544 lines
23 KiB
Python
544 lines
23 KiB
Python
![]() |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
||
|
#
|
||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
# you may not use this file except in compliance with the License.
|
||
|
# You may obtain a copy of the License at
|
||
|
#
|
||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
#
|
||
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
# See the License for the specific language governing permissions and
|
||
|
# limitations under the License.
|
||
|
# ==============================================================================
|
||
|
"""Keras model saving code."""
|
||
|
|
||
|
import os
|
||
|
|
||
|
import tensorflow.compat.v2 as tf
|
||
|
|
||
|
from keras import backend
|
||
|
from keras.saving import object_registration
|
||
|
from keras.saving.legacy import hdf5_format
|
||
|
from keras.saving.legacy import saving_utils
|
||
|
from keras.saving.legacy import serialization
|
||
|
from keras.saving.legacy.saved_model import load as saved_model_load
|
||
|
from keras.saving.legacy.saved_model import load_context
|
||
|
from keras.saving.legacy.saved_model import save as saved_model_save
|
||
|
from keras.saving.legacy.saved_model.utils import keras_option_scope
|
||
|
from keras.utils import io_utils
|
||
|
from keras.utils import traceback_utils
|
||
|
|
||
|
try:
|
||
|
import h5py
|
||
|
except ImportError:
|
||
|
h5py = None
|
||
|
|
||
|
|
||
|
@traceback_utils.filter_traceback
|
||
|
def save_model(
|
||
|
model,
|
||
|
filepath,
|
||
|
overwrite=True,
|
||
|
include_optimizer=True,
|
||
|
save_format=None,
|
||
|
signatures=None,
|
||
|
options=None,
|
||
|
save_traces=True,
|
||
|
):
|
||
|
"""Saves a model as a TensorFlow SavedModel or HDF5 file.
|
||
|
|
||
|
See the [Serialization and Saving
|
||
|
guide](https://keras.io/guides/serialization_and_saving/) for details.
|
||
|
|
||
|
Usage:
|
||
|
|
||
|
>>> model = tf.keras.Sequential([
|
||
|
... tf.keras.layers.Dense(5, input_shape=(3,)),
|
||
|
... tf.keras.layers.Softmax()])
|
||
|
>>> model.save('/tmp/model')
|
||
|
>>> loaded_model = tf.keras.models.load_model('/tmp/model')
|
||
|
>>> x = tf.random.uniform((10, 3))
|
||
|
>>> assert np.allclose(model.predict(x), loaded_model.predict(x))
|
||
|
|
||
|
Note that `model.save()` is an alias for `tf.keras.models.save_model()`.
|
||
|
|
||
|
The SavedModel and HDF5 file contains:
|
||
|
|
||
|
- the model's configuration (topology)
|
||
|
- the model's weights
|
||
|
- the model's optimizer's state (if any)
|
||
|
|
||
|
Thus models can be reinstantiated in the exact same state, without any of
|
||
|
the code used for model definition or training.
|
||
|
|
||
|
Note that the model weights may have different scoped names after being
|
||
|
loaded. Scoped names include the model/layer names, such as
|
||
|
`"dense_1/kernel:0"`. It is recommended that you use the layer properties to
|
||
|
access specific variables, e.g. `model.get_layer("dense_1").kernel`.
|
||
|
|
||
|
__SavedModel serialization format__
|
||
|
|
||
|
Keras SavedModel uses `tf.saved_model.save` to save the model and all
|
||
|
trackable objects attached to the model (e.g. layers and variables). The
|
||
|
model config, weights, and optimizer are saved in the SavedModel.
|
||
|
Additionally, for every Keras layer attached to the model, the SavedModel
|
||
|
stores:
|
||
|
|
||
|
* the config and metadata -- e.g. name, dtype, trainable status
|
||
|
* traced call and loss functions, which are stored as TensorFlow
|
||
|
subgraphs.
|
||
|
|
||
|
The traced functions allow the SavedModel format to save and load custom
|
||
|
layers without the original class definition.
|
||
|
|
||
|
You can choose to not save the traced functions by disabling the
|
||
|
`save_traces` option. This will decrease the time it takes to save the model
|
||
|
and the amount of disk space occupied by the output SavedModel. If you
|
||
|
enable this option, then you _must_ provide all custom class definitions
|
||
|
when loading the model. See the `custom_objects` argument in
|
||
|
`tf.keras.models.load_model`.
|
||
|
|
||
|
Args:
|
||
|
model: Keras model instance to be saved.
|
||
|
filepath: One of the following:
|
||
|
- String or `pathlib.Path` object, path where to save the model
|
||
|
- `h5py.File` object where to save the model
|
||
|
overwrite: Whether we should overwrite any existing model at the target
|
||
|
location, or instead ask the user with a manual prompt.
|
||
|
include_optimizer: If True, save optimizer's state together.
|
||
|
save_format: Either 'tf' or 'h5', indicating whether to save the model
|
||
|
to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5'
|
||
|
in TF 1.X.
|
||
|
signatures: Signatures to save with the SavedModel. Applicable to the
|
||
|
'tf' format only. Please see the `signatures` argument in
|
||
|
`tf.saved_model.save` for details.
|
||
|
options: (only applies to SavedModel format)
|
||
|
`tf.saved_model.SaveOptions` object that specifies options for saving
|
||
|
to SavedModel.
|
||
|
save_traces: (only applies to SavedModel format) When enabled, the
|
||
|
SavedModel will store the function traces for each layer. This
|
||
|
can be disabled, so that only the configs of each layer are stored.
|
||
|
Defaults to `True`. Disabling this will decrease serialization time
|
||
|
and reduce file size, but it requires that all custom layers/models
|
||
|
implement a `get_config()` method.
|
||
|
|
||
|
Raises:
|
||
|
ImportError: If save format is hdf5, and h5py is not available.
|
||
|
"""
|
||
|
|
||
|
from keras.engine import sequential
|
||
|
|
||
|
default_format = "tf" if tf.__internal__.tf2.enabled() else "h5"
|
||
|
save_format = save_format or default_format
|
||
|
|
||
|
filepath = io_utils.path_to_string(filepath)
|
||
|
|
||
|
# If the user has not already called fit or built the underlying metrics, we
|
||
|
# should do that before saving to ensure the metric names have all
|
||
|
# appropriate name transformations applied.
|
||
|
saving_utils.try_build_compiled_arguments(model)
|
||
|
|
||
|
if (
|
||
|
save_format == "h5"
|
||
|
or (h5py is not None and isinstance(filepath, h5py.File))
|
||
|
or saving_utils.is_hdf5_filepath(filepath)
|
||
|
):
|
||
|
# TODO(b/130258301): add utility method for detecting model type.
|
||
|
if not model._is_graph_network and not isinstance(
|
||
|
model, sequential.Sequential
|
||
|
):
|
||
|
raise NotImplementedError(
|
||
|
"Saving the model to HDF5 format requires the model to be a "
|
||
|
"Functional model or a Sequential model. It does not work for "
|
||
|
"subclassed models, because such models are defined via the "
|
||
|
"body of a Python method, which isn't safely serializable. "
|
||
|
"Consider saving to the Tensorflow SavedModel format (by "
|
||
|
'setting save_format="tf") or using `save_weights`.'
|
||
|
)
|
||
|
hdf5_format.save_model_to_hdf5(
|
||
|
model, filepath, overwrite, include_optimizer
|
||
|
)
|
||
|
else:
|
||
|
with serialization.SharedObjectSavingScope():
|
||
|
with keras_option_scope(
|
||
|
save_traces=save_traces, in_tf_saved_model_scope=True
|
||
|
):
|
||
|
saved_model_save.save(
|
||
|
model,
|
||
|
filepath,
|
||
|
overwrite,
|
||
|
include_optimizer,
|
||
|
signatures,
|
||
|
options,
|
||
|
save_traces,
|
||
|
)
|
||
|
|
||
|
|
||
|
@traceback_utils.filter_traceback
|
||
|
def load_model(filepath, custom_objects=None, compile=True, options=None):
|
||
|
"""Loads a model saved via `model.save()`.
|
||
|
|
||
|
Usage:
|
||
|
|
||
|
>>> model = tf.keras.Sequential([
|
||
|
... tf.keras.layers.Dense(5, input_shape=(3,)),
|
||
|
... tf.keras.layers.Softmax()])
|
||
|
>>> model.save('/tmp/model')
|
||
|
>>> loaded_model = tf.keras.models.load_model('/tmp/model')
|
||
|
>>> x = tf.random.uniform((10, 3))
|
||
|
>>> assert np.allclose(model.predict(x), loaded_model.predict(x))
|
||
|
|
||
|
Note that the model weights may have different scoped names after being
|
||
|
loaded. Scoped names include the model/layer names, such as
|
||
|
`"dense_1/kernel:0"`. It is recommended that you use the layer properties to
|
||
|
access specific variables, e.g. `model.get_layer("dense_1").kernel`.
|
||
|
|
||
|
Args:
|
||
|
filepath: One of the following:
|
||
|
- String or `pathlib.Path` object, path to the saved model
|
||
|
- `h5py.File` object from which to load the model
|
||
|
custom_objects: Optional dictionary mapping names
|
||
|
(strings) to custom classes or functions to be
|
||
|
considered during deserialization.
|
||
|
compile: Boolean, whether to compile the model
|
||
|
after loading.
|
||
|
options: Optional `tf.saved_model.LoadOptions` object that specifies
|
||
|
options for loading from SavedModel.
|
||
|
|
||
|
Returns:
|
||
|
A Keras model instance. If the original model was compiled, and saved
|
||
|
with the optimizer, then the returned model will be compiled. Otherwise,
|
||
|
the model will be left uncompiled. In the case that an uncompiled model
|
||
|
is returned, a warning is displayed if the `compile` argument is set to
|
||
|
`True`.
|
||
|
|
||
|
Raises:
|
||
|
ImportError: if loading from an hdf5 file and h5py is not available.
|
||
|
IOError: In case of an invalid savefile.
|
||
|
"""
|
||
|
with serialization.SharedObjectLoadingScope():
|
||
|
with object_registration.CustomObjectScope(custom_objects or {}):
|
||
|
with keras_option_scope(
|
||
|
save_traces=False, in_tf_saved_model_scope=True
|
||
|
):
|
||
|
with load_context.load_context(options):
|
||
|
filepath_str = io_utils.path_to_string(filepath)
|
||
|
if isinstance(filepath_str, str):
|
||
|
if not tf.io.gfile.exists(filepath_str):
|
||
|
raise IOError(
|
||
|
f"No file or directory found at {filepath_str}"
|
||
|
)
|
||
|
|
||
|
if tf.io.gfile.isdir(filepath_str):
|
||
|
return saved_model_load.load(
|
||
|
filepath_str, compile, options
|
||
|
)
|
||
|
else:
|
||
|
if h5py is None:
|
||
|
raise ImportError(
|
||
|
"Filepath looks like a hdf5 file but h5py"
|
||
|
"is not available."
|
||
|
f" filepath={filepath_str}"
|
||
|
)
|
||
|
return hdf5_format.load_model_from_hdf5(
|
||
|
tf.io.gfile.GFile(filepath_str, mode="rb"),
|
||
|
custom_objects,
|
||
|
compile,
|
||
|
)
|
||
|
elif h5py is not None and isinstance(filepath, h5py.File):
|
||
|
return hdf5_format.load_model_from_hdf5(
|
||
|
filepath, custom_objects, compile
|
||
|
)
|
||
|
|
||
|
raise IOError(
|
||
|
"Unable to load model. Filepath is not an hdf5 file (or h5py is not "
|
||
|
f"available) or SavedModel. Received: filepath={filepath}"
|
||
|
)
|
||
|
|
||
|
|
||
|
def save_weights(
|
||
|
model, filepath, overwrite=True, save_format=None, options=None
|
||
|
):
|
||
|
"""Saves all layer weights.
|
||
|
|
||
|
Either saves in HDF5 or in TensorFlow format based on the `save_format`
|
||
|
argument.
|
||
|
|
||
|
When saving in HDF5 format, the weight file has:
|
||
|
- `layer_names` (attribute), a list of strings
|
||
|
(ordered names of model layers).
|
||
|
- For every layer, a `group` named `layer.name`
|
||
|
- For every such layer group, a group attribute `weight_names`,
|
||
|
a list of strings
|
||
|
(ordered names of weights tensor of the layer).
|
||
|
- For every weight in the layer, a dataset
|
||
|
storing the weight value, named after the weight tensor.
|
||
|
|
||
|
When saving in TensorFlow format, all objects referenced by the network
|
||
|
are saved in the same format as `tf.train.Checkpoint`, including any
|
||
|
`Layer` instances or `Optimizer` instances assigned to object
|
||
|
attributes. For networks constructed from inputs and outputs using
|
||
|
`tf.keras.Model(inputs, outputs)`, `Layer` instances used by the network
|
||
|
are tracked/saved automatically. For user-defined classes which inherit
|
||
|
from `tf.keras.Model`, `Layer` instances must be assigned to object
|
||
|
attributes, typically in the constructor. See the documentation of
|
||
|
`tf.train.Checkpoint` and `tf.keras.Model` for details.
|
||
|
|
||
|
While the formats are the same, do not mix `save_weights` and
|
||
|
`tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should
|
||
|
be loaded using `Model.load_weights`. Checkpoints saved using
|
||
|
`tf.train.Checkpoint.save` should be restored using the corresponding
|
||
|
`tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over
|
||
|
`save_weights` for training checkpoints.
|
||
|
|
||
|
The TensorFlow format matches objects and variables by starting at a
|
||
|
root object, `self` for `save_weights`, and greedily matching attribute
|
||
|
names. For `Model.save` this is the `Model`, and for `Checkpoint.save`
|
||
|
this is the `Checkpoint` even if the `Checkpoint` has a model attached.
|
||
|
This means saving a `tf.keras.Model` using `save_weights` and loading
|
||
|
into a `tf.train.Checkpoint` with a `Model` attached (or vice versa)
|
||
|
will not match the `Model`'s variables. See the
|
||
|
[guide to training checkpoints](
|
||
|
https://www.tensorflow.org/guide/checkpoint) for details on
|
||
|
the TensorFlow format.
|
||
|
|
||
|
Args:
|
||
|
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
|
||
|
'.keras' will default to HDF5 if `save_format` is `None`.
|
||
|
Otherwise `None` defaults to 'tf'.
|
||
|
options: Optional `tf.train.CheckpointOptions` object that specifies
|
||
|
options for saving weights.
|
||
|
|
||
|
Raises:
|
||
|
ImportError: If `h5py` is not available when attempting to save in
|
||
|
HDF5 format.
|
||
|
"""
|
||
|
model._assert_weights_created()
|
||
|
filepath = io_utils.path_to_string(filepath)
|
||
|
filepath_is_h5 = saving_utils.is_hdf5_filepath(filepath)
|
||
|
if save_format is None:
|
||
|
if filepath_is_h5:
|
||
|
save_format = "h5"
|
||
|
else:
|
||
|
save_format = "tf"
|
||
|
else:
|
||
|
user_format = save_format.lower().strip()
|
||
|
if user_format in ("tensorflow", "tf"):
|
||
|
save_format = "tf"
|
||
|
elif user_format in ("hdf5", "h5", "keras"):
|
||
|
save_format = "h5"
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
f"Unknown format. Received: `save_format`={save_format}. "
|
||
|
'Was expecting one of {"tf", "h5"}.'
|
||
|
)
|
||
|
if save_format == "tf" and filepath_is_h5:
|
||
|
raise ValueError(
|
||
|
'save_weights got save_format="tf"/"tensorflow", but the '
|
||
|
f"filepath ({filepath}) looks like an HDF5 file. "
|
||
|
'Omit the ".h5"/".keras" when saving in TensorFlow format.'
|
||
|
)
|
||
|
|
||
|
if save_format == "h5" and h5py is None:
|
||
|
raise ImportError(
|
||
|
"`save_weights` requires h5py when saving in hdf5, but h5py is "
|
||
|
"not available. Try installing h5py package."
|
||
|
)
|
||
|
if save_format == "tf":
|
||
|
check_filepath = filepath + ".index"
|
||
|
else:
|
||
|
check_filepath = filepath
|
||
|
# If file exists and should not be overwritten:
|
||
|
if not overwrite and os.path.isfile(check_filepath):
|
||
|
proceed = io_utils.ask_to_proceed_with_overwrite(check_filepath)
|
||
|
if not proceed:
|
||
|
return
|
||
|
if save_format == "h5":
|
||
|
with h5py.File(filepath, "w") as f:
|
||
|
hdf5_format.save_weights_to_hdf5_group(f, model)
|
||
|
else:
|
||
|
if not tf.executing_eagerly():
|
||
|
# Call `get_session` to initialize any uninitialized variables.
|
||
|
backend.get_session()
|
||
|
model._checkpoint.write(filepath, options=options)
|
||
|
|
||
|
# Record this checkpoint so it's visible from
|
||
|
# tf.train.latest_checkpoint.
|
||
|
tf.__internal__.train.update_checkpoint_state(
|
||
|
save_dir=os.path.dirname(filepath),
|
||
|
model_checkpoint_path=filepath,
|
||
|
save_relative_paths=True,
|
||
|
all_model_checkpoint_paths=[filepath],
|
||
|
)
|
||
|
|
||
|
|
||
|
def load_weights(
|
||
|
model, filepath, by_name=False, skip_mismatch=False, options=None
|
||
|
):
|
||
|
"""Loads all layer weights, either from a SavedModel or H5 weights file.
|
||
|
|
||
|
If `by_name` is False weights are loaded based on the network's
|
||
|
topology. This means the architecture should be the same as when the
|
||
|
weights were saved. Note that layers that don't have weights are not
|
||
|
taken into account in the topological ordering, so adding or removing
|
||
|
layers is fine as long as they don't have weights.
|
||
|
|
||
|
If `by_name` is True, weights are loaded into layers only if they share
|
||
|
the same name. This is useful for fine-tuning or transfer-learning
|
||
|
models where some of the layers have changed.
|
||
|
|
||
|
Only topological loading (`by_name=False`) is supported when loading
|
||
|
weights from the TensorFlow format. Note that topological loading
|
||
|
differs slightly between TensorFlow and HDF5 formats for user-defined
|
||
|
classes inheriting from `tf.keras.Model`: HDF5 loads based on a
|
||
|
flattened list of weights, while the TensorFlow format loads based on
|
||
|
the object-local names of attributes to which layers are assigned in the
|
||
|
`Model`'s constructor.
|
||
|
|
||
|
Args:
|
||
|
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`). This can also be a path to a
|
||
|
SavedModel saved from `model.save`.
|
||
|
by_name: Boolean, whether to load weights by name or by topological
|
||
|
order. Only topological loading is supported for weight files in
|
||
|
TensorFlow format.
|
||
|
skip_mismatch: Boolean, whether to skip loading of layers where
|
||
|
there is a mismatch in the number of weights, or a mismatch in
|
||
|
the shape of the weight (only valid when `by_name=True`).
|
||
|
options: Optional `tf.train.CheckpointOptions` object that specifies
|
||
|
options for loading weights.
|
||
|
|
||
|
Returns:
|
||
|
When loading a weight file in TensorFlow format, returns the same
|
||
|
status object as `tf.train.Checkpoint.restore`. When graph building,
|
||
|
restore ops are run automatically as soon as the network is built
|
||
|
(on first call for user-defined classes inheriting from `Model`,
|
||
|
immediately if it is already built).
|
||
|
|
||
|
When loading weights in HDF5 format, returns `None`.
|
||
|
|
||
|
Raises:
|
||
|
ImportError: If `h5py` is not available and the weight file is in
|
||
|
HDF5 format.
|
||
|
ValueError: If `skip_mismatch` is set to `True` when `by_name` is
|
||
|
`False`.
|
||
|
"""
|
||
|
if backend.is_tpu_strategy(model._distribution_strategy):
|
||
|
if model._distribution_strategy.extended.steps_per_run > 1 and (
|
||
|
not saving_utils.is_hdf5_filepath(filepath)
|
||
|
):
|
||
|
spr = model._distribution_strategy.extended.steps_per_run
|
||
|
raise ValueError(
|
||
|
"Load weights is not implemented with TPUStrategy "
|
||
|
"with `steps_per_run` greater than 1. The "
|
||
|
f"`steps_per_run` is {spr}"
|
||
|
)
|
||
|
if skip_mismatch and not by_name:
|
||
|
raise ValueError(
|
||
|
"When calling model.load_weights, skip_mismatch can only be "
|
||
|
"set to True when by_name is True."
|
||
|
)
|
||
|
|
||
|
filepath, save_format = _detect_save_format(filepath)
|
||
|
if save_format == "tf":
|
||
|
status = model._checkpoint.read(filepath, options)
|
||
|
if by_name:
|
||
|
raise NotImplementedError(
|
||
|
"Weights may only be loaded based on topology into Models "
|
||
|
"when loading TensorFlow-formatted weights "
|
||
|
"(got by_name=True to load_weights)."
|
||
|
)
|
||
|
if not tf.executing_eagerly():
|
||
|
session = backend.get_session()
|
||
|
# Restore existing variables (if any) immediately, and set up a
|
||
|
# streaming restore for any variables created in the future.
|
||
|
tf.__internal__.tracking.streaming_restore(
|
||
|
status=status, session=session
|
||
|
)
|
||
|
status.assert_nontrivial_match()
|
||
|
else:
|
||
|
status = None
|
||
|
if h5py is None:
|
||
|
raise ImportError(
|
||
|
"`load_weights` requires h5py package when loading weights "
|
||
|
"from HDF5. Try installing h5py."
|
||
|
)
|
||
|
if not model._is_graph_network and not model.built:
|
||
|
raise ValueError(
|
||
|
"Unable to load weights saved in HDF5 format into a "
|
||
|
"subclassed Model which has not created its variables yet. "
|
||
|
"Call the Model first, then load the weights."
|
||
|
)
|
||
|
model._assert_weights_created()
|
||
|
with h5py.File(filepath, "r") as f:
|
||
|
if "layer_names" not in f.attrs and "model_weights" in f:
|
||
|
f = f["model_weights"]
|
||
|
if by_name:
|
||
|
hdf5_format.load_weights_from_hdf5_group_by_name(
|
||
|
f, model, skip_mismatch
|
||
|
)
|
||
|
else:
|
||
|
hdf5_format.load_weights_from_hdf5_group(f, model)
|
||
|
|
||
|
# Perform any layer defined finalization of the layer state.
|
||
|
for layer in model.layers:
|
||
|
layer.finalize_state()
|
||
|
return status
|
||
|
|
||
|
|
||
|
def _detect_save_format(filepath):
|
||
|
"""Returns path to weights file and save format."""
|
||
|
|
||
|
filepath = io_utils.path_to_string(filepath)
|
||
|
if saving_utils.is_hdf5_filepath(filepath):
|
||
|
return filepath, "h5"
|
||
|
|
||
|
# Filepath could be a TensorFlow checkpoint file prefix or SavedModel
|
||
|
# directory. It's possible for filepath to be both a prefix and directory.
|
||
|
# Prioritize checkpoint over SavedModel.
|
||
|
if _is_readable_tf_checkpoint(filepath):
|
||
|
save_format = "tf"
|
||
|
elif tf.saved_model.contains_saved_model(filepath):
|
||
|
ckpt_path = os.path.join(
|
||
|
filepath,
|
||
|
tf.saved_model.VARIABLES_DIRECTORY,
|
||
|
tf.saved_model.VARIABLES_FILENAME,
|
||
|
)
|
||
|
if _is_readable_tf_checkpoint(ckpt_path):
|
||
|
filepath = ckpt_path
|
||
|
save_format = "tf"
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Unable to load weights. filepath {} appears to be a "
|
||
|
"SavedModel directory, but checkpoint either doesn't "
|
||
|
"exist, or is incorrectly formatted.".format(filepath)
|
||
|
)
|
||
|
else:
|
||
|
# Not a TensorFlow checkpoint. This filepath is likely an H5 file that
|
||
|
# doesn't have the hdf5/keras extensions.
|
||
|
save_format = "h5"
|
||
|
return filepath, save_format
|
||
|
|
||
|
|
||
|
def _is_readable_tf_checkpoint(filepath):
|
||
|
try:
|
||
|
tf.compat.v1.train.NewCheckpointReader(filepath)
|
||
|
return True
|
||
|
except tf.errors.DataLossError:
|
||
|
# The checkpoint is not readable in TensorFlow format.
|
||
|
return False
|
||
|
|
||
|
|
||
|
# Inject the load_model function to keras_deps to remove the dependency
|
||
|
# from TFLite to Keras.
|
||
|
tf.__internal__.register_load_model_function(load_model)
|