Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_ssn.py 8.02 KB
Newer Older
unknown's avatar
Initial add code.  
unknown committed
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
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206 
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform

import mmcv
import pytest
import torch

from mmaction.models import build_localizer


@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_ssn_train():
    train_cfg = mmcv.ConfigDict(
        dict(
            ssn=dict(
                assigner=dict(
                    positive_iou_threshold=0.7,
                    background_iou_threshold=0.01,
                    incomplete_iou_threshold=0.3,
                    background_coverage_threshold=0.02,
                    incomplete_overlap_threshold=0.01),
                sampler=dict(
                    num_per_video=8,
                    positive_ratio=1,
                    background_ratio=1,
                    incomplete_ratio=6,
                    add_gt_as_proposals=True),
                loss_weight=dict(comp_loss_weight=0.1, reg_loss_weight=0.1),
                debug=False)))
    base_model_cfg = dict(
        type='SSN',
        backbone=dict(
            type='ResNet', pretrained=None, depth=18, norm_eval=True),
        spatial_type='avg',
        dropout_ratio=0.8,
        loss_cls=dict(type='SSNLoss'),
        cls_head=dict(
            type='SSNHead',
            dropout_ratio=0.,
            in_channels=512,
            num_classes=20,
            consensus=dict(
                type='STPPTrain',
                stpp_stage=(1, 1, 1),
                num_segments_list=(2, 5, 2)),
            use_regression=True),
        train_cfg=train_cfg)
    dropout_cfg = copy.deepcopy(base_model_cfg)
    dropout_cfg['dropout_ratio'] = 0
    dropout_cfg['cls_head']['dropout_ratio'] = 0.5
    non_regression_cfg = copy.deepcopy(base_model_cfg)
    non_regression_cfg['cls_head']['use_regression'] = False

    imgs = torch.rand(1, 8, 9, 3, 224, 224)
    proposal_scale_factor = torch.Tensor([[[1.0345, 1.0345], [1.0028, 0.0028],
                                           [1.0013, 1.0013], [1.0008, 1.0008],
                                           [0.3357, 1.0006], [1.0006, 1.0006],
                                           [0.0818, 1.0005], [1.0030,
                                                              1.0030]]])
    proposal_type = torch.Tensor([[0, 1, 1, 1, 1, 1, 1, 2]])
    proposal_labels = torch.LongTensor([[8, 8, 8, 8, 8, 8, 8, 0]])
    reg_targets = torch.Tensor([[[0.2929, 0.2694], [0.0000, 0.0000],
                                 [0.0000, 0.0000], [0.0000, 0.0000],
                                 [0.0000, 0.0000], [0.0000, 0.0000],
                                 [0.0000, 0.0000], [0.0000, 0.0000]]])

    localizer_ssn = build_localizer(base_model_cfg)
    localizer_ssn_dropout = build_localizer(dropout_cfg)
    localizer_ssn_non_regression = build_localizer(non_regression_cfg)

    if torch.cuda.is_available():
        localizer_ssn = localizer_ssn.cuda()
        localizer_ssn_dropout = localizer_ssn_dropout.cuda()
        localizer_ssn_non_regression = localizer_ssn_non_regression.cuda()
        imgs = imgs.cuda()
        proposal_scale_factor = proposal_scale_factor.cuda()
        proposal_type = proposal_type.cuda()
        proposal_labels = proposal_labels.cuda()
        reg_targets = reg_targets.cuda()

    # Train normal case
    losses = localizer_ssn(
        imgs,
        proposal_scale_factor=proposal_scale_factor,
        proposal_type=proposal_type,
        proposal_labels=proposal_labels,
        reg_targets=reg_targets)
    assert isinstance(losses, dict)

    # Train SSN without dropout in model, with dropout in head
    losses = localizer_ssn_dropout(
        imgs,
        proposal_scale_factor=proposal_scale_factor,
        proposal_type=proposal_type,
        proposal_labels=proposal_labels,
        reg_targets=reg_targets)
    assert isinstance(losses, dict)

    # Train SSN model without regression
    losses = localizer_ssn_non_regression(
        imgs,
        proposal_scale_factor=proposal_scale_factor,
        proposal_type=proposal_type,
        proposal_labels=proposal_labels,
        reg_targets=reg_targets)
    assert isinstance(losses, dict)


@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_ssn_test():
    test_cfg = mmcv.ConfigDict(
        dict(
            ssn=dict(
                sampler=dict(test_interval=6, batch_size=16),
                evaluater=dict(
                    top_k=2000,
                    nms=0.2,
                    softmax_before_filter=True,
                    cls_score_dict=None,
                    cls_top_k=2))))
    base_model_cfg = dict(
        type='SSN',
        backbone=dict(
            type='ResNet', pretrained=None, depth=18, norm_eval=True),
        spatial_type='avg',
        dropout_ratio=0.8,
        cls_head=dict(
            type='SSNHead',
            dropout_ratio=0.,
            in_channels=512,
            num_classes=20,
            consensus=dict(type='STPPTest', stpp_stage=(1, 1, 1)),
            use_regression=True),
        test_cfg=test_cfg)
    maxpool_model_cfg = copy.deepcopy(base_model_cfg)
    maxpool_model_cfg['spatial_type'] = 'max'
    non_regression_cfg = copy.deepcopy(base_model_cfg)
    non_regression_cfg['cls_head']['use_regression'] = False
    non_regression_cfg['cls_head']['consensus']['use_regression'] = False
    tuple_stage_cfg = copy.deepcopy(base_model_cfg)
    tuple_stage_cfg['cls_head']['consensus']['stpp_stage'] = (1, (1, 2), 1)
    str_stage_cfg = copy.deepcopy(base_model_cfg)
    str_stage_cfg['cls_head']['consensus']['stpp_stage'] = ('error', )

    imgs = torch.rand(1, 8, 3, 224, 224)
    relative_proposal_list = torch.Tensor([[[0.2500, 0.6250], [0.3750,
                                                               0.7500]]])
    scale_factor_list = torch.Tensor([[[1.0000, 1.0000], [1.0000, 0.2661]]])
    proposal_tick_list = torch.LongTensor([[[1, 2, 5, 7], [20, 30, 60, 80]]])
    reg_norm_consts = torch.Tensor([[[-0.0603, 0.0325], [0.0752, 0.1596]]])

    localizer_ssn = build_localizer(base_model_cfg)
    localizer_ssn_maxpool = build_localizer(maxpool_model_cfg)
    localizer_ssn_non_regression = build_localizer(non_regression_cfg)
    localizer_ssn_tuple_stage_cfg = build_localizer(tuple_stage_cfg)
    with pytest.raises(ValueError):
        build_localizer(str_stage_cfg)

    if torch.cuda.is_available():
        localizer_ssn = localizer_ssn.cuda()
        localizer_ssn_maxpool = localizer_ssn_maxpool.cuda()
        localizer_ssn_non_regression = localizer_ssn_non_regression.cuda()
        localizer_ssn_tuple_stage_cfg = localizer_ssn_tuple_stage_cfg.cuda()
        imgs = imgs.cuda()
        relative_proposal_list = relative_proposal_list.cuda()
        scale_factor_list = scale_factor_list.cuda()
        proposal_tick_list = proposal_tick_list.cuda()
        reg_norm_consts = reg_norm_consts.cuda()

    with torch.no_grad():
        # Test normal case
        localizer_ssn(
            imgs,
            relative_proposal_list=relative_proposal_list,
            scale_factor_list=scale_factor_list,
            proposal_tick_list=proposal_tick_list,
            reg_norm_consts=reg_norm_consts,
            return_loss=False)

        # Test SSN model with max spatial pooling
        localizer_ssn_maxpool(
            imgs,
            relative_proposal_list=relative_proposal_list,
            scale_factor_list=scale_factor_list,
            proposal_tick_list=proposal_tick_list,
            reg_norm_consts=reg_norm_consts,
            return_loss=False)

        # Test SSN model without regression
        localizer_ssn_non_regression(
            imgs,
            relative_proposal_list=relative_proposal_list,
            scale_factor_list=scale_factor_list,
            proposal_tick_list=proposal_tick_list,
            reg_norm_consts=reg_norm_consts,
            return_loss=False)

        # Test SSN model with tuple stage cfg.
        localizer_ssn_tuple_stage_cfg(
            imgs,
            relative_proposal_list=relative_proposal_list,
            scale_factor_list=scale_factor_list,
            proposal_tick_list=proposal_tick_list,
            reg_norm_consts=reg_norm_consts,
            return_loss=False)