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44 lines
1.3 KiB
44 lines
1.3 KiB
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 bilinear_sampler(img, coords, mode='bilinear', mask=False):
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""" Wrapper for grid_sample, uses pixel coordinates """
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H, W = img.shape[-2:]
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xgrid, ygrid = coords.split([1,1], dim=-1)
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xgrid = 2*xgrid/(W-1) - 1
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ygrid = 2*ygrid/(H-1) - 1
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grid = torch.cat([xgrid, ygrid], dim=-1)
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img = F.grid_sample(img, grid, align_corners=True)
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if mask:
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mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
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return img, mask.float()
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return img
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def test_bilinear_sampler():
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# Getting back the megengine objects:
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with open('test_data/bilinear_sampler_test.pickle', 'rb') as f:
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right_feature_prev, coords, right_feature = pickle.load(f)
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right_feature_prev = torch.tensor(right_feature_prev.numpy())
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coords = torch.tensor(coords.numpy())
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right_feature = right_feature.numpy()
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# Test Pytorch
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right_feature_pytorch = bilinear_sampler(right_feature_prev, coords).numpy()
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error = np.mean(right_feature_pytorch-right_feature)
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print(f"test_coords_grid - Avg. Error: {error}, \n \
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Original shape: {coords.numpy().shape},\n \
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Obtained shape: {right_feature_pytorch.shape}, Expected shape: {right_feature.shape}")
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if __name__ == '__main__':
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test_bilinear_sampler() |