def test_bbox_head_get_bboxes(num_sample, num_batch):
self = BBoxHead(reg_class_agnostic=True)
num_class = 6
rois = torch.rand((num_sample, 5))
cls_score = torch.rand((num_sample, num_class))
bbox_pred = torch.rand((num_sample, 4))
scale_factor = np.array([2.0, 2.0, 2.0, 2.0])
det_bboxes, det_labels = self.get_bboxes(rois,
cls_score,
bbox_pred,
None,
scale_factor,
rescale=True)
if num_sample == 0:
assert len(det_bboxes) == 0 and len(det_labels) == 0
else:
assert det_bboxes.shape == bbox_pred.shape
assert det_labels.shape == cls_score.shape
rois = torch.rand((num_batch, num_sample, 5))
cls_score = torch.rand((num_batch, num_sample, num_class))
bbox_pred = torch.rand((num_batch, num_sample, 4))
scale_factor = (np.array([2.0, 2.0, 2.0, 2.0]), ) * num_batch
det_bboxes, det_labels = self.get_bboxes(rois,
cls_score,
bbox_pred,
None,
scale_factor,
rescale=True)
assert len(det_bboxes) == num_batch and len(det_labels) == num_batch
def test_bbox_head_loss():
"""
Tests bbox head loss when truth is empty and non-empty
"""
self = BBoxHead(in_channels=8, roi_feat_size=3)
# Dummy proposals
proposal_list = [
torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
]
target_cfg = mmcv.Config(dict(pos_weight=1))
# Test bbox loss when truth is empty
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
rois = bbox2roi([res.bboxes for res in sampling_results])
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero'
# Test bbox loss when truth is non-empty
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero'
def test_refine_boxes():
"""Mirrors the doctest in
``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for
multiple values of n_roi / n_img."""
self = BBoxHead(reg_class_agnostic=True)
test_settings = [
# Corner case: less rois than images
{
'n_roi': 2,
'n_img': 4,
'rng': 34285940
},
# Corner case: no images
{
'n_roi': 0,
'n_img': 0,
'rng': 52925222
},
# Corner cases: few images / rois
{
'n_roi': 1,
'n_img': 1,
'rng': 1200281
},
{
'n_roi': 2,
'n_img': 1,
'rng': 1200282
},
{
'n_roi': 2,
'n_img': 2,
'rng': 1200283
},
{
'n_roi': 1,
'n_img': 2,
'rng': 1200284
},
# Corner case: no rois few images
{
'n_roi': 0,
'n_img': 1,
'rng': 23955860
},
{
'n_roi': 0,
'n_img': 2,
'rng': 25830516
},
# Corner case: no rois many images
{
'n_roi': 0,
'n_img': 10,
'rng': 671346
},
{
'n_roi': 0,
'n_img': 20,
'rng': 699807
},
# Corner case: cal_similarity num rois and images
{
'n_roi': 20,
'n_img': 20,
'rng': 1200238
},
{
'n_roi': 10,
'n_img': 20,
'rng': 1200238
},
{
'n_roi': 5,
'n_img': 5,
'rng': 1200238
},
# ----------------------------------
# Common case: more rois than images
{
'n_roi': 100,
'n_img': 1,
'rng': 337156
},
{
'n_roi': 150,
'n_img': 2,
'rng': 275898
},
{
'n_roi': 500,
'n_img': 5,
'rng': 4903221
},
]
for demokw in test_settings:
try:
n_roi = demokw['n_roi']
n_img = demokw['n_img']
rng = demokw['rng']
print(f'Test refine_boxes case: {demokw!r}')
tup = _demodata_refine_boxes(n_roi, n_img, rng=rng)
rois, labels, bbox_preds, pos_is_gts, img_metas = tup
bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
pos_is_gts, img_metas)
assert len(bboxes_list) == n_img
assert sum(map(len, bboxes_list)) <= n_roi
assert all(b.shape[1] == 4 for b in bboxes_list)
except Exception:
print(f'Test failed with demokw={demokw!r}')
raise
