added main() for programmatic call to convert pytorch->tf

pytorch_pretrained_bert/convert_pytorch_checkpoint_to_tf.py

@@ -17,16 +17,18 @@
 
 import os
 import argparse
+import torch
 import numpy as np
 import tensorflow as tf
-from pytorch_pretrained_bert.modeling import BertConfig, BertModel
+from pytorch_pretrained_bert.modeling import BertModel
 
 
-def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str):
+def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:str):
 
     """
     :param model:BertModel Pytorch model instance to be converted
-    :param ckpt_dir: directory to save Tensorflow model
+    :param ckpt_dir: Tensorflow model directory
+    :param model_name: model name
     :return:
 
     Currently supported HF models:
@@ -87,35 +89,42 @@ def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str):
         print("{0}{1}initialized".format(tf_name, " " * (60 - len(tf_name))))
 
     saver = tf.train.Saver(tf_vars)
-    saver.save(session, os.path.join(ckpt_dir, args.pytorch_model_name))
+    saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))
 
 
-if __name__ == "__main__":
-
+def main(raw_args=None):
     parser = argparse.ArgumentParser()
-    parser.add_argument("--pytorch_model_dir",
-                        default=None,
-                        type=str,
-                        required=False,
-                        help="Directory containing pytorch model")
-    parser.add_argument("--pytorch_model_name",
-                        default=None,
+    parser.add_argument("--model_name",
                         type=str,
                         required=True,
-                        help="model name (e.g. bert-base-uncased)")
-    parser.add_argument("--config_file_path",
+                        help="model name e.g. bert-base-uncased")
+    parser.add_argument("--cache_dir",
+                        type=str,
                         default=None,
+                        required=False,
+                        help="Directory containing pytorch model")
+    parser.add_argument("--pytorch_model_path",
                         type=str,
                         required=True,
-                        help="Path to bert config file")
-    parser.add_argument("--tf_checkpoint_dir",
-                        default="",
+                        help="/path/to/<pytorch-model-name>.bin")
+    parser.add_argument("--tf_cache_dir",
                         type=str,
                         required=True,
                         help="Directory in which to save tensorflow model")
-    args = parser.parse_args()
+    args = parser.parse_args(raw_args)
+    
+    model = BertModel.from_pretrained(
+        pretrained_model_name_or_path=args.model_name,
+        state_dict=torch.load(args.pytorch_model_path),
+        cache_dir=args.cache_dir
+    )
+    
+    convert_pytorch_checkpoint_to_tf(
+        model=model,
+        ckpt_dir=args.tf_cache_dir,
+        model_name=args.model_name
+    )
+
 
-    model = BertModel(
-        config=BertConfig(args.config_file_path)
-    ).from_pretrained(args.pytorch_model_name, cache_dir=args.pytorch_model_dir)
-    convert_pytorch_checkpoint_to_tf(model=model, ckpt_dir=args.tf_checkpoint_dir)
+if __name__ == "__main__":
+    main()