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Fix BigGAN-Deep generator; Support 512x512 generator #72

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Fix BigGAN-Deep generator; Support 512x512 generator #72

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40 changes: 25 additions & 15 deletions BigGANdeep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
# Channel ratio is the ratio of
class GBlock(nn.Module):
def __init__(self, in_channels, out_channels,
which_conv=nn.Conv2d, which_bn=layers.bn, activation=None,
which_conv=layers.SNConv2d, which_bn=layers.bn, activation=None,
upsample=None, channel_ratio=4):
super(GBlock, self).__init__()

Expand Down Expand Up @@ -50,36 +50,43 @@ def forward(self, x, y):
h = self.conv1(self.activation(self.bn1(x, y)))
# Apply next BN-ReLU
h = self.activation(self.bn2(h, y))
# Drop channels in x if necessary
if self.in_channels != self.out_channels:
x = x[:, :self.out_channels]
# Upsample both h and x at this point
# Upsample h
if self.upsample:
h = self.upsample(h)
x = self.upsample(x)
# 3x3 convs
h = self.conv2(h)
h = self.conv3(self.activation(self.bn3(h, y)))
# Final 1x1 conv
h = self.conv4(self.activation(self.bn4(h, y)))
# Drop channels in x if necessary
if self.in_channels != self.out_channels:
x = x[:, :self.out_channels]
# Upsample x
if self.upsample:
x = self.upsample(x)
return h + x

def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'):
arch = {}
arch[512] = {'in_channels' : [ch * item for item in [16, 16, 8, 8, 4, 2, 1]],
'out_channels' : [ch * item for item in [16, 8, 8, 4, 2, 1, 1]],
'upsample' : [True] * 7,
'resolution' : [8, 16, 32, 64, 128, 256, 512],
'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3,10)}}
arch[256] = {'in_channels' : [ch * item for item in [16, 16, 8, 8, 4, 2]],
'out_channels' : [ch * item for item in [16, 8, 8, 4, 2, 1]],
'upsample' : [True] * 6,
'resolution' : [8, 16, 32, 64, 128, 256],
'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
for i in range(3,9)}}
arch[128] = {'in_channels' : [ch * item for item in [16, 16, 8, 4, 2]],
'out_channels' : [ch * item for item in [16, 8, 4, 2, 1]],
'out_channels' : [ch * item for item in [16, 8, 4, 2, 1]],
'upsample' : [True] * 5,
'resolution' : [8, 16, 32, 64, 128],
'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
for i in range(3,8)}}
arch[64] = {'in_channels' : [ch * item for item in [16, 16, 8, 4]],
'out_channels' : [ch * item for item in [16, 8, 4, 2]],
'out_channels' : [ch * item for item in [16, 8, 4, 2]],
'upsample' : [True] * 4,
'resolution' : [8, 16, 32, 64],
'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
Expand All @@ -94,10 +101,10 @@ def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'):
return arch

class Generator(nn.Module):
def __init__(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, resolution=128,
def __init__(self, G_ch=128, G_depth=2, dim_z=128, bottom_width=4, resolution=512,
G_kernel_size=3, G_attn='64', n_classes=1000,
num_G_SVs=1, num_G_SV_itrs=1,
G_shared=True, shared_dim=0, hier=False,
G_shared=True, shared_dim=0, hier=True,
cross_replica=False, mybn=False,
G_activation=nn.ReLU(inplace=False),
G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8,
Expand Down Expand Up @@ -213,7 +220,7 @@ def __init__(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, resolution=128
cross_replica=self.cross_replica,
mybn=self.mybn),
self.activation,
self.which_conv(self.arch['out_channels'][-1], 3))
self.which_conv(self.arch['out_channels'][-1], 128))

# Initialize weights. Optionally skip init for testing.
if not skip_init:
Expand Down Expand Up @@ -265,7 +272,7 @@ def init_weights(self):
def forward(self, z, y):
# If hierarchical, concatenate zs and ys
if self.hier:
z = torch.cat([y, z], 1)
z = torch.cat([z, y], 1)
y = z
# First linear layer
h = self.linear(z)
Expand All @@ -276,9 +283,12 @@ def forward(self, z, y):
# Second inner loop in case block has multiple layers
for block in blocklist:
h = block(h, y)

# Apply batchnorm-relu-conv-tanh at output
return torch.tanh(self.output_layer(h))
# Apply batchnorm-relu-conv
h = self.output_layer(h)
# Take the rgb channels
h = h[:, :3, :, :]
# Apply final tanh at output
return torch.tanh(h)

class DBlock(nn.Module):
def __init__(self, in_channels, out_channels, which_conv=layers.SNConv2d, wide=True,
Expand Down