File size: 6,463 Bytes
0efc562
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase

import numpy as np

from mmpose.codecs import SPR
from mmpose.registry import KEYPOINT_CODECS
from mmpose.testing import get_coco_sample
from mmpose.utils.tensor_utils import to_numpy, to_tensor


class TestSPR(TestCase):

    def setUp(self) -> None:
        pass

    def _make_multi_instance_data(self, data):
        keypoints = data['keypoints']
        keypoints_visible = data['keypoints_visible']

        keypoints_visible[..., 0] = 0

        keypoints_outside = keypoints - keypoints.max(axis=-1, keepdims=True)
        keypoints_outside_visible = np.zeros(keypoints_visible.shape)

        keypoint_overlap = keypoints.mean(
            axis=-1, keepdims=True) + 0.8 * (
                keypoints - keypoints.mean(axis=-1, keepdims=True))
        keypoint_overlap_visible = keypoints_visible

        data['keypoints'] = np.concatenate(
            (keypoints, keypoints_outside, keypoint_overlap), axis=0)
        data['keypoints_visible'] = np.concatenate(
            (keypoints_visible, keypoints_outside_visible,
             keypoint_overlap_visible),
            axis=0)

        return data

    def test_build(self):
        cfg = dict(
            type='SPR',
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=4,
        )
        codec = KEYPOINT_CODECS.build(cfg)
        self.assertIsInstance(codec, SPR)

    def test_encode(self):
        data = get_coco_sample(img_shape=(512, 512), num_instances=1)
        data = self._make_multi_instance_data(data)

        # w/o keypoint heatmaps
        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=4,
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])

        heatmaps = encoded['heatmaps']
        displacements = encoded['displacements']
        heatmap_weights = encoded['heatmap_weights']
        displacement_weights = encoded['displacement_weights']

        self.assertEqual(heatmaps.shape, (1, 128, 128))
        self.assertEqual(heatmap_weights.shape, (1, 128, 128))
        self.assertEqual(displacements.shape, (34, 128, 128))
        self.assertEqual(displacement_weights.shape, (34, 128, 128))

        # w/ keypoint heatmaps
        with self.assertRaises(AssertionError):
            codec = SPR(
                input_size=(512, 512),
                heatmap_size=(128, 128),
                sigma=4,
                generate_keypoint_heatmaps=True,
            )

        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=(4, 2),
            generate_keypoint_heatmaps=True,
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])

        heatmaps = encoded['heatmaps']
        displacements = encoded['displacements']
        heatmap_weights = encoded['heatmap_weights']
        displacement_weights = encoded['displacement_weights']

        self.assertEqual(heatmaps.shape, (18, 128, 128))
        self.assertEqual(heatmap_weights.shape, (18, 128, 128))
        self.assertEqual(displacements.shape, (34, 128, 128))
        self.assertEqual(displacement_weights.shape, (34, 128, 128))

        # root_type
        with self.assertRaises(ValueError):
            codec = SPR(
                input_size=(512, 512),
                heatmap_size=(128, 128),
                sigma=(4, ),
                root_type='box_center',
            )
            encoded = codec.encode(data['keypoints'],
                                   data['keypoints_visible'])

        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=(4, ),
            root_type='bbox_center',
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])

        heatmaps = encoded['heatmaps']
        displacements = encoded['displacements']
        heatmap_weights = encoded['heatmap_weights']
        displacement_weights = encoded['displacement_weights']

        self.assertEqual(heatmaps.shape, (1, 128, 128))
        self.assertEqual(heatmap_weights.shape, (1, 128, 128))
        self.assertEqual(displacements.shape, (34, 128, 128))
        self.assertEqual(displacement_weights.shape, (34, 128, 128))

    def test_decode(self):
        data = get_coco_sample(img_shape=(512, 512), num_instances=1)

        # decode w/o keypoint heatmaps
        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=(4, ),
            generate_keypoint_heatmaps=False,
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])
        decoded = codec.decode(
            to_tensor(encoded['heatmaps']),
            to_tensor(encoded['displacements']))

        keypoints, (root_scores, keypoint_scores) = decoded
        self.assertIsNone(keypoint_scores)
        self.assertEqual(keypoints.shape, data['keypoints'].shape)
        self.assertEqual(root_scores.shape, data['keypoints'].shape[:1])

        # decode w/ keypoint heatmaps
        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=(4, 2),
            generate_keypoint_heatmaps=True,
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])
        decoded = codec.decode(
            to_tensor(encoded['heatmaps']),
            to_tensor(encoded['displacements']))

        keypoints, (root_scores, keypoint_scores) = decoded
        self.assertIsNotNone(keypoint_scores)
        self.assertEqual(keypoints.shape, data['keypoints'].shape)
        self.assertEqual(root_scores.shape, data['keypoints'].shape[:1])
        self.assertEqual(keypoint_scores.shape, data['keypoints'].shape[:2])

    def test_cicular_verification(self):
        data = get_coco_sample(img_shape=(512, 512), num_instances=1)

        codec = SPR(
            input_size=(512, 512),
            heatmap_size=(128, 128),
            sigma=(4, ),
            generate_keypoint_heatmaps=False,
        )

        encoded = codec.encode(data['keypoints'], data['keypoints_visible'])
        decoded = codec.decode(
            to_tensor(encoded['heatmaps']),
            to_tensor(encoded['displacements']))

        keypoints, _ = decoded
        self.assertTrue(
            np.allclose(to_numpy(keypoints), data['keypoints'], atol=5.))