/* * 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_RGB.c * Description: Q7 version of convolution for RGB image * * $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 Q7 convolution function for RGB image * @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 * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. * * @details * * Buffer size: * * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel * * bufferB size: 0 * * Input dimension constraints: * * ch_im_in equals 3 * * This kernel is written exclusively for convolution with ch_im_in * equals 3. This applies on the first layer of CNNs which has input * image with RGB format. */ arm_status arm_convolve_HWC_q7_RGB(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; // check if number of input channels is 3 if (ch_im_in != 3) { return ARM_MATH_SIZE_MISMATCH; } // This part implements the im2col function for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) { for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) { 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) { /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */ *__SIMD32(pBuffer) = 0x0; *(pBuffer + 2) = 0; pBuffer += 3; } else { /* * Equivalent to: * arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3); */ const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3; q31_t buf = *__SIMD32(pPixel); union arm_nnword top; union arm_nnword bottom; top.word = __SXTB16(buf); bottom.word = __SXTB16(__ROR(buf, 8)); #ifndef ARM_MATH_BIG_ENDIAN /* * little-endian, | omit | 3rd | 2nd | 1st | * MSB LSB * top | 3rd | 1st |; bottom | omit | 2nd | * * version 1, need to swap 2nd and 3rd weight * *__SIMD32(pBuffer) = top.word; * *(pBuffer+2) = bottom.half_words[0]; * * version 2, no weight shuffling required */ *pBuffer++ = top.half_words[0]; *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0); #else /* * big-endian, | 1st | 2nd | 3rd | omit | * MSB LSB * top | 2nd | omit |; bottom | 1st | 3rd | * * version 1, need to swap 2nd and 3rd weight * *__SIMD32(pBuffer) = bottom.word; * *(pBuffer+2) = top.half_words[1]; * * version 2, no weight shuffling required */ *pBuffer++ = bottom.half_words[0]; *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0); #endif pBuffer += 2; } } } if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel) { pOut = arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, ch_im_out, 3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); /* counter reset */ pBuffer = bufferA; } } } /* left-over because odd number of output pixels */ 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; /* basically each time it process 4 entries */ uint16_t colCnt = 3 * dim_kernel * dim_kernel >> 2; while (colCnt) { q31_t inA1, inA2; q31_t inB1, inB2; pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2); inB1 = *__SIMD32(pB)++; sum = __SMLAD(inA1, inB1, sum); inB2 = *__SIMD32(pB)++; sum = __SMLAD(inA2, inB2, sum); colCnt--; } colCnt = 3 * 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); } } #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; // check if number of input channels is 3 if (ch_im_in != 3) { 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 */