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Source code for mmedit.models.backbones.sr_backbones.rrdb_net

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import load_checkpoint

from mmedit.models.common import (default_init_weights, make_layer,
                                  pixel_unshuffle)
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger


class ResidualDenseBlock(nn.Module):
    """Residual Dense Block.

    Used in RRDB block in ESRGAN.

    Args:
        mid_channels (int): Channel number of intermediate features.
        growth_channels (int): Channels for each growth.
    """

    def __init__(self, mid_channels=64, growth_channels=32):
        super().__init__()
        for i in range(5):
            out_channels = mid_channels if i == 4 else growth_channels
            self.add_module(
                f'conv{i+1}',
                nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
                          1, 1))
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

        self.init_weights()

    def init_weights(self):
        """Init weights for ResidualDenseBlock.

        Use smaller std for better stability and performance. We empirically
        use 0.1. See more details in "ESRGAN: Enhanced Super-Resolution
        Generative Adversarial Networks"
        """
        for i in range(5):
            default_init_weights(getattr(self, f'conv{i+1}'), 0.1)

    def forward(self, x):
        """Forward function.

        Args:
            x (Tensor): Input tensor with shape (n, c, h, w).

        Returns:
            Tensor: Forward results.
        """
        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:
        mid_channels (int): Channel number of intermediate features.
        growth_channels (int): Channels for each growth.
    """

    def __init__(self, mid_channels, growth_channels=32):
        super().__init__()
        self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
        self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
        self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)

    def forward(self, x):
        """Forward function.

        Args:
            x (Tensor): Input tensor with shape (n, c, h, w).

        Returns:
            Tensor: Forward results.
        """
        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


[docs]@BACKBONES.register_module() class RRDBNet(nn.Module): """Networks consisting of Residual in Residual Dense Block, which is used in ESRGAN and Real-ESRGAN. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. # noqa: E501 Currently, it supports [x1/x2/x4] upsampling scale factor. Args: in_channels (int): Channel number of inputs. out_channels (int): Channel number of outputs. mid_channels (int): Channel number of intermediate features. Default: 64 num_blocks (int): Block number in the trunk network. Defaults: 23 growth_channels (int): Channels for each growth. Default: 32. upscale_factor (int): Upsampling factor. Support x1, x2 and x4. Default: 4. """ _supported_upscale_factors = [1, 2, 4] def __init__(self, in_channels, out_channels, mid_channels=64, num_blocks=23, growth_channels=32, upscale_factor=4): super().__init__() if upscale_factor in self._supported_upscale_factors: in_channels = in_channels * ((4 // upscale_factor)**2) else: raise ValueError(f'Unsupported scale factor {upscale_factor}. ' f'Currently supported ones are ' f'{self._supported_upscale_factors}.') self.upscale_factor = upscale_factor self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) self.body = make_layer( RRDB, num_blocks, mid_channels=mid_channels, growth_channels=growth_channels) self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) # upsample self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
[docs] def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ if self.upscale_factor in [1, 2]: feat = pixel_unshuffle(x, scale=4 // self.upscale_factor) else: feat = x feat = self.conv_first(feat) body_feat = self.conv_body(self.body(feat)) feat = feat + body_feat # upsample feat = self.lrelu( self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) feat = self.lrelu( self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) out = self.conv_last(self.lrelu(self.conv_hr(feat))) return out
[docs] def init_weights(self, pretrained=None, strict=True): """Init weights for models. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None. strict (boo, optional): Whether strictly load the pretrained model. Defaults to True. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=strict, logger=logger) elif pretrained is None: # Use smaller std for better stability and performance. We # use 0.1. See more details in "ESRGAN: Enhanced Super-Resolution # Generative Adversarial Networks" for m in [ self.conv_first, self.conv_body, self.conv_up1, self.conv_up2, self.conv_hr, self.conv_last ]: default_init_weights(m, 0.1) else: raise TypeError(f'"pretrained" must be a str or None. ' f'But received {type(pretrained)}.')
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