Download testbed/open-mmlab__mmengine/tests/test_structures/test_pixel_data.py from hc99/FEA-Bench: direct link, hf CLI and curl.
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https://huggingface.co/datasets/hc99/FEA-Bench/resolve/main/testbed/open-mmlab__mmengine/tests/test_structures/test_pixel_data.py
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hf download hf://datasets/hc99/FEA-Bench/testbed/open-mmlab__mmengine/tests/test_structures/test_pixel_data.py
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curl -L -o test_pixel_data.py https://huggingface.co/datasets/hc99/FEA-Bench/resolve/main/testbed/open-mmlab__mmengine/tests/test_structures/test_pixel_data.py
2.75 kB
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import random | |
| from unittest import TestCase | |
| import numpy as np | |
| import pytest | |
| import torch | |
| from mmengine.structures import PixelData | |
| class TestPixelData(TestCase): | |
| def setup_data(self): | |
| metainfo = dict( | |
| img_id=random.randint(0, 100), | |
| img_shape=(random.randint(400, 600), random.randint(400, 600))) | |
| image = np.random.randint(0, 255, (4, 20, 40)) | |
| featmap = torch.randint(0, 255, (10, 20, 40)) | |
| pixel_data = PixelData(metainfo=metainfo, image=image, featmap=featmap) | |
| return pixel_data | |
| def test_set_data(self): | |
| pixel_data = self.setup_data() | |
| # test set '_metainfo_fields' or '_data_fields' | |
| with self.assertRaises(AttributeError): | |
| pixel_data._metainfo_fields = 1 | |
| with self.assertRaises(AttributeError): | |
| pixel_data._data_fields = 1 | |
| # value only supports (torch.Tensor, np.ndarray) | |
| with self.assertRaises(AssertionError): | |
| pixel_data.v = 'value' | |
| # The width and height must be the same | |
| with self.assertRaises(AssertionError): | |
| pixel_data.map2 = torch.randint(0, 255, (3, 21, 41)) | |
| # The dimension must be 3 or 2 | |
| with self.assertRaises(AssertionError): | |
| pixel_data.map2 = torch.randint(0, 255, (1, 3, 20, 40)) | |
| pixel_data.map2 = torch.randint(0, 255, (3, 20, 40)) | |
| assert 'map2' in pixel_data | |
| pixel_data.map3 = torch.randint(0, 255, (20, 40)) | |
| assert tuple(pixel_data.map3.shape) == (1, 20, 40) | |
| def test_getitem(self): | |
| pixel_data = PixelData() | |
| pixel_data = self.setup_data() | |
| slice_pixel_data = pixel_data[10:15, 20:30] | |
| assert slice_pixel_data.shape == (5, 10) | |
| pixel_data = self.setup_data() | |
| slice_pixel_data = pixel_data[10, 20:30] | |
| assert slice_pixel_data.shape == (1, 10) | |
| # must be tuple | |
| item = torch.Tensor([1, 2, 3, 4]) | |
| with pytest.raises( | |
| TypeError, | |
| match=f'Unsupported type {type(item)} for slicing PixelData'): | |
| pixel_data[item] | |
| item = 1 | |
| with pytest.raises( | |
| TypeError, | |
| match=f'Unsupported type {type(item)} for slicing PixelData'): | |
| pixel_data[item] | |
| item = (5.5, 5) | |
| with pytest.raises( | |
| TypeError, | |
| match=('The type of element in input must be int or slice, ' | |
| f'but got {type(item[0])}')): | |
| pixel_data[item] | |
| def test_shape(self): | |
| pixel_data = self.setup_data() | |
| assert pixel_data.shape == (20, 40) | |
| pixel_data = PixelData() | |
| assert pixel_data.shape is None | |