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Bump version for 0.12.6 release (#674)

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/deepcell/utils/export_utils.py

# Copyright 2016-2023 The Van Valen Lab at the California Institute of
# Technology (Caltech), with support from the Paul Allen Family Foundation,
# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
# All rights reserved.
#
# Licensed under a modified 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.github.com/vanvalenlab/deepcell-tf/LICENSE
#
# The Work provided may be used for non-commercial academic purposes only.
# For any other use of the Work, including commercial use, please contact:
# vanvalenlab@gmail.com
#
# Neither the name of Caltech nor the names of its contributors may be used
# to endorse or promote products derived from this software without specific
# prior written permission.
#
# 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.
# ==============================================================================
"""Save Keras models as a SavedModel for TensorFlow Serving"""


import os
import numpy as np
import tensorflow as tf

from tensorflow.python.platform import tf_logging


def export_model(keras_model, export_path, model_version=0, weights_path=None,
                 include_optimizer=True, overwrite=True, save_format='tf'):
    """Export a model for use with TensorFlow Serving.

    DEPRECATED: ``tf.keras.models.save_model`` is preferred.

    Args:
        keras_model (tensorflow.keras.Model): Instantiated Keras model.
        export_path (str): Destination to save the exported model files.
        model_version (int): Integer version of the model.
        weights_path (str): Path to a ``.h5`` or ``.tf`` weights file.
        include_optimizer (bool): Whether to export the optimizer.
        overwrite (bool): Whether to overwrite any existing files in
            ``export_path``.
        save_format (str): Saved model format, one of ``'tf'`` or ``'h5'``.
    """
    tf_logging.warn('`export_model` is deprecated. '
                    'Please use `tf.keras.models.save_model` instead.')

    if weights_path:
        keras_model.load_weights(weights_path, by_name=True)

    tf.keras.models.save_model(
        keras_model,
        os.path.join(export_path, str(int(model_version))),
        overwrite=overwrite,
        include_optimizer=include_optimizer,
        save_format=save_format,
        signatures=None,
    )


def export_model_to_tflite(model_file, export_path, calibration_images,
                           norm=True, location=True, file_name='model.tflite'):
    """Export a saved keras model to tensorflow-lite with int8 precision.

    .. deprecated:: 0.12.4

       The ``export_model_to_tflite`` function is deprecated and will be
       removed in 0.13. Use ``tf.keras.models.save_model`` instead.

    This export function has only been tested with ``PanopticNet`` models.
    For the export to be successful, the ``PanopticNet`` model must have
    ``norm_method`` set to ``None``, ``location`` set to ``False``,
    and the upsampling layers must use ``bilinear`` interpolation.

    Args:
        model_file (str): Path to saved model file
        export_path (str): Directory to save the exported tflite model
        calibration_images (numpy.array): Array of images used for calibration
            during model quantization
        norm (bool): Whether to normalize calibration images.
        location (bool): Whether to append a location image
            to calibration images.
        file_name (str): File name for the exported model. Defaults to
            'model.tflite'
    """
    import warnings

    warnings.warn(
        (
            "\n\nexport_model_to_tflite is deprecated and will be removed.\n"
            "Use tf.keras.models.save_model instead.\n"
        ),
        DeprecationWarning,
        stacklevel=2,
    )

    # Define helper function - normalization
    def norm_images(images):
        mean = np.mean(images, axis=(1, 2), keepdims=True)
        std = np.std(images, axis=(1, 2), keepdims=True)
        norm = (images - mean) / std
        return norm

    # Define helper function - add location layer
    def add_location(images):
        x = np.arange(0, images.shape[1], dtype='float32')
        y = np.arange(0, images.shape[2], dtype='float32')

        x = x / max(x)
        y = y / max(y)

        loc_x, loc_y = np.meshgrid(x, y, indexing='ij')
        loc = np.stack([loc_x, loc_y], axis=-1)
        loc = np.expand_dims(loc, axis=0)
        loc = np.tile(loc, (images.shape[0], 1, 1, 1))
        images_with_loc = np.concatenate([images, loc], axis=-1)
        return images_with_loc

    # Create generator to calibrate model quantization
    calibration_images = calibration_images.astype('float32')
    if norm:
        calibration_images = norm_images(calibration_images)
    if location:
        calibration_images = add_location(calibration_images)

    def representative_data_gen():
        for image in calibration_images:
            data = [np.expand_dims(image, axis=0)]
            yield data

    converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(model_file)
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.uint8
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.representative_dataset = representative_data_gen
    tflite_model = converter.convert()

    # Save converted model
    save_path = os.path.join(export_path, file_name)
    open(save_path, "wb").write(tflite_quant_model)

    return tflite_model

1	# Copyright 2016-2023 The Van Valen Lab at the California Institute of
2	# Technology (Caltech), with support from the Paul Allen Family Foundation,
3	# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
4	# All rights reserved.
5	#
6	# Licensed under a modified Apache License, Version 2.0 (the "License");
7	# you may not use this file except in compliance with the License.
8	# You may obtain a copy of the License at
9	#
10	# http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
11	#
12	# The Work provided may be used for non-commercial academic purposes only.
13	# For any other use of the Work, including commercial use, please contact:
14	# vanvalenlab@gmail.com
15	#
16	# Neither the name of Caltech nor the names of its contributors may be used
17	# to endorse or promote products derived from this software without specific
18	# prior written permission.
19	#
20	# Unless required by applicable law or agreed to in writing, software
21	# distributed under the License is distributed on an "AS IS" BASIS,
22	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
23	# See the License for the specific language governing permissions and
24	# limitations under the License.
25	# ==============================================================================
26	"""Save Keras models as a SavedModel for TensorFlow Serving"""	×
27
28
29	import os	×
30	import numpy as np	×
31	import tensorflow as tf	×
32
33	from tensorflow.python.platform import tf_logging	×
34
35
36	def export_model(keras_model, export_path, model_version=0, weights_path=None,	×
37	include_optimizer=True, overwrite=True, save_format='tf'):
38	"""Export a model for use with TensorFlow Serving.
39
40	DEPRECATED: ``tf.keras.models.save_model`` is preferred.
41
42	Args:
43	keras_model (tensorflow.keras.Model): Instantiated Keras model.
44	export_path (str): Destination to save the exported model files.
45	model_version (int): Integer version of the model.
46	weights_path (str): Path to a ``.h5`` or ``.tf`` weights file.
47	include_optimizer (bool): Whether to export the optimizer.
48	overwrite (bool): Whether to overwrite any existing files in
49	``export_path``.
50	save_format (str): Saved model format, one of ``'tf'`` or ``'h5'``.
51	"""
52	tf_logging.warn('`export_model` is deprecated. '	×
53	'Please use `tf.keras.models.save_model` instead.')
54
55	if weights_path:	×
56	keras_model.load_weights(weights_path, by_name=True)	×
57
58	tf.keras.models.save_model(	×
59	keras_model,
60	os.path.join(export_path, str(int(model_version))),
61	overwrite=overwrite,
62	include_optimizer=include_optimizer,
63	save_format=save_format,
64	signatures=None,
65	)
66
67
68	def export_model_to_tflite(model_file, export_path, calibration_images,	×
69	norm=True, location=True, file_name='model.tflite'):
70	"""Export a saved keras model to tensorflow-lite with int8 precision.
71
72	.. deprecated:: 0.12.4
73
74	The ``export_model_to_tflite`` function is deprecated and will be
75	removed in 0.13. Use ``tf.keras.models.save_model`` instead.
76
77	This export function has only been tested with ``PanopticNet`` models.
78	For the export to be successful, the ``PanopticNet`` model must have
79	``norm_method`` set to ``None``, ``location`` set to ``False``,
80	and the upsampling layers must use ``bilinear`` interpolation.
81
82	Args:
83	model_file (str): Path to saved model file
84	export_path (str): Directory to save the exported tflite model
85	calibration_images (numpy.array): Array of images used for calibration
86	during model quantization
87	norm (bool): Whether to normalize calibration images.
88	location (bool): Whether to append a location image
89	to calibration images.
90	file_name (str): File name for the exported model. Defaults to
91	'model.tflite'
92	"""
93	import warnings	×
94
95	warnings.warn(	×
96	(
97	"\n\nexport_model_to_tflite is deprecated and will be removed.\n"
98	"Use tf.keras.models.save_model instead.\n"
99	),
100	DeprecationWarning,
101	stacklevel=2,
102	)
103
104	# Define helper function - normalization
105	def norm_images(images):	×
106	mean = np.mean(images, axis=(1, 2), keepdims=True)	×
107	std = np.std(images, axis=(1, 2), keepdims=True)	×
108	norm = (images - mean) / std	×
109	return norm	×
110
111	# Define helper function - add location layer
112	def add_location(images):	×
113	x = np.arange(0, images.shape[1], dtype='float32')	×
114	y = np.arange(0, images.shape[2], dtype='float32')	×
115
116	x = x / max(x)	×
117	y = y / max(y)	×
118
119	loc_x, loc_y = np.meshgrid(x, y, indexing='ij')	×
120	loc = np.stack([loc_x, loc_y], axis=-1)	×
121	loc = np.expand_dims(loc, axis=0)	×
122	loc = np.tile(loc, (images.shape[0], 1, 1, 1))	×
123	images_with_loc = np.concatenate([images, loc], axis=-1)	×
124	return images_with_loc	×
125
126	# Create generator to calibrate model quantization
127	calibration_images = calibration_images.astype('float32')	×
128	if norm:	×
129	calibration_images = norm_images(calibration_images)	×
130	if location:	×
131	calibration_images = add_location(calibration_images)	×
132
133	def representative_data_gen():	×
134	for image in calibration_images:	×
135	data = [np.expand_dims(image, axis=0)]	×
136	yield data	×
137
138	converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(model_file)	×
139	converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]	×
140	converter.inference_input_type = tf.uint8	×
141	converter.inference_output_type = tf.uint8	×
142	converter.optimizations = [tf.lite.Optimize.DEFAULT]	×
143	converter.representative_dataset = representative_data_gen	×
144	tflite_model = converter.convert()	×
145
146	# Save converted model
147	save_path = os.path.join(export_path, file_name)	×
148	open(save_path, "wb").write(tflite_quant_model)	×
149
150	return tflite_model	×

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