COMPMID-846: Create a ConvolutionLayer for NEON
Change-Id: I98bbef40bfac5b05134be4ef9fb54d14c0c9e8e8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118806
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/runtime/NEON/NEFunctions.h b/arm_compute/runtime/NEON/NEFunctions.h
index 077cf57..1531377e 100644
--- a/arm_compute/runtime/NEON/NEFunctions.h
+++ b/arm_compute/runtime/NEON/NEFunctions.h
@@ -60,6 +60,7 @@
#include "arm_compute/runtime/NEON/functions/NEFloor.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMInterleave4x4.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpAssemblyMatrixMultiplyCore.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
index f80f67d..6ab1350 100644
--- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -26,79 +26,27 @@
#include "arm_compute/runtime/IFunction.h"
-#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
-#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
-#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
-#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/MemoryGroup.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
-#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
-#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h"
#include <memory>
namespace arm_compute
{
class ITensor;
-/** Function to reshape and perform 1xW transposition on the weights. This function calls the following kernels:
- * -# @ref NEWeightsReshapeKernel
- * -# @ref NEGEMMTranspose1xWKernel (executed in case GEMM is required for the operation)
- */
-class NEConvolutionLayerReshapeWeights : public IFunction
-{
-public:
- /** Constructor */
- NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
- /** Set the input and output tensors.
- *
- * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F32.
- * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
- * @param[out] output Destination tensor. Data types supported: Same as @p weights.
- * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
- * Data types supported: Same as @p weights.
- */
- void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
- /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
- *
- * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F16/F32.
- * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
- * @param[in] output Destination tensor. Data types supported: Same as @p weights.
- * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
- * Data types supported: Same as @p weights.
- *
- * @return an error status
- */
- static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW);
-
- // Inherited methods overridden:
- void run() override;
-
-private:
- MemoryGroup _memory_group;
- NEWeightsReshapeKernel _weights_reshape_kernel;
- NEGEMMTranspose1xWKernel _weights_transposed_kernel;
- Tensor _weights_reshaped;
- bool _transpose1xW;
-};
-
-/** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
- * -# @ref NEWeightsReshapeKernel (executed only once for each configuration)
- * -# @ref NEIm2ColKernel
- * -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
- * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
- * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
- * -# @ref NECol2ImKernel
+/** Basic function to simulate a convolution layer. This function calls one of the following NEON functions:
+ * -# @ref NEGEMMConvolutionLayer (executed only in case GEMM is required for the operation)
+ * -# @ref NEWinogradLayer (executed only in case Winograd is required for the operation)
+ * -# @ref NEDirectConvolutionLayer (executed only in case Direct Convolution is required for the operation)
*/
class NEConvolutionLayer : public IFunction
{
public:
/** Constructor */
- NEConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
+ NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Set the input and output tensors.
*
@@ -114,7 +62,7 @@
* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
* tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
*/
- void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo());
+ void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo());
/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayer
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
@@ -133,51 +81,31 @@
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo());
+ /** Static function to check if given info will return the convolution called by @ref NEConvolutionLayer
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+ * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
+ * tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
+ *
+ * @return the Convolution Method Hint
+ */
+ static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info = WeightsInfo());
// Inherited methods overridden:
void run() override;
private:
- /** Configures the appropriate matrix multiply routine
- *
- * @param[in] input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32.
- * @param[in] weights Weights tensor. Data type supported: Same as @p input.
- * @param[out] output Output tensor. Data types supported: Same as @p input,
- * except for input of QASYMM8 type where output should be of S32 type.
- */
- void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output);
- /** Prepare the appropriate assembly optimized kernel
- *
- * @param[in] ci CPU information
- * @param[in] M M parameter of matrix multiplication
- * @param[in] N N parameter of matrix multiplication
- * @param[in] K K parameter of matrix multiplication
- */
- void configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K);
-
-private:
- MemoryGroup _memory_group;
- NEIm2ColKernel _input_im2col_kernel;
- NEGEMMInterleave4x4Kernel _input_interleave_kernel;
- NEConvolutionLayerReshapeWeights _reshape_weights;
- NEGEMMMatrixMultiplyKernel _mm_kernel;
- std::unique_ptr<NEGEMMAssemblyBaseKernel> _mm_optimised_kernel;
- NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
- NECol2ImKernel _output_col2im_kernel;
-
- Tensor _input_im2col_reshaped;
- Tensor _input_interleaved_reshaped;
- Tensor _weights_reshaped;
- Tensor _gemm_output;
- Tensor _tmp_output;
- Tensor _workspace;
-
- bool _append_bias;
- bool _is_fully_connected_convolution;
- bool _are_weights_reshaped;
- bool _is_quantized;
- bool _is_interleaved_transposed;
+ std::shared_ptr<IMemoryManager> _memory_manager;
+ std::unique_ptr<IFunction> _function; /**< Function to run */
};
}
#endif /* __ARM_COMPUTE_NECONVOLUTIONLAYER_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
new file mode 100644
index 0000000..c3c7f82
--- /dev/null
+++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2017-2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H__
+#define __ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H__
+
+#include "arm_compute/runtime/IFunction.h"
+
+#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
+#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
+#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
+#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/MemoryGroup.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
+#include "arm_compute/runtime/Tensor.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+class ITensor;
+
+/** Function to reshape and perform 1xW transposition on the weights. This function calls the following kernels:
+ * -# @ref NEWeightsReshapeKernel
+ * -# @ref NEGEMMTranspose1xWKernel (executed in case GEMM is required for the operation)
+ */
+class NEConvolutionLayerReshapeWeights : public IFunction
+{
+public:
+ /** Constructor */
+ NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ /** Set the input and output tensors.
+ *
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F32.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[out] output Destination tensor. Data types supported: Same as @p weights.
+ * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
+ * Data types supported: Same as @p weights.
+ */
+ void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
+ /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
+ *
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[in] output Destination tensor. Data types supported: Same as @p weights.
+ * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
+ * Data types supported: Same as @p weights.
+ *
+ * @return an error status
+ */
+ static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW);
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ MemoryGroup _memory_group;
+ NEWeightsReshapeKernel _weights_reshape_kernel;
+ NEGEMMTranspose1xWKernel _weights_transposed_kernel;
+ Tensor _weights_reshaped;
+ bool _transpose1xW;
+};
+
+/** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
+ * -# @ref NEWeightsReshapeKernel (executed only once for each configuration)
+ * -# @ref NEIm2ColKernel
+ * -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
+ * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
+ * -# @ref NECol2ImKernel
+ */
+class NEGEMMConvolutionLayer : public IFunction
+{
+public:
+ /** Constructor */
+ NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
+
+ /** Set the input and output tensors.
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: QS8/QASYMM8/QS16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+ * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
+ * tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
+ */
+ void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
+ * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
+ * tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info = WeightsInfo());
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ /** Configures the appropriate matrix multiply routine
+ *
+ * @param[in] input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Data type supported: Same as @p input.
+ * @param[out] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ */
+ void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output);
+ /** Prepare the appropriate assembly optimized kernel
+ *
+ * @param[in] ci CPU information
+ * @param[in] M M parameter of matrix multiplication
+ * @param[in] N N parameter of matrix multiplication
+ * @param[in] K K parameter of matrix multiplication
+ */
+ void configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K);
+
+private:
+ MemoryGroup _memory_group;
+ NEIm2ColKernel _input_im2col_kernel;
+ NEGEMMInterleave4x4Kernel _input_interleave_kernel;
+ NEConvolutionLayerReshapeWeights _reshape_weights;
+ NEGEMMMatrixMultiplyKernel _mm_kernel;
+ std::unique_ptr<NEGEMMAssemblyBaseKernel> _mm_optimised_kernel;
+ NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
+ NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
+ NECol2ImKernel _output_col2im_kernel;
+
+ Tensor _input_im2col_reshaped;
+ Tensor _input_interleaved_reshaped;
+ Tensor _weights_reshaped;
+ Tensor _gemm_output;
+ Tensor _tmp_output;
+ Tensor _workspace;
+
+ bool _append_bias;
+ bool _is_fully_connected_convolution;
+ bool _are_weights_reshaped;
+ bool _is_quantized;
+ bool _is_interleaved_transposed;
+};
+}
+#endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEWinogradLayer.h b/arm_compute/runtime/NEON/functions/NEWinogradLayer.h
index f57be69..a939f82 100644
--- a/arm_compute/runtime/NEON/functions/NEWinogradLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEWinogradLayer.h
@@ -67,6 +67,22 @@
// Inherited methods overridden:
void run() override;
+ /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+ * while every optional dimension from 4 and above represent a batch of inputs.
+ * Data types supported: F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
+ * Currently only 3x3 kernels are supported.
+ * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * Data types supported: Same as @p input.
+ * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info);
+
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradLayer(const NEWinogradLayer &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */