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mmagic.models.editors.esrgan.rrdb_net 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.model import BaseModule

from mmagic.models.archs import pixel_unshuffle
from mmagic.models.utils import default_init_weights, make_layer
from mmagic.registry import MODELS


@MODELS.register_module()
[文档]class RRDBNet(BaseModule): """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. init_cfg (dict, optional): Initialization config dict. Default: None. """
[文档] _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, init_cfg=None): super().__init__(init_cfg=init_cfg) 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)
[文档] 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
[文档] def init_weights(self): """Init weights for models.""" if self.init_cfg: super().init_weights() else: # 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)
[文档]class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. Default: 64. growth_channels (int): Channels for each growth. Default: 32. """ 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()
[文档] 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)
[文档] 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)) # Empirically, we use 0.2 to scale the residual for better performance return x5 * 0.2 + x
[文档]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. Default: 32. """ 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)
[文档] 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) # Empirically, we use 0.2 to scale the residual for better performance return out * 0.2 + x
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