v0.2.4

.github/workflows/no-response.yml

@@ -1,12 +1,11 @@
 name: No Response
 
+# TODO: it seems not to work
 # Modified from: https://raw.githubusercontent.com/github/docs/main/.github/workflows/no-response.yaml
 
-# **What it does**: Closes issues that don't have enough information to be
-#                   actionable.
-# **Why we have it**: To remove the need for maintainers to remember to check
-#                     back on issues periodically to see if contributors have
-#                     responded.
+# **What it does**: Closes issues that don't have enough information to be actionable.
+# **Why we have it**: To remove the need for maintainers to remember to check back on issues periodically
+#                     to see if contributors have responded.
 # **Who does it impact**: Everyone that works on docs or docs-internal.
 
 on:

VERSION

@@ -1 +1 @@
-0.2.1
+0.2.4

gfpgan/__init__.py

@@ -3,4 +3,4 @@ from .archs import *
 from .data import *
 from .models import *
 from .utils import *
-from .version import __gitsha__, __version__
+from .version import *

gfpgan/archs/arcface_arch.py

@@ -2,13 +2,27 @@ import torch.nn as nn
 from basicsr.utils.registry import ARCH_REGISTRY
 
 
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+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):
-    expansion = 1
+    """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__()
@@ -40,7 +54,16 @@ class BasicBlock(nn.Module):
 
 
 class IRBlock(nn.Module):
-    expansion = 1
+    """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__()
@@ -78,7 +101,15 @@ class IRBlock(nn.Module):
 
 
 class Bottleneck(nn.Module):
-    expansion = 4
+    """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__()
@@ -116,10 +147,16 @@ class Bottleneck(nn.Module):
 
 
 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)
+        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())
@@ -133,6 +170,15 @@ class SEBlock(nn.Module):
 
 @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':
@@ -140,6 +186,7 @@ class ResNetArcFace(nn.Module):
         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()
@@ -153,6 +200,7 @@ class ResNetArcFace(nn.Module):
         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)
@@ -163,7 +211,7 @@ class ResNetArcFace(nn.Module):
                 nn.init.xavier_normal_(m.weight)
                 nn.init.constant_(m.bias, 0)
 
-    def _make_layer(self, block, planes, blocks, stride=1):
+    def _make_layer(self, block, planes, num_blocks, stride=1):
         downsample = None
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
@@ -173,7 +221,7 @@ class ResNetArcFace(nn.Module):
         layers = []
         layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
         self.inplanes = planes
-        for _ in range(1, blocks):
+        for _ in range(1, num_blocks):
             layers.append(block(self.inplanes, planes, use_se=self.use_se))
 
         return nn.Sequential(*layers)

gfpgan/archs/gfpganv1_arch.py

@@ -10,18 +10,18 @@ from torch.nn import functional as F
 
 
 class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator.
+    """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].
+        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,
@@ -53,21 +53,18 @@ class StyleGAN2GeneratorSFT(StyleGAN2Generator):
                 truncation_latent=None,
                 inject_index=None,
                 return_latents=False):
-        """Forward function for StyleGAN2Generator.
+        """Forward function for StyleGAN2GeneratorSFT.
 
         Args:
             styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
+            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): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
+            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:
@@ -84,7 +81,7 @@ class StyleGAN2GeneratorSFT(StyleGAN2Generator):
             for style in styles:
                 style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
             styles = style_truncation
-        # get style latent with injection
+        # get style latents with injection
         if len(styles) == 1:
             inject_index = self.num_latent
 
@@ -113,15 +110,15 @@ class StyleGAN2GeneratorSFT(StyleGAN2Generator):
             # the conditions may have fewer levels
             if i < len(conditions):
                 # SFT part to combine the conditions
-                if self.sft_half:
+                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:
+                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)
+            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
             i += 2
 
         image = skip
@@ -133,17 +130,15 @@ class StyleGAN2GeneratorSFT(StyleGAN2Generator):
 
 
 class ConvUpLayer(nn.Module):
-    """Conv Up Layer. Bilinear upsample + Conv.
+    """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``.
+        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.
     """
@@ -163,6 +158,7 @@ class ConvUpLayer(nn.Module):
         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))
@@ -223,7 +219,26 @@ class ResUpBlock(nn.Module):
 
 @ARCH_REGISTRY.register()
 class GFPGANv1(nn.Module):
-    """Unet + StyleGAN2 decoder with SFT."""
+    """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,
@@ -246,7 +261,7 @@ class GFPGANv1(nn.Module):
         self.different_w = different_w
         self.num_style_feat = num_style_feat
 
-        unet_narrow = narrow * 0.5
+        unet_narrow = narrow * 0.5  # by default, use a half of input channels
         channels = {
             '4': int(512 * unet_narrow),
             '8': int(512 * unet_narrow),
@@ -295,6 +310,7 @@ class GFPGANv1(nn.Module):
         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,
@@ -305,14 +321,16 @@ class GFPGANv1(nn.Module):
             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
+        # 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):
@@ -332,13 +350,15 @@ class GFPGANv1(nn.Module):
                     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,
-                save_feat_path=None,
-                load_feat_path=None,
-                return_rgb=True,
-                randomize_noise=True):
+    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 = []
@@ -362,7 +382,7 @@ class GFPGANv1(nn.Module):
             feat = feat + unet_skips[i]
             # ResUpLayer
             feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layer
+            # generate scale and shift for SFT layers
             scale = self.condition_scale[i](feat)
             conditions.append(scale.clone())
             shift = self.condition_shift[i](feat)
@@ -371,12 +391,6 @@ class GFPGANv1(nn.Module):
             if return_rgb:
                 out_rgbs.append(self.toRGB[i](feat))
 
-        if save_feat_path is not None:
-            torch.save(conditions, save_feat_path)
-        if load_feat_path is not None:
-            conditions = torch.load(load_feat_path)
-            conditions = [v.cuda() for v in conditions]
-
         # decoder
         image, _ = self.stylegan_decoder([style_code],
                                          conditions,
@@ -389,10 +403,12 @@ class GFPGANv1(nn.Module):
 
 @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)
@@ -401,6 +417,12 @@ class FacialComponentDiscriminator(nn.Module):
         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 = []

gfpgan/archs/gfpganv1_clean_arch.py

@@ -1,6 +1,7 @@
 import math
 import random
 import torch
+from basicsr.utils.registry import ARCH_REGISTRY
 from torch import nn
 from torch.nn import functional as F
 
@@ -8,14 +9,17 @@ from .stylegan2_clean_arch import StyleGAN2GeneratorClean
 
 
 class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
-    """StyleGAN2 Generator.
+    """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.
+        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):
@@ -25,7 +29,6 @@ class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
             num_mlp=num_mlp,
             channel_multiplier=channel_multiplier,
             narrow=narrow)
-
         self.sft_half = sft_half
 
     def forward(self,
@@ -38,21 +41,18 @@ class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
                 truncation_latent=None,
                 inject_index=None,
                 return_latents=False):
-        """Forward function for StyleGAN2Generator.
+        """Forward function for StyleGAN2GeneratorCSFT.
 
         Args:
             styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
+            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): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
+            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:
@@ -69,7 +69,7 @@ class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
             for style in styles:
                 style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
             styles = style_truncation
-        # get style latent with injection
+        # get style latents with injection
         if len(styles) == 1:
             inject_index = self.num_latent
 
@@ -98,15 +98,15 @@ class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
             # the conditions may have fewer levels
             if i < len(conditions):
                 # SFT part to combine the conditions
-                if self.sft_half:
+                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:
+                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)
+            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
             i += 2
 
         image = skip
@@ -118,11 +118,12 @@ class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
 
 
 class ResBlock(nn.Module):
-    """Residual block with upsampling/downsampling.
+    """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'):
@@ -148,8 +149,27 @@ class ResBlock(nn.Module):
         return out
 
 
+@ARCH_REGISTRY.register()
 class GFPGANv1Clean(nn.Module):
-    """GFPGANv1 Clean version."""
+    """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,
@@ -170,7 +190,7 @@ class GFPGANv1Clean(nn.Module):
         self.different_w = different_w
         self.num_style_feat = num_style_feat
 
-        unet_narrow = narrow * 0.5
+        unet_narrow = narrow * 0.5  # by default, use a half of input channels
         channels = {
             '4': int(512 * unet_narrow),
             '8': int(512 * unet_narrow),
@@ -218,6 +238,7 @@ class GFPGANv1Clean(nn.Module):
 
         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,
@@ -226,14 +247,16 @@ class GFPGANv1Clean(nn.Module):
             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
+        # 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):
@@ -251,13 +274,15 @@ class GFPGANv1Clean(nn.Module):
                     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,
-                save_feat_path=None,
-                load_feat_path=None,
-                return_rgb=True,
-                randomize_noise=True):
+    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 = []
@@ -273,13 +298,14 @@ class GFPGANv1Clean(nn.Module):
         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 layer
+            # generate scale and shift for SFT layers
             scale = self.condition_scale[i](feat)
             conditions.append(scale.clone())
             shift = self.condition_shift[i](feat)
@@ -288,12 +314,6 @@ class GFPGANv1Clean(nn.Module):
             if return_rgb:
                 out_rgbs.append(self.toRGB[i](feat))
 
-        if save_feat_path is not None:
-            torch.save(conditions, save_feat_path)
-        if load_feat_path is not None:
-            conditions = torch.load(load_feat_path)
-            conditions = [v.cuda() for v in conditions]
-
         # decoder
         image, _ = self.stylegan_decoder([style_code],
                                          conditions,

gfpgan/archs/stylegan2_clean_arch.py

@@ -31,12 +31,9 @@ class ModulatedConv2d(nn.Module):
         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.
+        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,
@@ -87,6 +84,7 @@ class ModulatedConv2d(nn.Module):
 
         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':
@@ -101,14 +99,12 @@ class ModulatedConv2d(nn.Module):
         return out
 
     def __repr__(self):
-        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, '
-                f'out_channels={self.out_channels}, '
-                f'kernel_size={self.kernel_size}, '
-                f'demodulate={self.demodulate}, sample_mode={self.sample_mode})')
+        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.
+    """Style conv used in StyleGAN2.
 
     Args:
         in_channels (int): Channel number of the input.
@@ -116,8 +112,7 @@ class StyleConv(nn.Module):
         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.
+        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):
@@ -144,7 +139,7 @@ class StyleConv(nn.Module):
 
 
 class ToRGB(nn.Module):
-    """To RGB from features.
+    """To RGB (image space) from features.
 
     Args:
         in_channels (int): Channel number of input.
@@ -204,8 +199,7 @@ class StyleGAN2GeneratorClean(nn.Module):
         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.
+        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
         narrow (float): Narrow ratio for channels. Default: 1.0.
     """
 
@@ -222,6 +216,7 @@ class StyleGAN2GeneratorClean(nn.Module):
         # 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),
@@ -309,21 +304,17 @@ class StyleGAN2GeneratorClean(nn.Module):
                 truncation_latent=None,
                 inject_index=None,
                 return_latents=False):
-        """Forward function for StyleGAN2Generator.
+        """Forward function for StyleGAN2GeneratorClean.
 
         Args:
             styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
+            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): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
+            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:
@@ -340,7 +331,7 @@ class StyleGAN2GeneratorClean(nn.Module):
             for style in styles:
                 style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
             styles = style_truncation
-        # get style latent with injection
+        # get style latents with injection
         if len(styles) == 1:
             inject_index = self.num_latent
 
@@ -366,7 +357,7 @@ class StyleGAN2GeneratorClean(nn.Module):
                                                         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)
+            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
             i += 2
 
         image = skip

gfpgan/data/ffhq_degradation_dataset.py

@@ -15,6 +15,19 @@ from torchvision.transforms.functional import (adjust_brightness, adjust_contras
 
 @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__()
@@ -29,11 +42,13 @@ class FFHQDegradationDataset(data.Dataset):
         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)
+        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'):
@@ -41,9 +56,10 @@ class FFHQDegradationDataset(data.Dataset):
             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)
 
-        # degradations
+        # degradation configurations
         self.blur_kernel_size = opt['blur_kernel_size']
         self.kernel_list = opt['kernel_list']
         self.kernel_prob = opt['kernel_prob']
@@ -60,22 +76,20 @@ class FFHQDegradationDataset(data.Dataset):
         self.gray_prob = opt.get('gray_prob')
 
         logger = get_root_logger()
-        logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, '
-                    f'sigma: [{", ".join(map(str, self.blur_sigma))}]')
+        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}, '
-                        f'shift: {self.color_jitter_shift}')
+            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)
@@ -83,6 +97,7 @@ class FFHQDegradationDataset(data.Dataset):
 
     @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:
@@ -103,6 +118,7 @@ class FFHQDegradationDataset(data.Dataset):
         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
@@ -131,6 +147,7 @@ class FFHQDegradationDataset(data.Dataset):
             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)
@@ -139,6 +156,7 @@ class FFHQDegradationDataset(data.Dataset):
         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
@@ -173,9 +191,9 @@ class FFHQDegradationDataset(data.Dataset):
         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'):
+            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])
+                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)

gfpgan/models/gfpgan_model.py

@@ -16,11 +16,11 @@ from tqdm import tqdm
 
 @MODEL_REGISTRY.register()
 class GFPGANModel(BaseModel):
-    """GFPGAN model for <Towards real-world blind face restoratin with generative facial prior>"""
+    """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
+        self.idx = 0  # it is used for saving data for check
 
         # define network
         self.net_g = build_network(opt['network_g'])
@@ -51,8 +51,7 @@ class GFPGANModel(BaseModel):
             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
+        # 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)
@@ -65,7 +64,7 @@ class GFPGANModel(BaseModel):
         self.net_d.train()
         self.net_g_ema.eval()
 
-        # ----------- facial components networks ----------- #
+        # ----------- 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:
@@ -102,17 +101,19 @@ class GFPGANModel(BaseModel):
             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 used in pyramid loss, component style loss and identity loss
+        # 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)
@@ -179,6 +180,7 @@ class GFPGANModel(BaseModel):
         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')
@@ -221,6 +223,7 @@ class GFPGANModel(BaseModel):
             #     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):
@@ -229,7 +232,6 @@ class GFPGANModel(BaseModel):
         return pyramid_gt
 
     def get_roi_regions(self, eye_out_size=80, mouth_out_size=120):
-        # hard code
         face_ratio = int(self.opt['network_g']['out_size'] / 512)
         eye_out_size *= face_ratio
         mouth_out_size *= face_ratio
@@ -288,6 +290,7 @@ class GFPGANModel(BaseModel):
             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
@@ -419,11 +422,12 @@ class GFPGANModel(BaseModel):
         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 benegative
+        # 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)
@@ -434,8 +438,9 @@ class GFPGANModel(BaseModel):
 
         self.optimizer_d.step()
 
+        # optimize facial component discriminators
         if self.use_facial_disc:
-            # lefe eye
+            # 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(
@@ -485,22 +490,32 @@ class GFPGANModel(BaseModel):
     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:
-            self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
-        pbar = tqdm(total=len(dataloader), unit='image')
+            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()
 
-            visuals = self.get_current_visuals()
-            sr_img = tensor2img([visuals['sr']], min_max=(-1, 1))
-            gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
-
-            if 'gt' in visuals:
-                gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
+            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
@@ -522,35 +537,38 @@ class GFPGANModel(BaseModel):
             if with_metrics:
                 # calculate metrics
                 for name, opt_ in self.opt['val']['metrics'].items():
-                    metric_data = dict(img1=sr_img, img2=gt_img)
                     self.metric_results[name] += calculate_metric(metric_data, opt_)
-            pbar.update(1)
-            pbar.set_description(f'Test {img_name}')
-        pbar.close()
+            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}\n'
+            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/{metric}', value, current_iter)
-
-    def get_current_visuals(self):
-        out_dict = OrderedDict()
-        out_dict['gt'] = self.gt.detach().cpu()
-        out_dict['sr'] = self.output.detach().cpu()
-        return out_dict
+                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
@@ -558,4 +576,5 @@ class GFPGANModel(BaseModel):
             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/utils.py

@@ -2,10 +2,9 @@ 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 torch.hub import download_url_to_file, get_dir
 from torchvision.transforms.functional import normalize
-from urllib.parse import urlparse
 
 from gfpgan.archs.gfpganv1_arch import GFPGANv1
 from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
@@ -14,6 +13,20 @@ 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
@@ -56,7 +69,8 @@ class GFPGANer():
             device=self.device)
 
         if model_path.startswith('https://'):
-            model_path = load_file_from_url(url=model_path, model_dir='gfpgan/weights', progress=True, file_name=None)
+            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'
@@ -70,7 +84,7 @@ class GFPGANer():
     def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
         self.face_helper.clean_all()
 
-        if has_aligned:
+        if has_aligned:  # the inputs are already aligned
             img = cv2.resize(img, (512, 512))
             self.face_helper.cropped_faces = [img]
         else:
@@ -78,6 +92,7 @@ class GFPGANer():
             # 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()
 
@@ -100,9 +115,9 @@ class GFPGANer():
             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
+                # Now only support RealESRGAN for upsampling background
                 bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
             else:
                 bg_img = None
@@ -113,23 +128,3 @@ class GFPGANer():
             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
-
-
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
-    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
-    """
-    if model_dir is None:
-        hub_dir = get_dir()
-        model_dir = os.path.join(hub_dir, 'checkpoints')
-
-    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
-
-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    if file_name is not None:
-        filename = file_name
-    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
-    return cached_file

inference_gfpgan.py

@@ -10,20 +10,22 @@ from gfpgan import GFPGANer
 
 
 def main():
+    """Inference demo for GFPGAN.
+    """
     parser = argparse.ArgumentParser()
-
-    parser.add_argument('--upscale', type=int, default=2)
-    parser.add_argument('--arch', type=str, default='clean')
-    parser.add_argument('--channel', type=int, default=2)
+    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')
-    parser.add_argument('--bg_tile', type=int, default=400)
-    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
+    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')
-    parser.add_argument('--aligned', action='store_true')
-    parser.add_argument('--paste_back', action='store_false')
-    parser.add_argument('--save_root', type=str, default='results')
+    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,
@@ -44,10 +46,13 @@ def main():
                           '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,
@@ -70,6 +75,7 @@ def main():
         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)
 
@@ -85,7 +91,7 @@ def main():
                 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 cmp image
+            # 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'))
 

options/train_gfpgan_v1.yml

@@ -1,7 +1,7 @@
 # general settings
 name: train_GFPGANv1_512
 model_type: GFPGANModel
-num_gpu: 4
+num_gpu: auto  # officially, we use 4 GPUs
 manual_seed: 0
 
 # dataset and data loader settings
@@ -194,7 +194,7 @@ val:
   save_img: true
 
   metrics:
-    psnr: # metric name, can be arbitrary
+    psnr: # metric name
       type: calculate_psnr
       crop_border: 0
       test_y_channel: false

options/train_gfpgan_v1_simple.yml

@@ -1,7 +1,7 @@
 # general settings
 name: train_GFPGANv1_512_simple
 model_type: GFPGANModel
-num_gpu: 4
+num_gpu: auto  # officially, we use 4 GPUs
 manual_seed: 0
 
 # dataset and data loader settings
@@ -40,10 +40,6 @@ datasets:
     # gray_prob: 0.01
     # gt_gray: True
 
-    # crop_components: false
-    # 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
@@ -86,20 +82,6 @@ network_d:
   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:
@@ -107,13 +89,7 @@ path:
   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:
@@ -173,16 +149,6 @@ train:
     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
@@ -194,7 +160,7 @@ val:
   save_img: true
 
   metrics:
-    psnr: # metric name, can be arbitrary
+    psnr: # metric name
       type: calculate_psnr
       crop_border: 0
       test_y_channel: false

scripts/parse_landmark.py

@@ -1,24 +1,31 @@
 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 json file in FFHQ dataset
-with open('ffhq-dataset-v2.json', 'rb') as f:
+# 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='datasets/ffhq/ffhq_512.lmdb')
-with open('datasets/ffhq/ffhq_512.lmdb/meta_info.txt') as fin:
+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_img = False
-scale = 0.5  # 0.5 for official FFHQ (512x512), 1 for others
-enlarge_ratio = 1.4  # only for eyes
 save_dict = {}
 
 for item_idx, item in enumerate(json_data.values()):
@@ -34,6 +41,7 @@ for item_idx, item in enumerate(json_data.values()):
         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))
@@ -74,4 +82,4 @@ for item_idx, item in enumerate(json_data.values()):
     save_dict[f'{item_idx:08d}'] = item_dict
 
 print('Save...')
-torch.save(save_dict, './FFHQ_eye_mouth_landmarks_512.pth')
+torch.save(save_dict, save_path)

setup.cfg

@@ -17,7 +17,7 @@ line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = gfpgan
-known_third_party = basicsr,cv2,facexlib,numpy,torch,torchvision,tqdm
+known_third_party = basicsr,cv2,facexlib,numpy,pytest,torch,torchvision,tqdm,yaml
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 
@@ -25,3 +25,9 @@ default_section = THIRDPARTY
 skip = .git,./docs/build
 count =
 quiet-level = 3
+
+[aliases]
+test=pytest
+
+[tool:pytest]
+addopts=tests/

setup.py

@@ -43,12 +43,6 @@ def get_git_hash():
 def get_hash():
     if os.path.exists('.git'):
         sha = get_git_hash()[:7]
-    elif os.path.exists(version_file):
-        try:
-            from gfpgan.version import __version__
-            sha = __version__.split('+')[-1]
-        except ImportError:
-            raise ImportError('Unable to get git version')
     else:
         sha = 'unknown'
 

tests/data/ffhq_gt.lmdb/meta_info.txt

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

tests/data/test_ffhq_degradation_dataset.yml

@@ -0,0 +1,24 @@
+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

@@ -0,0 +1,140 @@
+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

@@ -0,0 +1,49 @@
+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

@@ -0,0 +1,96 @@
+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

@@ -0,0 +1,203 @@
+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

@@ -0,0 +1,132 @@
+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

@@ -0,0 +1,52 @@
+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

@@ -0,0 +1,43 @@
+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