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tensoreflusso:: ops:: Dequantizzare
#include <array_ops.h>
Dequantizza il tensore 'input' in un float o bfloat16 Tensor .
Riepilogo
[min_range, max_range] sono float scalari che specificano l'intervallo per l'output. L'attributo 'mode' controlla esattamente quali calcoli vengono utilizzati per convertire i valori float nei loro equivalenti quantizzati.
Nella modalità 'MIN_COMBINED', ciascun valore del tensore subirà quanto segue:
if T == qint8: in[i] += (range(T) + 1)/ 2.0
out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
qui
range(T) = numeric_limits ::max() - numeric_limits ::min()
range(T) = numeric_limits ::max() - numeric_limits ::min()
range(T) = numeric_limits ::max() - numeric_limits ::min()
Esempio di modalità MIN_COMBINED
Se l'input proviene da un QuantizedRelu6 , il tipo di output è quint8 (intervallo 0-255) ma il possibile intervallo di QuantizedRelu6 è 0-6. I valori min_range e max_range sono quindi 0,0 e 6,0. Dequantizza su quint8 prenderà ogni valore, lo convertirà in float e lo moltiplicherà per 6/255. Tieni presente che se quantizedtype è qint8, l'operazione aggiungerà inoltre ciascun valore per 128 prima dell'esecuzione del cast.
Se la modalità è "MIN_FIRST", viene utilizzato questo approccio:
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)
Se la modalità è SCALED
, la dequantizzazione viene eseguita moltiplicando ciascun valore di input per uno scaling_factor. (Pertanto un input pari a 0 corrisponde sempre a 0,0).
Lo scaling_factor è determinato da min_range
, max_range
e narrow_range
in modo compatibile con QuantizeAndDequantize{V2|V3}
e QuantizeV2
, utilizzando il seguente algoritmo:
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);
Argomenti:
- scope: un oggetto Scope
- min_range: il valore scalare minimo eventualmente prodotto per l'input.
- max_range: il valore scalare massimo possibilmente prodotto per l'input.
Attributi facoltativi (vedi Attrs
):
- dtype: tipo del tensore di uscita. Attualmente Dequantize supporta float e bfloat16. Se 'dtype' è 'bfloat16', supporta solo la modalità 'MIN_COMBINED'.
Resi:
-
Output
: il tensore di uscita.
Attributi pubblici
Funzioni pubbliche
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operatore::tensorflow::Output
operator::tensorflow::Output() const
Funzioni pubbliche statiche
Asse
Attrs Axis(
int64 x
)
Tipo D
Attrs Dtype(
DataType x
)
Modalità
Attrs Mode(
StringPiece x
)
Raggio ristretto
Attrs NarrowRange(
bool x
)
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-27 UTC.
[null,null,["Ultimo aggiornamento 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::Dequantize Class Reference\n\ntensorflow::ops::Dequantize\n===========================\n\n`#include \u003carray_ops.h\u003e`\n\n[Dequantize](/versions/r2.3/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_dequantize) the 'input' tensor into a float or bfloat16 [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor).\n\nSummary\n-------\n\n\\[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.\n\nIn 'MIN_COMBINED' mode, each value of the tensor will undergo the following:\n\n\u003cbr /\u003e\n\n```transact-sql\nif T == qint8: in[i] += (range(T) + 1)/ 2.0\nout[i] = min_range + (in[i]* (max_range - min_range) / range(T))\n```\nhere `range(T) = numeric_limits`::max() - numeric_limits::min()\n\n\u003cbr /\u003e\n\n\n*MIN_COMBINED Mode Example*\n\nIf the input comes from a [QuantizedRelu6](/versions/r2.3/api_docs/cc/class/tensorflow/ops/quantized-relu6#classtensorflow_1_1ops_1_1_quantized_relu6), the output type is quint8 (range of 0-255) but the possible range of [QuantizedRelu6](/versions/r2.3/api_docs/cc/class/tensorflow/ops/quantized-relu6#classtensorflow_1_1ops_1_1_quantized_relu6) is 0-6. The min_range and max_range values are therefore 0.0 and 6.0. [Dequantize](/versions/r2.3/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_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.\n\nIf the mode is 'MIN_FIRST', then this approach is used:\n\n\n```gdscript\nnum_discrete_values = 1 \u003c\u003c (# of bits in T)\nrange_adjust = num_discrete_values / (num_discrete_values - 1)\nrange = (range_max - range_min) * range_adjust\nrange_scale = range / num_discrete_values\nconst double offset_input = static_cast(input) - lowest_quantized;\nresult = range_min + ((input - numeric_limits::min()) * range_scale)\n```\n\n\u003cbr /\u003e\n\nIf 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).\n\nThe scaling_factor is determined from `min_range`, `max_range`, and `narrow_range` in a way that is compatible with `QuantizeAndDequantize{V2|V3}` and [QuantizeV2](/versions/r2.3/api_docs/cc/class/tensorflow/ops/quantize-v2#classtensorflow_1_1ops_1_1_quantize_v2), using the following algorithm:\n\n\n````gdscript\n \n \n const int min_expected_T = std::numeric_limits::min() +\n (narrow_range ? 1 : 0);\n const int max_expected_T = std::numeric_limits::max();\n const float max_expected_T = std::numeric_limits::max();\n \n \n \n```gdscript\n const float scale_factor =\n (std::numeric_limits::min() == 0) ? (max_range / max_expected_T)\n : std::max(min_range / min_expected_T,\n max_range / max_expected_T);\n```\n\n \n Arguments:\n \n- scope: A /versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope object\n\n \n- min_range: The minimum scalar value possibly produced for the input.\n\n \n- max_range: The maximum scalar value possibly produced for the input.\n\n \n\n Optional attributes (see /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs):\n \n- dtype: Type of the output tensor. Currently /versions/r2.3/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_dequantize supports float and bfloat16. If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode.\n\n \n Returns:\n \n- /versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: The output tensor. \n\n \n \n \n \n \n### Constructors and Destructors\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ace6411557abc00c6e59649720be7d579`(const ::`/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope` & scope, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` input, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` min_range, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` max_range)`\n \n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1afb71f46f9e4fc4922578ecd9116ad9b1`(const ::`/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope` & scope, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` input, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` min_range, ::`/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` max_range, const `/versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs` & attrs)`\n \n\n \n \n \n \n \n \n \n### Public attributes\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a917ce29fbec6ef49406db9a374bde9aa\n \n \n \n /versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a5c4618ae3d058bcd8547217612f8f41e\n \n \n \n `::`/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output\n \n \n \n \n \n \n \n \n### Public functions\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a4bdeb613e4b88880638a67528cbd01f0`() const `\n \n \n \n `::tensorflow::Node *`\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ab1b62ee39a382d6e124eb62156c05525`() const `\n \n \n \n `\n `\n`\n `\n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ae01ee2df9b62f7729848ca15ed70e8fc`() const `\n \n \n \n `\n `\n`\n `\n \n \n \n \n \n \n### Public static functions\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ac0b7d9ea267e2c8719f63ff4434b5250`(int64 x)`\n \n \n \n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1aeb2c0e323cdc6f85554c6e03de751730`(DataType x)`\n \n \n \n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ac9873b34c5c0eb36296e0fe726644fc9`(StringPiece x)`\n \n \n \n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a4409107547aae6b42715813687850b35`(bool x)`\n \n \n \n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n \n \n \n### Structs\n\n\n \n \n \n \n /versions/r2.3/api_docs/cc/struct/tensorflow/ops/dequantize/attrs\n \n \n Optional attribute setters for /versions/r2.3/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_dequantize. \n\n \n \n \n Public attributes\n \n \n### operation\n\n\n \n\n\n```text\nOperation operation\n```\n\n \n\n \n \n \n### output\n\n\n \n\n\n```text\n::tensorflow::Output output\n```\n\n \n\n \n Public functions\n \n \n### Dequantize\n\n\n \n\n\n```gdscript\n Dequantize(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input min_range,\n ::tensorflow::Input max_range\n)\n```\n\n \n\n \n \n \n### Dequantize\n\n\n \n\n\n```gdscript\n Dequantize(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input min_range,\n ::tensorflow::Input max_range,\n const Dequantize::Attrs & attrs\n)\n```\n\n \n\n \n \n \n### node\n\n\n \n\n\n```gdscript\n::tensorflow::Node * node() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Input\n\n\n \n\n\n```gdscript\n operator::tensorflow::Input() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Output\n\n\n \n\n\n```gdscript\n operator::tensorflow::Output() const \n```\n\n \n\n \n Public static functions\n \n \n### Axis\n\n\n \n\n\n```text\nAttrs Axis(\n int64 x\n)\n```\n\n \n\n \n \n \n### Dtype\n\n\n \n\n\n```carbon\nAttrs Dtype(\n DataType x\n)\n```\n\n \n\n \n \n \n### Mode\n\n\n \n\n\n```text\nAttrs Mode(\n StringPiece x\n)\n```\n\n \n\n \n \n \n### NarrowRange\n\n\n \n\n\n```text\nAttrs NarrowRange(\n bool x\n)\n```\n\n \n\n \n\n \n\n \n````"]]