converting python code to kotlin (numpy, tensor)

Question

I'm trying to migrate deep learning process in python and now need to convert it into kotlin (android studio) but struglling with this part for few days :(

can someone help me to convert python code to kotlin?

The type of out is Tensor. IMAGE_SIZE is 350 and half is 175.

    out = torch.argmax(out, dim=1, keepdims=True).permute(0, 2, 3, 1)[0].numpy().squeeze().astype(np.int32)
    r_H, r_W = out.shape

    _out_extended = np.zeros((IMAGE_SIZE + r_H, IMAGE_SIZE + r_W), dtype=out.dtype)
    _out_extended[half : half + IMAGE_SIZE, half : half + IMAGE_SIZE] = out * 255
    out = _out_extended.copy()

I also tried to use argmax function from Multik library but the input type is not the same. (not Tensor, but MultiArray).

Any other suggestion for migration from python to kotlin would be welcomed!

Answer 1

I kinda figured it out by myself.
In kotlin, I was not able to use argmax function so had to convert it somehow.

'outputs' are equivalent to the 'out' from my question.

        val outTensors = module.forward(IValue.from(inputTensor)).toDictStringKey()

        // the key "out" of the output tensor contains the semantic masks
        val outputTensor = outTensors["out"]!!.toTensor()
        val outputs = outputTensor .dataAsFloatArray

        val width: Int = imageModel.width
        val height: Int = imageModel.height

        val intVal = IntArray(width * height)

        // output processing
        // go through each element in the output of size [WIDTH, HEIGHT] and
        // set different color(black&white) for different classnum
        for (j in 0 until width) {
            for (k in 0 until height) {
                // maxi: the index of the 2 classnum with the max probability
                var maxi = 0
                var maxj = 0
                var maxk = 0
                var maxnum = -100000.0
                for (i in 0 until classnum) {
                    if (outputs[i * (width * height) + j * width + k] > maxnum) {
                        maxnum = outputs[i * (width * height) + j * width + k].toDouble()
                        maxi = i
                        maxj = j
                        maxk = k
                    }
                }
                // color coding for paper in white
                // black color for background and others
                if (maxi == 1)
                    intVal[maxj * width + maxk] = -0x000f // white
                else
                    intVal[maxj * width + maxk] = 0x1000000 // black
            }
        }

And for the _out_extended part, after I convert the intVal to mat (= originalImage), I used this method in order to extend the output.

val padding = 255 
Core.copyMakeBorder(
            originalImage,
            borderImage,
            padding, padding, padding, padding,
            Core.BORDER_CONSTANT, Scalar(0.0)
        )