57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
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# holder of all proprietary rights on this computer program.
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# You can only use this computer program if you have closed
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# a license agreement with MPG or you get the right to use the computer
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# program from someone who is authorized to grant you that right.
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# Any use of the computer program without a valid license is prohibited and
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# liable to prosecution.
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#
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# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
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# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
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# for Intelligent Systems. All rights reserved.
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#
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# Contact: ps-license@tuebingen.mpg.de
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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 NonLocalAttention(nn.Module):
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def __init__(
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self,
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in_channels=256,
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out_channels=256,
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):
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super(NonLocalAttention, self).__init__()
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self.conv1x1 = nn.Conv1d(in_channels, out_channels, kernel_size=1)
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def forward(self, input):
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'''
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input [N, Feats, J, 1]
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output [N, Feats, J, 1]
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'''
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batch_size, n_feats, n_joints, _ = input.shape
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input = input.squeeze(-1)
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# Compute attention weights
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attention = torch.matmul(input.transpose(2, 1), input)
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norm_attention = F.softmax(attention, dim=-1)
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# Compute final dot product
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out = torch.matmul(input, norm_attention)
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out = self.conv1x1(out)
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out = out.unsqueeze(-1) # [N, F, J, 1]
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return out
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if __name__ == '__main__':
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nla = NonLocalAttention()
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inp = torch.rand(32, 256, 24, 1)
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out = nla(inp)
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print(out.shape) |