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Add capability to place additional conv into discriminator

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This should allow us to support larger images sizes. May need
to add another one of these.
This commit is contained in:
James Betker 2020-06-23 09:40:33 -06:00
parent bad33de906
commit dfcbe5f2db
2 changed files with 17 additions and 3 deletions
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18
codes/models/archs/discriminator_vgg_arch.py
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@ -5,7 +5,7 @@ import torchvision
class Discriminator_VGG_128(nn.Module): class Discriminator_VGG_128(nn.Module):
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported. # input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
def __init__(self, in_nc, nf, input_img_factor=1): def __init__(self, in_nc, nf, input_img_factor=1, extra_conv=False):
super(Discriminator_VGG_128, self).__init__() super(Discriminator_VGG_128, self).__init__()
# [64, 128, 128] # [64, 128, 128]
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
@ -31,8 +31,18 @@ class Discriminator_VGG_128(nn.Module):
self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True) self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True) self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
final_nf = nf * 8
self.linear1 = nn.Linear(int(nf * 8 * 4 * input_img_factor * 4 * input_img_factor), 100) self.extra_conv = extra_conv
if self.extra_conv:
self.conv5_0 = nn.Conv2d(nf * 8, nf * 16, 3, 1, 1, bias=False)
self.bn5_0 = nn.BatchNorm2d(nf * 16, affine=True)
self.conv5_1 = nn.Conv2d(nf * 16, nf * 16, 4, 2, 1, bias=False)
self.bn5_1 = nn.BatchNorm2d(nf * 16, affine=True)
input_img_factor = input_img_factor // 2
final_nf = nf * 16
self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100)
self.linear2 = nn.Linear(100, 1) self.linear2 = nn.Linear(100, 1)
# activation function # activation function
@ -57,6 +67,10 @@ class Discriminator_VGG_128(nn.Module):
fea = self.lrelu(self.bn4_0(self.conv4_0(fea))) fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
fea = self.lrelu(self.bn4_1(self.conv4_1(fea))) fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
if self.extra_conv:
fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
fea = fea.contiguous().view(fea.size(0), -1) fea = fea.contiguous().view(fea.size(0), -1)
fea = self.lrelu(self.linear1(fea)) fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea) out = self.linear2(fea)

2
codes/models/networks.py
View File

@ -95,7 +95,7 @@ def define_D(opt):
which_model = opt_net['which_model_D'] which_model = opt_net['which_model_D']
if which_model == 'discriminator_vgg_128': if which_model == 'discriminator_vgg_128':
netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128) netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz // 128, extra_conv=opt_net['extra_conv'])
elif which_model == 'discriminator_resnet': elif which_model == 'discriminator_resnet':
netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz) netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz)
elif which_model == 'discriminator_resnet_passthrough': elif which_model == 'discriminator_resnet_passthrough':