| /* |
| * 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_nonsquare.c |
| * Description: Fast Q7 version of convolution (non-sqaure shape) |
| * |
| * $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 (non-sqaure shape) |
| * @param[in] Im_in pointer to input tensor |
| * @param[in] dim_im_in_x input tensor dimention x |
| * @param[in] dim_im_in_y input tensor dimention y |
| * @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_x filter kernel size x |
| * @param[in] dim_kernel_y filter kernel size y |
| * @param[in] padding_x padding size x |
| * @param[in] padding_y padding size y |
| * @param[in] stride_x convolution stride x |
| * @param[in] stride_y convolution stride y |
| * @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_x output tensor dimension x |
| * @param[in] dim_im_out_y output tensor dimension y |
| * @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. |
| * |
| * This function is the version with full list of optimization tricks, but with |
| * some contraints: |
| * ch_im_in is multiple of 4 |
| * ch_im_out is multiple of 2 |
| */ |
| |
| arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in, |
| const uint16_t dim_im_in_x, |
| const uint16_t dim_im_in_y, |
| const uint16_t ch_im_in, |
| const q7_t * wt, |
| const uint16_t ch_im_out, |
| const uint16_t dim_kernel_x, |
| const uint16_t dim_kernel_y, |
| const uint16_t padding_x, |
| const uint16_t padding_y, |
| const uint16_t stride_x, |
| const uint16_t stride_y, |
| const q7_t * bias, |
| const uint16_t bias_shift, |
| const uint16_t out_shift, |
| q7_t * Im_out, |
| const uint16_t dim_im_out_x, |
| const uint16_t dim_im_out_y, |
| 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_y; i_out_y++) |
| { |
| for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; |
| i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; |
| i_ker_x++) |
| { |
| if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) |
| { |
| /* 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_x + i_ker_x) * ch_im_in, |
| pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, |
| 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_y - padding_y; i_out_y++) |
| { |
| |
| /* left part */ |
| for (i_out_x = 0; i_out_x < padding_x; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; |
| i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; |
| i_ker_x++) |
| { |
| if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) |
| { |
| /* 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_x + i_ker_x) * ch_im_in, |
| pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, |
| bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| |
| /* mid part */ |
| for (; i_out_x < dim_im_out_x - padding_x; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; |
| i_ker_y++) |
| { |
| arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + |
| (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in, |
| pBuffer, ch_im_in * dim_kernel_x); |
| pBuffer += ch_im_in * dim_kernel_x; |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, |
| bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| |
| /* right part */ |
| for (; i_out_x < dim_im_out_x; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; |
| i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; |
| i_ker_x++) |
| { |
| if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) |
| { |
| /* 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_x + i_ker_x) * ch_im_in, |
| pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, |
| bias_shift, out_shift, bias, pOut); |
| /* counter reset */ |
| pBuffer = bufferA; |
| } |
| } |
| } |
| |
| for (; i_out_y < dim_im_out_y; i_out_y++) |
| { |
| for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) |
| { |
| /* This part implements the im2col function */ |
| for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; |
| i_ker_y++) |
| { |
| for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; |
| i_ker_x++) |
| { |
| if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) |
| { |
| /* 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_x + i_ker_x) * ch_im_in, |
| pBuffer, ch_im_in); |
| } |
| pBuffer += ch_im_in; |
| } |
| } |
| |
| if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) |
| { |
| pOut = |
| arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, |
| 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; |
| /* basically each time it process 4 entries */ |
| uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2; |
| |
| while (colCnt) |
| { |
| |
| q31_t inA1, inA2; |
| q31_t inB1, inB2; |
| |
| pA = (const 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_y * dim_kernel_x) & 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 */ |
| int i, j, k, l, m, n; |
| int conv_out; |
| int 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_y; j++) |
| { |
| for (k = 0; k < dim_im_out_x; k++) |
| { |
| conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); |
| for (m = 0; m < dim_kernel_y; m++) |
| { |
| for (n = 0; n < dim_kernel_x; n++) |
| { |
| /* if-for implementation */ |
| in_row = stride_y * j + m - padding_y; |
| in_col = stride_x * k + n - padding_x; |
| if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) |
| { |
| for (l = 0; l < ch_im_in; l++) |
| { |
| conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * |
| wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l]; |
| } |
| } |
| } |
| } |
| Im_out[i + (j * dim_im_out_x + 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 |
| */ |
