##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # ##################################################### import os, sys, time, torch, random, argparse from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from copy import deepcopy from pathlib import Path lib_dir = (Path(__file__).parent / ".." / "lib").resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) from config_utils import load_config, dict2config from procedures import get_procedures, get_optim_scheduler from datasets import get_datasets from models import obtain_model from utils import get_model_infos from log_utils import PrintLogger, time_string assert torch.cuda.is_available(), "torch.cuda is not available" def main(args): assert os.path.isdir(args.data_path), "invalid data-path : {:}".format( args.data_path ) assert os.path.isfile(args.checkpoint), "invalid checkpoint : {:}".format( args.checkpoint ) checkpoint = torch.load(args.checkpoint) xargs = checkpoint["args"] train_data, valid_data, xshape, class_num = get_datasets( xargs.dataset, args.data_path, xargs.cutout_length ) valid_loader = torch.utils.data.DataLoader( valid_data, batch_size=xargs.batch_size, shuffle=False, num_workers=xargs.workers, pin_memory=True, ) logger = PrintLogger() model_config = dict2config(checkpoint["model-config"], logger) base_model = obtain_model(model_config) flop, param = get_model_infos(base_model, xshape) logger.log("model ====>>>>:\n{:}".format(base_model)) logger.log("model information : {:}".format(base_model.get_message())) logger.log("-" * 50) logger.log( "Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format( param, flop, flop / 1e3 ) ) logger.log("-" * 50) logger.log("valid_data : {:}".format(valid_data)) optim_config = dict2config(checkpoint["optim-config"], logger) _, _, criterion = get_optim_scheduler(base_model.parameters(), optim_config) logger.log("criterion : {:}".format(criterion)) base_model.load_state_dict(checkpoint["base-model"]) _, valid_func = get_procedures(xargs.procedure) logger.log("initialize the CNN done, evaluate it using {:}".format(valid_func)) network = torch.nn.DataParallel(base_model).cuda() try: valid_loss, valid_acc1, valid_acc5 = valid_func( valid_loader, network, criterion, optim_config, "pure-evaluation", xargs.print_freq_eval, logger, ) except: _, valid_func = get_procedures("basic") valid_loss, valid_acc1, valid_acc5 = valid_func( valid_loader, network, criterion, optim_config, "pure-evaluation", xargs.print_freq_eval, logger, ) num_bytes = torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0 logger.log( "***{:s}*** EVALUATION loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f}, error@1 = {:.2f}, error@5 = {:.2f}".format( time_string(), valid_loss, valid_acc1, valid_acc5, 100 - valid_acc1, 100 - valid_acc5, ) ) logger.log( "[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format( next(network.parameters()).device, int(num_bytes), num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9, ) ) logger.close() if __name__ == "__main__": parser = argparse.ArgumentParser("Evaluate-CNN") parser.add_argument("--data_path", type=str, help="Path to dataset.") parser.add_argument( "--checkpoint", type=str, help="Choose between Cifar10/100 and ImageNet." ) args = parser.parse_args() main(args)