Reformulate via black
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D-X-Y
@@ -2,67 +2,83 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import os, sys, time, torch, random, argparse
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from PIL import ImageFile
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config
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from procedures import get_procedures, get_optim_scheduler
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from datasets import get_datasets
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from models import obtain_model
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from utils import get_model_infos
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from log_utils import PrintLogger, time_string
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from procedures import get_procedures, get_optim_scheduler
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from datasets import get_datasets
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from models import obtain_model
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from utils import get_model_infos
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from log_utils import PrintLogger, time_string
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assert torch.cuda.is_available(), 'torch.cuda is not available'
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assert torch.cuda.is_available(), "torch.cuda is not available"
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def main(args):
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assert os.path.isdir ( args.data_path ) , 'invalid data-path : {:}'.format(args.data_path)
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assert os.path.isfile( args.checkpoint ), 'invalid checkpoint : {:}'.format(args.checkpoint)
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assert os.path.isdir(args.data_path), "invalid data-path : {:}".format(args.data_path)
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assert os.path.isfile(args.checkpoint), "invalid checkpoint : {:}".format(args.checkpoint)
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checkpoint = torch.load( args.checkpoint )
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xargs = checkpoint['args']
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, args.data_path, xargs.cutout_length)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=xargs.batch_size, shuffle=False, num_workers=xargs.workers, pin_memory=True)
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checkpoint = torch.load(args.checkpoint)
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xargs = checkpoint["args"]
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, args.data_path, xargs.cutout_length)
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valid_loader = torch.utils.data.DataLoader(
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valid_data, batch_size=xargs.batch_size, shuffle=False, num_workers=xargs.workers, pin_memory=True
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)
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logger = PrintLogger()
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model_config = dict2config(checkpoint['model-config'], logger)
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base_model = obtain_model(model_config)
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flop, param = get_model_infos(base_model, xshape)
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logger.log('model ====>>>>:\n{:}'.format(base_model))
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logger.log('model information : {:}'.format(base_model.get_message()))
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logger.log('-'*50)
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logger.log('Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(param, flop, flop/1e3))
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logger.log('-'*50)
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logger.log('valid_data : {:}'.format(valid_data))
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optim_config = dict2config(checkpoint['optim-config'], logger)
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_, _, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
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logger.log('criterion : {:}'.format(criterion))
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base_model.load_state_dict( checkpoint['base-model'] )
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_, valid_func = get_procedures(xargs.procedure)
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logger.log('initialize the CNN done, evaluate it using {:}'.format(valid_func))
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network = torch.nn.DataParallel(base_model).cuda()
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try:
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valid_loss, valid_acc1, valid_acc5 = valid_func(valid_loader, network, criterion, optim_config, 'pure-evaluation', xargs.print_freq_eval, logger)
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except:
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_, valid_func = get_procedures('basic')
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valid_loss, valid_acc1, valid_acc5 = valid_func(valid_loader, network, criterion, optim_config, 'pure-evaluation', xargs.print_freq_eval, logger)
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num_bytes = torch.cuda.max_memory_cached( next(network.parameters()).device ) * 1.0
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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))
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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))
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logger.close()
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logger = PrintLogger()
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model_config = dict2config(checkpoint["model-config"], logger)
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base_model = obtain_model(model_config)
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flop, param = get_model_infos(base_model, xshape)
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logger.log("model ====>>>>:\n{:}".format(base_model))
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logger.log("model information : {:}".format(base_model.get_message()))
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logger.log("-" * 50)
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logger.log("Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(param, flop, flop / 1e3))
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logger.log("-" * 50)
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logger.log("valid_data : {:}".format(valid_data))
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optim_config = dict2config(checkpoint["optim-config"], logger)
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_, _, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
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logger.log("criterion : {:}".format(criterion))
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base_model.load_state_dict(checkpoint["base-model"])
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_, valid_func = get_procedures(xargs.procedure)
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logger.log("initialize the CNN done, evaluate it using {:}".format(valid_func))
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network = torch.nn.DataParallel(base_model).cuda()
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try:
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valid_loss, valid_acc1, valid_acc5 = valid_func(
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valid_loader, network, criterion, optim_config, "pure-evaluation", xargs.print_freq_eval, logger
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)
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except:
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_, valid_func = get_procedures("basic")
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valid_loss, valid_acc1, valid_acc5 = valid_func(
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valid_loader, network, criterion, optim_config, "pure-evaluation", xargs.print_freq_eval, logger
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)
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num_bytes = torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
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logger.log(
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"***{:s}*** EVALUATION loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f}, error@1 = {:.2f}, error@5 = {:.2f}".format(
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time_string(), valid_loss, valid_acc1, valid_acc5, 100 - valid_acc1, 100 - valid_acc5
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)
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)
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logger.log(
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"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
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next(network.parameters()).device, int(num_bytes), num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9
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)
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)
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logger.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Evaluate-CNN")
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parser.add_argument('--data_path', type=str, help='Path to dataset.')
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parser.add_argument('--checkpoint', type=str, help='Choose between Cifar10/100 and ImageNet.')
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args = parser.parse_args()
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main(args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Evaluate-CNN")
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parser.add_argument("--data_path", type=str, help="Path to dataset.")
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parser.add_argument("--checkpoint", type=str, help="Choose between Cifar10/100 and ImageNet.")
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args = parser.parse_args()
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main(args)
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