Update model card files

README.md

@@ -1,36 +1,25 @@
-# Real-ESRGAN
-PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.
-
-You can try it in [google colab](https://colab.research.google.com/drive/1yO6deHTscL7FBcB6_SRzbxRr1nVtuZYE?usp=sharing)
-
-- Paper: [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://arxiv.org/abs/2107.10833)
-- [Official github](https://github.com/xinntao/Real-ESRGAN)
-
-### Installation
-
+---
+language:
+- ru
+- en
+tags:
+- PyTorch
+thumbnail: "https://github.com/sberbank-ai/Real-ESRGAN"
 ---
 
-1. Clone repo
-
-   ```bash
-   git clone https://https://github.com/sberbank-ai/Real-ESRGAN
-   cd Real-ESRGAN
-   ```
-
-2. Install requirements
-
-   ```bash
-   pip install -r requirements.txt
-   ```
+# Real-ESRGAN
 
-3. Download [pretrained weights](https://drive.google.com/drive/folders/16PlVKhTNkSyWFx52RPb2hXPIQveNGbxS) and put them into `weights/` folder
+PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.
 
-### Usage
+Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing details while removing annoying artifacts for common real-world images.
 
----
+- [Paper](https://arxiv.org/abs/2107.10833)
+- [Official github](https://github.com/xinntao/Real-ESRGAN)
+- [Ours github](https://github.com/sberbank-ai/Real-ESRGAN)
 
-Basic example:
+## Usage
 
+Code for using model you can obtain in our [repo](https://github.com/sberbank-ai/Real-ESRGAN).
 ```python
 import torch
 from PIL import Image
@@ -48,5 +37,4 @@ image = Image.open(path_to_image).convert('RGB')
 sr_image = model.predict(image)
 
 sr_image.save('results/sr_image.png')
-```
-
+```

arch_util.py

@@ -1,197 +0,0 @@
-import math
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (nn.module): nn.module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-class ResidualBlockNoBN(nn.Module):
-    """Residual block without BN.
-
-    It has a style of:
-        ---Conv-ReLU-Conv-+-
-         |________________|
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-            Default: 64.
-        res_scale (float): Residual scale. Default: 1.
-        pytorch_init (bool): If set to True, use pytorch default init,
-            otherwise, use default_init_weights. Default: False.
-    """
-
-    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
-        super(ResidualBlockNoBN, self).__init__()
-        self.res_scale = res_scale
-        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-
-        if not pytorch_init:
-            default_init_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = self.conv2(self.relu(self.conv1(x)))
-        return identity + out * self.res_scale
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
-    """Warp an image or feature map with optical flow.
-
-    Args:
-        x (Tensor): Tensor with size (n, c, h, w).
-        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
-        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
-        padding_mode (str): 'zeros' or 'border' or 'reflection'.
-            Default: 'zeros'.
-        align_corners (bool): Before pytorch 1.3, the default value is
-            align_corners=True. After pytorch 1.3, the default value is
-            align_corners=False. Here, we use the True as default.
-
-    Returns:
-        Tensor: Warped image or feature map.
-    """
-    assert x.size()[-2:] == flow.size()[1:3]
-    _, _, h, w = x.size()
-    # create mesh grid
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
-    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
-    grid.requires_grad = False
-
-    vgrid = grid + flow
-    # scale grid to [-1,1]
-    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
-    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
-    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
-    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
-
-    # TODO, what if align_corners=False
-    return output
-
-
-def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
-    """Resize a flow according to ratio or shape.
-
-    Args:
-        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
-        size_type (str): 'ratio' or 'shape'.
-        sizes (list[int | float]): the ratio for resizing or the final output
-            shape.
-            1) The order of ratio should be [ratio_h, ratio_w]. For
-            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
-            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
-            ratio > 1.0).
-            2) The order of output_size should be [out_h, out_w].
-        interp_mode (str): The mode of interpolation for resizing.
-            Default: 'bilinear'.
-        align_corners (bool): Whether align corners. Default: False.
-
-    Returns:
-        Tensor: Resized flow.
-    """
-    _, _, flow_h, flow_w = flow.size()
-    if size_type == 'ratio':
-        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
-    elif size_type == 'shape':
-        output_h, output_w = sizes[0], sizes[1]
-    else:
-        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
-
-    input_flow = flow.clone()
-    ratio_h = output_h / flow_h
-    ratio_w = output_w / flow_w
-    input_flow[:, 0, :, :] *= ratio_w
-    input_flow[:, 1, :, :] *= ratio_h
-    resized_flow = F.interpolate(
-        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
-    return resized_flow
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)

realesrgan.py

@@ -1,55 +0,0 @@
-import torch
-from torch.nn import functional as F
-from PIL import Image
-import numpy as np
-import cv2
-
-from rrdbnet_arch import RRDBNet
-from utils_sr import *
-
-
-class RealESRGAN:
-    def __init__(self, device, scale=4):
-        self.device = device
-        self.scale = scale
-        self.model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
-        
-    def load_weights(self, model_path):
-        loadnet = torch.load(model_path)
-        if 'params' in loadnet:
-            self.model.load_state_dict(loadnet['params'], strict=True)
-        elif 'params_ema' in loadnet:
-            self.model.load_state_dict(loadnet['params_ema'], strict=True)
-        else:
-            self.model.load_state_dict(loadnet, strict=True)
-        self.model.eval()
-        self.model.to(self.device)
-        
-    def predict(self, lr_image, batch_size=4, patches_size=192,
-                padding=24, pad_size=15):
-        scale = self.scale
-        device = self.device
-        lr_image = np.array(lr_image)
-        lr_image = pad_reflect(lr_image, pad_size)
-
-        patches, p_shape = split_image_into_overlapping_patches(lr_image, patch_size=patches_size, 
-                                                                padding_size=padding)
-        img = torch.FloatTensor(patches/255).permute((0,3,1,2)).to(device).detach()
-
-        with torch.no_grad():
-            res = self.model(img[0:batch_size])
-            for i in range(batch_size, img.shape[0], batch_size):
-                res = torch.cat((res, self.model(img[i:i+batch_size])), 0)
-
-        sr_image = res.permute((0,2,3,1)).cpu().clamp_(0, 1)
-        np_sr_image = sr_image.numpy()
-
-        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
-        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
-        np_sr_image = stich_together(np_sr_image, padded_image_shape=padded_size_scaled,
-                                target_shape=scaled_image_shape, padding_size=padding * scale)
-        sr_img = (np_sr_image*255).astype(np.uint8)
-        sr_img = unpad_image(sr_img, pad_size*scale)
-        sr_img = Image.fromarray(sr_img)
-
-        return sr_img

requirements.txt

@@ -1,6 +0,0 @@
-numpy
-opencv-python
-Pillow
-torch>=1.7
-torchvision>=0.8.0
-tqdm

rrdbnet_arch.py

@@ -1,121 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        if scale == 8:
-            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        if self.scale == 8:
-            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out

utils_sr.py

@@ -1,141 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-import os
-import io
-import imageio
-
-def pad_reflect(image, pad_size):
-    imsize = image.shape
-    height, width = imsize[:2]
-    new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
-    new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
-    
-    new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top
-    new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom
-    new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left
-    new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right
-    
-    return new_img
-
-def unpad_image(image, pad_size):
-    return image[pad_size:-pad_size, pad_size:-pad_size, :]
-
-
-def jpegBlur(im,q):
-    buf = io.BytesIO()
-    imageio.imwrite(buf,im,format='jpg',quality=q)
-    s = buf.getbuffer()
-    return imageio.imread(s,format='jpg')
-
-
-def process_array(image_array, expand=True):
-    """ Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """
-    
-    image_batch = image_array / 255.0
-    if expand:
-        image_batch = np.expand_dims(image_batch, axis=0)
-    return image_batch
-
-
-def process_output(output_tensor):
-    """ Transforms the 4-dimensional output tensor into a suitable image format. """
-    
-    sr_img = output_tensor.clip(0, 1) * 255
-    sr_img = np.uint8(sr_img)
-    return sr_img
-
-
-def pad_patch(image_patch, padding_size, channel_last=True):
-    """ Pads image_patch with with padding_size edge values. """
-    
-    if channel_last:
-        return np.pad(
-            image_patch,
-            ((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
-            'edge',
-        )
-    else:
-        return np.pad(
-            image_patch,
-            ((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
-            'edge',
-        )
-
-
-def unpad_patches(image_patches, padding_size):
-    return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
-
-
-def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
-    """ Splits the image into partially overlapping patches.
-    The patches overlap by padding_size pixels.
-    Pads the image twice:
-        - first to have a size multiple of the patch size,
-        - then to have equal padding at the borders.
-    Args:
-        image_array: numpy array of the input image.
-        patch_size: size of the patches from the original image (without padding).
-        padding_size: size of the overlapping area.
-    """
-    
-    xmax, ymax, _ = image_array.shape
-    x_remainder = xmax % patch_size
-    y_remainder = ymax % patch_size
-    
-    # modulo here is to avoid extending of patch_size instead of 0
-    x_extend = (patch_size - x_remainder) % patch_size
-    y_extend = (patch_size - y_remainder) % patch_size
-    
-    # make sure the image is divisible into regular patches
-    extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
-    
-    # add padding around the image to simplify computations
-    padded_image = pad_patch(extended_image, padding_size, channel_last=True)
-    
-    xmax, ymax, _ = padded_image.shape
-    patches = []
-    
-    x_lefts = range(padding_size, xmax - padding_size, patch_size)
-    y_tops = range(padding_size, ymax - padding_size, patch_size)
-    
-    for x in x_lefts:
-        for y in y_tops:
-            x_left = x - padding_size
-            y_top = y - padding_size
-            x_right = x + patch_size + padding_size
-            y_bottom = y + patch_size + padding_size
-            patch = padded_image[x_left:x_right, y_top:y_bottom, :]
-            patches.append(patch)
-    
-    return np.array(patches), padded_image.shape
-
-
-def stich_together(patches, padded_image_shape, target_shape, padding_size=4):
-    """ Reconstruct the image from overlapping patches.
-    After scaling, shapes and padding should be scaled too.
-    Args:
-        patches: patches obtained with split_image_into_overlapping_patches
-        padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches
-        target_shape: shape of the final image
-        padding_size: size of the overlapping area.
-    """
-    
-    xmax, ymax, _ = padded_image_shape
-    patches = unpad_patches(patches, padding_size)
-    patch_size = patches.shape[1]
-    n_patches_per_row = ymax // patch_size
-    
-    complete_image = np.zeros((xmax, ymax, 3))
-    
-    row = -1
-    col = 0
-    for i in range(len(patches)):
-        if i % n_patches_per_row == 0:
-            row += 1
-            col = 0
-        complete_image[
-        row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,:
-        ] = patches[i]
-        col += 1
-    return complete_image[0: target_shape[0], 0: target_shape[1], :]

weights/README.md

@@ -1,4 +0,0 @@
-# Pretrained weights
-Download pretrained weights there: 
-https://drive.google.com/drive/folders/16PlVKhTNkSyWFx52RPb2hXPIQveNGbxS
-