consistent checkpoint filepattern

returnn/engine/base.py

@@ -8,9 +8,9 @@
 import os
 import sys
 import typing
-from returnn.util.basic import BackendEngine, model_epoch_from_filename, get_model_filename_postfix
 from returnn.log import log
 from returnn.pretrain import Pretrain
+from returnn.util import basic as util
 
 
 class EngineBase(object):
@@ -52,7 +52,7 @@ def get_existing_models(cls, config):
         if os.path.exists(fn):
           file_list[epoch] = fn
           break
-        if BackendEngine.is_tensorflow_selected():
+        if util.BackendEngine.is_tensorflow_selected():
           if os.path.exists(fn + ".index"):
             file_list[epoch] = fn
             break
@@ -72,32 +72,24 @@ def get_epoch_model(cls, config):
       start_epoch = int(start_epoch_mode)
       assert start_epoch >= 1
 
-    load_model_epoch_filename = config.value('load', '')
-    if load_model_epoch_filename.endswith(".meta"):
-      load_model_epoch_filename = load_model_epoch_filename[:-len(".meta")]
-    elif load_model_epoch_filename.endswith(".index"):
-      load_model_epoch_filename = load_model_epoch_filename[:-len(".index")]
+    load_model_epoch_filename = util.get_checkpoint_filepattern(config.value('load', ''))
     if load_model_epoch_filename:
-      assert os.path.exists(load_model_epoch_filename + get_model_filename_postfix())
+      assert os.path.exists(load_model_epoch_filename + util.get_model_filename_postfix())
 
-    import_model_train_epoch1 = config.value('import_model_train_epoch1', '')
-    if import_model_train_epoch1.endswith(".meta"):
-      import_model_train_epoch1 = import_model_train_epoch1[:-len(".meta")]
-    elif import_model_train_epoch1.endswith(".index"):
-      import_model_train_epoch1 = import_model_train_epoch1[:-len(".index")]
+    import_model_train_epoch1 = util.get_checkpoint_filepattern(config.value('import_model_train_epoch1', ''))
     if import_model_train_epoch1:
-      assert os.path.exists(import_model_train_epoch1 + get_model_filename_postfix())
+      assert os.path.exists(import_model_train_epoch1 + util.get_model_filename_postfix())
 
     existing_models = cls.get_existing_models(config)
     load_epoch = config.int("load_epoch", -1)
     if load_model_epoch_filename:
       if load_epoch <= 0:
-        load_epoch = model_epoch_from_filename(load_model_epoch_filename)
+        load_epoch = util.model_epoch_from_filename(load_model_epoch_filename)
     else:
       if load_epoch > 0:  # ignore if load_epoch == 0
         assert load_epoch in existing_models
         load_model_epoch_filename = existing_models[load_epoch]
-        assert model_epoch_from_filename(load_model_epoch_filename) == load_epoch
+        assert util.model_epoch_from_filename(load_model_epoch_filename) == load_epoch
 
     # Only use this when we don't train.
     # For training, we first consider existing models before we take the 'load' into account when in auto epoch mode.

returnn/tf/network.py

@@ -16,6 +16,7 @@
 import returnn.tf.compat as tf_compat
 import returnn.tf.util.basic as tf_util
 from returnn.tf.util.basic import Data, DimensionTag, reuse_name_scope, VariableAssigner
+from returnn.util import basic as util
 
 
 class DataNotFound(Exception):
@@ -3473,7 +3474,7 @@ def __init__(self, filename, saveable_params, params_prefix="", load_if_prefix="
                ignore_params=(), ignore_params_prefixes=(), var_name_mapping=None,
                network=None):
     """
-    :param str filename: filepattern for NewCheckpointReader
+    :param str filename: filepattern for NewCheckpointReader or .index/.meta file path
     :param list[tf.Variable|tensorflow.python.training.saver.BaseSaverBuilder.SaveableObject] saveable_params:
     :param str params_prefix: expect that all vars in saveable_params have this prefix, and remove it
     :param str load_if_prefix: if given, only load variables with a name containing this string.
@@ -3486,7 +3487,7 @@ def __init__(self, filename, saveable_params, params_prefix="", load_if_prefix="
       renamed vars in the checkpoint
     :param TFNetwork network:
     """
-    self.filename = filename
+    self.filepattern = util.get_checkpoint_filepattern(filename)
     self.network = network
     self.ignore_missing = ignore_missing
     self.params_prefix = params_prefix
@@ -3510,7 +3511,7 @@ def __init__(self, filename, saveable_params, params_prefix="", load_if_prefix="
         continue
       self.saveable_params.append(param)
     assert count > 0, "%s: no saveable vars" % self
-    self.reader = tf_compat.v1.train.NewCheckpointReader(filename)
+    self.reader = tf_compat.v1.train.NewCheckpointReader(self.filepattern)
     self.net_vars = [v for v in self.saveable_params if isinstance(v, tf.Variable)]
     self.net_saveables = [v for v in self.saveable_params if not isinstance(v, tf.Variable)]
     # All variables in the checkpoint:
@@ -3918,7 +3919,7 @@ def get_lazy_dict(self):
           if self.ignore_missing and v_name not in var_name_map:
             print(
               "Warning, did not find match for var %r (%r, params_prefix %r, load_if_prefix %r) in checkpoint %r." % (
-                v, v_name, self.params_prefix, self.load_if_prefix, self.filename), file=log.v3)
+                v, v_name, self.params_prefix, self.load_if_prefix, self.filepattern), file=log.v3)
             continue
           variable_values[v] = self.VariableValue(value=var_name_map[v_name]())
       assert variable_values, "no vars to load; saveable vars are %r. load_if_prefix %r." % (

returnn/util/basic.py

@@ -288,6 +288,21 @@ def get_model_filename_postfix():
   return ""
 
 
+def get_checkpoint_filepattern(filepath):
+  """
+  Removes optional .index or .meta extension
+
+  :param str filepath:
+  :return: CheckpointLoader compatible filepattern
+  :rtype: str
+  """
+  if filepath.endswith(".meta"):
+    return filepath[:-len(".meta")]
+  elif filepath.endswith(".index"):
+    return filepath[:-len(".index")]
+  return filepath
+
+
 def sys_cmd_out_lines(s):
   """
   :param str s: shell command