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| r"""Tool to export an object detection model for inference.
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| Prepares an object detection tensorflow graph for inference using model
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| configuration and a trained checkpoint. Outputs associated checkpoint files,
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| a SavedModel, and a copy of the model config.
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|
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| The inference graph contains one of three input nodes depending on the user
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| specified option.
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| * `image_tensor`: Accepts a uint8 4-D tensor of shape [1, None, None, 3]
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| * `float_image_tensor`: Accepts a float32 4-D tensor of shape
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| [1, None, None, 3]
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| * `encoded_image_string_tensor`: Accepts a 1-D string tensor of shape [None]
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| containing encoded PNG or JPEG images. Image resolutions are expected to be
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| the same if more than 1 image is provided.
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| * `tf_example`: Accepts a 1-D string tensor of shape [None] containing
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| serialized TFExample protos. Image resolutions are expected to be the same
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| if more than 1 image is provided.
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| * `image_and_boxes_tensor`: Accepts a 4-D image tensor of size
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| [1, None, None, 3] and a boxes tensor of size [1, None, 4] of normalized
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| bounding boxes. To be able to support this option, the model needs
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| to implement a predict_masks_from_boxes method. See the documentation
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| for DetectionFromImageAndBoxModule for details.
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|
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| and the following output nodes returned by the model.postprocess(..):
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| * `num_detections`: Outputs float32 tensors of the form [batch]
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| that specifies the number of valid boxes per image in the batch.
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| * `detection_boxes`: Outputs float32 tensors of the form
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| [batch, num_boxes, 4] containing detected boxes.
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| * `detection_scores`: Outputs float32 tensors of the form
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| [batch, num_boxes] containing class scores for the detections.
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| * `detection_classes`: Outputs float32 tensors of the form
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| [batch, num_boxes] containing classes for the detections.
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| Example Usage:
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| --------------
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| python exporter_main_v2.py \
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| --input_type image_tensor \
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| --pipeline_config_path path/to/ssd_inception_v2.config \
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| --trained_checkpoint_dir path/to/checkpoint \
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| --output_directory path/to/exported_model_directory
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| --use_side_inputs True/False \
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| --side_input_shapes dim_0,dim_1,...dim_a/.../dim_0,dim_1,...,dim_z \
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| --side_input_names name_a,name_b,...,name_c \
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| --side_input_types type_1,type_2
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|
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| The expected output would be in the directory
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| path/to/exported_model_directory (which is created if it does not exist)
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| holding two subdirectories (corresponding to checkpoint and SavedModel,
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| respectively) and a copy of the pipeline config.
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| Config overrides (see the `config_override` flag) are text protobufs
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| (also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
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| certain fields in the provided pipeline_config_path. These are useful for
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| making small changes to the inference graph that differ from the training or
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| eval config.
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|
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| Example Usage (in which we change the second stage post-processing score
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| threshold to be 0.5):
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|
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| python exporter_main_v2.py \
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| --input_type image_tensor \
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| --pipeline_config_path path/to/ssd_inception_v2.config \
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| --trained_checkpoint_dir path/to/checkpoint \
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| --output_directory path/to/exported_model_directory \
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| --config_override " \
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| model{ \
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| faster_rcnn { \
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| second_stage_post_processing { \
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| batch_non_max_suppression { \
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| score_threshold: 0.5 \
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| } \
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| } \
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| } \
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| }"
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|
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| If side inputs are desired, the following arguments could be appended
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| (the example below is for Context R-CNN).
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| --use_side_inputs True \
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| --side_input_shapes 1,2000,2057/1 \
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| --side_input_names context_features,valid_context_size \
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| --side_input_types tf.float32,tf.int32
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| """
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| from absl import app
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| from absl import flags
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|
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| import tensorflow.compat.v2 as tf
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| from google.protobuf import text_format
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| from object_detection import exporter_lib_v2
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| from object_detection.protos import pipeline_pb2
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|
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| tf.enable_v2_behavior()
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|
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| FLAGS = flags.FLAGS
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|
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| flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
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| 'one of [`image_tensor`, `encoded_image_string_tensor`, '
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| '`tf_example`, `float_image_tensor`, '
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| '`image_and_boxes_tensor`]')
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| flags.DEFINE_string('pipeline_config_path', None,
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| 'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
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| 'file.')
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| flags.DEFINE_string('trained_checkpoint_dir', None,
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| 'Path to trained checkpoint directory')
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| flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
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| flags.DEFINE_string('config_override', '',
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| 'pipeline_pb2.TrainEvalPipelineConfig '
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| 'text proto to override pipeline_config_path.')
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| flags.DEFINE_boolean('use_side_inputs', False,
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| 'If True, uses side inputs as well as image inputs.')
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| flags.DEFINE_string('side_input_shapes', '',
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| 'If use_side_inputs is True, this explicitly sets '
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| 'the shape of the side input tensors to a fixed size. The '
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| 'dimensions are to be provided as a comma-separated list '
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| 'of integers. A value of -1 can be used for unknown '
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| 'dimensions. A `/` denotes a break, starting the shape of '
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| 'the next side input tensor. This flag is required if '
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| 'using side inputs.')
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| flags.DEFINE_string('side_input_types', '',
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| 'If use_side_inputs is True, this explicitly sets '
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| 'the type of the side input tensors. The '
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| 'dimensions are to be provided as a comma-separated list '
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| 'of types, each of `string`, `integer`, or `float`. '
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| 'This flag is required if using side inputs.')
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| flags.DEFINE_string('side_input_names', '',
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| 'If use_side_inputs is True, this explicitly sets '
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| 'the names of the side input tensors required by the model '
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| 'assuming the names will be a comma-separated list of '
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| 'strings. This flag is required if using side inputs.')
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|
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| flags.mark_flag_as_required('pipeline_config_path')
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| flags.mark_flag_as_required('trained_checkpoint_dir')
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| flags.mark_flag_as_required('output_directory')
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|
|
|
|
| def main(_):
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| pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
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| with tf.io.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
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| text_format.Merge(f.read(), pipeline_config)
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| text_format.Merge(FLAGS.config_override, pipeline_config)
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| exporter_lib_v2.export_inference_graph(
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| FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_dir,
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| FLAGS.output_directory, FLAGS.use_side_inputs, FLAGS.side_input_shapes,
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| FLAGS.side_input_types, FLAGS.side_input_names)
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|
|
|
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| if __name__ == '__main__':
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| app.run(main)
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|
|
