#include "activations.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <float.h>
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char *get_activation_string(ACTIVATION a)
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{
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switch(a){
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case LOGISTIC:
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return "logistic";
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case LOGGY:
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return "loggy";
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case RELU:
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return "relu";
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case ELU:
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return "elu";
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case SELU:
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return "selu";
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case GELU:
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return "gelu";
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case RELIE:
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return "relie";
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case RAMP:
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return "ramp";
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case LINEAR:
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return "linear";
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case TANH:
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return "tanh";
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case PLSE:
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return "plse";
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case LEAKY:
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return "leaky";
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case STAIR:
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return "stair";
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case HARDTAN:
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return "hardtan";
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case LHTAN:
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return "lhtan";
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default:
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break;
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}
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return "relu";
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}
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ACTIVATION get_activation(char *s)
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{
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if (strcmp(s, "logistic")==0) return LOGISTIC;
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if (strcmp(s, "swish") == 0) return SWISH;
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if (strcmp(s, "mish") == 0) return MISH;
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if (strcmp(s, "hard_mish") == 0) return HARD_MISH;
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if (strcmp(s, "normalize_channels") == 0) return NORM_CHAN;
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if (strcmp(s, "normalize_channels_softmax") == 0) return NORM_CHAN_SOFTMAX;
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if (strcmp(s, "normalize_channels_softmax_maxval") == 0) return NORM_CHAN_SOFTMAX_MAXVAL;
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if (strcmp(s, "loggy")==0) return LOGGY;
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if (strcmp(s, "relu")==0) return RELU;
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if (strcmp(s, "relu6") == 0) return RELU6;
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if (strcmp(s, "elu")==0) return ELU;
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if (strcmp(s, "selu") == 0) return SELU;
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if (strcmp(s, "gelu") == 0) return GELU;
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if (strcmp(s, "relie")==0) return RELIE;
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if (strcmp(s, "plse")==0) return PLSE;
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if (strcmp(s, "hardtan")==0) return HARDTAN;
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if (strcmp(s, "lhtan")==0) return LHTAN;
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if (strcmp(s, "linear")==0) return LINEAR;
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if (strcmp(s, "ramp")==0) return RAMP;
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if (strcmp(s, "revleaky") == 0) return REVLEAKY;
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if (strcmp(s, "leaky")==0) return LEAKY;
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if (strcmp(s, "tanh")==0) return TANH;
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if (strcmp(s, "stair")==0) return STAIR;
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fprintf(stderr, "Couldn't find activation function %s, going with ReLU\n", s);
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return RELU;
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}
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float activate(float x, ACTIVATION a)
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{
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switch(a){
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case LINEAR:
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return linear_activate(x);
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case LOGISTIC:
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return logistic_activate(x);
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case LOGGY:
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return loggy_activate(x);
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case RELU:
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return relu_activate(x);
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case ELU:
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return elu_activate(x);
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case SELU:
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return selu_activate(x);
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case GELU:
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return gelu_activate(x);
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case RELIE:
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return relie_activate(x);
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case RAMP:
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return ramp_activate(x);
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case REVLEAKY:
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case LEAKY:
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return leaky_activate(x);
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case TANH:
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return tanh_activate(x);
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case PLSE:
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return plse_activate(x);
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case STAIR:
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return stair_activate(x);
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case HARDTAN:
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return hardtan_activate(x);
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case LHTAN:
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return lhtan_activate(x);
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}
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return 0;
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}
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void activate_array(float *x, const int n, const ACTIVATION a)
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{
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int i;
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if (a == LINEAR) {}
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else if (a == LEAKY) {
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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x[i] = leaky_activate(x[i]);
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}
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}
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else if (a == LOGISTIC) {
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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x[i] = logistic_activate(x[i]);
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}
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}
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else {
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for (i = 0; i < n; ++i) {
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x[i] = activate(x[i], a);
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}
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}
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}
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void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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float x_val = x[i];
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float sigmoid = logistic_activate(x_val);
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output_sigmoid[i] = sigmoid;
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output[i] = x_val * sigmoid;
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}
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}
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// https://github.com/digantamisra98/Mish
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void activate_array_mish(float *x, const int n, float * activation_input, float * output)
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{
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const float MISH_THRESHOLD = 20;
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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float x_val = x[i];
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activation_input[i] = x_val; // store value before activation
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output[i] = x_val * tanh_activate( softplus_activate(x_val, MISH_THRESHOLD) );
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}
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}
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static float hard_mish_yashas(float x)
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{
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if (x > 0)
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return x;
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if (x > -2)
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return x * x / 2 + x;
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return 0;
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}
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void activate_array_hard_mish(float *x, const int n, float * activation_input, float * output)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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float x_val = x[i];
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activation_input[i] = x_val; // store value before activation
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output[i] = hard_mish_yashas(x_val);
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}
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}
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void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output)
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{
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int size = n / channels;
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int wh_i = i % wh_step;
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int b = i / wh_step;
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const float eps = 0.0001;
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if (i < size) {
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float sum = eps;
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int k;
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for (k = 0; k < channels; ++k) {
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float val = x[wh_i + k * wh_step + b*wh_step*channels];
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if (val > 0) sum += val;
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}
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for (k = 0; k < channels; ++k) {
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float val = x[wh_i + k * wh_step + b*wh_step*channels];
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if (val > 0) val = val / sum;
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else val = 0;
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output[wh_i + k * wh_step + b*wh_step*channels] = val;
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}
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}
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}
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}
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void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output, int use_max_val)
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{
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int size = n / channels;
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int wh_i = i % wh_step;
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int b = i / wh_step;
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const float eps = 0.0001;
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if (i < size) {
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float sum = eps;
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float max_val = -FLT_MAX;
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int k;
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if (use_max_val) {
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for (k = 0; k < channels; ++k) {
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float val = x[wh_i + k * wh_step + b*wh_step*channels];
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if (val > max_val || k == 0) max_val = val;
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}
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}
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else
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max_val = 0;
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for (k = 0; k < channels; ++k) {
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float val = x[wh_i + k * wh_step + b*wh_step*channels];
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sum += expf(val - max_val);
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}
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for (k = 0; k < channels; ++k) {
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float val = x[wh_i + k * wh_step + b*wh_step*channels];
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val = expf(val - max_val) / sum;
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output[wh_i + k * wh_step + b*wh_step*channels] = val;
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}
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}
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}
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}
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void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta)
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{
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int size = n / channels;
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int wh_i = i % wh_step;
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int b = i / wh_step;
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if (i < size) {
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float grad = 0;
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int k;
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for (k = 0; k < channels; ++k) {
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const int index = wh_i + k * wh_step + b*wh_step*channels;
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float out = x[index];
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float d = delta[index];
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grad += out*d;
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}
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for (k = 0; k < channels; ++k) {
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const int index = wh_i + k * wh_step + b*wh_step*channels;
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float d = delta[index];
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d = d * grad;
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delta[index] = d;
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}
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}
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}
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}
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void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta)
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{
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int size = n / channels;
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int wh_i = i % wh_step;
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int b = i / wh_step;
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if (i < size) {
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float grad = 0;
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int k;
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for (k = 0; k < channels; ++k) {
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const int index = wh_i + k * wh_step + b*wh_step*channels;
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float out = x[index];
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float d = delta[index];
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grad += out*d;
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}
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for (k = 0; k < channels; ++k) {
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const int index = wh_i + k * wh_step + b*wh_step*channels;
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if (x[index] > 0) {
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float d = delta[index];
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d = d * grad;
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delta[index] = d;
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}
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}
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}
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}
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}
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float gradient(float x, ACTIVATION a)
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{
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switch(a){
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case LINEAR:
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return linear_gradient(x);
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case LOGISTIC:
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return logistic_gradient(x);
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case LOGGY:
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return loggy_gradient(x);
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case RELU:
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return relu_gradient(x);
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case RELU6:
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return relu6_gradient(x);
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case NORM_CHAN:
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//return relu_gradient(x);
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case NORM_CHAN_SOFTMAX_MAXVAL:
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//...
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case NORM_CHAN_SOFTMAX:
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printf(" Error: should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient \n");
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exit(0);
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return 0;
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case ELU:
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return elu_gradient(x);
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case SELU:
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return selu_gradient(x);
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case GELU:
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return gelu_gradient(x);
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case RELIE:
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return relie_gradient(x);
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case RAMP:
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return ramp_gradient(x);
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case REVLEAKY:
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case LEAKY:
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return leaky_gradient(x);
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case TANH:
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return tanh_gradient(x);
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case PLSE:
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return plse_gradient(x);
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case STAIR:
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return stair_gradient(x);
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case HARDTAN:
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return hardtan_gradient(x);
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case LHTAN:
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return lhtan_gradient(x);
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}
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return 0;
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}
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void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta)
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{
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int i;
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#pragma omp parallel for
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for(i = 0; i < n; ++i){
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delta[i] *= gradient(x[i], a);
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}
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}
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// https://github.com/BVLC/caffe/blob/04ab089db018a292ae48d51732dd6c66766b36b6/src/caffe/layers/swish_layer.cpp#L54-L56
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void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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float swish = x[i];
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delta[i] *= swish + sigmoid[i]*(1 - swish);
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}
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}
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// https://github.com/digantamisra98/Mish
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void gradient_array_mish(const int n, const float * activation_input, float * delta)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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const float MISH_THRESHOLD = 20.0f;
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// implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed
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// implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31
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float inp = activation_input[i];
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const float sp = softplus_activate(inp, MISH_THRESHOLD);
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const float grad_sp = 1 - exp(-sp);
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const float tsp = tanh(sp);
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const float grad_tsp = (1 - tsp*tsp) * grad_sp;
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const float grad = inp * grad_tsp + tsp;
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delta[i] *= grad;
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//float x = activation_input[i];
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//float d = 2 * expf(x) + expf(2 * x) + 2;
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//float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6);
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//float derivative = expf(x) * w / (d * d);
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//delta[i] *= derivative;
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}
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}
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static float hard_mish_yashas_grad(float x)
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{
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if (x > 0)
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return 1;
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if (x > -2)
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return x + 1;
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return 0;
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}
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void gradient_array_hard_mish(const int n, const float * activation_input, float * delta)
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{
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int i;
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#pragma omp parallel for
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for (i = 0; i < n; ++i) {
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float inp = activation_input[i];
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delta[i] *= hard_mish_yashas_grad(inp);
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}
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}
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