def getlabel(self):
im_tensor, im_info, gt_boxes = self._data
# all stacked image share same anchors
_, out_shape, _ = self._feat_sym.infer_shape(data=im_tensor.shape)
feat_height, feat_width = out_shape[0][-2:]
anchors = self._ag.generate(feat_height, feat_width)
# assign anchor according to their real size encoded in im_info
label, bbox_target, bbox_weight = [], [], []
for batch_ind in range(im_info.shape[0]):
b_im_info = im_info[batch_ind].asnumpy()
b_gt_boxes = gt_boxes[batch_ind].asnumpy()
b_im_height, b_im_width = b_im_info[:2]
b_label, b_bbox_target, b_bbox_weight = self._as.assign(anchors, b_gt_boxes, b_im_height, b_im_width)
b_label = b_label.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1)).flatten()
b_bbox_target = b_bbox_target.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1))
b_bbox_weight = b_bbox_weight.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1))
label.append(b_label)
bbox_target.append(b_bbox_target)
bbox_weight.append(b_bbox_weight)
label = mx.nd.array(tensor_vstack(label, pad=-1))
bbox_target = mx.nd.array(tensor_vstack(bbox_target, pad=0))
bbox_weight = mx.nd.array(tensor_vstack(bbox_weight, pad=0))
self._label = label, bbox_target, bbox_weight
return self._label
def getlabel(self):
im_tensor, im_info, gt_boxes = self._data
# all stacked image share same anchors
_, out_shape, _ = self._feat_sym.infer_shape(data=im_tensor.shape)
feat_height, feat_width = out_shape[0][-2:]
anchors = self._ag.generate(feat_height, feat_width)
# assign anchor according to their real size encoded in im_info
label, bbox_target, bbox_weight = [], [], []
for batch_ind in range(im_info.shape[0]):
b_im_info = im_info[batch_ind].asnumpy()
b_gt_boxes = gt_boxes[batch_ind].asnumpy()
b_im_height, b_im_width = b_im_info[:2]
b_label, b_bbox_target, b_bbox_weight = self._as.assign(
anchors, b_gt_boxes, b_im_height, b_im_width)
b_label = b_label.reshape((feat_height, feat_width, -1)).transpose(
(2, 0, 1)).flatten()
b_bbox_target = b_bbox_target.reshape(
(feat_height, feat_width, -1)).transpose((2, 0, 1))
b_bbox_weight = b_bbox_weight.reshape(
(feat_height, feat_width, -1)).transpose((2, 0, 1))
label.append(b_label)
bbox_target.append(b_bbox_target)
bbox_weight.append(b_bbox_weight)
label = mx.nd.array(tensor_vstack(label, pad=-1))
bbox_target = mx.nd.array(tensor_vstack(bbox_target, pad=0))
bbox_weight = mx.nd.array(tensor_vstack(bbox_weight, pad=0))
self._label = label, bbox_target, bbox_weight
return self._label
def getdata(self):
indices = self.getindex()
im_tensor, im_info = [], []
for index in indices:
roi_rec = self._roidb[index]
b_im_tensor, b_im_info, _ = get_image(roi_rec, self._short, self._max_size, self._mean, self._std)
im_tensor.append(b_im_tensor)
im_info.append(b_im_info)
im_tensor = mx.nd.array(tensor_vstack(im_tensor, pad=0))
im_info = mx.nd.array(tensor_vstack(im_info, pad=0))
self._data = im_tensor, im_info
return self._data
def getdata(self):
indices = self.getindex()
im_tensor, im_info = [], []
for index in indices:
roi_rec = self._roidb[index]
b_im_tensor, b_im_info, b_gt_boxes, b_seg = get_image(roi_rec, self._short, self._max_size, self._mean, self._std)
im_tensor.append(b_im_tensor)
im_info.append(b_im_info)
im_tensor = mx.nd.array(tensor_vstack(im_tensor, pad=0))
im_info = mx.nd.array(tensor_vstack(im_info, pad=0))
self._data = im_tensor, im_info
return self._data
def getdata(self):
indices = self.getindex()
im_tensor, im_info, gt_boxes = [], [], []
for index in indices:
roi_rec = self._roidb[index]
b_im_tensor, b_im_info, b_gt_boxes = get_image(
roi_rec, self._short, self._max_size, self._mean, self._std)
im_tensor.append(b_im_tensor)
im_info.append(b_im_info)
gt_boxes.append(b_gt_boxes)
im_tensor = mx.nd.array(tensor_vstack(im_tensor, pad=0))
im_info = mx.nd.array(tensor_vstack(im_info, pad=0))
gt_boxes = mx.nd.array(tensor_vstack(gt_boxes, pad=-1))
#print ("gt_boxes_shape=",gt_boxes.shape)
self._data = im_tensor, im_info, gt_boxes
return self._data
def getdata(self):
indices = self.getindex()
# short_side = np.floor(np.random.uniform(800, 1025))
im_tensor, im_info, gt_boxes, seg = [], [], [], []
for index in indices:
roi_rec = self._roidb[index]
b_im_tensor, b_im_info, b_gt_boxes, b_seg = get_image(roi_rec, self._short, self._max_size, self._mean, self._std)
im_tensor.append(b_im_tensor)
im_info.append(b_im_info)
gt_boxes.append(b_gt_boxes)
seg.append(b_seg)
# print(b_seg)
im_tensor = mx.nd.array(tensor_vstack(im_tensor, pad=0))
im_info = mx.nd.array(tensor_vstack(im_info, pad=0))
gt_boxes = mx.nd.array(tensor_vstack(gt_boxes, pad=-1))
seg = mx.nd.array(tensor_vstack(seg, pad=0))
self._data = im_tensor, im_info, gt_boxes, seg
return self._data
def getlabel(self):
im_tensor, im_info, gt_boxes = self._data
label_list = []
bbox_target_list = []
bbox_weight_list = []
# all stacked image share same anchors
# print(type(self._feat_sym))
for i, feat_sym in enumerate(self._feat_sym):
_, out_shape, _ = feat_sym.infer_shape(data=im_tensor.shape)
feat_height, feat_width = out_shape[0][-2:]
# print('feat height & width', feat_height, feat_width)
anchors = self._ag[i].generate(feat_height, feat_width)
# assign anchor according to their real size encoded in im_info
label, bbox_target, bbox_weight = [], [], []
for batch_ind in range(im_info.shape[0]):
b_im_info = im_info[batch_ind].asnumpy()
b_gt_boxes = gt_boxes[batch_ind].asnumpy()
b_im_height, b_im_width = b_im_info[:2]
b_label, b_bbox_target, b_bbox_weight = self._as.assign(anchors, b_gt_boxes, b_im_height, b_im_width)
b_label = b_label.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1)).flatten()
b_bbox_target = b_bbox_target.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1))
b_bbox_weight = b_bbox_weight.reshape((feat_height, feat_width, -1)).transpose((2, 0, 1))
label.append(b_label)
bbox_target.append(b_bbox_target)
bbox_weight.append(b_bbox_weight)
label_list.append(mx.nd.array(tensor_vstack(label, pad=-1)))
bbox_target_list.append(mx.nd.array(tensor_vstack(bbox_target, pad=0)))
bbox_weight_list.append(mx.nd.array(tensor_vstack(bbox_weight, pad=0)))
self._label = label_list[0], label_list[1], label_list[2], label_list[3], label_list[4], bbox_target_list[0], bbox_target_list[1], bbox_target_list[2], bbox_target_list[3], bbox_target_list[4], bbox_weight_list[0], bbox_weight_list[1], bbox_weight_list[2], bbox_weight_list[3], bbox_weight_list[4]
return self._label
