Created
April 19, 2019 07:49
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SPADE model from the paper 1903.07291, my implementation.
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class SPADE(Module): | |
def __init__(self, args, k): | |
super().__init__() | |
num_filters = args.spade_filter | |
kernel_size = args.spade_kernel | |
self.conv = spectral_norm(Conv2d(1, num_filters, kernel_size=(kernel_size, kernel_size), padding=1)) | |
self.conv_gamma = spectral_norm(Conv2d(num_filters, k, kernel_size=(kernel_size, kernel_size), padding=1)) | |
self.conv_beta = spectral_norm(Conv2d(num_filters, k, kernel_size=(kernel_size, kernel_size), padding=1)) | |
def forward(self, x, seg): | |
N, C, H, W = x.size() | |
sum_channel = torch.sum(x.reshape(N, C, H*W), dim=-1) | |
mean = sum_channel / (N*H*W) | |
std = torch.sqrt((sum_channel**2 - mean**2) / (N*H*W)) | |
mean = torch.unsqueeze(torch.unsqueeze(mean, -1), -1) | |
std = torch.unsqueeze(torch.unsqueeze(std, -1), -1) | |
x = (x - mean) / std | |
seg = F.interpolate(seg, size=(H,W), mode='nearest') | |
seg = relu(self.conv(seg)) | |
seg_gamma = self.conv_gamma(seg) | |
seg_beta = self.conv_beta(seg) | |
x = torch.matmul(seg_gamma, x) + seg_beta | |
return x |
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