2019-11-15 17:15:07 +11:00
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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2019-09-28 18:24:47 +10:00
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import torch
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import torch.nn as nn
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from SoftSelect import ChannelWiseInter
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if __name__ == '__main__':
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tensors = torch.rand((16, 128, 7, 7))
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for oc in range(200, 210):
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out_v1 = ChannelWiseInter(tensors, oc, 'v1')
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out_v2 = ChannelWiseInter(tensors, oc, 'v2')
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assert (out_v1 == out_v2).any().item() == 1
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for oc in range(48, 160):
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out_v1 = ChannelWiseInter(tensors, oc, 'v1')
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out_v2 = ChannelWiseInter(tensors, oc, 'v2')
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assert (out_v1 == out_v2).any().item() == 1
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