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| Rev | Author | Line No. | Line |
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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_softmax_q7.c |
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| 22 | * Description: Q7 softmax function |
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| 23 | * |
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| 24 | * $Date: 20. February 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 Softmax |
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| 40 | * @{ |
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| 41 | */ |
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| 42 | |||
| 43 | /** |
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| 44 | * @brief Q7 softmax function |
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| 45 | * @param[in] vec_in pointer to input vector |
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| 46 | * @param[in] dim_vec input vector dimention |
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| 47 | * @param[out] p_out pointer to output vector |
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| 48 | * @return none. |
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| 49 | * |
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| 50 | * @details |
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| 51 | * |
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| 52 | * Here, instead of typical natural logarithm e based softmax, we use |
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| 53 | * 2-based softmax here, i.e.,: |
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| 54 | * |
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| 55 | * y_i = 2^(x_i) / sum(2^x_j) |
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| 56 | * |
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| 57 | * The relative output will be different here. |
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| 58 | * But mathematically, the gradient will be the same |
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| 59 | * with a log(2) scaling factor. |
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| 60 | * |
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| 61 | */ |
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| 62 | |||
| 63 | void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out) |
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| 64 | { |
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| 65 | q31_t sum; |
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| 66 | int16_t i; |
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| 67 | uint8_t shift; |
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| 68 | q15_t base; |
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| 69 | base = -257; |
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| 70 | |||
| 71 | /* We first search for the maximum */ |
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| 72 | for (i = 0; i < dim_vec; i++) |
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| 73 | { |
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| 74 | if (vec_in[i] > base) |
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| 75 | { |
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| 76 | base = vec_in[i]; |
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| 77 | } |
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| 78 | } |
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| 79 | |||
| 80 | /* |
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| 81 | * So the base is set to max-8, meaning |
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| 82 | * that we ignore really small values. |
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| 83 | * anyway, they will be 0 after shrinking to q7_t. |
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| 84 | */ |
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| 85 | base = base - 8; |
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| 86 | |||
| 87 | sum = 0; |
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| 88 | |||
| 89 | for (i = 0; i < dim_vec; i++) |
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| 90 | { |
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| 91 | if (vec_in[i] > base) |
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| 92 | { |
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| 93 | shift = (uint8_t)__USAT(vec_in[i] - base, 5); |
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| 94 | sum += 0x1 << shift; |
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| 95 | } |
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| 96 | } |
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| 97 | |||
| 98 | /* This is effectively (0x1 << 20) / sum */ |
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| 99 | int output_base = 0x100000 / sum; |
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| 100 | |||
| 101 | /* |
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| 102 | * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) ) |
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| 103 | * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12 |
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| 104 | * and vec_in[i]-base = 8 |
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| 105 | */ |
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| 106 | for (i = 0; i < dim_vec; i++) |
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| 107 | { |
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| 108 | if (vec_in[i] > base) |
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| 109 | { |
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| 110 | /* Here minimum value of 13+base-vec_in[i] will be 5 */ |
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| 111 | shift = (uint8_t)__USAT(13+base-vec_in[i], 5); |
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| 112 | p_out[i] = (q7_t) __SSAT((output_base >> shift), 8); |
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| 113 | } else { |
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| 114 | p_out[i] = 0; |
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| 115 | } |
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| 116 | } |
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| 117 | } |
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| 118 | |||
| 119 | /** |
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| 120 | * @} end of Softmax group |
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| 121 | */ |