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_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