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2 | mjames | 1 | /* |
2 | * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. |
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3 | * |
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4 | * SPDX-License-Identifier: Apache-2.0 |
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5 | * |
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6 | * Licensed under the Apache License, Version 2.0 (the License); you may |
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7 | * not use this file except in compliance with the License. |
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8 | * You may obtain a copy of the License at |
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9 | * |
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10 | * www.apache.org/licenses/LICENSE-2.0 |
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11 | * |
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12 | * Unless required by applicable law or agreed to in writing, software |
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13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT |
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14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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15 | * See the License for the specific language governing permissions and |
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16 | * limitations under the License. |
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17 | */ |
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18 | |||
19 | /* ---------------------------------------------------------------------- |
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20 | * Project: CMSIS NN Library |
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21 | * Title: arm_convolve_HWC_q7_fast.c |
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22 | * Description: Fast Q7 version of convolution |
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23 | * |
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24 | * $Date: 17. January 2018 |
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25 | * $Revision: V.1.0.0 |
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26 | * |
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27 | * Target Processor: Cortex-M cores |
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28 | * |
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29 | * -------------------------------------------------------------------- */ |
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30 | |||
31 | #include "arm_math.h" |
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32 | #include "arm_nnfunctions.h" |
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33 | |||
34 | /** |
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35 | * @ingroup groupNN |
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36 | */ |
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37 | |||
38 | /** |
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39 | * @addtogroup NNConv |
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40 | * @{ |
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41 | */ |
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42 | |||
43 | /** |
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44 | * @brief Fast Q7 convolution function |
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45 | * @param[in] Im_in pointer to input tensor |
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46 | * @param[in] dim_im_in input tensor dimention |
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47 | * @param[in] ch_im_in number of input tensor channels |
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48 | * @param[in] wt pointer to kernel weights |
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49 | * @param[in] ch_im_out number of filters, i.e., output tensor channels |
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50 | * @param[in] dim_kernel filter kernel size |
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51 | * @param[in] padding padding sizes |
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52 | * @param[in] stride convolution stride |
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53 | * @param[in] bias pointer to bias |
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54 | * @param[in] bias_shift amount of left-shift for bias |
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55 | * @param[in] out_shift amount of right-shift for output |
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56 | * @param[in,out] Im_out pointer to output tensor |
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57 | * @param[in] dim_im_out output tensor dimension |
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58 | * @param[in,out] bufferA pointer to buffer space for input |
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59 | * @param[in,out] bufferB pointer to buffer space for output |
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60 | * @return The function returns either |
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61 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. |
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62 | * |
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63 | * @details |
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64 | * |
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65 | * <b>Buffer size:</b> |
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66 | * |
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67 | * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel |
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68 | * |
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69 | * bufferB size: 0 |
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70 | * |
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71 | * <b>Input dimension constraints:</b> |
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72 | * |
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73 | * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap ) |
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74 | * |
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75 | * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel ) |
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76 | * |
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77 | * The im2col converts the Q7 tensor input into Q15 column, which is stored in |
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78 | * bufferA. There is reordering happenning during this im2col process with |
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79 | * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and |
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80 | * third elements are swapped. |
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81 | * |
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82 | * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the |
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83 | * GEMM computation with the reordered columns. |
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84 | * |
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85 | * To speed-up the determination of the padding condition, we split the |
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86 | * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. |
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87 | * This reduces the total number of boundary condition checks and improves |
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88 | * the data copying performance. |
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89 | */ |
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90 | |||
91 | arm_status |
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92 | arm_convolve_HWC_q7_fast(const q7_t * Im_in, |
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93 | const uint16_t dim_im_in, |
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94 | const uint16_t ch_im_in, |
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95 | const q7_t * wt, |
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96 | const uint16_t ch_im_out, |
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97 | const uint16_t dim_kernel, |
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98 | const uint16_t padding, |
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99 | const uint16_t stride, |
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100 | const q7_t * bias, |
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101 | const uint16_t bias_shift, |
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102 | const uint16_t out_shift, |
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103 | q7_t * Im_out, |
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104 | const uint16_t dim_im_out, |
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105 | q15_t * bufferA, |
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106 | q7_t * bufferB) |
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107 | { |
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108 | |||
109 | #if defined (ARM_MATH_DSP) |
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110 | /* Run the following code for Cortex-M4 and Cortex-M7 */ |
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111 | |||
112 | int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; |
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113 | |||
114 | /* |
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115 | * Here we use bufferA as q15_t internally as computation are done with q15_t level |
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116 | * im2col are done to output in q15_t format from q7_t input |
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117 | */ |
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118 | |||
119 | q15_t *pBuffer = bufferA; |
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120 | q7_t *pOut = Im_out; |
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121 | |||
122 | if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
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123 | { |
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124 | /* check if the input dimension meets the constraints */ |
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125 | return ARM_MATH_SIZE_MISMATCH; |
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126 | } |
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127 | |||
128 | /* |
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129 | * Here we split the entire matrix into three regions depending on the padding situation |
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130 | * Top: i_out_y from 0 to padding - 1 |
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131 | * Middle: i_out_y from padding to dim_im_out-padding-1 |
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132 | * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 |
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133 | */ |
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134 | |||
135 | /* top part */ |
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136 | for (i_out_y = 0; i_out_y < padding; i_out_y++) |
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137 | { |
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138 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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139 | { |
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140 | /* This part implements the im2col function */ |
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141 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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142 | { |
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143 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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144 | { |
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145 | if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
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146 | { |
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147 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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148 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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149 | } else |
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150 | { |
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151 | arm_q7_to_q15_reordered_no_shift |
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152 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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153 | } |
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154 | pBuffer += ch_im_in; |
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155 | } |
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156 | } |
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157 | |||
158 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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159 | { |
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160 | pOut = |
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161 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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162 | bufferA, |
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163 | ch_im_out, |
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164 | ch_im_in |
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165 | * |
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166 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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167 | /* counter reset */ |
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168 | pBuffer = bufferA; |
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169 | } |
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170 | } |
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171 | } |
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172 | |||
173 | /* middle part, here we also divide the x into left, mid and right */ |
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174 | for (; i_out_y < dim_im_out - padding; i_out_y++) |
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175 | { |
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176 | |||
177 | /* left part */ |
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178 | for (i_out_x = 0; i_out_x < padding; i_out_x++) |
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179 | { |
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180 | /* This part implements the im2col function */ |
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181 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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182 | { |
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183 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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184 | { |
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185 | if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
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186 | { |
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187 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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188 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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189 | } else |
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190 | { |
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191 | arm_q7_to_q15_reordered_no_shift |
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192 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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193 | } |
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194 | pBuffer += ch_im_in; |
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195 | } |
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196 | } |
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197 | |||
198 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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199 | { |
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200 | pOut = |
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201 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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202 | bufferA, |
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203 | ch_im_out, |
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204 | ch_im_in |
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205 | * |
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206 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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207 | /* counter reset */ |
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208 | pBuffer = bufferA; |
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209 | } |
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210 | } |
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211 | |||
212 | /* mid part */ |
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213 | for (; i_out_x < dim_im_out - padding; i_out_x++) |
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214 | { |
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215 | /* This part implements the im2col function */ |
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216 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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217 | { |
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218 | arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in |
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219 | + |
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220 | (i_ker_y * |
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221 | dim_im_in + |
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222 | i_out_x * |
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223 | stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel); |
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224 | pBuffer += ch_im_in * dim_kernel; |
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225 | } |
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226 | |||
227 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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228 | { |
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229 | pOut = |
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230 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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231 | bufferA, |
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232 | ch_im_out, |
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233 | ch_im_in |
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234 | * |
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235 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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236 | /* counter reset */ |
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237 | pBuffer = bufferA; |
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238 | } |
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239 | } |
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240 | |||
241 | /* right part */ |
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242 | for (; i_out_x < dim_im_out; i_out_x++) |
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243 | { |
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244 | /* This part implements the im2col function */ |
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245 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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246 | { |
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247 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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248 | { |
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249 | if (i_ker_x < 0 || i_ker_x >= dim_im_in) |
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250 | { |
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251 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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252 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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253 | } else |
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254 | { |
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255 | arm_q7_to_q15_reordered_no_shift |
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256 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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257 | } |
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258 | pBuffer += ch_im_in; |
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259 | } |
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260 | } |
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261 | |||
262 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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263 | { |
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264 | pOut = |
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265 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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266 | bufferA, |
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267 | ch_im_out, |
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268 | ch_im_in |
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269 | * |
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270 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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271 | /* counter reset */ |
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272 | pBuffer = bufferA; |
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273 | } |
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274 | } |
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275 | } |
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276 | |||
277 | for (; i_out_y < dim_im_out; i_out_y++) |
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278 | { |
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279 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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280 | { |
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281 | /* This part implements the im2col function */ |
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282 | for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) |
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283 | { |
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284 | for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) |
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285 | { |
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286 | if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) |
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287 | { |
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288 | /* arm_fill_q15(0, pBuffer, ch_im_in); */ |
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289 | memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); |
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290 | } else |
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291 | { |
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292 | arm_q7_to_q15_reordered_no_shift |
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293 | ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); |
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294 | } |
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295 | pBuffer += ch_im_in; |
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296 | } |
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297 | } |
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298 | |||
299 | if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) |
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300 | { |
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301 | pOut = |
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302 | arm_nn_mat_mult_kernel_q7_q15_reordered(wt, |
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303 | bufferA, |
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304 | ch_im_out, |
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305 | ch_im_in |
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306 | * |
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307 | dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); |
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308 | /* counter reset */ |
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309 | pBuffer = bufferA; |
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310 | } |
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311 | } |
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312 | } |
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313 | |||
314 | /* check if there is left-over for compute */ |
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315 | if (pBuffer != bufferA) |
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316 | { |
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317 | const q7_t *pA = wt; |
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318 | int i; |
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319 | |||
320 | for (i = 0; i < ch_im_out; i++) |
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321 | { |
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322 | q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); |
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323 | q15_t *pB = bufferA; |
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324 | /* each time it process 4 entries */ |
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325 | uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; |
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326 | |||
327 | while (colCnt) |
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328 | { |
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329 | |||
330 | q31_t inA1, inA2; |
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331 | q31_t inB1, inB2; |
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332 | |||
333 | pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2); |
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334 | |||
335 | inB1 = *__SIMD32(pB)++; |
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336 | sum = __SMLAD(inA1, inB1, sum); |
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337 | inB2 = *__SIMD32(pB)++; |
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338 | sum = __SMLAD(inA2, inB2, sum); |
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339 | |||
340 | colCnt--; |
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341 | } |
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342 | colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; |
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343 | while (colCnt) |
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344 | { |
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345 | q7_t inA1 = *pA++; |
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346 | q15_t inB1 = *pB++; |
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347 | sum += inA1 * inB1; |
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348 | colCnt--; |
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349 | } |
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350 | *pOut = (q7_t) __SSAT((sum >> out_shift), 8); |
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351 | pOut++; |
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352 | |||
353 | } |
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354 | |||
355 | } |
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356 | #else |
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357 | /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ |
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358 | |||
359 | uint16_t i, j, k, l, m, n; |
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360 | int conv_out; |
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361 | signed char in_row, in_col; |
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362 | |||
363 | if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) |
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364 | { |
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365 | /* check if the input dimension meets the constraints */ |
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366 | return ARM_MATH_SIZE_MISMATCH; |
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367 | } |
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368 | |||
369 | for (i = 0; i < ch_im_out; i++) |
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370 | { |
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371 | for (j = 0; j < dim_im_out; j++) |
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372 | { |
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373 | for (k = 0; k < dim_im_out; k++) |
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374 | { |
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375 | conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); |
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376 | for (m = 0; m < dim_kernel; m++) |
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377 | { |
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378 | for (n = 0; n < dim_kernel; n++) |
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379 | { |
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380 | // if-for implementation |
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381 | in_row = stride * j + m - padding; |
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382 | in_col = stride * k + n - padding; |
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383 | if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) |
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384 | { |
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385 | for (l = 0; l < ch_im_in; l++) |
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386 | { |
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387 | conv_out += |
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388 | Im_in[(in_row * dim_im_in + in_col) * ch_im_in + |
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389 | l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + |
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390 | n) * ch_im_in + l]; |
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391 | } |
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392 | } |
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393 | } |
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394 | } |
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395 | Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); |
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396 | } |
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397 | } |
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398 | } |
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399 | |||
400 | #endif /* ARM_MATH_DSP */ |
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401 | |||
402 | /* Return to application */ |
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403 | return ARM_MATH_SUCCESS; |
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404 | } |
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405 | |||
406 | /** |
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407 | * @} end of NNConv group |
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408 | */ |