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@@ -520,12 +520,22 @@ class Generator(nn.Module): |
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device = self.input.input.device |
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noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] |
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noises.append(torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)) |
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for i in range(3, self.log_size + 1): |
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for _ in range(3): |
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for _ in range(2): |
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noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) |
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return noises |
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forward_noises=[] |
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target=[2,2+4,2+4+6,2+4+6+8,2+4+6+8+10] |
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j=0 |
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for i in range(1,(self.log_size-1)*(self.log_size-2)+1): |
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if i>target[j]: |
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j+=1 |
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# forward_noises.append(torch.randn(1, 1, 2 ** (j+3), 2 ** (j+3), device=device)) |
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# else: |
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# j+=1 |
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forward_noises.append(torch.randn(1, 1, 2 ** (j+3), 2 ** (j+3), device=device)) |
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# forward_noise=[None]* () |
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return noises,forward_noises |
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def mean_latent(self, n_latent): |
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latent_in = torch.randn( |
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@@ -547,6 +557,7 @@ class Generator(nn.Module): |
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truncation_latent=None, |
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input_is_latent=False, |
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noise=None, |
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forward_noise=None, |
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randomize_noise=True, |
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): |
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if not input_is_latent: |
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@@ -587,7 +598,8 @@ class Generator(nn.Module): |
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latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) |
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latent = torch.cat([latent, latent2], 1) |
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forward_noise=[None]* ((self.log_size-1)*(self.log_size-2)) |
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if forward_noise is None: |
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forward_noise=[None]* ((self.log_size-1)*(self.log_size-2)) |
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out = self.input(latent) |
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out = self.conv1(out, latent[:, 0], noise=noise[0]) |
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out = self.conv1_1(out, latent[:, 1], noise=noise[1]) |
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