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