################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ################################################## 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)