COMPMID-556 Improved indentation and error handling in format_doxygen.py

Change-Id: I6f51ffe6c324d9da500716b52c97c344f2a2a164
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110486
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

6 files changed, 70 insertions, 70 deletions

diff --git a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
index 3433e77ba1..8757bc63aa 100644
--- a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
@@ -68,18 +68,18 @@ public:
     /** Constructor */
     NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Set the input, weights, biases and output tensors.
-      *
-      * @param[in,out] input    Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
-      * @param[in]     weights  The 4d weights with dimensions [width, height, OFM, IFM]. Data type supported: Same as @p input.
-      * @param[in]     bias     Optional, ignored if NULL. The biases have one dimension. Data type supported: Same as @p input.
-      * @param[out]    output   Output tensor. The output has the same number of dimensions as the @p input.
-      * @param[in]     info     Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
-      * @param[in]     ax       The number of zeros added to right edge of the input.
-      * @param[in]     ay       The number of zeros added to top edge of the input.
-      * @param[in]     upscalex How much to scale the X axis.
-      * @param[in]     upscaley How much to scale the Y axis.
-      *
-      */
+     *
+     * @param[in,out] input    Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
+     * @param[in]     weights  The 4d weights with dimensions [width, height, OFM, IFM]. Data type supported: Same as @p input.
+     * @param[in]     bias     Optional, ignored if NULL. The biases have one dimension. Data type supported: Same as @p input.
+     * @param[out]    output   Output tensor. The output has the same number of dimensions as the @p input.
+     * @param[in]     info     Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     * @param[in]     ax       The number of zeros added to right edge of the input.
+     * @param[in]     ay       The number of zeros added to top edge of the input.
+     * @param[in]     upscalex How much to scale the X axis.
+     * @param[in]     upscaley How much to scale the Y axis.
+     *
+     */
     void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
                    unsigned int ax, unsigned int ay, float upscalex, float upscaley);
 
diff --git a/arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h
index daaf18f297..c731bf278f 100644
--- a/arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h
@@ -51,22 +51,22 @@ public:
     /** Constructor */
     NEDirectConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Set the input, weights, biases and output tensors.
-      *
-      * @note: DirectConvolution only works in the following configurations:
-      *    1x1 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = QS8/QS16/F16/F32
-      *    3x3 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = QS8/F16/F32
-      *    5x5 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = F32
-      *
-      * @param[in, out] input     Input tensor. Data types supported: QS8/QS16/F16/F32.
-      * @param[in]      weights   Set of kernels to convolve the input volume.
-      *                           Supported sizes: 1x1, 3x3 and 5x5.
-      *                           The 3rd dimension must be the same as the input's volume 3rd dimension.
-      *                           Data type supported: Same as @p input.
-      * @param[in]      bias      Set of biases. Data type supported: Same as @p input.
-      * @param[out]     output    Output tensor.
-      *                           The 3rd dimensions must be equal to the 4th dimension of the @p kernels tensor. Data types supported: Same as @p input.
-      * @param[in]      conv_info Contains padding and stride information described in @ref PadStrideInfo.
-      */
+     *
+     * @note: DirectConvolution only works in the following configurations:
+     *    1x1 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = QS8/QS16/F16/F32
+     *    3x3 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = QS8/F16/F32
+     *    5x5 convolution with stride_x = 1/2/3, stride_y = 1/2/3 data type = F32
+     *
+     * @param[in, out] input     Input tensor. Data types supported: QS8/QS16/F16/F32.
+     * @param[in]      weights   Set of kernels to convolve the input volume.
+     *                           Supported sizes: 1x1, 3x3 and 5x5.
+     *                           The 3rd dimension must be the same as the input's volume 3rd dimension.
+     *                           Data type supported: Same as @p input.
+     * @param[in]      bias      Set of biases. Data type supported: Same as @p input.
+     * @param[out]     output    Output tensor.
+     *                           The 3rd dimensions must be equal to the 4th dimension of the @p kernels tensor. Data types supported: Same as @p input.
+     * @param[in]      conv_info Contains padding and stride information described in @ref PadStrideInfo.
+     */
     void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &conv_info);
 
     // Inherited methods overridden:
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpAssemblyMatrixMultiplyCore.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpAssemblyMatrixMultiplyCore.h
index 3b6aa1c7db..3d213a7668 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpAssemblyMatrixMultiplyCore.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpAssemblyMatrixMultiplyCore.h
@@ -46,11 +46,11 @@ public:
     /** Constructor */
     NEGEMMLowpAssemblyMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Initialise the kernel's inputs, output
-    *
-    * @param[in]  a      First input tensor  (Matrix A). Data type supported: U8, S8.
-    * @param[in]  b      Second input tensor (Matrix B). Data type supported: same as @p a
-    * @param[out] output Output tensor. Data type supported: Data type supported: S32
-    */
+     *
+     * @param[in]  a      First input tensor  (Matrix A). Data type supported: U8, S8.
+     * @param[in]  b      Second input tensor (Matrix B). Data type supported: same as @p a
+     * @param[out] output Output tensor. Data type supported: Data type supported: S32
+     */
     void configure(const ITensor *a, const ITensor *b, ITensor *output);
 
     // Inherited methods overridden:
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
index 598756e435..889bbca7f2 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
@@ -58,18 +58,18 @@ public:
     /** Constructor */
     NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Initialise the kernel's inputs, output
-    *
-    * @note GEMM_LOWP:  low precision GEMM kernel
-    *  This kernel performs the following computations:
-    *
-    *  -# Convert a values from QASYMM8 to int32 and add a_offset to each of them.
-    *  -# Convert b values from QASYMM8 to int32 add b_offset to each of them.
-    *  -# Compute the matrix product of the resulting a * b in int32.
-    *
-    * @param[in]  a      First input tensor  (Matrix A). Data type supported: QASYMM8.
-    * @param[in]  b      Second input tensor (Matrix B). Data type supported: same as @p a
-    * @param[out] output Output tensor. Data type supported: Data type supported: S32
-    */
+     *
+     * @note GEMM_LOWP:  low precision GEMM kernel
+     *  This kernel performs the following computations:
+     *
+     *  -# Convert a values from QASYMM8 to int32 and add a_offset to each of them.
+     *  -# Convert b values from QASYMM8 to int32 add b_offset to each of them.
+     *  -# Compute the matrix product of the resulting a * b in int32.
+     *
+     * @param[in]  a      First input tensor  (Matrix A). Data type supported: QASYMM8.
+     * @param[in]  b      Second input tensor (Matrix B). Data type supported: same as @p a
+     * @param[out] output Output tensor. Data type supported: Data type supported: S32
+     */
     void configure(const ITensor *a, const ITensor *b, ITensor *output);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixMultiplyCore
      *
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
index 9270d5581f..533a41c888 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
@@ -60,29 +60,29 @@ class NEGEMMLowpQuantizeDownInt32ToUint8Scale : public INESimpleFunction
 {
 public:
     /** Initialise the kernel's inputs, output
-    *
-    * @param[in]  input           Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
-    * @param[in]  bias            Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
-    *                             Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-    * @param[out] output          Output tensor. Data type supported: Data type supported: QASYMM8
-    * @param[in]  result_offset   Offset to be added to each element of the input matrix
-    * @param[in]  result_mult_int Value to be multiplied to each element of the input matrix when once the result_offset has been add
-    * @param[in]  result_shift    Number of bits to shift right the result before converting back to QASYMM8
-    * @param[in]  min             (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-    * @param[in]  max             (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
-    *                             Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-    */
+     *
+     * @param[in]  input           Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+     * @param[in]  bias            Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+     *                             Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[out] output          Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in]  result_offset   Offset to be added to each element of the input matrix
+     * @param[in]  result_mult_int Value to be multiplied to each element of the input matrix when once the result_offset has been add
+     * @param[in]  result_shift    Number of bits to shift right the result before converting back to QASYMM8
+     * @param[in]  min             (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in]  max             (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     *                             Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     */
     void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min = 0, int max = 0);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale
-    *
-    * @param[in] input  Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
-    * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
-    *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-    * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
-    * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-    * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
-    *                   Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-    */
+     *
+     * @param[in] input  Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+     * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+     *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     *                   Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     */
     static Error validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
 };
 }
diff --git a/arm_compute/runtime/NEON/functions/NEIntegralImage.h b/arm_compute/runtime/NEON/functions/NEIntegralImage.h
index 6d7dd697e8..1ac501c994 100644
--- a/arm_compute/runtime/NEON/functions/NEIntegralImage.h
+++ b/arm_compute/runtime/NEON/functions/NEIntegralImage.h
@@ -35,10 +35,10 @@ class NEIntegralImage : public INESimpleFunction
 {
 public:
     /** Initialise the function's source, destinations and border mode.
-        *
-        * @param[in]  input  Source tensor. Data type supported: U8.
-        * @param[out] output Destination tensor. Data type supported: U32.
-        */
+     *
+     * @param[in]  input  Source tensor. Data type supported: U8.
+     * @param[out] output Destination tensor. Data type supported: U32.
+     */
     void configure(const ITensor *input, ITensor *output);
 };
 }