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# 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)