add autodl

AutoDL-Projects/exps/basic/xmain.py

@@ -0,0 +1,157 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
+#####################################################
+# python exps/basic/xmain.py --save_dir outputs/x   #
+#####################################################
+import os, sys, time, torch, random, argparse
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+print("LIB-DIR: {:}".format(lib_dir))
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+from xautodl import xmisc
+
+
+def main(args):
+
+    train_data = xmisc.nested_call_by_yaml(args.train_data_config, args.data_path)
+    valid_data = xmisc.nested_call_by_yaml(args.valid_data_config, args.data_path)
+    logger = xmisc.Logger(args.save_dir, prefix="seed-{:}-".format(args.rand_seed))
+
+    logger.log("Create the logger: {:}".format(logger))
+    logger.log("Arguments : -------------------------------")
+    for name, value in args._get_kwargs():
+        logger.log("{:16} : {:}".format(name, value))
+    logger.log("Python  Version  : {:}".format(sys.version.replace("\n", " ")))
+    logger.log("PyTorch Version  : {:}".format(torch.__version__))
+    logger.log("cuDNN   Version  : {:}".format(torch.backends.cudnn.version()))
+    logger.log("CUDA available   : {:}".format(torch.cuda.is_available()))
+    logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
+    logger.log(
+        "CUDA_VISIBLE_DEVICES : {:}".format(
+            os.environ["CUDA_VISIBLE_DEVICES"]
+            if "CUDA_VISIBLE_DEVICES" in os.environ
+            else "None"
+        )
+    )
+    logger.log("The training data is:\n{:}".format(train_data))
+    logger.log("The validation data is:\n{:}".format(valid_data))
+
+    model = xmisc.nested_call_by_yaml(args.model_config)
+    logger.log("The model is:\n{:}".format(model))
+    logger.log("The model size is {:.4f} M".format(xmisc.count_parameters(model)))
+
+    train_loader = torch.utils.data.DataLoader(
+        train_data,
+        batch_sampler=xmisc.BatchSampler(train_data, args.batch_size, args.steps),
+        num_workers=args.workers,
+        pin_memory=True,
+    )
+    valid_loader = torch.utils.data.DataLoader(
+        valid_data,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.workers,
+        pin_memory=True,
+        drop_last=False,
+    )
+    iters_per_epoch = len(train_data) // args.batch_size
+
+    logger.log("The training loader: {:}".format(train_loader))
+    logger.log("The validation loader: {:}".format(valid_loader))
+    optimizer = xmisc.nested_call_by_yaml(
+        args.optim_config,
+        model.parameters(),
+        lr=args.lr,
+        weight_decay=args.weight_decay,
+    )
+    objective = xmisc.nested_call_by_yaml(args.loss_config)
+    metric = xmisc.nested_call_by_yaml(args.metric_config)
+
+    logger.log("The optimizer is:\n{:}".format(optimizer))
+    logger.log("The objective is {:}".format(objective))
+    logger.log("The metric is {:}".format(metric))
+    logger.log(
+        "The iters_per_epoch = {:}, estimated epochs = {:}".format(
+            iters_per_epoch, args.steps // iters_per_epoch
+        )
+    )
+
+    model, objective = torch.nn.DataParallel(model).cuda(), objective.cuda()
+    scheduler = xmisc.LRMultiplier(
+        optimizer, xmisc.get_scheduler(args.scheduler, args.lr), args.steps
+    )
+
+    start_time, iter_time = time.time(), xmisc.AverageMeter()
+    for xiter, data in enumerate(train_loader):
+        need_time = "Time Left: {:}".format(
+            xmisc.time_utils.convert_secs2time(
+                iter_time.avg * (len(train_loader) - xiter), True
+            )
+        )
+        iter_str = "{:6d}/{:06d}".format(xiter, len(train_loader))
+
+        inputs, targets = data
+        targets = targets.cuda(non_blocking=True)
+        model.train()
+
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = objective(outputs, targets)
+
+        loss.backward()
+        optimizer.step()
+        scheduler.step()
+
+        if xiter % iters_per_epoch == 0:
+            logger.log("TRAIN [{:}] loss = {:.6f}".format(iter_str, loss.item()))
+
+        # measure elapsed time
+        iter_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Train a classification model with a loss function.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--save_dir", type=str, help="Folder to save checkpoints and log."
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument("--model_config", type=str, help="The path to the model config")
+    parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
+    parser.add_argument("--loss_config", type=str, help="The loss config file.")
+    parser.add_argument("--metric_config", type=str, help="The metric config file.")
+    parser.add_argument(
+        "--train_data_config", type=str, help="The training dataset config path."
+    )
+    parser.add_argument(
+        "--valid_data_config", type=str, help="The validation dataset config path."
+    )
+    parser.add_argument("--data_path", type=str, help="The path to the dataset.")
+    # Optimization options
+    parser.add_argument("--lr", type=float, help="The learning rate")
+    parser.add_argument("--weight_decay", type=float, help="The weight decay")
+    parser.add_argument("--scheduler", type=str, help="The scheduler indicator.")
+    parser.add_argument("--steps", type=int, help="The total number of steps.")
+    parser.add_argument("--batch_size", type=int, default=256, help="The batch size.")
+    parser.add_argument("--workers", type=int, default=4, help="The number of workers")
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+
+    args = parser.parse_args()
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    if args.save_dir is None:
+        raise ValueError("The save-path argument can not be None")
+
+    main(args)