Understanding export_tflite_ssd_graph.py - tensorflow

Question

Here is tutorial about converting Mobilenet+SSD to tflite at some point they use export_tflite_ssd_graph.py, as I understand this custom script is used to support tf.image.non_max_suppression operation.
export CONFIG_FILE=gs://${YOUR_GCS_BUCKET}/data/pipeline.config
export CHECKPOINT_PATH=gs://${YOUR_GCS_BUCKET}/train/model.ckpt-2000
export OUTPUT_DIR=/tmp/tflite
python object_detection/export_tflite_ssd_graph.py \
--pipeline_config_path=$CONFIG_FILE \
--trained_checkpoint_prefix=$CHECKPOINT_PATH \
--output_directory=$OUTPUT_DIR \
--add_postprocessing_op=true
But I wonder what is pipeline.config and how to create it if I use custom model(for example FaceBoxes) that use tf.image.non_max_suppression operation?

Answer 1

The main objective of export_tflite_ssd_graph.py is to export the training checkpoint files into a frozen graph that you can later use for transfer learning or for straight inference (because they contain the model structure info as well as the trained weights info). In fact, all the models listed in model zoo are the frozen graph generated this way.
As for the tf.image.non_max_suppression, export_tflite_ssd_graph.py is not used to 'support' it but if --add_postprocessing_op is set true there will be another custom op node added to the frozen graph, this custom node will have the functionality similar to op tf.image.non_max_suppression. See reference here.
Finally the pipeline.config file directly corresponds to a config file in the you use for training (--pipeline_config_path), it is a copy of it but often with a modified score threshold (See description here about pipeline.config.), so you will have to create it before the training if you use a custom model. And to create a custom config file, here is the official tutorial.