mirror of
https://github.com/pjreddie/darknet.git
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135 lines
4.1 KiB
C
135 lines
4.1 KiB
C
#include "route_layer.h"
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#include "dark_cuda.h"
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#include "blas.h"
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#include <stdio.h>
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route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes)
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{
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fprintf(stderr,"route ");
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route_layer l = { (LAYER_TYPE)0 };
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l.type = ROUTE;
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l.batch = batch;
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l.n = n;
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l.input_layers = input_layers;
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l.input_sizes = input_sizes;
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int i;
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int outputs = 0;
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for(i = 0; i < n; ++i){
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fprintf(stderr," %d", input_layers[i]);
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outputs += input_sizes[i];
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}
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fprintf(stderr, "\n");
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l.outputs = outputs;
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l.inputs = outputs;
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l.delta = (float*)calloc(outputs * batch, sizeof(float));
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l.output = (float*)calloc(outputs * batch, sizeof(float));
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l.forward = forward_route_layer;
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l.backward = backward_route_layer;
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#ifdef GPU
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l.forward_gpu = forward_route_layer_gpu;
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l.backward_gpu = backward_route_layer_gpu;
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l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
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l.output_gpu = cuda_make_array(l.output, outputs*batch);
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#endif
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return l;
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}
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void resize_route_layer(route_layer *l, network *net)
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{
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int i;
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layer first = net->layers[l->input_layers[0]];
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l->out_w = first.out_w;
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l->out_h = first.out_h;
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l->out_c = first.out_c;
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l->outputs = first.outputs;
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l->input_sizes[0] = first.outputs;
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for(i = 1; i < l->n; ++i){
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int index = l->input_layers[i];
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layer next = net->layers[index];
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l->outputs += next.outputs;
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l->input_sizes[i] = next.outputs;
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if(next.out_w == first.out_w && next.out_h == first.out_h){
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l->out_c += next.out_c;
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}else{
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printf("%d %d, %d %d\n", next.out_w, next.out_h, first.out_w, first.out_h);
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l->out_h = l->out_w = l->out_c = 0;
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}
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}
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l->inputs = l->outputs;
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l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float));
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l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float));
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#ifdef GPU
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cuda_free(l->output_gpu);
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cuda_free(l->delta_gpu);
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l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
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l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
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#endif
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}
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void forward_route_layer(const route_layer l, network_state state)
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{
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int i, j;
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int offset = 0;
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for(i = 0; i < l.n; ++i){
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int index = l.input_layers[i];
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float *input = state.net.layers[index].output;
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int input_size = l.input_sizes[i];
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for(j = 0; j < l.batch; ++j){
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copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1);
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}
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offset += input_size;
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}
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}
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void backward_route_layer(const route_layer l, network_state state)
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{
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int i, j;
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int offset = 0;
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for(i = 0; i < l.n; ++i){
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int index = l.input_layers[i];
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float *delta = state.net.layers[index].delta;
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int input_size = l.input_sizes[i];
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for(j = 0; j < l.batch; ++j){
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axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1);
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}
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offset += input_size;
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}
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}
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#ifdef GPU
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void forward_route_layer_gpu(const route_layer l, network_state state)
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{
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int i, j;
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int offset = 0;
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for(i = 0; i < l.n; ++i){
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int index = l.input_layers[i];
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float *input = state.net.layers[index].output_gpu;
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int input_size = l.input_sizes[i];
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for(j = 0; j < l.batch; ++j){
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//copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1);
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simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs);
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}
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offset += input_size;
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}
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}
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void backward_route_layer_gpu(const route_layer l, network_state state)
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{
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int i, j;
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int offset = 0;
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for(i = 0; i < l.n; ++i){
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int index = l.input_layers[i];
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float *delta = state.net.layers[index].delta_gpu;
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int input_size = l.input_sizes[i];
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for(j = 0; j < l.batch; ++j){
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axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1);
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}
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offset += input_size;
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}
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}
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#endif
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