Reorganize the kernels into nhwc, nchw and common folders

The Following kernels have been split into nchw/nhwc kernels files:

- batchnormalization_layer
- batch_to_space
- channel_shuffle
- depth_to_space
- dequantization_layer
- im2col
- normalization_layer
- normalize_planar_yuv_layer
- normalize_planar_yuv_layer_quantized
- pooling_layer
- pooling_layer_quantized
- remap
- reorg_layer
- scale
- scale_quantized
- space_to_batch
- space_to_depth
- upsample_layer
- winograd_filter_transform
- winograd_input_transform
- winograd_output_transform

The following kernels have been moved to nchw folder:
- direct_convolution1x1
- direct_convolution3x3
- direct_convolution5x5
- direct_convolution_quantized
- prior_box_layer

The following kernels have been moved to nhwc folder:
- direct_convolution
- dwc_native_fp_nhwc
- dwc_native_quantized_nhwc

The following kernels have been removed:
- sobel_filter

While the rest kerenls have been moved to the common folder.

Partially resolves COMPMID-4453

Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com>
Change-Id: Ic327ac935687ec351c610c65a3c6357f364a5a58
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5919
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 118 insertions, 0 deletions

diff --git a/src/core/CL/cl_kernels/common/mean_stddev_normalization.cl b/src/core/CL/cl_kernels/common/mean_stddev_normalization.cl
new file mode 100644
index 0000000000..05727a6aa6
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/mean_stddev_normalization.cl
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2019-2021 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.
+ */
+#include "helpers.h"
+
+#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH)
+/** This function normalizes the input 2D tensor across the first dimension with respect to mean and standard deviation of the same dimension.
+ *
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
+ * @attention Width of the input tensor should be passed using the -DWIDTH compile flag, e.g. -DWIDTH=16
+ * @attention Normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f
+ *
+ * @param[in]  input_ptr                            Pointer to the first source tensor. Supported data types: F16/F32
+ * @param[in]  input_stride_x                       Stride of the first source tensor in X dimension (in bytes)
+ * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  input_stride_y                       Stride of the first source tensor in Y dimension (in bytes)
+ * @param[in]  input_step_y                         input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the first source tensor
+ * @param[out] output_ptr                           (Optional) Pointer to the destination tensor. Supported data types: same as @p input_ptr
+ * @param[in]  output_stride_x                      (Optional) Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  output_step_x                        (Optional) output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  output_stride_y                      (Optional) Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  output_step_y                        (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor
+ */
+__kernel void mean_stddev_normalization(
+    IMAGE_DECLARATION(input)
+#ifndef IN_PLACE
+    ,
+    IMAGE_DECLARATION(output)
+#endif /* IN_PLACE */
+)
+{
+    // Get pixels pointer
+    Image in = CONVERT_TO_IMAGE_STRUCT(input);
+#ifdef IN_PLACE
+    Image out = in;
+#else  /* IN_PLACE */
+    Image out = CONVERT_TO_IMAGE_STRUCT(output);
+#endif /* IN_PLACE */
+
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    sum = 0.f;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    sum_sq = 0.f;
+    // Calculate partial sum
+    int i = 0;
+    for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE)
+    {
+        // Load data
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+        data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0));
+
+        sum += data;
+        sum_sq += data * data;
+    }
+    // Perform reduction
+    sum    = SUM_REDUCE(sum, VEC_SIZE);
+    sum_sq = SUM_REDUCE(sum_sq, VEC_SIZE);
+
+#if VEC_SIZE > 1
+#define sum sum.s0
+#define sum_sq sum_sq.s0
+#endif // VEC_SIZE > 1
+
+    // Left-overs loop
+    for(; i < WIDTH; ++i)
+    {
+        DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0));
+
+        sum += data;
+        sum_sq += data * data;
+    }
+
+    DATA_TYPE mean       = sum / WIDTH;
+    DATA_TYPE var        = (sum_sq / WIDTH) - (mean * mean);
+    DATA_TYPE stddev_inv = 1.f / sqrt(var + EPSILON);
+
+    i = 0;
+    for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE)
+    {
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+        data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0));
+
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+        res = (data - mean) * stddev_inv;
+        VSTORE(VEC_SIZE)
+        (res, 0, (__global DATA_TYPE *)offset(&out, i, 0));
+    }
+    for(; i < WIDTH; ++i)
+    {
+        DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0));
+
+        *((__global DATA_TYPE *)offset(&out, i, 0)) = (data - mean) * stddev_inv;
+    }
+}
+#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH) */