-
Notifications
You must be signed in to change notification settings - Fork 2
/
export_model.py
201 lines (163 loc) · 7.37 KB
/
export_model.py
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
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
# Lint as: python2, python3
# Copyright 2018 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.
# ==============================================================================
"""Exports trained model to TensorFlow frozen graph."""
import os
import tensorflow as tf
from tensorflow.contrib import quantize as contrib_quantize
from tensorflow.python.tools import freeze_graph
from deeplab import common
from deeplab import input_preprocess
from deeplab import model
slim = tf.contrib.slim
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('checkpoint_path', None, 'Checkpoint path')
flags.DEFINE_string('export_path', None,
'Path to output Tensorflow frozen graph.')
flags.DEFINE_integer('num_classes', 21, 'Number of classes.')
flags.DEFINE_multi_integer('crop_size', [513, 513],
'Crop size [height, width].')
# For `xception_65`, use atrous_rates = [12, 24, 36] if output_stride = 8, or
# rates = [6, 12, 18] if output_stride = 16. For `mobilenet_v2`, use None. Note
# one could use different atrous_rates/output_stride during training/evaluation.
flags.DEFINE_multi_integer('atrous_rates', None,
'Atrous rates for atrous spatial pyramid pooling.')
flags.DEFINE_integer('output_stride', 8,
'The ratio of input to output spatial resolution.')
# Change to [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] for multi-scale inference.
flags.DEFINE_multi_float('inference_scales', [1.0],
'The scales to resize images for inference.')
flags.DEFINE_bool('add_flipped_images', False,
'Add flipped images during inference or not.')
flags.DEFINE_integer(
'quantize_delay_step', -1,
'Steps to start quantized training. If < 0, will not quantize model.')
flags.DEFINE_bool('save_inference_graph', False,
'Save inference graph in text proto.')
# Input name of the exported model.
_INPUT_NAME = 'ImageTensor'
# Output name of the exported predictions.
_OUTPUT_NAME = 'SemanticPredictions'
_RAW_OUTPUT_NAME = 'RawSemanticPredictions'
# Output name of the exported probabilities.
_OUTPUT_PROB_NAME = 'SemanticProbabilities'
_RAW_OUTPUT_PROB_NAME = 'RawSemanticProbabilities'
def _create_input_tensors():
"""Creates and prepares input tensors for DeepLab model.
This method creates a 4-D uint8 image tensor 'ImageTensor' with shape
[1, None, None, 3]. The actual input tensor name to use during inference is
'ImageTensor:0'.
Returns:
image: Preprocessed 4-D float32 tensor with shape [1, crop_height,
crop_width, 3].
original_image_size: Original image shape tensor [height, width].
resized_image_size: Resized image shape tensor [height, width].
"""
# input_preprocess takes 4-D image tensor as input.
input_image = tf.placeholder(tf.uint8, [1, None, None, 3], name=_INPUT_NAME)
original_image_size = tf.shape(input_image)[1:3]
# Squeeze the dimension in axis=0 since `preprocess_image_and_label` assumes
# image to be 3-D.
image = tf.squeeze(input_image, axis=0)
resized_image, image, _ = input_preprocess.preprocess_image_and_label(
image,
label=None,
crop_height=FLAGS.crop_size[0],
crop_width=FLAGS.crop_size[1],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
is_training=False,
model_variant=FLAGS.model_variant)
resized_image_size = tf.shape(resized_image)[:2]
# Expand the dimension in axis=0, since the following operations assume the
# image to be 4-D.
image = tf.expand_dims(image, 0)
return image, original_image_size, resized_image_size
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info('Prepare to export model to: %s', FLAGS.export_path)
with tf.Graph().as_default():
image, image_size, resized_image_size = _create_input_tensors()
model_options = common.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: FLAGS.num_classes},
crop_size=FLAGS.crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
if tuple(FLAGS.inference_scales) == (1.0,):
tf.logging.info('Exported model performs single-scale inference.')
predictions = model.predict_labels(
image,
model_options=model_options,
image_pyramid=FLAGS.image_pyramid)
else:
tf.logging.info('Exported model performs multi-scale inference.')
if FLAGS.quantize_delay_step >= 0:
raise ValueError(
'Quantize mode is not supported with multi-scale test.')
predictions = model.predict_labels_multi_scale(
image,
model_options=model_options,
eval_scales=FLAGS.inference_scales,
add_flipped_images=FLAGS.add_flipped_images)
raw_predictions = tf.identity(
tf.cast(predictions[common.OUTPUT_TYPE], tf.float32),
_RAW_OUTPUT_NAME)
raw_probabilities = tf.identity(
predictions[common.OUTPUT_TYPE + model.PROB_SUFFIX],
_RAW_OUTPUT_PROB_NAME)
# Crop the valid regions from the predictions.
semantic_predictions = raw_predictions[
:, :resized_image_size[0], :resized_image_size[1]]
semantic_probabilities = raw_probabilities[
:, :resized_image_size[0], :resized_image_size[1]]
# Resize back the prediction to the original image size.
def _resize_label(label, label_size):
# Expand dimension of label to [1, height, width, 1] for resize operation.
label = tf.expand_dims(label, 3)
resized_label = tf.image.resize_images(
label,
label_size,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True)
return tf.cast(tf.squeeze(resized_label, 3), tf.int32)
semantic_predictions = _resize_label(semantic_predictions, image_size)
semantic_predictions = tf.identity(semantic_predictions, name=_OUTPUT_NAME)
semantic_probabilities = tf.image.resize_bilinear(
semantic_probabilities, image_size, align_corners=True,
name=_OUTPUT_PROB_NAME)
if FLAGS.quantize_delay_step >= 0:
contrib_quantize.create_eval_graph()
saver = tf.train.Saver(tf.all_variables())
dirname = os.path.dirname(FLAGS.export_path)
tf.gfile.MakeDirs(dirname)
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
freeze_graph.freeze_graph_with_def_protos(
graph_def,
saver.as_saver_def(),
FLAGS.checkpoint_path,
_OUTPUT_NAME + ',' + _OUTPUT_PROB_NAME,
restore_op_name=None,
filename_tensor_name=None,
output_graph=FLAGS.export_path,
clear_devices=True,
initializer_nodes=None)
if FLAGS.save_inference_graph:
tf.train.write_graph(graph_def, dirname, 'inference_graph.pbtxt')
if __name__ == '__main__':
flags.mark_flag_as_required('checkpoint_path')
flags.mark_flag_as_required('export_path')
tf.app.run()