def __init__(self, inputs, outputs, latent_size, has_last_conv=True, fused_scale=True): #分辨率大于128用fused_scale,即conv完成上采样
super().__init__()
self.has_last_conv = has_last_conv
self.noise_weight_1 = nn.Parameter(torch.Tensor(1, inputs, 1, 1))
self.noise_weight_1.data.zero_()
self.bias_1 = nn.Parameter(torch.Tensor(1, inputs, 1, 1))
self.instance_norm_1 = nn.InstanceNorm2d(inputs, affine=False, eps=1e-8)
self.inver_mod1 = ln.Linear(2 * inputs, latent_size, gain=1) # [n, 2c] -> [n,512]
self.conv_1 = ln.Conv2d(inputs, inputs, 3, 1, 1, bias=False)
self.noise_weight_2 = nn.Parameter(torch.Tensor(1, inputs, 1, 1))
self.noise_weight_2.data.zero_()
self.bias_2 = nn.Parameter(torch.Tensor(1, inputs, 1, 1))
self.instance_norm_2 = nn.InstanceNorm2d(inputs, affine=False, eps=1e-8)
self.inver_mod2 = ln.Linear(2 * inputs, latent_size, gain=1)
self.blur = Blur(inputs)
if has_last_conv:
if fused_scale:
self.conv_2 = ln.Conv2d(inputs, outputs, 3, 2, 1, bias=False, transform_kernel=True)
else:
self.conv_2 = ln.Conv2d(inputs, outputs, 3, 1, 1, bias=False)
self.fused_scale = fused_scale
self.inputs = inputs
self.outputs = outputs
if self.inputs != self.outputs:
self.conv_3 = ln.Conv2d(inputs, outputs, 1, 1, 0)
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, inputs, outputs, last=False, fused_scale=True):
super(DiscriminatorBlock, self).__init__()
self.conv_1 = ln.Conv2d(inputs + (1 if last else 0),
inputs,
3,
1,
1,
bias=False)
self.bias_1 = nn.Parameter(torch.Tensor(1, inputs, 1, 1))
self.blur = Blur(inputs)
self.last = last
self.fused_scale = fused_scale
if last:
self.dense = ln.Linear(inputs * 4 * 4, outputs)
else:
if fused_scale:
self.conv_2 = ln.Conv2d(inputs,
outputs,
3,
2,
1,
bias=False,
transform_kernel=True)
else:
self.conv_2 = ln.Conv2d(inputs, outputs, 3, 1, 1, bias=False)
self.bias_2 = nn.Parameter(torch.Tensor(1, outputs, 1, 1))
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, startf=32, maxf=256, layer_count=3, channels=3):
super(Discriminator, self).__init__()
self.maxf = maxf
self.startf = startf
self.layer_count = layer_count
self.from_rgb = nn.ModuleList()
self.channels = channels
mul = 2
inputs = startf
self.encode_block: nn.ModuleList[DiscriminatorBlock] = nn.ModuleList()
resolution = 2**(self.layer_count + 1)
for i in range(self.layer_count):
outputs = min(self.maxf, startf * mul)
self.from_rgb.append(FromRGB(channels, inputs))
fused_scale = resolution >= 128
block = DiscriminatorBlock(inputs,
outputs,
i == self.layer_count - 1,
fused_scale=fused_scale)
resolution //= 2
#print("encode_block%d %s" % ((i + 1), millify(count_parameters(block))))
self.encode_block.append(block)
inputs = outputs
mul *= 2
self.fc2 = ln.Linear(inputs, 1, gain=1)
def __init__(self,
inputs,
outputs,
latent_size,
has_first_conv=True,
fused_scale=True):
super(DecodeBlock, self).__init__()
self.has_first_conv = has_first_conv
self.inputs = inputs
self.has_first_conv = has_first_conv
self.fused_scale = fused_scale
if has_first_conv:
if fused_scale:
self.conv_1 = ln.ConvTranspose2d(inputs,
outputs,
3,
2,
1,
bias=False,
transform_kernel=True)
else:
self.conv_1 = ln.Conv2d(inputs, outputs, 3, 1, 1, bias=False)
self.blur = Blur(outputs)
self.noise_weight_1 = nn.Parameter(torch.Tensor(1, outputs, 1, 1))
self.noise_weight_1.data.zero_()
self.bias_1 = nn.Parameter(torch.Tensor(1, outputs, 1, 1))
self.instance_norm_1 = nn.InstanceNorm2d(outputs,
affine=False,
eps=1e-8)
self.style_1 = ln.Linear(latent_size, 2 * outputs, gain=1)
self.conv_2 = ln.Conv2d(outputs, outputs, 3, 1, 1, bias=False)
self.noise_weight_2 = nn.Parameter(torch.Tensor(1, outputs, 1, 1))
self.noise_weight_2.data.zero_()
self.bias_2 = nn.Parameter(torch.Tensor(1, outputs, 1, 1))
self.instance_norm_2 = nn.InstanceNorm2d(outputs,
affine=False,
eps=1e-8)
self.style_2 = ln.Linear(latent_size, 2 * outputs, gain=1)
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, inputs, output, lrmul=0.01):
super(MappingBlock, self).__init__()
self.fc = ln.Linear(inputs, output, lrmul=lrmul)
