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def gating_distance(self,
mean,
covariance,
measurements,
only_position=False,
metric='maha'):
"""
Compute gating distance between state distribution and measurements.
A suitable distance threshold can be obtained from `chi2inv95`. If
`only_position` is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
Args:
mean (ndarray): Mean vector over the state distribution (8
dimensional).
covariance (ndarray): Covariance of the state distribution (8x8
dimensional).
measurements (ndarray): An Nx4 dimensional matrix of N measurements,
each in format (x, y, a, h) where (x, y) is the bounding box center
position, a the aspect ratio, and h the height.
only_position (Optional[bool]): If True, distance computation is
done with respect to the bounding box center position only.
metric (str): Metric type, 'gaussian' or 'maha'.
Returns
An array of length N, where the i-th element contains the squared
Mahalanobis distance between (mean, covariance) and `measurements[i]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
mean, covariance = mean[:2], covariance[:2, :2]
measurements = measurements[:, :2]
d = measurements - mean
if metric == 'gaussian':
return np.sum(d * d, axis=1)
elif metric == 'maha':
cholesky_factor = np.linalg.cholesky(covariance)
z = scipy.linalg.solve_triangular(
cholesky_factor,
d.T,
lower=True,
check_finite=False,
overwrite_b=True)
squared_maha = np.sum(z * z, axis=0)
return squared_maha
else:
raise ValueError('invalid distance metric') |
Compute gating distance between state distribution and measurements.
A suitable distance threshold can be obtained from `chi2inv95`. If
`only_position` is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
Args:
mean (ndarray): Mean vector over the state distribution (8
dimensional).
covariance (ndarray): Covariance of the state distribution (8x8
dimensional).
measurements (ndarray): An Nx4 dimensional matrix of N measurements,
each in format (x, y, a, h) where (x, y) is the bounding box center
position, a the aspect ratio, and h the height.
only_position (Optional[bool]): If True, distance computation is
done with respect to the bounding box center position only.
metric (str): Metric type, 'gaussian' or 'maha'.
Returns
An array of length N, where the i-th element contains the squared
Mahalanobis distance between (mean, covariance) and `measurements[i]`.
| gating_distance | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def sub_cluster(cid_tid_dict,
scene_cluster,
use_ff=True,
use_rerank=True,
use_camera=False,
use_st_filter=False):
'''
cid_tid_dict: all camera_id and track_id
scene_cluster: like [41, 42, 43, 44, 45, 46] in AIC21 MTMCT S06 test videos
'''
assert (len(scene_cluster) != 0), "Error: scene_cluster length equals 0"
cid_tids = sorted(
[key for key in cid_tid_dict.keys() if key[0] in scene_cluster])
if use_camera:
clu = get_labels_with_camera(
cid_tid_dict,
cid_tids,
use_ff=use_ff,
use_rerank=use_rerank,
use_st_filter=use_st_filter)
else:
clu = get_labels(
cid_tid_dict,
cid_tids,
use_ff=use_ff,
use_rerank=use_rerank,
use_st_filter=use_st_filter)
new_clu = list()
for c_list in clu:
if len(c_list) <= 1: continue
cam_list = [cid_tids[c][0] for c in c_list]
if len(cam_list) != len(set(cam_list)): continue
new_clu.append([cid_tids[c] for c in c_list])
all_clu = new_clu
cid_tid_label = dict()
for i, c_list in enumerate(all_clu):
for c in c_list:
cid_tid_label[c] = i + 1
return cid_tid_label |
cid_tid_dict: all camera_id and track_id
scene_cluster: like [41, 42, 43, 44, 45, 46] in AIC21 MTMCT S06 test videos
| sub_cluster | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/postprocess.py | Apache-2.0 |
def getData(fpath, names=None, sep='\s+|\t+|,'):
""" Get the necessary track data from a file handle.
Args:
fpath (str) : Original path of file reading from.
names (list[str]): List of column names for the data.
sep (str): Allowed separators regular expression string.
Return:
df (pandas.DataFrame): Data frame containing the data loaded from the
stream with optionally assigned column names. No index is set on the data.
"""
try:
df = pd.read_csv(
fpath,
sep=sep,
index_col=None,
skipinitialspace=True,
header=None,
names=names,
engine='python')
return df
except Exception as e:
raise ValueError("Could not read input from %s. Error: %s" %
(fpath, repr(e))) | Get the necessary track data from a file handle.
Args:
fpath (str) : Original path of file reading from.
names (list[str]): List of column names for the data.
sep (str): Allowed separators regular expression string.
Return:
df (pandas.DataFrame): Data frame containing the data loaded from the
stream with optionally assigned column names. No index is set on the data.
| getData | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/utils.py | Apache-2.0 |
def init_count(num_classes):
"""
Initiate _count for all object classes
:param num_classes:
"""
for cls_id in range(num_classes):
BaseTrack._count_dict[cls_id] = 0 |
Initiate _count for all object classes
:param num_classes:
| init_count | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
"""
if self.mean is None:
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret | Get current position in bounding box format `(top left x, top left y,
width, height)`.
| tlwh | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlbr(self):
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret | Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
| tlbr | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlwh_to_xyah(tlwh):
"""Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = np.asarray(tlwh).copy()
ret[:2] += ret[2:] / 2
ret[2] /= ret[3]
return ret | Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
| tlwh_to_xyah | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def to_tlwh(self):
"""Get position in format `(top left x, top left y, width, height)`."""
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret | Get position in format `(top left x, top left y, width, height)`. | to_tlwh | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def to_tlbr(self):
"""Get position in bounding box format `(min x, miny, max x, max y)`."""
ret = self.to_tlwh()
ret[2:] = ret[:2] + ret[2:]
return ret | Get position in bounding box format `(min x, miny, max x, max y)`. | to_tlbr | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def predict(self, kalman_filter):
"""
Propagate the state distribution to the current time step using a Kalman
filter prediction step.
"""
self.mean, self.covariance = kalman_filter.predict(self.mean,
self.covariance)
self.age += 1
self.time_since_update += 1 |
Propagate the state distribution to the current time step using a Kalman
filter prediction step.
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def update(self, kalman_filter, detection):
"""
Perform Kalman filter measurement update step and update the associated
detection feature cache.
"""
self.mean, self.covariance = kalman_filter.update(self.mean,
self.covariance,
detection.to_xyah())
self.features.append(detection.feature)
self.feat = detection.feature
self.cls_id = detection.cls_id
self.score = detection.score
self.hits += 1
self.time_since_update = 0
if self.state == TrackState.Tentative and self.hits >= self._n_init:
self.state = TrackState.Confirmed |
Perform Kalman filter measurement update step and update the associated
detection feature cache.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def mark_missed(self):
"""Mark this track as missed (no association at the current time step).
"""
if self.state == TrackState.Tentative:
self.state = TrackState.Deleted
elif self.time_since_update > self._max_age:
self.state = TrackState.Deleted | Mark this track as missed (no association at the current time step).
| mark_missed | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs):
"""
Perform measurement update and track management.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128], usually pred_embs.shape[1] is a multiple of 128.
"""
pred_cls_ids = pred_dets[:, 0:1]
pred_scores = pred_dets[:, 1:2]
pred_xyxys = pred_dets[:, 2:6]
pred_tlwhs = np.concatenate(
(pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
axis=1)
detections = [
Detection(tlwh, score, feat, cls_id)
for tlwh, score, feat, cls_id in zip(pred_tlwhs, pred_scores,
pred_embs, pred_cls_ids)
]
# Run matching cascade.
matches, unmatched_tracks, unmatched_detections = \
self._match(detections)
# Update track set.
for track_idx, detection_idx in matches:
self.tracks[track_idx].update(self.motion,
detections[detection_idx])
for track_idx in unmatched_tracks:
self.tracks[track_idx].mark_missed()
for detection_idx in unmatched_detections:
self._initiate_track(detections[detection_idx])
self.tracks = [t for t in self.tracks if not t.is_deleted()]
# Update distance metric.
active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
features, targets = [], []
for track in self.tracks:
if not track.is_confirmed():
continue
features += track.features
targets += [track.track_id for _ in track.features]
track.features = []
self.metric.partial_fit(
np.asarray(features), np.asarray(targets), active_targets)
output_stracks = self.tracks
return output_stracks |
Perform measurement update and track management.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128], usually pred_embs.shape[1] is a multiple of 128.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/deepsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/deepsort_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs=None):
"""
Processes the image frame and finds bounding box(detections).
Associates the detection with corresponding tracklets and also handles
lost, removed, refound and active tracklets.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or [N, 512].
Return:
output_stracks_dict (dict(list)): The list contains information
regarding the online_tracklets for the received image tensor.
"""
self.frame_id += 1
if self.frame_id == 1:
STrack.init_count(self.num_classes)
activated_tracks_dict = defaultdict(list)
refined_tracks_dict = defaultdict(list)
lost_tracks_dict = defaultdict(list)
removed_tracks_dict = defaultdict(list)
output_tracks_dict = defaultdict(list)
pred_dets_dict = defaultdict(list)
pred_embs_dict = defaultdict(list)
# unify single and multi classes detection and embedding results
for cls_id in range(self.num_classes):
cls_idx = (pred_dets[:, 0:1] == cls_id).squeeze(-1)
pred_dets_dict[cls_id] = pred_dets[cls_idx]
if pred_embs is not None:
pred_embs_dict[cls_id] = pred_embs[cls_idx]
else:
pred_embs_dict[cls_id] = None
for cls_id in range(self.num_classes):
""" Step 1: Get detections by class"""
pred_dets_cls = pred_dets_dict[cls_id]
pred_embs_cls = pred_embs_dict[cls_id]
remain_inds = (pred_dets_cls[:, 1:2] > self.conf_thres).squeeze(-1)
if remain_inds.sum() > 0:
pred_dets_cls = pred_dets_cls[remain_inds]
if pred_embs_cls is None:
# in original ByteTrack
detections = [
STrack(
STrack.tlbr_to_tlwh(tlbrs[2:6]),
tlbrs[1],
cls_id,
30,
temp_feat=None) for tlbrs in pred_dets_cls
]
else:
pred_embs_cls = pred_embs_cls[remain_inds]
detections = [
STrack(
STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id,
30, temp_feat)
for (tlbrs, temp_feat
) in zip(pred_dets_cls, pred_embs_cls)
]
else:
detections = []
''' Add newly detected tracklets to tracked_stracks'''
unconfirmed_dict = defaultdict(list)
tracked_tracks_dict = defaultdict(list)
for track in self.tracked_tracks_dict[cls_id]:
if not track.is_activated:
# previous tracks which are not active in the current frame are added in unconfirmed list
unconfirmed_dict[cls_id].append(track)
else:
# Active tracks are added to the local list 'tracked_stracks'
tracked_tracks_dict[cls_id].append(track)
""" Step 2: First association, with embedding"""
# building tracking pool for the current frame
track_pool_dict = defaultdict(list)
track_pool_dict[cls_id] = joint_stracks(
tracked_tracks_dict[cls_id], self.lost_tracks_dict[cls_id])
# Predict the current location with KalmanFilter
STrack.multi_predict(track_pool_dict[cls_id], self.motion)
if pred_embs_cls is None:
# in original ByteTrack
dists = matching.iou_distance(track_pool_dict[cls_id],
detections)
matches, u_track, u_detection = matching.linear_assignment(
dists, thresh=self.match_thres) # not self.tracked_thresh
else:
dists = matching.embedding_distance(
track_pool_dict[cls_id],
detections,
metric=self.metric_type)
dists = matching.fuse_motion(
self.motion, dists, track_pool_dict[cls_id], detections)
matches, u_track, u_detection = matching.linear_assignment(
dists, thresh=self.tracked_thresh)
for i_tracked, idet in matches:
# i_tracked is the id of the track and idet is the detection
track = track_pool_dict[cls_id][i_tracked]
det = detections[idet]
if track.state == TrackState.Tracked:
# If the track is active, add the detection to the track
track.update(detections[idet], self.frame_id)
activated_tracks_dict[cls_id].append(track)
else:
# We have obtained a detection from a track which is not active,
# hence put the track in refind_stracks list
track.re_activate(det, self.frame_id, new_id=False)
refined_tracks_dict[cls_id].append(track)
# None of the steps below happen if there are no undetected tracks.
""" Step 3: Second association, with IOU"""
if self.use_byte:
inds_low = pred_dets_dict[cls_id][:, 1:2] > self.low_conf_thres
inds_high = pred_dets_dict[cls_id][:, 1:2] < self.conf_thres
inds_second = np.logical_and(inds_low, inds_high).squeeze(-1)
pred_dets_cls_second = pred_dets_dict[cls_id][inds_second]
# association the untrack to the low score detections
if len(pred_dets_cls_second) > 0:
if pred_embs_dict[cls_id] is None:
# in original ByteTrack
detections_second = [
STrack(
STrack.tlbr_to_tlwh(tlbrs[2:6]),
tlbrs[1],
cls_id,
30,
temp_feat=None)
for tlbrs in pred_dets_cls_second
]
else:
pred_embs_cls_second = pred_embs_dict[cls_id][
inds_second]
detections_second = [
STrack(
STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1],
cls_id, 30, temp_feat)
for (tlbrs, temp_feat) in zip(pred_dets_cls_second,
pred_embs_cls_second)
]
else:
detections_second = []
r_tracked_stracks = [
track_pool_dict[cls_id][i] for i in u_track
if track_pool_dict[cls_id][i].state == TrackState.Tracked
]
dists = matching.iou_distance(r_tracked_stracks,
detections_second)
matches, u_track, u_detection_second = matching.linear_assignment(
dists, thresh=0.4) # not r_tracked_thresh
else:
detections = [detections[i] for i in u_detection]
r_tracked_stracks = []
for i in u_track:
if track_pool_dict[cls_id][i].state == TrackState.Tracked:
r_tracked_stracks.append(track_pool_dict[cls_id][i])
dists = matching.iou_distance(r_tracked_stracks, detections)
matches, u_track, u_detection = matching.linear_assignment(
dists, thresh=self.r_tracked_thresh)
for i_tracked, idet in matches:
track = r_tracked_stracks[i_tracked]
det = detections[
idet] if not self.use_byte else detections_second[idet]
if track.state == TrackState.Tracked:
track.update(det, self.frame_id)
activated_tracks_dict[cls_id].append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refined_tracks_dict[cls_id].append(track)
for it in u_track:
track = r_tracked_stracks[it]
if not track.state == TrackState.Lost:
track.mark_lost()
lost_tracks_dict[cls_id].append(track)
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
detections = [detections[i] for i in u_detection]
dists = matching.iou_distance(unconfirmed_dict[cls_id], detections)
matches, u_unconfirmed, u_detection = matching.linear_assignment(
dists, thresh=self.unconfirmed_thresh)
for i_tracked, idet in matches:
unconfirmed_dict[cls_id][i_tracked].update(detections[idet],
self.frame_id)
activated_tracks_dict[cls_id].append(unconfirmed_dict[cls_id][
i_tracked])
for it in u_unconfirmed:
track = unconfirmed_dict[cls_id][it]
track.mark_removed()
removed_tracks_dict[cls_id].append(track)
""" Step 4: Init new stracks"""
for inew in u_detection:
track = detections[inew]
if track.score < self.det_thresh:
continue
track.activate(self.motion, self.frame_id)
activated_tracks_dict[cls_id].append(track)
""" Step 5: Update state"""
for track in self.lost_tracks_dict[cls_id]:
if self.frame_id - track.end_frame > self.max_time_lost:
track.mark_removed()
removed_tracks_dict[cls_id].append(track)
self.tracked_tracks_dict[cls_id] = [
t for t in self.tracked_tracks_dict[cls_id]
if t.state == TrackState.Tracked
]
self.tracked_tracks_dict[cls_id] = joint_stracks(
self.tracked_tracks_dict[cls_id],
activated_tracks_dict[cls_id])
self.tracked_tracks_dict[cls_id] = joint_stracks(
self.tracked_tracks_dict[cls_id], refined_tracks_dict[cls_id])
self.lost_tracks_dict[cls_id] = sub_stracks(
self.lost_tracks_dict[cls_id],
self.tracked_tracks_dict[cls_id])
self.lost_tracks_dict[cls_id].extend(lost_tracks_dict[cls_id])
self.lost_tracks_dict[cls_id] = sub_stracks(
self.lost_tracks_dict[cls_id],
self.removed_tracks_dict[cls_id])
self.removed_tracks_dict[cls_id].extend(removed_tracks_dict[
cls_id])
self.tracked_tracks_dict[cls_id], self.lost_tracks_dict[
cls_id] = remove_duplicate_stracks(
self.tracked_tracks_dict[cls_id],
self.lost_tracks_dict[cls_id])
# get scores of lost tracks
output_tracks_dict[cls_id] = [
track for track in self.tracked_tracks_dict[cls_id]
if track.is_activated
]
return output_tracks_dict |
Processes the image frame and finds bounding box(detections).
Associates the detection with corresponding tracklets and also handles
lost, removed, refound and active tracklets.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or [N, 512].
Return:
output_stracks_dict (dict(list)): The list contains information
regarding the online_tracklets for the received image tensor.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/jde_tracker.py | Apache-2.0 |
def convert_bbox_to_z(bbox):
"""
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
"""
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.
y = bbox[1] + h / 2.
s = w * h # scale is just area
r = w / float(h + 1e-6)
return np.array([x, y, s, r]).reshape((4, 1)) |
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
| convert_bbox_to_z | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def convert_x_to_bbox(x, score=None):
"""
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
"""
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if (score == None):
return np.array(
[x[0] - w / 2., x[1] - h / 2., x[0] + w / 2.,
x[1] + h / 2.]).reshape((1, 4))
else:
score = np.array([score])
return np.array([
x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score
]).reshape((1, 5)) |
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
| convert_x_to_bbox | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
if bbox is not None:
if self.last_observation.sum() >= 0: # no previous observation
previous_box = None
for i in range(self.delta_t):
dt = self.delta_t - i
if self.age - dt in self.observations:
previous_box = self.observations[self.age - dt]
break
if previous_box is None:
previous_box = self.last_observation
"""
Estimate the track speed direction with observations \Delta t steps away
"""
self.velocity = speed_direction(previous_box, bbox)
"""
Insert new observations. This is a ugly way to maintain both self.observations
and self.history_observations. Bear it for the moment.
"""
self.last_observation = bbox
self.observations[self.age] = bbox
self.history_observations.append(bbox)
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
self.kf.update(convert_bbox_to_z(bbox))
else:
self.kf.update(bbox) |
Updates the state vector with observed bbox.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def predict(self):
"""
Advances the state vector and returns the predicted bounding box estimate.
"""
if ((self.kf.x[6] + self.kf.x[2]) <= 0):
self.kf.x[6] *= 0.0
self.kf.predict()
self.age += 1
if (self.time_since_update > 0):
self.hit_streak = 0
self.time_since_update += 1
self.history.append(convert_x_to_bbox(self.kf.x, score=self.score))
return self.history[-1] |
Advances the state vector and returns the predicted bounding box estimate.
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs=None):
"""
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or [N, 512], default as None.
Return:
tracking boxes (np.array): [M, 6], means 'x0, y0, x1, y1, score, id'.
"""
if pred_dets is None:
return np.empty((0, 6))
self.frame_count += 1
bboxes = pred_dets[:, 2:]
scores = pred_dets[:, 1:2]
dets = np.concatenate((bboxes, scores), axis=1)
scores = scores.squeeze(-1)
inds_low = scores > 0.1
inds_high = scores < self.det_thresh
inds_second = np.logical_and(inds_low, inds_high)
# self.det_thresh > score > 0.1, for second matching
dets_second = dets[inds_second] # detections for second matching
remain_inds = scores > self.det_thresh
dets = dets[remain_inds]
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
velocities = np.array([
trk.velocity if trk.velocity is not None else np.array((0, 0))
for trk in self.trackers
])
last_boxes = np.array([trk.last_observation for trk in self.trackers])
k_observations = np.array([
k_previous_obs(trk.observations, trk.age, self.delta_t)
for trk in self.trackers
])
"""
First round of association
"""
matched, unmatched_dets, unmatched_trks = associate(
dets, trks, self.iou_threshold, velocities, k_observations,
self.inertia)
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
"""
Second round of associaton by OCR
"""
# BYTE association
if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[
0] > 0:
u_trks = trks[unmatched_trks]
iou_left = iou_batch(
dets_second,
u_trks) # iou between low score detections and unmatched tracks
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
matched_indices = linear_assignment(-iou_left)
to_remove_trk_indices = []
for m in matched_indices:
det_ind, trk_ind = m[0], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.trackers[trk_ind].update(dets_second[det_ind, :])
to_remove_trk_indices.append(trk_ind)
unmatched_trks = np.setdiff1d(unmatched_trks,
np.array(to_remove_trk_indices))
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
left_dets = dets[unmatched_dets]
left_trks = last_boxes[unmatched_trks]
iou_left = iou_batch(left_dets, left_trks)
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
rematched_indices = linear_assignment(-iou_left)
to_remove_det_indices = []
to_remove_trk_indices = []
for m in rematched_indices:
det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[
1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.trackers[trk_ind].update(dets[det_ind, :])
to_remove_det_indices.append(det_ind)
to_remove_trk_indices.append(trk_ind)
unmatched_dets = np.setdiff1d(unmatched_dets,
np.array(to_remove_det_indices))
unmatched_trks = np.setdiff1d(unmatched_trks,
np.array(to_remove_trk_indices))
for m in unmatched_trks:
self.trackers[m].update(None)
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t)
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if trk.last_observation.sum() < 0:
d = trk.get_state()[0]
else:
d = trk.last_observation # tlbr + score
if (trk.time_since_update < 1) and (
trk.hit_streak >= self.min_hits or
self.frame_count <= self.min_hits):
# +1 as MOT benchmark requires positive
ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
i -= 1
# remove dead tracklet
if (trk.time_since_update > self.max_age):
self.trackers.pop(i)
if (len(ret) > 0):
return np.concatenate(ret)
return np.empty((0, 6)) |
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or [N, 512], default as None.
Return:
tracking boxes (np.array): [M, 6], means 'x0, y0, x1, y1, score, id'.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
"""
# PaddleInferBenchmark Log Version
self.log_version = "1.0.3"
# Paddle Version
self.paddle_version = paddle.__version__
self.paddle_commit = paddle.__git_commit__
paddle_infer_info = paddle_infer.get_version()
self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
# model info
self.model_info = model_info
# data info
self.data_info = data_info
# perf info
self.perf_info = perf_info
try:
# required value
self.model_name = model_info['model_name']
self.precision = model_info['precision']
self.batch_size = data_info['batch_size']
self.shape = data_info['shape']
self.data_num = data_info['data_num']
self.inference_time_s = round(perf_info['inference_time_s'], 4)
except:
self.print_help()
raise ValueError(
"Set argument wrong, please check input argument and its type")
self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
self.with_tracker = True if 'tracking_time_s' in perf_info else False
self.tracking_time_s = perf_info.get('tracking_time_s', 0)
self.total_time_s = perf_info.get('total_time_s', 0)
self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
self.succ_rate = perf_info.get("succ_rate", "")
self.qps = perf_info.get("qps", "")
# conf info
self.config_status = self.parse_config(config)
# mem info
if isinstance(resource_info, dict):
self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
else:
self.cpu_rss_mb = 0
self.cpu_vms_mb = 0
self.cpu_shared_mb = 0
self.cpu_dirty_mb = 0
self.cpu_util = 0
self.gpu_rss_mb = 0
self.gpu_util = 0
self.gpu_mem_util = 0
# init benchmark logger
self.benchmark_logger() |
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
| __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
config_status = {}
config_status['runtime_device'] = "gpu" if config.use_gpu(
) else "cpu"
config_status['ir_optim'] = config.ir_optim()
config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
config_status['precision'] = self.precision
config_status['enable_mkldnn'] = config.mkldnn_enabled()
config_status[
'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
)
elif isinstance(config, dict):
config_status['runtime_device'] = config.get('runtime_device', "")
config_status['ir_optim'] = config.get('ir_optim', "")
config_status['enable_tensorrt'] = config.get('enable_tensorrt',
"")
config_status['precision'] = config.get('precision', "")
config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
config_status['cpu_math_library_num_threads'] = config.get(
'cpu_math_library_num_threads', "")
else:
self.print_help()
raise ValueError(
"Set argument config wrong, please check input argument and its type"
)
return config_status |
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
| parse_config | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
"---------------------- Paddle info ----------------------")
self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
self.logger.info(f"{identifier} log_api_version: {self.log_version}")
self.logger.info(
"----------------------- Conf info -----------------------")
self.logger.info(
f"{identifier} runtime_device: {self.config_status['runtime_device']}"
)
self.logger.info(
f"{identifier} ir_optim: {self.config_status['ir_optim']}")
self.logger.info(f"{identifier} enable_memory_optim: {True}")
self.logger.info(
f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
)
self.logger.info(
f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}"
)
self.logger.info(
f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
)
self.logger.info(
"----------------------- Model info ----------------------")
self.logger.info(f"{identifier} model_name: {self.model_name}")
self.logger.info(f"{identifier} precision: {self.precision}")
self.logger.info(
"----------------------- Data info -----------------------")
self.logger.info(f"{identifier} batch_size: {self.batch_size}")
self.logger.info(f"{identifier} input_shape: {self.shape}")
self.logger.info(f"{identifier} data_num: {self.data_num}")
self.logger.info(
"----------------------- Perf info -----------------------")
self.logger.info(
f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
)
self.logger.info(
f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
)
self.logger.info(
f"{identifier} total time spent(s): {self.total_time_s}")
if self.with_tracker:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}, "
f"tracking_time(ms): {round(self.tracking_time_s*1000, 1)}")
else:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
)
if self.inference_time_s_90:
self.looger.info(
f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
)
if self.qps:
self.logger.info(f"{identifier} QPS: {self.qps}") |
print log report
args:
identifier(string): identify log
| report | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'masks': np.ndarray:
shape: [N, im_h, im_w]
'''
# model prediction
np_boxes_num, np_boxes, np_masks = np.array([0]), None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
boxes_num = self.predictor.get_output_handle(output_names[1])
np_boxes_num = boxes_num.copy_to_cpu()
if self.pred_config.mask:
masks_tensor = self.predictor.get_output_handle(output_names[
2])
np_masks = masks_tensor.copy_to_cpu()
result = dict(boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
return result |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'masks': np.ndarray:
shape: [N, im_h, im_w]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
'''
np_label, np_score, np_segms = None, None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
np_boxes_num = self.predictor.get_output_handle(output_names[
0]).copy_to_cpu()
np_label = self.predictor.get_output_handle(output_names[
1]).copy_to_cpu()
np_score = self.predictor.get_output_handle(output_names[
2]).copy_to_cpu()
np_segms = self.predictor.get_output_handle(output_names[
3]).copy_to_cpu()
result = dict(
segm=np_segms,
label=np_label,
score=np_score,
boxes_num=np_boxes_num)
return result |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
'''
np_score_list, np_boxes_list = [], []
for i in range(repeats):
self.predictor.run()
np_score_list.clear()
np_boxes_list.clear()
output_names = self.predictor.get_output_names()
num_outs = int(len(output_names) / 2)
for out_idx in range(num_outs):
np_score_list.append(
self.predictor.get_output_handle(output_names[out_idx])
.copy_to_cpu())
np_boxes_list.append(
self.predictor.get_output_handle(output_names[
out_idx + num_outs]).copy_to_cpu())
result = dict(boxes=np_score_list, boxes_num=np_boxes_list)
return result |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
im_shape = []
scale_factor = []
if len(imgs) == 1:
inputs['image'] = np.array((imgs[0], )).astype('float32')
inputs['im_shape'] = np.array(
(im_info[0]['im_shape'], )).astype('float32')
inputs['scale_factor'] = np.array(
(im_info[0]['scale_factor'], )).astype('float32')
return inputs
for e in im_info:
im_shape.append(np.array((e['im_shape'], )).astype('float32'))
scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
inputs['im_shape'] = np.concatenate(im_shape, axis=0)
inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
max_shape_h = max([e[0] for e in imgs_shape])
max_shape_w = max([e[1] for e in imgs_shape])
padding_imgs = []
for img in imgs:
im_c, im_h, im_w = img.shape[:]
padding_im = np.zeros(
(im_c, max_shape_h, max_shape_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = img
padding_imgs.append(padding_im)
inputs['image'] = np.stack(padding_imgs, axis=0)
return inputs | generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
'arch'], SUPPORT_MODELS)) |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def load_predictor(model_dir,
run_mode='paddle',
batch_size=1,
device='CPU',
min_subgraph_size=3,
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
enable_mkldnn_bfloat16=False,
delete_shuffle_pass=False):
"""set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
Used by action model.
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
ValueError: predict by TensorRT need device == 'GPU'.
"""
if device != 'GPU' and run_mode != 'paddle':
raise ValueError(
"Predict by TensorRT mode: {}, expect device=='GPU', but device == {}"
.format(run_mode, device))
infer_model = os.path.join(model_dir, 'model.pdmodel')
infer_params = os.path.join(model_dir, 'model.pdiparams')
if not os.path.exists(infer_model):
infer_model = os.path.join(model_dir, 'inference.pdmodel')
infer_params = os.path.join(model_dir, 'inference.pdiparams')
if not os.path.exists(infer_model):
raise ValueError("Cannot find any inference model in dir: {},".
format(model_dir))
config = Config(infer_model, infer_params)
if device == 'GPU':
# initial GPU memory(M), device ID
config.enable_use_gpu(200, 0)
# optimize graph and fuse op
config.switch_ir_optim(True)
elif device == 'XPU':
config.enable_lite_engine()
config.enable_xpu(10 * 1024 * 1024)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(cpu_threads)
if enable_mkldnn:
try:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
if enable_mkldnn_bfloat16:
config.enable_mkldnn_bfloat16()
except Exception as e:
print(
"The current environment does not support `mkldnn`, so disable mkldnn."
)
pass
precision_map = {
'trt_int8': Config.Precision.Int8,
'trt_fp32': Config.Precision.Float32,
'trt_fp16': Config.Precision.Half
}
if run_mode in precision_map.keys():
config.enable_tensorrt_engine(
workspace_size=(1 << 25) * batch_size,
max_batch_size=batch_size,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[run_mode],
use_static=False,
use_calib_mode=trt_calib_mode)
if use_dynamic_shape:
min_input_shape = {
'image': [batch_size, 3, trt_min_shape, trt_min_shape]
}
max_input_shape = {
'image': [batch_size, 3, trt_max_shape, trt_max_shape]
}
opt_input_shape = {
'image': [batch_size, 3, trt_opt_shape, trt_opt_shape]
}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
print('trt set dynamic shape done!')
# disable print log when predict
config.disable_glog_info()
# enable shared memory
config.enable_memory_optim()
# disable feed, fetch OP, needed by zero_copy_run
config.switch_use_feed_fetch_ops(False)
if delete_shuffle_pass:
config.delete_pass("shuffle_channel_detect_pass")
predictor = create_predictor(config)
return predictor, config | set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
Used by action model.
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
ValueError: predict by TensorRT need device == 'GPU'.
| load_predictor | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
assert infer_dir is None or os.path.isdir(infer_dir), \
"{} is not a directory".format(infer_dir)
# infer_img has a higher priority
if infer_img and os.path.isfile(infer_img):
return [infer_img]
images = set()
infer_dir = os.path.abspath(infer_dir)
assert os.path.isdir(infer_dir), \
"infer_dir {} is not a directory".format(infer_dir)
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for ext in exts:
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
images = list(images)
assert len(images) > 0, "no image found in {}".format(infer_dir)
print("Found {} inference images in total.".format(len(images)))
return images |
Get image path list in TEST mode
| get_test_images | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape: [N, im_h, im_w]
'''
# model prediction
np_heatmap, np_masks = None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
heatmap_tensor = self.predictor.get_output_handle(output_names[0])
np_heatmap = heatmap_tensor.copy_to_cpu()
if self.pred_config.tagmap:
masks_tensor = self.predictor.get_output_handle(output_names[
1])
heat_k = self.predictor.get_output_handle(output_names[2])
inds_k = self.predictor.get_output_handle(output_names[3])
np_masks = [
masks_tensor.copy_to_cpu(), heat_k.copy_to_cpu(),
inds_k.copy_to_cpu()
]
result = dict(heatmap=np_heatmap, masks=np_masks)
return result |
Args:
repeats (int): repeat number for prediction
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape: [N, im_h, im_w]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of image (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
inputs['image'] = np.stack(imgs, axis=0).astype('float32')
im_shape = []
for e in im_info:
im_shape.append(np.array((e['im_shape'])).astype('float32'))
inputs['im_shape'] = np.stack(im_shape, axis=0)
return inputs | generate input for different model type
Args:
imgs (list(numpy)): list of image (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in KEYPOINT_SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
'arch'], KEYPOINT_SUPPORT_MODELS)) |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def warp_affine_joints(joints, mat):
"""Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate of joints.
"""
joints = np.array(joints)
shape = joints.shape
joints = joints.reshape(-1, 2)
return np.dot(np.concatenate(
(joints, joints[:, 0:1] * 0 + 1), axis=1),
mat.T).reshape(shape) | Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate of joints.
| warp_affine_joints | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_max_preds(self, heatmaps):
"""get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
"""
assert isinstance(heatmaps,
np.ndarray), 'heatmaps should be numpy.ndarray'
assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
batch_size = heatmaps.shape[0]
num_joints = heatmaps.shape[1]
width = heatmaps.shape[3]
heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
idx = np.argmax(heatmaps_reshaped, 2)
maxvals = np.amax(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals | get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
| get_max_preds | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def dark_postprocess(self, hm, coords, kernelsize):
"""
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
"""
hm = self.gaussian_blur(hm, kernelsize)
hm = np.maximum(hm, 1e-10)
hm = np.log(hm)
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
return coords |
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
| dark_postprocess | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
"""the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
"""
coords, maxvals = self.get_max_preds(heatmaps)
heatmap_height = heatmaps.shape[2]
heatmap_width = heatmaps.shape[3]
if self.use_dark:
coords = self.dark_postprocess(heatmaps, coords, kernelsize)
else:
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
hm = heatmaps[n][p]
px = int(math.floor(coords[n][p][0] + 0.5))
py = int(math.floor(coords[n][p][1] + 0.5))
if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
diff = np.array([
hm[py][px + 1] - hm[py][px - 1],
hm[py + 1][px] - hm[py - 1][px]
])
coords[n][p] += np.sign(diff) * .25
preds = coords.copy()
# Transform back
for i in range(coords.shape[0]):
preds[i] = transform_preds(coords[i], center[i], scale[i],
[heatmap_width, heatmap_height])
return preds, maxvals | the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
| get_final_preds | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_affine_transform(center,
input_size,
rot,
output_size,
shift=(0., 0.),
inv=False):
"""Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
center (np.ndarray[2, ]): Center of the bounding box (x, y).
scale (np.ndarray[2, ]): Scale of the bounding box
wrt [width, height].
rot (float): Rotation angle (degree).
output_size (np.ndarray[2, ]): Size of the destination heatmaps.
shift (0-100%): Shift translation ratio wrt the width/height.
Default (0., 0.).
inv (bool): Option to inverse the affine transform direction.
(inv=False: src->dst or inv=True: dst->src)
Returns:
np.ndarray: The transform matrix.
"""
assert len(center) == 2
assert len(output_size) == 2
assert len(shift) == 2
if not isinstance(input_size, (np.ndarray, list)):
input_size = np.array([input_size, input_size], dtype=np.float32)
scale_tmp = input_size
shift = np.array(shift)
src_w = scale_tmp[0]
dst_w = output_size[0]
dst_h = output_size[1]
rot_rad = np.pi * rot / 180
src_dir = rotate_point([0., src_w * -0.5], rot_rad)
dst_dir = np.array([0., dst_w * -0.5])
src = np.zeros((3, 2), dtype=np.float32)
src[0, :] = center + scale_tmp * shift
src[1, :] = center + src_dir + scale_tmp * shift
src[2, :] = _get_3rd_point(src[0, :], src[1, :])
dst = np.zeros((3, 2), dtype=np.float32)
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
return trans | Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
center (np.ndarray[2, ]): Center of the bounding box (x, y).
scale (np.ndarray[2, ]): Scale of the bounding box
wrt [width, height].
rot (float): Rotation angle (degree).
output_size (np.ndarray[2, ]): Size of the destination heatmaps.
shift (0-100%): Shift translation ratio wrt the width/height.
Default (0., 0.).
inv (bool): Option to inverse the affine transform direction.
(inv=False: src->dst or inv=True: dst->src)
Returns:
np.ndarray: The transform matrix.
| get_affine_transform | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def get_warp_matrix(theta, size_input, size_dst, size_target):
"""This code is based on
https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
Data Processing for Human Pose Estimation (CVPR 2020).
Args:
theta (float): Rotation angle in degrees.
size_input (np.ndarray): Size of input image [w, h].
size_dst (np.ndarray): Size of output image [w, h].
size_target (np.ndarray): Size of ROI in input plane [w, h].
Returns:
matrix (np.ndarray): A matrix for transformation.
"""
theta = np.deg2rad(theta)
matrix = np.zeros((2, 3), dtype=np.float32)
scale_x = size_dst[0] / size_target[0]
scale_y = size_dst[1] / size_target[1]
matrix[0, 0] = np.cos(theta) * scale_x
matrix[0, 1] = -np.sin(theta) * scale_x
matrix[0, 2] = scale_x * (
-0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
np.sin(theta) + 0.5 * size_target[0])
matrix[1, 0] = np.sin(theta) * scale_y
matrix[1, 1] = np.cos(theta) * scale_y
matrix[1, 2] = scale_y * (
-0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
np.cos(theta) + 0.5 * size_target[1])
return matrix | This code is based on
https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
Data Processing for Human Pose Estimation (CVPR 2020).
Args:
theta (float): Rotation angle in degrees.
size_input (np.ndarray): Size of input image [w, h].
size_dst (np.ndarray): Size of output image [w, h].
size_target (np.ndarray): Size of ROI in input plane [w, h].
Returns:
matrix (np.ndarray): A matrix for transformation.
| get_warp_matrix | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def rotate_point(pt, angle_rad):
"""Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
"""
assert len(pt) == 2
sn, cs = np.sin(angle_rad), np.cos(angle_rad)
new_x = pt[0] * cs - pt[1] * sn
new_y = pt[0] * sn + pt[1] * cs
rotated_pt = [new_x, new_y]
return rotated_pt | Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
| rotate_point | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def _get_3rd_point(a, b):
"""To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.ndarray): point(x,y)
b (np.ndarray): point(x,y)
Returns:
np.ndarray: The 3rd point.
"""
assert len(a) == 2
assert len(b) == 2
direction = a - b
third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
return third_pt | To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.ndarray): point(x,y)
b (np.ndarray): point(x,y)
Returns:
np.ndarray: The 3rd point.
| _get_3rd_point | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
shape: [N, 128]
'''
# model prediction
np_pred_dets, np_pred_embs = None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_pred_dets = boxes_tensor.copy_to_cpu()
embs_tensor = self.predictor.get_output_handle(output_names[1])
np_pred_embs = embs_tensor.copy_to_cpu()
result = dict(pred_dets=np_pred_dets, pred_embs=np_pred_embs)
return result |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
shape: [N, 128]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/mot_jde_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/mot_jde_infer.py | Apache-2.0 |
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
indexes = np.argsort(scores)
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(
current_box, axis=0), )
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :] |
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
| hard_nms | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def iou_of(boxes0, boxes1, eps=1e-5):
"""Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
"""
overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
overlap_area = area_of(overlap_left_top, overlap_right_bottom)
area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
return overlap_area / (area0 + area1 - overlap_area + eps) | Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
| iou_of | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def area_of(left_top, right_bottom):
"""Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
"""
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1] | Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
| area_of | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def decode_image(im_file, im_info):
"""read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
if isinstance(im_file, str):
with open(im_file, 'rb') as f:
im_read = f.read()
data = np.frombuffer(im_read, dtype='uint8')
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
else:
im = im_file
im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
return im, im_info | read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| decode_image | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im_channel = im.shape[2]
im_scale_y, im_scale_x = self.generate_scale(im)
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
im_info['scale_factor'] = np.array(
[im_scale_y, im_scale_x]).astype('float32')
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def generate_scale(self, img):
"""
Args:
img (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
limit_side_len = self.limit_side_len
h, w, c = img.shape
# limit the max side
if self.limit_type == 'max':
if h > w:
ratio = float(limit_side_len) / h
else:
ratio = float(limit_side_len) / w
elif self.limit_type == 'min':
if h < w:
ratio = float(limit_side_len) / h
else:
ratio = float(limit_side_len) / w
elif self.limit_type == 'resize_long':
ratio = float(limit_side_len) / max(h, w)
else:
raise Exception('not support limit type, image ')
resize_h = int(h * ratio)
resize_w = int(w * ratio)
resize_h = max(int(round(resize_h / 32) * 32), 32)
resize_w = max(int(round(resize_w / 32) * 32), 32)
im_scale_y = resize_h / float(h)
im_scale_x = resize_w / float(w)
return im_scale_y, im_scale_x |
Args:
img (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_size) == 2
assert self.target_size[0] > 0 and self.target_size[1] > 0
im_channel = im.shape[2]
im_scale_y, im_scale_x = self.generate_scale(im)
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
im_info['scale_factor'] = np.array(
[im_scale_y, im_scale_x]).astype('float32')
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def generate_scale(self, im):
"""
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.keep_ratio:
im_size_min = np.min(origin_shape)
im_size_max = np.max(origin_shape)
target_size_min = np.min(self.target_size)
target_size_max = np.max(self.target_size)
im_scale = float(target_size_min) / float(im_size_min)
if np.round(im_scale * im_size_max) > target_size_max:
im_scale = float(target_size_max) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
else:
resize_h, resize_w = self.target_size
im_scale_y = resize_h / float(origin_shape[0])
im_scale_x = resize_w / float(origin_shape[1])
return im_scale_y, im_scale_x |
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, img):
"""
Performs resize operations.
Args:
img (PIL.Image): a PIL.Image.
return:
resized_img: a PIL.Image after scaling.
"""
result_img = None
if isinstance(img, np.ndarray):
h, w, _ = img.shape
elif isinstance(img, Image.Image):
w, h = img.size
else:
raise NotImplementedError
if w <= h:
ow = self.short_size
if self.fixed_ratio: # default is True
oh = int(self.short_size * 4.0 / 3.0)
elif not self.keep_ratio: # no
oh = self.short_size
else:
scale_factor = self.short_size / w
oh = int(h * float(scale_factor) +
0.5) if self.do_round else int(h * self.short_size /
w)
ow = int(w * float(scale_factor) +
0.5) if self.do_round else int(w * self.short_size /
h)
else:
oh = self.short_size
if self.fixed_ratio:
ow = int(self.short_size * 4.0 / 3.0)
elif not self.keep_ratio: # no
ow = self.short_size
else:
scale_factor = self.short_size / h
oh = int(h * float(scale_factor) +
0.5) if self.do_round else int(h * self.short_size /
w)
ow = int(w * float(scale_factor) +
0.5) if self.do_round else int(w * self.short_size /
h)
if type(img) == np.ndarray:
img = Image.fromarray(img, mode='RGB')
if self.backend == 'pillow':
result_img = img.resize((ow, oh), Image.BILINEAR)
elif self.backend == 'cv2' and (self.keep_ratio is not None):
result_img = cv2.resize(
img, (ow, oh), interpolation=cv2.INTER_LINEAR)
else:
result_img = Image.fromarray(
cv2.resize(
np.asarray(img), (ow, oh), interpolation=cv2.INTER_LINEAR))
return result_img |
Performs resize operations.
Args:
img (PIL.Image): a PIL.Image.
return:
resized_img: a PIL.Image after scaling.
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.astype(np.float32, copy=False)
if self.is_scale:
scale = 1.0 / 255.0
im *= scale
if self.norm_type == 'mean_std':
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
im -= mean
im /= std
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.transpose((2, 0, 1)).copy()
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
coarsest_stride = self.coarsest_stride
if coarsest_stride <= 0:
return im, im_info
im_c, im_h, im_w = im.shape
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
return padding_im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __init__(self, target_size):
"""
Resize image to target size, convert normalized xywh to pixel xyxy
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
Args:
target_size (int|list): image target size.
"""
super(LetterBoxResize, self).__init__()
if isinstance(target_size, int):
target_size = [target_size, target_size]
self.target_size = target_size |
Resize image to target size, convert normalized xywh to pixel xyxy
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
Args:
target_size (int|list): image target size.
| __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_size) == 2
assert self.target_size[0] > 0 and self.target_size[1] > 0
height, width = self.target_size
h, w = im.shape[:2]
im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
new_shape = [round(h * ratio), round(w * ratio)]
im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
"""
Pad image to a specified size.
Args:
size (list[int]): image target size
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
"""
super(Pad, self).__init__()
if isinstance(size, int):
size = [size, size]
self.size = size
self.fill_value = fill_value |
Pad image to a specified size.
Args:
size (list[int]): image target size
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
| __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
h, w = img.shape[:2]
if self.keep_res:
input_h = (h | self.pad) + 1
input_w = (w | self.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
c = np.array([w // 2, h // 2], dtype=np.float32)
else:
s = max(h, w) * 1.0
input_h, input_w = self.input_h, self.input_w
c = np.array([w / 2., h / 2.], dtype=np.float32)
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
img = cv2.resize(img, (w, h))
inp = cv2.warpAffine(
img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
return inp, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def get_current_memory_mb():
"""
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
"""
import pynvml
import psutil
import GPUtil
gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
pid = os.getpid()
p = psutil.Process(pid)
info = p.memory_full_info()
cpu_mem = info.uss / 1024. / 1024.
gpu_mem = 0
gpu_percent = 0
gpus = GPUtil.getGPUs()
if gpu_id is not None and len(gpus) > 0:
gpu_percent = gpus[gpu_id].load
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
gpu_mem = meminfo.used / 1024. / 1024.
return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4) |
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
| get_current_memory_mb | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/utils.py | Apache-2.0 |
def nms(dets, match_threshold=0.6, match_metric='iou'):
""" Apply NMS to avoid detecting too many overlapping bounding boxes.
Args:
dets: shape [N, 5], [score, x1, y1, x2, y2]
match_metric: 'iou' or 'ios'
match_threshold: overlap thresh for match metric.
"""
if dets.shape[0] == 0:
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
for _i in range(ndets):
i = order[_i]
if suppressed[i] == 1:
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range(_i + 1, ndets):
j = order[_j]
if suppressed[j] == 1:
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, xx2 - xx1 + 1)
h = max(0.0, yy2 - yy1 + 1)
inter = w * h
if match_metric == 'iou':
union = iarea + areas[j] - inter
match_value = inter / union
elif match_metric == 'ios':
smaller = min(iarea, areas[j])
match_value = inter / smaller
else:
raise ValueError()
if match_value >= match_threshold:
suppressed[j] = 1
keep = np.where(suppressed == 0)[0]
dets = dets[keep, :]
return dets | Apply NMS to avoid detecting too many overlapping bounding boxes.
Args:
dets: shape [N, 5], [score, x1, y1, x2, y2]
match_metric: 'iou' or 'ios'
match_threshold: overlap thresh for match metric.
| nms | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/utils.py | Apache-2.0 |
def visualize_box_mask(im, results, labels, threshold=0.5):
"""
Args:
im (str/np.ndarray): path of image/np.ndarray read by cv2
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape:[N, im_h, im_w]
labels (list): labels:['class1', ..., 'classn']
threshold (float): Threshold of score.
Returns:
im (PIL.Image.Image): visualized image
"""
if isinstance(im, str):
im = Image.open(im).convert('RGB')
elif isinstance(im, np.ndarray):
im = Image.fromarray(im)
if 'masks' in results and 'boxes' in results and len(results['boxes']) > 0:
im = draw_mask(
im,
results['boxes'],
results['masks'],
labels,
threshold=threshold)
if 'boxes' in results and len(results['boxes']) > 0:
im = draw_box(im, results['boxes'], labels, threshold=threshold)
if 'segm' in results:
im = draw_segm(
im,
results['segm'],
results['label'],
results['score'],
labels,
threshold=threshold)
return im |
Args:
im (str/np.ndarray): path of image/np.ndarray read by cv2
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape:[N, im_h, im_w]
labels (list): labels:['class1', ..., 'classn']
threshold (float): Threshold of score.
Returns:
im (PIL.Image.Image): visualized image
| visualize_box_mask | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def get_color_map_list(num_classes):
"""
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
"""
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
return color_map |
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
| get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def draw_mask(im, np_boxes, np_masks, labels, threshold=0.5):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
np_masks (np.ndarray): shape:[N, im_h, im_w]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of mask
Returns:
im (PIL.Image.Image): visualized image
"""
color_list = get_color_map_list(len(labels))
w_ratio = 0.4
alpha = 0.7
im = np.array(im).astype('float32')
clsid2color = {}
expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
np_boxes = np_boxes[expect_boxes, :]
np_masks = np_masks[expect_boxes, :, :]
im_h, im_w = im.shape[:2]
np_masks = np_masks[:, :im_h, :im_w]
for i in range(len(np_masks)):
clsid, score = int(np_boxes[i][0]), np_boxes[i][1]
mask = np_masks[i]
if clsid not in clsid2color:
clsid2color[clsid] = color_list[clsid]
color_mask = clsid2color[clsid]
for c in range(3):
color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
idx = np.nonzero(mask)
color_mask = np.array(color_mask)
im[idx[0], idx[1], :] *= 1.0 - alpha
im[idx[0], idx[1], :] += alpha * color_mask
return Image.fromarray(im.astype('uint8')) |
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
np_masks (np.ndarray): shape:[N, im_h, im_w]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of mask
Returns:
im (PIL.Image.Image): visualized image
| draw_mask | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def draw_box(im, np_boxes, labels, threshold=0.5):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of box
Returns:
im (PIL.Image.Image): visualized image
"""
draw_thickness = min(im.size) // 320
draw = ImageDraw.Draw(im)
clsid2color = {}
color_list = get_color_map_list(len(labels))
expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
np_boxes = np_boxes[expect_boxes, :]
for dt in np_boxes:
clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
if clsid not in clsid2color:
clsid2color[clsid] = color_list[clsid]
color = tuple(clsid2color[clsid])
if len(bbox) == 4:
xmin, ymin, xmax, ymax = bbox
print('class_id:{:d}, confidence:{:.4f}, left_top:[{:.2f},{:.2f}],'
'right_bottom:[{:.2f},{:.2f}]'.format(
int(clsid), score, xmin, ymin, xmax, ymax))
# draw bbox
draw.line(
[(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
(xmin, ymin)],
width=draw_thickness,
fill=color)
elif len(bbox) == 8:
x1, y1, x2, y2, x3, y3, x4, y4 = bbox
draw.line(
[(x1, y1), (x2, y2), (x3, y3), (x4, y4), (x1, y1)],
width=2,
fill=color)
xmin = min(x1, x2, x3, x4)
ymin = min(y1, y2, y3, y4)
# draw label
text = "{} {:.4f}".format(labels[clsid], score)
tw, th = draw.textsize(text)
draw.rectangle(
[(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
return im |
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of box
Returns:
im (PIL.Image.Image): visualized image
| draw_box | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def __init__(self, cfg):
"""
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
"""
self.cfg = DeployConfig(cfg)
self._init_base_config()
self._init_cpu_config()
self.predictor = create_predictor(self.pred_cfg) |
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
| __init__ | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_pseudo_color_map(self, pred, color_map=None):
"""
Get the pseudo color image.
Args:
pred (numpy.ndarray): the origin predicted image.
color_map (list, optional): the palette color map. Default: None,
use paddleseg's default color map.
Returns:
(numpy.ndarray): the pseduo image.
"""
pred_mask = PILImage.fromarray(pred.astype(np.uint8), mode='P')
if color_map is None:
color_map = self.get_color_map_list(256)
pred_mask.putpalette(color_map)
return pred_mask |
Get the pseudo color image.
Args:
pred (numpy.ndarray): the origin predicted image.
color_map (list, optional): the palette color map. Default: None,
use paddleseg's default color map.
Returns:
(numpy.ndarray): the pseduo image.
| get_pseudo_color_map | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_color_map_list(self, num_classes, custom_color=None):
"""
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
Returns:
(list). The color map.
"""
num_classes += 1
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = color_map[3:]
if custom_color:
color_map[:len(custom_color)] = custom_color
return color_map |
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
Returns:
(list). The color map.
| get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_output(img, size, bg, download_size):
"""
Get the special size and background photo.
Args:
img(numpy:ndarray): The image array.
size(str): The size user specified.
bg(str): The background color user specified.
download_size(str): The size for image saving.
"""
alpha = predictor.run(img)
res = utils.bg_replace(img, alpha, bg_name=bg)
size_index = sizes_play.index(size)
res = utils.adjust_size(res, size_index)
res_download = utils.download(res, download_size)
return res, res_download |
Get the special size and background photo.
Args:
img(numpy:ndarray): The image array.
size(str): The size user specified.
bg(str): The background color user specified.
download_size(str): The size for image saving.
| get_output | python | PaddlePaddle/models | modelcenter/PP-Matting/APP1/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Matting/APP1/app.py | Apache-2.0 |
def __init__(self, cfg):
"""
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
"""
self.cfg = DeployConfig(cfg)
self._init_base_config()
self._init_cpu_config()
self.predictor = create_predictor(self.pred_cfg) |
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
| __init__ | python | PaddlePaddle/models | modelcenter/PP-Matting/APP1/predict.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Matting/APP1/predict.py | Apache-2.0 |
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') \
or path.startswith('https://') \
or path.startswith('paddlecv://') |
Whether path is URL.
Args:
path (string): URL string or not.
| is_url | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_model_path(path):
"""Get model path from WEIGHTS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, WEIGHTS_HOME, path_depth=2)
return path | Get model path from WEIGHTS_HOME, if not exists,
download it from url.
| get_model_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_config_path(path):
"""Get config path from CONFIGS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, CONFIGS_HOME)
return path | Get config path from CONFIGS_HOME, if not exists,
download it from url.
| get_config_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_dict_path(path):
"""Get config path from CONFIGS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, DICTS_HOME)
return path | Get config path from CONFIGS_HOME, if not exists,
download it from url.
| get_dict_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_path(url, root_dir, md5sum=None, check_exist=True, path_depth=1):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url, return the path.
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_HOME
md5sum (str): md5 sum of download package
"""
# parse path after download to decompress under root_dir
fullpath, dirname = map_path(url, root_dir, path_depth)
if osp.exists(fullpath) and check_exist:
if not osp.isfile(fullpath) or \
_check_exist_file_md5(fullpath, md5sum, url):
return fullpath, True
else:
os.remove(fullpath)
fullname = _download(url, dirname, md5sum)
return fullpath, False | Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url, return the path.
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_HOME
md5sum (str): md5 sum of download package
| get_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum,
url)):
if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1
else:
raise RuntimeError("Download from {} failed. "
"Retry limit reached".format(url))
# NOTE: windows path join may incur \, which is invalid in url
if sys.platform == "win32":
url = url.replace('\\', '/')
req = requests.get(url, stream=True)
if req.status_code != 200:
raise RuntimeError("Downloading from {} failed with code "
"{}!".format(url, req.status_code))
# For protecting download interupted, download to
# tmp_fullname firstly, move tmp_fullname to fullname
# after download finished
tmp_fullname = fullname + "_tmp"
total_size = req.headers.get('content-length')
with open(tmp_fullname, 'wb') as f:
if total_size:
for chunk in tqdm.tqdm(
req.iter_content(chunk_size=1024),
total=(int(total_size) + 1023) // 1024,
unit='KB'):
f.write(chunk)
else:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
shutil.move(tmp_fullname, fullname)
return fullname |
Download from url, save to path.
url (str): download url
path (str): download to given path
| _download | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def decode_image(im_file, im_info):
"""read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
if isinstance(im_file, str):
with open(im_file, 'rb') as f:
im_read = f.read()
data = np.frombuffer(im_read, dtype='uint8')
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
else:
im = im_file
im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
return im, im_info | read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| decode_image | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_size) == 2
assert self.target_size[0] > 0 and self.target_size[1] > 0
im_channel = im.shape[2]
im_scale_y, im_scale_x = self.generate_scale(im)
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
im_info['scale_factor'] = np.array(
[im_scale_y, im_scale_x]).astype('float32')
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def generate_scale(self, im):
"""
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.keep_ratio:
im_size_min = np.min(origin_shape)
im_size_max = np.max(origin_shape)
target_size_min = np.min(self.target_size)
target_size_max = np.max(self.target_size)
im_scale = float(target_size_min) / float(im_size_min)
if np.round(im_scale * im_size_max) > target_size_max:
im_scale = float(target_size_max) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
else:
resize_h, resize_w = self.target_size
im_scale_y = resize_h / float(origin_shape[0])
im_scale_x = resize_w / float(origin_shape[1])
return im_scale_y, im_scale_x |
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.astype(np.float32, copy=False)
if self.is_scale:
scale = 1.0 / 255.0
im *= scale
if self.norm_type == 'mean_std':
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
im -= mean
im /= std
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.transpose((2, 0, 1)).copy()
return im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
coarsest_stride = self.coarsest_stride
if coarsest_stride <= 0:
return im, im_info
im_c, im_h, im_w = im.shape
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
return padding_im, im_info |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def get_color_map_list(num_classes):
"""
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
"""
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
return color_map |
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
| get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/visualize.py | Apache-2.0 |
def download_with_progressbar(url: str, save_path: str):
"""Download file from given url and decompress it
Args:
url (str): url
save_path (str): path for saving downloaded file
Raises:
Exception: exception
"""
print(f"Auto downloading {url} to {save_path}")
if os.path.exists(save_path):
print("File already exist, skip...")
else:
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(
total=total_size_in_bytes, unit="iB", unit_scale=True)
with open(save_path, "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes or not os.path.isfile(
save_path):
raise Exception(
f"Something went wrong while downloading file from {url}")
print("Finished downloading")
print(f"Try decompression at {save_path}")
os.system(f"tar -xf {save_path}")
print(f"Finished decompression at {save_path}") | Download file from given url and decompress it
Args:
url (str): url
save_path (str): path for saving downloaded file
Raises:
Exception: exception
| download_with_progressbar | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def model_inference(image) -> tuple:
"""send given image to inference model and get result from output
Args:
image (gr.Image): input image
Returns:
tuple: (drawn image to display, result in json format)
"""
results = clas_engine.predict(image, print_pred=True, predict_type="shitu")
# bs = 1, fetch the first result
results = list(results)[0]
image_draw_box = draw_bbox_results(image, results)
im_show = Image.fromarray(image_draw_box)
json_out = {"base64": image_to_base64(im_show), "result": str(results)}
return im_show, json_out | send given image to inference model and get result from output
Args:
image (gr.Image): input image
Returns:
tuple: (drawn image to display, result in json format)
| model_inference | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def draw_bbox_results(image: Union[np.ndarray, Image.Image],
results: List[Dict[str, Any]]) -> np.ndarray:
"""draw bounding box(es)
Args:
image (Union[np.ndarray, Image.Image]): image to be drawn
results (List[Dict[str, Any]]): information for drawing bounding box
Returns:
np.ndarray: drawn image
"""
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
draw = ImageDraw.Draw(image)
font_size = 18
font = ImageFont.truetype("./simfang.ttf", font_size, encoding="utf-8")
color = (0, 102, 255)
for result in results:
# empty results
if result["rec_docs"] is None:
continue
xmin, ymin, xmax, ymax = result["bbox"]
text = "{}, {:.2f}".format(result["rec_docs"], result["rec_scores"])
th = font_size
tw = font.getsize(text)[0]
start_y = max(0, ymin - th)
draw.rectangle(
[(xmin + 1, start_y), (xmin + tw + 1, start_y + th)], fill=color)
draw.text((xmin + 1, start_y), text, fill=(255, 255, 255), font=font)
draw.rectangle(
[(xmin, ymin), (xmax, ymax)], outline=(255, 0, 0), width=2)
return np.array(image) | draw bounding box(es)
Args:
image (Union[np.ndarray, Image.Image]): image to be drawn
results (List[Dict[str, Any]]): information for drawing bounding box
Returns:
np.ndarray: drawn image
| draw_bbox_results | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
"""
# PaddleInferBenchmark Log Version
self.log_version = "1.0.3"
# Paddle Version
self.paddle_version = paddle.__version__
self.paddle_commit = paddle.__git_commit__
paddle_infer_info = paddle_infer.get_version()
self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
# model info
self.model_info = model_info
# data info
self.data_info = data_info
# perf info
self.perf_info = perf_info
try:
# required value
self.model_name = model_info['model_name']
self.precision = model_info['precision']
self.batch_size = data_info['batch_size']
self.shape = data_info['shape']
self.data_num = data_info['data_num']
self.inference_time_s = round(perf_info['inference_time_s'], 4)
except:
self.print_help()
raise ValueError(
"Set argument wrong, please check input argument and its type")
self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
self.with_tracker = True if 'tracking_time_s' in perf_info else False
self.tracking_time_s = perf_info.get('tracking_time_s', 0)
self.total_time_s = perf_info.get('total_time_s', 0)
self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
self.succ_rate = perf_info.get("succ_rate", "")
self.qps = perf_info.get("qps", "")
# conf info
self.config_status = self.parse_config(config)
# mem info
if isinstance(resource_info, dict):
self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
else:
self.cpu_rss_mb = 0
self.cpu_vms_mb = 0
self.cpu_shared_mb = 0
self.cpu_dirty_mb = 0
self.cpu_util = 0
self.gpu_rss_mb = 0
self.gpu_util = 0
self.gpu_mem_util = 0
# init benchmark logger
self.benchmark_logger() |
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
| __init__ | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
config_status = {}
config_status['runtime_device'] = "gpu" if config.use_gpu(
) else "cpu"
config_status['ir_optim'] = config.ir_optim()
config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
config_status['precision'] = self.precision
config_status['enable_mkldnn'] = config.mkldnn_enabled()
config_status[
'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
)
elif isinstance(config, dict):
config_status['runtime_device'] = config.get('runtime_device', "")
config_status['ir_optim'] = config.get('ir_optim', "")
config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
config_status['precision'] = config.get('precision', "")
config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
config_status['cpu_math_library_num_threads'] = config.get(
'cpu_math_library_num_threads', "")
else:
self.print_help()
raise ValueError(
"Set argument config wrong, please check input argument and its type"
)
return config_status |
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
| parse_config | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
"---------------------- Paddle info ----------------------")
self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
self.logger.info(f"{identifier} log_api_version: {self.log_version}")
self.logger.info(
"----------------------- Conf info -----------------------")
self.logger.info(
f"{identifier} runtime_device: {self.config_status['runtime_device']}"
)
self.logger.info(
f"{identifier} ir_optim: {self.config_status['ir_optim']}")
self.logger.info(f"{identifier} enable_memory_optim: {True}")
self.logger.info(
f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
)
self.logger.info(
f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
self.logger.info(
f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
)
self.logger.info(
"----------------------- Model info ----------------------")
self.logger.info(f"{identifier} model_name: {self.model_name}")
self.logger.info(f"{identifier} precision: {self.precision}")
self.logger.info(
"----------------------- Data info -----------------------")
self.logger.info(f"{identifier} batch_size: {self.batch_size}")
self.logger.info(f"{identifier} input_shape: {self.shape}")
self.logger.info(f"{identifier} data_num: {self.data_num}")
self.logger.info(
"----------------------- Perf info -----------------------")
self.logger.info(
f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
)
self.logger.info(
f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
)
self.logger.info(
f"{identifier} total time spent(s): {self.total_time_s}")
if self.with_tracker:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}, "
f"tracking_time(ms): {round(self.tracking_time_s*1000, 1)}")
else:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
)
if self.inference_time_s_90:
self.looger.info(
f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
)
if self.qps:
self.logger.info(f"{identifier} QPS: {self.qps}") |
print log report
args:
identifier(string): identify log
| report | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') \
or path.startswith('https://') \
or path.startswith('ppdet://') |
Whether path is URL.
Args:
path (string): URL string or not.
| is_url | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum,
url)):
if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1
else:
raise RuntimeError("Download from {} failed. "
"Retry limit reached".format(url))
# NOTE: windows path join may incur \, which is invalid in url
if sys.platform == "win32":
url = url.replace('\\', '/')
req = requests.get(url, stream=True)
if req.status_code != 200:
raise RuntimeError("Downloading from {} failed with code "
"{}!".format(url, req.status_code))
# For protecting download interupted, download to
# tmp_fullname firstly, move tmp_fullname to fullname
# after download finished
tmp_fullname = fullname + "_tmp"
total_size = req.headers.get('content-length')
with open(tmp_fullname, 'wb') as f:
if total_size:
for chunk in tqdm.tqdm(
req.iter_content(chunk_size=1024),
total=(int(total_size) + 1023) // 1024,
unit='KB'):
f.write(chunk)
else:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
shutil.move(tmp_fullname, fullname)
return fullname |
Download from url, save to path.
url (str): download url
path (str): download to given path
| _download | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _move_and_merge_tree(src, dst):
"""
Move src directory to dst, if dst is already exists,
merge src to dst
"""
if not osp.exists(dst):
shutil.move(src, dst)
elif osp.isfile(src):
shutil.move(src, dst)
else:
for fp in os.listdir(src):
src_fp = osp.join(src, fp)
dst_fp = osp.join(dst, fp)
if osp.isdir(src_fp):
if osp.isdir(dst_fp):
_move_and_merge_tree(src_fp, dst_fp)
else:
shutil.move(src_fp, dst_fp)
elif osp.isfile(src_fp) and \
not osp.isfile(dst_fp):
shutil.move(src_fp, dst_fp) |
Move src directory to dst, if dst is already exists,
merge src to dst
| _move_and_merge_tree | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _decompress(fname):
"""
Decompress for zip and tar file
"""
# For protecting decompressing interupted,
# decompress to fpath_tmp directory firstly, if decompress
# successed, move decompress files to fpath and delete
# fpath_tmp and remove download compress file.
fpath = osp.split(fname)[0]
fpath_tmp = osp.join(fpath, 'tmp')
if osp.isdir(fpath_tmp):
shutil.rmtree(fpath_tmp)
os.makedirs(fpath_tmp)
if fname.find('tar') >= 0:
with tarfile.open(fname) as tf:
tf.extractall(path=fpath_tmp)
elif fname.find('zip') >= 0:
with zipfile.ZipFile(fname) as zf:
zf.extractall(path=fpath_tmp)
elif fname.find('.txt') >= 0:
return
else:
raise TypeError("Unsupport compress file type {}".format(fname))
for f in os.listdir(fpath_tmp):
src_dir = osp.join(fpath_tmp, f)
dst_dir = osp.join(fpath, f)
_move_and_merge_tree(src_dir, dst_dir)
shutil.rmtree(fpath_tmp)
os.remove(fname) |
Decompress for zip and tar file
| _decompress | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def get_path(url, root_dir=WEIGHTS_HOME, md5sum=None, check_exist=True):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root_dir (str): root dir for downloading
md5sum (str): md5 sum of download package
"""
# parse path after download to decompress under root_dir
fullpath = map_path(url, root_dir)
# For same zip file, decompressed directory name different
# from zip file name, rename by following map
decompress_name_map = {"ppTSM_fight": "ppTSM", }
for k, v in decompress_name_map.items():
if fullpath.find(k) >= 0:
fullpath = osp.join(osp.split(fullpath)[0], v)
if osp.exists(fullpath) and check_exist:
if not osp.isfile(fullpath) or \
_check_exist_file_md5(fullpath, md5sum, url):
return fullpath, True
else:
os.remove(fullpath)
fullname = _download_dist(url, root_dir, md5sum)
# new weights format which postfix is 'pdparams' not
# need to decompress
if osp.splitext(fullname)[-1] not in ['.pdparams', '.yml']:
_decompress_dist(fullname)
return fullpath, False | Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root_dir (str): root dir for downloading
md5sum (str): md5 sum of download package
| get_path | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def get_weights_path(url):
"""Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
"""
url = parse_url(url)
md5sum = None
if url in MODEL_URL_MD5_DICT.keys():
md5sum = MODEL_URL_MD5_DICT[url]
path, _ = get_path(url, WEIGHTS_HOME, md5sum)
return path | Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
| get_weights_path | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'masks': np.ndarray:
shape: [N, im_h, im_w]
'''
# model prediction
np_boxes_num, np_boxes, np_masks = np.array([0]), None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
boxes_num = self.predictor.get_output_handle(output_names[1])
np_boxes_num = boxes_num.copy_to_cpu()
if self.pred_config.mask:
masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu()
result = dict(boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
return result |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'masks': np.ndarray:
shape: [N, im_h, im_w]
| predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
'''
np_label, np_score, np_segms = None, None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
np_boxes_num = self.predictor.get_output_handle(output_names[
0]).copy_to_cpu()
np_label = self.predictor.get_output_handle(output_names[
1]).copy_to_cpu()
np_score = self.predictor.get_output_handle(output_names[
2]).copy_to_cpu()
np_segms = self.predictor.get_output_handle(output_names[
3]).copy_to_cpu()
result = dict(
segm=np_segms,
label=np_label,
score=np_score,
boxes_num=np_boxes_num)
return result |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
| predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
'''
np_score_list, np_boxes_list = [], []
for i in range(repeats):
self.predictor.run()
np_score_list.clear()
np_boxes_list.clear()
output_names = self.predictor.get_output_names()
num_outs = int(len(output_names) / 2)
for out_idx in range(num_outs):
np_score_list.append(
self.predictor.get_output_handle(output_names[out_idx])
.copy_to_cpu())
np_boxes_list.append(
self.predictor.get_output_handle(output_names[
out_idx + num_outs]).copy_to_cpu())
result = dict(boxes=np_score_list, boxes_num=np_boxes_list)
return result |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
| predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
im_shape = []
scale_factor = []
if len(imgs) == 1:
inputs['image'] = np.array((imgs[0], )).astype('float32')
inputs['im_shape'] = np.array(
(im_info[0]['im_shape'], )).astype('float32')
inputs['scale_factor'] = np.array(
(im_info[0]['scale_factor'], )).astype('float32')
return inputs
for e in im_info:
im_shape.append(np.array((e['im_shape'], )).astype('float32'))
scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
inputs['im_shape'] = np.concatenate(im_shape, axis=0)
inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
max_shape_h = max([e[0] for e in imgs_shape])
max_shape_w = max([e[1] for e in imgs_shape])
padding_imgs = []
for img in imgs:
im_c, im_h, im_w = img.shape[:]
padding_im = np.zeros(
(im_c, max_shape_h, max_shape_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = img
padding_imgs.append(padding_im)
inputs['image'] = np.stack(padding_imgs, axis=0)
return inputs | generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
'arch'], SUPPORT_MODELS)) |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def load_predictor(model_dir,
run_mode='paddle',
batch_size=1,
device='CPU',
min_subgraph_size=3,
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
enable_mkldnn_bfloat16=False,
delete_shuffle_pass=False):
"""set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
Used by action model.
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
ValueError: predict by TensorRT need device == 'GPU'.
"""
if device != 'GPU' and run_mode != 'paddle':
raise ValueError(
"Predict by TensorRT mode: {}, expect device=='GPU', but device == {}"
.format(run_mode, device))
infer_model = os.path.join(model_dir, 'model.pdmodel')
infer_params = os.path.join(model_dir, 'model.pdiparams')
if not os.path.exists(infer_model):
infer_model = os.path.join(model_dir, 'inference.pdmodel')
infer_params = os.path.join(model_dir, 'inference.pdiparams')
if not os.path.exists(infer_model):
raise ValueError(
"Cannot find any inference model in dir: {},".format(model_dir))
config = Config(infer_model, infer_params)
if device == 'GPU':
# initial GPU memory(M), device ID
config.enable_use_gpu(200, 0)
# optimize graph and fuse op
config.switch_ir_optim(True)
elif device == 'XPU':
config.enable_lite_engine()
config.enable_xpu(10 * 1024 * 1024)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(cpu_threads)
if enable_mkldnn:
try:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
if enable_mkldnn_bfloat16:
config.enable_mkldnn_bfloat16()
except Exception as e:
print(
"The current environment does not support `mkldnn`, so disable mkldnn."
)
pass
precision_map = {
'trt_int8': Config.Precision.Int8,
'trt_fp32': Config.Precision.Float32,
'trt_fp16': Config.Precision.Half
}
if run_mode in precision_map.keys():
config.enable_tensorrt_engine(
workspace_size=(1 << 25) * batch_size,
max_batch_size=batch_size,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[run_mode],
use_static=False,
use_calib_mode=trt_calib_mode)
if use_dynamic_shape:
min_input_shape = {
'image': [batch_size, 3, trt_min_shape, trt_min_shape]
}
max_input_shape = {
'image': [batch_size, 3, trt_max_shape, trt_max_shape]
}
opt_input_shape = {
'image': [batch_size, 3, trt_opt_shape, trt_opt_shape]
}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
print('trt set dynamic shape done!')
# disable print log when predict
config.disable_glog_info()
# enable shared memory
config.enable_memory_optim()
# disable feed, fetch OP, needed by zero_copy_run
config.switch_use_feed_fetch_ops(False)
if delete_shuffle_pass:
config.delete_pass("shuffle_channel_detect_pass")
predictor = create_predictor(config)
return predictor, config | set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
Used by action model.
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
ValueError: predict by TensorRT need device == 'GPU'.
| load_predictor | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
assert infer_dir is None or os.path.isdir(infer_dir), \
"{} is not a directory".format(infer_dir)
# infer_img has a higher priority
if infer_img and os.path.isfile(infer_img):
return [infer_img]
images = set()
infer_dir = os.path.abspath(infer_dir)
assert os.path.isdir(infer_dir), \
"infer_dir {} is not a directory".format(infer_dir)
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for ext in exts:
images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
images = list(images)
assert len(images) > 0, "no image found in {}".format(infer_dir)
print("Found {} inference images in total.".format(len(images)))
return images |
Get image path list in TEST mode
| get_test_images | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
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