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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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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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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) /
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psr_imgs3d.std)
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