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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 . |
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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/python/tf/quantization/dequantize