From 6e1791b1bfabc81f08d3117939f6eb5264ed4edf Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 2 Dec 2019 19:01:25 +0000 Subject: COMPMID-2764: Add support for QASYMM8_SIGNED in NEConvolutionLayer. Change-Id: I8fbbd2e399f48968337a60147098d04f27c2d1c0 Signed-off-by: Georgios Pinitas Reviewed-on: https://review.mlplatform.org/c/2402 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- arm_compute/core/NEON/kernels/NECol2ImKernel.h | 6 +- ...NEGEMMLowpOffsetContributionOutputStageKernel.h | 7 +- .../core/NEON/kernels/NEWeightsReshapeKernel.h | 6 +- arm_compute/core/Utils.h | 83 ++++++++++++++++++++++ arm_compute/runtime/NEON/functions/NECol2Im.h | 6 +- .../runtime/NEON/functions/NEConvolutionLayer.h | 12 ++-- .../NEON/functions/NEGEMMConvolutionLayer.h | 54 +++++++------- 7 files changed, 130 insertions(+), 44 deletions(-) (limited to 'arm_compute') diff --git a/arm_compute/core/NEON/kernels/NECol2ImKernel.h b/arm_compute/core/NEON/kernels/NECol2ImKernel.h index f02858e7d9..9858d4fd56 100644 --- a/arm_compute/core/NEON/kernels/NECol2ImKernel.h +++ b/arm_compute/core/NEON/kernels/NECol2ImKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -72,7 +72,7 @@ public: /** Set the input and output of the kernel. * - * @param[in] input The input tensor to convert. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input The input tensor to convert. Data types supported: Any * @param[out] output The output 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] convolved_dims Output convolved dimensions. @@ -80,7 +80,7 @@ public: void configure(const ITensor *input, ITensor *output, const Size2D &convolved_dims); /** Static function to check if given info will lead to a valid configuration of @ref NECol2ImKernel * - * @param[in] input The input tensor to convert. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input The input tensor to convert. Data types supported: Any * @param[in] output The output 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] convolved_dims Output convolved dimensions. diff --git a/arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h b/arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h index dadc5c221b..ac17b2efa5 100644 --- a/arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h +++ b/arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h @@ -37,13 +37,14 @@ class ITensor; * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), * and adds to it the offset contribution of matrix A and matrix B in-place. * - * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint. + * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8. + * The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8. * - * For QuantizeDownInt32ToUint8Scale the final result is: + * For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is: * * ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift * - * For QuantizeDownInt32ToUint8ScaleByFixedPoint the final result is: + * For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is: * * (FixedPointMul(mm_result'[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift * diff --git a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h index 585c707bb6..d432b731c2 100644 --- a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h +++ b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h @@ -75,7 +75,8 @@ public: /** Set the input and output of the kernel. * * @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared, - * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QASYMM8/QSYMM8_PER_CHANNEL/FP16/F32 + * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. + * Data types supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/FP16/F32 * @param[in] bias The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with * dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input * @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types. @@ -85,7 +86,8 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref NEWeightsReshapeKernel * * @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared, - * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32 + * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. + * Data types supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32 * @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with * dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input * @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types. diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h index 366d5dcc68..590e281bb0 100644 --- a/arm_compute/core/Utils.h +++ b/arm_compute/core/Utils.h @@ -549,6 +549,72 @@ inline DataType get_promoted_data_type(DataType dt) return DataType::UNKNOWN; } +/** Compute the mininum and maximum values a data type can take + * + * @param[in] dt Data type to get the min/max bounds of + * + * @return A tuple (min,max) with the minimum and maximum values respectively wrapped in PixelValue. + */ +inline std::tuple get_min_max(DataType dt) +{ + PixelValue min(0); + PixelValue max(0); + switch(dt) + { + case DataType::U8: + case DataType::QASYMM8: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::S8: + case DataType::QSYMM8: + case DataType::QASYMM8_SIGNED: + case DataType::QSYMM8_PER_CHANNEL: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::U16: + case DataType::QASYMM16: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::S16: + case DataType::QSYMM16: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::U32: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::S32: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + case DataType::F32: + { + min = PixelValue(std::numeric_limits::lowest()); + max = PixelValue(std::numeric_limits::max()); + break; + } + default: + ARM_COMPUTE_ERROR("Undefined data type!"); + } + return std::make_tuple(min, max); +} + /** Return true if the given format has horizontal subsampling. * * @param[in] format Format to determine subsampling. @@ -1054,6 +1120,23 @@ inline bool is_data_type_quantized_asymmetric(DataType dt) } } +/** Check if a given data type is of asymmetric quantized signed type + * + * @param[in] dt Input data type. + * + * @return True if data type is of asymmetric quantized signed type, else false. + */ +inline bool is_data_type_quantized_asymmetric_signed(DataType dt) +{ + switch(dt) + { + case DataType::QASYMM8_SIGNED: + return true; + default: + return false; + } +} + /** Check if a given data type is of symmetric quantized type * * @param[in] dt Input data type. diff --git a/arm_compute/runtime/NEON/functions/NECol2Im.h b/arm_compute/runtime/NEON/functions/NECol2Im.h index 64ce9944e2..613507cf6a 100644 --- a/arm_compute/runtime/NEON/functions/NECol2Im.h +++ b/arm_compute/runtime/NEON/functions/NECol2Im.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -39,7 +39,7 @@ class NECol2Im : public INESimpleFunctionNoBorder public: /** Configure the col2im NEON kernel * - * @param[in] input The input tensor to convert. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input The input tensor to convert. Data types supported: Any * @param[out] output The output 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] convolved_dims Output convolved dimensions. @@ -47,7 +47,7 @@ public: void configure(const ITensor *input, ITensor *output, const Size2D &convolved_dims); /** Static function to check if given info will lead to a valid configuration of @ref NECol2Im * - * @param[in] input The input tensor to convert. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input The input tensor to convert. Data types supported: Any * @param[in] output The output 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] convolved_dims Output convolved dimensions. diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h index 4310ab4b41..91fcef5971 100644 --- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h @@ -80,10 +80,10 @@ public: * * @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: QASYMM8/F16/F32. + * Data types supported: QASYMM8/QASYMM8_SIGNED/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. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED 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. @@ -101,10 +101,10 @@ public: * * @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: QASYMM8/F16/F32. + * Data types supported: QASYMM8/QASYMM8_SIGNED/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. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED 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. @@ -125,7 +125,7 @@ public: * * @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: QASYMM8/F16/F32. + * Data types supported: QASYMM8/QASYMM8_SIGNED/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] 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. @@ -149,5 +149,5 @@ private: std::shared_ptr _memory_manager; std::unique_ptr _function; /**< Function to run */ }; -} +} // namespace arm_compute #endif /* __ARM_COMPUTE_NECONVOLUTIONLAYER_H__ */ diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h index 6452fc9249..c513afa790 100644 --- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h @@ -63,16 +63,16 @@ public: NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = default; /** 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: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/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[out] output Destination tensor. Data types supported: Same as @p weights. */ void configure(const ITensor *weights, const ITensor *biases, ITensor *output); /** 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: QASYMM8/QSYMM8_PER_CHANNEL/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] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. + * @param[in] output Destination tensor info. Data types supported: Same as @p weights. * * @return an error status */ @@ -135,8 +135,8 @@ private: * * -# @ref NEIm2ColKernel * -# @ref NEGEMM (if the data type is FP32 or FP16) - * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8) - * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8) + * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED) + * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED) * -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout) * -# @ref NECol2ImKernel (if NCHW data layout) * @@ -158,10 +158,10 @@ public: * * @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: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. + * Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. * @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. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED 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. @@ -175,13 +175,13 @@ public: const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1); /** 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], + * @param[in] input Source tensor info. 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: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. - * @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: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. + * @param[in] output Destination tensor info. 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 @@ -202,24 +202,24 @@ public: private: /** Configures the appropriate matrix multiply routine * - * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. + * @param[in] input Input tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] weights Weights tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. * @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. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. * @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. + * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) */ void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1); /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines * - * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/F16/F32. - * @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 Output tensor. Data types supported: Same as @p input, - * except for input of QASYMM8 type where output should be of S32 type. + * @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] weights Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. + * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. + * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. + * @param[in] output Output tensor info. Data types supported: Same as @p input, + * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false) @@ -230,8 +230,8 @@ private: int gemm_3d_depth = 1, bool skip_im2col = false); /** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore * - * @param[in] input_info Input tensor info. Data types supported: QASYMM8/F16/F32. - * @param[in] weights_info Weights tensor info. Data types supported: QASYMM8/F16/F32. + * @param[in] input_info Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. + * @param[in] weights_info Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. * @param[in] act_info Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * @param[in] gemm_3d_depth Depth of GEMM 3D * @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout -- cgit v1.2.1