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Add a checkpointable discriminator

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This commit is contained in:
James Betker 2020-10-18 09:57:47 -06:00
parent 9ead2c0a08
commit 6a0d5f4813
3 changed files with 63 additions and 6 deletions
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63
codes/models/archs/discriminator_vgg_arch.py
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@ -77,7 +77,6 @@ class Discriminator_VGG_128(nn.Module):
out = self.linear2(fea)
return out
class Discriminator_VGG_128_GN(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):
@ -108,12 +107,12 @@ class Discriminator_VGG_128_GN(nn.Module):
self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True)
final_nf = nf * 8
self.linear1 = nn.Linear(int(final_nf * 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)
self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
self.linear2 = nn.Linear(100, 1)
def forward(self, x):
fea = self.lrelu(self.conv0_0(x))
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
@ -138,6 +137,62 @@ class Discriminator_VGG_128_GN(nn.Module):
return out
from utils.util import checkpoint
class Discriminator_VGG_128_GN_Checkpointed(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_GN_Checkpointed, 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.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)
# [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)
final_nf = nf * 8
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.body = nn.Sequential(self.conv0_0, self.lrelu,
self.conv0_1, self.bn0_1, self.lrelu,
self.conv1_0, self.bn1_0, self.lrelu,
self.conv1_1, self.bn1_1, self.lrelu,
self.conv2_0, self.bn2_0, self.lrelu,
self.conv2_1, self.bn2_1, self.lrelu,
self.conv3_0, self.bn3_0, self.lrelu,
self.conv3_1, self.bn3_1, self.lrelu,
self.conv4_0, self.bn4_0, self.lrelu,
self.conv4_1, self.bn4_1, self.lrelu)
self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100)
self.linear2 = nn.Linear(100, 1)
def forward(self, x):
fea = checkpoint(self.body, x)
fea = fea.contiguous().view(fea.size(0), -1)
fea = self.lrelu(self.linear1(fea))
out = self.linear2(fea)
return out
class CrossCompareBlock(nn.Module):
def __init__(self, nf_in, nf_out):
super(CrossCompareBlock, self).__init__()

2
codes/models/networks.py
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@ -171,6 +171,8 @@ def define_D_net(opt_net, img_sz=None, wrap=False):
netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
if wrap:
netD = GradDiscWrapper(netD)
elif which_model == 'discriminator_vgg_128_gn_checkpointed':
netD = SRGAN_arch.Discriminator_VGG_128_GN_Checkpointed(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
elif which_model == 'discriminator_resnet':
netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz)
elif which_model == 'discriminator_resnet_50':

2
codes/models/steps/progressive_zoom.py
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@ -95,7 +95,7 @@ class ProgressiveGeneratorInjector(Injector):
chain_output.append(recurrent_hq)
results_hq.append(hq_inputs[:, link.index])
if self.env['step'] % 1 == 0:
if self.env['step'] % 50 == 0:
self.produce_progressive_visual_debugs(chain_input, chain_output, debug_index)
debug_index += 1
results[self.hq_output_key] = results_hq