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| 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 | |