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Added efficientnet-lite3.cfg and activation=relu6

This commit is contained in:
AlexeyAB 2020-03-23 21:56:32 +03:00
parent bb5eb481d1
commit a234a50223
6 changed files with 2047 additions and 2 deletions
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1009
build/darknet/x64/cfg/efficientnet-lite3.cfg Normal file
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1009
cfg/efficientnet-lite3.cfg Normal file
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2
include/darknet.h
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@ -104,7 +104,7 @@ typedef struct tree {
// activations.h
typedef enum {
LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL
LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL
}ACTIVATION;
// parser.h

25
src/activation_kernels.cu
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@ -7,7 +7,6 @@
#include "activations.h"
#include "dark_cuda.h"
__device__ float lhtan_activate_kernel(float x)
{
if(x < 0) return .001*x;
@ -30,6 +29,7 @@ __device__ float linear_activate_kernel(float x){return x;}
__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));}
__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;}
__device__ float relu_activate_kernel(float x){return x*(x>0);}
__device__ float relu6_activate_kernel(float x) { return min_val_cmp(max_val_cmp(x, 0), 6); }
__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);}
__device__ float selu_activate_kernel(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); }
__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;}
@ -68,6 +68,7 @@ __device__ float loggy_gradient_kernel(float x)
return 2*(1-y)*y;
}
__device__ float relu_gradient_kernel(float x){return (x>0);}
__device__ float relu6_gradient_kernel(float x) { return (x > 0 && x < 6); }
__device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);}
__device__ float selu_gradient_kernel(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); }
__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;}
@ -92,6 +93,8 @@ __device__ float activate_kernel(float x, ACTIVATION a)
return loggy_activate_kernel(x);
case RELU:
return relu_activate_kernel(x);
case RELU6:
return relu6_activate_kernel(x);
case ELU:
return elu_activate_kernel(x);
case SELU:
@ -127,6 +130,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a)
return loggy_gradient_kernel(x);
case RELU:
return relu_gradient_kernel(x);
case RELU6:
return relu6_gradient_kernel(x);
case NORM_CHAN:
return relu_gradient_kernel(x);
case ELU:
@ -272,6 +277,14 @@ __global__ void activate_array_relu_kernel(float *x, int n)
}
}
__global__ void activate_array_relu6_kernel(float *x, int n)
{
int index = blockIdx.x*blockDim.x + threadIdx.x;
if (index < n) {
x[index] = relu6_activate_kernel(x[index]);
}
}
__global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
@ -362,6 +375,14 @@ __global__ void gradient_array_relu_kernel(float *x, int n, float *delta)
}
}
__global__ void gradient_array_relu6_kernel(float *x, int n, float *delta)
{
int index = blockIdx.x*blockDim.x + threadIdx.x;
if (index < n) {
delta[index] *= relu6_gradient_kernel(x[index]);
}
}
extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a)
{
const int num_blocks = get_number_of_blocks(n, BLOCK);
@ -371,6 +392,7 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a)
else if (a == TANH) activate_array_tanh_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n);
else if (a == HARDTAN) activate_array_hardtan_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n);
else if (a == RELU) activate_array_relu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n);
else if (a == RELU6) activate_array_relu6_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n);
else if (a == SELU) activate_array_selu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n);
else
activate_array_kernel<<<cuda_gridsize(n), BLOCK, 0, get_cuda_stream()>>>(x, n, a);
@ -400,6 +422,7 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta
else if (a == TANH) gradient_array_tanh_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n, delta);
else if (a == HARDTAN) gradient_array_hardtan_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n, delta);
else if (a == RELU) gradient_array_relu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n, delta);
else if (a == RELU6) gradient_array_relu6_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n, delta);
//else if (a == NORM_CHAN) gradient_array_relu_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(x, n, delta);
else if (a == NORM_CHAN_SOFTMAX || a == NORM_CHAN) {
printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n");

1
src/activations.c
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@ -53,6 +53,7 @@ ACTIVATION get_activation(char *s)
if (strcmp(s, "normalize_channels_softmax_maxval") == 0) return NORM_CHAN_SOFTMAX_MAXVAL;
if (strcmp(s, "loggy")==0) return LOGGY;
if (strcmp(s, "relu")==0) return RELU;
if (strcmp(s, "relu6") == 0) return RELU6;
if (strcmp(s, "elu")==0) return ELU;
if (strcmp(s, "selu") == 0) return SELU;
if (strcmp(s, "relie")==0) return RELIE;

3
src/activations.h
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@ -3,6 +3,7 @@
#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
@ -56,6 +57,7 @@ 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;}
@ -105,6 +107,7 @@ static inline float stair_gradient(float x)
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;}