def test_activitynet_proposals2json(self):
activitynet_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix)
results = [
dict(video_name='v_test1',
proposal_list=[dict(segment=[0.1, 0.9], score=0.1)]),
dict(video_name='v_test2',
proposal_list=[dict(segment=[10.1, 20.9], score=0.9)])
]
result_dict = activitynet_dataset.proposals2json(results)
assert result_dict == dict(test1=[{
'segment': [0.1, 0.9],
'score': 0.1
}],
test2=[{
'segment': [10.1, 20.9],
'score': 0.9
}])
result_dict = activitynet_dataset.proposals2json(results, True)
assert result_dict == dict(test1=[{
'segment': [0.1, 0.9],
'score': 0.1
}],
test2=[{
'segment': [10.1, 20.9],
'score': 0.9
}])
def test_activitynet_evaluate(self):
activitynet_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix)
with pytest.raises(TypeError):
# results must be a list
activitynet_dataset.evaluate('0.5')
with pytest.raises(AssertionError):
# The length of results must be equal to the dataset len
activitynet_dataset.evaluate([0] * 5)
with pytest.raises(KeyError):
# unsupported metric
activitynet_dataset.evaluate([0] * len(activitynet_dataset),
metrics='iou')
# evaluate AR@AN metric
results = [
dict(video_name='v_test1',
proposal_list=[dict(segment=[0.1, 0.9], score=0.1)]),
dict(video_name='v_test2',
proposal_list=[dict(segment=[10.1, 20.9], score=0.9)])
]
eval_result = activitynet_dataset.evaluate(results, metrics=['AR@AN'])
assert set(eval_result) == set(
['auc', 'AR@1', 'AR@5', 'AR@10', 'AR@100'])
def test_action_pipeline(self):
target_keys = ['video_name', 'data_prefix']
# ActivityNet Dataset not in test mode
action_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix,
test_mode=False)
result = action_dataset[0]
assert self.check_keys_contain(result.keys(), target_keys)
# ActivityNet Dataset in test mode
action_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix,
test_mode=True)
result = action_dataset[0]
assert self.check_keys_contain(result.keys(), target_keys)
def test_activitynet_dump_results(self):
activitynet_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix)
# test dumping json file
results = [
dict(
video_name='v_test1',
proposal_list=[dict(segment=[0.1, 0.9], score=0.1)]),
dict(
video_name='v_test2',
proposal_list=[dict(segment=[10.1, 20.9], score=0.9)])
]
dump_results = {
'version': 'VERSION 1.3',
'results': {
'test1': [{
'segment': [0.1, 0.9],
'score': 0.1
}],
'test2': [{
'segment': [10.1, 20.9],
'score': 0.9
}]
},
'external_data': {}
}
with tempfile.TemporaryDirectory() as tmpdir:
tmp_filename = osp.join(tmpdir, 'result.json')
activitynet_dataset.dump_results(results, tmp_filename, 'json')
assert osp.isfile(tmp_filename)
with open(tmp_filename, 'r+') as f:
load_obj = mmcv.load(f, file_format='json')
assert load_obj == dump_results
# test dumping csv file
results = [('test_video', np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9,
10]]))]
with tempfile.TemporaryDirectory() as tmpdir:
activitynet_dataset.dump_results(results, tmpdir, 'csv')
load_obj = np.loadtxt(
osp.join(tmpdir, 'test_video.csv'),
dtype=np.float32,
delimiter=',',
skiprows=1)
assert_array_equal(
load_obj,
np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]],
dtype=np.float32))
def test_activitynet_dataset(self):
activitynet_dataset = ActivityNetDataset(self.action_ann_file,
self.action_pipeline,
self.data_prefix)
activitynet_infos = activitynet_dataset.video_infos
assert activitynet_infos == [
dict(video_name='v_test1',
duration_second=1,
duration_frame=30,
annotations=[dict(segment=[0.3, 0.6], label='Rock climbing')],
feature_frame=30,
fps=30.0,
rfps=30),
dict(video_name='v_test2',
duration_second=2,
duration_frame=48,
annotations=[dict(segment=[1.0, 2.0], label='Drinking beer')],
feature_frame=48,
fps=24.0,
rfps=24.0)
]