158 lines
6.2 KiB
Python
158 lines
6.2 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
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#####################################################
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# python exps/basic/xmain.py --save_dir outputs/x #
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#####################################################
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import os, sys, time, torch, random, argparse
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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 / ".." / "..").resolve()
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print("LIB-DIR: {:}".format(lib_dir))
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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 xautodl import xmisc
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def main(args):
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train_data = xmisc.nested_call_by_yaml(args.train_data_config, args.data_path)
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valid_data = xmisc.nested_call_by_yaml(args.valid_data_config, args.data_path)
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logger = xmisc.Logger(args.save_dir, prefix="seed-{:}-".format(args.rand_seed))
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logger.log("Create the logger: {:}".format(logger))
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logger.log("Arguments : -------------------------------")
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for name, value in args._get_kwargs():
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logger.log("{:16} : {:}".format(name, value))
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logger.log("Python Version : {:}".format(sys.version.replace("\n", " ")))
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logger.log("PyTorch Version : {:}".format(torch.__version__))
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logger.log("cuDNN Version : {:}".format(torch.backends.cudnn.version()))
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logger.log("CUDA available : {:}".format(torch.cuda.is_available()))
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logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
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logger.log(
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"CUDA_VISIBLE_DEVICES : {:}".format(
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os.environ["CUDA_VISIBLE_DEVICES"]
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if "CUDA_VISIBLE_DEVICES" in os.environ
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else "None"
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)
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)
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logger.log("The training data is:\n{:}".format(train_data))
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logger.log("The validation data is:\n{:}".format(valid_data))
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model = xmisc.nested_call_by_yaml(args.model_config)
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logger.log("The model is:\n{:}".format(model))
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logger.log("The model size is {:.4f} M".format(xmisc.count_parameters(model)))
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_sampler=xmisc.BatchSampler(train_data, args.batch_size, args.steps),
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num_workers=args.workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.workers,
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pin_memory=True,
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drop_last=False,
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)
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iters_per_epoch = len(train_data) // args.batch_size
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logger.log("The training loader: {:}".format(train_loader))
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logger.log("The validation loader: {:}".format(valid_loader))
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optimizer = xmisc.nested_call_by_yaml(
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args.optim_config,
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model.parameters(),
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lr=args.lr,
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weight_decay=args.weight_decay,
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)
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objective = xmisc.nested_call_by_yaml(args.loss_config)
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metric = xmisc.nested_call_by_yaml(args.metric_config)
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logger.log("The optimizer is:\n{:}".format(optimizer))
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logger.log("The objective is {:}".format(objective))
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logger.log("The metric is {:}".format(metric))
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logger.log(
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"The iters_per_epoch = {:}, estimated epochs = {:}".format(
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iters_per_epoch, args.steps // iters_per_epoch
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)
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)
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model, objective = torch.nn.DataParallel(model).cuda(), objective.cuda()
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scheduler = xmisc.LRMultiplier(
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optimizer, xmisc.get_scheduler(args.scheduler, args.lr), args.steps
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)
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start_time, iter_time = time.time(), xmisc.AverageMeter()
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for xiter, data in enumerate(train_loader):
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need_time = "Time Left: {:}".format(
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xmisc.time_utils.convert_secs2time(
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iter_time.avg * (len(train_loader) - xiter), True
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)
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)
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iter_str = "{:6d}/{:06d}".format(xiter, len(train_loader))
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inputs, targets = data
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targets = targets.cuda(non_blocking=True)
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model.train()
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = objective(outputs, targets)
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loss.backward()
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optimizer.step()
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scheduler.step()
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if xiter % iters_per_epoch == 0:
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logger.log("TRAIN [{:}] loss = {:.6f}".format(iter_str, loss.item()))
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# measure elapsed time
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iter_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log("-" * 200 + "\n")
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Train a classification model with a loss function.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--save_dir", type=str, help="Folder to save checkpoints and log."
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)
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parser.add_argument("--resume", type=str, help="Resume path.")
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parser.add_argument("--init_model", type=str, help="The initialization model path.")
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parser.add_argument("--model_config", type=str, help="The path to the model config")
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parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
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parser.add_argument("--loss_config", type=str, help="The loss config file.")
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parser.add_argument("--metric_config", type=str, help="The metric config file.")
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parser.add_argument(
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"--train_data_config", type=str, help="The training dataset config path."
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)
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parser.add_argument(
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"--valid_data_config", type=str, help="The validation dataset config path."
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)
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parser.add_argument("--data_path", type=str, help="The path to the dataset.")
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# Optimization options
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parser.add_argument("--lr", type=float, help="The learning rate")
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parser.add_argument("--weight_decay", type=float, help="The weight decay")
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parser.add_argument("--scheduler", type=str, help="The scheduler indicator.")
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parser.add_argument("--steps", type=int, help="The total number of steps.")
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parser.add_argument("--batch_size", type=int, default=256, help="The batch size.")
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parser.add_argument("--workers", type=int, default=4, help="The number of workers")
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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if args.save_dir is None:
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raise ValueError("The save-path argument can not be None")
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main(args)
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