# if map_type not set, use default 11point, only use in VOC eval map_type = cfg.map_type if 'map_type' in cfg else '11point' save_only = getattr(cfg, 'save_prediction_only', False) eval_results( results, cfg.metric, cfg.num_classes, resolution, is_bbox_normalized, FLAGS.output_eval, map_type, dataset=dataset, save_only=save_only) if __name__ == '__main__': parser = ArgsParser() parser.add_argument( "--json_eval", action='store_true', default=False, help="Whether to re eval with already exists bbox.json or mask.json") parser.add_argument( "-f", "--output_eval", default=None, type=str, help="Evaluation file directory, default is current directory.") FLAGS = parser.parse_args() main()
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step) vdl_mAP_step += 1 if box_ap_stats[0] > best_box_ap_list[0]: best_box_ap_list[0] = box_ap_stats[0] best_box_ap_list[1] = it checkpoint.save(exe, train_prog, os.path.join(save_dir, "best_model")) logger.info("Best test box ap: {}, in iter: {}".format( best_box_ap_list[0], best_box_ap_list[1])) train_loader.reset() if __name__ == '__main__': parser = ArgsParser() parser.add_argument("-r", "--resume_checkpoint", default=None, type=str, help="Checkpoint path for resuming training.") parser.add_argument("--fp16", action='store_true', default=False, help="Enable mixed precision training.") parser.add_argument("--loss_scale", default=8., type=float, help="Mixed precision training loss scale.") parser.add_argument("--eval", action='store_true',
def parse_args(): parser = ArgsParser() parser.add_argument( "--infer_dir", type=str, default=None, help="Directory for images to perform inference on.") parser.add_argument( "--infer_img", type=str, default=None, help="Image path, has higher priority over --infer_dir") parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output visualization files.") parser.add_argument( "--draw_threshold", type=float, default=0.5, help="Threshold to reserve the result for visualization.") parser.add_argument( "--slim_config", default=None, type=str, help="Configuration file of slim method.") parser.add_argument( "--use_vdl", type=bool, default=False, help="Whether to record the data to VisualDL.") parser.add_argument( '--vdl_log_dir', type=str, default="vdl_log_dir/image", help='VisualDL logging directory for image.') parser.add_argument( "--save_txt", type=bool, default=False, help="Whether to save inference result in txt.") args = parser.parse_args() return args
if box_ap_stats[0] > best_box_ap_list[0]: best_box_ap_list[0] = box_ap_stats[0] best_box_ap_list[1] = it save_checkpoint(exe, eval_prog, os.path.join(save_dir, "best_model"), train_prog) logger.info("Best test box ap: {}, in iter: {}".format( best_box_ap_list[0], best_box_ap_list[1])) train_loader.reset() if __name__ == '__main__': enable_static_mode() parser = ArgsParser() parser.add_argument("--loss_scale", default=8., type=float, help="Mixed precision training loss scale.") parser.add_argument("--eval", action='store_true', default=False, help="Whether to perform evaluation in train") parser.add_argument( "--output_eval", default=None, type=str, help="Evaluation directory, default is current directory.") parser.add_argument( "--not_quant_pattern",
inputs_def = cfg['TestReader']['inputs_def'] inputs_def['use_dataloader'] = False feed_vars, _ = model.build_inputs(**inputs_def) # postprocess not need in exclude_nms, exclude NMS in exclude_nms mode test_fetches = model.test(feed_vars, exclude_nms=FLAGS.exclude_nms) infer_prog = infer_prog.clone(True) check_py_func(infer_prog) exe.run(startup_prog) checkpoint.load_params(exe, infer_prog, cfg.weights) dump_infer_config(FLAGS, cfg) save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog) if __name__ == '__main__': parser = ArgsParser() parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output model files.") parser.add_argument( "--exclude_nms", action='store_true', default=False, help="Whether prune NMS for benchmark") FLAGS = parser.parse_args() main()
image = visualize_results(image, int(im_id), catid2name, FLAGS.draw_threshold, bbox_results, mask_results) save_name = get_save_image_name(FLAGS.output_dir, image_path) logger.info( "Detection bbox results save in {}".format(save_name)) image.save(save_name, quality=95) except (StopIteration, fluid.core.EOFException): loader.reset() if __name__ == '__main__': enable_static_mode() parser = ArgsParser() parser.add_argument("--infer_dir", type=str, default=None, help="Directory for images to perform inference on.") parser.add_argument( "--infer_img", type=str, default=None, help="Image path, has higher priority over --infer_dir") parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output visualization files.") parser.add_argument(