mirror of https://github.com/open-mmlab/mmcv.git
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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class Loss(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, input, target):
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input = input.view(-1)
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target = target.view(-1)
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return torch.mean(input - target)
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class TestCrissCrossAttention(object):
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def test_cc_attention(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import CrissCrossAttention
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loss_func = Loss()
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input = np.fromfile(
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'tests/data/for_ccattention/ccattention_input.bin',
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dtype=np.float32)
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output = np.fromfile(
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'tests/data/for_ccattention/ccattention_output.bin',
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dtype=np.float32)
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input = input.reshape((1, 32, 45, 45))
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output = output.reshape((1, 32, 45, 45))
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label = torch.ones((1, 32, 45, 45))
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input = torch.FloatTensor(input)
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output = torch.FloatTensor(output)
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input.requires_grad = True
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shape = input.shape
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channel = shape[1]
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cca = CrissCrossAttention(channel)
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cca.cuda()
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input = input.cuda()
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label = label.cuda()
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cca.train()
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test_output = cca(input)
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test_loss = loss_func(test_output, label)
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test_loss.backward()
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test_output = test_output.detach().cpu().numpy()
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output = output.numpy()
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assert np.allclose(test_output, output)
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assert test_output.shape == shape
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