Update xmisc with yaml

exps/basic/xmain.py

@@ -1,35 +1,28 @@
 #####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
+#####################################################
+# python exps/basic/xmain.py --save_dir outputs/x   #
 #####################################################
 import sys, time, torch, random, argparse
-from PIL import ImageFile
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
 from copy import deepcopy
 from pathlib import Path
 
-from xautodl.datasets import get_datasets
-from xautodl.config_utils import load_config, obtain_basic_args as obtain_args
-from xautodl.procedures import (
-    prepare_seed,
-    prepare_logger,
-    save_checkpoint,
-    copy_checkpoint,
-)
-from xautodl.procedures import get_optim_scheduler, get_procedures
-from xautodl.models import obtain_model
-from xautodl.xmodels import obtain_model as obtain_xmodel
-from xautodl.nas_infer_model import obtain_nas_infer_model
-from xautodl.utils import get_model_infos
-from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
+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.xmisc import nested_call_by_yaml
 
 
 def main(args):
-    assert torch.cuda.is_available(), "CUDA is not available."
-    torch.backends.cudnn.enabled = True
-    torch.backends.cudnn.benchmark = True
-    # torch.backends.cudnn.deterministic = True
-    # torch.set_num_threads(args.workers)
+
+    train_data = nested_call_by_yaml(args.train_data_config, args.data_path)
+    valid_data = nested_call_by_yaml(args.valid_data_config, args.data_path)
+
+    import pdb
+
+    pdb.set_trace()
 
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)
@@ -290,5 +283,44 @@ def main(args):
 
 
 if __name__ == "__main__":
-    args = obtain_args()
+    parser = argparse.ArgumentParser(
+        description="Train a 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 path to the optimizer config"
+    )
+    parser.add_argument(
+        "--train_data_config", type=str, help="The dataset config path."
+    )
+    parser.add_argument(
+        "--valid_data_config", type=str, help="The dataset config path."
+    )
+    parser.add_argument(
+        "--data_path", type=str, help="The path to the dataset."
+    )
+    parser.add_argument("--algorithm", type=str, help="The algorithm.")
+    # Optimization options
+    parser.add_argument("--batch_size", type=int, default=2, help="The batch size.")
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=8,
+        help="number of data loading workers (default: 8)",
+    )
+    # 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)