Merge pull request #37955 from ngc92:pathlib

PiperOrigin-RevId: 305264725
Change-Id: Ia73b4865e0886cc01d52e772745476ddd89b21f7

tensorflow/python/keras/callbacks.py

@@ -44,6 +44,7 @@ from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.keras.utils import version_utils
 from tensorflow.python.keras.utils.data_utils import Sequence
 from tensorflow.python.keras.utils.generic_utils import Progbar
+from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.keras.utils.mode_keys import ModeKeys
 from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
@@ -1044,12 +1045,12 @@ class ModelCheckpoint(Callback):
   ```
 
   Arguments:
-      filepath: string, path to save the model file. `filepath` can contain
-        named formatting options, which will be filled the value of `epoch` and
-        keys in `logs` (passed in `on_epoch_end`). For example: if `filepath` is
-        `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model checkpoints
-        will be saved with the epoch number and the validation loss in the
-        filename.
+      filepath: string or `PathLike`, path to save the model file. `filepath`
+        can contain named formatting options, which will be filled the value of
+        `epoch` and keys in `logs` (passed in `on_epoch_end`). For example: if
+        `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model
+        checkpoints will be saved with the epoch number and the validation loss
+        in the filename.
       monitor: quantity to monitor.
       verbose: verbosity mode, 0 or 1.
       save_best_only: if `save_best_only=True`, the latest best model according
@@ -1090,7 +1091,7 @@ class ModelCheckpoint(Callback):
     self._supports_tf_logs = True
     self.monitor = monitor
     self.verbose = verbose
-    self.filepath = filepath
+    self.filepath = path_to_string(filepath)
     self.save_best_only = save_best_only
     self.save_weights_only = save_weights_only
     self.save_freq = save_freq
@@ -1780,7 +1781,7 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
     self._supports_tf_logs = True
     self._validate_kwargs(kwargs)
 
-    self.log_dir = log_dir
+    self.log_dir = path_to_string(log_dir)
     self.histogram_freq = histogram_freq
     self.write_graph = write_graph
     self.write_images = write_images
@@ -2280,7 +2281,7 @@ class CSVLogger(Callback):
 
   def __init__(self, filename, separator=',', append=False):
     self.sep = separator
-    self.filename = filename
+    self.filename = path_to_string(filename)
     self.append = append
     self.writer = None
     self.keys = None

tensorflow/python/keras/engine/network.py

@@ -51,6 +51,7 @@ from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
+from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.ragged import ragged_tensor
@@ -1030,7 +1031,8 @@ class Network(base_layer.Layer):
      access specific variables, e.g. `model.get_layer("dense_1").kernel`.
 
     Arguments:
-        filepath: String, path to SavedModel or H5 file to save the model.
+        filepath: String, PathLike, path to SavedModel or H5 file to save the
+            model.
         overwrite: Whether to silently overwrite any existing file at the
             target location, or provide the user with a manual prompt.
         include_optimizer: If True, save optimizer's state together.
@@ -1103,10 +1105,10 @@ class Network(base_layer.Layer):
     on the TensorFlow format.
 
     Arguments:
-        filepath: String, path to the file to save the weights to. When saving
-            in TensorFlow format, this is the prefix used for checkpoint files
-            (multiple files are generated). Note that the '.h5' suffix causes
-            weights to be saved in HDF5 format.
+        filepath: String or PathLike, path to the file to save the weights to.
+            When saving in TensorFlow format, this is the prefix used for
+            checkpoint files (multiple files are generated). Note that the '.h5'
+            suffix causes weights to be saved in HDF5 format.
         overwrite: Whether to silently overwrite any existing file at the
             target location, or provide the user with a manual prompt.
         save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or
@@ -1119,6 +1121,7 @@ class Network(base_layer.Layer):
         ValueError: For invalid/unknown format arguments.
     """
     self._assert_weights_created()
+    filepath = path_to_string(filepath)
     filepath_is_h5 = _is_hdf5_filepath(filepath)
     if save_format is None:
       if filepath_is_h5:
@@ -1201,9 +1204,9 @@ class Network(base_layer.Layer):
     which layers are assigned in the `Model`'s constructor.
 
     Arguments:
-        filepath: String, path to the weights file to load. For weight files in
-            TensorFlow format, this is the file prefix (the same as was passed
-            to `save_weights`).
+        filepath: String or PathLike, path to the weights file to load. For
+            weight files in TensorFlow format, this is the file prefix (the
+            same as was passed to `save_weights`).
         by_name: Boolean, whether to load weights by name or by topological
             order. Only topological loading is supported for weight files in
             TensorFlow format.
@@ -1232,6 +1235,7 @@ class Network(base_layer.Layer):
           'When calling model.load_weights, skip_mismatch can only be set to '
           'True when by_name is True.')
 
+    filepath = path_to_string(filepath)
     if _is_hdf5_filepath(filepath):
       save_format = 'h5'
     else:

tensorflow/python/keras/saving/save.py

@@ -19,8 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import os
-import sys
-
 import six
 
 from tensorflow.python import tf2
@@ -28,12 +26,11 @@ from tensorflow.python.keras.saving import hdf5_format
 from tensorflow.python.keras.saving.saved_model import load as saved_model_load
 from tensorflow.python.keras.saving.saved_model import save as saved_model_save
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.saved_model import loader_impl
 from tensorflow.python.util.tf_export import keras_export
 
 # pylint: disable=g-import-not-at-top
-if sys.version_info >= (3, 4):
-  import pathlib
 try:
   import h5py
 except ImportError:
@@ -115,8 +112,7 @@ def save_model(model,
   default_format = 'tf' if tf2.enabled() else 'h5'
   save_format = save_format or default_format
 
-  if sys.version_info >= (3, 4) and isinstance(filepath, pathlib.Path):
-    filepath = str(filepath)
+  filepath = path_to_string(filepath)
 
   if (save_format == 'h5' or
       (h5py is not None and isinstance(filepath, h5py.File)) or
@@ -183,8 +179,7 @@ def load_model(filepath, custom_objects=None, compile=True):  # pylint: disable=
         isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
       return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
 
-    if sys.version_info >= (3, 4) and isinstance(filepath, pathlib.Path):
-      filepath = str(filepath)
+    filepath = path_to_string(filepath)
     if isinstance(filepath, six.string_types):
       loader_impl.parse_saved_model(filepath)
       return saved_model_load.load(filepath, compile)

tensorflow/python/keras/saving/save_test.py

@@ -41,7 +41,7 @@ from tensorflow.python.ops import lookup_ops
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import loader_impl
 
-if sys.version_info >= (3, 4):
+if sys.version_info >= (3, 6):
   import pathlib  # pylint:disable=g-import-not-at-top
 try:
   import h5py  # pylint:disable=g-import-not-at-top
@@ -100,7 +100,7 @@ class TestSaveModel(test.TestCase, parameterized.TestCase):
 
   @test_util.run_v2_only
   def test_save_load_tf_pathlib(self):
-    if sys.version_info >= (3, 4):
+    if sys.version_info >= (3, 6):
       path = pathlib.Path(self.get_temp_dir()) / 'model'
       save.save_model(self.model, path, save_format='tf')
       save.load_model(path)

tensorflow/python/keras/utils/data_utils.py

@@ -46,6 +46,7 @@ from six.moves.urllib.error import URLError
 from tensorflow.python.framework import ops
 from six.moves.urllib.request import urlopen
 from tensorflow.python.keras.utils.generic_utils import Progbar
+from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
@@ -137,6 +138,9 @@ def _extract_archive(file_path, path='.', archive_format='auto'):
   if isinstance(archive_format, six.string_types):
     archive_format = [archive_format]
 
+  file_path = path_to_string(file_path)
+  path = path_to_string(path)
+
   for archive_type in archive_format:
     if archive_type == 'tar':
       open_fn = tarfile.open
@@ -230,6 +234,8 @@ def get_file(fname,
   datadir = os.path.join(datadir_base, cache_subdir)
   _makedirs_exist_ok(datadir)
 
+  fname = path_to_string(fname)
+
   if untar:
     untar_fpath = os.path.join(datadir, fname)
     fpath = untar_fpath + '.tar.gz'

tensorflow/python/keras/utils/io_utils.py

@@ -19,6 +19,8 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import os
+import sys
 
 import numpy as np
 import six
@@ -33,6 +35,48 @@ except ImportError:
   h5py = None
 
 
+if sys.version_info >= (3, 6):
+
+  def _path_to_string(path):
+    if isinstance(path, os.PathLike):
+      return os.fspath(path)
+    return path
+elif sys.version_info >= (3, 4):
+
+  def _path_to_string(path):
+    import pathlib
+    if isinstance(path, pathlib.Path):
+      return str(path)
+    return path
+else:
+
+  def _path_to_string(path):
+    return path
+
+
+def path_to_string(path):
+  """Convert `PathLike` objects to their string representation.
+
+  If given a non-string typed path object, converts it to its string
+  representation. Depending on the python version used, this function
+  can handle the following arguments:
+  python >= 3.6: Everything supporting the fs path protocol
+    https://www.python.org/dev/peps/pep-0519
+  python >= 3.4: Only `pathlib.Path` objects
+
+  If the object passed to `path` is not among the above, then it is
+  returned unchanged. This allows e.g. passthrough of file objects
+  through this function.
+
+  Args:
+    path: `PathLike` object that represents a path
+
+  Returns:
+    A string representation of the path argument, if Python support exists.
+  """
+  return _path_to_string(path)
+
+
 @keras_export('keras.utils.HDF5Matrix')
 class HDF5Matrix(object):
   """Representation of HDF5 dataset to be used instead of a Numpy array.

tensorflow/python/keras/utils/io_utils_test.py

@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import os
 import shutil
+import sys
 
 import numpy as np
 import six
@@ -137,6 +138,25 @@ class TestIOUtils(keras_parameterized.TestCase):
       self.assertFalse(
           io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))
 
+  def test_path_to_string(self):
+
+    class PathLikeDummy(object):
+
+      def __fspath__(self):
+        return 'dummypath'
+
+    dummy = object()
+    if sys.version_info >= (3, 4):
+      from pathlib import Path  # pylint:disable=g-import-not-at-top
+      # conversion of PathLike
+      self.assertEqual(io_utils.path_to_string(Path('path')), 'path')
+    if sys.version_info >= (3, 6):
+      self.assertEqual(io_utils.path_to_string(PathLikeDummy()), 'dummypath')
+
+    # pass-through, works for all versions of python
+    self.assertEqual(io_utils.path_to_string('path'), 'path')
+    self.assertIs(io_utils.path_to_string(dummy), dummy)
+
 
 if __name__ == '__main__':
   test.main()

tensorflow/python/keras/utils/vis_utils.py

@@ -21,6 +21,7 @@ from __future__ import print_function
 
 import os
 import sys
+from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import keras_export
 
@@ -299,6 +300,7 @@ def plot_model(model,
                      rankdir=rankdir,
                      expand_nested=expand_nested,
                      dpi=dpi)
+  to_file = path_to_string(to_file)
   if dot is None:
     return
   _, extension = os.path.splitext(to_file)