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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_depthwise_separable_conv_HWC_q7.c |
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22 | * Description: Q7 depthwise separable convolution function |
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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 Q7 depthwise separable 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 equals ch_im_out |
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74 | * |
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75 | * Implementation: |
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76 | * There are 3 nested loop here: |
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77 | * Inner loop: calculate each output value with MAC instruction over an accumulator |
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78 | * Mid loop: loop over different output channel |
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79 | * Outer loop: loop over different output (x, y) |
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80 | */ |
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81 | |||
82 | arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in, |
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83 | const uint16_t dim_im_in, |
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84 | const uint16_t ch_im_in, |
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85 | const q7_t * wt, |
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86 | const uint16_t ch_im_out, |
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87 | const uint16_t dim_kernel, |
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88 | const uint16_t padding, |
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89 | const uint16_t stride, |
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90 | const q7_t * bias, |
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91 | const uint16_t bias_shift, |
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92 | const uint16_t out_shift, |
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93 | q7_t * Im_out, |
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94 | const uint16_t dim_im_out, |
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95 | q15_t * bufferA, |
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96 | q7_t * bufferB) |
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97 | { |
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98 | |||
99 | #if defined (ARM_MATH_DSP) |
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100 | /* Run the following code for Cortex-M4 and Cortex-M7 */ |
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101 | |||
102 | int16_t i_out_y, i_out_x; |
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103 | int16_t i_ker_y, i_ker_x; |
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104 | q7_t *colBuffer = (q7_t *) bufferA; |
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105 | q7_t *pBuffer = colBuffer; |
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106 | const q7_t *pBias = bias; |
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107 | q7_t *pOut = Im_out; |
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108 | uint16_t rowCnt; |
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109 | uint16_t row_shift; |
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110 | |||
111 | /* do some checking here, basically ch_im_in == ch_im_out */ |
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112 | if (ch_im_in != ch_im_out) |
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113 | { |
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114 | return ARM_MATH_SIZE_MISMATCH; |
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115 | } |
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116 | |||
117 | for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) |
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118 | { |
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119 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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120 | { |
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121 | /* we first do im2col here */ |
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122 | 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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123 | { |
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124 | 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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125 | { |
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126 | 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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127 | { |
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128 | /* arm_fill_q7(0, pBuffer, ch_im_in); */ |
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129 | memset(pBuffer, 0, ch_im_in); |
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130 | } else |
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131 | { |
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132 | /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ |
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133 | memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in); |
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134 | } |
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135 | pBuffer += ch_im_in; |
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136 | } |
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137 | } |
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138 | |||
139 | /* we will do the computation here for each channel */ |
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140 | rowCnt = ch_im_out >> 2; |
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141 | row_shift = 0; |
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142 | pBias = bias; |
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143 | |||
144 | while (rowCnt) |
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145 | { |
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146 | q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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147 | q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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148 | q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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149 | q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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150 | |||
151 | uint16_t colCnt = (dim_kernel * dim_kernel) >> 1; |
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152 | q7_t *pB = colBuffer + row_shift; |
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153 | const q7_t *pA = wt + row_shift; |
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154 | row_shift += 4; |
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155 | |||
156 | #ifdef USE_INTRINSIC |
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157 | |||
158 | #ifndef ARM_MATH_BIG_ENDIAN |
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159 | |||
160 | while (colCnt) |
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161 | { |
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162 | q31_t inA1, inA2, inB1, inB2, opA, opB; |
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163 | |||
164 | inB1 = *__SIMD32(pB); |
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165 | pB += ch_im_in; |
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166 | opB = *__SIMD32(pB); |
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167 | pB += ch_im_in; |
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168 | inB2 = __PKHTB(opB, inB1, 16); |
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169 | inB1 = __PKHBT(inB1, opB, 16); |
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170 | inA1 = *__SIMD32(pA); |
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171 | pA += ch_im_in; |
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172 | opB = *__SIMD32(pA); |
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173 | pA += ch_im_in; |
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174 | inA2 = __PKHTB(opB, inA1, 16); |
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175 | inA1 = __PKHBT(inA1, opB, 16); |
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176 | opA = __SXTB16(inA1); |
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177 | opB = __SXTB16(inB1); |
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178 | sum = __SMLAD(opA, opB, sum); |
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179 | opA = __SXTB16(__ROR(inA1, 8)); |
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180 | opB = __SXTB16(__ROR(inB1, 8)); |
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181 | sum2 = __SMLAD(opA, opB, sum2); |
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182 | opA = __SXTB16(inA2); |
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183 | opB = __SXTB16(inB2); |
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184 | sum3 = __SMLAD(opA, opB, sum3); |
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185 | opA = __SXTB16(__ROR(inA2, 8)); |
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186 | opB = __SXTB16(__ROR(inB2, 8)); |
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187 | sum4 = __SMLAD(opA, opB, sum4); |
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188 | colCnt--; |
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189 | } |
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190 | #else |
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191 | |||
192 | while (colCnt) |
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193 | { |
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194 | q31_t inA1, inA2, inB1, inB2, opA, opB; |
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195 | |||
196 | inB1 = *__SIMD32(pB); |
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197 | pB += ch_im_in; |
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198 | opB = *__SIMD32(pB); |
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199 | pB += ch_im_in; |
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200 | inB2 = __PKHBT(opB, inB1, 16); |
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201 | inB1 = __PKHTB(inB1, opB, 16); |
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202 | inA1 = *__SIMD32(pA); |
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203 | pA += ch_im_in; |
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204 | opB = *__SIMD32(pA); |
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205 | pA += ch_im_in; |
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206 | inA2 = __PKHBT(opB, inA1, 16); |
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207 | inA1 = __PKHTB(inA1, opB, 16); |
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208 | opA = __SXTB16(inA1); |
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209 | opB = __SXTB16(inB1); |
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210 | sum2 = __SMLAD(opA, opB, sum2); |
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211 | opA = __SXTB16(__ROR(inA1, 8)); |
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212 | opB = __SXTB16(__ROR(inB1, 8)); |
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213 | sum = __SMLAD(opA, opB, sum); |
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214 | opA = __SXTB16(inA2); |
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215 | opB = __SXTB16(inB2); |
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216 | sum4 = __SMLAD(opA, opB, sum4); |
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217 | opA = __SXTB16(__ROR(inA2, 8)); |
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218 | opB = __SXTB16(__ROR(inB2, 8)); |
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219 | sum3 = __SMLAD(opA, opB, sum3); |
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220 | colCnt--; |
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221 | } |
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222 | |||
223 | #endif /* ARM_MATH_BIG_ENDIAN */ |
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224 | |||
225 | #else |
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226 | |||
227 | #ifndef ARM_MATH_BIG_ENDIAN |
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228 | /* |
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229 | * r0 r1 r2 r3 r4 r5 |
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230 | * inA1, inA2, inB1, inB2, opA, opB |
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231 | */ |
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232 | |||
233 | asm volatile ("COL_LOOP_%=:\n" |
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234 | "ldr.w r2, [%[pB], #0]\n" |
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235 | "add.w %[pB], %[pB], %[ch_im_in]\n" |
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236 | "ldr.w r5, [%[pB], #0]\n" |
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237 | "add.w %[pB], %[pB], %[ch_im_in]\n" |
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238 | "pkhtb r3, r5, r2, ASR #16\n" |
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239 | "pkhbt r2, r2, r5, LSL #16\n" |
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240 | "ldr.w r0, [%[pA], #0]\n" |
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241 | "add.w %[pA], %[pA], %[ch_im_in]\n" |
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242 | "ldr.w r5, [%[pA], #0]\n" |
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243 | "add.w %[pA], %[pA], %[ch_im_in]\n" |
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244 | "pkhtb r1, r5, r0, ASR #16\n" |
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245 | "pkhbt r0, r0, r5, LSL #16\n" |
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246 | "sxtb16 r4, r0\n" |
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247 | "sxtb16 r5, r2\n" |
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248 | "smlad %[sum], r4, r5, %[sum]\n" |
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249 | "mov.w r4, r0, ror #8\n" |
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250 | "mov.w r5, r2, ror #8\n" |
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251 | "sxtb16 r4, r4\n" |
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252 | "sxtb16 r5, r5\n" |
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253 | "smlad %[sum2], r4, r5, %[sum2]\n" |
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254 | "sxtb16 r4, r1\n" |
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255 | "sxtb16 r5, r3\n" |
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256 | "smlad %[sum3], r4, r5, %[sum3]\n" |
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257 | "mov.w r4, r1, ror #8\n" |
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258 | "mov.w r5, r3, ror #8\n" |
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259 | "sxtb16 r4, r4\n" |
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260 | "sxtb16 r5, r5\n" |
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261 | "smlad %[sum4], r4, r5, %[sum4]\n" |
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262 | "subs %[colCnt], #1\n" |
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263 | "bne COL_LOOP_%=\n":[sum] |
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264 | "+r"(sum),[sum2] "+r"(sum2), |
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265 | [sum3] "+r"(sum3), |
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266 | [sum4] "+r"(sum4),[pB] "+r"(pB), |
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267 | [pA] "+r"(pA):[colCnt] |
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268 | "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); |
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269 | #else |
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270 | /* |
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271 | * r0 r1 r2 r3 r4 r5 |
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272 | * inA1, inA2, inB1, inB2, opA, opB |
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273 | */ |
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274 | asm volatile ("COL_LOOP_%=:\n" |
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275 | "ldr.w r2, [%[pB], #0]\n" |
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276 | "add.w %[pB], %[pB], %[ch_im_in]\n" |
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277 | "ldr.w r5, [%[pB], #0]\n" |
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278 | "add.w %[pB], %[pB], %[ch_im_in]\n" |
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279 | "pkhbt r3, r5, r2, LSL #16\n" |
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280 | "pkhtb r2, r2, r5, ASR #16\n" |
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281 | "ldr.w r0, [%[pA], #0]\n" |
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282 | "add.w %[pA], %[pA], %[ch_im_in]\n" |
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283 | "ldr.w r5, [%[pA], #0]\n" |
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284 | "add.w %[pA], %[pA], %[ch_im_in]\n" |
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285 | "pkhbt r1, r5, r0, LSL #16\n" |
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286 | "pkhtb r0, r0, r5, ASR #16\n" |
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287 | "sxtb16 r4, r0\n" |
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288 | "sxtb16 r5, r2\n" |
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289 | "smlad %[sum2], r4, r5, %[sum2]\n" |
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290 | "mov.w r4, r0, ror #8\n" |
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291 | "mov.w r5, r2, ror #8\n" |
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292 | "sxtb16 r4, r4\n" |
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293 | "sxtb16 r5, r5\n" |
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294 | "smlad %[sum], r4, r5, %[sum]\n" |
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295 | "sxtb16 r4, r1\n" |
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296 | "sxtb16 r5, r3\n" |
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297 | "smlad %[sum4], r4, r5, %[sum4]\n" |
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298 | "mov.w r4, r1, ror #8\n" |
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299 | "mov.w r5, r3, ror #8\n" |
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300 | "sxtb16 r4, r4\n" |
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301 | "sxtb16 r5, r5\n" |
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302 | "smlad %[sum3], r4, r5, %[sum3]\n" |
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303 | "subs %[colCnt], #1\n" |
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304 | "bne COL_LOOP_%=\n":[sum] |
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305 | "+r"(sum),[sum2] "+r"(sum2), |
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306 | [sum3] "+r"(sum3), |
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307 | [sum4] "+r"(sum4),[pB] "+r"(pB), |
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308 | [pA] "+r"(pA):[colCnt] |
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309 | "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); |
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310 | |||
311 | #endif /* ARM_MATH_BIG_ENDIAN */ |
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312 | |||
313 | #endif /* USE_INTRINSIC */ |
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314 | |||
315 | colCnt = (dim_kernel * dim_kernel) & 0x1; |
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316 | while (colCnt) |
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317 | { |
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318 | union arm_nnword inA, inB; |
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319 | inA.word = *__SIMD32(pA); |
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320 | pA += ch_im_in; |
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321 | inB.word = *__SIMD32(pB); |
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322 | pB += ch_im_in; |
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323 | sum += inA.bytes[0] * inB.bytes[0]; |
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324 | sum2 += inA.bytes[1] * inB.bytes[1]; |
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325 | sum3 += inA.bytes[2] * inB.bytes[2]; |
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326 | sum4 += inA.bytes[3] * inB.bytes[3]; |
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327 | colCnt--; |
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328 | } |
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329 | |||
330 | *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); |
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331 | *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8); |
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332 | *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); |
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333 | *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8); |
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334 | |||
335 | rowCnt--; |
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336 | } |
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337 | |||
338 | rowCnt = ch_im_out & 0x3; |
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339 | while (rowCnt) |
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340 | { |
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341 | q7_t *pB = colBuffer + row_shift; |
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342 | const q7_t *pA = wt + row_shift; |
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343 | q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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344 | uint16_t colCnt = (dim_kernel * dim_kernel); |
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345 | |||
346 | row_shift += 1; |
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347 | |||
348 | while (colCnt) |
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349 | { |
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350 | q7_t A1 = *pA; |
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351 | q7_t B1 = *pB; |
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352 | pA += ch_im_in; |
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353 | pB += ch_im_in; |
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354 | sum += A1 * B1; |
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355 | |||
356 | colCnt--; |
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357 | } |
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358 | *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); |
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359 | rowCnt--; |
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360 | } |
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361 | |||
362 | /* clear counter and pointers */ |
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363 | pBuffer = colBuffer; |
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364 | } |
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365 | } |
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366 | |||
367 | #else |
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368 | /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ |
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369 | int i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y; |
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370 | int conv_out; |
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371 | |||
372 | /* do some checking here, basically ch_im_in == ch_im_out */ |
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373 | if (ch_im_in != ch_im_out) |
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374 | { |
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375 | return ARM_MATH_SIZE_MISMATCH; |
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376 | } |
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377 | |||
378 | for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) |
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379 | { |
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380 | for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) |
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381 | { |
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382 | for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) |
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383 | { |
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384 | // for each output |
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385 | conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); |
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386 | for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++) |
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387 | { |
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388 | for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++) |
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389 | { |
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390 | int in_row = stride * i_out_y + i_ker_y - padding; |
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391 | int in_col = stride * i_out_x + i_ker_x - padding; |
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392 | if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) |
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393 | { |
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394 | conv_out += |
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395 | Im_in[(in_row * |
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396 | dim_im_in + |
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397 | in_col) * |
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398 | ch_im_in + |
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399 | i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out]; |
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400 | } |
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401 | } |
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402 | } |
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403 | Im_out[(i_out_y * dim_im_out + |
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404 | i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); |
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405 | } |
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406 | } |
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407 | } |
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408 | |||
409 | #endif /* ARM_MATH_DSP */ |
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410 | |||
411 | /* Return to application */ |
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412 | return ARM_MATH_SUCCESS; |
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413 | |||
414 | } |
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415 | |||
416 | /** |
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417 | * @} end of NNConv group |
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418 | */ |