COMPMID-3687: Remove deprecated functions in 20.05 release
Change-Id: I90e09e460b5d5d4f9ead8e3905833c5da3b9fbd6
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3762
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
index 8f7a7c3..c6e9588 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
@@ -38,76 +38,6 @@
{
class ITensor;
-/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8Scale on OpenCL.
- *
- * CLGEMMLowpQuantizeDownInt32ToUint8Scale depends on 3 parameters: result_offset, result_mult_int, result_shift
- * The final result is:
- *
- * ((input[i][k] + result_offset) * result_mult_int) >> result_shift
- *
- * In case the bias tensor is provided, the final result is:
- *
- * ((input[i][k] + bias[k] + result_offset) * result_mult_int) >> result_shift
- *
- * This function calls the following OpenCL kernels:
- *
- * -# @ref CLGEMMLowpQuantizeDownInt32ScaleKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class CLGEMMLowpQuantizeDownInt32ToUint8Scale : public ICLSimpleFunction
-{
-public:
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: QASYMM8
- * @param[in] result_offset Offset to be added to each element of the input matrix
- * @param[in] result_mult_int Value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result before converting back to QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min = std::numeric_limits<int32_t>::lowest(),
- int max = std::numeric_limits<int32_t>::max());
- /** Initialise the kernel's inputs, output
- *
- * @param[in] compile_context The compile context to be used.
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: QASYMM8
- * @param[in] result_offset Offset to be added to each element of the input matrix
- * @param[in] result_mult_int Value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result before converting back to QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift,
- int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
- /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8Scale
- *
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- *
- * @return a status
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
-};
-
/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint on OpenCL.
*
* CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint depends on 3 parameters:
@@ -264,65 +194,6 @@
static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
};
-/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat on OpenCL.
- *
- * This function calls the following OpenCL kernels:
- *
- * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat : public ICLSimpleFunction
-{
-public:
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: QASYMM8
- * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix
- * @param[in] offset Offset to be applied to result before converting it back to QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = std::numeric_limits<int32_t>::lowest(),
- int max = std::numeric_limits<int32_t>::max());
- /** Initialise the kernel's inputs, output
- *
- * @param[in] compile_context The compile context to be used.
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: QASYMM8
- * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix
- * @param[in] offset Offset to be applied to result before converting it back to QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset,
- int min = std::numeric_limits<int32_t>::lowest(),
- int max = std::numeric_limits<int32_t>::max());
- /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- *
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- *
- * @return a status
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
-};
/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint on OpenCL.
*
* CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint depends on 2 parameters:
@@ -442,4 +313,4 @@
static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info);
};
} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H */
\ No newline at end of file
+#endif /*ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H */
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
index 486b9c6..f29d5d4 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
@@ -38,59 +38,6 @@
{
class ITensor;
-/** Basic function to execute NEGEMMLowpQuantizeDownInt32ToUint8Scale on NEON.
- *
- * NEGEMMLowpQuantizeDownInt32ToUint8Scale depends on 3 parameters: result_offset, result_mult_int, result_shift
- * The final result is:
- *
- * ((input[i][k] + result_offset) * result_mult_int) >> result_shift
- *
- * In case the bias tensor is provided, the final result is:
- *
- * ((input[i][k] + bias[k] + result_offset) * result_mult_int) >> result_shift
- *
- * This function calls the following NEON kernels:
- *
- * -# @ref NEGEMMLowpQuantizeDownInt32ScaleKernel
- *
- * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
- * after the result is shifted right by result_shift
-*/
-class NEGEMMLowpQuantizeDownInt32ToUint8Scale : public INESimpleFunctionNoBorder
-{
-public:
- /** Initialise the kernel's inputs, output
- *
- * @param[in] input Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8
- * @param[in] result_offset Offset to be added to each element of the input matrix
- * @param[in] result_mult_int Value to be multiplied to each element of the input matrix when once the result_offset has been add
- * @param[in] result_shift Number of bits to shift right the result before converting back to QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min = std::numeric_limits<int32_t>::lowest(),
- int max = std::numeric_limits<int32_t>::max());
- /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale
- *
- * @param[in] input Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
- * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
- *
- * @return a status
- */
- ARM_COMPUTE_DEPRECATED_REL(20.05)
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
-};
-
/** Basic function to execute NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint on NEON.
*
* NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint depends on 3 parameters:
diff --git a/docs/00_introduction.dox b/docs/00_introduction.dox
index c281cb2..35fff36 100644
--- a/docs/00_introduction.dox
+++ b/docs/00_introduction.dox
@@ -257,6 +257,11 @@
- graph_yolov3_output_detector
- Removed padding from:
- @ref NEPixelWiseMultiplicationKernel
+ - Removedd OpenCL kernels / functions:
+ - CLGEMMLowpQuantizeDownInt32ToUint8Scale
+ - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat
+ - Removed NEON kernels / functions:
+ - NEGEMMLowpQuantizeDownInt32ToUint8Scale
- GEMMTuner improvements:
- Added fp16 support
- Output json files for easier integration
diff --git a/docs/06_functions_list.dox b/docs/06_functions_list.dox
index 4a7c9fa..99eab12 100644
--- a/docs/06_functions_list.dox
+++ b/docs/06_functions_list.dox
@@ -91,7 +91,6 @@
- @ref NEFullyConnectedLayerReshapeWeights
- @ref NEGather
- @ref NEGEMMInterleave4x4
- - @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale
- @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- @ref NEGEMMTranspose1xW
@@ -297,10 +296,8 @@
- @ref CLFullyConnectedLayerReshapeWeights
- @ref CLGather
- @ref CLGaussian3x3
- - @ref CLGEMMLowpQuantizeDownInt32ToUint8Scale
- @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
- @ref CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint
- - @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat
- @ref CLMagnitude
- @ref CLMeanStdDevNormalizationLayer
- @ref CLMedian3x3
diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
index d27b764..a499e18 100644
--- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
@@ -33,45 +33,6 @@
namespace arm_compute
{
-void CLGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_offset = result_offset;
- info.gemmlowp_multiplier = result_mult_int;
- info.gemmlowp_shift = result_shift;
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
- k->configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, &info);
- _kernel = std::move(k);
-}
-
-void CLGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset,
- int result_mult_int,
- int result_shift, int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_offset = result_offset;
- info.gemmlowp_multiplier = result_mult_int;
- info.gemmlowp_shift = result_shift;
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
- k->configure(compile_context, input, bias, output, &info);
- _kernel = std::move(k);
-}
-
-Status CLGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
-}
-
void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
int min, int max)
@@ -118,45 +79,6 @@
return CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, min, max);
}
-void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
- float multiplier, int offset,
- int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_offset = offset;
- info.gemmlowp_real_multiplier = multiplier;
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel>();
- k->configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, &info);
- _kernel = std::move(k);
-}
-
-void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
- float multiplier, int offset,
- int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_offset = offset;
- info.gemmlowp_real_multiplier = multiplier;
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel>();
- k->configure(compile_context, input, bias, output, &info);
- _kernel = std::move(k);
-}
-
-Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
- int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
- return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info);
-}
-
void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
int result_fixedpoint_multiplier, int result_shift,
int min, int max)
@@ -268,4 +190,4 @@
return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
}
}
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index ed9b449..239a8e6 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -33,29 +33,6 @@
namespace arm_compute
{
-void NEGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_offset = result_offset;
- info.gemmlowp_multiplier = result_mult_int;
- info.gemmlowp_shift = result_shift;
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
- k->configure(input, bias, output, &info);
- _kernel = std::move(k);
-}
-
-Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
- GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
- info.gemmlowp_min_bound = min;
- info.gemmlowp_max_bound = max;
-
- return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
-}
-
void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
int result_offset_after_shift, int min, int max)
{