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from torch.nn.utils import spectral_norm | |
import torch.nn.functional as F | |
import torch.nn as nn | |
import torch | |
class NonLocalBlock(nn.Module): | |
"""Non-local block.""" | |
def __init__(self, conv_dim): | |
super(NonLocalBlock, self).__init__() | |
self.conv1 = spectral_norm(nn.Conv2d(conv_dim, conv_dim//8, 1, 1, 0)) | |
self.conv2 = spectral_norm(nn.Conv2d(conv_dim, conv_dim//8, 1, 1, 0)) | |
self.conv3 = spectral_norm(nn.Conv2d(conv_dim, conv_dim//2, 1, 1, 0)) | |
self.conv4 = spectral_norm(nn.Conv2d(conv_dim//2, conv_dim, 1, 1, 0)) | |
self.downsample = nn.MaxPool2d(2, 2) | |
self.gamma = nn.Parameter(torch.zeros(1)) | |
def forward(self, x): | |
N, C, H, W = x.size() # x: if (?, 1024, 8, 8) | |
query = self.conv1(x) # (?, 128, 8, 8) | |
query = query.reshape(N, C//8, -1) # (?, 128, 64) | |
key = self.conv2(x) # (?, 128, 8, 8) | |
key = self.downsample(key) # (?, 128, 4, 4) | |
key = key.reshape(N, C//8, -1) # (?, 128, 16) | |
attn = torch.bmm(query.transpose(1, 2), key) # (?, 64, 16) | |
attn = F.softmax(attn, dim=2) # (?, 64, 16) | |
value = self.conv3(x) # (?, 512, 8, 8) | |
value = self.downsample(value) # (?, 512, 4, 4) | |
value = value.reshape(N, C//2, -1) # (?, 512, 16) | |
out = torch.bmm(value, attn.transpose(1, 2)) # (?, 512, 64) | |
out = out.reshape(N, C//2, H, W) # (?, 512, 8, 8) | |
return x + self.gamma * self.conv4(out) # (?, 1024, 8, 8) |
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