3e4c8751ecea36db6c23f05ccff1019fbd3e1b55,lib/model/rpn/proposal_target_layer_cascade.py,_ProposalTargetLayer,_sample_rois_pytorch,#_ProposalTargetLayer#,114

Before Change

                // sampling bg
                //rand_num = torch.floor(torch.rand(rois_per_image) * bg_num_rois).long().cuda()
                rand_num = np.floor(np.random.rand(rois_per_image) * bg_num_rois)
                rand_num = torch.from_numpy(rand_num).long().cuda()

                bg_inds = bg_inds[rand_num]
                bg_rois_per_this_image = rois_per_image
                fg_rois_per_this_image = 0
            else:
                print("bg_num_rois = 0 and fg_num_rois = 0, this should not happen!")
                pdb.set_trace()

            // The indices that we"re selecting (both fg and bg)
            keep_inds = torch.cat([fg_inds, bg_inds], 0)

            // Select sampled values from various arrays:
            labels_batch[i].copy_(labels[i][keep_inds])

After Change

                // sampling fg
                //rand_num = torch.floor(torch.rand(rois_per_image) * fg_num_rois).long().cuda()
                rand_num = np.floor(np.random.rand(rois_per_image) * fg_num_rois)
                rand_num = torch.from_numpy(rand_num).type_as(gt_boxes).long()
                fg_inds = fg_inds[rand_num]
                fg_rois_per_this_image = rois_per_image
                bg_rois_per_this_image = 0
            elif bg_num_rois > 0 and fg_num_rois == 0:
                // sampling bg
                //rand_num = torch.floor(torch.rand(rois_per_image) * bg_num_rois).long().cuda()
                rand_num = np.floor(np.random.rand(rois_per_image) * bg_num_rois)
                rand_num = torch.from_numpy(rand_num).type_as(gt_boxes).long()

                bg_inds = bg_inds[rand_num]
                bg_rois_per_this_image = rois_per_image
                fg_rois_per_this_image = 0
            else:
                error("bg_num_rois = 0 and fg_num_rois = 0, this should not happen!")
                
            // The indices that we"re selecting (both fg and bg)
            keep_inds = torch.cat([fg_inds, bg_inds], 0)

            // Select sampled values from various arrays:
            labels_batch[i].copy_(labels[i][keep_inds])

Instances