Reshaping tensorflow output tensors - tensorflow

Question

I am training an object detection model with Azure customvision.ai. The model output is with tensorflow, either saved model .pb, .tf or .tflite.
The model output type is designated as float32[1,13,13,50]
I then push the .tflite onto a Google Coral Edge device and attempt to run it (previous .tflite models trained with Google Cloud worked, but I'm now bound to corporate Azure and need to use customvision.ai). These commands are with
$ mdt shell
$ export DEMO_FILES="/usr/lib/python3/dist*/edgetpu/demo"
$ export DISPLAY=:0 && edgetpu_detect \
$ --source /dev/video1:YUY2:1280x720:20/1 \
$ --model ${DEMO_FILES}/model.tflite
Finally, the model attempts to run, but results in a ValueError
'This model has a {}.'.format(output_tensors_sizes.size)))
ValueError: Detection model should have 4 output tensors! This model has 1.
What is happening here? How do I reshape my tensorflow model to match the device requirements of 4 output tensors?
The model that works
The model that does not work
Edit, this outputs a tflite model, but still has only one output
python tflite_convert.py \
--output_file=model.tflite \
--graph_def_file=saved_model.pb \
--saved_model_dir="C:\Users\b0588718\AppData\Roaming\Python\Python37\site-packages\tensorflow\lite\python" \
--inference_type=FLOAT \
--input_shapes=1,416,416,3 \
--input_arrays=Placeholder \
--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \
--mean_values=128 \
--std_dev_values=128 \
--allow_custom_ops \
--change_concat_input_ranges=false \
--allow_nudging_weights_to_use_fast_gemm_kernel=true

Answer 1

You are running an object detection demo where the engine expects 4 outputs from the model and your model only have one outputs. Maybe you had the tflite conversion incorrect? For instance, if you grabbed the Face SSD model from our zoo, conversion should be like this:
$ tflite_convert \
--output_file=face_ssd.tflite \
--graph_def_file=tflite_graph.pb \
--inference_type=QUANTIZED_UINT8 \
--input_shapes=1,320,320,3 \
--input_arrays normalized_input_image_tensor \
--output_arrays "TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3" \
--mean_values 128 \
--std_dev_values 128 \
--allow_custom_ops \
--change_concat_input_ranges=false \
--allow_nudging_weights_to_use_fast_gemm_kernel=true
Take a look at a similar query for more details:
https://github.com/google-coral/edgetpu/issues/135#issuecomment-640677917