pytorch_quantization.utils

pytorch_quantization.utils.amp_wrapper

pytorch_quantization.utils.quant_logging

A WAR for codes that messes up logging format

pytorch_quantization.utils.quant_logging.reset_logger_handler()[source]

Remove all handler in root logger

pytorch_quantization.utils.reduce_amax

Function to get absolute maximum of a tensor Follow numpy fashion, which is more generic as pytorch’s

pytorch_quantization.utils.reduce_amax.reduce_amax(input, axis=None, keepdims=True)[source]

Compute the absolute maximum value of a tensor.

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

Note

Gradient computeation is disabled as this function is never meant learning reduces amax

Parameters

  • input – Input tensor

  • axis – The dimensions to reduce. None or int or tuple of ints. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).

  • keepdims – A boolean. If true, retains reduced dimensions with length 1. Default True

  • granularity – DEPRECTED. specifies if the statistic has to be calculated at tensor or channel granularity

Returns

The reduced tensor.

Raises

  • ValueError – Any axis which doesn’t make sense or is not supported

  • ValueError – If unknown granularity is passed in.