DL-Art-School/codes/models/archs/discriminator_vgg_arch.py

65 lines
2.6 KiB
Python
Raw Normal View History

2019-08-23 13:42:47 +00:00
import torch
import torch.nn as nn
import torchvision
class Discriminator_VGG_128(nn.Module):
2020-04-21 22:32:59 +00:00
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
def __init__(self, in_nc, nf, input_img_factor=1):
2019-08-23 13:42:47 +00:00
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]
2020-05-12 16:08:12 +00:00
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
2019-08-23 13:42:47 +00:00
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]
2020-05-12 16:08:12 +00:00
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
2019-08-23 13:42:47 +00:00
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)
2019-08-23 13:42:47 +00:00
self.linear2 = nn.Linear(100, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
2020-05-27 19:31:22 +00:00
x = x[0]
2019-08-23 13:42:47 +00:00
fea = self.lrelu(self.conv0_0(x))
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
2020-05-12 16:08:12 +00:00
#fea = torch.cat([fea, skip_med], dim=1)
2019-08-23 13:42:47 +00:00
fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
2020-05-12 16:08:12 +00:00
#fea = torch.cat([fea, skip_lo], dim=1)
2019-08-23 13:42:47 +00:00
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.contiguous().view(fea.size(0), -1)
2019-08-23 13:42:47 +00:00
fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea)
return out