Issues exporting ssdlite_mobilenet_v2 to tensorflow-lite

System information

• What is the top-level directory of the model you are using:

  • ssdlite_mobilenet_v2_coco_2018_05_09 pretrained model

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  Linux ubuntu 16.04

• TensorFlow installed from binary (CPU):

• TensorFlow version (use command below):

  • 1.5

Describe the problem

Hi:

I am trying to export ssdlite_Mobilenet_v2 model to tf-lite:
I downloaded from this path
I tried with these two ways:

1.- following this tutorial with export_tflite_ssd_graph.py, using the checkpoint

• I use export_tflite_ssd_graph.py like this:

  export CHECKPOINT_PATH=/home/VICOMTECH/uelordi/projects/tflite_models/ssdlite_mobilenet_v2_coco_2018_05_09/model.ckpt

  export OUTPUT_DIR=/home/VICOMTECH/uelordi/projects/tflite_models/ssdlite_mobilenet_v2_coco_2018_05_09/tflite

    python /home/VICOMTECH/uelordi/SDK/tensorflow1/models/models/research/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

• then I created toco script:

toco \
--input_file=tflite_graph.pb \
--output_file=latest_ssdlite_mobilenetv2.tflite \
  --input_format=TENSORFLOW_GRAPHDEF \
--input_shapes=1,300,300,3 \
  --output_format=TFLITE \
--input_arrays=normalized_input_image_tensor \
--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3'  \
--inference_type=FLOAT \
--mean_values=128 \
--std_values=128 \
--change_concat_input_ranges=false \

and I got this errors:

Some of the operators in the model are not supported by the standard TensorFlow Lite runtime. If you have a custom implementation for them you can disable this error with --allow_custom_ops. Here is a list of operators for which you will need custom implementations: DIV, Squeeze, TFLite_Detection_PostProcess.

So I added allow-custom-ops, and when I use the tf-lite interperter in android:

d.tfLite = new Interpreter(loadModelFile(assetManager, modelFilename));

And as I supposed I have custom operation error.

Internal error: Cannot create interpreter: Didn't find custom op for name 'DIV' with version 1
Didn't find custom op for name 'Squeeze' with version 1
Registration failed.

2.- using toco with the pretrained model.

my toco script is:

toco \
--input_file=frozen_inference_graph.pb \
--output_file=ssd_lite_v2.tflite \
  --input_format=TENSORFLOW_GRAPHDEF \
--input_shapes=1,300,300,3 \
  --output_format=TFLITE \
--input_arrays=normalized_input_image_tensor \
--output_arrays='detection_boxes,detection_scores,detection_classes,num_detections' \
--inference_type=FLOAT \
--mean_values=128 \
--std_values=128 \
--change_concat_input_ranges=false \

As I have some errors with the operators I added allow-custom-ops, and when I use the tf-lite interperter in android:

d.tfLite = new Interpreter(loadModelFile(assetManager, modelFilename));

But as I supposed I have custom operation error.

Internal error: Cannot create interpreter: Didn't find custom op for name 'DIV' with version 1
Didn't find custom op for name 'Squeeze' with version 1
Registration failed.

So my questions are:

• What makes different export_tflite_ssd_graph.py (with the checkpoint) from a common toco script with pretrained model? It creates custom operations? complementary postprocessing operations?
  *In case of toco pretrained problems: *
• This means that I have to create my custom operation of DIV and Squeeze operations?