Subversion Repositories dashGPS

Rev

Go to most recent revision | Blame | Compare with Previous | Last modification | View Log | Download | RSS feed

  1. /*
  2.  * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
  3.  *
  4.  * SPDX-License-Identifier: Apache-2.0
  5.  *
  6.  * Licensed under the Apache License, Version 2.0 (the License); you may
  7.  * not use this file except in compliance with the License.
  8.  * You may obtain a copy of the License at
  9.  *
  10.  * www.apache.org/licenses/LICENSE-2.0
  11.  *
  12.  * Unless required by applicable law or agreed to in writing, software
  13.  * distributed under the License is distributed on an AS IS BASIS, WITHOUT
  14.  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  15.  * See the License for the specific language governing permissions and
  16.  * limitations under the License.
  17.  */
  18.  
  19. /* ----------------------------------------------------------------------
  20.  * Project:      CMSIS NN Library
  21.  * Title:        arm_softmax_q7.c
  22.  * Description:  Q7 softmax function
  23.  *
  24.  * $Date:        20. February 2018
  25.  * $Revision:    V.1.0.0
  26.  *
  27.  * Target Processor:  Cortex-M cores
  28.  *
  29.  * -------------------------------------------------------------------- */
  30.  
  31. #include "arm_math.h"
  32. #include "arm_nnfunctions.h"
  33.  
  34. /**
  35.  *  @ingroup groupNN
  36.  */
  37.  
  38. /**
  39.  * @addtogroup Softmax
  40.  * @{
  41.  */
  42.  
  43.   /**
  44.    * @brief Q7 softmax function
  45.    * @param[in]       vec_in      pointer to input vector
  46.    * @param[in]       dim_vec     input vector dimention
  47.    * @param[out]      p_out       pointer to output vector
  48.    * @return none.
  49.    *
  50.    * @details
  51.    *
  52.    *  Here, instead of typical natural logarithm e based softmax, we use
  53.    *  2-based softmax here, i.e.,:
  54.    *
  55.    *  y_i = 2^(x_i) / sum(2^x_j)
  56.    *
  57.    *  The relative output will be different here.
  58.    *  But mathematically, the gradient will be the same
  59.    *  with a log(2) scaling factor.
  60.    *
  61.    */
  62.  
  63. void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
  64. {
  65.     q31_t     sum;
  66.     int16_t   i;
  67.     uint8_t   shift;
  68.     q15_t     base;
  69.     base = -257;
  70.  
  71.     /* We first search for the maximum */
  72.     for (i = 0; i < dim_vec; i++)
  73.     {
  74.         if (vec_in[i] > base)
  75.         {
  76.             base = vec_in[i];
  77.         }
  78.     }
  79.  
  80.     /*
  81.      * So the base is set to max-8, meaning
  82.      * that we ignore really small values.
  83.      * anyway, they will be 0 after shrinking to q7_t.
  84.      */
  85.     base = base - 8;
  86.  
  87.     sum = 0;
  88.  
  89.     for (i = 0; i < dim_vec; i++)
  90.     {
  91.         if (vec_in[i] > base)
  92.         {
  93.             shift = (uint8_t)__USAT(vec_in[i] - base, 5);
  94.             sum += 0x1 << shift;
  95.         }
  96.     }
  97.  
  98.     /* This is effectively (0x1 << 20) / sum */
  99.     int output_base = 0x100000 / sum;
  100.  
  101.     /*
  102.      * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
  103.      * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12
  104.      * and vec_in[i]-base = 8
  105.      */
  106.     for (i = 0; i < dim_vec; i++)
  107.     {
  108.         if (vec_in[i] > base)
  109.         {
  110.             /* Here minimum value of 13+base-vec_in[i] will be 5 */
  111.             shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
  112.             p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
  113.         } else {
  114.             p_out[i] = 0;
  115.         }
  116.     }
  117. }
  118.  
  119. /**
  120.  * @} end of Softmax group
  121.  */
  122.