Use tf.io instead of os for checking if paths exist in Keras applications.

PiperOrigin-RevId: 312468401
Change-Id: Ibe9c4a9719be5bb8b72f6db84036791031e26760

tensorflow/python/keras/applications/BUILD

@@ -35,10 +35,16 @@ py_library(
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
     deps = [
-        "//tensorflow/python:util",
+        "//tensorflow/python:lib",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:tf_export",
+        "//tensorflow/python/keras:activations",
         "//tensorflow/python/keras:backend",
-        "//tensorflow/python/keras:engine",
+        "//tensorflow/python/keras/engine",
         "//tensorflow/python/keras/layers",
+        "//tensorflow/python/keras/utils:data_utils",
+        "//tensorflow/python/keras/utils:layer_utils",
+        "//third_party/py/numpy",
     ],
 )
 

tensorflow/python/keras/applications/densenet.py

@@ -23,14 +23,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -193,7 +192,7 @@ def DenseNet(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/efficientnet.py

@@ -26,7 +26,6 @@ from __future__ import print_function
 
 import copy
 import math
-import os
 
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
@@ -34,6 +33,7 @@ from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -269,7 +269,7 @@ def EfficientNet(
   if blocks_args == 'default':
     blocks_args = DEFAULT_BLOCKS_ARGS
 
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/inception_resnet_v2.py

@@ -25,14 +25,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -113,7 +112,7 @@ def InceptionResNetV2(include_top=True,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/inception_v3.py

@@ -23,14 +23,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -109,7 +108,7 @@ def InceptionV3(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/mobilenet.py

@@ -64,14 +64,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export
 
@@ -164,7 +163,7 @@ def MobileNet(input_shape=None,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/mobilenet_v2.py

@@ -77,14 +77,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export
 
@@ -181,7 +180,7 @@ def MobileNetV2(input_shape=None,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/nasnet.py

@@ -41,14 +41,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export
 
@@ -151,7 +150,7 @@ def NASNet(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/resnet.py

@@ -23,14 +23,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -138,7 +137,7 @@ def ResNet(stack_fn,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/vgg16.py

@@ -23,14 +23,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -114,7 +113,7 @@ def VGG16(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/vgg19.py

@@ -23,14 +23,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -114,7 +113,7 @@ def VGG19(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '

tensorflow/python/keras/applications/xception.py

@@ -27,14 +27,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -114,7 +113,7 @@ def Xception(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or os.path.exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '