673 lines
29 KiB
Python
673 lines
29 KiB
Python
import torch
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import torch.nn as nn
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from models.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
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import torch.nn.functional as F
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from utils.util import checkpoint
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class Discriminator_VGG_128(nn.Module):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, in_nc, nf, input_img_factor=1, extra_conv=False):
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super(Discriminator_VGG_128, self).__init__()
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# [64, 128, 128]
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self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
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# [64, 64, 64]
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self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
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self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
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# [128, 32, 32]
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self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
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self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
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# [256, 16, 16]
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self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
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self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
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# [512, 8, 8]
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self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
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final_nf = nf * 8
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self.extra_conv = extra_conv
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if self.extra_conv:
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self.conv5_0 = nn.Conv2d(nf * 8, nf * 16, 3, 1, 1, bias=False)
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self.bn5_0 = nn.BatchNorm2d(nf * 16, affine=True)
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self.conv5_1 = nn.Conv2d(nf * 16, nf * 16, 4, 2, 1, bias=False)
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self.bn5_1 = nn.BatchNorm2d(nf * 16, affine=True)
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input_img_factor = input_img_factor // 2
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final_nf = nf * 16
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self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100)
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self.linear2 = nn.Linear(100, 1)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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fea = self.lrelu(self.conv0_0(x))
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fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
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#fea = torch.cat([fea, skip_med], dim=1)
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fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
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fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
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#fea = torch.cat([fea, skip_lo], dim=1)
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fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
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fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
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fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
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fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
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fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
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fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
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if self.extra_conv:
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fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
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fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
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fea = fea.contiguous().view(fea.size(0), -1)
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fea = self.lrelu(self.linear1(fea))
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out = self.linear2(fea)
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return out
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class Discriminator_VGG_128_GN(nn.Module):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, in_nc, nf, input_img_factor=1, do_checkpointing=False, extra_conv=False):
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super(Discriminator_VGG_128_GN, self).__init__()
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self.do_checkpointing = do_checkpointing
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# [64, 128, 128]
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self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
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# [64, 64, 64]
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self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
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self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
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# [128, 32, 32]
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self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
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self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
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# [256, 16, 16]
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self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
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self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
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# [512, 8, 8]
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self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
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self.extra_conv = extra_conv
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if extra_conv:
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self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn5_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn5_1 = nn.GroupNorm(8, nf * 8, affine=True)
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input_img_factor = input_img_factor / 2
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final_nf = nf * 8
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
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self.linear2 = nn.Linear(100, 1)
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def compute_body(self, x):
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fea = self.lrelu(self.conv0_0(x))
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fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
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#fea = torch.cat([fea, skip_med], dim=1)
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fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
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fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
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#fea = torch.cat([fea, skip_lo], dim=1)
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fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
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fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
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fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
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fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
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fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
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fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
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if self.extra_conv:
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fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
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fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
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return fea
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def forward(self, x):
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if self.do_checkpointing:
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fea = checkpoint(self.compute_body, x)
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else:
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fea = self.compute_body(x)
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fea = fea.contiguous().view(fea.size(0), -1)
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fea = self.lrelu(self.linear1(fea))
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out = self.linear2(fea)
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return out
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class CrossCompareBlock(nn.Module):
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def __init__(self, nf_in, nf_out):
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super(CrossCompareBlock, self).__init__()
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self.conv_hr_merge = ConvGnLelu(nf_in * 2, nf_in, kernel_size=1, bias=False, activation=False, norm=True)
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self.proc_hr = ConvGnLelu(nf_in, nf_out, kernel_size=3, bias=False, activation=True, norm=True)
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self.proc_lr = ConvGnLelu(nf_in, nf_out, kernel_size=3, bias=False, activation=True, norm=True)
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self.reduce_hr = ConvGnLelu(nf_out, nf_out, kernel_size=3, stride=2, bias=False, activation=True, norm=True)
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self.reduce_lr = ConvGnLelu(nf_out, nf_out, kernel_size=3, stride=2, bias=False, activation=True, norm=True)
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def forward(self, hr, lr):
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hr = self.conv_hr_merge(torch.cat([hr, lr], dim=1))
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hr = self.proc_hr(hr)
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hr = self.reduce_hr(hr)
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lr = self.proc_lr(lr)
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lr = self.reduce_lr(lr)
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return hr, lr
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class CrossCompareDiscriminator(nn.Module):
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def __init__(self, in_nc, ref_channels, nf, scale=4):
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super(CrossCompareDiscriminator, self).__init__()
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assert scale == 2 or scale == 4
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self.init_conv_hr = ConvGnLelu(in_nc, nf, stride=2, norm=False, bias=True, activation=True)
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self.init_conv_lr = ConvGnLelu(ref_channels, nf, stride=1, norm=False, bias=True, activation=True)
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if scale == 4:
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strd_2 = 2
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else:
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strd_2 = 1
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self.second_conv = ConvGnLelu(nf, nf, stride=strd_2, norm=True, bias=False, activation=True)
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self.cross1 = CrossCompareBlock(nf, nf * 2)
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self.cross2 = CrossCompareBlock(nf * 2, nf * 4)
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self.cross3 = CrossCompareBlock(nf * 4, nf * 8)
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self.cross4 = CrossCompareBlock(nf * 8, nf * 8)
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self.fproc_conv = ConvGnLelu(nf * 8, nf, norm=True, bias=True, activation=True)
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self.out_conv = ConvGnLelu(nf, 1, norm=False, bias=False, activation=False)
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self.scale = scale * 16
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def forward(self, hr, lr):
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hr = self.init_conv_hr(hr)
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hr = self.second_conv(hr)
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lr = self.init_conv_lr(lr)
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hr, lr = self.cross1(hr, lr)
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hr, lr = self.cross2(hr, lr)
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hr, lr = self.cross3(hr, lr)
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hr, _ = self.cross4(hr, lr)
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return self.out_conv(self.fproc_conv(hr)).view(-1, 1)
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# Returns tuple of (number_output_channels, scale_of_output_reduction (1/n))
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def pixgan_parameters(self):
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return 3, self.scale
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class Discriminator_VGG_PixLoss(nn.Module):
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def __init__(self, in_nc, nf):
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super(Discriminator_VGG_PixLoss, self).__init__()
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# [64, 128, 128]
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self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
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# [64, 64, 64]
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self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
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self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
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# [128, 32, 32]
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self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
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self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
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# [256, 16, 16]
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self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
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self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
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# [512, 8, 8]
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self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
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self.reduce_1 = ConvGnLelu(nf * 8, nf * 4, bias=False)
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self.pix_loss_collapse = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False)
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# Pyramid network: upsample with residuals and produce losses at multiple resolutions.
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self.up3_decimate = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=True, activation=False)
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self.up3_converge = ConvGnLelu(nf * 16, nf * 8, kernel_size=3, bias=False)
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self.up3_proc = ConvGnLelu(nf * 8, nf * 8, bias=False)
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self.up3_reduce = ConvGnLelu(nf * 8, nf * 4, bias=False)
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self.up3_pix = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False)
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self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, activation=False)
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self.up2_converge = ConvGnLelu(nf * 8, nf * 4, kernel_size=3, bias=False)
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self.up2_proc = ConvGnLelu(nf * 4, nf * 4, bias=False)
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self.up2_reduce = ConvGnLelu(nf * 4, nf * 2, bias=False)
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self.up2_pix = ConvGnLelu(nf * 2, 1, bias=False, norm=False, activation=False)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x, flatten=True):
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fea0 = self.lrelu(self.conv0_0(x))
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fea0 = self.lrelu(self.bn0_1(self.conv0_1(fea0)))
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fea1 = self.lrelu(self.bn1_0(self.conv1_0(fea0)))
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fea1 = self.lrelu(self.bn1_1(self.conv1_1(fea1)))
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fea2 = self.lrelu(self.bn2_0(self.conv2_0(fea1)))
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fea2 = self.lrelu(self.bn2_1(self.conv2_1(fea2)))
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fea3 = self.lrelu(self.bn3_0(self.conv3_0(fea2)))
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fea3 = self.lrelu(self.bn3_1(self.conv3_1(fea3)))
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fea4 = self.lrelu(self.bn4_0(self.conv4_0(fea3)))
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fea4 = self.lrelu(self.bn4_1(self.conv4_1(fea4)))
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loss = self.reduce_1(fea4)
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# "Weight" all losses the same by interpolating them to the highest dimension.
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loss = self.pix_loss_collapse(loss)
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loss = F.interpolate(loss, scale_factor=4, mode="nearest")
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# And the pyramid network!
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dec3 = self.up3_decimate(F.interpolate(fea4, scale_factor=2, mode="nearest"))
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dec3 = torch.cat([dec3, fea3], dim=1)
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dec3 = self.up3_converge(dec3)
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dec3 = self.up3_proc(dec3)
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loss3 = self.up3_reduce(dec3)
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loss3 = self.up3_pix(loss3)
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loss3 = F.interpolate(loss3, scale_factor=2, mode="nearest")
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dec2 = self.up2_decimate(F.interpolate(dec3, scale_factor=2, mode="nearest"))
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dec2 = torch.cat([dec2, fea2], dim=1)
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dec2 = self.up2_converge(dec2)
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dec2 = self.up2_proc(dec2)
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dec2 = self.up2_reduce(dec2)
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loss2 = self.up2_pix(dec2)
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# Compress all of the loss values into the batch dimension. The actual loss attached to this output will
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# then know how to handle them.
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combined_losses = torch.cat([loss, loss3, loss2], dim=1)
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return combined_losses.view(-1, 1)
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def pixgan_parameters(self):
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return 3, 8
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class Discriminator_UNet(nn.Module):
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def __init__(self, in_nc, nf):
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super(Discriminator_UNet, self).__init__()
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# [64, 128, 128]
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self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False)
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self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False)
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# [64, 64, 64]
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self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False)
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self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False)
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# [128, 32, 32]
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self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False)
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self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False)
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# [256, 16, 16]
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self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False)
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self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
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# [512, 8, 8]
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self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False)
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self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
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self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu)
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self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False)
|
|
self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up2 = ExpansionBlock(nf * 8, nf * 4, block=ConvGnLelu)
|
|
self.proc2 = ConvGnLelu(nf * 4, nf * 4, bias=False)
|
|
self.collapse2 = ConvGnLelu(nf * 4, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.up3 = ExpansionBlock(nf * 4, nf * 2, block=ConvGnLelu)
|
|
self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False)
|
|
self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False)
|
|
|
|
def forward(self, x, flatten=True):
|
|
fea0 = self.conv0_0(x)
|
|
fea0 = self.conv0_1(fea0)
|
|
|
|
fea1 = self.conv1_0(fea0)
|
|
fea1 = self.conv1_1(fea1)
|
|
|
|
fea2 = self.conv2_0(fea1)
|
|
fea2 = self.conv2_1(fea2)
|
|
|
|
fea3 = self.conv3_0(fea2)
|
|
fea3 = self.conv3_1(fea3)
|
|
|
|
fea4 = self.conv4_0(fea3)
|
|
fea4 = self.conv4_1(fea4)
|
|
|
|
# And the pyramid network!
|
|
u1 = self.up1(fea4, fea3)
|
|
loss1 = self.collapse1(self.proc1(u1))
|
|
u2 = self.up2(u1, fea2)
|
|
loss2 = self.collapse2(self.proc2(u2))
|
|
u3 = self.up3(u2, fea1)
|
|
loss3 = self.collapse3(self.proc3(u3))
|
|
res = loss3.shape[2:]
|
|
|
|
# Compress all of the loss values into the batch dimension. The actual loss attached to this output will
|
|
# then know how to handle them.
|
|
combined_losses = torch.cat([F.interpolate(loss1, scale_factor=4),
|
|
F.interpolate(loss2, scale_factor=2),
|
|
F.interpolate(loss3, scale_factor=1)], dim=1)
|
|
return combined_losses.view(-1, 1)
|
|
|
|
def pixgan_parameters(self):
|
|
return 3, 4
|
|
|
|
|
|
class Discriminator_UNet_FeaOut(nn.Module):
|
|
def __init__(self, in_nc, nf, feature_mode=False):
|
|
super(Discriminator_UNet_FeaOut, self).__init__()
|
|
# [64, 128, 128]
|
|
self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False)
|
|
self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False)
|
|
self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False)
|
|
self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False)
|
|
self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False)
|
|
self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False)
|
|
|
|
self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu)
|
|
self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False)
|
|
self.fea_proc = ConvGnLelu(nf * 8, nf * 8, bias=True, norm=False, activation=False)
|
|
self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False)
|
|
|
|
self.feature_mode = feature_mode
|
|
|
|
def forward(self, x, output_feature_vector=False):
|
|
fea0 = self.conv0_0(x)
|
|
fea0 = self.conv0_1(fea0)
|
|
|
|
fea1 = self.conv1_0(fea0)
|
|
fea1 = self.conv1_1(fea1)
|
|
|
|
fea2 = self.conv2_0(fea1)
|
|
fea2 = self.conv2_1(fea2)
|
|
|
|
fea3 = self.conv3_0(fea2)
|
|
fea3 = self.conv3_1(fea3)
|
|
|
|
fea4 = self.conv4_0(fea3)
|
|
fea4 = self.conv4_1(fea4)
|
|
|
|
# And the pyramid network!
|
|
u1 = self.up1(fea4, fea3)
|
|
loss1 = self.collapse1(self.proc1(u1))
|
|
fea_out = self.fea_proc(u1)
|
|
|
|
combined_losses = F.interpolate(loss1, scale_factor=4)
|
|
if output_feature_vector:
|
|
return combined_losses.view(-1, 1), fea_out
|
|
else:
|
|
return combined_losses.view(-1, 1)
|
|
|
|
def pixgan_parameters(self):
|
|
return 1, 4
|
|
|
|
|
|
class Vgg128GnHead(nn.Module):
|
|
def __init__(self, in_nc, nf, depth=5):
|
|
super(Vgg128GnHead, self).__init__()
|
|
assert depth == 4 or depth == 5 # Nothing stopping others from being implemented, just not done yet.
|
|
self.depth = depth
|
|
|
|
# [64, 128, 128]
|
|
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
|
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.bn0_1 = nn.GroupNorm(8, nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True)
|
|
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True)
|
|
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True)
|
|
# [256, 16, 16]
|
|
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
if depth > 4:
|
|
# [512, 8, 8]
|
|
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
def forward(self, x):
|
|
fea = self.lrelu(self.conv0_0(x))
|
|
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
|
|
|
|
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
|
|
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
|
|
|
|
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
|
|
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
|
|
|
|
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
|
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
|
|
|
if self.depth > 4:
|
|
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
|
|
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
|
|
return fea
|
|
|
|
|
|
class RefDiscriminatorVgg128(nn.Module):
|
|
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
|
|
def __init__(self, in_nc, nf, input_img_factor=1):
|
|
super(RefDiscriminatorVgg128, self).__init__()
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
self.feature_head = Vgg128GnHead(in_nc, nf)
|
|
self.ref_head = Vgg128GnHead(in_nc+1, nf, depth=4)
|
|
final_nf = nf * 8
|
|
|
|
self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 512)
|
|
self.ref_linear = nn.Linear(nf * 8, 128)
|
|
|
|
self.output_linears = nn.Sequential(
|
|
nn.Linear(128+512, 512),
|
|
self.lrelu,
|
|
nn.Linear(512, 256),
|
|
self.lrelu,
|
|
nn.Linear(256, 128),
|
|
self.lrelu,
|
|
nn.Linear(128, 1)
|
|
)
|
|
|
|
def forward(self, x, ref, ref_center_point):
|
|
ref = self.ref_head(ref)
|
|
ref_center_point = ref_center_point // 16
|
|
from models.SwitchedResidualGenerator_arch import gather_2d
|
|
ref_vector = gather_2d(ref, ref_center_point)
|
|
ref_vector = self.ref_linear(ref_vector)
|
|
|
|
fea = self.feature_head(x)
|
|
fea = fea.contiguous().view(fea.size(0), -1)
|
|
fea = self.lrelu(self.linear1(fea))
|
|
|
|
out = self.output_linears(torch.cat([fea, ref_vector], dim=1))
|
|
return out
|
|
|
|
|
|
class PsnrApproximator(nn.Module):
|
|
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
|
|
def __init__(self, nf, input_img_factor=1):
|
|
super(PsnrApproximator, self).__init__()
|
|
|
|
# [64, 128, 128]
|
|
self.fake_conv0_0 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
|
|
self.fake_conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.fake_bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.fake_conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.fake_bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
self.fake_conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.fake_bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.fake_conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.fake_bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
self.fake_conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.fake_bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
|
|
# [64, 128, 128]
|
|
self.real_conv0_0 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
|
|
self.real_conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.real_bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.real_conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.real_bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
self.real_conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.real_bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.real_conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.real_bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
self.real_conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.real_bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
|
|
# [512, 16, 16]
|
|
self.conv3_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
|
self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
final_nf = nf * 8
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 1024)
|
|
self.linear2 = nn.Linear(1024, 512)
|
|
self.linear3 = nn.Linear(512, 128)
|
|
self.linear4 = nn.Linear(128, 1)
|
|
|
|
def compute_body1(self, real):
|
|
fea = self.lrelu(self.real_conv0_0(real))
|
|
fea = self.lrelu(self.real_bn0_1(self.real_conv0_1(fea)))
|
|
fea = self.lrelu(self.real_bn1_0(self.real_conv1_0(fea)))
|
|
fea = self.lrelu(self.real_bn1_1(self.real_conv1_1(fea)))
|
|
fea = self.lrelu(self.real_bn2_0(self.real_conv2_0(fea)))
|
|
fea = self.lrelu(self.real_bn2_1(self.real_conv2_1(fea)))
|
|
return fea
|
|
|
|
def compute_body2(self, fake):
|
|
fea = self.lrelu(self.fake_conv0_0(fake))
|
|
fea = self.lrelu(self.fake_bn0_1(self.fake_conv0_1(fea)))
|
|
fea = self.lrelu(self.fake_bn1_0(self.fake_conv1_0(fea)))
|
|
fea = self.lrelu(self.fake_bn1_1(self.fake_conv1_1(fea)))
|
|
fea = self.lrelu(self.fake_bn2_0(self.fake_conv2_0(fea)))
|
|
fea = self.lrelu(self.fake_bn2_1(self.fake_conv2_1(fea)))
|
|
return fea
|
|
|
|
def forward(self, real, fake):
|
|
real_fea = checkpoint(self.compute_body1, real)
|
|
fake_fea = checkpoint(self.compute_body2, fake)
|
|
fea = torch.cat([real_fea, fake_fea], dim=1)
|
|
|
|
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
|
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
|
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
|
|
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
|
|
|
|
fea = fea.contiguous().view(fea.size(0), -1)
|
|
fea = self.lrelu(self.linear1(fea))
|
|
fea = self.lrelu(self.linear2(fea))
|
|
fea = self.lrelu(self.linear3(fea))
|
|
out = self.linear4(fea)
|
|
return out.squeeze()
|
|
|
|
|
|
class SingleImageQualityEstimator(nn.Module):
|
|
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
|
|
def __init__(self, nf, input_img_factor=1):
|
|
super(SingleImageQualityEstimator, self).__init__()
|
|
|
|
# [64, 128, 128]
|
|
self.fake_conv0_0 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
|
|
self.fake_conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
|
self.fake_bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.fake_conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.fake_bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
self.fake_conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.fake_bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
# [128, 32, 32]
|
|
self.fake_conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
|
self.fake_bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
self.fake_conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.fake_bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
|
|
# [512, 16, 16]
|
|
self.conv3_0 = nn.Conv2d(nf * 4, nf * 4, 3, 1, 1, bias=False)
|
|
self.bn3_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
|
self.conv3_1 = nn.Conv2d(nf * 4, nf * 8, 4, 2, 1, bias=False)
|
|
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
|
# [512, 8, 8]
|
|
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=True)
|
|
self.conv4_1 = nn.Conv2d(nf * 8, nf * 2, 3, 1, 1, bias=True)
|
|
self.conv4_2 = nn.Conv2d(nf * 2, nf, 3, 1, 1, bias=True)
|
|
self.conv4_3 = nn.Conv2d(nf, 3, 3, 1, 1, bias=True)
|
|
self.sigmoid = nn.Sigmoid()
|
|
self.lrelu = nn.LeakyReLU(negative_slope=.2, inplace=True)
|
|
|
|
def compute_body(self, fake):
|
|
fea = self.lrelu(self.fake_conv0_0(fake))
|
|
fea = self.lrelu(self.fake_bn0_1(self.fake_conv0_1(fea)))
|
|
fea = self.lrelu(self.fake_bn1_0(self.fake_conv1_0(fea)))
|
|
fea = self.lrelu(self.fake_bn1_1(self.fake_conv1_1(fea)))
|
|
fea = self.lrelu(self.fake_bn2_0(self.fake_conv2_0(fea)))
|
|
fea = self.lrelu(self.fake_bn2_1(self.fake_conv2_1(fea)))
|
|
return fea
|
|
|
|
def forward(self, fake):
|
|
fea = checkpoint(self.compute_body, fake)
|
|
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
|
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
|
fea = self.lrelu(self.conv4_0(fea))
|
|
fea = self.lrelu(self.conv4_1(fea))
|
|
fea = self.lrelu(self.conv4_2(fea))
|
|
fea = self.sigmoid(self.conv4_3(fea))
|
|
return fea
|