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CVPR Experiments

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This commit is contained in:
Joseph Redmon
2015-11-03 19:23:17 -08:00
parent c40cdeb402
commit 8fd18add6e
27 changed files with 1426 additions and 491 deletions
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161
src/convolutional_kernels.cu
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@ -8,21 +8,65 @@ extern "C" {
#include "cuda.h"
}
__global__ void bias_output_kernel(float *output, float *biases, int n, int size)
__global__ void scale_bias_kernel(float *output, float *biases, int n, int size)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
int filter = blockIdx.y;
int batch = blockIdx.z;
if(offset < size) output[(batch*n+filter)*size + offset] = biases[filter];
if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter];
}
void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
void scale_bias_gpu(float *output, float *biases, int batch, int n, int size)
{
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
bias_output_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
__shared__ float part[BLOCK];
int i,b;
int filter = blockIdx.x;
int p = threadIdx.x;
float sum = 0;
for(b = 0; b < batch; ++b){
for(i = 0; i < size; i += BLOCK){
int index = p + i + size*(filter + n*b);
sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
}
}
part[p] = sum;
__syncthreads();
if (p == 0) {
for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
}
}
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);
check_error(cudaPeekAtLastError());
}
__global__ void add_bias_kernel(float *output, float *biases, int n, int size)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
int filter = blockIdx.y;
int batch = blockIdx.z;
if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter];
}
void add_bias_gpu(float *output, float *biases, int batch, int n, int size)
{
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
add_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
@ -41,7 +85,7 @@ __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batc
}
part[p] = sum;
__syncthreads();
if(p == 0){
if (p == 0) {
for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
}
}
@ -52,53 +96,88 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int
check_error(cudaPeekAtLastError());
}
void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
int n = convolutional_out_height(layer)*
convolutional_out_width(layer);
int m = l.n;
int k = l.size*l.size*l.c;
int n = convolutional_out_height(l)*
convolutional_out_width(l);
bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
for(i = 0; i < layer.batch; ++i){
im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
float * a = layer.filters_gpu;
float * b = layer.col_image_gpu;
float * c = layer.output_gpu;
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
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;
float * b = l.col_image_gpu;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
}
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
if(l.batch_normalize){
if(state.train){
fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_mean_gpu, l.mean_gpu);
fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_variance_gpu, l.variance_gpu);
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);
axpy_ongpu(l.n, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
// cuda_pull_array(l.variance_gpu, l.mean, l.n);
// printf("%f\n", l.mean[0]);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_h*l.out_w);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
} else {
normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.n, l.out_h*l.out_w);
}
scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w);
}
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);
}
void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
convolutional_out_width(layer);
int m = l.n;
int n = l.size*l.size*l.c;
int k = convolutional_out_height(l)*
convolutional_out_width(l);
gradient_array_ongpu(layer.output_gpu, m*k*layer.batch, layer.activation, layer.delta_gpu);
backward_bias_gpu(layer.bias_updates_gpu, layer.delta_gpu, layer.batch, layer.n, k);
gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu);
for(i = 0; i < layer.batch; ++i){
float * a = layer.delta_gpu;
float * b = layer.col_image_gpu;
float * c = layer.filter_updates_gpu;
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
if(l.batch_normalize){
backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.scale_updates_gpu);
scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w);
fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_mean_delta_gpu, l.mean_delta_gpu);
fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_variance_delta_gpu, l.variance_delta_gpu);
normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.delta_gpu);
}
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;
float * b = l.col_image_gpu;
float * c = l.filter_updates_gpu;
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);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
float * a = layer.filters_gpu;
float * b = layer.delta_gpu;
float * c = layer.col_image_gpu;
float * a = l.filters_gpu;
float * b = l.delta_gpu;
float * c = l.col_image_gpu;
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
col2im_ongpu(layer.col_image_gpu, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, state.delta + i*layer.c*layer.h*layer.w);
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);
}
}
}
@ -109,6 +188,11 @@ void pull_convolutional_layer(convolutional_layer layer)
cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_pull_array(layer.scales_gpu, layer.scales, layer.n);
cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
}
void push_convolutional_layer(convolutional_layer layer)
@ -117,6 +201,11 @@ void push_convolutional_layer(convolutional_layer layer)
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_push_array(layer.scales_gpu, layer.scales, layer.n);
cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
}
void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
@ -126,8 +215,12 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}