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31 lines
951 B
31 lines
951 B
3 years ago
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import pickle
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import numpy as np
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import megengine as mge
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import torch
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import torch.nn.functional as F
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def test_offset():
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# Getting back the megengine objects:
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with open('test_data/offset_test.pkl', 'rb') as f:
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x_grid, y_grid, reshape_shape, transpose_order, expand_size, repeat_size, repeat_axis, offsets = pickle.load(f)
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x_grid = torch.tensor(x_grid.numpy())
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y_grid = torch.tensor(y_grid.numpy())
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offsets_mge = offsets.numpy()
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N = repeat_size
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# Test Pytorch
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offsets = torch.stack((x_grid, y_grid))
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offsets = offsets.reshape(2, -1).permute(1, 0)
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for d in sorted((0, 2, 3)):
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offsets = offsets.unsqueeze(d)
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offsets = offsets.repeat_interleave(N, dim=0)
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error = np.mean(offsets.numpy()-offsets_mge)
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print(f"test_offset - Avg. Error: {error}, \n \
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Obtained shape: {offsets.numpy().shape}, Expected shape: {offsets_mge.shape}")
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if __name__ == '__main__':
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test_offset()
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