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Subdivisions for batches

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This commit is contained in:
Joseph Redmon
2015-03-22 09:56:40 -07:00
parent 9d418102f4
commit 664c5dd2f2
10 changed files with 44 additions and 83 deletions
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33
src/convolutional_kernels.cu
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@ -48,15 +48,12 @@ __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batc
extern "C" void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
{
float alpha = 1./batch;
backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size, alpha);
backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size, 1);
check_error(cudaPeekAtLastError());
}
extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
//clock_t time = clock();
int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
@ -64,36 +61,18 @@ extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, netwo
convolutional_out_width(layer);
bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
//cudaDeviceSynchronize();
//printf("bias %f\n", sec(clock() - time));
//time = clock();
//float imt=0;
//float gemt = 0;
for(i = 0; i < layer.batch; ++i){
//time = clock();
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);
//cudaDeviceSynchronize();
//imt += sec(clock()-time);
//time = clock();
float * a = layer.filters_gpu;
float * b = layer.col_image_gpu;
float * c = layer.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
//cudaDeviceSynchronize();
//gemt += sec(clock()-time);
//time = clock();
}
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
//cudaDeviceSynchronize();
//printf("activate %f\n", sec(clock() - time));
//printf("im2col %f\n", imt);
//printf("gemm %f\n", gemt);
}
extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
@ -111,7 +90,7 @@ extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, netw
float * c = layer.filter_updates_gpu;
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);
gemm_ongpu(0,1,m,n,k,alpha,a + i*m*k,k,b,k,1,c,n);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
@ -142,15 +121,15 @@ extern "C" void push_convolutional_layer(convolutional_layer layer)
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, float learning_rate, float momentum, float decay)
extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
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(size, -decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
axpy_ongpu(size, learning_rate, layer.filter_updates_gpu, 1, layer.filters_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);
}