update gfpgan demo (#4)

- update gfpgan (a7308990ea5b8c9666e3eccd533bbe287d18005b)

.gitattributes

@@ -1,7 +1,6 @@
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
-*.bin.* filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
@@ -10,9 +9,13 @@
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
@@ -21,7 +24,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -1,351 +0,0 @@
-Tencent is pleased to support the open source community by making GFPGAN available.
-
-Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
-
-GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
-
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MANIFEST.in

@@ -1,8 +0,0 @@
-include assets/*
-include inputs/*
-include scripts/*.py
-include inference_gfpgan.py
-include VERSION
-include LICENSE
-include requirements.txt
-include gfpgan/weights/README.md

PaperModel.md

@@ -1,76 +0,0 @@
-# Installation
-
-We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.<br>
-If you want want to use the original model in our paper, please follow the instructions below.
-
-1. Clone repo
-
-    ```bash
-    git clone https://github.com/xinntao/GFPGAN.git
-    cd GFPGAN
-    ```
-
-1. Install dependent packages
-
-    As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**.
-    You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
-
-    **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
-
-    ```bash
-    # Install basicsr - https://github.com/xinntao/BasicSR
-    # We use BasicSR for both training and inference
-    pip install basicsr
-
-    # Install facexlib - https://github.com/xinntao/facexlib
-    # We use face detection and face restoration helper in the facexlib package
-    pip install facexlib
-
-    pip install -r requirements.txt
-    python setup.py develop
-
-    # remember to set BASICSR_JIT=True before your running commands
-    ```
-
-    **Option 2: Compile extensions during installation** (For those need to train/inference for many times)
-
-    ```bash
-    # Install basicsr - https://github.com/xinntao/BasicSR
-    # We use BasicSR for both training and inference
-    # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
-    # Add -vvv for detailed log prints
-    BASICSR_EXT=True pip install basicsr -vvv
-
-    # Install facexlib - https://github.com/xinntao/facexlib
-    # We use face detection and face restoration helper in the facexlib package
-    pip install facexlib
-
-    pip install -r requirements.txt
-    python setup.py develop
-    ```
-
-## :zap: Quick Inference
-
-Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
-
-```bash
-wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
-```
-
-- Option 1: Load extensions just-in-time(JIT)
-
-    ```bash
-    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-
-    # for aligned images
-    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
-    ```
-
-- Option 2: Have successfully compiled extensions during installation
-
-    ```bash
-    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
-
-    # for aligned images
-    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
-    ```

README.md

@@ -1,34 +1,13 @@
 ---
 title: GFPGAN
-emoji: 📚
+emoji: 😁
 colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 3.1.3
+sdk_version: 3.1.7
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

VERSION

@@ -1 +0,0 @@
-0.2.4

app.py

@@ -1,68 +1,116 @@
 import os
-os.system("pip install gfpgan")
 
-os.system("pip freeze")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
+import cv2
 import gradio as gr
-from PIL import Image
 import torch
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
 
+os.system("pip freeze")
+os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
+os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
+os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
 
+torch.hub.download_url_to_file(
+    'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
+    'lincoln.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
+    'AI-generate.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
+    'Blake_Lively.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
+    '10045.png')
 
+# background enhancer with RealESRGAN
+model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+model_path = 'realesr-general-x4v3.pth'
+half = True if torch.cuda.is_available() else False
+upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
+# Use GFPGAN for face enhancement
+face_enhancer_v3 = GFPGANer(
+    model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+face_enhancer_v2 = GFPGANer(
+    model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+os.makedirs('output', exist_ok=True)
+
+
+def inference(img, version, scale):
+    print(img, version, scale)
+    try:
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        else:
+            img_mode = None
+
+        h, w = img.shape[0:2]
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
-import warnings
-warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-              'If you really want to use it, please modify the corresponding codes.')
-bg_upsampler = None
+        if version == 'v1.2':
+            face_enhancer = face_enhancer_v2
+        else:
+            face_enhancer = face_enhancer_v3
+        try:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
+        else:
+            extension = 'png'
 
+        try:
+            if scale != 2:
+                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+                h, w = img.shape[0:2]
+                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+        except Exception as error:
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+        save_path = f'output/out.{extension}'
+        cv2.imwrite(save_path, output)
 
- 
-# set up GFPGAN restorer
-restorer = GFPGANer(
-    model_path='GFPGANCleanv1-NoCE-C2.pth',
-    upscale=2,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler)
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output, save_path
+    except Exception as error:
+        print('global exception', error)
+        return None, None
 
 
+title = "GFPGAN: Practical Face Restoration Algorithm"
+description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
+It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
+To use it, simply upload your image.<br>
+If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
+"""
+article = r"""
 
+[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
 
+If you have any question, please email 📧 `[email protected]` or `[email protected]`.
 
-def inference(img):
-    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
-    cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=False, only_center_face=False, paste_back=True)
-    
-    return Image.fromarray(restored_faces[0][:,:,::-1])
-        
-title = "GFP-GAN"
-description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061' target='_blank'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
+"""
 gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
+    inference, [
+        gr.inputs.Image(type="filepath", label="Input"),
+        gr.inputs.Radio(['v1.2', 'v1.3'], type="value", default='v1.3', label='GFPGAN version'),
+        gr.inputs.Number(label="Rescaling factor", default=2)
+    ], [
+        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
+        gr.outputs.File(label="Download the output image")
+    ],
     title=title,
     description=description,
     article=article,
-    examples=[
-    ['lincoln.jpg'],
-    ['einstein.png'],
-    ['edison.jpg'],
-    ['Henry.jpg'],
-    ['Frida.jpg']
-    ]
-    ).launch()
+    examples=[['AI-generate.jpg', 'v1.3', 2], ['lincoln.jpg', 'v1.3', 2], ['Blake_Lively.jpg', 'v1.3', 2],
+              ['10045.png', 'v1.3', 2]]).launch()

experiments/pretrained_models/README.md

@@ -1,7 +0,0 @@
-# Pre-trained Models and Other Data
-
-Download pre-trained models and other data. Put them in this folder.
-
-1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
-1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
-1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)

gfpgan/__init__.py

@@ -1,7 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-
-# from .version import *

gfpgan/archs/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import arch modules for registry
-# scan all the files that end with '_arch.py' under the archs folder
-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'gfpgan.archs.{file_name}') for file_name in arch_filenames]

gfpgan/archs/arcface_arch.py

@@ -1,245 +0,0 @@
-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

gfpgan/archs/gfpganv1_arch.py

@@ -1,439 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
-                                          StyleGAN2Generator)
-from basicsr.ops.fused_act import FusedLeakyReLU
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(self,
-                 out_size,
-                 num_style_feat=512,
-                 num_mlp=8,
-                 channel_multiplier=2,
-                 resample_kernel=(1, 3, 3, 1),
-                 lr_mlp=0.01,
-                 narrow=1,
-                 sft_half=False):
-        super(StyleGAN2GeneratorSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow)
-        self.sft_half = sft_half
-
-    def forward(self,
-                styles,
-                conditions,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorSFT.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ConvUpLayer(nn.Module):
-    """Convolutional upsampling layer. It uses bilinear upsampler + Conv.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input. Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 bias=True,
-                 bias_init_val=0,
-                 activate=True):
-        super(ConvUpLayer, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        # self.scale is used to scale the convolution weights, which is related to the common initializations.
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
-
-        if bias and not activate:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter('bias', None)
-
-        # activation
-        if activate:
-            if bias:
-                self.activation = FusedLeakyReLU(out_channels)
-            else:
-                self.activation = ScaledLeakyReLU(0.2)
-        else:
-            self.activation = None
-
-    def forward(self, x):
-        # bilinear upsample
-        out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        # conv
-        out = F.conv2d(
-            out,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-        # activation
-        if self.activation is not None:
-            out = self.activation(out)
-        return out
-
-
-class ResUpBlock(nn.Module):
-    """Residual block with upsampling.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(ResUpBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True)
-        self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class GFPGANv1(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-            self,
-            out_size,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=False):
-
-        super(GFPGANv1, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            '4': int(512 * unet_narrow),
-            '8': int(512 * unet_narrow),
-            '16': int(512 * unet_narrow),
-            '32': int(512 * unet_narrow),
-            '64': int(256 * channel_multiplier * unet_narrow),
-            '128': int(128 * channel_multiplier * unet_narrow),
-            '256': int(64 * channel_multiplier * unet_narrow),
-            '512': int(32 * channel_multiplier * unet_narrow),
-            '1024': int(16 * channel_multiplier * unet_narrow)
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2**(int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True)
-
-        # downsample
-        in_channels = channels[f'{first_out_size}']
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f'{2**(i - 1)}']
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel))
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True)
-
-        # upsample
-        in_channels = channels['4']
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half)
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1)))
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0)))
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANv1.
-
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder([style_code],
-                                         conditions,
-                                         return_latents=return_latents,
-                                         input_is_latent=self.input_is_latent,
-                                         randomize_noise=randomize_noise)
-
-        return image, out_rgbs
-
-
-@ARCH_REGISTRY.register()
-class FacialComponentDiscriminator(nn.Module):
-    """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.
-    """
-
-    def __init__(self):
-        super(FacialComponentDiscriminator, self).__init__()
-        # It now uses a VGG-style architectrue with fixed model size
-        self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
-
-    def forward(self, x, return_feats=False):
-        """Forward function for FacialComponentDiscriminator.
-
-        Args:
-            x (Tensor): Input images.
-            return_feats (bool): Whether to return intermediate features. Default: False.
-        """
-        feat = self.conv1(x)
-        feat = self.conv3(self.conv2(feat))
-        rlt_feats = []
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        feat = self.conv5(self.conv4(feat))
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        out = self.final_conv(feat)
-
-        if return_feats:
-            return out, rlt_feats
-        else:
-            return out, None

gfpgan/archs/gfpganv1_clean_arch.py

@@ -1,324 +0,0 @@
-import math
-import random
-import torch
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-from .stylegan2_clean_arch import StyleGAN2GeneratorClean
-
-
-class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False):
-        super(StyleGAN2GeneratorCSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow)
-        self.sft_half = sft_half
-
-    def forward(self,
-                styles,
-                conditions,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorCSFT.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ResBlock(nn.Module):
-    """Residual block with bilinear upsampling/downsampling.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
-    """
-
-    def __init__(self, in_channels, out_channels, mode='down'):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
-        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
-        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
-        if mode == 'down':
-            self.scale_factor = 0.5
-        elif mode == 'up':
-            self.scale_factor = 2
-
-    def forward(self, x):
-        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
-        # upsample/downsample
-        out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
-        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
-        # skip
-        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
-        skip = self.skip(x)
-        out = out + skip
-        return out
-
-
-@ARCH_REGISTRY.register()
-class GFPGANv1Clean(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-            self,
-            out_size,
-            num_style_feat=512,
-            channel_multiplier=1,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=False):
-
-        super(GFPGANv1Clean, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            '4': int(512 * unet_narrow),
-            '8': int(512 * unet_narrow),
-            '16': int(512 * unet_narrow),
-            '32': int(512 * unet_narrow),
-            '64': int(256 * channel_multiplier * unet_narrow),
-            '128': int(128 * channel_multiplier * unet_narrow),
-            '256': int(64 * channel_multiplier * unet_narrow),
-            '512': int(32 * channel_multiplier * unet_narrow),
-            '1024': int(16 * channel_multiplier * unet_narrow)
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2**(int(math.log(out_size, 2)))
-
-        self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1)
-
-        # downsample
-        in_channels = channels[f'{first_out_size}']
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f'{2**(i - 1)}']
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down'))
-            in_channels = out_channels
-
-        self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1)
-
-        # upsample
-        in_channels = channels['4']
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up'))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorCSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow,
-            sft_half=sft_half)
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
-            self.condition_shift.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANv1Clean.
-
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder([style_code],
-                                         conditions,
-                                         return_latents=return_latents,
-                                         input_is_latent=self.input_is_latent,
-                                         randomize_noise=randomize_noise)
-
-        return image, out_rgbs

gfpgan/archs/stylegan2_clean_arch.py

@@ -1,368 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.arch_util import default_init_weights
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class NormStyleCode(nn.Module):
-
-    def forward(self, x):
-        """Normalize the style codes.
-
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-
-    There is no bias in ModulatedConv2d.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 num_style_feat,
-                 demodulate=True,
-                 sample_mode=None,
-                 eps=1e-8):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        # modulation inside each modulated conv
-        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
-        # initialization
-        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
-            math.sqrt(in_channels * kernel_size**2))
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
-
-        # upsample or downsample if necessary
-        if self.sample_mode == 'upsample':
-            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        elif self.sample_mode == 'downsample':
-            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
-                f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
-
-
-class StyleConv(nn.Module):
-    """Style conv used in StyleGAN2.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-    """
-
-    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
-        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # add bias
-        out = out + self.bias
-        # activation
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB (image space) from features.
-
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(self, in_channels, num_style_feat, upsample=True):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.modulated_conv = ModulatedConv2d(
-            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class StyleGAN2GeneratorClean(nn.Module):
-    """Clean version of StyleGAN2 Generator.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1):
-        super(StyleGAN2GeneratorClean, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.extend(
-                [nn.Linear(num_style_feat, num_style_feat, bias=True),
-                 nn.LeakyReLU(negative_slope=0.2, inplace=True)])
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-        # initialization
-        default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu')
-
-        # channel list
-        channels = {
-            '4': int(512 * narrow),
-            '8': int(512 * narrow),
-            '16': int(512 * narrow),
-            '32': int(512 * narrow),
-            '64': int(256 * channel_multiplier * narrow),
-            '128': int(128 * channel_multiplier * narrow),
-            '256': int(64 * channel_multiplier * narrow),
-            '512': int(32 * channel_multiplier * narrow),
-            '1024': int(16 * channel_multiplier * narrow)
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels['4'], size=4)
-        self.style_conv1 = StyleConv(
-            channels['4'],
-            channels['4'],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None)
-        self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False)
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels['4']
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2**((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode='upsample'))
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None))
-            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device)
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(self,
-                styles,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorClean.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None

gfpgan/data/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import dataset modules for registry
-# scan all the files that end with '_dataset.py' under the data folder
-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'gfpgan.data.{file_name}') for file_name in dataset_filenames]

gfpgan/data/ffhq_degradation_dataset.py

@@ -1,230 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os.path as osp
-import torch
-import torch.utils.data as data
-from basicsr.data import degradations as degradations
-from basicsr.data.data_util import paths_from_folder
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
-                                               normalize)
-
-
-@DATASET_REGISTRY.register()
-class FFHQDegradationDataset(data.Dataset):
-    """FFHQ dataset for GFPGAN.
-
-    It reads high resolution images, and then generate low-quality (LQ) images on-the-fly.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            io_backend (dict): IO backend type and other kwarg.
-            mean (list | tuple): Image mean.
-            std (list | tuple): Image std.
-            use_hflip (bool): Whether to horizontally flip.
-            Please see more options in the codes.
-    """
-
-    def __init__(self, opt):
-        super(FFHQDegradationDataset, self).__init__()
-        self.opt = opt
-        # file client (io backend)
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-
-        self.gt_folder = opt['dataroot_gt']
-        self.mean = opt['mean']
-        self.std = opt['std']
-        self.out_size = opt['out_size']
-
-        self.crop_components = opt.get('crop_components', False)  # facial components
-        self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1)  # whether enlarge eye regions
-
-        if self.crop_components:
-            # load component list from a pre-process pth files
-            self.components_list = torch.load(opt.get('component_path'))
-
-        # file client (lmdb io backend)
-        if self.io_backend_opt['type'] == 'lmdb':
-            self.io_backend_opt['db_paths'] = self.gt_folder
-            if not self.gt_folder.endswith('.lmdb'):
-                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
-            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
-                self.paths = [line.split('.')[0] for line in fin]
-        else:
-            # disk backend: scan file list from a folder
-            self.paths = paths_from_folder(self.gt_folder)
-
-        # degradation configurations
-        self.blur_kernel_size = opt['blur_kernel_size']
-        self.kernel_list = opt['kernel_list']
-        self.kernel_prob = opt['kernel_prob']
-        self.blur_sigma = opt['blur_sigma']
-        self.downsample_range = opt['downsample_range']
-        self.noise_range = opt['noise_range']
-        self.jpeg_range = opt['jpeg_range']
-
-        # color jitter
-        self.color_jitter_prob = opt.get('color_jitter_prob')
-        self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob')
-        self.color_jitter_shift = opt.get('color_jitter_shift', 20)
-        # to gray
-        self.gray_prob = opt.get('gray_prob')
-
-        logger = get_root_logger()
-        logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]')
-        logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]')
-        logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]')
-        logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]')
-
-        if self.color_jitter_prob is not None:
-            logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}')
-        if self.gray_prob is not None:
-            logger.info(f'Use random gray. Prob: {self.gray_prob}')
-        self.color_jitter_shift /= 255.
-
-    @staticmethod
-    def color_jitter(img, shift):
-        """jitter color: randomly jitter the RGB values, in numpy formats"""
-        jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
-        img = img + jitter_val
-        img = np.clip(img, 0, 1)
-        return img
-
-    @staticmethod
-    def color_jitter_pt(img, brightness, contrast, saturation, hue):
-        """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats"""
-        fn_idx = torch.randperm(4)
-        for fn_id in fn_idx:
-            if fn_id == 0 and brightness is not None:
-                brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
-                img = adjust_brightness(img, brightness_factor)
-
-            if fn_id == 1 and contrast is not None:
-                contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
-                img = adjust_contrast(img, contrast_factor)
-
-            if fn_id == 2 and saturation is not None:
-                saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
-                img = adjust_saturation(img, saturation_factor)
-
-            if fn_id == 3 and hue is not None:
-                hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
-                img = adjust_hue(img, hue_factor)
-        return img
-
-    def get_component_coordinates(self, index, status):
-        """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file"""
-        components_bbox = self.components_list[f'{index:08d}']
-        if status[0]:  # hflip
-            # exchange right and left eye
-            tmp = components_bbox['left_eye']
-            components_bbox['left_eye'] = components_bbox['right_eye']
-            components_bbox['right_eye'] = tmp
-            # modify the width coordinate
-            components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0]
-            components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0]
-            components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0]
-
-        # get coordinates
-        locations = []
-        for part in ['left_eye', 'right_eye', 'mouth']:
-            mean = components_bbox[part][0:2]
-            half_len = components_bbox[part][2]
-            if 'eye' in part:
-                half_len *= self.eye_enlarge_ratio
-            loc = np.hstack((mean - half_len + 1, mean + half_len))
-            loc = torch.from_numpy(loc).float()
-            locations.append(loc)
-        return locations
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
-        # load gt image
-        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
-        gt_path = self.paths[index]
-        img_bytes = self.file_client.get(gt_path)
-        img_gt = imfrombytes(img_bytes, float32=True)
-
-        # random horizontal flip
-        img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True)
-        h, w, _ = img_gt.shape
-
-        # get facial component coordinates
-        if self.crop_components:
-            locations = self.get_component_coordinates(index, status)
-            loc_left_eye, loc_right_eye, loc_mouth = locations
-
-        # ------------------------ generate lq image ------------------------ #
-        # blur
-        kernel = degradations.random_mixed_kernels(
-            self.kernel_list,
-            self.kernel_prob,
-            self.blur_kernel_size,
-            self.blur_sigma,
-            self.blur_sigma, [-math.pi, math.pi],
-            noise_range=None)
-        img_lq = cv2.filter2D(img_gt, -1, kernel)
-        # downsample
-        scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1])
-        img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR)
-        # noise
-        if self.noise_range is not None:
-            img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range)
-        # jpeg compression
-        if self.jpeg_range is not None:
-            img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range)
-
-        # resize to original size
-        img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR)
-
-        # random color jitter (only for lq)
-        if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob):
-            img_lq = self.color_jitter(img_lq, self.color_jitter_shift)
-        # random to gray (only for lq)
-        if self.gray_prob and np.random.uniform() < self.gray_prob:
-            img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY)
-            img_lq = np.tile(img_lq[:, :, None], [1, 1, 3])
-            if self.opt.get('gt_gray'):  # whether convert GT to gray images
-                img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY)
-                img_gt = np.tile(img_gt[:, :, None], [1, 1, 3])  # repeat the color channels
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
-
-        # random color jitter (pytorch version) (only for lq)
-        if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob):
-            brightness = self.opt.get('brightness', (0.5, 1.5))
-            contrast = self.opt.get('contrast', (0.5, 1.5))
-            saturation = self.opt.get('saturation', (0, 1.5))
-            hue = self.opt.get('hue', (-0.1, 0.1))
-            img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue)
-
-        # round and clip
-        img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255.
-
-        # normalize
-        normalize(img_gt, self.mean, self.std, inplace=True)
-        normalize(img_lq, self.mean, self.std, inplace=True)
-
-        if self.crop_components:
-            return_dict = {
-                'lq': img_lq,
-                'gt': img_gt,
-                'gt_path': gt_path,
-                'loc_left_eye': loc_left_eye,
-                'loc_right_eye': loc_right_eye,
-                'loc_mouth': loc_mouth
-            }
-            return return_dict
-        else:
-            return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path}
-
-    def __len__(self):
-        return len(self.paths)

gfpgan/models/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]

gfpgan/models/gfpgan_model.py

@@ -1,580 +0,0 @@
-import math
-import os.path as osp
-import torch
-from basicsr.archs import build_network
-from basicsr.losses import build_loss
-from basicsr.losses.gan_loss import r1_penalty
-from basicsr.metrics import calculate_metric
-from basicsr.models.base_model import BaseModel
-from basicsr.utils import get_root_logger, imwrite, tensor2img
-from basicsr.utils.registry import MODEL_REGISTRY
-from collections import OrderedDict
-from torch.nn import functional as F
-from torchvision.ops import roi_align
-from tqdm import tqdm
-
-
-@MODEL_REGISTRY.register()
-class GFPGANModel(BaseModel):
-    """The GFPGAN model for Towards real-world blind face restoratin with generative facial prior"""
-
-    def __init__(self, opt):
-        super(GFPGANModel, self).__init__(opt)
-        self.idx = 0  # it is used for saving data for check
-
-        # define network
-        self.net_g = build_network(opt['network_g'])
-        self.net_g = self.model_to_device(self.net_g)
-        self.print_network(self.net_g)
-
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_g', None)
-        if load_path is not None:
-            param_key = self.opt['path'].get('param_key_g', 'params')
-            self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
-
-        self.log_size = int(math.log(self.opt['network_g']['out_size'], 2))
-
-        if self.is_train:
-            self.init_training_settings()
-
-    def init_training_settings(self):
-        train_opt = self.opt['train']
-
-        # ----------- define net_d ----------- #
-        self.net_d = build_network(self.opt['network_d'])
-        self.net_d = self.model_to_device(self.net_d)
-        self.print_network(self.net_d)
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_d', None)
-        if load_path is not None:
-            self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
-
-        # ----------- define net_g with Exponential Moving Average (EMA) ----------- #
-        # net_g_ema only used for testing on one GPU and saving. There is no need to wrap with DistributedDataParallel
-        self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_g', None)
-        if load_path is not None:
-            self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
-        else:
-            self.model_ema(0)  # copy net_g weight
-
-        self.net_g.train()
-        self.net_d.train()
-        self.net_g_ema.eval()
-
-        # ----------- facial component networks ----------- #
-        if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt):
-            self.use_facial_disc = True
-        else:
-            self.use_facial_disc = False
-
-        if self.use_facial_disc:
-            # left eye
-            self.net_d_left_eye = build_network(self.opt['network_d_left_eye'])
-            self.net_d_left_eye = self.model_to_device(self.net_d_left_eye)
-            self.print_network(self.net_d_left_eye)
-            load_path = self.opt['path'].get('pretrain_network_d_left_eye')
-            if load_path is not None:
-                self.load_network(self.net_d_left_eye, load_path, True, 'params')
-            # right eye
-            self.net_d_right_eye = build_network(self.opt['network_d_right_eye'])
-            self.net_d_right_eye = self.model_to_device(self.net_d_right_eye)
-            self.print_network(self.net_d_right_eye)
-            load_path = self.opt['path'].get('pretrain_network_d_right_eye')
-            if load_path is not None:
-                self.load_network(self.net_d_right_eye, load_path, True, 'params')
-            # mouth
-            self.net_d_mouth = build_network(self.opt['network_d_mouth'])
-            self.net_d_mouth = self.model_to_device(self.net_d_mouth)
-            self.print_network(self.net_d_mouth)
-            load_path = self.opt['path'].get('pretrain_network_d_mouth')
-            if load_path is not None:
-                self.load_network(self.net_d_mouth, load_path, True, 'params')
-
-            self.net_d_left_eye.train()
-            self.net_d_right_eye.train()
-            self.net_d_mouth.train()
-
-            # ----------- define facial component gan loss ----------- #
-            self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device)
-
-        # ----------- define losses ----------- #
-        # pixel loss
-        if train_opt.get('pixel_opt'):
-            self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
-        else:
-            self.cri_pix = None
-
-        # perceptual loss
-        if train_opt.get('perceptual_opt'):
-            self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
-        else:
-            self.cri_perceptual = None
-
-        # L1 loss is used in pyramid loss, component style loss and identity loss
-        self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device)
-
-        # gan loss (wgan)
-        self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
-
-        # ----------- define identity loss ----------- #
-        if 'network_identity' in self.opt:
-            self.use_identity = True
-        else:
-            self.use_identity = False
-
-        if self.use_identity:
-            # define identity network
-            self.network_identity = build_network(self.opt['network_identity'])
-            self.network_identity = self.model_to_device(self.network_identity)
-            self.print_network(self.network_identity)
-            load_path = self.opt['path'].get('pretrain_network_identity')
-            if load_path is not None:
-                self.load_network(self.network_identity, load_path, True, None)
-            self.network_identity.eval()
-            for param in self.network_identity.parameters():
-                param.requires_grad = False
-
-        # regularization weights
-        self.r1_reg_weight = train_opt['r1_reg_weight']  # for discriminator
-        self.net_d_iters = train_opt.get('net_d_iters', 1)
-        self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
-        self.net_d_reg_every = train_opt['net_d_reg_every']
-
-        # set up optimizers and schedulers
-        self.setup_optimizers()
-        self.setup_schedulers()
-
-    def setup_optimizers(self):
-        train_opt = self.opt['train']
-
-        # ----------- optimizer g ----------- #
-        net_g_reg_ratio = 1
-        normal_params = []
-        for _, param in self.net_g.named_parameters():
-            normal_params.append(param)
-        optim_params_g = [{  # add normal params first
-            'params': normal_params,
-            'lr': train_opt['optim_g']['lr']
-        }]
-        optim_type = train_opt['optim_g'].pop('type')
-        lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
-        betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
-        self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
-        self.optimizers.append(self.optimizer_g)
-
-        # ----------- optimizer d ----------- #
-        net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
-        normal_params = []
-        for _, param in self.net_d.named_parameters():
-            normal_params.append(param)
-        optim_params_d = [{  # add normal params first
-            'params': normal_params,
-            'lr': train_opt['optim_d']['lr']
-        }]
-        optim_type = train_opt['optim_d'].pop('type')
-        lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
-        betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
-        self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
-        self.optimizers.append(self.optimizer_d)
-
-        # ----------- optimizers for facial component networks ----------- #
-        if self.use_facial_disc:
-            # setup optimizers for facial component discriminators
-            optim_type = train_opt['optim_component'].pop('type')
-            lr = train_opt['optim_component']['lr']
-            # left eye
-            self.optimizer_d_left_eye = self.get_optimizer(
-                optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99))
-            self.optimizers.append(self.optimizer_d_left_eye)
-            # right eye
-            self.optimizer_d_right_eye = self.get_optimizer(
-                optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99))
-            self.optimizers.append(self.optimizer_d_right_eye)
-            # mouth
-            self.optimizer_d_mouth = self.get_optimizer(
-                optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99))
-            self.optimizers.append(self.optimizer_d_mouth)
-
-    def feed_data(self, data):
-        self.lq = data['lq'].to(self.device)
-        if 'gt' in data:
-            self.gt = data['gt'].to(self.device)
-
-        if 'loc_left_eye' in data:
-            # get facial component locations, shape (batch, 4)
-            self.loc_left_eyes = data['loc_left_eye']
-            self.loc_right_eyes = data['loc_right_eye']
-            self.loc_mouths = data['loc_mouth']
-
-            # uncomment to check data
-            # import torchvision
-            # if self.opt['rank'] == 0:
-            #     import os
-            #     os.makedirs('tmp/gt', exist_ok=True)
-            #     os.makedirs('tmp/lq', exist_ok=True)
-            #     print(self.idx)
-            #     torchvision.utils.save_image(
-            #         self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
-            #     torchvision.utils.save_image(
-            #         self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
-            #     self.idx = self.idx + 1
-
-    def construct_img_pyramid(self):
-        """Construct image pyramid for intermediate restoration loss"""
-        pyramid_gt = [self.gt]
-        down_img = self.gt
-        for _ in range(0, self.log_size - 3):
-            down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False)
-            pyramid_gt.insert(0, down_img)
-        return pyramid_gt
-
-    def get_roi_regions(self, eye_out_size=80, mouth_out_size=120):
-        face_ratio = int(self.opt['network_g']['out_size'] / 512)
-        eye_out_size *= face_ratio
-        mouth_out_size *= face_ratio
-
-        rois_eyes = []
-        rois_mouths = []
-        for b in range(self.loc_left_eyes.size(0)):  # loop for batch size
-            # left eye and right eye
-            img_inds = self.loc_left_eyes.new_full((2, 1), b)
-            bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0)  # shape: (2, 4)
-            rois = torch.cat([img_inds, bbox], dim=-1)  # shape: (2, 5)
-            rois_eyes.append(rois)
-            # mouse
-            img_inds = self.loc_left_eyes.new_full((1, 1), b)
-            rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1)  # shape: (1, 5)
-            rois_mouths.append(rois)
-
-        rois_eyes = torch.cat(rois_eyes, 0).to(self.device)
-        rois_mouths = torch.cat(rois_mouths, 0).to(self.device)
-
-        # real images
-        all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
-        self.left_eyes_gt = all_eyes[0::2, :, :, :]
-        self.right_eyes_gt = all_eyes[1::2, :, :, :]
-        self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
-        # output
-        all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
-        self.left_eyes = all_eyes[0::2, :, :, :]
-        self.right_eyes = all_eyes[1::2, :, :, :]
-        self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
-
-    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
-
-    def gray_resize_for_identity(self, out, size=128):
-        out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :])
-        out_gray = out_gray.unsqueeze(1)
-        out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False)
-        return out_gray
-
-    def optimize_parameters(self, current_iter):
-        # optimize net_g
-        for p in self.net_d.parameters():
-            p.requires_grad = False
-        self.optimizer_g.zero_grad()
-
-        # do not update facial component net_d
-        if self.use_facial_disc:
-            for p in self.net_d_left_eye.parameters():
-                p.requires_grad = False
-            for p in self.net_d_right_eye.parameters():
-                p.requires_grad = False
-            for p in self.net_d_mouth.parameters():
-                p.requires_grad = False
-
-        # image pyramid loss weight
-        if current_iter < self.opt['train'].get('remove_pyramid_loss', float('inf')):
-            pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 1)
-        else:
-            pyramid_loss_weight = 1e-12  # very small loss
-        if pyramid_loss_weight > 0:
-            self.output, out_rgbs = self.net_g(self.lq, return_rgb=True)
-            pyramid_gt = self.construct_img_pyramid()
-        else:
-            self.output, out_rgbs = self.net_g(self.lq, return_rgb=False)
-
-        # get roi-align regions
-        if self.use_facial_disc:
-            self.get_roi_regions(eye_out_size=80, mouth_out_size=120)
-
-        l_g_total = 0
-        loss_dict = OrderedDict()
-        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
-            # pixel loss
-            if self.cri_pix:
-                l_g_pix = self.cri_pix(self.output, self.gt)
-                l_g_total += l_g_pix
-                loss_dict['l_g_pix'] = l_g_pix
-
-            # image pyramid loss
-            if pyramid_loss_weight > 0:
-                for i in range(0, self.log_size - 2):
-                    l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight
-                    l_g_total += l_pyramid
-                    loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid
-
-            # perceptual loss
-            if self.cri_perceptual:
-                l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
-                if l_g_percep is not None:
-                    l_g_total += l_g_percep
-                    loss_dict['l_g_percep'] = l_g_percep
-                if l_g_style is not None:
-                    l_g_total += l_g_style
-                    loss_dict['l_g_style'] = l_g_style
-
-            # gan loss
-            fake_g_pred = self.net_d(self.output)
-            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
-            l_g_total += l_g_gan
-            loss_dict['l_g_gan'] = l_g_gan
-
-            # facial component loss
-            if self.use_facial_disc:
-                # left eye
-                fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True)
-                l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False)
-                l_g_total += l_g_gan
-                loss_dict['l_g_gan_left_eye'] = l_g_gan
-                # right eye
-                fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True)
-                l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False)
-                l_g_total += l_g_gan
-                loss_dict['l_g_gan_right_eye'] = l_g_gan
-                # mouth
-                fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True)
-                l_g_gan = self.cri_component(fake_mouth, True, is_disc=False)
-                l_g_total += l_g_gan
-                loss_dict['l_g_gan_mouth'] = l_g_gan
-
-                if self.opt['train'].get('comp_style_weight', 0) > 0:
-                    # get gt feat
-                    _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True)
-                    _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True)
-                    _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True)
-
-                    def _comp_style(feat, feat_gt, criterion):
-                        return criterion(self._gram_mat(feat[0]), self._gram_mat(
-                            feat_gt[0].detach())) * 0.5 + criterion(
-                                self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach()))
-
-                    # facial component style loss
-                    comp_style_loss = 0
-                    comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1)
-                    comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1)
-                    comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1)
-                    comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight']
-                    l_g_total += comp_style_loss
-                    loss_dict['l_g_comp_style_loss'] = comp_style_loss
-
-            # identity loss
-            if self.use_identity:
-                identity_weight = self.opt['train']['identity_weight']
-                # get gray images and resize
-                out_gray = self.gray_resize_for_identity(self.output)
-                gt_gray = self.gray_resize_for_identity(self.gt)
-
-                identity_gt = self.network_identity(gt_gray).detach()
-                identity_out = self.network_identity(out_gray)
-                l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight
-                l_g_total += l_identity
-                loss_dict['l_identity'] = l_identity
-
-            l_g_total.backward()
-            self.optimizer_g.step()
-
-        # EMA
-        self.model_ema(decay=0.5**(32 / (10 * 1000)))
-
-        # ----------- optimize net_d ----------- #
-        for p in self.net_d.parameters():
-            p.requires_grad = True
-        self.optimizer_d.zero_grad()
-        if self.use_facial_disc:
-            for p in self.net_d_left_eye.parameters():
-                p.requires_grad = True
-            for p in self.net_d_right_eye.parameters():
-                p.requires_grad = True
-            for p in self.net_d_mouth.parameters():
-                p.requires_grad = True
-            self.optimizer_d_left_eye.zero_grad()
-            self.optimizer_d_right_eye.zero_grad()
-            self.optimizer_d_mouth.zero_grad()
-
-        fake_d_pred = self.net_d(self.output.detach())
-        real_d_pred = self.net_d(self.gt)
-        l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True)
-        loss_dict['l_d'] = l_d
-        # In WGAN, real_score should be positive and fake_score should be negative
-        loss_dict['real_score'] = real_d_pred.detach().mean()
-        loss_dict['fake_score'] = fake_d_pred.detach().mean()
-        l_d.backward()
-
-        # regularization loss
-        if current_iter % self.net_d_reg_every == 0:
-            self.gt.requires_grad = True
-            real_pred = self.net_d(self.gt)
-            l_d_r1 = r1_penalty(real_pred, self.gt)
-            l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
-            loss_dict['l_d_r1'] = l_d_r1.detach().mean()
-            l_d_r1.backward()
-
-        self.optimizer_d.step()
-
-        # optimize facial component discriminators
-        if self.use_facial_disc:
-            # left eye
-            fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach())
-            real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt)
-            l_d_left_eye = self.cri_component(
-                real_d_pred, True, is_disc=True) + self.cri_gan(
-                    fake_d_pred, False, is_disc=True)
-            loss_dict['l_d_left_eye'] = l_d_left_eye
-            l_d_left_eye.backward()
-            # right eye
-            fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach())
-            real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt)
-            l_d_right_eye = self.cri_component(
-                real_d_pred, True, is_disc=True) + self.cri_gan(
-                    fake_d_pred, False, is_disc=True)
-            loss_dict['l_d_right_eye'] = l_d_right_eye
-            l_d_right_eye.backward()
-            # mouth
-            fake_d_pred, _ = self.net_d_mouth(self.mouths.detach())
-            real_d_pred, _ = self.net_d_mouth(self.mouths_gt)
-            l_d_mouth = self.cri_component(
-                real_d_pred, True, is_disc=True) + self.cri_gan(
-                    fake_d_pred, False, is_disc=True)
-            loss_dict['l_d_mouth'] = l_d_mouth
-            l_d_mouth.backward()
-
-            self.optimizer_d_left_eye.step()
-            self.optimizer_d_right_eye.step()
-            self.optimizer_d_mouth.step()
-
-        self.log_dict = self.reduce_loss_dict(loss_dict)
-
-    def test(self):
-        with torch.no_grad():
-            if hasattr(self, 'net_g_ema'):
-                self.net_g_ema.eval()
-                self.output, _ = self.net_g_ema(self.lq)
-            else:
-                logger = get_root_logger()
-                logger.warning('Do not have self.net_g_ema, use self.net_g.')
-                self.net_g.eval()
-                self.output, _ = self.net_g(self.lq)
-                self.net_g.train()
-
-    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        if self.opt['rank'] == 0:
-            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        dataset_name = dataloader.dataset.opt['name']
-        with_metrics = self.opt['val'].get('metrics') is not None
-        use_pbar = self.opt['val'].get('pbar', False)
-
-        if with_metrics:
-            if not hasattr(self, 'metric_results'):  # only execute in the first run
-                self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
-            # initialize the best metric results for each dataset_name (supporting multiple validation datasets)
-            self._initialize_best_metric_results(dataset_name)
-            # zero self.metric_results
-            self.metric_results = {metric: 0 for metric in self.metric_results}
-
-        metric_data = dict()
-        if use_pbar:
-            pbar = tqdm(total=len(dataloader), unit='image')
-
-        for idx, val_data in enumerate(dataloader):
-            img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
-            self.feed_data(val_data)
-            self.test()
-
-            sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1))
-            metric_data['img'] = sr_img
-            if hasattr(self, 'gt'):
-                gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1))
-                metric_data['img2'] = gt_img
-                del self.gt
-
-            # tentative for out of GPU memory
-            del self.lq
-            del self.output
-            torch.cuda.empty_cache()
-
-            if save_img:
-                if self.opt['is_train']:
-                    save_img_path = osp.join(self.opt['path']['visualization'], img_name,
-                                             f'{img_name}_{current_iter}.png')
-                else:
-                    if self.opt['val']['suffix']:
-                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
-                                                 f'{img_name}_{self.opt["val"]["suffix"]}.png')
-                    else:
-                        save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
-                                                 f'{img_name}_{self.opt["name"]}.png')
-                imwrite(sr_img, save_img_path)
-
-            if with_metrics:
-                # calculate metrics
-                for name, opt_ in self.opt['val']['metrics'].items():
-                    self.metric_results[name] += calculate_metric(metric_data, opt_)
-            if use_pbar:
-                pbar.update(1)
-                pbar.set_description(f'Test {img_name}')
-        if use_pbar:
-            pbar.close()
-
-        if with_metrics:
-            for metric in self.metric_results.keys():
-                self.metric_results[metric] /= (idx + 1)
-                # update the best metric result
-                self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter)
-
-            self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
-
-    def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
-        log_str = f'Validation {dataset_name}\n'
-        for metric, value in self.metric_results.items():
-            log_str += f'\t # {metric}: {value:.4f}'
-            if hasattr(self, 'best_metric_results'):
-                log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ '
-                            f'{self.best_metric_results[dataset_name][metric]["iter"]} iter')
-            log_str += '\n'
-
-        logger = get_root_logger()
-        logger.info(log_str)
-        if tb_logger:
-            for metric, value in self.metric_results.items():
-                tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter)
-
-    def save(self, epoch, current_iter):
-        # save net_g and net_d
-        self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
-        self.save_network(self.net_d, 'net_d', current_iter)
-        # save component discriminators
-        if self.use_facial_disc:
-            self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter)
-            self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter)
-            self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter)
-        # save training state
-        self.save_training_state(epoch, current_iter)

gfpgan/train.py

@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import gfpgan.archs
-import gfpgan.data
-import gfpgan.models
-
-if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)

gfpgan/utils.py

@@ -1,130 +0,0 @@
-import cv2
-import os
-import torch
-from basicsr.utils import img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class GFPGANer():
-    """Helper for restoration with GFPGAN.
-
-    It will detect and crop faces, and then resize the faces to 512x512.
-    GFPGAN is used to restored the resized faces.
-    The background is upsampled with the bg_upsampler.
-    Finally, the faces will be pasted back to the upsample background image.
-
-    Args:
-        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
-        upscale (float): The upscale of the final output. Default: 2.
-        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
-    """
-
-    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
-        self.upscale = upscale
-        self.bg_upsampler = bg_upsampler
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        # initialize the GFP-GAN
-        if arch == 'clean':
-            self.gfpgan = GFPGANv1Clean(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        else:
-            self.gfpgan = GFPGANv1(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=True,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        # initialize face helper
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model='retinaface_resnet50',
-            save_ext='png',
-            device=self.device)
-
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
-        self.gfpgan.eval()
-        self.gfpgan = self.gfpgan.to(self.device)
-
-    @torch.no_grad()
-    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
-        self.face_helper.clean_all()
-
-        if has_aligned:  # the inputs are already aligned
-            img = cv2.resize(img, (512, 512))
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
-            # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
-            # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration
-        for cropped_face in self.face_helper.cropped_faces:
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-            except RuntimeError as error:
-                print(f'\tFailed inference for GFPGAN: {error}.')
-                restored_face = cropped_face
-
-            restored_face = restored_face.astype('uint8')
-            self.face_helper.add_restored_face(restored_face)
-
-        if not has_aligned and paste_back:
-            # upsample the background
-            if self.bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
-            else:
-                bg_img = None
-
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
-        else:
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None

gfpgan/weights/README.md

@@ -1,3 +0,0 @@
-# Weights
-
-Put the downloaded weights to this folder.

inference_gfpgan.py

@@ -1,116 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-import os
-import torch
-from basicsr.utils import imwrite
-
-from gfpgan import GFPGANer
-
-
-def main():
-    """Inference demo for GFPGAN.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image')
-    parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original')
-    parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2')
-    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
-    parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler')
-    parser.add_argument(
-        '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing')
-    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder')
-    parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
-    parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
-    parser.add_argument('--aligned', action='store_true', help='Input are aligned faces')
-    parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images')
-    parser.add_argument('--save_root', type=str, default='results', help='Path to save root')
-    parser.add_argument(
-        '--ext',
-        type=str,
-        default='auto',
-        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
-    args = parser.parse_args()
-
-    args = parser.parse_args()
-    if args.test_path.endswith('/'):
-        args.test_path = args.test_path[:-1]
-    os.makedirs(args.save_root, exist_ok=True)
-
-    # background upsampler
-    if args.bg_upsampler == 'realesrgan':
-        if not torch.cuda.is_available():  # CPU
-            import warnings
-            warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-                          'If you really want to use it, please modify the corresponding codes.')
-            bg_upsampler = None
-        else:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
-            from realesrgan import RealESRGANer
-            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-            bg_upsampler = RealESRGANer(
-                scale=2,
-                model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-                model=model,
-                tile=args.bg_tile,
-                tile_pad=10,
-                pre_pad=0,
-                half=True)  # need to set False in CPU mode
-    else:
-        bg_upsampler = None
-    # set up GFPGAN restorer
-    restorer = GFPGANer(
-        model_path=args.model_path,
-        upscale=args.upscale,
-        arch=args.arch,
-        channel_multiplier=args.channel,
-        bg_upsampler=bg_upsampler)
-
-    img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
-    for img_path in img_list:
-        # read image
-        img_name = os.path.basename(img_path)
-        print(f'Processing {img_name} ...')
-        basename, ext = os.path.splitext(img_name)
-        input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-
-        # restore faces and background if necessary
-        cropped_faces, restored_faces, restored_img = restorer.enhance(
-            input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
-
-        # save faces
-        for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
-            # save cropped face
-            save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
-            imwrite(cropped_face, save_crop_path)
-            # save restored face
-            if args.suffix is not None:
-                save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
-            else:
-                save_face_name = f'{basename}_{idx:02d}.png'
-            save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name)
-            imwrite(restored_face, save_restore_path)
-            # save comparison image
-            cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
-            imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png'))
-
-        # save restored img
-        if restored_img is not None:
-            if args.ext == 'auto':
-                extension = ext[1:]
-            else:
-                extension = args.ext
-
-            if args.suffix is not None:
-                save_restore_path = os.path.join(args.save_root, 'restored_imgs',
-                                                 f'{basename}_{args.suffix}.{extension}')
-            else:
-                save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}')
-            imwrite(restored_img, save_restore_path)
-
-    print(f'Results are in the [{args.save_root}] folder.')
-
-
-if __name__ == '__main__':
-    main()

options/train_gfpgan_v1.yml

@@ -1,216 +0,0 @@
-# general settings
-name: train_GFPGANv1_512
-model_type: GFPGANModel
-num_gpu: auto  # officially, we use 4 GPUs
-manual_seed: 0
-
-# dataset and data loader settings
-datasets:
-  train:
-    name: FFHQ
-    type: FFHQDegradationDataset
-    # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
-    dataroot_gt: datasets/ffhq/ffhq_512
-    io_backend:
-      # type: lmdb
-      type: disk
-
-    use_hflip: true
-    mean: [0.5, 0.5, 0.5]
-    std: [0.5, 0.5, 0.5]
-    out_size: 512
-
-    blur_kernel_size: 41
-    kernel_list: ['iso', 'aniso']
-    kernel_prob: [0.5, 0.5]
-    blur_sigma: [0.1, 10]
-    downsample_range: [0.8, 8]
-    noise_range: [0, 20]
-    jpeg_range: [60, 100]
-
-    # color jitter and gray
-    color_jitter_prob: 0.3
-    color_jitter_shift: 20
-    color_jitter_pt_prob: 0.3
-    gray_prob: 0.01
-
-    # If you do not want colorization, please set
-    # color_jitter_prob: ~
-    # color_jitter_pt_prob: ~
-    # gray_prob: 0.01
-    # gt_gray: True
-
-    crop_components: true
-    component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
-    eye_enlarge_ratio: 1.4
-
-    # data loader
-    use_shuffle: true
-    num_worker_per_gpu: 6
-    batch_size_per_gpu: 3
-    dataset_enlarge_ratio: 1
-    prefetch_mode: ~
-
-  val:
-    # Please modify accordingly to use your own validation
-    # Or comment the val block if do not need validation during training
-    name: validation
-    type: PairedImageDataset
-    dataroot_lq: datasets/faces/validation/input
-    dataroot_gt: datasets/faces/validation/reference
-    io_backend:
-      type: disk
-    mean: [0.5, 0.5, 0.5]
-    std: [0.5, 0.5, 0.5]
-    scale: 1
-
-# network structures
-network_g:
-  type: GFPGANv1
-  out_size: 512
-  num_style_feat: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-  decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
-  fix_decoder: true
-  num_mlp: 8
-  lr_mlp: 0.01
-  input_is_latent: true
-  different_w: true
-  narrow: 1
-  sft_half: true
-
-network_d:
-  type: StyleGAN2Discriminator
-  out_size: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-
-network_d_left_eye:
-  type: FacialComponentDiscriminator
-
-network_d_right_eye:
-  type: FacialComponentDiscriminator
-
-network_d_mouth:
-  type: FacialComponentDiscriminator
-
-network_identity:
-  type: ResNetArcFace
-  block: IRBlock
-  layers: [2, 2, 2, 2]
-  use_se: False
-
-# path
-path:
-  pretrain_network_g: ~
-  param_key_g: params_ema
-  strict_load_g: ~
-  pretrain_network_d: ~
-  pretrain_network_d_left_eye: ~
-  pretrain_network_d_right_eye: ~
-  pretrain_network_d_mouth: ~
-  pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
-  # resume
-  resume_state: ~
-  ignore_resume_networks: ['network_identity']
-
-# training settings
-train:
-  optim_g:
-    type: Adam
-    lr: !!float 2e-3
-  optim_d:
-    type: Adam
-    lr: !!float 2e-3
-  optim_component:
-    type: Adam
-    lr: !!float 2e-3
-
-  scheduler:
-    type: MultiStepLR
-    milestones: [600000, 700000]
-    gamma: 0.5
-
-  total_iter: 800000
-  warmup_iter: -1  # no warm up
-
-  # losses
-  # pixel loss
-  pixel_opt:
-    type: L1Loss
-    loss_weight: !!float 1e-1
-    reduction: mean
-  # L1 loss used in pyramid loss, component style loss and identity loss
-  L1_opt:
-    type: L1Loss
-    loss_weight: 1
-    reduction: mean
-
-  # image pyramid loss
-  pyramid_loss_weight: 1
-  remove_pyramid_loss: 50000
-  # perceptual loss (content and style losses)
-  perceptual_opt:
-    type: PerceptualLoss
-    layer_weights:
-      # before relu
-      'conv1_2': 0.1
-      'conv2_2': 0.1
-      'conv3_4': 1
-      'conv4_4': 1
-      'conv5_4': 1
-    vgg_type: vgg19
-    use_input_norm: true
-    perceptual_weight: !!float 1
-    style_weight: 50
-    range_norm: true
-    criterion: l1
-  # gan loss
-  gan_opt:
-    type: GANLoss
-    gan_type: wgan_softplus
-    loss_weight: !!float 1e-1
-  # r1 regularization for discriminator
-  r1_reg_weight: 10
-  # facial component loss
-  gan_component_opt:
-    type: GANLoss
-    gan_type: vanilla
-    real_label_val: 1.0
-    fake_label_val: 0.0
-    loss_weight: !!float 1
-  comp_style_weight: 200
-  # identity loss
-  identity_weight: 10
-
-  net_d_iters: 1
-  net_d_init_iters: 0
-  net_d_reg_every: 16
-
-# validation settings
-val:
-  val_freq: !!float 5e3
-  save_img: true
-
-  metrics:
-    psnr: # metric name
-      type: calculate_psnr
-      crop_border: 0
-      test_y_channel: false
-
-# logging settings
-logger:
-  print_freq: 100
-  save_checkpoint_freq: !!float 5e3
-  use_tb_logger: true
-  wandb:
-    project: ~
-    resume_id: ~
-
-# dist training settings
-dist_params:
-  backend: nccl
-  port: 29500
-
-find_unused_parameters: true

options/train_gfpgan_v1_simple.yml

@@ -1,182 +0,0 @@
-# general settings
-name: train_GFPGANv1_512_simple
-model_type: GFPGANModel
-num_gpu: auto  # officially, we use 4 GPUs
-manual_seed: 0
-
-# dataset and data loader settings
-datasets:
-  train:
-    name: FFHQ
-    type: FFHQDegradationDataset
-    # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
-    dataroot_gt: datasets/ffhq/ffhq_512
-    io_backend:
-      # type: lmdb
-      type: disk
-
-    use_hflip: true
-    mean: [0.5, 0.5, 0.5]
-    std: [0.5, 0.5, 0.5]
-    out_size: 512
-
-    blur_kernel_size: 41
-    kernel_list: ['iso', 'aniso']
-    kernel_prob: [0.5, 0.5]
-    blur_sigma: [0.1, 10]
-    downsample_range: [0.8, 8]
-    noise_range: [0, 20]
-    jpeg_range: [60, 100]
-
-    # color jitter and gray
-    color_jitter_prob: 0.3
-    color_jitter_shift: 20
-    color_jitter_pt_prob: 0.3
-    gray_prob: 0.01
-
-    # If you do not want colorization, please set
-    # color_jitter_prob: ~
-    # color_jitter_pt_prob: ~
-    # gray_prob: 0.01
-    # gt_gray: True
-
-    # data loader
-    use_shuffle: true
-    num_worker_per_gpu: 6
-    batch_size_per_gpu: 3
-    dataset_enlarge_ratio: 1
-    prefetch_mode: ~
-
-  val:
-    # Please modify accordingly to use your own validation
-    # Or comment the val block if do not need validation during training
-    name: validation
-    type: PairedImageDataset
-    dataroot_lq: datasets/faces/validation/input
-    dataroot_gt: datasets/faces/validation/reference
-    io_backend:
-      type: disk
-    mean: [0.5, 0.5, 0.5]
-    std: [0.5, 0.5, 0.5]
-    scale: 1
-
-# network structures
-network_g:
-  type: GFPGANv1
-  out_size: 512
-  num_style_feat: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-  decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
-  fix_decoder: true
-  num_mlp: 8
-  lr_mlp: 0.01
-  input_is_latent: true
-  different_w: true
-  narrow: 1
-  sft_half: true
-
-network_d:
-  type: StyleGAN2Discriminator
-  out_size: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-
-
-# path
-path:
-  pretrain_network_g: ~
-  param_key_g: params_ema
-  strict_load_g: ~
-  pretrain_network_d: ~
-  resume_state: ~
-
-# training settings
-train:
-  optim_g:
-    type: Adam
-    lr: !!float 2e-3
-  optim_d:
-    type: Adam
-    lr: !!float 2e-3
-  optim_component:
-    type: Adam
-    lr: !!float 2e-3
-
-  scheduler:
-    type: MultiStepLR
-    milestones: [600000, 700000]
-    gamma: 0.5
-
-  total_iter: 800000
-  warmup_iter: -1  # no warm up
-
-  # losses
-  # pixel loss
-  pixel_opt:
-    type: L1Loss
-    loss_weight: !!float 1e-1
-    reduction: mean
-  # L1 loss used in pyramid loss, component style loss and identity loss
-  L1_opt:
-    type: L1Loss
-    loss_weight: 1
-    reduction: mean
-
-  # image pyramid loss
-  pyramid_loss_weight: 1
-  remove_pyramid_loss: 50000
-  # perceptual loss (content and style losses)
-  perceptual_opt:
-    type: PerceptualLoss
-    layer_weights:
-      # before relu
-      'conv1_2': 0.1
-      'conv2_2': 0.1
-      'conv3_4': 1
-      'conv4_4': 1
-      'conv5_4': 1
-    vgg_type: vgg19
-    use_input_norm: true
-    perceptual_weight: !!float 1
-    style_weight: 50
-    range_norm: true
-    criterion: l1
-  # gan loss
-  gan_opt:
-    type: GANLoss
-    gan_type: wgan_softplus
-    loss_weight: !!float 1e-1
-  # r1 regularization for discriminator
-  r1_reg_weight: 10
-
-  net_d_iters: 1
-  net_d_init_iters: 0
-  net_d_reg_every: 16
-
-# validation settings
-val:
-  val_freq: !!float 5e3
-  save_img: true
-
-  metrics:
-    psnr: # metric name
-      type: calculate_psnr
-      crop_border: 0
-      test_y_channel: false
-
-# logging settings
-logger:
-  print_freq: 100
-  save_checkpoint_freq: !!float 5e3
-  use_tb_logger: true
-  wandb:
-    project: ~
-    resume_id: ~
-
-# dist training settings
-dist_params:
-  backend: nccl
-  port: 29500
-
-find_unused_parameters: true

packages.txt

@@ -1,3 +1,3 @@
-ffmpeg 
-libsm6 
+ffmpeg
+libsm6
 libxext6

requirements.txt

@@ -1,10 +1,13 @@
 torch>=1.7
-numpy<1.21  # numba requires numpy<1.21,>=1.17
+basicsr>=1.4.2
+facexlib>=0.2.5
+gfpgan>=1.3.4
+realesrgan>=0.2.5
+numpy
 opencv-python
 torchvision
 scipy
 tqdm
 lmdb
 pyyaml
-tb-nightly
-yapf
+yapf

scripts/parse_landmark.py

@@ -1,85 +0,0 @@
-import cv2
-import json
-import numpy as np
-import os
-import torch
-from basicsr.utils import FileClient, imfrombytes
-from collections import OrderedDict
-
-# ---------------------------- This script is used to parse facial landmarks ------------------------------------- #
-# Configurations
-save_img = False
-scale = 0.5  # 0.5 for official FFHQ (512x512), 1 for others
-enlarge_ratio = 1.4  # only for eyes
-json_path = 'ffhq-dataset-v2.json'
-face_path = 'datasets/ffhq/ffhq_512.lmdb'
-save_path = './FFHQ_eye_mouth_landmarks_512.pth'
-
-print('Load JSON metadata...')
-# use the official json file in FFHQ dataset
-with open(json_path, 'rb') as f:
-    json_data = json.load(f, object_pairs_hook=OrderedDict)
-
-print('Open LMDB file...')
-# read ffhq images
-file_client = FileClient('lmdb', db_paths=face_path)
-with open(os.path.join(face_path, 'meta_info.txt')) as fin:
-    paths = [line.split('.')[0] for line in fin]
-
-save_dict = {}
-
-for item_idx, item in enumerate(json_data.values()):
-    print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True)
-
-    # parse landmarks
-    lm = np.array(item['image']['face_landmarks'])
-    lm = lm * scale
-
-    item_dict = {}
-    # get image
-    if save_img:
-        img_bytes = file_client.get(paths[item_idx])
-        img = imfrombytes(img_bytes, float32=True)
-
-    # get landmarks for each component
-    map_left_eye = list(range(36, 42))
-    map_right_eye = list(range(42, 48))
-    map_mouth = list(range(48, 68))
-
-    # eye_left
-    mean_left_eye = np.mean(lm[map_left_eye], 0)  # (x, y)
-    half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16))
-    item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye]
-    # mean_left_eye[0] = 512 - mean_left_eye[0]  # for testing flip
-    half_len_left_eye *= enlarge_ratio
-    loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int)
-    if save_img:
-        eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :]
-        cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255)
-
-    # eye_right
-    mean_right_eye = np.mean(lm[map_right_eye], 0)
-    half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16))
-    item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye]
-    # mean_right_eye[0] = 512 - mean_right_eye[0]  # # for testing flip
-    half_len_right_eye *= enlarge_ratio
-    loc_right_eye = np.hstack(
-        (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int)
-    if save_img:
-        eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :]
-        cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255)
-
-    # mouth
-    mean_mouth = np.mean(lm[map_mouth], 0)
-    half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16))
-    item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth]
-    # mean_mouth[0] = 512 - mean_mouth[0]  # for testing flip
-    loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int)
-    if save_img:
-        mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :]
-        cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255)
-
-    save_dict[f'{item_idx:08d}'] = item_dict
-
-print('Save...')
-torch.save(save_dict, save_path)

setup.cfg

@@ -1,33 +0,0 @@
-[flake8]
-ignore =
-    # line break before binary operator (W503)
-    W503,
-    # line break after binary operator (W504)
-    W504,
-max-line-length=120
-
-[yapf]
-based_on_style = pep8
-column_limit = 120
-blank_line_before_nested_class_or_def = true
-split_before_expression_after_opening_paren = true
-
-[isort]
-line_length = 120
-multi_line_output = 0
-known_standard_library = pkg_resources,setuptools
-known_first_party = gfpgan
-known_third_party = basicsr,cv2,facexlib,numpy,pytest,torch,torchvision,tqdm,yaml
-no_lines_before = STDLIB,LOCALFOLDER
-default_section = THIRDPARTY
-
-[codespell]
-skip = .git,./docs/build
-count =
-quiet-level = 3
-
-[aliases]
-test=pytest
-
-[tool:pytest]
-addopts=tests/

setup.py

@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'gfpgan/version.py'
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-def get_git_hash():
-
-    def _minimal_ext_cmd(cmd):
-        # construct minimal environment
-        env = {}
-        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-            v = os.environ.get(k)
-            if v is not None:
-                env[k] = v
-        # LANGUAGE is used on win32
-        env['LANGUAGE'] = 'C'
-        env['LANG'] = 'C'
-        env['LC_ALL'] = 'C'
-        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-        return out
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-    except OSError:
-        sha = 'unknown'
-
-    return sha
-
-
-def get_hash():
-    if os.path.exists('.git'):
-        sha = get_git_hash()[:7]
-    else:
-        sha = 'unknown'
-
-    return sha
-
-
-def write_version_py():
-    content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
-    sha = get_hash()
-    with open('VERSION', 'r') as f:
-        SHORT_VERSION = f.read().strip()
-    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
-    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
-    with open(version_file, 'w') as f:
-        f.write(version_file_str)
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
-    here = os.path.dirname(os.path.realpath(__file__))
-    with open(os.path.join(here, filename), 'r') as f:
-        requires = [line.replace('\n', '') for line in f.readlines()]
-    return requires
-
-
-if __name__ == '__main__':
-    write_version_py()
-    setup(
-        name='gfpgan',
-        version=get_version(),
-        description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        author='Xintao Wang',
-        author_email='[email protected]',
-        keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
-        url='https://github.com/TencentARC/GFPGAN',
-        include_package_data=True,
-        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-        ],
-        license='Apache License Version 2.0',
-        setup_requires=['cython', 'numpy'],
-        install_requires=get_requirements(),
-        zip_safe=False)

tests/data/ffhq_gt.lmdb/meta_info.txt

@@ -1 +0,0 @@
-00000000.png (512,512,3) 1

tests/data/test_eye_mouth_landmarks.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:131583fca2cc346652f8754eb3c5a0bdeda808686039ff10ead7a26254b72358
-size 943

tests/data/test_ffhq_degradation_dataset.yml

@@ -1,24 +0,0 @@
-name: UnitTest
-type: FFHQDegradationDataset
-dataroot_gt: tests/data/gt
-io_backend:
-  type: disk
-
-use_hflip: true
-mean: [0.5, 0.5, 0.5]
-std: [0.5, 0.5, 0.5]
-out_size: 512
-
-blur_kernel_size: 41
-kernel_list: ['iso', 'aniso']
-kernel_prob: [0.5, 0.5]
-blur_sigma: [0.1, 10]
-downsample_range: [0.8, 8]
-noise_range: [0, 20]
-jpeg_range: [60, 100]
-
-# color jitter and gray
-color_jitter_prob: 1
-color_jitter_shift: 20
-color_jitter_pt_prob: 1
-gray_prob: 1

tests/data/test_gfpgan_model.yml

@@ -1,140 +0,0 @@
-num_gpu: 1
-manual_seed: 0
-is_train: True
-dist: False
-
-# network structures
-network_g:
-  type: GFPGANv1
-  out_size: 512
-  num_style_feat: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-  decoder_load_path: ~
-  fix_decoder: true
-  num_mlp: 8
-  lr_mlp: 0.01
-  input_is_latent: true
-  different_w: true
-  narrow: 0.5
-  sft_half: true
-
-network_d:
-  type: StyleGAN2Discriminator
-  out_size: 512
-  channel_multiplier: 1
-  resample_kernel: [1, 3, 3, 1]
-
-network_d_left_eye:
-  type: FacialComponentDiscriminator
-
-network_d_right_eye:
-  type: FacialComponentDiscriminator
-
-network_d_mouth:
-  type: FacialComponentDiscriminator
-
-network_identity:
-  type: ResNetArcFace
-  block: IRBlock
-  layers: [2, 2, 2, 2]
-  use_se: False
-
-# path
-path:
-  pretrain_network_g: ~
-  param_key_g: params_ema
-  strict_load_g: ~
-  pretrain_network_d: ~
-  pretrain_network_d_left_eye: ~
-  pretrain_network_d_right_eye: ~
-  pretrain_network_d_mouth: ~
-  pretrain_network_identity: ~
-  # resume
-  resume_state: ~
-  ignore_resume_networks: ['network_identity']
-
-# training settings
-train:
-  optim_g:
-    type: Adam
-    lr: !!float 2e-3
-  optim_d:
-    type: Adam
-    lr: !!float 2e-3
-  optim_component:
-    type: Adam
-    lr: !!float 2e-3
-
-  scheduler:
-    type: MultiStepLR
-    milestones: [600000, 700000]
-    gamma: 0.5
-
-  total_iter: 800000
-  warmup_iter: -1  # no warm up
-
-  # losses
-  # pixel loss
-  pixel_opt:
-    type: L1Loss
-    loss_weight: !!float 1e-1
-    reduction: mean
-  # L1 loss used in pyramid loss, component style loss and identity loss
-  L1_opt:
-    type: L1Loss
-    loss_weight: 1
-    reduction: mean
-
-  # image pyramid loss
-  pyramid_loss_weight: 1
-  remove_pyramid_loss: 50000
-  # perceptual loss (content and style losses)
-  perceptual_opt:
-    type: PerceptualLoss
-    layer_weights:
-      # before relu
-      'conv1_2': 0.1
-      'conv2_2': 0.1
-      'conv3_4': 1
-      'conv4_4': 1
-      'conv5_4': 1
-    vgg_type: vgg19
-    use_input_norm: true
-    perceptual_weight: !!float 1
-    style_weight: 50
-    range_norm: true
-    criterion: l1
-  # gan loss
-  gan_opt:
-    type: GANLoss
-    gan_type: wgan_softplus
-    loss_weight: !!float 1e-1
-  # r1 regularization for discriminator
-  r1_reg_weight: 10
-  # facial component loss
-  gan_component_opt:
-    type: GANLoss
-    gan_type: vanilla
-    real_label_val: 1.0
-    fake_label_val: 0.0
-    loss_weight: !!float 1
-  comp_style_weight: 200
-  # identity loss
-  identity_weight: 10
-
-  net_d_iters: 1
-  net_d_init_iters: 0
-  net_d_reg_every: 1
-
-# validation settings
-val:
-  val_freq: !!float 5e3
-  save_img: True
-  use_pbar: True
-
-  metrics:
-    psnr: # metric name
-      type: calculate_psnr
-      crop_border: 0
-      test_y_channel: false

tests/test_arcface_arch.py

@@ -1,49 +0,0 @@
-import torch
-
-from gfpgan.archs.arcface_arch import BasicBlock, Bottleneck, ResNetArcFace
-
-
-def test_resnetarcface():
-    """Test arch: ResNetArcFace."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=True).cuda().eval()
-        img = torch.rand((1, 1, 128, 128), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output.shape == (1, 512)
-
-        # -------------------- without SE block ----------------------- #
-        net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=False).cuda().eval()
-        output = net(img)
-        assert output.shape == (1, 512)
-
-
-def test_basicblock():
-    """Test the BasicBlock in arcface_arch"""
-    block = BasicBlock(1, 3, stride=1, downsample=None).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 3, 12, 12)
-
-    # ----------------- use the downsmaple module--------------- #
-    downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
-    block = BasicBlock(1, 3, stride=2, downsample=downsample).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 3, 6, 6)
-
-
-def test_bottleneck():
-    """Test the Bottleneck in arcface_arch"""
-    block = Bottleneck(1, 1, stride=1, downsample=None).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 4, 12, 12)
-
-    # ----------------- use the downsmaple module--------------- #
-    downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
-    block = Bottleneck(1, 1, stride=2, downsample=downsample).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 4, 6, 6)

tests/test_ffhq_degradation_dataset.py

@@ -1,96 +0,0 @@
-import pytest
-import yaml
-
-from gfpgan.data.ffhq_degradation_dataset import FFHQDegradationDataset
-
-
-def test_ffhq_degradation_dataset():
-
-    with open('tests/data/test_ffhq_degradation_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 1
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test with probability = 0 -------------------- #
-    opt['color_jitter_prob'] = 0
-    opt['color_jitter_pt_prob'] = 0
-    opt['gray_prob'] = 0
-    opt['io_backend'] = dict(type='disk')
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/ffhq_gt.lmdb'
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-
-    # ------------------ test with crop_components -------------------- #
-    opt['crop_components'] = True
-    opt['component_path'] = 'tests/data/test_eye_mouth_landmarks.pth'
-    opt['eye_enlarge_ratio'] = 1.4
-    opt['gt_gray'] = True
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.crop_components is True
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'loc_left_eye', 'loc_right_eye', 'loc_mouth']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-    assert result['loc_left_eye'].shape == (4, )
-    assert result['loc_right_eye'].shape == (4, )
-    assert result['loc_mouth'].shape == (4, )
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend'] = dict(type='lmdb')
-        dataset = FFHQDegradationDataset(opt)

tests/test_gfpgan_arch.py

@@ -1,203 +0,0 @@
-import torch
-
-from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT
-
-
-def test_stylegan2generatorsft():
-    """Test arch: StyleGAN2GeneratorSFT."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorSFT(
-            out_size=32,
-            num_style_feat=512,
-            num_mlp=8,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            lr_mlp=0.01,
-            narrow=1,
-            sft_half=False).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
-        condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
-        condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
-        conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
-        output = net([style], conditions)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], conditions, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], conditions, randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-
-def test_gfpganv1():
-    """Test arch: GFPGANv1."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = GFPGANv1(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-        # -------------------- with different_w = True ----------------------- #
-        net = GFPGANv1(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=True,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-
-def test_facialcomponentdiscriminator():
-    """Test arch: FacialComponentDiscriminator."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = FacialComponentDiscriminator().cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert len(output) == 2
-        assert output[0].shape == (1, 1, 8, 8)
-        assert output[1] is None
-
-        # -------------------- return intermediate features ----------------------- #
-        output = net(img, return_feats=True)
-        assert len(output) == 2
-        assert output[0].shape == (1, 1, 8, 8)
-        assert len(output[1]) == 2
-        assert output[1][0].shape == (1, 128, 16, 16)
-        assert output[1][1].shape == (1, 256, 8, 8)
-
-
-def test_stylegan2generatorcsft():
-    """Test arch: StyleGAN2GeneratorCSFT."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorCSFT(
-            out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda()
-        condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda()
-        condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda()
-        conditions = [condition1, condition1, condition2, condition2, condition3, condition3]
-        output = net([style], conditions)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], conditions, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], conditions, randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], conditions, truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-
-def test_gfpganv1clean():
-    """Test arch: GFPGANv1Clean."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = GFPGANv1Clean(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=True).cuda().eval()
-
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)
-
-        # -------------------- with different_w = True ----------------------- #
-        net = GFPGANv1Clean(
-            out_size=32,
-            num_style_feat=512,
-            channel_multiplier=1,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=False,
-            different_w=True,
-            narrow=1,
-            sft_half=True).cuda().eval()
-        img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 3
-        # check out_rgbs for intermediate loss
-        assert output[1][0].shape == (1, 3, 8, 8)
-        assert output[1][1].shape == (1, 3, 16, 16)
-        assert output[1][2].shape == (1, 3, 32, 32)

tests/test_gfpgan_model.py

@@ -1,132 +0,0 @@
-import tempfile
-import torch
-import yaml
-from basicsr.archs.stylegan2_arch import StyleGAN2Discriminator
-from basicsr.data.paired_image_dataset import PairedImageDataset
-from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
-
-from gfpgan.archs.arcface_arch import ResNetArcFace
-from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1
-from gfpgan.models.gfpgan_model import GFPGANModel
-
-
-def test_gfpgan_model():
-    with open('tests/data/test_gfpgan_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = GFPGANModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'GFPGANModel'
-    assert isinstance(model.net_g, GFPGANv1)  # generator
-    assert isinstance(model.net_d, StyleGAN2Discriminator)  # discriminator
-    # facial component discriminators
-    assert isinstance(model.net_d_left_eye, FacialComponentDiscriminator)
-    assert isinstance(model.net_d_right_eye, FacialComponentDiscriminator)
-    assert isinstance(model.net_d_mouth, FacialComponentDiscriminator)
-    # identity network
-    assert isinstance(model.network_identity, ResNetArcFace)
-    # losses
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.cri_perceptual, PerceptualLoss)
-    assert isinstance(model.cri_gan, GANLoss)
-    assert isinstance(model.cri_l1, L1Loss)
-    # optimizer
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-    assert isinstance(model.optimizers[1], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 512, 512), dtype=torch.float32)
-    lq = torch.rand((1, 3, 512, 512), dtype=torch.float32)
-    loc_left_eye = torch.rand((1, 4), dtype=torch.float32)
-    loc_right_eye = torch.rand((1, 4), dtype=torch.float32)
-    loc_mouth = torch.rand((1, 4), dtype=torch.float32)
-    data = dict(gt=gt, lq=lq, loc_left_eye=loc_left_eye, loc_right_eye=loc_right_eye, loc_mouth=loc_mouth)
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 512, 512)
-    assert model.gt.shape == (1, 3, 512, 512)
-    assert model.loc_left_eyes.shape == (1, 4)
-    assert model.loc_right_eyes.shape == (1, 4)
-    assert model.loc_mouths.shape == (1, 4)
-
-    # ----------------- test optimize_parameters -------------------- #
-    model.feed_data(data)
-    model.optimize_parameters(1)
-    assert model.output.shape == (1, 3, 512, 512)
-    assert isinstance(model.log_dict, dict)
-    # check returned keys
-    expected_keys = [
-        'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth',
-        'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye',
-        'l_d_right_eye', 'l_d_mouth'
-    ]
-    assert set(expected_keys).issubset(set(model.log_dict.keys()))
-
-    # ----------------- remove pyramid_loss_weight-------------------- #
-    model.feed_data(data)
-    model.optimize_parameters(100000)  # large than remove_pyramid_loss = 50000
-    assert model.output.shape == (1, 3, 512, 512)
-    assert isinstance(model.log_dict, dict)
-    # check returned keys
-    expected_keys = [
-        'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth',
-        'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye',
-        'l_d_right_eye', 'l_d_mouth'
-    ]
-    assert set(expected_keys).issubset(set(model.log_dict.keys()))
-
-    # ----------------- test save -------------------- #
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model.opt['path']['models'] = tmpdir
-        model.opt['path']['training_states'] = tmpdir
-        model.save(0, 1)
-
-    # ----------------- test the test function -------------------- #
-    model.test()
-    assert model.output.shape == (1, 3, 512, 512)
-    # delete net_g_ema
-    model.__delattr__('net_g_ema')
-    model.test()
-    assert model.output.shape == (1, 3, 512, 512)
-    assert model.net_g.training is True  # should back to training mode after testing
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/gt',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model.opt['path']['visualization'] = tmpdir
-        model.nondist_validation(dataloader, 1, None, save_img=True)
-        assert model.is_train is True
-        # check metric_results
-        assert 'psnr' in model.metric_results
-        assert isinstance(model.metric_results['psnr'], float)
-
-    # validation
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model.opt['is_train'] = False
-        model.opt['val']['suffix'] = 'test'
-        model.opt['path']['visualization'] = tmpdir
-        model.opt['val']['pbar'] = True
-        model.nondist_validation(dataloader, 1, None, save_img=True)
-        # check metric_results
-        assert 'psnr' in model.metric_results
-        assert isinstance(model.metric_results['psnr'], float)
-
-        # if opt['val']['suffix'] is None
-        model.opt['val']['suffix'] = None
-        model.opt['name'] = 'demo'
-        model.opt['path']['visualization'] = tmpdir
-        model.nondist_validation(dataloader, 1, None, save_img=True)
-        # check metric_results
-        assert 'psnr' in model.metric_results
-        assert isinstance(model.metric_results['psnr'], float)

tests/test_stylegan2_clean_arch.py

@@ -1,52 +0,0 @@
-import torch
-
-from gfpgan.archs.stylegan2_clean_arch import StyleGAN2GeneratorClean
-
-
-def test_stylegan2generatorclean():
-    """Test arch: StyleGAN2GeneratorClean."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = StyleGAN2GeneratorClean(
-            out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=0.5).cuda().eval()
-        style = torch.rand((1, 512), dtype=torch.float32).cuda()
-        output = net([style], input_is_latent=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with return_latents ----------------------- #
-        output = net([style], input_is_latent=True, return_latents=True)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert len(output[1]) == 1
-        # check latent
-        assert output[1][0].shape == (8, 512)
-
-        # -------------------- with randomize_noise = False ----------------------- #
-        output = net([style], randomize_noise=False)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # -------------------- with truncation = 0.5 and mixing----------------------- #
-        output = net([style, style], truncation=0.5, truncation_latent=style)
-        assert output[0].shape == (1, 3, 32, 32)
-        assert output[1] is None
-
-        # ------------------ test make_noise ----------------------- #
-        out = net.make_noise()
-        assert len(out) == 7
-        assert out[0].shape == (1, 1, 4, 4)
-        assert out[1].shape == (1, 1, 8, 8)
-        assert out[2].shape == (1, 1, 8, 8)
-        assert out[3].shape == (1, 1, 16, 16)
-        assert out[4].shape == (1, 1, 16, 16)
-        assert out[5].shape == (1, 1, 32, 32)
-        assert out[6].shape == (1, 1, 32, 32)
-
-        # ------------------ test get_latent ----------------------- #
-        out = net.get_latent(style)
-        assert out.shape == (1, 512)
-
-        # ------------------ test mean_latent ----------------------- #
-        out = net.mean_latent(2)
-        assert out.shape == (1, 512)

tests/test_utils.py

@@ -1,43 +0,0 @@
-import cv2
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-from gfpgan.utils import GFPGANer
-
-
-def test_gfpganer():
-    # initialize with the clean model
-    restorer = GFPGANer(
-        model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth',
-        upscale=2,
-        arch='clean',
-        channel_multiplier=2,
-        bg_upsampler=None)
-    # test attribute
-    assert isinstance(restorer.gfpgan, GFPGANv1Clean)
-    assert isinstance(restorer.face_helper, FaceRestoreHelper)
-
-    # initialize with the original model
-    restorer = GFPGANer(
-        model_path='experiments/pretrained_models/GFPGANv1.pth',
-        upscale=2,
-        arch='original',
-        channel_multiplier=1,
-        bg_upsampler=None)
-    # test attribute
-    assert isinstance(restorer.gfpgan, GFPGANv1)
-    assert isinstance(restorer.face_helper, FaceRestoreHelper)
-
-    # ------------------ test enhance ---------------- #
-    img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR)
-    result = restorer.enhance(img, has_aligned=False, paste_back=True)
-    assert result[0][0].shape == (512, 512, 3)
-    assert result[1][0].shape == (512, 512, 3)
-    assert result[2].shape == (1024, 1024, 3)
-
-    # with has_aligned=True
-    result = restorer.enhance(img, has_aligned=True, paste_back=False)
-    assert result[0][0].shape == (512, 512, 3)
-    assert result[1][0].shape == (512, 512, 3)
-    assert result[2] is None