box_delta_right = box_delta_right * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
dim_orien = dim_orien * torch.FloatTensor(cfg.TRAIN.DIM_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.DIM_NORMALIZE_MEANS).cuda()
box_delta_left = box_delta_left.view(1, -1, 4 * len(kitti_classes))
box_delta_right = box_delta_right.view(1, -1, 4 * len(kitti_classes))
dim_orien = dim_orien.view(1, -1, 5 * len(kitti_classes))
kpts_delta = kpts_delta.view(1, -1, 1)
left_delta = left_delta.view(1, -1, 1)
right_delta = right_delta.view(1, -1, 1)
max_prob = max_prob.view(1, -1, 1)
pred_boxes_left = bbox_transform_inv(boxes_left, box_delta_left, 1)
pred_boxes_right = bbox_transform_inv(boxes_right, box_delta_right, 1)
pred_kpts, kpts_type = kpts_transform_inv(boxes_left, kpts_delta,
cfg.KPTS_GRID)
pred_left = border_transform_inv(boxes_left, left_delta, cfg.KPTS_GRID)
pred_right = border_transform_inv(boxes_left, right_delta,
cfg.KPTS_GRID)
pred_boxes_left = clip_boxes(pred_boxes_left, im_info.data, 1)
pred_boxes_right = clip_boxes(pred_boxes_right, im_info.data, 1)
pred_boxes_left /= im_info[0, 2].data
pred_boxes_right /= im_info[0, 2].data
pred_kpts /= im_info[0, 2].data
pred_left /= im_info[0, 2].data
pred_right /= im_info[0, 2].data
scores = scores.squeeze()
pred_boxes_left = pred_boxes_left.squeeze()
box_delta_left = box_delta_left.view(1, -1, 4 * len(imdb._classes))
box_delta_right = box_delta_right.view(1, -1,
4 * len(imdb._classes))
dim_orien = dim_orien.view(1, -1, 5 * len(imdb._classes))
kpts_delta = kpts_delta.view(1, -1, 1)
left_delta = left_delta.view(1, -1, 1)
right_delta = right_delta.view(1, -1, 1)
max_prob = max_prob.view(1, -1, 1)
pred_boxes_left = \
bbox_transform_inv(boxes_left, box_delta_left, 1)
pred_boxes_right = \
bbox_transform_inv(boxes_right, box_delta_right, 1)
pred_kpts, kpts_type = \
kpts_transform_inv(boxes_left, kpts_delta, cfg.KPTS_GRID)
pred_left = \
border_transform_inv(boxes_left, left_delta, cfg.KPTS_GRID)
pred_right = \
border_transform_inv(boxes_left, right_delta, cfg.KPTS_GRID)
pred_boxes_left = clip_boxes(pred_boxes_left, im_info.data, 1)
pred_boxes_right = clip_boxes(pred_boxes_right, im_info.data, 1)
pred_boxes_left /= im_info[0, 2].data
pred_boxes_right /= im_info[0, 2].data
pred_kpts /= im_info[0, 2].data
pred_left /= im_info[0, 2].data
pred_right /= im_info[0, 2].data
scores = scores.squeeze()