File size: 3,842 Bytes
d3dbf03 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import pytest
import torch
from numpy.testing import assert_array_equal
from mmaction.models import MultiModalDataPreprocessor
from mmaction.structures import ActionDataSample
from mmaction.utils import register_all_modules
def generate_dummy_data(batch_size, input_keys, input_shapes):
data = dict()
data['data_samples'] = [
ActionDataSample().set_gt_label(2) for _ in range(batch_size)
]
data['inputs'] = dict()
for key, shape in zip(input_keys, input_shapes):
data['inputs'][key] = [
torch.randint(0, 255, shape) for _ in range(batch_size)
]
return data
def test_multimodal_data_preprocessor():
with pytest.raises(AssertionError):
MultiModalDataPreprocessor(
preprocessors=dict(imgs=dict(format_shape='NCTHW')))
register_all_modules()
data_keys = ('imgs', 'heatmap_imgs')
data_shapes = ((1, 3, 8, 224, 224), (1, 17, 32, 64, 64))
raw_data = generate_dummy_data(2, data_keys, data_shapes)
psr = MultiModalDataPreprocessor(
preprocessors=dict(
imgs=dict(
type='ActionDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
format_shape='NCTHW'),
heatmap_imgs=dict(type='ActionDataPreprocessor')))
data = psr(copy.deepcopy(raw_data))
assert data['inputs']['imgs'].shape == (2, 1, 3, 8, 224, 224)
assert data['inputs']['heatmap_imgs'].shape == (2, 1, 17, 32, 64, 64)
psr_imgs = psr.preprocessors['imgs']
assert_array_equal(data['inputs']['imgs'][0],
(raw_data['inputs']['imgs'][0] - psr_imgs.mean) /
psr_imgs.std)
assert_array_equal(data['inputs']['imgs'][1],
(raw_data['inputs']['imgs'][1] - psr_imgs.mean) /
psr_imgs.std)
assert_array_equal(data['inputs']['heatmap_imgs'][0],
raw_data['inputs']['heatmap_imgs'][0])
assert_array_equal(data['inputs']['heatmap_imgs'][1],
raw_data['inputs']['heatmap_imgs'][1])
data_keys = ('imgs_2D', 'imgs_3D')
data_shapes = ((1, 3, 224, 224), (1, 3, 8, 224, 224))
raw_data = generate_dummy_data(2, data_keys, data_shapes)
psr = MultiModalDataPreprocessor(
preprocessors=dict(
imgs_2D=dict(
type='ActionDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
format_shape='NCHW'),
imgs_3D=dict(
type='ActionDataPreprocessor',
mean=[127.5, 127.5, 127.5],
std=[57.5, 57.5, 57.5],
format_shape='NCTHW')))
data = psr(copy.deepcopy(raw_data))
assert data['inputs']['imgs_2D'].shape == (2, 1, 3, 224, 224)
assert data['inputs']['imgs_3D'].shape == (2, 1, 3, 8, 224, 224)
psr_imgs2d = psr.preprocessors['imgs_2D']
psr_imgs3d = psr.preprocessors['imgs_3D']
assert_array_equal(data['inputs']['imgs_2D'][0],
(raw_data['inputs']['imgs_2D'][0] - psr_imgs2d.mean) /
psr_imgs2d.std)
assert_array_equal(data['inputs']['imgs_2D'][1],
(raw_data['inputs']['imgs_2D'][1] - psr_imgs2d.mean) /
psr_imgs2d.std)
assert_array_equal(data['inputs']['imgs_3D'][0],
(raw_data['inputs']['imgs_3D'][0] - psr_imgs3d.mean) /
psr_imgs3d.std)
assert_array_equal(data['inputs']['imgs_3D'][1],
(raw_data['inputs']['imgs_3D'][1] - psr_imgs3d.mean) /
psr_imgs3d.std)
|