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https://github.com/pjreddie/darknet.git
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This commit is contained in:
Joseph Redmon
275
src/local_layer.c
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275
src/local_layer.c
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#include "local_layer.h"
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#include "utils.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "blas.h"
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#include "gemm.h"
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#include <stdio.h>
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#include <time.h>
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int local_out_height(local_layer l)
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{
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int h = l.h;
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if (!l.pad) h -= l.size;
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else h -= 1;
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return h/l.stride + 1;
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}
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int local_out_width(local_layer l)
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{
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int w = l.w;
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if (!l.pad) w -= l.size;
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else w -= 1;
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return w/l.stride + 1;
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}
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local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
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{
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int i;
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local_layer l = {0};
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l.type = LOCAL;
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l.h = h;
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l.w = w;
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l.c = c;
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l.n = n;
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l.batch = batch;
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l.stride = stride;
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l.size = size;
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l.pad = pad;
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int out_h = local_out_height(l);
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int out_w = local_out_width(l);
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int locations = out_h*out_w;
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l.out_h = out_h;
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l.out_w = out_w;
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l.out_c = n;
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l.outputs = l.out_h * l.out_w * l.out_c;
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l.inputs = l.w * l.h * l.c;
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l.filters = calloc(c*n*size*size*locations, sizeof(float));
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l.filter_updates = calloc(c*n*size*size*locations, sizeof(float));
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l.biases = calloc(l.outputs, sizeof(float));
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l.bias_updates = calloc(l.outputs, sizeof(float));
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// float scale = 1./sqrt(size*size*c);
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float scale = sqrt(2./(size*size*c));
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for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale;
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l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
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l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
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l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
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#ifdef GPU
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l.filters_gpu = cuda_make_array(l.filters, c*n*size*size*locations);
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l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size*locations);
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l.biases_gpu = cuda_make_array(l.biases, l.outputs);
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l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs);
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l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
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l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
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l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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#endif
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l.activation = activation;
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fprintf(stderr, "Local Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
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return l;
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}
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void forward_local_layer(const local_layer l, network_state state)
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{
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int out_h = local_out_height(l);
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int out_w = local_out_width(l);
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int i, j;
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int locations = out_h * out_w;
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for(i = 0; i < l.batch; ++i){
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copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
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}
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for(i = 0; i < l.batch; ++i){
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float *input = state.input + i*l.w*l.h*l.c;
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im2col_cpu(input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, l.col_image);
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float *output = l.output + i*l.outputs;
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for(j = 0; j < locations; ++j){
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float *a = l.filters + j*l.size*l.size*l.c*l.n;
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float *b = l.col_image + j;
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float *c = output + j;
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int m = l.n;
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int n = 1;
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int k = l.size*l.size*l.c;
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gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
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}
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}
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activate_array(l.output, l.outputs*l.batch, l.activation);
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}
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void backward_local_layer(local_layer l, network_state state)
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{
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int i, j;
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int locations = l.out_w*l.out_h;
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gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
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for(i = 0; i < l.batch; ++i){
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axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
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}
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for(i = 0; i < l.batch; ++i){
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float *input = state.input + i*l.w*l.h*l.c;
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im2col_cpu(input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, l.col_image);
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for(j = 0; j < locations; ++j){
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float *a = l.delta + i*l.outputs + j;
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float *b = l.col_image + j;
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float *c = l.filter_updates + j*l.size*l.size*l.c*l.n;
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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 = 1;
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gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
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}
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if(state.delta){
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for(j = 0; j < locations; ++j){
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float *a = l.filters + j*l.size*l.size*l.c*l.n;
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float *b = l.delta + i*l.outputs + j;
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float *c = l.col_image + j;
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int m = l.size*l.size*l.c;
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int n = 1;
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int k = l.n;
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gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
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}
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col2im_cpu(l.col_image, 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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}
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}
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}
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void update_local_layer(local_layer l, int batch, float learning_rate, float momentum, float decay)
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{
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int locations = l.out_w*l.out_h;
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int size = l.size*l.size*l.c*l.n*locations;
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axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
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scal_cpu(l.outputs, momentum, l.bias_updates, 1);
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axpy_cpu(size, -decay*batch, l.filters, 1, l.filter_updates, 1);
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axpy_cpu(size, learning_rate/batch, l.filter_updates, 1, l.filters, 1);
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scal_cpu(size, momentum, l.filter_updates, 1);
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}
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#ifdef GPU
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void forward_local_layer_gpu(const local_layer l, network_state state)
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{
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int out_h = local_out_height(l);
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int out_w = local_out_width(l);
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int i, j;
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int locations = out_h * out_w;
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for(i = 0; i < l.batch; ++i){
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copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
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}
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for(i = 0; i < l.batch; ++i){
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float *input = state.input + i*l.w*l.h*l.c;
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im2col_ongpu(input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, l.col_image_gpu);
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float *output = l.output_gpu + i*l.outputs;
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for(j = 0; j < locations; ++j){
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float *a = l.filters_gpu + j*l.size*l.size*l.c*l.n;
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float *b = l.col_image_gpu + j;
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float *c = output + j;
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int m = l.n;
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int n = 1;
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int k = l.size*l.size*l.c;
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gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
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}
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}
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activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
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}
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void backward_local_layer_gpu(local_layer l, network_state state)
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{
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int i, j;
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int locations = l.out_w*l.out_h;
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gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
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for(i = 0; i < l.batch; ++i){
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axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
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}
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for(i = 0; i < l.batch; ++i){
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float *input = state.input + i*l.w*l.h*l.c;
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im2col_ongpu(input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, l.col_image_gpu);
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for(j = 0; j < locations; ++j){
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float *a = l.delta_gpu + i*l.outputs + j;
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float *b = l.col_image_gpu + j;
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float *c = l.filter_updates_gpu + j*l.size*l.size*l.c*l.n;
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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 = 1;
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gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
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}
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if(state.delta){
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for(j = 0; j < locations; ++j){
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float *a = l.filters_gpu + j*l.size*l.size*l.c*l.n;
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float *b = l.delta_gpu + i*l.outputs + j;
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float *c = l.col_image_gpu + j;
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int m = l.size*l.size*l.c;
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int n = 1;
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int k = l.n;
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gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
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}
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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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}
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}
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}
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void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay)
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{
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int locations = l.out_w*l.out_h;
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int size = l.size*l.size*l.c*l.n*locations;
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axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
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scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
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axpy_ongpu(size, -decay*batch, l.filters_gpu, 1, l.filter_updates_gpu, 1);
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axpy_ongpu(size, learning_rate/batch, l.filter_updates_gpu, 1, l.filters_gpu, 1);
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scal_ongpu(size, momentum, l.filter_updates_gpu, 1);
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}
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void pull_local_layer(local_layer l)
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{
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int locations = l.out_w*l.out_h;
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int size = l.size*l.size*l.c*l.n*locations;
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cuda_pull_array(l.filters_gpu, l.filters, size);
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cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
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}
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void push_local_layer(local_layer l)
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{
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int locations = l.out_w*l.out_h;
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int size = l.size*l.size*l.c*l.n*locations;
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cuda_push_array(l.filters_gpu, l.filters, size);
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cuda_push_array(l.biases_gpu, l.biases, l.outputs);
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}
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#endif
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