|
|
| from copy import deepcopy
|
|
|
| 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 ActionDataPreprocessor
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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_shape):
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| data = {
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| 'inputs':
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| [torch.randint(0, 255, input_shape) for _ in range(batch_size)],
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| 'data_samples':
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| [ActionDataSample().set_gt_label(2) for _ in range(batch_size)]
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| }
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| return data
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|
|
|
|
| def test_data_preprocessor():
|
| with pytest.raises(ValueError):
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| ActionDataPreprocessor(
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| mean=[1, 1], std=[0, 0], format_shape='NCTHW_Heatmap')
|
| with pytest.raises(ValueError):
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| psr = ActionDataPreprocessor(format_shape='NCTHW_Heatmap', to_rgb=True)
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| psr(generate_dummy_data(1, (3, 224, 224)))
|
|
|
| raw_data = generate_dummy_data(2, (1, 3, 8, 224, 224))
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| psr = 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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| data = psr(deepcopy(raw_data))
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| assert data['inputs'].shape == (2, 1, 3, 8, 224, 224)
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| assert_array_equal(data['inputs'][0],
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| (raw_data['inputs'][0] - psr.mean) / psr.std)
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| assert_array_equal(data['inputs'][1],
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| (raw_data['inputs'][1] - psr.mean) / psr.std)
|
|
|
| psr = ActionDataPreprocessor(format_shape='NCTHW', to_rgb=True)
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| data = psr(deepcopy(raw_data))
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| assert data['inputs'].shape == (2, 1, 3, 8, 224, 224)
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| assert_array_equal(data['inputs'][0], raw_data['inputs'][0][:, [2, 1, 0]])
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| assert_array_equal(data['inputs'][1], raw_data['inputs'][1][:, [2, 1, 0]])
|
|
|
| register_all_modules()
|
| psr = ActionDataPreprocessor(
|
| mean=[123.675, 116.28, 103.53],
|
| std=[58.395, 57.12, 57.375],
|
| format_shape='NCTHW',
|
| blending=dict(type='MixupBlending', num_classes=5))
|
| data = psr(deepcopy(raw_data), training=True)
|
| assert data['data_samples'][0].gt_label.shape == (5, )
|
| assert data['data_samples'][1].gt_label.shape == (5, )
|
|
|
| raw_data = generate_dummy_data(2, (1, 3, 224, 224))
|
| psr = ActionDataPreprocessor(
|
| mean=[123.675, 116.28, 103.53],
|
| std=[58.395, 57.12, 57.375],
|
| format_shape='NCHW',
|
| to_rgb=True)
|
| data = psr(deepcopy(raw_data))
|
| assert_array_equal(data['inputs'][0],
|
| (raw_data['inputs'][0][:, [2, 1, 0]] - psr.mean) /
|
| psr.std)
|
| assert_array_equal(data['inputs'][1],
|
| (raw_data['inputs'][1][:, [2, 1, 0]] - psr.mean) /
|
| psr.std)
|
|
|
| psr = ActionDataPreprocessor()
|
| data = psr(deepcopy(raw_data))
|
| assert data['inputs'].shape == (2, 1, 3, 224, 224)
|
| assert_array_equal(data['inputs'][0], raw_data['inputs'][0])
|
| assert_array_equal(data['inputs'][1], raw_data['inputs'][1])
|
|
|
| raw_2d_data = generate_dummy_data(2, (3, 224, 224))
|
| raw_3d_data = generate_dummy_data(2, (1, 3, 8, 224, 224))
|
| raw_data = (raw_2d_data, raw_3d_data)
|
|
|
| psr = ActionDataPreprocessor(
|
| mean=[123.675, 116.28, 103.53],
|
| std=[58.395, 57.12, 57.375],
|
| format_shape='MIX2d3d')
|
| data = psr(raw_data)
|
| assert_array_equal(data[0]['inputs'][0],
|
| (raw_2d_data['inputs'][0] - psr.mean.view(-1, 1, 1)) /
|
| psr.std.view(-1, 1, 1))
|
| assert_array_equal(data[0]['inputs'][1],
|
| (raw_2d_data['inputs'][1] - psr.mean.view(-1, 1, 1)) /
|
| psr.std.view(-1, 1, 1))
|
| assert_array_equal(data[1]['inputs'][0],
|
| (raw_3d_data['inputs'][0] - psr.mean) / psr.std)
|
| assert_array_equal(data[1]['inputs'][1],
|
| (raw_3d_data['inputs'][1] - psr.mean) / psr.std)
|
|
|
| raw_data = generate_dummy_data(2, (77, ))
|
| psr = ActionDataPreprocessor(to_float32=False)
|
| data = psr(raw_data)
|
| assert data['inputs'].dtype == raw_data['inputs'][0].dtype
|
|
|
