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# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import List, Sequence, Tuple
import torch
from detectron2.structures import ImageList
class TestImageList(unittest.TestCase):
def test_imagelist_padding_tracing(self):
# test that the trace does not contain hard-coded constant sizes
def to_imagelist(tensors: Sequence[torch.Tensor]):
image_list = ImageList.from_tensors(tensors, 4)
return image_list.tensor, image_list.image_sizes
def _tensor(*shape):
return torch.ones(shape, dtype=torch.float32)
# test CHW (inputs needs padding vs. no padding)
for shape in [(3, 10, 10), (3, 12, 12)]:
func = torch.jit.trace(to_imagelist, ([_tensor(*shape)],))
tensor, image_sizes = func([_tensor(3, 15, 20)])
self.assertEqual(tensor.shape, (1, 3, 16, 20), tensor.shape)
self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0])
# test HW
func = torch.jit.trace(to_imagelist, ([_tensor(10, 10)],))
tensor, image_sizes = func([_tensor(15, 20)])
self.assertEqual(tensor.shape, (1, 16, 20), tensor.shape)
self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0])
# test 2x CHW
func = torch.jit.trace(
to_imagelist,
([_tensor(3, 16, 10), _tensor(3, 13, 11)],),
)
tensor, image_sizes = func([_tensor(3, 25, 20), _tensor(3, 10, 10)])
self.assertEqual(tensor.shape, (2, 3, 28, 20), tensor.shape)
self.assertEqual(image_sizes[0].tolist(), [25, 20], image_sizes[0])
self.assertEqual(image_sizes[1].tolist(), [10, 10], image_sizes[1])
# support calling with different spatial sizes, but not with different #images
def test_imagelist_scriptability(self):
image_nums = 2
image_tensor = torch.randn((image_nums, 10, 20), dtype=torch.float32)
image_shape = [(10, 20)] * image_nums
def f(image_tensor, image_shape: List[Tuple[int, int]]):
return ImageList(image_tensor, image_shape)
ret = f(image_tensor, image_shape)
ret_script = torch.jit.script(f)(image_tensor, image_shape)
self.assertEqual(len(ret), len(ret_script))
for i in range(image_nums):
self.assertTrue(torch.equal(ret[i], ret_script[i]))
def test_imagelist_from_tensors_scriptability(self):
image_tensor_0 = torch.randn(10, 20, dtype=torch.float32)
image_tensor_1 = torch.randn(12, 22, dtype=torch.float32)
inputs = [image_tensor_0, image_tensor_1]
def f(image_tensor: List[torch.Tensor]):
return ImageList.from_tensors(image_tensor, 10)
ret = f(inputs)
ret_script = torch.jit.script(f)(inputs)
self.assertEqual(len(ret), len(ret_script))
self.assertTrue(torch.equal(ret.tensor, ret_script.tensor))
if __name__ == "__main__":
unittest.main()
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