From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- src/core/CL/cl_kernels/hog.cl | 455 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 455 insertions(+) create mode 100644 src/core/CL/cl_kernels/hog.cl (limited to 'src/core/CL/cl_kernels/hog.cl') diff --git a/src/core/CL/cl_kernels/hog.cl b/src/core/CL/cl_kernels/hog.cl new file mode 100644 index 0000000000..31dd57b767 --- /dev/null +++ b/src/core/CL/cl_kernels/hog.cl @@ -0,0 +1,455 @@ +/* + * Copyright (c) 2017 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 "types.h" + +#if(defined CELL_WIDTH && defined CELL_HEIGHT && defined NUM_BINS && defined PHASE_SCALE) + +/** This OpenCL kernel computes the HOG orientation binning + * + * @attention The following variables must be passed at compile time: + * + * -# -DCELL_WIDTH = Width of the cell + * -# -DCELL_HEIGHT = height of the cell + * -# -DNUM_BINS = Number of bins for each cell + * -# -DPHASE_SCALE = Scale factor used to evaluate the index of the local HOG + * + * @note Each work-item computes a single cell + * + * @param[in] mag_ptr Pointer to the source image which stores the magnitude of the gradient for each pixel. Supported data types: S16 + * @param[in] mag_stride_x Stride of the magnitude image in X dimension (in bytes) + * @param[in] mag_step_x mag_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] mag_stride_y Stride of the magnitude image in Y dimension (in bytes) + * @param[in] mag_step_y mag_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] mag_offset_first_element_in_bytes The offset of the first element in the magnitude image + * @param[in] phase_ptr Pointer to the source image which stores the phase of the gradient for each pixel. Supported data types: U8 + * @param[in] phase_stride_x Stride of the phase image in X dimension (in bytes) + * @param[in] phase_step_x phase_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] phase_stride_y Stride of the the phase image in Y dimension (in bytes) + * @param[in] phase_step_y phase_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] phase_offset_first_element_in_bytes The offset of the first element in the the phase image + * @param[out] dst_ptr Pointer to the destination image which stores the local HOG for each cell Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell + * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void hog_orientation_binning(IMAGE_DECLARATION(mag), + IMAGE_DECLARATION(phase), + IMAGE_DECLARATION(dst)) +{ + float bins[NUM_BINS] = { 0 }; + + // Compute address for the magnitude and phase images + Image mag = CONVERT_TO_IMAGE_STRUCT(mag); + Image phase = CONVERT_TO_IMAGE_STRUCT(phase); + + __global uchar *mag_row_ptr = mag.ptr; + __global uchar *phase_row_ptr = phase.ptr; + + for(int yc = 0; yc < CELL_HEIGHT; ++yc) + { + int xc = 0; + for(; xc <= (CELL_WIDTH - 4); xc += 4) + { + // Load magnitude and phase values + const float4 mag_f32 = convert_float4(vload4(0, (__global short *)mag_row_ptr + xc)); + float4 phase_f32 = convert_float4(vload4(0, phase_row_ptr + xc)); + + // Scale phase: phase * scale + 0.5f + phase_f32 = (float4)0.5f + phase_f32 * (float4)PHASE_SCALE; + + // Compute histogram index. + int4 hidx_s32 = convert_int4(phase_f32); + + // Compute magnitude weights (w0 and w1) + const float4 hidx_f32 = convert_float4(hidx_s32); + + // w1 = phase_f32 - hidx_s32 + const float4 w1_f32 = phase_f32 - hidx_f32; + + // w0 = 1.0 - w1 + const float4 w0_f32 = (float4)1.0f - w1_f32; + + // Calculate the weights for splitting vote + const float4 mag_w0_f32 = mag_f32 * w0_f32; + const float4 mag_w1_f32 = mag_f32 * w1_f32; + + // Weighted vote between 2 bins + + // Check if the histogram index is equal to NUM_BINS. If so, replace the index with 0 + hidx_s32 = select(hidx_s32, (int4)0, hidx_s32 == (int4)(NUM_BINS)); + + // Bin 0 + bins[hidx_s32.s0] += mag_w0_f32.s0; + bins[hidx_s32.s1] += mag_w0_f32.s1; + bins[hidx_s32.s2] += mag_w0_f32.s2; + bins[hidx_s32.s3] += mag_w0_f32.s3; + + hidx_s32 += (int4)1; + + // Check if the histogram index is equal to NUM_BINS. If so, replace the index with 0 + hidx_s32 = select(hidx_s32, (int4)0, hidx_s32 == (int4)(NUM_BINS)); + + // Bin1 + bins[hidx_s32.s0] += mag_w1_f32.s0; + bins[hidx_s32.s1] += mag_w1_f32.s1; + bins[hidx_s32.s2] += mag_w1_f32.s2; + bins[hidx_s32.s3] += mag_w1_f32.s3; + } + + // Left over computation + for(; xc < CELL_WIDTH; xc++) + { + const float mag_value = *((__global short *)mag_row_ptr + xc); + const float phase_value = *(mag_row_ptr + xc) * (float)PHASE_SCALE + 0.5f; + const float w1 = phase_value - floor(phase_value); + + // The quantised phase is the histogram index [0, NUM_BINS - 1] + // Check limit of histogram index. If hidx == NUM_BINS, hidx = 0 + const uint hidx = (uint)(phase_value) % NUM_BINS; + + // Weighted vote between 2 bins + bins[hidx] += mag_value * (1.0f - w1); + bins[(hidx + 1) % NUM_BINS] += mag_value * w1; + } + + // Point to the next row of magnitude and phase images + mag_row_ptr += mag_stride_y; + phase_row_ptr += phase_stride_y; + } + + // Compute address for the destination image + Image dst = CONVERT_TO_IMAGE_STRUCT(dst); + + // Store the local HOG in the global memory + int xc = 0; + for(; xc <= (NUM_BINS - 4); xc += 4) + { + float4 values = vload4(0, bins + xc); + + vstore4(values, 0, ((__global float *)dst.ptr) + xc); + } + + // Left over stores + for(; xc < NUM_BINS; ++xc) + { + ((__global float *)dst.ptr)[xc] = bins[xc]; + } +} +#endif // (defined CELL_WIDTH && defined CELL_HEIGHT && defined NUM_BINS && defined PHASE_SCALE) + +#if(defined NUM_CELLS_PER_BLOCK_HEIGHT && defined NUM_BINS_PER_BLOCK_X && defined NUM_BINS_PER_BLOCK && HOG_NORM_TYPE && defined L2_HYST_THRESHOLD) + +#ifndef L2_NORM +#error The value of enum class HOGNormType::L2_NORM has not be passed to the OpenCL kernel +#endif + +#ifndef L2HYS_NORM +#error The value of enum class HOGNormType::L2HYS_NORM has not be passed to the OpenCL kernel +#endif + +#ifndef L1_NORM +#error The value of enum class HOGNormType::L1_NORM has not be passed to the OpenCL kernel +#endif + +/** This OpenCL kernel computes the HOG block normalization + * + * @attention The following variables must be passed at compile time: + * + * -# -DNUM_CELLS_PER_BLOCK_HEIGHT = Number of cells for each block + * -# -DNUM_BINS_PER_BLOCK_X = Number of bins for each block along the X direction + * -# -DNUM_BINS_PER_BLOCK = Number of bins for each block + * -# -DHOG_NORM_TYPE = Normalization type + * -# -DL2_HYST_THRESHOLD = Threshold used for L2HYS_NORM normalization method + * -# -DL2_NORM = Value of the enum class HOGNormType::L2_NORM + * -# -DL2HYS_NORM = Value of the enum class HOGNormType::L2HYS_NORM + * -# -DL1_NORM = Value of the enum class HOGNormType::L1_NORM + * + * @note Each work-item computes a single block + * + * @param[in] src_ptr Pointer to the source image which stores the local HOG. Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell + * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] dst_ptr Pointer to the destination image which stores the normlized HOG Supported data types: F32. Number of channels supported: equal to the number of histogram bins per block + * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void hog_block_normalization(IMAGE_DECLARATION(src), + IMAGE_DECLARATION(dst)) +{ + float sum = 0.0f; + float4 sum_f32 = (float4)(0.0f); + + // Compute address for the source and destination tensor + Image src = CONVERT_TO_IMAGE_STRUCT(src); + Image dst = CONVERT_TO_IMAGE_STRUCT(dst); + + for(size_t yc = 0; yc < NUM_CELLS_PER_BLOCK_HEIGHT; ++yc) + { + const __global float *hist_ptr = (__global float *)(src.ptr + yc * src_stride_y); + + int xc = 0; + for(; xc <= (NUM_BINS_PER_BLOCK_X - 16); xc += 16) + { + const float4 val0 = vload4(0, hist_ptr + xc + 0); + const float4 val1 = vload4(0, hist_ptr + xc + 4); + const float4 val2 = vload4(0, hist_ptr + xc + 8); + const float4 val3 = vload4(0, hist_ptr + xc + 12); + +#if(HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) + // Compute val^2 for L2_NORM or L2HYS_NORM + sum_f32 += val0 * val0; + sum_f32 += val1 * val1; + sum_f32 += val2 * val2; + sum_f32 += val3 * val3; +#else + // Compute |val| for L1_NORM + sum_f32 += fabs(val0); + sum_f32 += fabs(val1); + sum_f32 += fabs(val2); + sum_f32 += fabs(val3); +#endif // (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) + + // Store linearly the input values un-normalized in the output image. These values will be reused for the normalization. + // This approach will help us to be cache friendly in the next for loop where the normalization will be done because all the values + // will be accessed consecutively + vstore4(val0, 0, ((__global float *)dst.ptr) + xc + 0 + yc * NUM_BINS_PER_BLOCK_X); + vstore4(val1, 0, ((__global float *)dst.ptr) + xc + 4 + yc * NUM_BINS_PER_BLOCK_X); + vstore4(val2, 0, ((__global float *)dst.ptr) + xc + 8 + yc * NUM_BINS_PER_BLOCK_X); + vstore4(val3, 0, ((__global float *)dst.ptr) + xc + 12 + yc * NUM_BINS_PER_BLOCK_X); + } + + // Compute left over + for(; xc < NUM_BINS_PER_BLOCK_X; ++xc) + { + const float val = hist_ptr[xc]; + +#if(HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) + sum += val * val; +#else + sum += fabs(val); +#endif // (HOG_NORM_TYPE == L2_NORM) || (HOG_NORM_TYPE == L2HYS_NORM) + + ((__global float *)dst.ptr)[xc + 0 + yc * NUM_BINS_PER_BLOCK_X] = val; + } + } + + sum += dot(sum_f32, (float4)1.0f); + + float scale = 1.0f / (sqrt(sum) + NUM_BINS_PER_BLOCK * 0.1f); + +#if(HOG_NORM_TYPE == L2HYS_NORM) + // Reset sum + sum_f32 = (float4)0.0f; + sum = 0.0f; + + int k = 0; + for(; k <= NUM_BINS_PER_BLOCK - 16; k += 16) + { + float4 val0 = vload4(0, ((__global float *)dst.ptr) + k + 0); + float4 val1 = vload4(0, ((__global float *)dst.ptr) + k + 4); + float4 val2 = vload4(0, ((__global float *)dst.ptr) + k + 8); + float4 val3 = vload4(0, ((__global float *)dst.ptr) + k + 12); + + // Scale val + val0 = val0 * (float4)scale; + val1 = val1 * (float4)scale; + val2 = val2 * (float4)scale; + val3 = val3 * (float4)scale; + + // Clip val if over _threshold_l2hys + val0 = fmin(val0, (float4)L2_HYST_THRESHOLD); + val1 = fmin(val1, (float4)L2_HYST_THRESHOLD); + val2 = fmin(val2, (float4)L2_HYST_THRESHOLD); + val3 = fmin(val3, (float4)L2_HYST_THRESHOLD); + + // Compute val^2 + sum_f32 += val0 * val0; + sum_f32 += val1 * val1; + sum_f32 += val2 * val2; + sum_f32 += val3 * val3; + + vstore4(val0, 0, ((__global float *)dst.ptr) + k + 0); + vstore4(val1, 0, ((__global float *)dst.ptr) + k + 4); + vstore4(val2, 0, ((__global float *)dst.ptr) + k + 8); + vstore4(val3, 0, ((__global float *)dst.ptr) + k + 12); + } + + // Compute left over + for(; k < NUM_BINS_PER_BLOCK; ++k) + { + float val = ((__global float *)dst.ptr)[k] * scale; + + // Clip scaled input_value if over L2_HYST_THRESHOLD + val = fmin(val, (float)L2_HYST_THRESHOLD); + + sum += val * val; + + ((__global float *)dst.ptr)[k] = val; + } + + sum += dot(sum_f32, (float4)1.0f); + + // We use the same constants of OpenCV + scale = 1.0f / (sqrt(sum) + 1e-3f); + +#endif // (HOG_NORM_TYPE == L2HYS_NORM) + + int i = 0; + for(; i <= (NUM_BINS_PER_BLOCK - 16); i += 16) + { + float4 val0 = vload4(0, ((__global float *)dst.ptr) + i + 0); + float4 val1 = vload4(0, ((__global float *)dst.ptr) + i + 4); + float4 val2 = vload4(0, ((__global float *)dst.ptr) + i + 8); + float4 val3 = vload4(0, ((__global float *)dst.ptr) + i + 12); + + // Multiply val by the normalization scale factor + val0 = val0 * (float4)scale; + val1 = val1 * (float4)scale; + val2 = val2 * (float4)scale; + val3 = val3 * (float4)scale; + + vstore4(val0, 0, ((__global float *)dst.ptr) + i + 0); + vstore4(val1, 0, ((__global float *)dst.ptr) + i + 4); + vstore4(val2, 0, ((__global float *)dst.ptr) + i + 8); + vstore4(val3, 0, ((__global float *)dst.ptr) + i + 12); + } + + for(; i < NUM_BINS_PER_BLOCK; ++i) + { + ((__global float *)dst.ptr)[i] *= scale; + } +} +#endif // (defined NUM_CELLS_PER_BLOCK_HEIGHT && defined NUM_BINS_PER_BLOCK_X && defined NUM_BINS_PER_BLOCK && HOG_NORM_TYPE && defined L2_HYST_THRESHOLD) + +#if(defined NUM_BLOCKS_PER_DESCRIPTOR_Y && defined NUM_BINS_PER_DESCRIPTOR_X && defined THRESHOLD && defined MAX_NUM_DETECTION_WINDOWS && defined IDX_CLASS && defined BLOCK_STRIDE_WIDTH && defined BLOCK_STRIDE_HEIGHT && defined DETECTION_WINDOW_WIDTH && defined DETECTION_WINDOW_HEIGHT) + +/** This OpenCL kernel computes the HOG detector using linear SVM + * + * @attention The following variables must be passed at compile time: + * + * -# -DNUM_BLOCKS_PER_DESCRIPTOR_Y = Number of blocks per descriptor along the Y direction + * -# -DNUM_BINS_PER_DESCRIPTOR_X = Number of bins per descriptor along the X direction + * -# -DTHRESHOLD = Threshold for the distance between features and SVM classifying plane + * -# -DMAX_NUM_DETECTION_WINDOWS = Maximum number of possible detection windows. It is equal to the size of the DetectioWindow array + * -# -DIDX_CLASS = Index of the class to detect + * -# -DBLOCK_STRIDE_WIDTH = Block stride for the X direction + * -# -DBLOCK_STRIDE_HEIGHT = Block stride for the Y direction + * -# -DDETECTION_WINDOW_WIDTH = Width of the detection window + * -# -DDETECTION_WINDOW_HEIGHT = Height of the detection window + * + * @note Each work-item computes a single detection window + * + * @param[in] src_ptr Pointer to the source image which stores the local HOG. Supported data types: F32. Number of channels supported: equal to the number of histogram bins per cell + * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] hog_descriptor Pointer to HOG descriptor. Supported data types: F32 + * @param[out] dst Pointer to DetectionWindow array + * @param[out] num_detection_windows Number of objects detected + */ +__kernel void hog_detector(IMAGE_DECLARATION(src), + __global float *hog_descriptor, + __global DetectionWindow *dst, + __global uint *num_detection_windows) +{ + // Check if the DetectionWindow array is full + if(*num_detection_windows >= MAX_NUM_DETECTION_WINDOWS) + { + return; + } + + Image src = CONVERT_TO_IMAGE_STRUCT(src); + + const int src_step_y_f32 = src_stride_y / sizeof(float); + + // Init score_f32 with 0 + float4 score_f32 = (float4)0.0f; + + // Init score with 0 + float score = 0.0f; + + __global float *src_row_ptr = (__global float *)src.ptr; + + // Compute Linear SVM + for(int yb = 0; yb < NUM_BLOCKS_PER_DESCRIPTOR_Y; ++yb, src_row_ptr += src_step_y_f32) + { + int xb = 0; + + const int offset_y = yb * NUM_BINS_PER_DESCRIPTOR_X; + + for(; xb < (int)NUM_BINS_PER_DESCRIPTOR_X - 8; xb += 8) + { + // Load descriptor values + float4 a0_f32 = vload4(0, src_row_ptr + xb + 0); + float4 a1_f32 = vload4(0, src_row_ptr + xb + 4); + + float4 b0_f32 = vload4(0, hog_descriptor + xb + 0 + offset_y); + float4 b1_f32 = vload4(0, hog_descriptor + xb + 4 + offset_y); + + // Multiply accumulate + score_f32 += a0_f32 * b0_f32; + score_f32 += a1_f32 * b1_f32; + } + + for(; xb < NUM_BINS_PER_DESCRIPTOR_X; ++xb) + { + const float a = src_row_ptr[xb]; + const float b = hog_descriptor[xb + offset_y]; + + score += a * b; + } + } + + score += dot(score_f32, (float4)1.0f); + + // Add the bias. The bias is located at the position (descriptor_size() - 1) + // (descriptor_size - 1) = NUM_BINS_PER_DESCRIPTOR_X * NUM_BLOCKS_PER_DESCRIPTOR_Y + score += hog_descriptor[NUM_BINS_PER_DESCRIPTOR_X * NUM_BLOCKS_PER_DESCRIPTOR_Y]; + + if(score > (float)THRESHOLD) + { + int id = atomic_inc(num_detection_windows); + if(id < MAX_NUM_DETECTION_WINDOWS) + { + dst[id].x = get_global_id(0) * BLOCK_STRIDE_WIDTH; + dst[id].y = get_global_id(1) * BLOCK_STRIDE_HEIGHT; + dst[id].width = DETECTION_WINDOW_WIDTH; + dst[id].height = DETECTION_WINDOW_HEIGHT; + dst[id].idx_class = IDX_CLASS; + dst[id].score = score; + } + } +} +#endif // defined BIAS && defined NUM_BLOCKS_PER_DESCRIPTOR_Y && defined NUM_BINS_PER_DESCRIPTOR_X && ... -- cgit v1.2.1