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# Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
import pytest
import torch
from numpy.testing import assert_array_equal
from mmaction.models import ActionDataPreprocessor
from mmaction.structures import ActionDataSample
from mmaction.utils import register_all_modules
def generate_dummy_data(batch_size, input_shape):
data = {
'inputs':
[torch.randint(0, 255, input_shape) for _ in range(batch_size)],
'data_samples':
[ActionDataSample().set_gt_label(2) for _ in range(batch_size)]
}
return data
def test_data_preprocessor():
with pytest.raises(ValueError):
ActionDataPreprocessor(
mean=[1, 1], std=[0, 0], format_shape='NCTHW_Heatmap')
with pytest.raises(ValueError):
psr = ActionDataPreprocessor(format_shape='NCTHW_Heatmap', to_rgb=True)
psr(generate_dummy_data(1, (3, 224, 224)))
raw_data = generate_dummy_data(2, (1, 3, 8, 224, 224))
psr = ActionDataPreprocessor(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
format_shape='NCTHW')
data = psr(deepcopy(raw_data))
assert data['inputs'].shape == (2, 1, 3, 8, 224, 224)
assert_array_equal(data['inputs'][0],
(raw_data['inputs'][0] - psr.mean) / psr.std)
assert_array_equal(data['inputs'][1],
(raw_data['inputs'][1] - psr.mean) / psr.std)
psr = ActionDataPreprocessor(format_shape='NCTHW', to_rgb=True)
data = psr(deepcopy(raw_data))
assert data['inputs'].shape == (2, 1, 3, 8, 224, 224)
assert_array_equal(data['inputs'][0], raw_data['inputs'][0][:, [2, 1, 0]])
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
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