import math import torch.nn as nn import models.basicblock as B import functools import torch.nn.functional as F import torch.nn.init as init """ # -------------------------------------------- # modified SRResNet # -- MSRResNet0 (v0.0) # -- MSRResNet1 (v0.1) # -------------------------------------------- References: @inproceedings{wang2018esrgan, title={Esrgan: Enhanced super-resolution generative adversarial networks}, author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen}, booktitle={European Concerence on Computer Vision (ECCV)}, pages={0--0}, year={2018} } @inproceedings{ledig2017photo, title={Photo-realistic single image super-resolution using a generative adversarial network}, author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others}, booktitle={IEEE concerence on computer vision and pattern recognition}, pages={4681--4690}, year={2017} } # -------------------------------------------- """ # -------------------------------------------- # modified SRResNet v0.0 # https://github.com/xinntao/ESRGAN # -------------------------------------------- class MSRResNet0(nn.Module): def __init__(self, in_nc=3, out_nc=3, nc=64, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'): """ in_nc: channel number of input out_nc: channel number of output nc: channel number nb: number of residual blocks upscale: up-scale factor act_mode: activation function upsample_mode: 'upconv' | 'pixelshuffle' | 'convtranspose' """ super(MSRResNet0, self).__init__() assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL' n_upscale = int(math.log(upscale, 2)) if upscale == 3: n_upscale = 1 m_head = B.conv(in_nc, nc, mode='C') m_body = [B.ResBlock(nc, nc, mode='C'+act_mode+'C') for _ in range(nb)] m_body.append(B.conv(nc, nc, mode='C')) if upsample_mode == 'upconv': upsample_block = B.upsample_upconv elif upsample_mode == 'pixelshuffle': upsample_block = B.upsample_pixelshuffle elif upsample_mode == 'convtranspose': upsample_block = B.upsample_convtranspose else: raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) if upscale == 3: m_uper = upsample_block(nc, nc, mode='3'+act_mode) else: m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)] H_conv0 = B.conv(nc, nc, mode='C'+act_mode) H_conv1 = B.conv(nc, out_nc, bias=False, mode='C') m_tail = B.sequential(H_conv0, H_conv1) self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail) def forward(self, x): x = self.model(x) return x # -------------------------------------------- # modified SRResNet v0.1 # https://github.com/xinntao/ESRGAN # -------------------------------------------- class MSRResNet1(nn.Module): def __init__(self, in_nc=3, out_nc=3, nc=64, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'): super(MSRResNet1, self).__init__() self.upscale = upscale self.conv_first = nn.Conv2d(in_nc, nc, 3, 1, 1, bias=True) basic_block = functools.partial(ResidualBlock_noBN, nc=nc) self.recon_trunk = make_layer(basic_block, nb) # upsampling if self.upscale == 2: self.upconv1 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True) self.pixel_shuffle = nn.PixelShuffle(2) elif self.upscale == 3: self.upconv1 = nn.Conv2d(nc, nc * 9, 3, 1, 1, bias=True) self.pixel_shuffle = nn.PixelShuffle(3) elif self.upscale == 4: self.upconv1 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True) self.upconv2 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True) self.pixel_shuffle = nn.PixelShuffle(2) self.HRconv = nn.Conv2d(nc, nc, 3, 1, 1, bias=True) self.conv_last = nn.Conv2d(nc, out_nc, 3, 1, 1, bias=True) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # initialization initialize_weights([self.conv_first, self.upconv1, self.HRconv, self.conv_last], 0.1) if self.upscale == 4: initialize_weights(self.upconv2, 0.1) def forward(self, x): fea = self.lrelu(self.conv_first(x)) out = self.recon_trunk(fea) if self.upscale == 4: out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) elif self.upscale == 3 or self.upscale == 2: out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) out = self.conv_last(self.lrelu(self.HRconv(out))) base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False) out += base return out def initialize_weights(net_l, scale=1): if not isinstance(net_l, list): net_l = [net_l] for net in net_l: for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data *= scale # for residual block if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data *= scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias.data, 0.0) def make_layer(block, n_layers): layers = [] for _ in range(n_layers): layers.append(block()) return nn.Sequential(*layers) class ResidualBlock_noBN(nn.Module): '''Residual block w/o BN ---Conv-ReLU-Conv-+- |________________| ''' def __init__(self, nc=64): super(ResidualBlock_noBN, self).__init__() self.conv1 = nn.Conv2d(nc, nc, 3, 1, 1, bias=True) self.conv2 = nn.Conv2d(nc, nc, 3, 1, 1, bias=True) # initialization initialize_weights([self.conv1, self.conv2], 0.1) def forward(self, x): identity = x out = F.relu(self.conv1(x), inplace=True) out = self.conv2(out) return identity + out