def decode(self, loc_preds, cls_preds, score_thresh=0.6, nms_thresh=0.45):
variances = (0.1, 0.2)
xy = loc_preds[:, :2] * variances[0] * self.default_boxes[:, 2:] + self.default_boxes[:, :2]
wh = torch.exp(loc_preds[:, 2:] * variances[1]) * self.default_boxes[:, 2:]
box_preds = torch.cat([xy - wh / 2, xy + wh / 2], 1)
boxes = []
labels = []
scores = []
num_classes = cls_preds.size(1)
for i in range(num_classes - 1):
score = cls_preds[:, i + 1] # class i corresponds to (i+1) column
mask = score > score_thresh
if not mask.any():
continue
box = box_preds[mask.nonzero().squeeze(1)]
score = score[mask]
keep = box_nms(box, score, nms_thresh)
boxes.append(box[keep])
labels.append(torch.LongTensor(len(box[keep])).fill_(i))
scores.append(score[keep])
boxes = torch.cat(boxes, 0)
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
return boxes, labels, scores
def decode(self, loc_preds, cls_preds, input_size):
'''Decode outputs back to bouding box locations and class labels.
Args:
loc_preds: (tensor) predicted locations, sized [#anchors, 4].
cls_preds: (tensor) predicted class labels, sized [#anchors, #classes].
input_size: (tuple) model input size of (w,h).
Returns:
boxes: (tensor) decode box locations, sized [#obj,4].
labels: (tensor) class labels for each box, sized [#obj,].
'''
CLS_THRESH = 0.5
NMS_THRESH = 0.5
input_size = torch.Tensor(input_size)
anchor_boxes = self._get_anchor_boxes(input_size) # xywh
loc_xy = loc_preds[:, :2]
loc_wh = loc_preds[:, 2:]
xy = loc_xy * anchor_boxes[:, 2:] + anchor_boxes[:, :2]
wh = loc_wh.exp() * anchor_boxes[:, 2:]
boxes = torch.cat([xy - wh / 2, xy + wh / 2], 1) # [#anchors,4]
score, labels = cls_preds.sigmoid().max(1) # [#anchors,]
ids = score > CLS_THRESH
ids = ids.nonzero().squeeze() # [#obj,]
keep = box_nms(boxes[ids], score[ids], threshold=NMS_THRESH)
return boxes[ids][keep], labels[ids][keep]
def decode(self, loc_preds, cls_preds, score_thresh=0.6, nms_thresh=0.45):
'''Decode predicted loc/cls back to real box locations and class labels.
Args:
loc_preds: (tensor) predicted loc, sized [8732,4].
cls_preds: (tensor) predicted conf, sized [8732,21].
score_thresh: (float) threshold for object confidence score.
nms_thresh: (float) threshold for box nms.
Returns:
boxes: (tensor) bbox locations, sized [#obj,4].
labels: (tensor) class labels, sized [#obj,].
'''
variances = (0.1, 0.2)
# variances = (1, 1)
xy = loc_preds[:, :2] * variances[
0] * self.default_boxes[:, 2:] + self.default_boxes[:, :2]
wh = torch.exp(
loc_preds[:, 2:] * variances[1]) * self.default_boxes[:, 2:]
box_preds = torch.cat([xy - wh / 2, xy + wh / 2], 1)
boxes = []
labels = []
scores = []
num_classes = cls_preds.size(1)
for i in range(num_classes - 1):
score = cls_preds[:, i + 1] # class i corresponds to (i+1) column
mask = score > score_thresh
if not mask.any():
continue
box = box_preds[mask.nonzero().squeeze()]
score = score[mask]
keep = box_nms(box, score, nms_thresh)
boxes.append(box[keep])
labels.append(torch.LongTensor(len(box[keep])).fill_(i))
scores.append(score[keep])
try:
boxes = torch.cat(boxes, 0)
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
except:
boxes = None
labels = None
scores = None
return boxes, labels, scores
def decode(self, loc_preds, cls_preds, score_thresh=0.6, nms_thresh=0.45):
'''Decode predicted loc/cls back to real box locations and class labels.
Args:
loc_preds: (tensor) predicted loc, sized [#anchors,4].
cls_preds: (tensor) predicted conf, sized [#anchors,#classes].
score_thresh: (float) threshold for object confidence score.
nms_thresh: (float) threshold for box nms.
Returns:
boxes: (tensor) bbox locations, sized [#obj,4].
labels: (tensor) class labels, sized [#obj,].
'''
anchor_boxes = change_box_order(self.anchor_boxes, 'xyxy2xywh')
xy = loc_preds[:, :2] * anchor_boxes[:, 2:] + anchor_boxes[:, :2]
wh = loc_preds[:, 2:].exp() * anchor_boxes[:, 2:]
box_preds = torch.cat([xy - wh / 2, xy + wh / 2], 1)
boxes = []
labels = []
scores = []
num_classes = cls_preds.size(1)
for i in range(num_classes - 1):
score = cls_preds[:, i + 1] # class i corresponds to (i+1) column
mask = score > score_thresh
if not mask.any():
continue
box = box_preds[mask]
score = score[mask]
# print(box.size())
# print(score.size())
keep = box_nms(box, score, nms_thresh)
boxes.append(box[keep])
labels.append(torch.empty_like(keep).fill_(i))
scores.append(score[keep])
boxes = torch.cat(boxes, 0)
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
return boxes, labels, scores
def decode(self,
loc_preds,
cls_preds,
input_size=conf.input_size,
cls_thred=conf.cls_thred,
max_output_size=conf.max_output_size,
nms_thred=conf.nms_thred,
return_score=False,
tf_box_order=True):
"""Decode outputs back to bouding box locations and class labels.
Args:
loc_preds: (tensor) predicted locations, sized [#anchors, 4].
cls_preds: (tensor) predicted class labels, sized [#anchors, #classes].
input_size: (int/tuple) model input size of (w, h), should be the same.
cls_thred: class score threshold
max_output_size: max output nums after nms
nms_thred: non-maximum suppression threshold
return_score: (bool) indicate whether to return score value.
tf_box_order: (bool) True: [ymin, xmin, ymax, xmax]
False: [xmin, ymin, xmax, ymax]
Returns:
boxes: (tensor) decode box locations, sized [#obj, 4].
order determined by param: tf_box_order
labels: (tensor) class labels for each box, sized [#obj, ].
NOTE: #obj == min(#detected_objs, #max_output_size)
"""
assert len(loc_preds.get_shape().as_list(
)) == 2, 'Ensure the location input shape to be [#anchors, 4]'
assert len(cls_preds.get_shape().as_list(
)) == 2, 'Ensure the class input shape to be [#anchors, #classes]'
input_size = _make_list_input_size(input_size)
anchor_boxes = self._get_anchor_boxes(input_size)
loc_xy = loc_preds[:, :2]
loc_wh = loc_preds[:, 2:]
xy = loc_xy * anchor_boxes[:, 2:] + anchor_boxes[:, :2]
wh = tf.exp(loc_wh) * anchor_boxes[:, 2:]
boxes = tf.concat([xy - wh / 2, xy + wh / 2], 1) # [#anchors, 4]
labels = tf.argmax(cls_preds, 1) # [#anchors, ]
score = tf.reduce_max(tf.sigmoid(cls_preds), 1)
ids = tf.cast(score > cls_thred, tf.int32)
ids = tf.where(tf.not_equal(ids, 0))
if not ids.numpy().any(): # Fail to detect, choose the max score
ids = tf.expand_dims(tf.argmax(score), axis=-1)
else:
ids = tf.squeeze(ids, -1)
if tf_box_order:
# [ymin, xmin, ymax, xmax]
boxes = tf.transpose(tf.gather(tf.transpose(boxes), [1, 0, 3, 2]))
keep = tf.image.non_max_suppression(
tf.gather(boxes, ids),
tf.gather(score, ids),
max_output_size=max_output_size,
iou_threshold=nms_thred)
else:
# [xmin, ymin, xmax, ymax]
keep = box_nms(tf.gather(boxes, ids),
tf.gather(score, ids),
threshold=nms_thred)
def _index(t, index):
"""Gather tensor successively
E.g., _index(boxes, [idx_1, idx_2]) = tf.gather(tf.gather(boxes, idx_1), idx_2)
"""
if not isinstance(index, (tuple, list)):
index = list(index)
for i in index:
t = tf.gather(t, i)
return t
if return_score:
return _index(boxes, [ids, keep]), _index(labels,
[ids, keep]), _index(
score, [ids, keep])
return _index(boxes, [ids, keep]), _index(labels, [ids, keep])
def decode(self,
loc_preds,
cls_preds,
input_size=conf.input_size,
output_size=None,
cls_thred=conf.cls_thred,
max_output_size=conf.max_output_size,
nms_thred=conf.nms_thred,
return_score=True,
tf_box_order=conf.tf_box_order):
"""Decode outputs back to bouding box locations and class labels.
We obey the Faster RCNN box coder:
tx = (x - anchor_x) / anchor_w
ty = (y - anchor_y) / anchor_h
tw = log(w / anchor_w)
th = log(h / anchor_h)
Args:
loc_preds: (tensor) predicted locations, sized [#anchors, 4].
cls_preds: (tensor) predicted class labels, sized [#anchors, #classes].
input_size: (int/tuple) model input size of (w, h), should be the same.
cls_thred: class score threshold
max_output_size: max output nums after nms
nms_thred: non-maximum suppression threshold
return_score: (bool) indicate whether to return score value.
tf_box_order: (bool) True: [ymin, xmin, ymax, xmax]
False: [xmin, ymin, xmax, ymax]
Returns:
boxes: (tensor) decode box locations, sized [#obj, 4].
order determined by param: tf_box_order
labels: (tensor) class labels for each box, sized [#obj, ].
NOTE: #obj == min(#detected_objs, #max_output_size)
"""
input_size = _make_list_input_size(input_size)
anchor_boxes = self._get_anchor_boxes(input_size)
loc_xy = loc_preds[:, :2]
loc_wh = loc_preds[:, 2:]
xy = loc_xy * anchor_boxes[:, 2:] + anchor_boxes[:, :2]
wh = tf.exp(loc_wh) * anchor_boxes[:, 2:]
boxes = tf.concat([xy - wh / 2, xy + wh / 2], 1) # [#anchors, 4]
labels = tf.argmax(cls_preds, 1) # [#anchors, ]
#score = tf.reduce_max(tf.sigmoid(cls_preds), 1)
score = tf.sigmoid(tf.reduce_max(cls_preds, 1)) ######### xpy
#ids = tf.where(tf.greater_equal(score, cls_thred+0.6) & tf.less_equal(score, cls_thred+0.8))
ids = tf.cast(score > cls_thred, tf.int32)
ids = tf.where(tf.not_equal(ids, 0))
#if not ids.numpy().any(): # Fail to detect, choose the max score
if ids.shape[0] == 0: # Fail to detect, choose the max score
ids = tf.expand_dims(tf.argmax(score), axis=-1)
print(
"!!! Box decode: Fail to detect, choose the max score !!!!!!!!!!!!!!!!!!"
)
else:
ids = tf.squeeze(ids, -1)
#print("Here!!!!!!!!!!!!!")
if tf_box_order:
# [ymin, xmin, ymax, xmax]
boxes = tf.transpose(tf.gather(tf.transpose(boxes), [1, 0, 3, 2]))
keep = tf.image.non_max_suppression(
tf.gather(boxes, ids),
tf.gather(score, ids),
max_output_size=max_output_size,
iou_threshold=nms_thred)
else:
# [xmin, ymin, xmax, ymax]
keep = box_nms(tf.gather(boxes, ids),
tf.gather(score, ids),
threshold=nms_thred)
def _index(t, index):
"""Gather tensor successively
E.g., _index(boxes, [idx_1, idx_2]) = tf.gather(tf.gather(boxes, idx_1), idx_2)
"""
if not isinstance(index, (tuple, list)):
index = list(index)
for i in index:
t = tf.gather(t, i)
#t = tf.gather(t, index[0])
return t
#return boxes,labels,score
bboxes = _index(boxes, [ids, keep])
if tf_box_order:
bbox = tf.split(axis=1, num_or_size_splits=4, value=bboxes)
bboxes = tf.concat([bbox[1], bbox[0], bbox[3], bbox[2]], axis=1)
#bboxes[:, [0, 1, 2, 3]] = bboxes[:, [1, 0, 3, 2]]
if return_score:
return bboxes, _index(labels,
[ids, keep]), _index(score, [ids, keep])
return _index(boxes, [ids, keep]), _index(labels, [ids, keep])
def decode___(self,
loc_preds,
cls_preds,
score_thresh=0.6,
nms_thresh=0.45):
'''Decode predicted loc/cls back to real box locations and class labels.
Args:
loc_preds: (tensor) predicted loc, sized [8732,4].
cls_preds: (tensor) predicted conf, sized [8732,21].
score_thresh: (float) threshold for object confidence score.
nms_thresh: (float) threshold for box nms.
Returns:
boxes: (tensor) bbox locations, sized [#obj,4].
labels: (tensor) class labels, sized [#obj,].
'''
self.steps = (4, 8, 16, 32, 64, 128, 256, 512)
self.box_sizes = (17.92, 35.84, 76.8, 153.6, 230.4, 307.2, 384.0,
460.8, 537.6)
self.aspect_ratios = ((), (2, ), (2, ), (2, ), (2, ), (2, ), (2, ),
(2, ))
boxes = []
score = []
for i in range(len(cls_preds)):
cls_preds[i] = F.sigmoid(cls_preds[i].squeeze())
for i in range(len(loc_preds)):
oreg, ocls = loc_preds[i].squeeze().data.cpu(
), cls_preds[i].data.cpu()
FH, FW, anchor_num = ocls.size() # feature map size
for Findex in range(FH * FW):
windex, hindex = Findex % FW, Findex // FW
cx = (windex + 0.5) * self.steps[i]
cy = (hindex + 0.5) * self.steps[i]
if ocls[hindex, windex, 0] > score_thresh:
s = self.box_sizes[i]
loc = oreg[hindex, windex, 0, :].unsqueeze(0)
prior = torch.Tensor([cx, cy, s, s]).unsqueeze(0)
variances = (1, 1)
xy = loc[:, :2] * variances[0] * prior[:,
2:] + prior[:, :2]
wh = torch.exp(loc[:, 2:] * variances[1]) * prior[:, 2:]
boxes.append(torch.cat([xy - wh / 2, xy + wh / 2], 1))
score.append(ocls[hindex, windex, 0])
if ocls[hindex, windex, 1] > score_thresh:
s = math.sqrt(self.box_sizes[i] * self.box_sizes[i + 1])
loc = oreg[hindex, windex, 1, :].unsqueeze(0)
prior = torch.Tensor([cx, cy, s, s]).unsqueeze(0)
variances = (1, 1)
xy = loc[:, :2] * variances[0] * prior[:,
2:] + prior[:, :2]
wh = torch.exp(loc[:, 2:] * variances[1]) * prior[:, 2:]
boxes.append(torch.cat([xy - wh / 2, xy + wh / 2], 1))
score.append(ocls[hindex, windex, 1])
s = self.box_sizes[i]
for j, ar in enumerate(self.aspect_ratios[i]):
if ocls[hindex, windex, 2 + j * 2] > score_thresh:
loc = oreg[hindex, windex, 2 + j * 2, :].unsqueeze(0)
prior = torch.Tensor(
[cx, cy, s * math.sqrt(ar),
s / math.sqrt(ar)]).unsqueeze(0)
variances = (1, 1)
xy = loc[:, :2] * variances[
0] * prior[:, 2:] + prior[:, :2]
wh = torch.exp(loc[:, 2:] * variances[1]) * prior[:,
2:]
boxes.append(torch.cat([xy - wh / 2, xy + wh / 2], 1))
score.append(ocls[hindex, windex, 2 + j * 2])
if ocls[hindex, windex, 2 + j * 2 + 1] > score_thresh:
loc = oreg[hindex, windex,
2 + j * 2 + 1, :].unsqueeze(0)
prior = torch.Tensor(
[cx, cy, s / math.sqrt(ar),
s * math.sqrt(ar)]).unsqueeze(0)
variances = (1, 1)
xy = loc[:, :2] * variances[
0] * prior[:, 2:] + prior[:, :2]
wh = torch.exp(loc[:, 2:] * variances[1]) * prior[:,
2:]
boxes.append(torch.cat([xy - wh / 2, xy + wh / 2], 1))
score.append(ocls[hindex, windex, 2 + j * 2 + 1])
try:
box = torch.cat(boxes, 0)
except:
boxes = None
labels = None
scores = None
return boxes, labels, scores
score = torch.Tensor(score)
boxes = []
labels = []
scores = []
keep = box_nms(box, score, nms_thresh)
boxes.append(box[keep])
labels.append(torch.LongTensor(len(box[keep])).fill_(i))
scores.append(score[keep])
boxes = torch.cat(boxes, 0)
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
return boxes, labels, scores
