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Dequantize the 'input' tensor into a float or bfloat16 Tensor.
tf.quantization.dequantize(
input,
min_range,
max_range,
mode='MIN_COMBINED',
name=None,
axis=None,
narrow_range=False,
dtype=tf.dtypes.float32
)
[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<T>::max() - numeric_limits<T>::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<double>(input) - lowest_quantized;
result = range_min + ((input - numeric_limits<T>::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<T>::min() +
(narrow_range ? 1 : 0);
const int max_expected_T = std::numeric_limits<T>::max();
const float max_expected_T = std::numeric_limits<float>::max();
const float scale_factor =
(std::numeric_limits<T>::min() == 0) ? (max_range / max_expected_T)
: std::max(min_range / min_expected_T,
max_range / max_expected_T);
Args | |
---|---|
input
|
A Tensor . Must be one of the following types: qint8 , quint8 , qint32 , qint16 , quint16 .
|
min_range
|
A Tensor of type float32 .
The minimum scalar value possibly produced for the input.
|
max_range
|
A Tensor of type float32 .
The maximum scalar value possibly produced for the input.
|
mode
|
An optional string from: "MIN_COMBINED", "MIN_FIRST", "SCALED" . Defaults to "MIN_COMBINED" .
|
narrow_range
|
An optional bool . Defaults to False .
|
axis
|
An optional int . Defaults to -1 .
|
dtype
|
An optional tf.DType from: tf.bfloat16, tf.float32 . Defaults to tf.float32 .
Type of the output tensor. Currently Dequantize supports float and bfloat16.
If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type dtype .
|