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PaddleDetection
未验证 提交 da0157cf 编写于 作者: W wangguanzhong 提交者： GitHub
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update numpy 1.24 (#7552)

上级 630304e0
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Showing with 135 addition and 101 deletion
deploy/pptracking/python/mot/matching/jde_matching.py
浏览文件 @ da0157cf
......@@ -82,8 +82,8 @@ def linear_assignment(cost_matrix, thresh):
def bbox_ious(atlbrs, btlbrs):
boxes = np.ascontiguousarray(atlbrs, dtype=np.float)
query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float)
boxes = np.ascontiguousarray(atlbrs, dtype=np.float32)
query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float32)
N = boxes.shape[0]
K = query_boxes.shape[0]
ious = np.zeros((N, K), dtype=boxes.dtype)
......@@ -127,13 +127,13 @@ def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray(
[track.curr_feat for track in detections], dtype=np.float)
[track.curr_feat for track in detections], dtype=np.float32)
track_features = np.asarray(
[track.smooth_feat for track in tracks], dtype=np.float)
[track.smooth_feat for track in tracks], dtype=np.float32)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features,
metric)) # Nomalized features
return cost_matrix
......
deploy/pptracking/python/mot/tracker/base_jde_tracker.py
浏览文件 @ da0157cf
......@@ -95,14 +95,9 @@ class BaseTrack(object):
class STrack(BaseTrack):
def __init__(self,
tlwh,
score,
cls_id,
buff_size=30,
temp_feat=None):
def __init__(self, tlwh, score, cls_id, buff_size=30, temp_feat=None):
# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=np.float32)
self.score = score
self.cls_id = cls_id
self.track_len = 0
......
deploy/python/utils.py
浏览文件 @ da0157cf
......@@ -357,7 +357,7 @@ def nms(dets, match_threshold=0.6, match_metric='iou'):
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
suppressed = np.zeros((ndets), dtype=np.int32)
for _i in range(ndets):
i = order[_i]
......
deploy/third_engine/demo_avh/convert_image.py
浏览文件 @ da0157cf
......@@ -78,13 +78,13 @@ def create_headers(image_name):
# Create input header file
create_header_file("inputs", "input", img_data, "./include")
# Create output header file
output_data = np.zeros([8500], np.float)
output_data = np.zeros([8500], np.float32)
create_header_file(
"outputs",
"output0",
output_data,
"./include", )
output_data = np.zeros([170000], np.float)
output_data = np.zeros([170000], np.float32)
create_header_file(
"outputs",
"output1",
......
ppdet/data/crop_utils/annotation_cropper.py
浏览文件 @ da0157cf
......@@ -27,14 +27,15 @@ from .chip_box_utils import intersection_over_box
class AnnoCropper(object):
def __init__(self, image_target_sizes: List[int],
def __init__(self,
image_target_sizes: List[int],
valid_box_ratio_ranges: List[List[float]],
chip_target_size: int, chip_target_stride: int,
use_neg_chip: bool = False,
max_neg_num_per_im: int = 8,
max_per_img: int = -1,
nms_thresh: int = 0.5
):
chip_target_size: int,
chip_target_stride: int,
use_neg_chip: bool=False,
max_neg_num_per_im: int=8,
max_per_img: int=-1,
nms_thresh: int=0.5):
"""
Generate chips by chip_target_size and chip_target_stride.
These two parameters just like kernel_size and stride in cnn.
......@@ -117,7 +118,8 @@ class AnnoCropper(object):
self.chip_records = []
self._global_chip_id = 1
for r in records:
self._cur_im_pos_chips = [] # element: (chip, boxes_idx), chip is [x1, y1, x2, y2], boxes_ids is List[int]
self._cur_im_pos_chips = [
] # element: (chip, boxes_idx), chip is [x1, y1, x2, y2], boxes_ids is List[int]
self._cur_im_neg_chips = [] # element: (chip, neg_box_num)
for scale_i in range(self.scale_num):
self._get_current_scale_parameters(scale_i, r)
......@@ -126,12 +128,16 @@ class AnnoCropper(object):
chips = self._create_chips(r['h'], r['w'], self._cur_scale)
# # dict: chipid->[box_id, ...]
pos_chip2boxes_idx = self._get_valid_boxes_and_pos_chips(r['gt_bbox'], chips)
pos_chip2boxes_idx = self._get_valid_boxes_and_pos_chips(
r['gt_bbox'], chips)
# dict: chipid->neg_box_num
neg_chip2box_num = self._get_neg_boxes_and_chips(chips, list(pos_chip2boxes_idx.keys()), r.get('proposals', None))
neg_chip2box_num = self._get_neg_boxes_and_chips(
chips,
list(pos_chip2boxes_idx.keys()), r.get('proposals', None))
self._add_to_cur_im_chips(chips, pos_chip2boxes_idx, neg_chip2box_num)
self._add_to_cur_im_chips(chips, pos_chip2boxes_idx,
neg_chip2box_num)
cur_image_records = self._trans_all_chips2annotations(r)
self.chip_records.extend(cur_image_records)
......@@ -156,20 +162,24 @@ class AnnoCropper(object):
gt_class = r['gt_class']
# gt_poly = r['gt_poly'] # [None]xN
# remaining keys: im_id, h, w
chip_records = self._trans_pos_chips2annotations(im_file, gt_bbox, is_crowd, gt_class)
chip_records = self._trans_pos_chips2annotations(im_file, gt_bbox,
is_crowd, gt_class)
if not self.use_neg_chip:
return chip_records
sampled_neg_chips = self._sample_neg_chips()
neg_chip_records = self._trans_neg_chips2annotations(im_file, sampled_neg_chips)
neg_chip_records = self._trans_neg_chips2annotations(im_file,
sampled_neg_chips)
chip_records.extend(neg_chip_records)
return chip_records
def _trans_pos_chips2annotations(self, im_file, gt_bbox, is_crowd, gt_class):
def _trans_pos_chips2annotations(self, im_file, gt_bbox, is_crowd,
gt_class):
chip_records = []
for chip, boxes_idx in self._cur_im_pos_chips:
chip_bbox, final_boxes_idx = transform_chip_box(gt_bbox, boxes_idx, chip)
chip_bbox, final_boxes_idx = transform_chip_box(gt_bbox, boxes_idx,
chip)
x1, y1, x2, y2 = chip
chip_h = y2 - y1
chip_w = x2 - x1
......@@ -197,12 +207,15 @@ class AnnoCropper(object):
return self._cur_im_neg_chips
candidate_num = int(sample_num * 1.5)
candidate_neg_chips = sorted(self._cur_im_neg_chips, key=lambda x: -x[1])[:candidate_num]
candidate_neg_chips = sorted(
self._cur_im_neg_chips, key=lambda x: -x[1])[:candidate_num]
random.shuffle(candidate_neg_chips)
sampled_neg_chips = candidate_neg_chips[:sample_num]
return sampled_neg_chips
def _trans_neg_chips2annotations(self, im_file: str, sampled_neg_chips: List[Tuple]):
def _trans_neg_chips2annotations(self,
im_file: str,
sampled_neg_chips: List[Tuple]):
chip_records = []
for chip, neg_box_num in sampled_neg_chips:
x1, y1, x2, y2 = chip
......@@ -213,9 +226,12 @@ class AnnoCropper(object):
'im_id': np.array([self._global_chip_id]),
'h': chip_h,
'w': chip_w,
'gt_bbox': np.zeros((0, 4), dtype=np.float32),
'is_crowd': np.zeros((0, 1), dtype=np.int32),
'gt_class': np.zeros((0, 1), dtype=np.int32),
'gt_bbox': np.zeros(
(0, 4), dtype=np.float32),
'is_crowd': np.zeros(
(0, 1), dtype=np.int32),
'gt_class': np.zeros(
(0, 1), dtype=np.int32),
# 'gt_poly': [],
'chip': chip
}
......@@ -247,7 +263,8 @@ class AnnoCropper(object):
assert chip_size >= stride
chip_overlap = chip_size - stride
if (width - chip_overlap) % stride > min_chip_location_diff: # 不能被stride整除的部分比较大，则保留
if (width - chip_overlap
) % stride > min_chip_location_diff: # 不能被stride整除的部分比较大，则保留
w_steps = max(1, int(math.ceil((width - chip_overlap) / stride)))
else: # 不能被stride整除的部分比较小，则丢弃
w_steps = max(1, int(math.floor((width - chip_overlap) / stride)))
......@@ -267,9 +284,10 @@ class AnnoCropper(object):
# check chip size
for item in chips:
if item[2] - item[0] > chip_size * 1.1 or item[3] - item[1] > chip_size * 1.1:
if item[2] - item[0] > chip_size * 1.1 or item[3] - item[
1] > chip_size * 1.1:
raise ValueError(item)
chips = np.array(chips, dtype=np.float)
chips = np.array(chips, dtype=np.float32)
raw_size_chips = chips / scale
return raw_size_chips
......@@ -279,12 +297,15 @@ class AnnoCropper(object):
im_size = self._cur_im_size
scale = self._cur_scale
# Nx4 N
valid_boxes, valid_boxes_idx = self._validate_boxes(valid_ratio_range, im_size, gt_bbox, scale)
valid_boxes, valid_boxes_idx = self._validate_boxes(
valid_ratio_range, im_size, gt_bbox, scale)
# dict: chipid->[box_id, ...]
pos_chip2boxes_idx = self._find_pos_chips(chips, valid_boxes, valid_boxes_idx)
pos_chip2boxes_idx = self._find_pos_chips(chips, valid_boxes,
valid_boxes_idx)
return pos_chip2boxes_idx
def _validate_boxes(self, valid_ratio_range: List[float],
def _validate_boxes(self,
valid_ratio_range: List[float],
im_size: int,
gt_boxes: 'np.array of Nx4',
scale: float):
......@@ -299,20 +320,26 @@ class AnnoCropper(object):
target_mins = mins * scale
low = valid_ratio_range[0] if valid_ratio_range[0] > 0 else 0
high = valid_ratio_range[1] if valid_ratio_range[1] > 0 else np.finfo(np.float).max
high = valid_ratio_range[1] if valid_ratio_range[1] > 0 else np.finfo(
np.float32).max
valid_boxes_idx = np.nonzero((low <= box_ratio) & (box_ratio < high) & (target_mins >= 2))[0]
valid_boxes_idx = np.nonzero((low <= box_ratio) & (box_ratio < high) & (
target_mins >= 2))[0]
valid_boxes = gt_boxes[valid_boxes_idx]
return valid_boxes, valid_boxes_idx
def _find_pos_chips(self, chips: 'Cx4', valid_boxes: 'Bx4', valid_boxes_idx: 'B'):
def _find_pos_chips(self,
chips: 'Cx4',
valid_boxes: 'Bx4',
valid_boxes_idx: 'B'):
"""
:return: pos_chip2boxes_idx, dict: chipid->[box_id, ...]
"""
iob = intersection_over_box(chips, valid_boxes) # overlap, CxB
iob_threshold_to_find_chips = 1.
pos_chip_ids, _ = self._find_chips_to_cover_overlaped_boxes(iob, iob_threshold_to_find_chips)
pos_chip_ids, _ = self._find_chips_to_cover_overlaped_boxes(
iob, iob_threshold_to_find_chips)
pos_chip_ids = set(pos_chip_ids)
iob_threshold_to_assign_box = 0.5
......@@ -323,7 +350,8 @@ class AnnoCropper(object):
def _find_chips_to_cover_overlaped_boxes(self, iob, overlap_threshold):
return find_chips_to_cover_overlaped_boxes(iob, overlap_threshold)
def _assign_boxes_to_pos_chips(self, iob, overlap_threshold, pos_chip_ids, valid_boxes_idx):
def _assign_boxes_to_pos_chips(self, iob, overlap_threshold, pos_chip_ids,
valid_boxes_idx):
chip_ids, box_ids = np.nonzero(iob >= overlap_threshold)
pos_chip2boxes_idx = defaultdict(list)
for chip_id, box_id in zip(chip_ids, box_ids):
......@@ -333,7 +361,10 @@ class AnnoCropper(object):
pos_chip2boxes_idx[chip_id].append(raw_gt_box_idx)
return pos_chip2boxes_idx
def _get_neg_boxes_and_chips(self, chips: 'Cx4', pos_chip_ids: 'D', proposals: 'Px4'):
def _get_neg_boxes_and_chips(self,
chips: 'Cx4',
pos_chip_ids: 'D',
proposals: 'Px4'):
"""
:param chips:
:param pos_chip_ids:
......@@ -351,12 +382,16 @@ class AnnoCropper(object):
im_size = self._cur_im_size
scale = self._cur_scale
valid_props, _ = self._validate_boxes(valid_ratio_range, im_size, proposals, scale)
valid_props, _ = self._validate_boxes(valid_ratio_range, im_size,
proposals, scale)
neg_boxes = self._find_neg_boxes(chips, pos_chip_ids, valid_props)
neg_chip2box_num = self._find_neg_chips(chips, pos_chip_ids, neg_boxes)
return neg_chip2box_num
def _find_neg_boxes(self, chips: 'Cx4', pos_chip_ids: 'D', valid_props: 'Px4'):
def _find_neg_boxes(self,
chips: 'Cx4',
pos_chip_ids: 'D',
valid_props: 'Px4'):
"""
:return: neg_boxes: Nx4
"""
......@@ -370,7 +405,8 @@ class AnnoCropper(object):
neg_boxes = valid_props[non_overlap_props_idx]
return neg_boxes
def _find_neg_chips(self, chips: 'Cx4', pos_chip_ids: 'D', neg_boxes: 'Nx4'):
def _find_neg_chips(self, chips: 'Cx4', pos_chip_ids: 'D',
neg_boxes: 'Nx4'):
"""
:return: neg_chip2box_num, dict: chipid->neg_box_num
"""
......@@ -469,7 +505,8 @@ class AnnoCropper(object):
for result in results:
bbox_locs = result['bbox']
bbox_nums = result['bbox_num']
if len(bbox_locs) == 1 and bbox_locs[0][0] == -1: # current batch has no detections
if len(bbox_locs) == 1 and bbox_locs[0][
0] == -1: # current batch has no detections
# bbox_locs = array([[-1.]], dtype=float32); bbox_nums = [[1]]
# MultiClassNMS output: If there is no detected boxes for all images, lod will be set to {1} and Out only contains one value which is -1.
continue
......@@ -479,20 +516,25 @@ class AnnoCropper(object):
for idx, im_id in enumerate(im_ids):
cur_bbox_len = bbox_nums[idx]
bboxes = bbox_locs[last_bbox_num: last_bbox_num + cur_bbox_len]
bboxes = bbox_locs[last_bbox_num:last_bbox_num + cur_bbox_len]
last_bbox_num += cur_bbox_len
# box: [num_id, score, xmin, ymin, xmax, ymax]
if len(bboxes) == 0: # current image has no detections
continue
chip_rec = records[int(im_id) - 1] # im_id starts from 1, type is np.int64
chip_rec = records[int(im_id) -
1] # im_id starts from 1, type is np.int64
image_size = max(chip_rec["ori_im_h"], chip_rec["ori_im_w"])
bboxes = transform_chip_boxes2image_boxes(bboxes, chip_rec["chip"], chip_rec["ori_im_h"], chip_rec["ori_im_w"])
bboxes = transform_chip_boxes2image_boxes(
bboxes, chip_rec["chip"], chip_rec["ori_im_h"],
chip_rec["ori_im_w"])
scale_i = chip_rec["scale_i"]
cur_scale = self._get_current_scale(self.target_sizes[scale_i], image_size)
_, valid_boxes_idx = self._validate_boxes(self.valid_box_ratio_ranges[scale_i], image_size,
cur_scale = self._get_current_scale(self.target_sizes[scale_i],
image_size)
_, valid_boxes_idx = self._validate_boxes(
self.valid_box_ratio_ranges[scale_i], image_size,
bboxes[:, 2:], cur_scale)
ori_img_id = self._global_chip_id2img_id[int(im_id)]
......@@ -507,7 +549,8 @@ class AnnoCropper(object):
nms_thresh = self.nms_thresh
for img_id in img_id2bbox:
box = img_id2bbox[img_id] # list of np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
box = img_id2bbox[
img_id] # list of np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
box = np.concatenate(box, axis=0)
nms_dets = nms(box, nms_thresh)
if max_per_img > 0:
......@@ -525,18 +568,13 @@ class AnnoCropper(object):
results = []
for img_id in im_ids: # output by original im_id order
if len(img_id2bbox[img_id]) == 0:
bbox = np.array([[-1., 0., 0., 0., 0., 0.]]) # edge case: no detections
bbox = np.array(
[[-1., 0., 0., 0., 0., 0.]]) # edge case: no detections
bbox_num = np.array([0])
else:
# np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
bbox = img_id2bbox[img_id]
bbox_num = np.array([len(bbox)])
res = dict(
im_id=np.array([[img_id]]),
bbox=bbox,
bbox_num=bbox_num
)
res = dict(im_id=np.array([[img_id]]), bbox=bbox, bbox_num=bbox_num)
results.append(res)
return results
ppdet/data/crop_utils/chip_box_utils.py
浏览文件 @ da0157cf
......@@ -33,8 +33,10 @@ def intersection_over_box(chips, boxes):
box_area = bbox_area(boxes) # B
inter_x2y2 = np.minimum(np.expand_dims(chips, 1)[:, :, 2:], boxes[:, 2:]) # CxBX2
inter_x1y1 = np.maximum(np.expand_dims(chips, 1)[:, :, :2], boxes[:, :2]) # CxBx2
inter_x2y2 = np.minimum(np.expand_dims(chips, 1)[:, :, 2:],
boxes[:, 2:]) # CxBX2
inter_x1y1 = np.maximum(np.expand_dims(chips, 1)[:, :, :2],
boxes[:, :2]) # CxBx2
inter_wh = inter_x2y2 - inter_x1y1
inter_wh = np.clip(inter_wh, a_min=0, a_max=None)
inter_area = inter_wh[:, :, 0] * inter_wh[:, :, 1] # CxB
......@@ -81,7 +83,8 @@ def transform_chip_box(gt_bbox: 'Gx4', boxes_idx: 'B', chip: '4'):
def find_chips_to_cover_overlaped_boxes(iob, overlap_threshold):
chip_ids, box_ids = np.nonzero(iob >= overlap_threshold)
chip_id2overlap_box_num = np.bincount(chip_ids) # 1d array
chip_id2overlap_box_num = np.pad(chip_id2overlap_box_num, (0, len(iob) - len(chip_id2overlap_box_num)),
chip_id2overlap_box_num = np.pad(
chip_id2overlap_box_num, (0, len(iob) - len(chip_id2overlap_box_num)),
constant_values=0)
chosen_chip_ids = []
......@@ -92,7 +95,8 @@ def find_chips_to_cover_overlaped_boxes(iob, overlap_threshold):
chosen_chip_ids.append(max_count_chip_id)
box_ids_in_cur_chip = box_ids[chip_ids == max_count_chip_id]
ids_not_in_cur_boxes_mask = np.logical_not(np.isin(box_ids, box_ids_in_cur_chip))
ids_not_in_cur_boxes_mask = np.logical_not(
np.isin(box_ids, box_ids_in_cur_chip))
chip_ids = chip_ids[ids_not_in_cur_boxes_mask]
box_ids = box_ids[ids_not_in_cur_boxes_mask]
return chosen_chip_ids, chip_id2overlap_box_num
......@@ -124,7 +128,7 @@ def nms(dets, thresh):
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
suppressed = np.zeros((ndets), dtype=np.int32)
# nominal indices
# _i, _j
......
ppdet/data/source/keypoint_coco.py
浏览文件 @ da0157cf
......@@ -487,9 +487,9 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
continue
joints = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
(self.ann_info['num_joints'], 3), dtype=np.float32)
joints_vis = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
(self.ann_info['num_joints'], 3), dtype=np.float32)
for ipt in range(self.ann_info['num_joints']):
joints[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
joints[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
......@@ -560,9 +560,10 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
continue
center, scale = self._box2cs(box)
joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
joints = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float32)
joints_vis = np.ones(
(self.ann_info['num_joints'], 3), dtype=np.float)
(self.ann_info['num_joints'], 3), dtype=np.float32)
kpt_db.append({
'image_file': img_name,
'im_id': im_id,
......@@ -633,8 +634,8 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
im_id = a['image_id'] if 'image_id' in a else int(
os.path.splitext(image_name)[0])
c = np.array(a['center'], dtype=np.float)
s = np.array([a['scale'], a['scale']], dtype=np.float)
c = np.array(a['center'], dtype=np.float32)
s = np.array([a['scale'], a['scale']], dtype=np.float32)
# Adjust center/scale slightly to avoid cropping limbs
if c[0] != -1:
......@@ -642,9 +643,10 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
s = s * 1.25
c = c - 1
joints = np.zeros((self.ann_info['num_joints'], 3), dtype=np.float)
joints = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float32)
joints_vis = np.zeros(
(self.ann_info['num_joints'], 3), dtype=np.float)
(self.ann_info['num_joints'], 3), dtype=np.float32)
if 'joints' in a:
joints_ = np.array(a['joints'])
joints_[:, 0:2] = joints_[:, 0:2] - 1
......
ppdet/data/source/pose3d_cmb.py
浏览文件 @ da0157cf
......@@ -68,7 +68,7 @@ class Pose3DDataset(DetDataset):
def get_mask(self, mvm_percent=0.3):
num_joints = self.num_joints
mjm_mask = np.ones((num_joints, 1)).astype(np.float)
mjm_mask = np.ones((num_joints, 1)).astype(np.float32)
if self.test_mode == False:
pb = np.random.random_sample()
masked_num = int(
......@@ -78,7 +78,7 @@ class Pose3DDataset(DetDataset):
np.arange(num_joints), replace=False, size=masked_num)
mjm_mask[indices, :] = 0.0
mvm_mask = np.ones((10, 1)).astype(np.float)
mvm_mask = np.ones((10, 1)).astype(np.float32)
if self.test_mode == False:
num_vertices = 10
pb = np.random.random_sample()
......
ppdet/modeling/mot/matching/jde_matching.py
浏览文件 @ da0157cf
......@@ -82,8 +82,8 @@ def linear_assignment(cost_matrix, thresh):
def bbox_ious(atlbrs, btlbrs):
boxes = np.ascontiguousarray(atlbrs, dtype=np.float)
query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float)
boxes = np.ascontiguousarray(atlbrs, dtype=np.float32)
query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float32)
N = boxes.shape[0]
K = query_boxes.shape[0]
ious = np.zeros((N, K), dtype=boxes.dtype)
......@@ -127,13 +127,13 @@ def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray(
[track.curr_feat for track in detections], dtype=np.float)
[track.curr_feat for track in detections], dtype=np.float32)
track_features = np.asarray(
[track.smooth_feat for track in tracks], dtype=np.float)
[track.smooth_feat for track in tracks], dtype=np.float32)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features,
metric)) # Nomalized features
return cost_matrix
......
ppdet/modeling/mot/tracker/base_jde_tracker.py
浏览文件 @ da0157cf
......@@ -102,14 +102,9 @@ class BaseTrack(object):
@register
@serializable
class STrack(BaseTrack):
def __init__(self,
tlwh,
score,
cls_id,
buff_size=30,
temp_feat=None):
def __init__(self, tlwh, score, cls_id, buff_size=30, temp_feat=None):
# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=np.float32)
self.score = score
self.cls_id = cls_id
self.track_len = 0
......
ppdet/modeling/post_process.py
浏览文件 @ da0157cf
......@@ -635,7 +635,7 @@ def nms(dets, match_threshold=0.6, match_metric='iou'):
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
suppressed = np.zeros((ndets), dtype=np.int32)
for _i in range(ndets):
i = order[_i]
......
ppdet/modeling/proposal_generator/target.py
浏览文件 @ da0157cf
......@@ -295,7 +295,7 @@ def polygons_to_mask(polygons, height, width):
assert len(polygons) > 0, "COCOAPI does not support empty polygons"
rles = mask_util.frPyObjects(polygons, height, width)
rle = mask_util.merge(rles)
return mask_util.decode(rle).astype(np.bool)
return mask_util.decode(rle).astype(np.bool_)
def rasterize_polygons_within_box(poly, box, resolution):
......@@ -448,7 +448,7 @@ def libra_sample_via_interval(max_overlaps, full_set, num_expected, floor_thr,
tmp_sampled_set = np.random.choice(
tmp_inds, size=per_num_expected, replace=False)
else:
tmp_sampled_set = np.array(tmp_inds, dtype=np.int)
tmp_sampled_set = np.array(tmp_inds, dtype=np.int32)
sampled_inds.append(tmp_sampled_set)
sampled_inds = np.concatenate(sampled_inds)
......@@ -509,13 +509,13 @@ def libra_sample_neg(max_overlaps,
size=num_expected_iou_sampling,
replace=False)
else:
iou_sampled_inds = np.array(iou_sampling_neg_inds, dtype=np.int)
iou_sampled_inds = np.array(iou_sampling_neg_inds, dtype=np.int32)
num_expected_floor = num_expected - len(iou_sampled_inds)
if len(floor_neg_inds) > num_expected_floor:
sampled_floor_inds = np.random.choice(
floor_neg_inds, size=num_expected_floor, replace=False)
else:
sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int)
sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int32)
sampled_inds = np.concatenate((sampled_floor_inds, iou_sampled_inds))
if len(sampled_inds) < num_expected:
num_extra = num_expected - len(sampled_inds)
......
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