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from copy import deepcopy
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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 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():
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with pytest.raises(ValueError):
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ActionDataPreprocessor(
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mean=[1, 1], std=[0, 0], format_shape='NCTHW_Heatmap')
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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)))
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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)
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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]])
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register_all_modules()
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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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blending=dict(type='MixupBlending', num_classes=5))
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data = psr(deepcopy(raw_data), training=True)
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assert data['data_samples'][0].gt_label.shape == (5, )
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assert data['data_samples'][1].gt_label.shape == (5, )
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raw_data = generate_dummy_data(2, (1, 3, 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='NCHW',
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to_rgb=True)
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data = psr(deepcopy(raw_data))
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assert_array_equal(data['inputs'][0],
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(raw_data['inputs'][0][:, [2, 1, 0]] - psr.mean) /
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psr.std)
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assert_array_equal(data['inputs'][1],
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(raw_data['inputs'][1][:, [2, 1, 0]] - psr.mean) /
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psr.std)
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psr = ActionDataPreprocessor()
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data = psr(deepcopy(raw_data))
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assert data['inputs'].shape == (2, 1, 3, 224, 224)
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assert_array_equal(data['inputs'][0], raw_data['inputs'][0])
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assert_array_equal(data['inputs'][1], raw_data['inputs'][1])
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raw_2d_data = generate_dummy_data(2, (3, 224, 224))
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raw_3d_data = generate_dummy_data(2, (1, 3, 8, 224, 224))
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raw_data = (raw_2d_data, raw_3d_data)
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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='MIX2d3d')
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data = psr(raw_data)
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assert_array_equal(data[0]['inputs'][0],
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(raw_2d_data['inputs'][0] - psr.mean.view(-1, 1, 1)) /
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psr.std.view(-1, 1, 1))
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assert_array_equal(data[0]['inputs'][1],
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(raw_2d_data['inputs'][1] - psr.mean.view(-1, 1, 1)) /
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psr.std.view(-1, 1, 1))
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assert_array_equal(data[1]['inputs'][0],
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(raw_3d_data['inputs'][0] - psr.mean) / psr.std)
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assert_array_equal(data[1]['inputs'][1],
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(raw_3d_data['inputs'][1] - psr.mean) / psr.std)
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raw_data = generate_dummy_data(2, (77, ))
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psr = ActionDataPreprocessor(to_float32=False)
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data = psr(raw_data)
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assert data['inputs'].dtype == raw_data['inputs'][0].dtype
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