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import math |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class CAResBlock(nn.Module): |
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def __init__(self, in_dim: int, out_dim: int, residual: bool = True): |
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super().__init__() |
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self.residual = residual |
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self.conv1 = nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1) |
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self.conv2 = nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1) |
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t = int((abs(math.log2(out_dim)) + 1) // 2) |
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k = t if t % 2 else t + 1 |
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self.pool = nn.AdaptiveAvgPool2d(1) |
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self.conv = nn.Conv1d(1, 1, kernel_size=k, padding=(k - 1) // 2, bias=False) |
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if self.residual: |
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if in_dim == out_dim: |
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self.downsample = nn.Identity() |
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else: |
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self.downsample = nn.Conv2d(in_dim, out_dim, kernel_size=1) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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r = x |
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x = self.conv1(F.relu(x)) |
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x = self.conv2(F.relu(x)) |
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b, c = x.shape[:2] |
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w = self.pool(x).view(b, 1, c) |
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w = self.conv(w).transpose(-1, -2).unsqueeze(-1).sigmoid() |
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if self.residual: |
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x = x * w + self.downsample(r) |
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else: |
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x = x * w |
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return x |
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