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Update Time_TravelRephotography/model.py
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Time_TravelRephotography/model.py
CHANGED
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@@ -9,7 +9,8 @@ from torch import nn
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from torch.nn import functional as F
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from torch.autograd import Function
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from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
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class PixelNorm(nn.Module):
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@@ -152,7 +153,7 @@ class EqualLinear(nn.Module):
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def forward(self, input):
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if self.activation:
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out = F.linear(input, self.weight * self.scale)
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out =
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else:
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out = F.linear(
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@@ -331,7 +332,7 @@ class StyledConv(nn.Module):
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self.noise = NoiseInjection()
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# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
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# self.activate = ScaledLeakyReLU(0.2)
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self.activate =
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def forward(self, input, style, noise=None):
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out = self.conv(input, style)
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@@ -606,7 +607,7 @@ class ConvLayer(nn.Sequential):
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if activate:
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if bias:
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layers.append(
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else:
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layers.append(ScaledLeakyReLU(0.2))
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from torch.nn import functional as F
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from torch.autograd import Function
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#from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
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from torch_utils.ops import bias_act, upfirdn2d
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class PixelNorm(nn.Module):
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def forward(self, input):
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if self.activation:
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out = F.linear(input, self.weight * self.scale)
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out = bias_act(out)
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else:
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out = F.linear(
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self.noise = NoiseInjection()
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# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
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# self.activate = ScaledLeakyReLU(0.2)
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self.activate = bias_act(out_channel)
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def forward(self, input, style, noise=None):
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out = self.conv(input, style)
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if activate:
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if bias:
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layers.append(bias_act(out_channel))
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else:
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layers.append(ScaledLeakyReLU(0.2))
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