| /* |
| * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. |
| * |
| * SPDX-License-Identifier: Apache-2.0 |
| * |
| * Licensed under the Apache License, Version 2.0 (the License); you may |
| * not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| /* ---------------------------------------------------------------------- |
| * Project: CMSIS NN Library |
| * Title: arm_convolve_HWC_q7_fast.c |
| * Description: Fast Q7 version of convolution |
| * |
| * $Date: 17. January 2018 |
| * $Revision: V.1.0.0 |
| * |
| * Target Processor: Cortex-M cores |
| * |
| * -------------------------------------------------------------------- */ |
| |
| #include "arm_math.h" |
| #include "arm_nnfunctions.h" |
| |
| /** |
| * @ingroup groupNN |
| */ |
| |
| /** |
| * @addtogroup NNConv |
| * @{ |
| */ |
| |
| /** |
| * @brief Fast Q7 convolution function |
| * @param[in] Im_in pointer to input tensor |
| * @param[in] dim_im_in input tensor dimention |
| * @param[in] ch_im_in number of input tensor channels |
| * @param[in] wt pointer to kernel weights |
| * @param[in] ch_im_out number of filters, i.e., output tensor channels |
| * @param[in] dim_kernel filter kernel size |
| * @param[in] padding padding sizes |
| * @param[in] stride convolution stride |
| * @param[in] bias pointer to bias |
| * @param[in] bias_shift amount of left-shift for bias |
| * @param[in] out_shift amount of right-shift for output |
| * @param[in,out] Im_out pointer to output tensor |
| * @param[in] dim_im_out output tensor dimension |
| * @param[in,out] bufferA pointer to buffer space for input |
| * @param[in,out] bufferB pointer to buffer space for output |
| * @return The function returns either |
| * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. |
| * |
| * @details |
| * |
| * <b>Buffer size:</b> |
| * |
| * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel |
| * |
| * bufferB size: 0 |
| * |
| * <b>Input dimension constraints:</b> |
| * |
| * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap ) |
| * |
| * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel ) |
| * |
| * The im2col converts the Q7 tensor input into Q15 column, which is stored in |
| * bufferA. There is reordering happenning during this im2col process with |
| * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and |
| * third elements are swapped. |
| * |
| * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the |
| * GEMM computation with the reordered columns. |
| * |
| * To speed-up the determination of the padding condition, we split the |
| * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. |
| * This reduces the total number of boundary condition checks and improves |
| * the data copying performance. |
| */ |
| |
| arm_status |
| arm_convolve_HWC_q7_fast(const q7_t * Im_in, |
| const uint16_t dim_im_in, |
| const uint16_t ch_im_in, |
| const q7_t * wt, |
| const uint16_t ch_im_out, |
| const uint16_t dim_kernel, |
| const uint16_t padding, |
| const uint16_t stride, |
| const q7_t * bias, |
| const uint16_t bias_shift, |
| const uint16_t out_shift, |
| q7_t * Im_out, |
| const uint16_t dim_im_out, |
| q15_t * bufferA, |
| q7_t * bufferB) |
| { |
| |
| #if defined (ARM_MATH_DSP) |
| /* Run the following code for Cortex-M4 and Cortex-M7 */ |
| |
| int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; |
| |
| /* |
| * Here we use bufferA as q15_t internally as computation are done with q15_t level |
| * im2col are done to output in q15_t format from q7_t input |
| */ |
| |
| q15_t *pBuffer = bufferA; |
| q7_t *pOut = Im_out; |
| |
| if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
| { |
| /* check if the input dimension meets the constraints */ |
| return ARM_MATH_SIZE_MISMATCH; |
| } |
| |
| /* |
| * Here we split the entire matrix into three regions depending on the padding situation |
| * Top: i_out_y from 0 to padding - 1 |
| * Middle: i_out_y from padding to dim_im_out-padding-1 |
| * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 |
| */ |
| |
| /* top part */ |
| for (i_out_y = 0; i_out_y < padding; i_out_y++) |
| { |
| for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
| { |
| if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
| { |
| /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
| memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
| } else |
| { |
| arm_q7_to_q15_reordered_no_shift |
| ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
| bufferA, |
| ch_im_out, |
| ch_im_in |
| * |
| dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| } |
| |
| /* middle part, here we also divide the x into left, mid and right */ |
| for (; i_out_y < dim_im_out - padding; i_out_y++) |
| { |
| |
| /* left part */ |
| for (i_out_x = 0; i_out_x < padding; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
| { |
| if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
| { |
| /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
| memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
| } else |
| { |
| arm_q7_to_q15_reordered_no_shift |
| ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
| bufferA, |
| ch_im_out, |
| ch_im_in |
| * |
| dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| |
| /* mid part */ |
| for (; i_out_x < dim_im_out - padding; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
| { |
| arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in |
| + |
| (i_ker_y * |
| dim_im_in + |
| i_out_x * |
| stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel); |
| pBuffer += ch_im_in * dim_kernel; |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
| bufferA, |
| ch_im_out, |
| ch_im_in |
| * |
| dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| |
| /* right part */ |
| for (; i_out_x < dim_im_out; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
| { |
| if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
| { |
| /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
| memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
| } else |
| { |
| arm_q7_to_q15_reordered_no_shift |
| ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
| bufferA, |
| ch_im_out, |
| ch_im_in |
| * |
| dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| } |
| |
| for (; i_out_y < dim_im_out; i_out_y++) |
| { |
| for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
| { |
| if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
| { |
| /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
| memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
| } else |
| { |
| arm_q7_to_q15_reordered_no_shift |
| ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
| bufferA, |
| ch_im_out, |
| ch_im_in |
| * |
| dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| } |
| |
| /* check if there is left-over for compute */ |
| if (pBuffer != bufferA) |
| { |
| const q7_t *pA = wt; |
| int i; |
| |
| for (i = 0; i < ch_im_out; i++) |
| { |
| q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); |
| q15_t *pB = bufferA; |
| /* each time it process 4 entries */ |
| uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; |
| |
| while (colCnt) |
| { |
| |
| q31_t inA1, inA2; |
| q31_t inB1, inB2; |
| |
| pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2); |
| |
| inB1 = *__SIMD32(pB)++; |
| sum = __SMLAD(inA1, inB1, sum); |
| inB2 = *__SIMD32(pB)++; |
| sum = __SMLAD(inA2, inB2, sum); |
| |
| colCnt--; |
| } |
| colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; |
| while (colCnt) |
| { |
| q7_t inA1 = *pA++; |
| q15_t inB1 = *pB++; |
| sum += inA1 * inB1; |
| colCnt--; |
| } |
| *pOut = (q7_t) __SSAT((sum >> out_shift), 8); |
| pOut++; |
| |
| } |
| |
| } |
| #else |
| /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ |
| |
| uint16_t i, j, k, l, m, n; |
| int conv_out; |
| signed char in_row, in_col; |
| |
| if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
| { |
| /* check if the input dimension meets the constraints */ |
| return ARM_MATH_SIZE_MISMATCH; |
| } |
| |
| for (i = 0; i < ch_im_out; i++) |
| { |
| for (j = 0; j < dim_im_out; j++) |
| { |
| for (k = 0; k < dim_im_out; k++) |
| { |
| conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); |
| for (m = 0; m < dim_kernel; m++) |
| { |
| for (n = 0; n < dim_kernel; n++) |
| { |
| // if-for implementation |
| in_row = stride * j + m - padding; |
| in_col = stride * k + n - padding; |
| if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) |
| { |
| for (l = 0; l < ch_im_in; l++) |
| { |
| conv_out += |
| Im_in[(in_row * dim_im_in + in_col) * ch_im_in + |
| l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + |
| n) * ch_im_in + l]; |
| } |
| } |
| } |
| } |
| Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); |
| } |
| } |
| } |
| |
| #endif /* ARM_MATH_DSP */ |
| |
| /* Return to application */ |
| return ARM_MATH_SUCCESS; |
| } |
| |
| /** |
| * @} end of NNConv group |
| */ |
