def __init__(self, inputs, outputs, latent_size, last=False, fused_scale=True):
super(EncodeBlock, self).__init__()
self.conv_1 = ln.Conv2d(inputs, inputs, 3, 1, 1, bias=False)
# 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.instance_norm_1 = nn.InstanceNorm2d(inputs, affine=False)
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))
self.instance_norm_2 = nn.InstanceNorm2d(outputs, affine=False)
self.style_1 = ln.Linear(2 * inputs, latent_size)
if last:
self.style_2 = ln.Linear(outputs, latent_size)
else:
self.style_2 = ln.Linear(2 * outputs, latent_size)
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, inputs, outputs, latent_size, has_first_conv=True, fused_scale=True, layer=0):
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)
self.layer = layer
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, inputs, outputs, has_first_conv=True, layer=0):
super(DecodeBlock, self).__init__()
self.has_first_conv = has_first_conv
self.inputs = inputs
self.has_first_conv = has_first_conv
if has_first_conv:
self.conv_1 = ln.Conv2d(inputs, outputs, 3, 1, 1, bias=False)
else:
self.dense = ln.Linear(inputs, outputs * 4 * 4)
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.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.layer = layer
with torch.no_grad():
self.bias_1.zero_()
self.bias_2.zero_()
def __init__(self, inputs, outputs, last=False):
super(EncodeBlock, self).__init__()
self.conv_1 = ln.Conv2d(inputs, inputs, 3, 1, 1, bias=False)
# 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
if last:
self.dense = ln.Linear(inputs * 4 * 4, outputs)
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, inputs, outputs, last=False, fused_scale=True, dense=False):
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.dense_ = dense
self.fused_scale = fused_scale
if self.dense_:
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, inputs, channels):
super(ToRGB, self).__init__()
self.inputs = inputs
self.channels = channels
self.to_rgb = ln.Conv2d(inputs, channels, 1, 1, 0, gain=0.03)
def __init__(self, channels, outputs):
super(FromRGB, self).__init__()
self.from_rgb = ln.Conv2d(channels, outputs, 1, 1, 0)
