yigitcolakoglu
/
MyCity


								# Copyright 2017 The TensorFlow Authors. All Rights Reserved.

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								# 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

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								#     http://www.apache.org/licenses/LICENSE-2.0

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								# 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"""Tool to export an object detection model for inference.


								Prepares an object detection tensorflow graph for inference using model

								configuration and a trained checkpoint. Outputs inference

								graph, associated checkpoint files, a frozen inference graph and a

								SavedModel (https://tensorflow.github.io/serving/serving_basic.html).


								The inference graph contains one of three input nodes depending on the user

								specified option.

								  * `image_tensor`: Accepts a uint8 4-D tensor of shape [None, None, None, 3]

								  * `encoded_image_string_tensor`: Accepts a 1-D string tensor of shape [None]

								    containing encoded PNG or JPEG images. Image resolutions are expected to be

								    the same if more than 1 image is provided.

								  * `tf_example`: Accepts a 1-D string tensor of shape [None] containing

								    serialized TFExample protos. Image resolutions are expected to be the same

								    if more than 1 image is provided.


								and the following output nodes returned by the model.postprocess(..):

								  * `num_detections`: Outputs float32 tensors of the form [batch]

								      that specifies the number of valid boxes per image in the batch.

								  * `detection_boxes`: Outputs float32 tensors of the form

								      [batch, num_boxes, 4] containing detected boxes.

								  * `detection_scores`: Outputs float32 tensors of the form

								      [batch, num_boxes] containing class scores for the detections.

								  * `detection_classes`: Outputs float32 tensors of the form

								      [batch, num_boxes] containing classes for the detections.

								  * `raw_detection_boxes`: Outputs float32 tensors of the form

								      [batch, raw_num_boxes, 4] containing detection boxes without

								      post-processing.

								  * `raw_detection_scores`: Outputs float32 tensors of the form

								      [batch, raw_num_boxes, num_classes_with_background] containing class score

								      logits for raw detection boxes.

								  * `detection_masks`: Outputs float32 tensors of the form

								      [batch, num_boxes, mask_height, mask_width] containing predicted instance

								      masks for each box if its present in the dictionary of postprocessed

								      tensors returned by the model.


								Notes:

								 * This tool uses `use_moving_averages` from eval_config to decide which

								   weights to freeze.


								Example Usage:

								--------------

								python export_inference_graph \

								    --input_type image_tensor \

								    --pipeline_config_path path/to/ssd_inception_v2.config \

								    --trained_checkpoint_prefix path/to/model.ckpt \

								    --output_directory path/to/exported_model_directory


								The expected output would be in the directory

								path/to/exported_model_directory (which is created if it does not exist)

								with contents:

								 - inference_graph.pbtxt

								 - model.ckpt.data-00000-of-00001

								 - model.ckpt.info

								 - model.ckpt.meta

								 - frozen_inference_graph.pb

								 + saved_model (a directory)


								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 (in which we change the second stage post-processing score

								threshold to be 0.5):


								python export_inference_graph \

								    --input_type image_tensor \

								    --pipeline_config_path path/to/ssd_inception_v2.config \

								    --trained_checkpoint_prefix path/to/model.ckpt \

								    --output_directory path/to/exported_model_directory \

								    --config_override " \

								            model{ \

								              faster_rcnn { \

								                second_stage_post_processing { \

								                  batch_non_max_suppression { \

								                    score_threshold: 0.5 \

								                  } \

								                } \

								              } \

								            }"

								"""

								import tensorflow as tf

								from google.protobuf import text_format

								from object_detection import exporter

								from object_detection.protos import pipeline_pb2


								slim = tf.contrib.slim

								flags = tf.app.flags


								flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '

								                    'one of [`image_tensor`, `encoded_image_string_tensor`, '

								                    '`tf_example`]')

								flags.DEFINE_string('input_shape', None,

								                    'If input_type is `image_tensor`, this can explicitly set '

								                    'the shape of this input tensor to a fixed size. The '

								                    'dimensions are to be provided as a comma-separated list '

								                    'of integers. A value of -1 can be used for unknown '

								                    'dimensions. If not specified, for an `image_tensor, the '

								                    'default shape will be partially specified as '

								                    '`[None, None, None, 3]`.')

								flags.DEFINE_string('pipeline_config_path', None,

								                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '

								                    'file.')

								flags.DEFINE_string('trained_checkpoint_prefix', None,

								                    'Path to trained checkpoint, typically of the form '

								                    'path/to/model.ckpt')

								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_boolean('write_inference_graph', False,

								                     'If true, writes inference graph to disk.')

								tf.app.flags.mark_flag_as_required('pipeline_config_path')

								tf.app.flags.mark_flag_as_required('trained_checkpoint_prefix')

								tf.app.flags.mark_flag_as_required('output_directory')

								FLAGS = flags.FLAGS


								def main(_):

								  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()

								  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:

								    text_format.Merge(f.read(), pipeline_config)

								  text_format.Merge(FLAGS.config_override, pipeline_config)

								  if FLAGS.input_shape:

								    input_shape = [

								        int(dim) if dim != '-1' else None

								        for dim in FLAGS.input_shape.split(',')

								    ]

								  else:

								    input_shape = None

								  exporter.export_inference_graph(

								      FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix,

								      FLAGS.output_directory, input_shape=input_shape,

								      write_inference_graph=FLAGS.write_inference_graph)


								if __name__ == '__main__':

								  tf.app.run()