-
Notifications
You must be signed in to change notification settings - Fork 6
/
export_tflite_graph_tf2.py
152 lines (141 loc) · 6.16 KB
/
export_tflite_graph_tf2.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
# Lint as: python2, python3
# Copyright 2020 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.
# ==============================================================================
r"""Exports TF2 detection SavedModel for conversion to TensorFlow Lite.
Link to the TF2 Detection Zoo:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
The output folder will contain an intermediate SavedModel that can be used with
the TfLite converter.
NOTE: This only supports SSD meta-architectures for now.
One input:
image: a float32 tensor of shape[1, height, width, 3] containing the
*normalized* input image.
NOTE: See the `preprocess` function defined in the feature extractor class
in the object_detection/models directory.
Four Outputs:
detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
locations
detection_classes: a float32 tensor of shape [1, num_boxes]
with class indices
detection_scores: a float32 tensor of shape [1, num_boxes]
with class scores
num_boxes: a float32 tensor of size 1 containing the number of detected boxes
Example Usage:
--------------
python object_detection/export_tflite_graph_tf2.py \
--pipeline_config_path path/to/ssd_model/pipeline.config \
--trained_checkpoint_dir path/to/ssd_model/checkpoint \
--output_directory path/to/exported_model_directory
The expected output SavedModel would be in the directory
path/to/exported_model_directory (which is created if it does not exist).
Config overrides (see the `config_override` flag) are text protobufs
(also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
certain fields in the provided pipeline_config_path. These are useful for
making small changes to the inference graph that differ from the training or
eval config.
Example Usage 1 (in which we change the NMS iou_threshold to be 0.5 and
NMS score_threshold to be 0.0):
python object_detection/export_tflite_model_tf2.py \
--pipeline_config_path path/to/ssd_model/pipeline.config \
--trained_checkpoint_dir path/to/ssd_model/checkpoint \
--output_directory path/to/exported_model_directory
--config_override " \
model{ \
ssd{ \
post_processing { \
batch_non_max_suppression { \
score_threshold: 0.0 \
iou_threshold: 0.5 \
} \
} \
} \
} \
"
Example Usage 2 (export CenterNet model for keypoint estimation task with fixed
shape resizer and customized input resolution):
python object_detection/export_tflite_model_tf2.py \
--pipeline_config_path path/to/ssd_model/pipeline.config \
--trained_checkpoint_dir path/to/ssd_model/checkpoint \
--output_directory path/to/exported_model_directory \
--keypoint_label_map_path path/to/label_map.txt \
--max_detections 10 \
--centernet_include_keypoints true \
--config_override " \
model{ \
center_net { \
image_resizer { \
fixed_shape_resizer { \
height: 320 \
width: 320 \
} \
} \
} \
}" \
"""
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
from google.protobuf import text_format
from object_detection import export_tflite_graph_lib_tf2
from object_detection.protos import pipeline_pb2
tf.enable_v2_behavior()
FLAGS = flags.FLAGS
flags.DEFINE_string(
'pipeline_config_path', None,
'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
'file.')
flags.DEFINE_string('trained_checkpoint_dir', None,
'Path to trained checkpoint directory')
flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
flags.DEFINE_string(
'config_override', '', 'pipeline_pb2.TrainEvalPipelineConfig '
'text proto to override pipeline_config_path.')
flags.DEFINE_integer('max_detections', 10,
'Maximum number of detections (boxes) to return.')
# SSD-specific flags
flags.DEFINE_bool(
'ssd_use_regular_nms', False,
'Flag to set postprocessing op to use Regular NMS instead of Fast NMS '
'(Default false).')
# CenterNet-specific flags
flags.DEFINE_bool(
'centernet_include_keypoints', False,
'Whether to export the predicted keypoint tensors. Only CenterNet model'
' supports this flag.'
)
flags.DEFINE_string(
'keypoint_label_map_path', None,
'Path of the label map used by CenterNet keypoint estimation task. If'
' provided, the label map path in the pipeline config will be replaced by'
' this one. Note that it is only used when exporting CenterNet model for'
' keypoint estimation task.'
)
def main(argv):
del argv # Unused.
flags.mark_flag_as_required('pipeline_config_path')
flags.mark_flag_as_required('trained_checkpoint_dir')
flags.mark_flag_as_required('output_directory')
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.io.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
text_format.Parse(f.read(), pipeline_config)
override_config = pipeline_pb2.TrainEvalPipelineConfig()
text_format.Parse(FLAGS.config_override, override_config)
pipeline_config.MergeFrom(override_config)
export_tflite_graph_lib_tf2.export_tflite_model(
pipeline_config, FLAGS.trained_checkpoint_dir, FLAGS.output_directory,
FLAGS.max_detections, FLAGS.ssd_use_regular_nms,
FLAGS.centernet_include_keypoints, FLAGS.keypoint_label_map_path)
if __name__ == '__main__':
app.run(main)