def generate_label(prop_loc, sample_list, index):
batch_size = prop_loc.shape[0]
iou_labels = []
lens = []
for idx in range(batch_size):
batch_prop_start = np.append([0.0], prop_loc[idx][:, 0])
batch_prop_end = np.append([0.0], prop_loc[idx][:, 1])
gt_start = sample_list[index[idx]][2]
gt_end = sample_list[index[idx]][3]
cur_labels = []
lens.append(len(gt_start) + 1)
for i in range(len(gt_start)):
tmp_iou_list = iou_with_anchors(batch_prop_start, batch_prop_end,
gt_start[i], gt_end[i])
cur_labels.append(tmp_iou_list)
end_prop = np.zeros([
len(batch_prop_start),
])
end_prop[0] = 1.
cur_labels.append(end_prop)
for i in range(len(gt_start) + 1, args.max_seq_len):
cur_labels.append(np.zeros([
len(batch_prop_start),
]))
iou_labels.append(np.array(cur_labels))
return np.array(iou_labels), np.array(lens)
def evaluate(props, sample_list, idx):
prop_start, prop_end = [prop[0] for prop in props], [prop[1] for prop in props]
prop_start, prop_end = np.array(prop_start), np.array(prop_end)
gt_start = sample_list[idx][2]
gt_end = sample_list[idx][3]
prop_gt_iou = []
for i in range(len(props)):
tmp_iou_list = iou_with_anchors(gt_start, gt_end, prop_start[i], prop_end[i])
prop_gt_iou.append(tmp_iou_list)
prop_gt_iou = np.array(prop_gt_iou)
p, r, f = [], [], []
for tiou in np.linspace(0.5, 0.95, 10):
cur_iou = copy.deepcopy(prop_gt_iou)
match = .0
matched = []
for i in range(len(props)):
j = np.argmax(cur_iou[i])
if prop_gt_iou[i][j] > tiou and j not in matched:
match += 1
matched.append(j)
cur_iou[:, j] = -1
cur_p = match / len(props)
cur_r = match / len(gt_start)
p.append(cur_p)
r.append(cur_r)
f.append(2 * cur_p * cur_r / (cur_p + cur_r + 1e-10))
return np.array(p), np.array(r), np.array(f)
def soft_nms(prop, alpha, t1, t2):
'''
prop: proposals generated by network, (score, start, end) for each row;
alpha: alpha value of Gaussian decaying function;
t1, t2: threshold for soft nms.
'''
idx = np.argsort(-prop[:, 0], axis=0)
prop = prop[idx]
tscore, tstart, tend = map(list, [prop[:, 0], prop[:, 1], prop[:, 2]])
rstart = []
rend = []
rscore = []
while len(tscore) > 1 and len(rscore) < 101:
max_index = tscore.index(max(tscore))
tmp_iou_list = iou_with_anchors(np.array(tstart), np.array(tend),
tstart[max_index], tend[max_index])
for idx in range(0, len(tscore)):
if idx != max_index:
tmp_iou = tmp_iou_list[idx]
tmp_width = (tend[max_index] - tstart[max_index]) / tscale
if tmp_iou > t1 + (t2 - t1) * tmp_width:
tscore[idx] = tscore[idx] * np.exp(
-np.square(tmp_iou) / alpha)
rstart.append(tstart[max_index])
rend.append(tend[max_index])
rscore.append(tscore[max_index])
tstart.pop(max_index)
tend.pop(max_index)
tscore.pop(max_index)
newprop = np.stack([rscore, rstart, rend]).transpose()
return newprop
def _get_train_label(self, start, end, anchor_xmin, anchor_xmax):
gt_bbox = []
gt_iou_map = []
for tmp_start, tmp_end in zip(start, end):
gt_bbox.append([tmp_start, tmp_end])
tmp_gt_iou_map = iou_with_anchors(self.match_map[:, 0], self.match_map[:, 1], tmp_start, tmp_end)
tmp_gt_iou_map = np.reshape(tmp_gt_iou_map, [self.maxdur, self.tscale])
gt_iou_map.append(tmp_gt_iou_map)
gt_iou_map = np.array(gt_iou_map)
gt_iou_map = np.max(gt_iou_map, axis=0)
gt_iou_map = torch.Tensor(gt_iou_map)
# generate R_s and R_e
gt_bbox = np.array(gt_bbox)
gt_xmins = gt_bbox[:, 0]
gt_xmaxs = gt_bbox[:, 1]
gt_lens = gt_xmaxs - gt_xmins
gt_len_small = 3 * self.temporal_gap # np.maximum(self.temporal_gap, self.boundary_ratio * gt_lens)
gt_start_bboxs = np.stack((gt_xmins - gt_len_small / 2, gt_xmins + gt_len_small / 2), axis=1)
gt_end_bboxs = np.stack((gt_xmaxs - gt_len_small / 2, gt_xmaxs + gt_len_small / 2), axis=1)
# calculate the ioa for all timestamp
match_score_start, match_score_end = [], []
for i in range(len(anchor_xmin)):
match_score_start.append(np.max(
ioa_with_anchors(anchor_xmin[i], anchor_xmax[i], gt_start_bboxs[:, 0], gt_start_bboxs[:, 1])))
match_score_end.append(np.max(
ioa_with_anchors(anchor_xmin[i], anchor_xmax[i], gt_end_bboxs[:, 0], gt_end_bboxs[:, 1])))
match_score_start = torch.Tensor(match_score_start)
match_score_end = torch.Tensor(match_score_end)
return match_score_start, match_score_end, gt_iou_map