convert_tf_ckpt_to_of.py added, evaluate-v1.1.py deleted

LanguageModeling/BERT/convert_tf_ckpt_to_of.py

+"""Convert tensorflow checkpoint to oneflow snapshot"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import argparse
+import tensorflow as tf
+import numpy as np
+import os
+
+parser = argparse.ArgumentParser()
+
+## Required parameters
+parser.add_argument("--tf_checkpoint_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path the TensorFlow checkpoint path.")
+parser.add_argument("--of_dump_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path to the output OneFlow model.")
+
+args = parser.parse_args()
+
+def _SaveWeightBlob2File(blob, folder):
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+    filename = os.path.join(folder, "out")
+    f = open(filename, 'wb')
+    f.write(blob.tobytes())
+    f.close()
+
+def convert():
+    path = args.tf_checkpoint_path
+    init_vars = tf.train.list_variables(path)
+    for name, shape in init_vars:
+        array = tf.train.load_variable(path, name)
+
+        sep = name.rfind('/')
+        blob_name = name[sep + 1:]
+        op_name = name[:sep].replace('/', '-')
+
+        if blob_name == "kernel":
+            blob_name = "weight"
+        elif blob_name in ['adam_m', 'adam_v']:
+            print("find m, v weights")
+
+        folder_name = op_name+"-"+blob_name
+        folder = os.path.join(args.of_dump_path, folder_name)
+        print("saved to:", folder)
+
+        _SaveWeightBlob2File(array, folder)
+
+
+if __name__ == "__main__":
+    convert()
+