def parse_args(): parser = argparse.ArgumentParser( description= "Evaluate a model for image classification/segmentation (Chainer)", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument( "--dataset", type=str, default="ImageNet1K", help= "dataset name. options are ImageNet1K, CUB200_2011, CIFAR10, CIFAR100, SVHN, VOC2012, ADE20K, Cityscapes, " "COCO") parser.add_argument( "--work-dir", type=str, default=os.path.join("..", "imgclsmob_data"), help="path to working directory only for dataset root path preset") args, _ = parser.parse_known_args() dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset) dataset_metainfo.add_dataset_parser_arguments(parser=parser, work_dir_path=args.work_dir) add_eval_parser_arguments(parser) args = parser.parse_args() return args
def test_model(args): """ Main test routine. Parameters: ---------- args : ArgumentParser Main script arguments. Returns ------- float Main accuracy value. """ ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset) ds_metainfo.update(args=args) assert (ds_metainfo.ml_type != "imgseg") or (args.batch_size == 1) assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune global_config.train = False use_gpus = prepare_ch_context(args.num_gpus) net = prepare_model( model_name=args.model, use_pretrained=args.use_pretrained, pretrained_model_file_path=args.resume.strip(), use_gpus=use_gpus, net_extra_kwargs=ds_metainfo.test_net_extra_kwargs, num_classes=(args.num_classes if ds_metainfo.ml_type != "hpe" else None), in_channels=args.in_channels) assert (hasattr(net, "classes") or (ds_metainfo.ml_type == "hpe")) assert (hasattr(net, "in_size")) get_test_data_source_class = get_val_data_source if args.data_subset == "val" else get_test_data_source test_data = get_test_data_source_class( ds_metainfo=ds_metainfo, batch_size=args.batch_size, num_workers=args.num_workers) if args.data_subset == "val": test_metric = get_composite_metric( metric_names=ds_metainfo.val_metric_names, metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs) else: test_metric = get_composite_metric( metric_names=ds_metainfo.test_metric_names, metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs) assert (args.use_pretrained or args.resume.strip()) acc_values = calc_model_accuracy( net=net, test_data=test_data, metric=test_metric, calc_weight_count=True, calc_flops_only=args.calc_flops_only, extended_log=True) return acc_values[ds_metainfo.saver_acc_ind] if len(acc_values) > 0 else None
def main(): """ Main body of script. """ args = parse_args() if args.disable_cudnn_autotune: os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0" _, log_file_exist = initialize_logging( logging_dir_path=args.save_dir, logging_file_name=args.logging_file_name, script_args=args, log_packages=args.log_packages, log_pip_packages=args.log_pip_packages) ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset) ds_metainfo.update(args=args) assert (ds_metainfo.ml_type != "imgseg") or (args.batch_size == 1) assert (ds_metainfo.ml_type != "imgseg") or args.disable_cudnn_autotune global_config.train = False use_gpus = prepare_ch_context(args.num_gpus) net = prepare_model(model_name=args.model, use_pretrained=args.use_pretrained, pretrained_model_file_path=args.resume.strip(), use_gpus=use_gpus, net_extra_kwargs=ds_metainfo.net_extra_kwargs, num_classes=args.num_classes, in_channels=args.in_channels) assert (hasattr(net, "classes")) assert (hasattr(net, "in_size")) if args.data_subset == "val": get_test_data_source_class = get_val_data_source test_metric = get_composite_metric( metric_names=ds_metainfo.val_metric_names, metric_extra_kwargs=ds_metainfo.val_metric_extra_kwargs) else: get_test_data_source_class = get_test_data_source test_metric = get_composite_metric( metric_names=ds_metainfo.test_metric_names, metric_extra_kwargs=ds_metainfo.test_metric_extra_kwargs) test_data = get_test_data_source_class(ds_metainfo=ds_metainfo, batch_size=args.batch_size, num_workers=args.num_workers) assert (args.use_pretrained or args.resume.strip()) test(net=net, test_data=test_data, metric=test_metric, calc_weight_count=True, extended_log=True)
def main(): args = parse_args() args.seed = init_rand(seed=args.seed) _, log_file_exist = initialize_logging( logging_dir_path=args.save_dir, logging_file_name=args.logging_file_name, script_args=args, log_packages=args.log_packages, log_pip_packages=args.log_pip_packages) ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset) ds_metainfo.update(args=args) use_gpus = prepare_ch_context(args.num_gpus) # batch_size = args.batch_size net = prepare_model( model_name=args.model, use_pretrained=args.use_pretrained, pretrained_model_file_path=args.resume.strip(), use_gpus=use_gpus, num_classes=args.num_classes, in_channels=args.in_channels) assert (hasattr(net, "classes")) assert (hasattr(net, "in_size")) train_data = get_train_data_source( ds_metainfo=ds_metainfo, batch_size=args.batch_size, num_workers=args.num_workers) val_data = get_val_data_source( ds_metainfo=ds_metainfo, batch_size=args.batch_size, num_workers=args.num_workers) trainer = prepare_trainer( net=net, optimizer_name=args.optimizer_name, lr=args.lr, momentum=args.momentum, num_epochs=args.num_epochs, train_data=train_data, val_data=val_data, logging_dir_path=args.save_dir, use_gpus=use_gpus) trainer.run()