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good chance I didn't break anything

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This commit is contained in:
Joseph Redmon
2016-09-12 13:55:20 -07:00
parent 8ec889f103
commit 5c067dc447
19 changed files with 558 additions and 298 deletions
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48
src/convolutional_kernels.cu
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@ -48,25 +48,25 @@ void binarize_input_gpu(float *input, int n, int size, float *binary)
}
__global__ void binarize_filters_kernel(float *filters, int n, int size, float *binary)
__global__ void binarize_weights_kernel(float *weights, 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 += abs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
//binary[f*size + i] = filters[f*size + i];
binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
//binary[f*size + i] = weights[f*size + i];
}
}
void binarize_filters_gpu(float *filters, int n, int size, float *binary)
void binarize_weights_gpu(float *weights, int n, int size, float *binary)
{
binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, binary);
binarize_weights_kernel<<<cuda_gridsize(n), BLOCK>>>(weights, n, size, binary);
check_error(cudaPeekAtLastError());
}
@ -74,12 +74,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
{
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);
binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
}
if(l.xnor){
binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
swap_binary(&l);
binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
state.input = l.binary_input_gpu;
@ -91,8 +91,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
&one,
l.srcTensorDesc,
state.input,
l.filterDesc,
l.filters_gpu,
l.weightDesc,
l.weights_gpu,
l.convDesc,
l.fw_algo,
state.workspace,
@ -108,7 +108,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
int n = l.out_w*l.out_h;
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, state.workspace);
float * a = l.filters_gpu;
float * a = l.weights_gpu;
float * b = state.workspace;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
@ -150,15 +150,15 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
state.workspace,
l.workspace_size,
&one,
l.dfilterDesc,
l.filter_updates_gpu);
l.dweightDesc,
l.weight_updates_gpu);
if(state.delta){
if(l.binary || l.xnor) swap_binary(&l);
cudnnConvolutionBackwardData(cudnn_handle(),
&one,
l.filterDesc,
l.filters_gpu,
l.weightDesc,
l.weights_gpu,
l.ddstTensorDesc,
l.delta_gpu,
l.convDesc,
@ -181,14 +181,14 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;
float * b = state.workspace;
float * c = l.filter_updates_gpu;
float * c = l.weight_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, state.workspace);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
if(l.binary || l.xnor) swap_binary(&l);
float * a = l.filters_gpu;
float * a = l.weights_gpu;
float * b = l.delta_gpu;
float * c = state.workspace;
@ -206,9 +206,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
void pull_convolutional_layer(convolutional_layer layer)
{
cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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.weight_updates_gpu, layer.weight_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);
@ -219,9 +219,9 @@ void pull_convolutional_layer(convolutional_layer layer)
void push_convolutional_layer(convolutional_layer layer)
{
cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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.weight_updates_gpu, layer.weight_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);
@ -240,9 +240,9 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
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);
axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
}