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07ef7ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # 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.
# ==============================================================================
"""Export quantized tflite model from a trained checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
from nets import nets_factory
from preprocessing import preprocessing_factory
flags.DEFINE_string("model_name", None,
"The name of the architecture to quantize.")
flags.DEFINE_string("checkpoint_path", None, "Path to the training checkpoint.")
flags.DEFINE_string("dataset_name", "imagenet2012",
"Name of the dataset to use for quantization calibration.")
flags.DEFINE_string("dataset_dir", None, "Dataset location.")
flags.DEFINE_string(
"dataset_split", "train",
"The dataset split (train, validation etc.) to use for calibration.")
flags.DEFINE_string("output_tflite", None, "Path to output tflite file.")
flags.DEFINE_boolean(
"use_model_specific_preprocessing", False,
"When true, uses the preprocessing corresponding to the model as specified "
"in preprocessing factory.")
flags.DEFINE_boolean("enable_ema", True,
"Load exponential moving average version of variables.")
flags.DEFINE_integer(
"num_steps", 1000,
"Number of post-training quantization calibration steps to run.")
flags.DEFINE_integer("image_size", 224, "Size of the input image.")
flags.DEFINE_integer("num_classes", 1001,
"Number of output classes for the model.")
FLAGS = flags.FLAGS
# Mean and standard deviation used for normalizing the image tensor.
_MEAN_RGB = 127.5
_STD_RGB = 127.5
def _preprocess_for_quantization(image_data, image_size, crop_padding=32):
"""Crops to center of image with padding then scales, normalizes image_size.
Args:
image_data: A 3D Tensor representing the RGB image data. Image can be of
arbitrary height and width.
image_size: image height/width dimension.
crop_padding: the padding size to use when centering the crop.
Returns:
A decoded and cropped image Tensor. Image is normalized to [-1,1].
"""
shape = tf.shape(image_data)
image_height = shape[0]
image_width = shape[1]
padded_center_crop_size = tf.cast(
(image_size * 1.0 / (image_size + crop_padding)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32), tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
image = tf.image.crop_to_bounding_box(
image_data,
offset_height=offset_height,
offset_width=offset_width,
target_height=padded_center_crop_size,
target_width=padded_center_crop_size)
image = tf.image.resize([image], [image_size, image_size],
method=tf.image.ResizeMethod.BICUBIC)[0]
image = tf.cast(image, tf.float32)
image -= tf.constant(_MEAN_RGB)
image /= tf.constant(_STD_RGB)
return image
def restore_model(sess, checkpoint_path, enable_ema=True):
"""Restore variables from the checkpoint into the provided session.
Args:
sess: A tensorflow session where the checkpoint will be loaded.
checkpoint_path: Path to the trained checkpoint.
enable_ema: (optional) Whether to load the exponential moving average (ema)
version of the tensorflow variables. Defaults to True.
"""
if enable_ema:
ema = tf.train.ExponentialMovingAverage(decay=0.0)
ema_vars = tf.trainable_variables() + tf.get_collection("moving_vars")
for v in tf.global_variables():
if "moving_mean" in v.name or "moving_variance" in v.name:
ema_vars.append(v)
ema_vars = list(set(ema_vars))
var_dict = ema.variables_to_restore(ema_vars)
else:
var_dict = None
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_dict, max_to_keep=1)
saver.restore(sess, checkpoint_path)
def _representative_dataset_gen():
"""Gets a python generator of numpy arrays for the given dataset."""
image_size = FLAGS.image_size
dataset = tfds.builder(FLAGS.dataset_name, data_dir=FLAGS.dataset_dir)
dataset.download_and_prepare()
data = dataset.as_dataset()[FLAGS.dataset_split]
iterator = tf.data.make_one_shot_iterator(data)
if FLAGS.use_model_specific_preprocessing:
preprocess_fn = functools.partial(
preprocessing_factory.get_preprocessing(name=FLAGS.model_name),
output_height=image_size,
output_width=image_size)
else:
preprocess_fn = functools.partial(
_preprocess_for_quantization, image_size=image_size)
features = iterator.get_next()
image = features["image"]
image = preprocess_fn(image)
image = tf.reshape(image, [1, image_size, image_size, 3])
for _ in range(FLAGS.num_steps):
yield [image.eval()]
def main(_):
with tf.Graph().as_default(), tf.Session() as sess:
network_fn = nets_factory.get_network_fn(
FLAGS.model_name, num_classes=FLAGS.num_classes, is_training=False)
image_size = FLAGS.image_size
images = tf.placeholder(
tf.float32, shape=(1, image_size, image_size, 3), name="images")
logits, _ = network_fn(images)
output_tensor = tf.nn.softmax(logits)
restore_model(sess, FLAGS.checkpoint_path, enable_ema=FLAGS.enable_ema)
converter = tf.lite.TFLiteConverter.from_session(sess, [images],
[output_tensor])
converter.representative_dataset = tf.lite.RepresentativeDataset(
_representative_dataset_gen)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
tflite_buffer = converter.convert()
with tf.gfile.GFile(FLAGS.output_tflite, "wb") as output_tflite:
output_tflite.write(tflite_buffer)
print("tflite model written to %s" % FLAGS.output_tflite)
if __name__ == "__main__":
flags.mark_flag_as_required("model_name")
flags.mark_flag_as_required("checkpoint_path")
flags.mark_flag_as_required("dataset_dir")
flags.mark_flag_as_required("output_tflite")
app.run(main)
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