forked from mrq/DL-Art-School
65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torchvision
|
|
|
|
|
|
class Discriminator_VGG_128(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(Discriminator_VGG_128, self).__init__()
|
|
# [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.BatchNorm2d(nf, affine=True)
|
|
# [64, 64, 64]
|
|
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
|
self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
|
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
|
self.bn1_1 = nn.BatchNorm2d(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.BatchNorm2d(nf * 4, affine=True)
|
|
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
|
self.bn2_1 = nn.BatchNorm2d(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.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)
|
|
|
|
self.linear1 = nn.Linear(int(nf * 8 * 4 * input_img_factor * 4 * input_img_factor), 100)
|
|
self.linear2 = nn.Linear(100, 1)
|
|
|
|
# activation function
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
|
|
|
def forward(self, x):
|
|
x = x[0]
|
|
fea = self.lrelu(self.conv0_0(x))
|
|
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
|
|
|
|
#fea = torch.cat([fea, skip_med], dim=1)
|
|
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
|
|
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
|
|
|
|
#fea = torch.cat([fea, skip_lo], dim=1)
|
|
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)))
|
|
|
|
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
|
|
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
|
|
|
|
fea = fea.view(fea.size(0), -1)
|
|
fea = self.lrelu(self.linear1(fea))
|
|
out = self.linear2(fea)
|
|
return out
|
|
|