#ifndef ACTIVATIONS_H #define ACTIVATIONS_H #include "darknet.h" #include "dark_cuda.h" #include "math.h" #include "utils.h" //typedef enum{ // LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH //}ACTIVATION; #ifdef __cplusplus extern "C" { #endif ACTIVATION get_activation(char *s); char *get_activation_string(ACTIVATION a); float activate(float x, ACTIVATION a); float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta); void gradient_array_mish(const int n, const float * activation_input, float * delta); void gradient_array_hard_mish(const int n, const float * activation_input, float * delta); void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); void activate_array_mish(float *x, const int n, float * activation_input, float * output); void activate_array_hard_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta); void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output, int use_max_val); void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu); void activate_array_hard_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu); void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); void gradient_array_hard_mish_ongpu(int n, float *activation_input_gpu, float *delta); void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); void gradient_array_normalize_channels_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu, int use_max_val); void gradient_array_normalize_channels_softmax_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); #endif static inline float stair_activate(float x) { int n = floorf(x); if (n%2 == 0) return floorf(x/2.f); else return (x - n) + floorf(x/2.f); } static inline float hardtan_activate(float x) { if (x < -1) return -1; if (x > 1) return 1; return x; } static inline float linear_activate(float x){return x;} static inline float logistic_activate(float x){return 1.f/(1.f + expf(-x));} static inline float loggy_activate(float x){return 2.f/(1.f + expf(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} static inline float relu6_activate(float x) { return min_val_cmp(max_val_cmp(x, 0), 6); } static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} static inline float selu_activate(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } static inline float relie_activate(float x){return (x>0) ? x : .01f*x;} static inline float ramp_activate(float x){return x*(x>0)+.1f*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1f*x;} //static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} static inline float tanh_activate(float x) { return (2 / (1 + expf(-2 * x)) - 1); } static inline float gelu_activate(float x) { return (0.5*x*(1 + tanhf(0.797885*x + 0.035677*powf(x, 3)))); } static inline float softplus_activate(float x, float threshold) { if (x > threshold) return x; // too large else if (x < -threshold) return expf(x); // too small return logf(expf(x) + 1); } static inline float plse_activate(float x) { if(x < -4) return .01f * (x + 4); if(x > 4) return .01f * (x - 4) + 1; return .125f*x + .5f; } static inline float lhtan_activate(float x) { if(x < 0) return .001f*x; if(x > 1) return .001f*(x-1) + 1; return x; } static inline float lhtan_gradient(float x) { if(x > 0 && x < 1) return 1; return .001f; } static inline float hardtan_gradient(float x) { if (x > -1 && x < 1) return 1; return 0; } static inline float linear_gradient(float x){return 1;} static inline float logistic_gradient(float x){return (1-x)*x;} static inline float loggy_gradient(float x) { float y = (x+1.f)/2.f; return 2*(1-y)*y; } static inline float stair_gradient(float x) { if (floor(x) == x) return 0; return 1.0f; } static inline float relu_gradient(float x){return (x>0);} static inline float relu6_gradient(float x) { return (x > 0 && x < 6); } static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} static inline float selu_gradient(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); } static inline float relie_gradient(float x){return (x>0) ? 1 : .01f;} static inline float ramp_gradient(float x){return (x>0)+.1f;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1f;} static inline float tanh_gradient(float x){return 1-x*x;} static inline float sech(float x) { return 2 / (expf(x) + expf(-x)); } static inline float gelu_gradient(float x) { const float x3 = powf(x, 3); return 0.5*tanhf(0.0356774*x3 + 0.797885*x) + (0.0535161*x3 + 0.398942*x) * powf(sech(0.0356774*x3 + 0.797885*x), 2) + 0.5; } static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01f : .125f;} #ifdef __cplusplus } #endif #endif