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https://github.com/pjreddie/darknet.git
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good chance I didn't break anything
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@ -48,25 +48,25 @@ void binarize_input_gpu(float *input, int n, int size, float *binary)
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}
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__global__ void binarize_filters_kernel(float *filters, int n, int size, float *binary)
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__global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary)
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{
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int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (f >= n) return;
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int i = 0;
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float mean = 0;
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for(i = 0; i < size; ++i){
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mean += abs(filters[f*size + i]);
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mean += abs(weights[f*size + i]);
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}
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mean = mean / size;
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for(i = 0; i < size; ++i){
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binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
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//binary[f*size + i] = filters[f*size + i];
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binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
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//binary[f*size + i] = weights[f*size + i];
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}
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}
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void binarize_filters_gpu(float *filters, int n, int size, float *binary)
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void binarize_weights_gpu(float *weights, int n, int size, float *binary)
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{
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binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, binary);
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binarize_weights_kernel<<<cuda_gridsize(n), BLOCK>>>(weights, n, size, binary);
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check_error(cudaPeekAtLastError());
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}
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@ -74,12 +74,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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{
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fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
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if(l.binary){
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binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
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binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
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swap_binary(&l);
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}
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if(l.xnor){
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binarize_filters_gpu(l.filters_gpu, l.n, l.c*l.size*l.size, l.binary_filters_gpu);
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binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
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swap_binary(&l);
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binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
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state.input = l.binary_input_gpu;
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@ -91,8 +91,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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&one,
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l.srcTensorDesc,
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state.input,
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l.filterDesc,
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l.filters_gpu,
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l.weightDesc,
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l.weights_gpu,
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l.convDesc,
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l.fw_algo,
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state.workspace,
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@ -108,7 +108,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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int n = l.out_w*l.out_h;
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for(i = 0; i < l.batch; ++i){
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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);
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float * a = l.filters_gpu;
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float * a = l.weights_gpu;
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float * b = state.workspace;
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float * c = l.output_gpu;
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
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@ -150,15 +150,15 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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state.workspace,
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l.workspace_size,
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&one,
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l.dfilterDesc,
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l.filter_updates_gpu);
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l.dweightDesc,
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l.weight_updates_gpu);
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if(state.delta){
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if(l.binary || l.xnor) swap_binary(&l);
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cudnnConvolutionBackwardData(cudnn_handle(),
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&one,
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l.filterDesc,
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l.filters_gpu,
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l.weightDesc,
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l.weights_gpu,
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l.ddstTensorDesc,
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l.delta_gpu,
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l.convDesc,
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@ -181,14 +181,14 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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for(i = 0; i < l.batch; ++i){
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float * a = l.delta_gpu;
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float * b = state.workspace;
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float * c = l.filter_updates_gpu;
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float * c = l.weight_updates_gpu;
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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);
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gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
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if(state.delta){
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if(l.binary || l.xnor) swap_binary(&l);
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float * a = l.filters_gpu;
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float * a = l.weights_gpu;
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float * b = l.delta_gpu;
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float * c = state.workspace;
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@ -206,9 +206,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
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void pull_convolutional_layer(convolutional_layer layer)
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{
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cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
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cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
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cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
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cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
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cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
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if (layer.batch_normalize){
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cuda_pull_array(layer.scales_gpu, layer.scales, layer.n);
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@ -219,9 +219,9 @@ void pull_convolutional_layer(convolutional_layer layer)
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void push_convolutional_layer(convolutional_layer layer)
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{
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cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
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cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
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cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
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cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
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cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
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cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
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if (layer.batch_normalize){
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cuda_push_array(layer.scales_gpu, layer.scales, layer.n);
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@ -240,9 +240,9 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
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axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
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scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
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axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
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axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
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scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
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axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
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axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
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scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
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}
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