Transformer running

utils/config.py

@@ -1,6 +1,9 @@
 import ast
+import configparser
 from pathlib import Path
+from typing import Mapping, Dict
 
+import torch
 from copy import deepcopy
 
 from abc import ABC
@@ -9,8 +12,67 @@ from argparse import Namespace, ArgumentParser
 from collections import defaultdict
 from configparser import ConfigParser, DuplicateSectionError
 import hashlib
+from pytorch_lightning import Trainer
 
-from ml_lib.utils.tools import locate_and_import_class
+from ml_lib.utils.loggers import Logger
+from ml_lib.utils.tools import locate_and_import_class, auto_cast
+
+
+# Argument Parser and default Values
+# =============================================================================
+def parse_comandline_args_add_defaults(filepath, overrides=None):
+
+    # Parse Command Line
+    parser = ArgumentParser()
+    parser.add_argument('--model_name', type=str)
+    parser.add_argument('--data_name', type=str)
+
+    # Load Defaults from _parameters.ini file
+    config = configparser.ConfigParser()
+    config.read(str(filepath))
+
+    new_defaults = dict()
+    for key in ['project', 'train', 'data']:
+        defaults = config[key]
+        new_defaults.update({key: auto_cast(val) for key, val in defaults.items()})
+
+    if new_defaults['debug']:
+        new_defaults.update(
+            max_epochs=2,
+            max_steps=2     # The seems to be the new "fast_dev_run"
+        )
+
+    args, _ = parser.parse_known_args()
+    overrides = overrides or dict()
+    default_data = overrides.get('data_name', None) or new_defaults['data_name']
+    default_model = overrides.get('model_name', None) or new_defaults['model_name']
+
+    data_name = args.__dict__.get('data_name', None) or default_data
+    model_name = args.__dict__.get('model_name', None) or default_model
+
+    new_defaults.update({key: auto_cast(val) for key, val in config[model_name].items()})
+
+    found_data_class = locate_and_import_class(data_name, 'datasets')
+    found_model_class = locate_and_import_class(model_name, 'models')
+
+    for module in [Logger, Trainer, found_data_class, found_model_class]:
+        parser = module.add_argparse_args(parser)
+
+    args, _ = parser.parse_known_args(namespace=Namespace(**new_defaults))
+
+    args = vars(args)
+    args.update({key: auto_cast(val) for key, val in args.items()})
+    args.update(gpus=[0] if torch.cuda.is_available() and not args['debug'] else None,
+                row_log_interval=1000,  # TODO: Better Value / Setting
+                log_save_interval=10000,  # TODO: Better Value / Setting
+                auto_lr_find=not args['debug'],
+                weights_summary='top',
+                check_val_every_n_epoch=1 if args['debug'] else args.get('check_val_every_n_epoch', 1)
+                )
+
+    if overrides is not None and isinstance(overrides, (Mapping, Dict)):
+        args.update(**overrides)
+    return args, found_data_class, found_model_class
 
 
 def is_jsonable(x):