def test_ava_dataset(self):
target_keys = [
'frame_dir', 'video_id', 'timestamp', 'img_key', 'shot_info',
'fps', 'ann'
]
ann_keys = ['labels', 'entity_boxes', 'entity_ids']
pkl_keys = ['0f39OWEqJ24,0902', '0f39OWEqJ24,0903', '_-Z6wFjXtGQ,0902']
ava_dataset = AVADataset(self.ann_file,
self.exclude_file,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert check_keys_contain(ava_dataset.proposals.keys(), pkl_keys)
assert check_keys_contain(ava_infos[0].keys(), target_keys)
assert check_keys_contain(ava_infos[0]['ann'].keys(), ann_keys)
assert len(ava_infos) == 1
assert ava_infos[0]['frame_dir'] == osp.join(self.data_prefix,
'0f39OWEqJ24')
assert ava_infos[0]['video_id'] == '0f39OWEqJ24'
assert ava_infos[0]['timestamp'] == 902
assert ava_infos[0]['img_key'] == '0f39OWEqJ24,0902'
assert ava_infos[0]['shot_info'] == (0, 26880)
assert ava_infos[0]['fps'] == 30
assert len(ava_infos[0]['ann']) == 3
target_labels = np.array([12, 17, 79] + [
-1,
] * 78)
target_labels = target_labels[None, ...]
assert_array_equal(ava_infos[0]['ann']['labels'], target_labels)
assert_array_equal(ava_infos[0]['ann']['entity_boxes'],
np.array([[0.031, 0.162, 0.67, 0.995]]))
assert_array_equal(ava_infos[0]['ann']['entity_ids'], np.array([0]))
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert len(ava_infos) == 3
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
test_mode=True,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert len(ava_infos) == 3
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
test_mode=True,
data_prefix=self.data_prefix,
proposal_file=None)
assert ava_dataset.proposals is None
def test_ava_evaluate(self):
data_prefix = osp.join(
osp.dirname(__file__), '../data/test_eval_detection')
ann_file = osp.join(data_prefix, 'gt.csv')
label_file = osp.join(data_prefix, 'action_list.txt')
ava_dataset = AVADataset(
ann_file, None, [], label_file=label_file, num_classes=4)
fake_result = [[
np.array([[0.362, 0.156, 0.969, 0.666, 0.106],
[0.442, 0.083, 0.721, 0.947, 0.162]]),
np.array([[0.288, 0.365, 0.766, 0.551, 0.706],
[0.178, 0.296, 0.707, 0.995, 0.223]]),
np.array([[0.417, 0.167, 0.843, 0.939, 0.015],
[0.35, 0.421, 0.57, 0.689, 0.427]])
],
[
np.array([[0.256, 0.338, 0.726, 0.799, 0.563],
[0.071, 0.256, 0.64, 0.75, 0.297]]),
np.array([[0.326, 0.036, 0.513, 0.991, 0.405],
[0.351, 0.035, 0.729, 0.936, 0.945]]),
np.array([[0.051, 0.005, 0.975, 0.942, 0.424],
[0.347, 0.05, 0.97, 0.944, 0.396]])
],
[
np.array([[0.39, 0.087, 0.833, 0.616, 0.447],
[0.461, 0.212, 0.627, 0.527, 0.036]]),
np.array([[0.022, 0.394, 0.93, 0.527, 0.109],
[0.208, 0.462, 0.874, 0.948, 0.954]]),
np.array([[0.206, 0.456, 0.564, 0.725, 0.685],
[0.106, 0.445, 0.782, 0.673, 0.367]])
]]
res = ava_dataset.evaluate(fake_result)
assert_array_almost_equal(res['[email protected]@100'], 0.33333333)
assert_array_almost_equal(res['AR@100'], 0.15833333)
assert_array_almost_equal(res['[email protected]'], 0.027777778)
def test_ava_dataset(self):
target_keys = [
'frame_dir', 'video_id', 'timestamp', 'img_key', 'shot_info',
'fps', 'ann'
]
ann_keys = ['gt_labels', 'gt_bboxes', 'entity_ids']
pkl_keys = ['0f39OWEqJ24,0902', '0f39OWEqJ24,0903', '_-Z6wFjXtGQ,0902']
ava_dataset = AVADataset(self.ann_file,
self.exclude_file,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert assert_dict_has_keys(ava_dataset.proposals, pkl_keys)
assert assert_dict_has_keys(ava_infos[0], target_keys)
assert assert_dict_has_keys(ava_infos[0]['ann'], ann_keys)
assert len(ava_infos) == 1
assert ava_infos[0]['frame_dir'] == osp.join(self.data_prefix,
'0f39OWEqJ24')
assert ava_infos[0]['video_id'] == '0f39OWEqJ24'
assert ava_infos[0]['timestamp'] == 902
assert ava_infos[0]['img_key'] == '0f39OWEqJ24,0902'
assert ava_infos[0]['shot_info'] == (0, 27000)
assert ava_infos[0]['fps'] == 30
assert len(ava_infos[0]['ann']) == 3
target_labels = np.array([12, 17, 79])
labels = np.zeros([81])
labels[target_labels] = 1.
target_labels = labels[None, ...]
assert_array_equal(ava_infos[0]['ann']['gt_labels'], target_labels)
assert_array_equal(ava_infos[0]['ann']['gt_bboxes'],
np.array([[0.031, 0.162, 0.67, 0.995]]))
assert_array_equal(ava_infos[0]['ann']['entity_ids'], np.array([0]))
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert len(ava_infos) == 3
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
test_mode=True,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
ava_infos = ava_dataset.video_infos
assert len(ava_infos) == 3
ava_dataset = AVADataset(self.ann_file,
None,
self.pipeline,
test_mode=True,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
def test_ava_pipeline(self):
target_keys = [
'frame_dir', 'video_id', 'timestamp', 'img_key', 'shot_info',
'fps', 'ann', 'filename_tmpl', 'modality', 'start_index',
'timestamp_start', 'timestamp_end', 'proposals', 'frame_inds',
'clip_len', 'frame_interval'
]
ann_keys = ['labels', 'entity_boxes', 'entity_ids']
ava_dataset = AVADataset(self.ann_file,
self.exclude_file,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
result = ava_dataset[0]
assert check_keys_contain(result.keys(), target_keys)
assert check_keys_contain(result['ann'].keys(), ann_keys)
assert result['filename_tmpl'] == 'img_{:05}.jpg'
assert result['modality'] == 'RGB'
assert result['start_index'] == 1
assert result['timestamp_start'] == 902
assert result['timestamp_end'] == 1798
assert_array_equal(result['proposals'],
np.array([[0.011, 0.157, 0.655, 0.983, 0.998163]]))
assert result['clip_len'] == 32
assert result['frame_interval'] == 2
assert len(result['frame_inds']) == 32
def test_assigner_sampler():
try:
from mmdet.core.bbox import build_assigner, build_sampler
except (ImportError, ModuleNotFoundError):
raise ImportError(
'Failed to import `build_assigner` and `build_sampler` '
'from `mmdet.core.bbox`. The two APIs are required for '
'the testing in `test_bbox.py`! ')
data_prefix = osp.normpath(
osp.join(osp.dirname(__file__), '../data/eval_detection'))
ann_file = osp.join(data_prefix, 'gt.csv')
label_file = osp.join(data_prefix, 'action_list.txt')
proposal_file = osp.join(data_prefix, 'proposal.pkl')
dataset = AVADataset(ann_file=ann_file,
exclude_file=None,
pipeline=[],
label_file=label_file,
proposal_file=proposal_file,
num_classes=4)
assigner = dict(type='MaxIoUAssignerAVA',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5)
assigner = build_assigner(assigner)
proposal = torch.tensor(dataset[0]['proposals'])
gt_bboxes = torch.tensor(dataset[0]['gt_bboxes'])
gt_labels = torch.tensor(dataset[0]['gt_labels'])
assign_result = assigner.assign(bboxes=proposal,
gt_bboxes=gt_bboxes,
gt_bboxes_ignore=None,
gt_labels=gt_labels)
assert assign_result.num_gts == 4
assert torch.all(
assign_result.gt_inds == torch.tensor([0, 0, 3, 3, 0, 0, 0, 1, 0, 0]))
assert torch.all(
torch.isclose(
assign_result.max_overlaps,
torch.tensor([
0.40386841, 0.47127257, 0.53544776, 0.58797631, 0.29281288,
0.40979504, 0.45902917, 0.50093938, 0.21560125, 0.32948171
],
dtype=torch.float64)))
assert torch.all(
torch.isclose(
assign_result.labels,
torch.tensor([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 0., 0.],
[0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.],
[0., 0., 0., 0.]])))
sampler = dict(type='RandomSampler', num=32, pos_fraction=1)
sampler = build_sampler(sampler)
sampling_result = sampler.sample(assign_result, proposal, gt_bboxes,
gt_labels)
assert (sampling_result.pos_inds.shape[0] ==
sampling_result.pos_bboxes.shape[0])
assert (sampling_result.neg_inds.shape[0] ==
sampling_result.neg_bboxes.shape[0])
return sampling_result
def test_ava_pipeline(self):
target_keys = [
'frame_dir', 'video_id', 'timestamp', 'img_key', 'shot_info',
'fps', 'filename_tmpl', 'modality', 'start_index',
'timestamp_start', 'timestamp_end', 'proposals', 'scores',
'frame_inds', 'clip_len', 'frame_interval', 'gt_labels',
'gt_bboxes', 'entity_ids'
]
ava_dataset = AVADataset(
self.ann_file,
self.exclude_file,
self.pipeline,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
result = ava_dataset[0]
assert assert_dict_has_keys(result, target_keys)
assert result['filename_tmpl'] == 'img_{:05}.jpg'
assert result['modality'] == 'RGB'
assert result['start_index'] == 1
assert result['timestamp_start'] == 900
assert result['timestamp_end'] == 1800
assert_array_equal(result['proposals'],
np.array([[0.011, 0.157, 0.655, 0.983]]))
assert_array_equal(result['scores'], np.array([0.998163]))
assert result['clip_len'] == 32
assert result['frame_interval'] == 2
assert len(result['frame_inds']) == 32
ava_dataset = AVADataset(
self.ann_file,
None,
self.pipeline,
test_mode=True,
data_prefix=self.data_prefix,
proposal_file=self.proposal_file)
# Try to get a sample
result = ava_dataset[0]
assert result['filename_tmpl'] == 'img_{:05}.jpg'
assert result['modality'] == 'RGB'
assert result['start_index'] == 1
assert result['timestamp_start'] == 900
assert result['timestamp_end'] == 1800
def test_assigner_sampler():
data_prefix = osp.join(osp.dirname(__file__), 'data/test_eval_detection')
ann_file = osp.join(data_prefix, 'gt.csv')
label_file = osp.join(data_prefix, 'action_list.txt')
proposal_file = osp.join(data_prefix, 'proposal.pkl')
dataset = AVADataset(ann_file=ann_file,
exclude_file=None,
pipeline=[],
label_file=label_file,
proposal_file=proposal_file,
num_classes=4)
assigner = dict(type='MaxIoUAssignerAVA',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5)
assigner = build_assigner(assigner)
proposal = torch.tensor(dataset[0]['proposals'])
gt_bboxes = torch.tensor(dataset[0]['gt_bboxes'])
gt_labels = torch.tensor(dataset[0]['gt_labels'])
assign_result = assigner.assign(bboxes=proposal,
gt_bboxes=gt_bboxes,
gt_bboxes_ignore=None,
gt_labels=gt_labels)
assert assign_result.num_gts == 4
assert torch.all(
assign_result.gt_inds == torch.tensor([0, 0, 3, 3, 0, 0, 0, 1, 0, 0]))
assert torch.all(
torch.isclose(
assign_result.max_overlaps,
torch.tensor([
0.40386841, 0.47127257, 0.53544776, 0.58797631, 0.29281288,
0.40979504, 0.45902917, 0.50093938, 0.21560125, 0.32948171
],
dtype=torch.float64)))
assert torch.all(
torch.isclose(
assign_result.labels,
torch.tensor([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 0., 0.],
[0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.],
[0., 0., 0., 0.]])))
sampler = dict(type='RandomSampler', num=32, pos_fraction=1)
sampler = build_sampler(sampler)
sampling_result = sampler.sample(assign_result, proposal, gt_bboxes,
gt_labels)
assert (sampling_result.pos_inds.shape[0] ==
sampling_result.pos_bboxes.shape[0])
assert (sampling_result.neg_inds.shape[0] ==
sampling_result.neg_bboxes.shape[0])
return sampling_result