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lots of stuff

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This commit is contained in:
Joseph Redmon
2016-01-28 12:30:38 -08:00
parent 1578ec70d7
commit 913d355ec1
35 changed files with 913 additions and 86 deletions
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42
src/convolutional_kernels.cu
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@ -12,6 +12,21 @@ extern "C" {
#include "cuda.h"
}
__global__ void binarize_filters_kernel(float *filters, int n, int size, float *binary)
{
int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (f >= n) return;
int i = 0;
float mean = 0;
for(i = 0; i < size; ++i){
mean += abs(filters[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
}
}
__global__ void scale_bias_kernel(float *output, float *biases, int n, int size)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
@ -50,6 +65,12 @@ __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, in
}
}
void binarize_filters_gpu(float *filters, int n, int size, float *mean)
{
binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, mean);
check_error(cudaPeekAtLastError());
}
void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
backward_scale_kernel<<<n, BLOCK>>>(x_norm, delta, batch, n, size, scale_updates);
@ -100,6 +121,13 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int
check_error(cudaPeekAtLastError());
}
void swap_binary(convolutional_layer l)
{
float *swap = l.filters_gpu;
l.filters_gpu = l.binary_filters_gpu;
l.binary_filters_gpu = swap;
}
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int i;
@ -109,6 +137,11 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
convolutional_out_width(l);
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
if(l.binary){
binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
swap_binary(l);
}
for(i = 0; i < l.batch; ++i){
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu);
float * a = l.filters_gpu;
@ -122,12 +155,6 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.mean_gpu);
fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.variance_gpu);
/*
cuda_pull_array(l.variance_gpu, l.mean, 1);
printf("%f\n", l.mean[0]);
*/
scal_ongpu(l.n, .95, l.rolling_mean_gpu, 1);
axpy_ongpu(l.n, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
scal_ongpu(l.n, .95, l.rolling_variance_gpu, 1);
@ -145,6 +172,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
if(l.binary) swap_binary(l);
}
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
@ -178,6 +206,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
if(l.binary) swap_binary(l);
float * a = l.filters_gpu;
float * b = l.delta_gpu;
float * c = l.col_image_gpu;
@ -185,6 +214,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w);
if(l.binary) swap_binary(l);
}
}
}