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Compact Bilinear Pooling in PyTorch using the new FFT support
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| import torch | |
| class CompactBilinearPooling(torch.nn.Module): | |
| def __init__(self, input_dim1, input_dim2, output_dim, sum_pool = True): | |
| super(CompactBilinearPooling, self).__init__() | |
| self.output_dim = output_dim | |
| self.sum_pool = sum_pool | |
| generate_sketch_matrix = lambda rand_h, rand_s, input_dim, output_dim: torch.sparse.FloatTensor(torch.stack([torch.arange(input_dim, out = torch.LongTensor()), rand_h.long()]), rand_s.float(), [input_dim, output_dim]).to_dense() | |
| self.sketch_matrix1 = torch.nn.Parameter(generate_sketch_matrix(torch.randint(output_dim, size = (input_dim1,)), 2 * torch.randint(2, size = (input_dim1,)) - 1, input_dim1, output_dim)) | |
| self.sketch_matrix2 = torch.nn.Parameter(generate_sketch_matrix(torch.randint(output_dim, size = (input_dim2,)), 2 * torch.randint(2, size = (input_dim2,)) - 1, input_dim2, output_dim)) | |
| def forward(self, x1, x2): | |
| fft1 = torch.rfft(x1.permute(0, 2, 3, 1).matmul(self.sketch_matrix1), 1) | |
| fft2 = torch.rfft(x2.permute(0, 2, 3, 1).matmul(self.sketch_matrix2), 1) | |
| fft_product = torch.stack([fft1[..., 0] * fft2[..., 0] - fft1[..., 1] * fft2[..., 1], fft1[..., 0] * fft2[..., 1] + fft1[..., 1] * fft2[..., 0]], dim = -1) | |
| cbp = torch.irfft(fft_product, 1, signal_sizes = (self.output_dim,)).view(len(x1), x1.size(-2), x1.size(-1), -1) * self.output_dim | |
| return cbp.sum(dim = 1).sum(dim = 1) if self.sum_pool else cbp.permute(0, 3, 1, 2) |
Author
Just install PyTorch from master branch or even 0.4 version probably has FFT
Thanks for your code first. I have a question that in the other implements, like Torch version and Tensorflow version, there is a zero_padding before feeding the tensor into the fft. But in this code, I don't see the zero_padding.
Thanks very much!
hello,
torch.stack([torch.arange(in_features), rand_h]) where in_features is not defined. How to fix it?
thanks!
Author
Thanks for noting this. Fixed! It should have been in_channels
Author
Some ways to improve the code: make use of the new PyTorch fft module, complex support. Figure out dense x sparse matmul (currently I'm materializing the sparse sketch)
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Thanks for your code, how to install the new fft support?