def test_crop_cuda():
inp = torch.cuda.FloatTensor([[
[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 0, 1, 1],
[0, 0, 1, 1],
]])
expected = torch.cuda.FloatTensor([[
[2, 2, 2, 2, 2],
[1, 0, 0, 2, 2],
]])
actual = crop(inp, -1, 1, 2, 5, padding_mode='constant', fill=2)
assert_allclose(actual, expected)
def test_crop():
inp = torch.FloatTensor([[
[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 0, 1, 1],
[0, 0, 1, 1],
]])
expected = torch.FloatTensor([[
[1, 0],
[0, 1],
[0, 1],
]])
actual = crop(inp, 1, 1, 3, 2)
assert_allclose(actual, expected)
def test_crop_random(data, t, l, w, h, device):
inp = torch.from_numpy(data).to(device)
actual = crop(inp, t, l, h, w)
assert actual.shape == (data.shape[0], h, w)