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author | Adnan AlSinan <adnan.alsinan@arm.com> | 2021-07-05 13:12:52 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-07-25 13:04:23 +0000 |
commit | 7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4 (patch) | |
tree | b65671bdf37eb1ef8cc30ef64ab572da795546fa /src/core/CL/cl_kernels/roi_pooling_layer.cl | |
parent | 22f5ed51f1b01f7cf6993a556a0b763e437926fc (diff) | |
download | ComputeLibrary-7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4.tar.gz |
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>
Diffstat (limited to 'src/core/CL/cl_kernels/roi_pooling_layer.cl')
-rw-r--r-- | src/core/CL/cl_kernels/roi_pooling_layer.cl | 196 |
1 files changed, 0 insertions, 196 deletions
diff --git a/src/core/CL/cl_kernels/roi_pooling_layer.cl b/src/core/CL/cl_kernels/roi_pooling_layer.cl deleted file mode 100644 index 6899b952e0..0000000000 --- a/src/core/CL/cl_kernels/roi_pooling_layer.cl +++ /dev/null @@ -1,196 +0,0 @@ -/* - * Copyright (c) 2017-2021 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" -#include "helpers_asymm.h" - -#if DATA_SIZE == 32 -#define VEC_SIZE 4 -#define VEC_MAX vec4_max -#elif DATA_SIZE == 16 -#define VEC_SIZE 8 -#define VEC_MAX vec8_max -#elif DATA_SIZE == 8 -#define VEC_SIZE 16 -#define VEC_MAX vec16_max -#else /* DATA_SIZE not equals 8, 16, 32 */ -#error "Unsupported data size" -#endif /* DATA_SIZE == 32 */ - -// Define whether to use max (Quantized datatype) or fmax (Float) functions -#if defined(OFFSET_OUT) && defined(SCALE_OUT) -#define MAX(x, y) max(x, y) -#else // !(defined(OFFSET_OUT) && defined(SCALE_OUT) -#define MAX(x, y) fmax(x, y) -#endif // defined(OFFSET_OUT) && defined(SCALE_OUT) - -inline DATA_TYPE vec4_max(VEC_DATA_TYPE(DATA_TYPE, 4) vec) -{ - VEC_DATA_TYPE(DATA_TYPE, 2) - temp = MAX(vec.lo, vec.hi); - return MAX(temp.x, temp.y); -} - -inline DATA_TYPE vec8_max(VEC_DATA_TYPE(DATA_TYPE, 8) vec) -{ - VEC_DATA_TYPE(DATA_TYPE, 4) - temp = MAX(vec.lo, vec.hi); - return vec4_max(temp); -} - -inline DATA_TYPE vec16_max(VEC_DATA_TYPE(DATA_TYPE, 16) vec) -{ - VEC_DATA_TYPE(DATA_TYPE, 8) - temp = MAX(vec.lo, vec.hi); - return vec8_max(temp); -} - -/** Performs a roi pooling on a single output pixel. - * - * @param[in] input Pointer to input Tensor3D struct. - * @param[in] region_start_x Start x index projected onto the input tensor. - * @param[in] region_end_x End x index projected onto the input tensor. - * @param[in] region_start_y Start y index projected onto the input tensor. - * @param[in] region_end_y End y index projected onto the input tensor. - * @param[in] pz z index of the input tensor. - * - * @return A max pooled value from the region specified in the input tensor. - */ -inline DATA_TYPE roi_pool_1x1(const Tensor3D *input, int region_start_x, int region_end_x, int region_start_y, int region_end_y, int pz) -{ - // Iterate through the pooling region - if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) - { - return (DATA_TYPE)0; - } - else - { - int num_iter = (int)((region_end_x - region_start_x) / VEC_SIZE); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - curr_max = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(MIN_VALUE); - - for(int j = region_start_y; j < region_end_y; ++j) - { - int i = region_start_x; - for(; i < region_start_x + num_iter * VEC_SIZE; i += VEC_SIZE) - { - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor3D_offset(input, i, j, pz)); - curr_max = MAX(val, curr_max); - } - for(; i < region_end_x; ++i) - { - DATA_TYPE val = *(__global DATA_TYPE *)tensor3D_offset(input, i, j, pz); - curr_max = MAX(curr_max, val); - } - } - - const DATA_TYPE temp = (DATA_TYPE)VEC_MAX(curr_max); - -#if defined(OFFSET_OUT) && defined(SCALE_OUT) - return QUANTIZE(temp, OFFSET_OUT, SCALE_OUT, DATA_TYPE, 1); -#endif /* if quantized, requantize and return */ - - return temp; - } -} - -/** Performs a roi pooling function. - * - * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32, QASYMM8; - * @note Datasize must be passed using -DDATA_SIZE e.g. -DDATA_SIZE=32; - * @note Input dimensions must be passed using -DMAX_DIM_X, -DMAX_DIM_Y and -DMAX_DIM_Z; - * @note Pooled region dimensions must be passed using -DPOOLED_DIM_X and -DPOOLED_DIM_Y; - * @note Spatial scale must be passed using -DSPATIAL_SCALE; - * - * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32, QASYMM8 - * @param[in] input_stride_x Stride of the source image 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 source image 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_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the pooled region of the source image as specifed by ROI - * @param[in] rois_ptr Pointer to the ROIs tensor. Layout: { batch_index, x1, y1, x2, y2 }. Supported data types: same as @p input_ptr - * @param[in] rois_stride_x Stride of the ROIs tensor in X dimension (in bytes) - * @param[in] rois_step_x Step of the ROIs tensor in X dimension (in bytes) - * @param[in] rois_stride_y Stride of the ROIs tensor in Y dimension (in bytes) - * @param[in] rois_step_y Step of the ROIs tensor in Y dimension (in bytes) - * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the ROIs tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as input - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image - * @param[in] input_stride_w Stride of the source image in W dimension (in bytes) - * @param[in] output_stride_w Stride of the destination image in W dimension (in bytes) - */ -__kernel void roi_pooling_layer( - TENSOR3D_DECLARATION(input), - IMAGE_DECLARATION(rois), - TENSOR3D_DECLARATION(output), - unsigned int input_stride_w, unsigned int output_stride_w) -{ - // Get pixels pointer - Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); - Image rois = CONVERT_TO_IMAGE_STRUCT_NO_STEP(rois); - Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); - - const int px = get_global_id(0); - const int py = get_global_id(1); - const int pw = get_global_id(2); - - // Load roi parameters - // roi is laid out as follows { batch_index, x1, y1, x2, y2 } - const ushort roi_batch = (ushort) * ((__global ushort *)offset(&rois, 0, pw)); - const VEC_DATA_TYPE(ushort, 4) - roi = vload4(0, (__global ushort *)offset(&rois, 1, pw)); - const int2 roi_anchor = convert_int2_sat(round(convert_float2(roi.s01) * (float)SPATIAL_SCALE)); - const int2 roi_dims = convert_int2_sat(fmax(round(convert_float2(roi.s23 - roi.s01) * (float)SPATIAL_SCALE), 1.f)); - - // Calculate pooled region start and end - const float2 spatial_indx = (float2)(px, py); - const float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y); - const int2 max_spatial_dims = (int2)(MAX_DIM_X, MAX_DIM_Y); - int2 region_start = convert_int2_sat(floor(spatial_indx / pooled_dims * convert_float2(roi_dims))) + roi_anchor; - int2 region_end = convert_int2_sat(floor((spatial_indx + 1) / pooled_dims * convert_float2(roi_dims))) + roi_anchor; - - region_start = clamp(region_start, 0, max_spatial_dims); - region_end = clamp(region_end, 0, max_spatial_dims); - - // Move input and output pointer across the fourth dimension - input.ptr += roi_batch * input_stride_w; - output.ptr += pw * output_stride_w; - - for(int pz = 0; pz < MAX_DIM_Z; ++pz) - { - *(__global DATA_TYPE *)tensor3D_offset(&output, px, py, pz) = (__global DATA_TYPE)roi_pool_1x1(&input, - region_start.x, - region_end.x, - region_start.y, - region_end.y, pz); - } -} |