forked from mrq/DL-Art-School
90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
import torch
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import torch.nn as nn
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import torchvision
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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):
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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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self.linear1 = nn.Linear(int(nf * 8 * 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 = 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 = 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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fea = fea.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 VGGFeatureExtractor(nn.Module):
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def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True,
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device=torch.device('cpu')):
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super(VGGFeatureExtractor, self).__init__()
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self.use_input_norm = use_input_norm
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if use_bn:
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model = torchvision.models.vgg19_bn(pretrained=True)
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else:
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model = torchvision.models.vgg19(pretrained=True)
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if self.use_input_norm:
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mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
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# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
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std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
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# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
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self.register_buffer('mean', mean)
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self.register_buffer('std', std)
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self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
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# No need to BP to variable
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for k, v in self.features.named_parameters():
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v.requires_grad = False
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def forward(self, x):
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# Assume input range is [0, 1]
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if self.use_input_norm:
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x = (x - self.mean) / self.std
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output = self.features(x)
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return output
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