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/TensorFlow C++ 2.3

tensorflow::ops::Dequantize

#include <array_ops.h>

Dequantize the 'input' tensor into a float or bfloat16 Tensor.

Summary

[min_range, max_range] are scalar floats that specify the range for the output. The 'mode' attribute controls exactly which calculations are used to convert the float values to their quantized equivalents.

In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:

if T == qint8: in[i] += (range(T) + 1)/ 2.0
out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
here

range(T) = numeric_limits::max() - numeric_limits::min()

MIN_COMBINED Mode Example

If the input comes from a QuantizedRelu6, the output type is quint8 (range of 0-255) but the possible range of QuantizedRelu6 is 0-6. The min_range and max_range values are therefore 0.0 and 6.0. Dequantize on quint8 will take each value, cast to float, and multiply by 6 / 255. Note that if quantizedtype is qint8, the operation will additionally add each value by 128 prior to casting.

If the mode is 'MIN_FIRST', then this approach is used:

num_discrete_values = 1 << (# of bits in T)
range_adjust = num_discrete_values / (num_discrete_values - 1)
range = (range_max - range_min) * range_adjust
range_scale = range / num_discrete_values
const double offset_input = static_cast(input) - lowest_quantized;
result = range_min + ((input - numeric_limits::min()) * range_scale)

If the mode is SCALED, dequantization is performed by multiplying each input value by a scaling_factor. (Thus an input of 0 always maps to 0.0).

The scaling_factor is determined from min_range, max_range, and narrow_range in a way that is compatible with QuantizeAndDequantize{V2|V3} and QuantizeV2, using the following algorithm:


    const int min_expected_T = std::numeric_limits::min() +
  (narrow_range ? 1 : 0);
const int max_expected_T = std::numeric_limits::max();
const float max_expected_T = std::numeric_limits::max();


    const float scale_factor =
  (std::numeric_limits::min() == 0) ? (max_range / max_expected_T)
                                       : std::max(min_range / min_expected_T,
                                                  max_range / max_expected_T);


Arguments:
  • scope: A Scope object
  • min_range: The minimum scalar value possibly produced for the input.
  • max_range: The maximum scalar value possibly produced for the input.

Optional attributes (see Attrs):

  • dtype: Type of the output tensor. Currently Dequantize supports float and bfloat16. If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode.

Returns:

Constructors and Destructors
Dequantize(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input min_range, ::tensorflow::Input max_range)
Dequantize(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input min_range, ::tensorflow::Input max_range, const Dequantize::Attrs & attrs)
Public attributes
operation
output
Public functions
node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const
Public static functions
Axis(int64 x)
Dtype(DataType x)
Mode(StringPiece x)
NarrowRange(bool x)
Structs
tensorflow::ops::Dequantize::Attrs

Optional attribute setters for Dequantize.

Public attributes

operation

Operation operation

output

::tensorflow::Output output

Public functions

Dequantize

 Dequantize(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input min_range,
  ::tensorflow::Input max_range
)

Dequantize

 Dequantize(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input min_range,
  ::tensorflow::Input max_range,
  const Dequantize::Attrs & attrs
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

operator::tensorflow::Input() const 

operator::tensorflow::Output

operator::tensorflow::Output() const 

Public static functions

Axis

Attrs Axis(
  int64 x
)

Dtype

Attrs Dtype(
  DataType x
)

Mode

Attrs Mode(
  StringPiece x
)

NarrowRange

Attrs NarrowRange(
  bool x
)

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/cc/class/tensorflow/ops/dequantize