :, :, :-1])
+
+        loss = x_diff + y_diff
+
+        return loss
+
+
+@LOSS_REGISTRY.register()
+class PerceptualLoss(nn.Module):
+    """Perceptual loss with commonly used style loss.
+
+    Args:
+        layer_weights (dict): The weight for each layer of vgg feature.
+            Here is an example: {'conv5_4': 1.}, which means the conv5_4
+            feature layer (before relu5_4) will be extracted with weight
+            1.0 in calculting losses.
+        vgg_type (str): The type of vgg network used as feature extractor.
+            Default: 'vgg19'.
+        use_input_norm (bool):  If True, normalize the input image in vgg.
+            Default: True.
+        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
+            Default: False.
+        perceptual_weight (float): If `perceptual_weight > 0`, the perceptual
+            loss will be calculated and the loss will multiplied by the
+            weight. Default: 1.0.
+        style_weight (float): If `style_weight > 0`, the style loss will be
+            calculated and the loss will multiplied by the weight.
+            Default: 0.
+        criterion (str): Criterion used for perceptual loss. Default: 'l1'.
+    """
+
+    def __init__(self,
+                 layer_weights,
+                 vgg_type='vgg19',
+                 use_input_norm=True,
+                 range_norm=False,
+                 perceptual_weight=1.0,
+                 style_weight=0.,
+                 criterion='l1'):
+        super(PerceptualLoss, self).__init__()
+        self.perceptual_weight = perceptual_weight
+        self.style_weight = style_weight
+        self.layer_weights = layer_weights
+        self.vgg = VGGFeatureExtractor(
+            layer_name_list=list(layer_weights.keys()),
+            vgg_type=vgg_type,
+            use_input_norm=use_input_norm,
+            range_norm=range_norm)
+
+        self.criterion_type = criterion
+        if self.criterion_type == 'l1':
+            self.criterion = torch.nn.L1Loss()
+        elif self.criterion_type == 'l2':
+            self.criterion = torch.nn.L2loss()
+        elif self.criterion_type == 'mse':
+            self.criterion = torch.nn.MSELoss(reduction='mean')
+        elif self.criterion_type == 'fro':
+            self.criterion = None
+        else:
+            raise NotImplementedError(f'{criterion} criterion has not been supported.')
+
+    def forward(self, x, gt):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+            gt (Tensor): Ground-truth tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor: Forward results.
+        """
+        # extract vgg features
+        x_features = self.vgg(x)
+        gt_features = self.vgg(gt.detach())
+
+        # calculate perceptual loss
+        if self.perceptual_weight > 0:
+            percep_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k]
+                else:
+                    percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k]
+            percep_loss *= self.perceptual_weight
+        else:
+            percep_loss = None
+
+        # calculate style loss
+        if self.style_weight > 0:
+            style_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    style_loss += torch.norm(
+                        self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k]
+                else:
+                    style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat(
+                        gt_features[k])) * self.layer_weights[k]
+            style_loss *= self.style_weight
+        else:
+            style_loss = None
+
+        return percep_loss, style_loss
+
+    def _gram_mat(self, x):
+        """Calculate Gram matrix.
+
+        Args:
+            x (torch.Tensor): Tensor with shape of (n, c, h, w).
+
+        Returns:
+            torch.Tensor: Gram matrix.
+        """
+        n, c, h, w = x.size()
+        features = x.view(n, c, w * h)
+        features_t = features.transpose(1, 2)
+        gram = features.bmm(features_t) / (c * h * w)
+        return gram
+
+
+@LOSS_REGISTRY.register()
+class LPIPSLoss(nn.Module):
+    def __init__(self, 
+            loss_weight=1.0, 
+            use_input_norm=True,
+            range_norm=False,):
+        super(LPIPSLoss, self).__init__()
+        self.perceptual = lpips.LPIPS(net="vgg", spatial=False).eval()
+        self.loss_weight = loss_weight
+        self.use_input_norm = use_input_norm
+        self.range_norm = range_norm
+
+        if self.use_input_norm:
+            # the mean is for image with range [0, 1]
+            self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+            # the std is for image with range [0, 1]
+            self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+    def forward(self, pred, target):
+        if self.range_norm:
+            pred   = (pred + 1) / 2
+            target = (target + 1) / 2
+        if self.use_input_norm:
+            pred   = (pred - self.mean) / self.std
+            target = (target - self.mean) / self.std
+        lpips_loss = self.perceptual(target.contiguous(), pred.contiguous())
+        return self.loss_weight * lpips_loss.mean()
+
+
+@LOSS_REGISTRY.register()
+class GANLoss(nn.Module):
+    """Define GAN loss.
+
+    Args:
+        gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'.
+        real_label_val (float): The value for real label. Default: 1.0.
+        fake_label_val (float): The value for fake label. Default: 0.0.
+        loss_weight (float): Loss weight. Default: 1.0.
+            Note that loss_weight is only for generators; and it is always 1.0
+            for discriminators.
+    """
+
+    def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0):
+        super(GANLoss, self).__init__()
+        self.gan_type = gan_type
+        self.loss_weight = loss_weight
+        self.real_label_val = real_label_val
+        self.fake_label_val = fake_label_val
+
+        if self.gan_type == 'vanilla':
+            self.loss = nn.BCEWithLogitsLoss()
+        elif self.gan_type == 'lsgan':
+            self.loss = nn.MSELoss()
+        elif self.gan_type == 'wgan':
+            self.loss = self._wgan_loss
+        elif self.gan_type == 'wgan_softplus':
+            self.loss = self._wgan_softplus_loss
+        elif self.gan_type == 'hinge':
+            self.loss = nn.ReLU()
+        else:
+            raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.')
+
+    def _wgan_loss(self, input, target):
+        """wgan loss.
+
+        Args:
+            input (Tensor): Input tensor.
+            target (bool): Target label.
+
+        Returns:
+            Tensor: wgan loss.
+        """
+        return -input.mean() if target else input.mean()
+
+    def _wgan_softplus_loss(self, input, target):
+        """wgan loss with soft plus. softplus is a smooth approximation to the
+        ReLU function.
+
+        In StyleGAN2, it is called:
+            Logistic loss for discriminator;
+            Non-saturating loss for generator.
+
+        Args:
+            input (Tensor): Input tensor.
+            target (bool): Target label.
+
+        Returns:
+            Tensor: wgan loss.
+        """
+        return F.softplus(-input).mean() if target else F.softplus(input).mean()
+
+    def get_target_label(self, input, target_is_real):
+        """Get target label.
+
+        Args:
+            input (Tensor): Input tensor.
+            target_is_real (bool): Whether the target is real or fake.
+
+        Returns:
+            (bool | Tensor): Target tensor. Return bool for wgan, otherwise,
+                return Tensor.
+        """
+
+        if self.gan_type in ['wgan', 'wgan_softplus']:
+            return target_is_real
+        target_val = (self.real_label_val if target_is_real else self.fake_label_val)
+        return input.new_ones(input.size()) * target_val
+
+    def forward(self, input, target_is_real, is_disc=False):
+        """
+        Args:
+            input (Tensor): The input for the loss module, i.e., the network
+                prediction.
+            target_is_real (bool): Whether the targe is real or fake.
+            is_disc (bool): Whether the loss for discriminators or not.
+                Default: False.
+
+        Returns:
+            Tensor: GAN loss value.
+        """
+        if self.gan_type == 'hinge':
+            if is_disc:  # for discriminators in hinge-gan
+                input = -input if target_is_real else input
+                loss = self.loss(1 + input).mean()
+            else:  # for generators in hinge-gan
+                loss = -input.mean()
+        else:  # other gan types
+            target_label = self.get_target_label(input, target_is_real)
+            loss = self.loss(input, target_label)
+
+        # loss_weight is always 1.0 for discriminators
+        return loss if is_disc else loss * self.loss_weight
+
+
+def r1_penalty(real_pred, real_img):
+    """R1 regularization for discriminator. The core idea is to
+        penalize the gradient on real data alone: when the
+        generator distribution produces the true data distribution
+        and the discriminator is equal to 0 on the data manifold, the
+        gradient penalty ensures that the discriminator cannot create
+        a non-zero gradient orthogonal to the data manifold without
+        suffering a loss in the GAN game.
+
+        Ref:
+        Eq. 9 in Which training methods for GANs do actually converge.
+        """
+    grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0]
+    grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean()
+    return grad_penalty
+
+
+def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
+    noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3])
+    grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0]
+    path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
+
+    path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
+
+    path_penalty = (path_lengths - path_mean).pow(2).mean()
+
+    return path_penalty, path_lengths.detach().mean(), path_mean.detach()
+
+
+def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None):
+    """Calculate gradient penalty for wgan-gp.
+
+    Args:
+        discriminator (nn.Module): Network for the discriminator.
+        real_data (Tensor): Real input data.
+        fake_data (Tensor): Fake input data.
+        weight (Tensor): Weight tensor. Default: None.
+
+    Returns:
+        Tensor: A tensor for gradient penalty.
+    """
+
+    batch_size = real_data.size(0)
+    alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1))
+
+    # interpolate between real_data and fake_data
+    interpolates = alpha * real_data + (1. - alpha) * fake_data
+    interpolates = autograd.Variable(interpolates, requires_grad=True)
+
+    disc_interpolates = discriminator(interpolates)
+    gradients = autograd.grad(
+        outputs=disc_interpolates,
+        inputs=interpolates,
+        grad_outputs=torch.ones_like(disc_interpolates),
+        create_graph=True,
+        retain_graph=True,
+        only_inputs=True)[0]
+
+    if weight is not None:
+        gradients = gradients * weight
+
+    gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()
+    if weight is not None:
+        gradients_penalty /= torch.mean(weight)
+
+    return gradients_penalty

CodeFormer/basicsr/metrics/__init__.py

@@ -0,0 +1,19 @@
+from copy import deepcopy
+
+from basicsr.utils.registry import METRIC_REGISTRY
+from .psnr_ssim import calculate_psnr, calculate_ssim
+
+__all__ = ['calculate_psnr', 'calculate_ssim']
+
+
+def calculate_metric(data, opt):
+    """Calculate metric from data and options.
+
+    Args:
+        opt (dict): Configuration. It must constain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    metric_type = opt.pop('type')
+    metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
+    return metric

CodeFormer/basicsr/metrics/metric_util.py

@@ -0,0 +1,45 @@
+import numpy as np
+
+from basicsr.utils.matlab_functions import bgr2ycbcr
+
+
+def reorder_image(img, input_order='HWC'):
+    """Reorder images to 'HWC' order.
+
+    If the input_order is (h, w), return (h, w, 1);
+    If the input_order is (c, h, w), return (h, w, c);
+    If the input_order is (h, w, c), return as it is.
+
+    Args:
+        img (ndarray): Input image.
+        input_order (str): Whether the input order is 'HWC' or 'CHW'.
+            If the input image shape is (h, w), input_order will not have
+            effects. Default: 'HWC'.
+
+    Returns:
+        ndarray: reordered image.
+    """
+
+    if input_order not in ['HWC', 'CHW']:
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
+    if len(img.shape) == 2:
+        img = img[..., None]
+    if input_order == 'CHW':
+        img = img.transpose(1, 2, 0)
+    return img
+
+
+def to_y_channel(img):
+    """Change to Y channel of YCbCr.
+
+    Args:
+        img (ndarray): Images with range [0, 255].
+
+    Returns:
+        (ndarray): Images with range [0, 255] (float type) without round.
+    """
+    img = img.astype(np.float32) / 255.
+    if img.ndim == 3 and img.shape[2] == 3:
+        img = bgr2ycbcr(img, y_only=True)
+        img = img[..., None]
+    return img * 255.