Models: Tflite interpreter of Centernet resnet50 v1 FPN keypoints fails

[orihash]

Prerequisites

• I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• I am reporting the issue to the correct repository. (Model Garden official or research directory)
• I checked to make sure that this issue has not been filed already.

2. Describe the bug

Not sure if it's a bug or not, but I am gonna mark it this way for now.

After downloading the pre-trained model of Centernet resnet50 V1 FPN Keypoints: http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_resnet50_v1_fpn_512x512_kpts_coco17_tpu-8.tar.gz, I tried to convert to tflite but I couldn't do that. I did some research and ran into a filed issue: https://github.com/tensorflow/tensorflow/issues/43495, that explained that I should re train the model and use tf-nightly to convert tflite model. After I got the tflite model I tried interpreting it with Interpreter interface for TensorFlow Lite Models (tf.lite.Interpreter(tflite_model)) but it kept failing and the error that I am getting is:

ValueError: Did not get operators or tensors in subgraph 1

3. Steps to reproduce

1. Download tflite model: https://we.tl/t-qnmBmOU6wD
2. Create a conda environment
3. Install tf-nightly
4. Try running the below code

import tensorflow as tf 

interpreter = tf.lite.Interpreter(path_to_tflite_model)
interpreter.allocate_tensors()
interpreter.invoke()

4. Expected behavior

I was expecting code posted above not to fail.

5. Additional context

I tried tflite model in Netron app works fine so it's not corrupted

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04
• Mobile device name if the issue happens on a mobile device:
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): tf-nightly
• Python version: 3.8.5
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:

[flamxi]

I tried that model with C++ API of current master and could reproduce it, I got this output when tried to build from FlatBufferModel::BuildFromFile

INFO: Initialized TensorFlow Lite runtime.
ERROR: Did not get operators or tensors in subgraph 1.

[srjoglekar246]

The Exporting script does not support CenterNet as of now, but we are looking into it.
Will update this bug when it lands.

[orihash]

@srjoglekar246 I have already exported to tflite CenterNet model, but I am having troubles to interpret it.
I am getting: ValueError: Did not get operators or tensors in subgraph 1

[srjoglekar246]

The architecture doesn't convert to TFLite as expected, so your exported model is not runnable

[orihash]

@srjoglekar246 Thanks a lot for your answer! Can I ask If you have any idea when the fix will be released?

[srjoglekar246]

We are working with the Research team to add support in our conversion tooling, and also train some new models that are mobile-friendly (smaller dimensions, MobileNet backbone instead of ResNet, etc). There was significant work involved in re-writing some parts of the model to make it convertible to TFLite.

Should be landing in a month or two. Sorry about the delay!

[gcervantes8]

I am getting the same bug when I try to convert my own machine translation model using TensorFlow 2.3.1 to convert to TFLite:

Internal error: Cannot create interpreter: Did not get operators or tensors in subgraph 3.

Is there any way to find out which operation in the model is causing this so I can avoid using it?

If I try converting the model using TF-nightly 2.5.0-dev20201111, and I use tensorflow-lite:0.0.0-nightly on Android Studio to run the model, then I get this different error:

Cannot create interpreter: Op builtin_code out of range: 127. Are you using old TFLite binary with newer model?
Registration failed.

Is there anything I can try doing to fix it?