COMPMID-2764: Add support for QASYMM8_SIGNED in NEConvolutionLayer.

Change-Id: I8fbbd2e399f48968337a60147098d04f27c2d1c0
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2402
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

3 files changed, 11 insertions, 8 deletions

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.