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