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=0.5, 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').cuda()
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)
# print (num_classes)
for i in range(num_classes - 1):
score = cls_preds[:, i + 1] # class i corresponds to (i+1) column
# print (score)
mask = score > score_thresh
# print (mask)
if not mask.any():
# print ("continue")
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])
# print (sizeof(boxes))
#print (np.array(boxes).shape)
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, score_thresh=0.2, nms_thresh=0.45):
print("decode called")
'''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)
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
idxs = mask.nonzero().squeeze()
if len(idxs.shape) == 0:
box = box_preds[None, idxs, :]
else:
box = box_preds[idxs, :]
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, 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)
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]
if nms_thresh < 1.0:
keep = box_nms(box, score, nms_thresh)
else:
keep = list(range(len(box)))
boxes.append(box[keep])
labels.append(torch.LongTensor(len(box[keep])).fill_(i))
scores.append(score[keep])
try:
boxes = torch.cat(boxes, 0)
except RuntimeError:
return torch.FloatTensor([]), torch.FloatTensor(
[]), torch.FloatTensor([])
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
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 [8732,4] or [8732,8].
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,].
'''
#print("decode")
#print(loc_preds.size())
#print(cls_preds.size())
variances = (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)
default_boxes = change_box_order(self.default_boxes, 'xywh2xyxyxyxy')
box_preds = loc_preds * variances[
0] * self.default_boxes[:, 2:].repeat(1, 4) + default_boxes
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])
boxes = torch.cat(boxes, 0)
labels = torch.cat(labels, 0)
scores = torch.cat(scores, 0)
return boxes, labels, scores