327 lines
12 KiB
Python
327 lines
12 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utils related to keras model saving."""
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import collections
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import copy
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import os
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from tensorflow.python.eager import def_function
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import losses
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from tensorflow.python.keras import optimizer_v1
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from tensorflow.python.keras import optimizers
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from tensorflow.python.keras.engine import base_layer_utils
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.keras.utils import version_utils
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from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util import nest
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def extract_model_metrics(model):
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"""Convert metrics from a Keras model `compile` API to dictionary.
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This is used for converting Keras models to Estimators and SavedModels.
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Args:
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model: A `tf.keras.Model` object.
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Returns:
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Dictionary mapping metric names to metric instances. May return `None` if
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the model does not contain any metrics.
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"""
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if getattr(model, '_compile_metrics', None):
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# TODO(psv/kathywu): use this implementation in model to estimator flow.
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# We are not using model.metrics here because we want to exclude the metrics
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# added using `add_metric` API.
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return {m.name: m for m in model._compile_metric_functions} # pylint: disable=protected-access
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return None
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def model_input_signature(model, keep_original_batch_size=False):
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"""Inspect model to get its input signature.
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The model's input signature is a list with a single (possibly-nested) object.
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This is due to the Keras-enforced restriction that tensor inputs must be
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passed in as the first argument.
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For example, a model with input {'feature1': <Tensor>, 'feature2': <Tensor>}
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will have input signature: [{'feature1': TensorSpec, 'feature2': TensorSpec}]
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Args:
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model: Keras Model object.
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keep_original_batch_size: A boolean indicating whether we want to keep using
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the original batch size or set it to None. Default is `False`, which means
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that the batch dim of the returned input signature will always be set to
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`None`.
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Returns:
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A list containing either a single TensorSpec or an object with nested
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TensorSpecs. This list does not contain the `training` argument.
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"""
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input_specs = model._get_save_spec(dynamic_batch=not keep_original_batch_size) # pylint: disable=protected-access
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if input_specs is None:
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return None
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input_specs = _enforce_names_consistency(input_specs)
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# Return a list with a single element as the model's input signature.
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if isinstance(input_specs,
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collections.abc.Sequence) and len(input_specs) == 1:
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# Note that the isinstance check filters out single-element dictionaries,
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# which should also be wrapped as a single-element list.
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return input_specs
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else:
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return [input_specs]
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def raise_model_input_error(model):
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raise ValueError(
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'Model {} cannot be saved because the input shapes have not been '
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'set. Usually, input shapes are automatically determined from calling'
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' `.fit()` or `.predict()`. To manually set the shapes, call '
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'`model.build(input_shape)`.'.format(model))
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def trace_model_call(model, input_signature=None):
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"""Trace the model call to create a tf.function for exporting a Keras model.
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Args:
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model: A Keras model.
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input_signature: optional, a list of tf.TensorSpec objects specifying the
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inputs to the model.
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Returns:
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A tf.function wrapping the model's call function with input signatures set.
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Raises:
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ValueError: if input signature cannot be inferred from the model.
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"""
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if input_signature is None:
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if isinstance(model.call, def_function.Function):
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input_signature = model.call.input_signature
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if input_signature is None:
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input_signature = model_input_signature(model)
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if input_signature is None:
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raise_model_input_error(model)
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@def_function.function(input_signature=input_signature)
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def _wrapped_model(*args):
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"""A concrete tf.function that wraps the model's call function."""
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# When given a single input, Keras models will call the model on the tensor
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# rather than a list consisting of the single tensor.
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inputs = args[0] if len(input_signature) == 1 else list(args)
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with base_layer_utils.call_context().enter(
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model, inputs=inputs, build_graph=False, training=False, saving=True):
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outputs = model(inputs, training=False)
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# Outputs always has to be a flat dict.
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output_names = model.output_names # Functional Model.
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if output_names is None: # Subclassed Model.
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from tensorflow.python.keras.engine import compile_utils # pylint: disable=g-import-not-at-top
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output_names = compile_utils.create_pseudo_output_names(outputs)
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outputs = nest.flatten(outputs)
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return {name: output for name, output in zip(output_names, outputs)}
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return _wrapped_model
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def model_metadata(model, include_optimizer=True, require_config=True):
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"""Returns a dictionary containing the model metadata."""
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from tensorflow.python.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
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from tensorflow.python.keras.optimizer_v2 import optimizer_v2 # pylint: disable=g-import-not-at-top
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model_config = {'class_name': model.__class__.__name__}
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try:
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model_config['config'] = model.get_config()
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except NotImplementedError as e:
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if require_config:
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raise e
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metadata = dict(
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keras_version=str(keras_version),
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backend=K.backend(),
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model_config=model_config)
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if model.optimizer and include_optimizer:
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if isinstance(model.optimizer, optimizer_v1.TFOptimizer):
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logging.warning(
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'TensorFlow optimizers do not '
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'make it possible to access '
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'optimizer attributes or optimizer state '
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'after instantiation. '
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'As a result, we cannot save the optimizer '
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'as part of the model save file. '
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'You will have to compile your model again after loading it. '
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'Prefer using a Keras optimizer instead '
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'(see keras.io/optimizers).')
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elif model._compile_was_called: # pylint: disable=protected-access
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training_config = model._get_compile_args(user_metrics=False) # pylint: disable=protected-access
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training_config.pop('optimizer', None) # Handled separately.
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metadata['training_config'] = _serialize_nested_config(training_config)
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if isinstance(model.optimizer, optimizer_v2.RestoredOptimizer):
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raise NotImplementedError(
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'As of now, Optimizers loaded from SavedModel cannot be saved. '
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'If you\'re calling `model.save` or `tf.keras.models.save_model`,'
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' please set the `include_optimizer` option to `False`. For '
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'`tf.saved_model.save`, delete the optimizer from the model.')
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else:
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optimizer_config = {
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'class_name':
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generic_utils.get_registered_name(model.optimizer.__class__),
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'config':
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model.optimizer.get_config()
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}
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metadata['training_config']['optimizer_config'] = optimizer_config
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return metadata
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def should_overwrite(filepath, overwrite):
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"""Returns whether the filepath should be overwritten."""
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# If file exists and should not be overwritten.
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if not overwrite and os.path.isfile(filepath):
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return ask_to_proceed_with_overwrite(filepath)
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return True
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def compile_args_from_training_config(training_config, custom_objects=None):
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"""Return model.compile arguments from training config."""
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if custom_objects is None:
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custom_objects = {}
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with generic_utils.CustomObjectScope(custom_objects):
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optimizer_config = training_config['optimizer_config']
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optimizer = optimizers.deserialize(optimizer_config)
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# Recover losses.
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loss = None
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loss_config = training_config.get('loss', None)
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if loss_config is not None:
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loss = _deserialize_nested_config(losses.deserialize, loss_config)
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# Recover metrics.
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metrics = None
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metrics_config = training_config.get('metrics', None)
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if metrics_config is not None:
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metrics = _deserialize_nested_config(_deserialize_metric, metrics_config)
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# Recover weighted metrics.
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weighted_metrics = None
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weighted_metrics_config = training_config.get('weighted_metrics', None)
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if weighted_metrics_config is not None:
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weighted_metrics = _deserialize_nested_config(_deserialize_metric,
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weighted_metrics_config)
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sample_weight_mode = training_config['sample_weight_mode'] if hasattr(
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training_config, 'sample_weight_mode') else None
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loss_weights = training_config['loss_weights']
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return dict(
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optimizer=optimizer,
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loss=loss,
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metrics=metrics,
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weighted_metrics=weighted_metrics,
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loss_weights=loss_weights,
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sample_weight_mode=sample_weight_mode)
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def _deserialize_nested_config(deserialize_fn, config):
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"""Deserializes arbitrary Keras `config` using `deserialize_fn`."""
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def _is_single_object(obj):
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if isinstance(obj, dict) and 'class_name' in obj:
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return True # Serialized Keras object.
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if isinstance(obj, str):
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return True # Serialized function or string.
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return False
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if config is None:
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return None
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if _is_single_object(config):
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return deserialize_fn(config)
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elif isinstance(config, dict):
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return {
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k: _deserialize_nested_config(deserialize_fn, v)
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for k, v in config.items()
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}
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elif isinstance(config, (tuple, list)):
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return [_deserialize_nested_config(deserialize_fn, obj) for obj in config]
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raise ValueError('Saved configuration not understood.')
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def _serialize_nested_config(config):
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"""Serialized a nested structure of Keras objects."""
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def _serialize_fn(obj):
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if callable(obj):
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return generic_utils.serialize_keras_object(obj)
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return obj
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return nest.map_structure(_serialize_fn, config)
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def _deserialize_metric(metric_config):
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"""Deserialize metrics, leaving special strings untouched."""
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from tensorflow.python.keras import metrics as metrics_module # pylint:disable=g-import-not-at-top
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if metric_config in ['accuracy', 'acc', 'crossentropy', 'ce']:
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# Do not deserialize accuracy and cross-entropy strings as we have special
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# case handling for these in compile, based on model output shape.
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return metric_config
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return metrics_module.deserialize(metric_config)
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def _enforce_names_consistency(specs):
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"""Enforces that either all specs have names or none do."""
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def _has_name(spec):
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return hasattr(spec, 'name') and spec.name is not None
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def _clear_name(spec):
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spec = copy.deepcopy(spec)
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if hasattr(spec, 'name'):
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spec._name = None # pylint:disable=protected-access
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return spec
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flat_specs = nest.flatten(specs)
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name_inconsistency = (
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any(_has_name(s) for s in flat_specs) and
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not all(_has_name(s) for s in flat_specs))
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if name_inconsistency:
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specs = nest.map_structure(_clear_name, specs)
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return specs
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def try_build_compiled_arguments(model):
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if (not version_utils.is_v1_layer_or_model(model) and
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model.outputs is not None):
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try:
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if not model.compiled_loss.built:
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model.compiled_loss.build(model.outputs)
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if not model.compiled_metrics.built:
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model.compiled_metrics.build(model.outputs, model.outputs)
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except: # pylint: disable=bare-except
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logging.warning(
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'Compiled the loaded model, but the compiled metrics have yet to '
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'be built. `model.compile_metrics` will be empty until you train '
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'or evaluate the model.')
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def is_hdf5_filepath(filepath):
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return (filepath.endswith('.h5') or filepath.endswith('.keras') or
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filepath.endswith('.hdf5'))
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