Missing / non-supported op during convert with tensorflowjs_convert

I was trying to use the general documentation of AutoML to use trained model in a web based application. As that way wasn’t working (issue was raised here) I tried a workaround and exported and downloaded the saved model of my custom training.

My idea was to use the tensorflowjs_converter and convert the saved model to graph model. I used the following command:

tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model --signature_name=serving_default --saved_model_tags=serve --control_flow_v2=true model-export/ converted/

This will stop with the following error:

tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model --signature_name=serving_default --saved_model_tags=serve --control_flow_v2=true model-export/ converted/
2020-10-15 13:12:12.115746: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-10-15 13:12:12.135884: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fbc2ba2d150 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-15 13:12:12.135902: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-10-15 13:12:13.772835: I tensorflow/core/grappler/devices.cc:78] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)
2020-10-15 13:12:13.773299: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2020-10-15 13:12:13.840628: E tensorflow/core/grappler/grappler_item_builder.cc:669] Init node index_to_string/table_init/LookupTableImportV2 doesn't exist in graph
WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py:379: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py:383: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.convert_variables_to_constants`
WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/framework/convert_to_constants.py:854: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
Traceback (most recent call last):
  File "/usr/local/bin/tensorflowjs_converter", line 8, in <module>
    sys.exit(pip_main())
  File "/usr/local/lib/python3.8/site-packages/tensorflowjs/converters/converter.py", line 757, in pip_main
    main([' '.join(sys.argv[1:])])
  File "/usr/local/lib/python3.8/site-packages/tensorflowjs/converters/converter.py", line 761, in main
    convert(argv[0].split(' '))
  File "/usr/local/lib/python3.8/site-packages/tensorflowjs/converters/converter.py", line 690, in convert
    tf_saved_model_conversion_v2.convert_tf_saved_model(
  File "/usr/local/lib/python3.8/site-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 607, in convert_tf_saved_model
    optimize_graph(frozen_graph, signature,
  File "/usr/local/lib/python3.8/site-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 145, in optimize_graph
    raise ValueError('Unsupported Ops in the model before optimization\n' +
ValueError: Unsupported Ops in the model before optimization
DecodeJpeg

Note: there was more error, but i realized there was an update/commit and some new ops were added, so I reinstalled tensorflow, still, the DecodeJpeg is still present.

Now I’m not sure how I can make my custom AutoML trained model work on web as neither the default export is working (see issue linked above) and I cannot convert it.

I tried to suppress the error message by passing the switch --skip_op_check, but than I have an error while trying to load the model. (the same error, just in the browser console)

tfjs:17 Uncaught (in promise) TypeError: Unknown op 'HashTableV2'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()
    at tfjs:17
    at UO (tfjs:17)
    at tfjs:17
    at tfjs:17
    at e.t.scopedRun (tfjs:17)
    at e.t.tidy (tfjs:17)
    at sx (tfjs:17)
    at e.t.execute (tfjs:17)
    at e.t.loadSync (tfjs:17)
    at e.<anonymous> (tfjs:17)
(anonymous) @ tfjs:17
UO @ tfjs:17
(anonymous) @ tfjs:17
(anonymous) @ tfjs:17
t.scopedRun @ tfjs:17
t.tidy @ tfjs:17
sx @ tfjs:17
t.execute @ tfjs:17
t.loadSync @ tfjs:17
(anonymous) @ tfjs:17
u @ tfjs:17
(anonymous) @ tfjs:17
forEach.e.<computed> @ tfjs:17
Um @ tfjs:17
o @ tfjs:17
async function (async)
run @ script.js:3
(anonymous) @ script.js:14

I have tensorflow@2.3.1 installed and running Python 3.8.5 on OSX (Mojave).

Sample HTML I use:

<!DOCTYPE html>
<html lang="en">
  <head>
    <title>Hello World - TensorFlow.js</title>
    <meta charset="utf-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <link rel="stylesheet" href="/style.css">
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-automl"></script>
    <script src="script.js"></script>
  </head>  
  <body>
    <img id="test" crossorigin="anonymous" src="test.jpg">
  </body>
</html>

Sample script I use:

async function run() {
    const MODEL_URL = 'model.json';
    const model = await tf.automl.loadObjectDetection(MODEL_URL);
  tf.enableDebugMode(); 
  const img = document.getElementById('test');
  const options = {score: 0.5, iou: 0.5, topk: 20};
  const predictions = await model.detect(img, options);
  console.log(predictions);
  const pre = document.createElement('pre');
  pre.textContent = JSON.stringify(predictions, null, 2);
  document.body.append(pre);
}
run();

Is there a way to use the exported AutoML models with TensorflowJS?

[rthadur]

@peterschmiz would you be intrested in contributing ? please check our contributing guidelines here.

[google-ml-butler[bot]]

Closing as stale. Please @mention us if this needs more attention.