def proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg,
_feat_stride,
anchor_scales,
gt_boxes=None):
#convert to numpy
rpn_cls_prob_reshape = rpn_cls_prob_reshape.data.cpu().numpy()
rpn_bbox_pred = rpn_bbox_pred.data.cpu().numpy()
rois, scores, anchor_inds, labels = proposal_layer_py(
rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg,
_feat_stride=_feat_stride,
anchor_scales=anchor_scales,
gt_boxes=gt_boxes)
rois = network.np_to_variable(rois, is_cuda=True)
anchor_inds = network.np_to_variable(anchor_inds,
is_cuda=True,
dtype=torch.LongTensor)
labels = network.np_to_variable(labels,
is_cuda=True,
dtype=torch.LongTensor)
#just get fg scores, make bg scores 0
scores = network.np_to_variable(scores, is_cuda=True)
return rois, scores, anchor_inds, labels
def proposal_layer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, cfg_key,
_feat_stride, anchor_scales):
rpn_cls_prob_reshape = rpn_cls_prob_reshape.data.cpu().numpy()
rpn_bbox_pred = rpn_bbox_pred.data.cpu().numpy()
x = proposal_layer_py(rpn_cls_prob_reshape, rpn_bbox_pred, im_info,
cfg_key, _feat_stride, anchor_scales)
x = network.np_to_variable(x, is_cuda=True)
return x.view(-1, 5)
def proposal_layer(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchor_scales):
# if isinstance(rpn_cls_prob_reshape, tuple):
# rpn_cls_prob_reshape = rpn_cls_prob_reshape[0]
rpn_cls_prob_reshape = rpn_cls_prob_reshape.data.cpu().numpy()
rpn_bbox_pred = rpn_bbox_pred.data.cpu().numpy()
x = proposal_layer_py(rpn_cls_prob_reshape, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchor_scales)
x = torch.from_numpy(x)
x = Variable(x).cuda()
return x.view(-1, 5)
def proposal_layer(rpn_cls_prob_reshape_vec, rpn_bbox_pred_vec, im_info, cfg_key, _feat_stride, anchor_scales):
x = proposal_layer_py(rpn_cls_prob_reshape_vec, rpn_bbox_pred_vec, im_info, cfg_key, _feat_stride, anchor_scales)
x = network.np_to_variable(x, is_cuda=True)
return x.view(-1, 5)
