|
|
| import copy
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|
|
| import pytest
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| import torch
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| from numpy.testing import assert_array_equal
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|
|
| from mmaction.models import MultiModalDataPreprocessor
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| from mmaction.structures import ActionDataSample
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| from mmaction.utils import register_all_modules
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|
|
|
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| def generate_dummy_data(batch_size, input_keys, input_shapes):
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| data = dict()
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| data['data_samples'] = [
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| ActionDataSample().set_gt_label(2) for _ in range(batch_size)
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| ]
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| data['inputs'] = dict()
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| for key, shape in zip(input_keys, input_shapes):
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| data['inputs'][key] = [
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| torch.randint(0, 255, shape) for _ in range(batch_size)
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| ]
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|
|
| return data
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|
|
|
|
| def test_multimodal_data_preprocessor():
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| with pytest.raises(AssertionError):
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| MultiModalDataPreprocessor(
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| preprocessors=dict(imgs=dict(format_shape='NCTHW')))
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|
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| register_all_modules()
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| data_keys = ('imgs', 'heatmap_imgs')
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| data_shapes = ((1, 3, 8, 224, 224), (1, 17, 32, 64, 64))
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| raw_data = generate_dummy_data(2, data_keys, data_shapes)
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|
|
| psr = MultiModalDataPreprocessor(
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| preprocessors=dict(
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| imgs=dict(
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| type='ActionDataPreprocessor',
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| mean=[123.675, 116.28, 103.53],
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| std=[58.395, 57.12, 57.375],
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| format_shape='NCTHW'),
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| heatmap_imgs=dict(type='ActionDataPreprocessor')))
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|
|
| data = psr(copy.deepcopy(raw_data))
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| assert data['inputs']['imgs'].shape == (2, 1, 3, 8, 224, 224)
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| assert data['inputs']['heatmap_imgs'].shape == (2, 1, 17, 32, 64, 64)
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| psr_imgs = psr.preprocessors['imgs']
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| assert_array_equal(data['inputs']['imgs'][0],
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| (raw_data['inputs']['imgs'][0] - psr_imgs.mean) /
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| psr_imgs.std)
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| assert_array_equal(data['inputs']['imgs'][1],
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| (raw_data['inputs']['imgs'][1] - psr_imgs.mean) /
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| psr_imgs.std)
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| assert_array_equal(data['inputs']['heatmap_imgs'][0],
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| raw_data['inputs']['heatmap_imgs'][0])
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| assert_array_equal(data['inputs']['heatmap_imgs'][1],
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| raw_data['inputs']['heatmap_imgs'][1])
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|
|
| data_keys = ('imgs_2D', 'imgs_3D')
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| data_shapes = ((1, 3, 224, 224), (1, 3, 8, 224, 224))
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| raw_data = generate_dummy_data(2, data_keys, data_shapes)
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|
|
| psr = MultiModalDataPreprocessor(
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| preprocessors=dict(
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| imgs_2D=dict(
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| type='ActionDataPreprocessor',
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| mean=[123.675, 116.28, 103.53],
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| std=[58.395, 57.12, 57.375],
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| format_shape='NCHW'),
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| imgs_3D=dict(
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| type='ActionDataPreprocessor',
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| mean=[127.5, 127.5, 127.5],
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| std=[57.5, 57.5, 57.5],
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| format_shape='NCTHW')))
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|
|
| data = psr(copy.deepcopy(raw_data))
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| assert data['inputs']['imgs_2D'].shape == (2, 1, 3, 224, 224)
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| assert data['inputs']['imgs_3D'].shape == (2, 1, 3, 8, 224, 224)
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| psr_imgs2d = psr.preprocessors['imgs_2D']
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| psr_imgs3d = psr.preprocessors['imgs_3D']
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| assert_array_equal(data['inputs']['imgs_2D'][0],
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| (raw_data['inputs']['imgs_2D'][0] - psr_imgs2d.mean) /
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| psr_imgs2d.std)
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| assert_array_equal(data['inputs']['imgs_2D'][1],
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| (raw_data['inputs']['imgs_2D'][1] - psr_imgs2d.mean) /
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| psr_imgs2d.std)
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| assert_array_equal(data['inputs']['imgs_3D'][0],
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| (raw_data['inputs']['imgs_3D'][0] - psr_imgs3d.mean) /
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| psr_imgs3d.std)
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| assert_array_equal(data['inputs']['imgs_3D'][1],
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| (raw_data['inputs']['imgs_3D'][1] - psr_imgs3d.mean) /
|
| psr_imgs3d.std)
|
|
|
