def test_ssn_dataset(self):
# test ssn dataset
ssn_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix)
ssn_infos = ssn_dataset.video_infos
assert ssn_infos[0]['video_id'] == 'test_imgs'
assert ssn_infos[0]['total_frames'] == 5
# test ssn dataset with verbose
ssn_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix,
verbose=True)
ssn_infos = ssn_dataset.video_infos
assert ssn_infos[0]['video_id'] == 'test_imgs'
assert ssn_infos[0]['total_frames'] == 5
# test ssn datatset with normalized proposal file
with pytest.raises(Exception):
ssn_dataset = SSNDataset(
self.proposal_norm_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix)
ssn_infos = ssn_dataset.video_infos
# test ssn dataset with reg_normalize_constants
ssn_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix,
reg_normalize_constants=[[[-0.0603, 0.0325], [0.0752, 0.1596]]])
ssn_infos = ssn_dataset.video_infos
assert ssn_infos[0]['video_id'] == 'test_imgs'
assert ssn_infos[0]['total_frames'] == 5
# test error case
with pytest.raises(TypeError):
ssn_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix,
aug_ratio=('error', 'error'))
ssn_infos = ssn_dataset.video_infos
def test_proposal_pipeline(self):
target_keys = [
'frame_dir', 'video_id', 'total_frames', 'gts', 'proposals',
'filename_tmpl', 'modality', 'out_proposals', 'reg_targets',
'proposal_scale_factor', 'proposal_labels', 'proposal_type',
'start_index'
]
# SSN Dataset not in test mode
proposal_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix)
result = proposal_dataset[0]
assert self.check_keys_contain(result.keys(), target_keys)
# SSN Dataset with random sampling proposals
proposal_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix,
video_centric=False)
result = proposal_dataset[0]
assert self.check_keys_contain(result.keys(), target_keys)
target_keys = [
'frame_dir', 'video_id', 'total_frames', 'gts', 'proposals',
'filename_tmpl', 'modality', 'relative_proposal_list',
'scale_factor_list', 'proposal_tick_list', 'reg_norm_consts',
'start_index'
]
# SSN Dataset in test mode
proposal_dataset = SSNDataset(
self.proposal_ann_file,
self.proposal_test_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix,
test_mode=True)
result = proposal_dataset[0]
assert self.check_keys_contain(result.keys(), target_keys)
def test_ssn_evaluate(self):
ssn_dataset = SSNDataset(self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg,
data_prefix=self.data_prefix)
ssn_dataset_topall = SSNDataset(self.proposal_ann_file,
self.proposal_pipeline,
self.proposal_train_cfg,
self.proposal_test_cfg_topall,
data_prefix=self.data_prefix)
with pytest.raises(TypeError):
# results must be a list
ssn_dataset.evaluate('0.5')
with pytest.raises(AssertionError):
# The length of results must be equal to the dataset len
ssn_dataset.evaluate([0] * 5)
with pytest.raises(KeyError):
# unsupported metric
ssn_dataset.evaluate([0] * len(ssn_dataset), metrics='iou')
# evaluate mAP metric
results_relative_proposal_list = np.random.randn(16, 2)
results_activity_scores = np.random.randn(16, 21)
results_completeness_scores = np.random.randn(16, 20)
results_bbox_preds = np.random.randn(16, 20, 2)
results = [[
results_relative_proposal_list, results_activity_scores,
results_completeness_scores, results_bbox_preds
]]
eval_result = ssn_dataset.evaluate(results, metrics=['mAP'])
assert set(eval_result) == set([
'[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]',
'[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]'
])
# evaluate mAP metric without filtering topk
results_relative_proposal_list = np.random.randn(16, 2)
results_activity_scores = np.random.randn(16, 21)
results_completeness_scores = np.random.randn(16, 20)
results_bbox_preds = np.random.randn(16, 20, 2)
results = [[
results_relative_proposal_list, results_activity_scores,
results_completeness_scores, results_bbox_preds
]]
eval_result = ssn_dataset_topall.evaluate(results, metrics=['mAP'])
assert set(eval_result) == set([
'[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]',
'[email protected]', '[email protected]', '[email protected]', '[email protected]', '[email protected]'
])