TypeError: Cannot read property 'backend' of undefined

I’m using the latest version of tfjs-node on npm:

{
 "peerDependencies": {
    "@tensorflow/tfjs-core": "^2.4.0"
  },
  "dependencies": {
      "@tensorflow/tfjs-converter": "^2.4.0",
      "@tensorflow/tfjs-node": "^2.4.0"
     }
}

I get this error when loading a saved model with loadSavedModel

TypeError: Cannot read property 'backend' of undefined
    at Engine.moveData (/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:3280:31)
    at DataStorage.get (/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:115:28)
    at NodeJSKernelBackend.getInputTensorIds (/node_modules/@tensorflow/tfjs-node/dist/nodejs_kernel_backend.js:153:43)
    at NodeJSKernelBackend.getMappedInputTensorIds (/node_modules/@tensorflow/tfjs-node/dist/nodejs_kernel_backend.js:1487:30)
    at NodeJSKernelBackend.runSavedModel (/node_modules/@tensorflow/tfjs-node/dist/nodejs_kernel_backend.js:1506:66)
    at TFSavedModel.predict (/node_modules/@tensorflow/tfjs-node/dist/saved_model.js:362:52)
    at /lib/tests/models/audio.js:44:22

const tf = require('@tensorflow/tfjs-node');

(function () {

    const modelPath='/root/saved_model/';
    
    // load model
    tf.node.loadSavedModel(modelPath)
    .then(model => {
       // it holds a waveform of audio file
        const data = require('fs').readFileSync('/root/test.json');
        const waveform = JSON.parse(data).data;
        const inputTensor = tf.tensor2d(waveform, [ waveform.length, 1], 'float32' );
        const inputs = {
            audio_id: '',
            mix_spectrogram: null,
            mix_stft: null,
            waveform: inputTensor
        };
        return model.predict(inputs);
    })
    .then(output => {
        console.dir(output, { depth: null, maxArrayLength: null });
    })
    .catch(error => {
        console.error(error);
    })

    // load model metadata
    tf.node.getMetaGraphsFromSavedModel(modelPath)
    .then(modelInfo => {
        console.dir(modelInfo[0].signatureDefs.serving_default.outputs, { depth: null, maxArrayLength: null });
        console.dir(modelInfo[0].signatureDefs.serving_default.inputs, { depth: null, maxArrayLength: null });
    })
    .catch(error => {
        console.error(error);
    })

}).call(this);

[tafsiri]

So the cause of the error you are seeing is that you are passing null values as inputs to the model. Your code has

const inputs = {
	audio_id: '',
	mix_spectrogram: null,
	mix_stft: null,
	waveform: inputTensor
};

All of those inputs need to be tensors. Here is the code i used to get past that point (just creating some random data):

tf.node.loadSavedModel(modelPath, ['serve'], 'serving_default')
  .then(model => {
      const inputs = {
          audio_id: tf.tensor(['id']),
          mix_spectrogram: tf.randomNormal([2, 512, 1024, 2]),
          mix_stft: tf.randomNormal([2, 2049, 2]),
          waveform: tf.randomNormal([2, 2])
      };
      return model.predict(inputs);
  })
  .then(output => {
      console.dir(output, { depth: null, maxArrayLength: null });
  })
  .catch(error => {
      console.error(error);
  })

Beyond that I still ran into an issue Error: Session fail to run with error: Placeholder_1:0 is both fed and fetched., and looking at some of the signature def info (see below), I noticed that audio_id is both an input and and output and is referring to the same placeholder. From what I can tell this isn’t allowed, it appears one should at least call tf.identity on the input placeholder if you are trying to pass it through. Have you been able to execute this model succesfully in python (or otherwise know that it works in python)?

inputs {
  audio_id: {
    dtype: 'string',
    tfDtype: 'DT_STRING',
    name: 'Placeholder_1:0',
    shape: []
  },
  mix_spectrogram: {
    dtype: 'float32',
    tfDtype: 'DT_FLOAT',
    name: 'strided_slice_3:0',
    shape: [ [Object], [Object], [Object], [Object] ]
  },
  mix_stft: {
    dtype: 'complex64',
    tfDtype: 'DT_COMPLEX64',
    name: 'transpose_1:0',
    shape: [ [Object], [Object], [Object] ]
  },
  waveform: {
    dtype: 'float32',
    tfDtype: 'DT_FLOAT',
    name: 'Placeholder:0',
    shape: [ [Object], [Object] ]
  }
}
outputs {
  accompaniment: {
    dtype: 'float32',
    tfDtype: 'DT_FLOAT',
    name: 'strided_slice_23:0',
    shape: [ [Object], [Object] ]
  },
  audio_id: {
    dtype: 'string',
    tfDtype: 'DT_STRING',
    name: 'Placeholder_1:0',
    shape: []
  },
  vocals: {
    dtype: 'float32',
    tfDtype: 'DT_FLOAT',
    name: 'strided_slice_13:0',
    shape: [ [Object], [Object] ]
  }
}

[loretoparisi]

@tafsiri thank you, I appreciate it! So we did some step forward, using the checkpoint directly! @shoegazerstella is trying in this way

import os
import tensorflow as tf
trained_checkpoint_prefix = 'pretrained_models/2stems/model'
export_dir = os.path.join('export_dir', '0')
graph = tf.Graph()
with tf.compat.v1.Session(graph=graph) as sess:
    loader = tf.compat.v1.train.import_meta_graph(trained_checkpoint_prefix + '.meta')
    loader.restore(sess, trained_checkpoint_prefix)
    builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
    builder.add_meta_graph_and_variables(sess,
                                         [tf.saved_model.TRAINING, tf.saved_model.SERVING],
                                         strip_default_attrs=True)
    builder.save()  

At this point it should be done, but we now get a Error: The SavedModel does not have signature: serving_default

2020-12-04 08:08:46.918697: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Error: The SavedModel does not have signature: serving_default
    at getSignatureDefEntryFromMetaGraphInfo (/node_modules/@tensorflow/tfjs-node/dist/saved_model.js:210:23)

Thank you!