Deeplab/Segmentation: Error: The dtype of dict['ImageTensor'] provided in model.execute(dict) must be int32, but was float32

TensorFlow.js version

“@tensorflow/tfjs-node”: “^2.0.1” “@tensorflow/tfjs”: “^2.0.1”

Browser version

Nodejs and expressjs, no browser

Describe the problem or feature request

In using a LOCALLY downloaded and served model… in the NPM/node module the index.js file requires me to cast a tensor to an int32 or it errors out about float32 no matter what I pass it.

Code to reproduce the bug / link to feature request

model loading

const loadModelDeepLab = async () => {
  const modelName = 'pascal';   // set to your preferred model, either `pascal`, `cityscapes` or `ade20k`
  const quantizationBytes = 2;  // either 1, 2 or 4
  const url = 'https://tfhub.dev/tensorflow/tfjs-model/deeplab/pascal/1/default/1/model.json?tfjs-format=file';
  return await deeplab.load({modelUrl: url,base: modelName, quantizationBytes});
};

info on the tensor

Tensor {
  kept: false,
  isDisposedInternal: false,
  shape: [ 1000, 1000, 3 ],
  dtype: 'int32',
  size: 3000000,
  strides: [ 3000, 3 ],
  dataId: {},
  id: 2,
  rankType: '3',
  scopeId: 0 }

the error out

"(node:35252) UnhandledPromiseRejectionWarning: Error: The dtype of dict['ImageTensor'] provided in model.execute(dict) must be int32, but was float32
"

I manually changed offending code to… and everything runs

    SemanticSegmentation.prototype.predict = function (input) {
        var _this = this;
        return tf.tidy(function () {
            var data = utils_1.toInputTensor(input);
            return tf.squeeze(_this.model.execute(tf.cast(data,"int32")));
        });
    };

[maxbauer]

Same problem for me when trying to run the TensorFlow SavedModel Import Demo with a custom ssd mobilenet v2 model.

@parthlathiya42 it worked for me with the following cast:

const integerTensor = imageToPredict.toInt();
return this.model.executeAsync(integerTensor); //execute() doesn't work for me here

[parthlathiya42]

I got the same error.

My Scenerio: I trained a pre-trained model from tensorflow model zoo using transfer learning using tensorflow api as saved model (model.pb file) and converted it into tfjs format (model.json and shared .bin files).

When I tried running this model.json on the javascript(web), it gives the below error:

Uncaught (in promise) Error: The dtype of dict['input_tensor'] provided in model.execute(dict) must be int32, but was float32

When I tried someone else’s working converted model (model.json and shared .bin files) on my javascript(web), it worked.

Conclusion: There is something wrong with my converted model. I converted it using tensorflowjs_converter. My original model in (model.pb) works accurately in python too.

I’m still trying out to convert my model.pb file with different tensorflowjs_converters as it seems to be the converters versioning issue.