Loading Python 2D ndarray into Android for inference on TFLite

I’d like to test inference on a TensorFlow Lite model I’ve loaded into an Android project.

I have some inputs generated in a Python environment I’d like to save to a file, load into my Android app and use for TFLite inference. My inputs are somewhat large, one example is:

<class 'numpy.ndarray'>, dtype: float32, shape: (1, 596, 80)

I need some way of serialising this ndarray and loading this into Android.

More information on TFLite inference can be found here. In essence, this should be a multi-dimensional array of primitive floats, or a ByteBuffer.

What is the most simple way to:

  • Serialise this ndarray on the Python side
  • Deserialise this blob on the Java side from a file



I figured this out in the end, there’s a handy Java library called JavaNpy that allows you to open .npy files in Java, and therefore Android.

On the Python side I saved a flattened .npy in the normal way:

data_flat = data.flatten()
np.save(file="data.npy", arr=data_flat)

In Android I placed this into the assets folder.

I then loaded it into JavaNpy:

InputStream stream = context.getAssets().open("data.npy")
Npy npy = new Npy(stream);
float[] npyData = npy.floatElements();

And finally converted it into a TensorBuffer:

int[] inputShape = new int[]{1, 596, 80};   //the data shape before I flattened it
TensorBuffer tensorBuffer = TensorBuffer.createFixedSize(inputShape, DataType.FLOAT32);

I then used this tensorBuffer for inference on my TFLite model.