Custom layer using numpy.inf conflicts with tensorflow lite interpreter


I have custom padding layer that fills the inputs/activations of my model with constant_values = -numpy.inf instead of 0 to then act with maxpooling2d (basically compare between -inf and the activation value). Once converted the tflite model to int8 (full-integer post-training quantization) and load the model with the tflite interpreter I get the following output

RuntimeError: tensorflow/lite/kernels/ op_context.input->type != op_context.constant_values->type (INT8 != FLOAT32)Node number 3 (PADV2) failed to prepare.

I guess this problem is due to the fact that padding cannot fill with -inf an input that is int8, I would appreciate if you can give me some advice regarding this, how can I fill an int8 input to compare its values with -inf in a quantized way

Thank you in advance

==== Status 23.09.21 ====
After changing the constant values of -numpy.inf in the custom padding layer in favor of x.dtype.min:

    def call(self, x):
        paddings = [[0, 0]] + self._explicit_pad + [[0, 0]]
        return tf.pad(x, paddings=paddings, mode='CONSTANT',
                      constant_values= 0 if self.layer_type == 'conv' else x.dtype.min)

for pooling layers the tflite quantized model is still comparing and int8 input with a float → x.dtype.min and when calling tf.lite.Interpreter.allocate_tensors():

Adding @tei.jeong @Thai_Nguyen for the visibility.

A hacky solution for this problem of quantization implies using 'SYMMETRIC' padding instead of 'CONSTANT' in such a way the maxpooling layers during quantization take max(a, a) = a instead of max(-inf, a) = a. However this only works for the case of a single pixel added to the borders of the input, for other cases the problem persists