def test(imgL, imgR, image_sizes=None, calibs_fu=None, calibs_baseline=None, calibs_Proj=None, calibs_Proj_R=None): model.eval() with torch.no_grad(): outputs = model(imgL, imgR, calibs_fu, calibs_baseline, calibs_Proj, calibs_Proj_R=calibs_Proj_R) if args.save_feat_map: # test feature hook print("*" * 5 + "hook record extractor features" + "*" * 5) print(module_name_extractors) print("*" * 5 + "hook record extractor features" + "*" * 5) pred_disp = outputs['depth_preds'] rets = [pred_disp] if cfg.RPN3D_ENABLE: box_pred = make_fcos3d_postprocessor(cfg)(outputs['bbox_cls'], outputs['bbox_reg'], outputs['bbox_centerness'], image_sizes=image_sizes, calibs_Proj=calibs_Proj) rets.append(box_pred) return rets
def test(imgL, imgR, image_sizes=None, calibs_fu=None, calibs_baseline=None, calibs_Proj=None, calibs_Proj_R=None): model.eval() with torch.no_grad(): outputs = model(imgL, imgR, calibs_fu, calibs_baseline, calibs_Proj, calibs_Proj_R=calibs_Proj_R) pred_disp = outputs['depth_preds'] rets = [pred_disp] if cfg.RPN3D_ENABLE: box_pred = make_fcos3d_postprocessor(cfg)(outputs['bbox_cls'], outputs['bbox_reg'], outputs['bbox_centerness'], image_sizes=image_sizes, calibs_Proj=calibs_Proj) rets.append(box_pred) return rets