#include "cuda_runtime.h"
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#include "curand.h"
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#include "cublas_v2.h"
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extern "C" {
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#include "convolutional_layer.h"
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#include "batchnorm_layer.h"
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#include "gemm.h"
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#include "blas.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "utils.h"
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#include "cuda.h"
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}
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__global__ void binarize_kernel(float *x, int n, float *binary)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= n) return;
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binary[i] = (x[i] > 0) ? 1 : -1;
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}
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void binarize_gpu(float *x, int n, float *binary)
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{
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binarize_kernel<<<cuda_gridsize(n), BLOCK>>>(x, n, binary);
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check_error(cudaPeekAtLastError());
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}
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__global__ void binarize_input_kernel(float *input, int n, int size, float *binary)
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{
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int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (s >= size) return;
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int i = 0;
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float mean = 0;
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for(i = 0; i < n; ++i){
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mean += abs(input[i*size + s]);
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}
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mean = mean / n;
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for(i = 0; i < n; ++i){
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binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
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}
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}
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void binarize_input_gpu(float *input, int n, int size, float *binary)
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{
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binarize_input_kernel<<<cuda_gridsize(size), BLOCK>>>(input, n, size, binary);
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check_error(cudaPeekAtLastError());
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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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{
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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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}
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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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}
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}
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void binarize_filters_gpu(float *filters, 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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check_error(cudaPeekAtLastError());
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}
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void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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{
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int i;
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int m = l.n;
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int k = l.size*l.size*l.c;
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int n = convolutional_out_height(l)*
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convolutional_out_width(l);
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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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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_gpu(l.filters_gpu, l.n*l.c*l.size*l.size, l.binary_filters_gpu);
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swap_binary(&l);
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for(i = 0; i < l.batch; ++i){
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binarize_input_gpu(state.input + i*l.inputs, l.c, l.h*l.w, l.binary_input_gpu + i*l.inputs);
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}
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state.input = l.binary_input_gpu;
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}
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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, l.col_image_gpu);
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float * a = l.filters_gpu;
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float * b = l.col_image_gpu;
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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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}
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if (l.batch_normalize) {
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forward_batchnorm_layer_gpu(l, state);
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}
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
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activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
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//if(l.dot > 0) dot_error_gpu(l);
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if(l.binary || l.xnor) swap_binary(&l);
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}
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void backward_convolutional_layer_gpu(convolutional_layer l, network_state state)
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{
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int i;
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int m = l.n;
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int n = l.size*l.size*l.c;
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int k = convolutional_out_height(l)*
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convolutional_out_width(l);
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gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu);
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backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
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if(l.batch_normalize){
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backward_batchnorm_layer_gpu(l, state);
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}
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if(l.xnor) state.input = l.binary_input_gpu;
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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 = l.col_image_gpu;
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float * c = l.filter_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, l.col_image_gpu);
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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 * b = l.delta_gpu;
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float * c = l.col_image_gpu;
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gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
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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);
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if(l.binary || l.xnor) swap_binary(&l);
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}
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}
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}
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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.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.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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cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
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cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
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}
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}
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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.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.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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cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
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cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
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}
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}
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void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
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{
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int size = layer.size*layer.size*layer.c*layer.n;
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axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
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scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
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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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}
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