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Running
on
A10G
File size: 1,220 Bytes
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CAResBlock(nn.Module):
def __init__(self, in_dim: int, out_dim: int, residual: bool = True):
super().__init__()
self.residual = residual
self.conv1 = nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1)
t = int((abs(math.log2(out_dim)) + 1) // 2)
k = t if t % 2 else t + 1
self.pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k, padding=(k - 1) // 2, bias=False)
if self.residual:
if in_dim == out_dim:
self.downsample = nn.Identity()
else:
self.downsample = nn.Conv2d(in_dim, out_dim, kernel_size=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
r = x
x = self.conv1(F.relu(x))
x = self.conv2(F.relu(x))
b, c = x.shape[:2]
w = self.pool(x).view(b, 1, c)
w = self.conv(w).transpose(-1, -2).unsqueeze(-1).sigmoid() # B*C*1*1
if self.residual:
x = x * w + self.downsample(r)
else:
x = x * w
return x
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