1 | /* |
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2 | Copyright (C) 2018 Paul Brossier <piem@aubio.org> |
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3 | |
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4 | This file is part of aubio. |
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5 | |
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6 | aubio is free software: you can redistribute it and/or modify |
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7 | it under the terms of the GNU General Public License as published by |
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8 | the Free Software Foundation, either version 3 of the License, or |
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9 | (at your option) any later version. |
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10 | |
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11 | aubio is distributed in the hope that it will be useful, |
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12 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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13 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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14 | GNU General Public License for more details. |
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15 | |
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16 | You should have received a copy of the GNU General Public License |
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17 | along with aubio. If not, see <http://www.gnu.org/licenses/>. |
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18 | |
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19 | */ |
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20 | |
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21 | #include "aubio_priv.h" |
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22 | #include "fmat.h" |
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23 | #include "tensor.h" |
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24 | #include "batchnorm.h" |
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25 | |
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26 | struct _aubio_batchnorm_t { |
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27 | uint_t n_outputs; |
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28 | fvec_t *gamma; |
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29 | fvec_t *beta; |
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30 | fvec_t *moving_mean; |
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31 | fvec_t *moving_variance; |
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32 | }; |
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33 | |
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34 | static void aubio_batchnorm_debug(aubio_batchnorm_t *c, |
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35 | aubio_tensor_t *input_tensor); |
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36 | |
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37 | aubio_batchnorm_t *new_aubio_batchnorm(uint_t n_outputs) |
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38 | { |
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39 | aubio_batchnorm_t *c = AUBIO_NEW(aubio_batchnorm_t); |
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40 | |
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41 | AUBIO_GOTO_FAILURE((sint_t)n_outputs > 0); |
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42 | |
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43 | c->n_outputs = n_outputs; |
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44 | |
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45 | c->gamma = new_fvec(n_outputs); |
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46 | c->beta = new_fvec(n_outputs); |
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47 | c->moving_mean = new_fvec(n_outputs); |
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48 | c->moving_variance = new_fvec(n_outputs); |
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49 | |
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50 | return c; |
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51 | |
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52 | failure: |
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53 | del_aubio_batchnorm(c); |
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54 | return NULL; |
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55 | } |
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56 | |
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57 | void del_aubio_batchnorm(aubio_batchnorm_t* c) { |
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58 | AUBIO_ASSERT(c); |
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59 | if (c->gamma) |
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60 | del_fvec(c->gamma); |
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61 | if (c->beta) |
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62 | del_fvec(c->beta); |
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63 | if (c->moving_mean) |
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64 | del_fvec(c->moving_mean); |
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65 | if (c->moving_variance) |
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66 | del_fvec(c->moving_variance); |
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67 | AUBIO_FREE(c); |
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68 | } |
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69 | |
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70 | void aubio_batchnorm_debug(aubio_batchnorm_t *c, aubio_tensor_t *input_tensor) |
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71 | { |
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72 | AUBIO_DBG("batchnorm: %15s -> %s (%d params) (4 * (%d,))\n", |
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73 | aubio_tensor_get_shape_string(input_tensor), |
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74 | aubio_tensor_get_shape_string(input_tensor), // same output shape |
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75 | c->n_outputs, 4 * c->n_outputs); |
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76 | } |
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77 | |
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78 | uint_t aubio_batchnorm_get_output_shape(aubio_batchnorm_t *c, |
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79 | aubio_tensor_t *input, uint_t *shape) |
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80 | { |
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81 | AUBIO_ASSERT(c && input && shape); |
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82 | |
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83 | shape[0] = input->shape[0]; |
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84 | shape[1] = input->shape[1]; |
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85 | shape[2] = input->shape[2]; |
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86 | |
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87 | aubio_batchnorm_debug(c, input); |
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88 | |
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89 | return AUBIO_OK; |
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90 | } |
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91 | |
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92 | void aubio_batchnorm_do(aubio_batchnorm_t *c, aubio_tensor_t *input_tensor, |
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93 | aubio_tensor_t *activations) |
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94 | { |
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95 | uint_t i, j, k; |
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96 | uint_t jj; |
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97 | smpl_t s; |
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98 | AUBIO_ASSERT(c); |
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99 | AUBIO_ASSERT_EQUAL_SHAPE(input_tensor, activations); |
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100 | if (input_tensor->ndim == 3) { |
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101 | for (i = 0; i < activations->shape[0]; i++) { |
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102 | jj = 0; |
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103 | for (j = 0; j < activations->shape[1]; j++) { |
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104 | for (k = 0; k < activations->shape[2]; k++) { |
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105 | s = input_tensor->data[i][jj + k]; |
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106 | s -= c->moving_mean->data[k]; |
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107 | s *= c->gamma->data[k]; |
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108 | s /= SQRT(c->moving_variance->data[k] + 1.e-4); |
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109 | s += c->beta->data[k]; |
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110 | activations->data[i][jj + k] = s; |
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111 | } |
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112 | jj += activations->shape[2]; |
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113 | } |
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114 | } |
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115 | } else if (input_tensor->ndim == 2) { |
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116 | for (i = 0; i < activations->shape[0]; i++) { |
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117 | for (j = 0; j < activations->shape[1]; j++) { |
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118 | s = input_tensor->data[i][j]; |
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119 | s -= c->moving_mean->data[j]; |
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120 | s *= c->gamma->data[j]; |
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121 | s /= SQRT(c->moving_variance->data[j] + 1.e-4); |
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122 | s += c->beta->data[j]; |
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123 | activations->data[i][j] = s; |
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124 | } |
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125 | } |
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126 | } |
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127 | } |
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128 | |
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129 | uint_t aubio_batchnorm_set_gamma(aubio_batchnorm_t *t, fvec_t *gamma) |
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130 | { |
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131 | AUBIO_ASSERT(t && t->gamma); |
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132 | AUBIO_ASSERT(gamma); |
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133 | if (t->gamma->length != gamma->length) return AUBIO_FAIL; |
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134 | fvec_copy(gamma, t->gamma); |
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135 | return AUBIO_OK; |
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136 | } |
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137 | |
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138 | uint_t aubio_batchnorm_set_beta(aubio_batchnorm_t *t, fvec_t *beta) |
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139 | { |
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140 | AUBIO_ASSERT(t && t->beta); |
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141 | AUBIO_ASSERT(beta); |
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142 | if (t->beta->length != beta->length) return AUBIO_FAIL; |
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143 | fvec_copy(beta, t->beta); |
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144 | return AUBIO_OK; |
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145 | } |
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146 | |
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147 | uint_t aubio_batchnorm_set_moving_mean(aubio_batchnorm_t *t, fvec_t *moving_mean) |
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148 | { |
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149 | AUBIO_ASSERT(t && t->moving_mean); |
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150 | AUBIO_ASSERT(moving_mean); |
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151 | if (t->moving_mean->length != moving_mean->length) return AUBIO_FAIL; |
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152 | fvec_copy(moving_mean, t->moving_mean); |
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153 | return AUBIO_OK; |
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154 | } |
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155 | |
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156 | uint_t aubio_batchnorm_set_moving_variance(aubio_batchnorm_t *t, fvec_t *moving_variance) |
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157 | { |
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158 | AUBIO_ASSERT(t && t->moving_variance); |
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159 | AUBIO_ASSERT(moving_variance); |
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160 | if (t->moving_variance->length != moving_variance->length) return AUBIO_FAIL; |
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161 | fvec_copy(moving_variance, t->moving_variance); |
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162 | return AUBIO_OK; |
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163 | } |
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164 | |
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165 | fvec_t *aubio_batchnorm_get_gamma(aubio_batchnorm_t *t) |
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166 | { |
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167 | AUBIO_ASSERT(t && t->gamma); |
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168 | return t->gamma; |
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169 | } |
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170 | |
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171 | fvec_t *aubio_batchnorm_get_beta(aubio_batchnorm_t *t) |
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172 | { |
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173 | AUBIO_ASSERT(t && t->beta); |
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174 | return t->beta; |
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175 | } |
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176 | |
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177 | fvec_t *aubio_batchnorm_get_moving_mean(aubio_batchnorm_t *t) |
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178 | { |
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179 | AUBIO_ASSERT(t && t->moving_mean); |
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180 | return t->moving_mean; |
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181 | } |
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182 | |
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183 | fvec_t *aubio_batchnorm_get_moving_variance(aubio_batchnorm_t *t) |
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184 | { |
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185 | AUBIO_ASSERT(t && t->moving_variance); |
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186 | return t->moving_variance; |
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187 | } |
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