COMPMID-417: Add in-place support for batch-normalization.
Change-Id: I4b0c9348f3bc2addc198a76fadd1b583abf42b60
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/84434
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h
index 6df7ae4..add1dfb 100644
--- a/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h
+++ b/arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h
@@ -50,22 +50,25 @@
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM]. Data types supported: QS8/QS16/F32.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
- * The rest are optional and used for representing batches.
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] epsilon Small value to avoid division with zero.
+ * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place
+ *
+ * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
*/
- void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon);
+ void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
private:
- const ICLTensor *_input;
+ ICLTensor *_input;
ICLTensor *_output;
const ICLTensor *_mean;
const ICLTensor *_var;
diff --git a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
index 29fcbd2..8ac70be 100644
--- a/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h
@@ -49,24 +49,27 @@
~NEBatchNormalizationLayerKernel() = default;
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM].
- * The rest are optional and used for representing batches. Data types supported: QS8/F32.
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] epsilon Small value to avoid division with zero.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
+ * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place
+ *
+ * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
*/
- void configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
+ void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
// Inherited methods overridden:
void run(const Window &window) override;
private:
- using BatchNormFunction = void(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window);
+ using BatchNormFunction = void(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window);
BatchNormFunction *_func;
- const ITensor *_input;
+ ITensor *_input;
ITensor *_output;
const ITensor *_mean;
const ITensor *_var;
diff --git a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h
index 882786f..ffb66be 100644
--- a/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h
+++ b/arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h
@@ -46,16 +46,19 @@
CLBatchNormalizationLayer();
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM].
- * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F32.
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] epsilon Small value to avoid division with zero.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
+ * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place
+ *
+ * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
*/
- void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon);
+ void configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon);
// Inherited methods overridden:
void run() override;
diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
index aba88bd..2057b6f 100644
--- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
@@ -34,8 +34,6 @@
#include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
#include "arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h"
#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTensor.h"
namespace arm_compute
diff --git a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
index b0b5c12..041b9e7 100644
--- a/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h
@@ -45,16 +45,19 @@
NEBatchNormalizationLayer();
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM].
- * The rest are optional and used for representing batches. Data types supported: QS8/F32.
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] epsilon Small value to avoid division with zero.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
+ * @note If the output tensor is a nullptr, the batch normalization function will be performed in-place
+ *
+ * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will store the result.
+ * 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * The rest are optional and used for representing batches. Data types supported: QS8/QS16/F16/F32.
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] gamma Gamma values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] beta Beta values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
+ * @param[in] epsilon Small value to avoid division with zero.
+ * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
*/
- void configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
+ void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon);
// Inherited methods overridden:
void run() override;
diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl
index cb4d0c8..904d5b3 100644
--- a/src/core/CL/cl_kernels/batchnormalization_layer.cl
+++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl
@@ -80,19 +80,25 @@
* @param[in] epsilon Epsilon parameter in the batch normalization equation
*/
__kernel void batchnormalization_layer(TENSOR3D_DECLARATION(input),
+#ifndef IN_PLACE
TENSOR3D_DECLARATION(output),
+#endif /* not IN_PLACE */
VECTOR_DECLARATION(mean),
VECTOR_DECLARATION(var),
VECTOR_DECLARATION(beta),
VECTOR_DECLARATION(gamma),
float epsilon)
{
- Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input);
- Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
- Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
- Vector var = CONVERT_TO_VECTOR_STRUCT(var);
- Vector beta = CONVERT_TO_VECTOR_STRUCT(beta);
- Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma);
+ Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input);
+#ifdef IN_PLACE
+ Tensor3D out = in;
+#else /* IN_PLACE */
+ Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
+#endif /* IN_PLACE */
+ Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
+ Vector var = CONVERT_TO_VECTOR_STRUCT(var);
+ Vector beta = CONVERT_TO_VECTOR_STRUCT(beta);
+ Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma);
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
_in = 0;
diff --git a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
index 02bf35a..18c0c97 100644
--- a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
@@ -42,20 +42,10 @@
{
}
-void CLBatchNormalizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma,
+void CLBatchNormalizationLayerKernel::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma,
float epsilon)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
- // Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
-
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
_input = input;
_output = output;
@@ -65,12 +55,31 @@
_gamma = gamma;
_epsilon = epsilon;
+ if(output != nullptr)
+ {
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, mean, var, beta, gamma);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var, beta, gamma);
+ }
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
+
const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
// Set build options
std::set<std::string> build_opts;
build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
build_opts.emplace(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
+ build_opts.emplace(output == nullptr ? "-DIN_PLACE" : "");
if(is_data_type_fixed_point(input->info()->data_type()))
{
build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
@@ -84,14 +93,18 @@
_kernel.setArg<cl_float>(idx++, _epsilon);
// Configure kernel window
- Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
-
+ Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
-
- update_window_and_padding(win, input_access, output_access);
- output_access.set_valid_region(win, input->info()->valid_region());
-
+ if(output != nullptr)
+ {
+ AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+ update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, input->info()->valid_region());
+ }
+ else
+ {
+ update_window_and_padding(win, input_access);
+ }
ICLKernel::configure(win);
}
@@ -115,7 +128,10 @@
{
idx = 0;
add_3D_tensor_argument(idx, _input, slice);
- add_3D_tensor_argument(idx, _output, slice);
+ if(_output != nullptr)
+ {
+ add_3D_tensor_argument(idx, _output, slice);
+ }
enqueue(queue, *this, slice);
}
while(window.slide_window_slice_3D(slice));
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
index 290a3c5..66f174e 100644
--- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -38,7 +38,7 @@
{
}
-void batch_normalization_q8(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+void batch_normalization_q8(ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
@@ -82,7 +82,7 @@
input, output);
}
-void batch_normalization_q16(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+void batch_normalization_q16(ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
@@ -126,7 +126,7 @@
input, output);
}
-void batch_normalization_fp32(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+void batch_normalization_fp32(ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
@@ -170,7 +170,7 @@
}
#ifdef ARM_COMPUTE_ENABLE_FP16
-void batch_normalization_fp16(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+void batch_normalization_fp16(ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
@@ -214,28 +214,33 @@
}
#endif /* ARM_COMPUTE_ENABLE_FP16 */
-void NEBatchNormalizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
+void NEBatchNormalizationLayerKernel::configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
- // Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
-
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
_input = input;
- _output = output;
+ _output = input;
_mean = mean;
_var = var;
_gamma = gamma;
_beta = beta;
_epsilon = epsilon;
+ if(output != nullptr)
+ {
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+
+ _output = output;
+ }
+
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var, beta, gamma);
+ ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
+
unsigned int num_elems_processed_per_iteration = 0;
switch(input->info()->data_type())
@@ -263,15 +268,18 @@
break;
}
- Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
-
+ Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
-
- update_window_and_padding(win, input_access, output_access);
-
- output_access.set_valid_region(win, input->info()->valid_region());
-
+ if(output != nullptr)
+ {
+ AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+ update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, input->info()->valid_region());
+ }
+ else
+ {
+ update_window_and_padding(win, input_access);
+ }
INEKernel::configure(win);
}
diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
index 176e3d6..4fed16b 100644
--- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
+++ b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
@@ -26,7 +26,6 @@
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/NEON/NEMath.h"
diff --git a/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp b/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
index 3df673c..68cdaac 100644
--- a/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
+++ b/src/runtime/CL/functions/CLBatchNormalizationLayer.cpp
@@ -37,7 +37,7 @@
{
}
-void CLBatchNormalizationLayer::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon)
+void CLBatchNormalizationLayer::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *var, const ICLTensor *beta, const ICLTensor *gamma, float epsilon)
{
_norm_kernel.configure(input, output, mean, var, beta, gamma, epsilon);
}
diff --git a/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp b/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
index a24429c..ef79b02 100644
--- a/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
+++ b/src/runtime/NEON/functions/NEBatchNormalizationLayer.cpp
@@ -37,7 +37,7 @@
{
}
-void NEBatchNormalizationLayer::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
+void NEBatchNormalizationLayer::configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
{
// Configure kernel
_norm_kernel.configure(input, output, mean, var, beta, gamma, epsilon);