Make it possible to run Keras Core without TensorFlow. (#475)

* Make it possible to run Keras Core without TensorFlow.

* Update requirements

examples/demo_functional.py

@@ -7,9 +7,15 @@
 from keras_core import optimizers
 
 inputs = layers.Input((100,))
-x = layers.Dense(128, activation="relu")(inputs)
+x = layers.Dense(512, activation="relu")(inputs)
 residual = x
-x = layers.Dense(128, activation="relu")(x)
+x = layers.Dense(512, activation="relu")(x)
+x = layers.Dense(512, activation="relu")(x)
+x += residual
+x = layers.Dense(512, activation="relu")(x)
+residual = x
+x = layers.Dense(512, activation="relu")(x)
+x = layers.Dense(512, activation="relu")(x)
 x += residual
 outputs = layers.Dense(16)(x)
 model = Model(inputs, outputs)

keras_core/applications/convnext.py

@@ -1,5 +1,4 @@
 import numpy as np
-from tensorflow.io import gfile
 
 from keras_core import backend
 from keras_core import initializers
@@ -390,7 +389,7 @@ def ConvNeXt(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/densenet.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -182,7 +180,7 @@ def DenseNet(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/efficientnet.py

@@ -1,8 +1,6 @@
 import copy
 import math
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -271,7 +269,7 @@ def EfficientNet(
     if blocks_args == "default":
         blocks_args = DEFAULT_BLOCKS_ARGS
 
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/efficientnet_v2.py

@@ -1,8 +1,6 @@
 import copy
 import math
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import initializers
 from keras_core import layers
@@ -892,7 +890,7 @@ def EfficientNetV2(
     if blocks_args == "default":
         blocks_args = DEFAULT_BLOCKS_ARGS[model_name]
 
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/inception_resnet_v2.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -94,7 +92,7 @@ def InceptionResNetV2(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/inception_v3.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -97,7 +95,7 @@ def InceptionV3(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/mobilenet.py

@@ -1,7 +1,5 @@
 import warnings
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -107,7 +105,7 @@ def MobileNet(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), 'imagenet' "

keras_core/applications/mobilenet_v2.py

@@ -1,7 +1,5 @@
 import warnings
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -109,7 +107,7 @@ def MobileNetV2(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/mobilenet_v3.py

@@ -1,7 +1,5 @@
 import warnings
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -165,7 +163,7 @@ def MobileNetV3(
     classifier_activation="softmax",
     include_preprocessing=True,
 ):
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/nasnet.py

@@ -1,7 +1,5 @@
 import warnings
 
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -111,7 +109,7 @@ def NASNet(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), `imagenet` "

keras_core/applications/resnet.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -107,7 +105,7 @@ def ResNet(
         A Model instance.
     """
 
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), 'imagenet' "

keras_core/applications/vgg16.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -94,7 +92,7 @@ def VGG16(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), 'imagenet' "

keras_core/applications/vgg19.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -94,7 +92,7 @@ def VGG19(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), 'imagenet' "

keras_core/applications/xception.py

@@ -1,5 +1,3 @@
-from tensorflow.io import gfile
-
 from keras_core import backend
 from keras_core import layers
 from keras_core.api_export import keras_core_export
@@ -92,7 +90,7 @@ def Xception(
     Returns:
         A model instance.
     """
-    if not (weights in {"imagenet", None} or gfile.exists(weights)):
+    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
         raise ValueError(
             "The `weights` argument should be either "
             "`None` (random initialization), 'imagenet' "

keras_core/backend/common/keras_tensor.py

@@ -1,4 +1,4 @@
-from tensorflow import nest
+import tree
 
 from keras_core.api_export import keras_core_export
 from keras_core.utils.naming import auto_name
@@ -232,7 +232,7 @@ def __getitem__(self, key):
 def any_symbolic_tensors(args=None, kwargs=None):
     args = args or ()
     kwargs = kwargs or {}
-    for x in nest.flatten((args, kwargs)):
+    for x in tree.flatten((args, kwargs)):
         if isinstance(x, KerasTensor):
             return True
     return False

keras_core/backend/jax/core.py

@@ -1,12 +1,13 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
-from tensorflow import nest
+import tree
 
 from keras_core.backend.common import KerasVariable
 from keras_core.backend.common import standardize_dtype
 from keras_core.backend.common.keras_tensor import KerasTensor
 from keras_core.backend.common.stateless_scope import StatelessScope
+from keras_core.utils.nest import pack_sequence_as
 
 DYNAMIC_SHAPES_OK = True
 
@@ -89,7 +90,7 @@ def index_all_ktensors(x):
             return x
 
         # Third, find out if there are dynamic shapes
-        maybe_symbolic_args, maybe_symbolic_kwargs = nest.map_structure(
+        maybe_symbolic_args, maybe_symbolic_kwargs = tree.map_structure(
             index_all_ktensors, (maybe_symbolic_args, maybe_symbolic_kwargs)
         )
         none_count = 0
@@ -115,24 +116,24 @@ def wrapped_fn(*args, **kwargs):
         jax_out = None
         if none_count:
             try:
-                ms_args_1, ms_kwargs_1 = nest.map_structure(
+                ms_args_1, ms_kwargs_1 = tree.map_structure(
                     lambda x: convert_keras_tensor_to_jax(x, fill_value=83),
                     (maybe_symbolic_args, maybe_symbolic_kwargs),
                 )
                 _, jax_out_1 = jax.make_jaxpr(wrapped_fn, return_shape=True)(
                     *ms_args_1, **ms_kwargs_1
                 )
 
-                ms_args_2, ms_kwargs_2 = nest.map_structure(
+                ms_args_2, ms_kwargs_2 = tree.map_structure(
                     lambda x: convert_keras_tensor_to_jax(x, fill_value=89),
                     (maybe_symbolic_args, maybe_symbolic_kwargs),
                 )
                 _, jax_out_2 = jax.make_jaxpr(wrapped_fn, return_shape=True)(
                     *ms_args_2, **ms_kwargs_2
                 )
 
-                flat_out_1 = nest.flatten(jax_out_1)
-                flat_out_2 = nest.flatten(jax_out_2)
+                flat_out_1 = tree.flatten(jax_out_1)
+                flat_out_2 = tree.flatten(jax_out_2)
 
                 flat_out = []
                 for x1, x2 in zip(flat_out_1, flat_out_2):
@@ -148,7 +149,7 @@ def wrapped_fn(*args, **kwargs):
                         )
                     else:
                         flat_out.append(x1)
-                jax_out = nest.pack_sequence_as(jax_out_1, flat_out)
+                jax_out = pack_sequence_as(jax_out_1, flat_out)
             except:
                 # Errors can happen when the filled dimensions
                 # are not compatible with the function
@@ -162,7 +163,7 @@ def wrapped_fn(*args, **kwargs):
                 pass
 
         if jax_out is None:
-            maybe_symbolic_args, maybe_symbolic_kwargs = nest.map_structure(
+            maybe_symbolic_args, maybe_symbolic_kwargs = tree.map_structure(
                 convert_keras_tensor_to_jax,
                 (maybe_symbolic_args, maybe_symbolic_kwargs),
             )
@@ -175,7 +176,7 @@ def convert_jax_spec_to_keras_tensor(x):
                 return KerasTensor(x.shape, x.dtype)
             return x
 
-        output_shape = nest.map_structure(
+        output_shape = tree.map_structure(
             convert_jax_spec_to_keras_tensor, jax_out
         )
     return output_shape