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>

4 files changed, 94 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.
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<PixelValue, PixelValue> get_min_max(DataType dt)
+{
+    PixelValue min(0);
+    PixelValue max(0);
+    switch(dt)
+    {
+        case DataType::U8:
+        case DataType::QASYMM8:
+        {
+            min = PixelValue(std::numeric_limits<uint8_t>::lowest());
+            max = PixelValue(std::numeric_limits<uint8_t>::max());
+            break;
+        }
+        case DataType::S8:
+        case DataType::QSYMM8:
+        case DataType::QASYMM8_SIGNED:
+        case DataType::QSYMM8_PER_CHANNEL:
+        {
+            min = PixelValue(std::numeric_limits<int8_t>::lowest());
+            max = PixelValue(std::numeric_limits<int8_t>::max());
+            break;
+        }
+        case DataType::U16:
+        case DataType::QASYMM16:
+        {
+            min = PixelValue(std::numeric_limits<uint16_t>::lowest());
+            max = PixelValue(std::numeric_limits<uint16_t>::max());
+            break;
+        }
+        case DataType::S16:
+        case DataType::QSYMM16:
+        {
+            min = PixelValue(std::numeric_limits<int16_t>::lowest());
+            max = PixelValue(std::numeric_limits<int16_t>::max());
+            break;
+        }
+        case DataType::U32:
+        {
+            min = PixelValue(std::numeric_limits<uint32_t>::lowest());
+            max = PixelValue(std::numeric_limits<uint32_t>::max());
+            break;
+        }
+        case DataType::S32:
+        {
+            min = PixelValue(std::numeric_limits<int32_t>::lowest());
+            max = PixelValue(std::numeric_limits<int32_t>::max());
+            break;
+        }
+        case DataType::F32:
+        {
+            min = PixelValue(std::numeric_limits<float>::lowest());
+            max = PixelValue(std::numeric_limits<float>::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.