#include "dark_cuda.h"
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#include <stdio.h>
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#include <time.h>
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#include <assert.h>
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#include "network.h"
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#include "image.h"
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#include "data.h"
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#include "utils.h"
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#include "parser.h"
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#include "crop_layer.h"
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#include "connected_layer.h"
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#include "rnn_layer.h"
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#include "gru_layer.h"
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#include "crnn_layer.h"
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#include "detection_layer.h"
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#include "region_layer.h"
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#include "convolutional_layer.h"
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#include "activation_layer.h"
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#include "maxpool_layer.h"
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#include "reorg_layer.h"
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#include "avgpool_layer.h"
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#include "normalization_layer.h"
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#include "batchnorm_layer.h"
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#include "cost_layer.h"
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#include "local_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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#include "route_layer.h"
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#include "shortcut_layer.h"
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#include "blas.h"
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//#ifdef OPENCV
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//#include <opencv2/highgui/highgui_c.h>
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//#endif
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#include "http_stream.h"
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float * get_network_output_gpu_layer(network net, int i);
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float * get_network_delta_gpu_layer(network net, int i);
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float * get_network_output_gpu(network net);
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typedef struct time_benchmark_layers {
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float time;
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int layer_id, layer_type;
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} time_benchmark_layers;
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int time_comparator(const void *pa, const void *pb)
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{
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time_benchmark_layers a = *(time_benchmark_layers *)pa;
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time_benchmark_layers b = *(time_benchmark_layers *)pb;
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float diff = a.time - b.time;
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if (diff < 0) return 1;
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else if (diff > 0) return -1;
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return 0;
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}
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void forward_network_gpu(network net, network_state state)
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{
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static time_benchmark_layers *avg_time_per_layer = NULL;
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static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
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double start_time, end_time;
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if (net.benchmark_layers) {
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if (!avg_time_per_layer) {
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avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
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sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
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}
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cudaDeviceSynchronize();
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}
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//printf("\n");
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state.workspace = net.workspace;
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int i;
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for(i = 0; i < net.n; ++i){
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state.index = i;
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layer l = net.layers[i];
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if(l.delta_gpu && state.train){
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fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
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}
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if (net.benchmark_layers) {
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start_time = get_time_point();
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}
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l.forward_gpu(l, state);
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if (net.benchmark_layers) {
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CHECK_CUDA(cudaDeviceSynchronize());
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end_time = get_time_point();
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const double took_time = (end_time - start_time) / 1000;
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const double alpha = 0.9;
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if (avg_time_per_layer[i].time == 0) {
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avg_time_per_layer[i].layer_id = i;
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avg_time_per_layer[i].layer_type = l.type;
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avg_time_per_layer[i].time = took_time;
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}
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else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
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sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
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printf("\n fw-layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time);
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}
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if(net.wait_stream)
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cudaStreamSynchronize(get_cuda_stream());
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state.input = l.output_gpu;
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//cudaDeviceSynchronize();
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/*
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cuda_pull_array(l.output_gpu, l.output, l.outputs);
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cudaStreamSynchronize(get_cuda_stream());
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float avg_val = 0;
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int k;
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for (k = 0; k < l.outputs; ++k) avg_val += l.output[k];
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printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs);
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*/
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/*
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cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
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if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) {
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int j;
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for (j = 0; j < l.out_c; ++j) {
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image img = make_image(l.out_w, l.out_h, 3);
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memcpy(img.data, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
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memcpy(img.data + l.out_w*l.out_h * 1, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
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memcpy(img.data + l.out_w*l.out_h * 2, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
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char buff[256];
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sprintf(buff, "layer-%d slice-%d", i, j);
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show_image(img, buff);
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save_image(img, buff);
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}
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cvWaitKey(0); // wait press-key in console
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cvDestroyAllWindows();
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}
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*/
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}
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if (net.benchmark_layers) {
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printf("\n\nSorted by time (forward):\n");
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qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
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for (i = 0; i < net.n; ++i) {
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//printf("layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time);
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printf("%d - fw-sort-layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time);
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}
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}
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//cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions
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//cudaDeviceSynchronize();
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}
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void backward_network_gpu(network net, network_state state)
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{
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static time_benchmark_layers *avg_time_per_layer = NULL;
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static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
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double start_time, end_time;
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if (net.benchmark_layers) {
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if (!avg_time_per_layer) {
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avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
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sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
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}
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cudaDeviceSynchronize();
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}
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state.workspace = net.workspace;
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int i;
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float * original_input = state.input;
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float * original_delta = state.delta;
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for(i = net.n-1; i >= 0; --i){
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state.index = i;
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layer l = net.layers[i];
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if (l.stopbackward == 1) break;
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if (l.stopbackward > get_current_iteration(net)) break;
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if(i == 0){
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state.input = original_input;
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state.delta = original_delta;
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}else{
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layer prev = net.layers[i-1];
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state.input = prev.output_gpu;
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state.delta = prev.delta_gpu;
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if (net.optimized_memory && !prev.keep_delta_gpu) {
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state.delta = net.state_delta_gpu;
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}
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}
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if (l.onlyforward) continue;
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if (net.benchmark_layers) {
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start_time = get_time_point();
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}
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l.backward_gpu(l, state);
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if (net.benchmark_layers) {
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CHECK_CUDA(cudaDeviceSynchronize());
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end_time = get_time_point();
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const double took_time = (end_time - start_time) / 1000;
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const double alpha = 0.9;
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if (avg_time_per_layer[i].time == 0) {
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avg_time_per_layer[i].layer_id = i;
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avg_time_per_layer[i].layer_type = l.type;
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avg_time_per_layer[i].time = took_time;
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}
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else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
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sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
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printf("\n bw-layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time);
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}
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if (i != 0) {
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layer prev = net.layers[i - 1];
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if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) {
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if (prev.delta_gpu != state.delta) simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu);
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fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1);
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}
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}
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/*
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if(i != 0)
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{
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layer l = net.layers[i - 1];
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int state_delta_nan_inf = is_nan_or_inf(state.delta, l.outputs * l.batch);
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int state_input_nan_inf = is_nan_or_inf(state.input, l.outputs * l.batch);
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printf("\n i - %d is_nan_or_inf(s.delta) = %d \n", i, state_delta_nan_inf);
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printf(" i - %d is_nan_or_inf(s.input) = %d \n", i, state_input_nan_inf);
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if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); getchar(); }
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}
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*/
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}
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if (net.adversarial && net.attention)
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{
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int img_size = net.w * net.h * net.c;
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float *original_input_cpu = (float *)xcalloc(img_size, sizeof(float));
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float *original_delta_cpu = (float *)xcalloc(img_size, sizeof(float));
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cuda_pull_array(original_input, original_input_cpu, img_size);
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cuda_pull_array(original_delta, original_delta_cpu, img_size);
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image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c);
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show_image(attention_img, "attention_img");
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resize_window_cv("attention_img", 500, 500);
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free_image(attention_img);
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free(original_input_cpu);
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free(original_delta_cpu);
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}
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if (net.adversarial) {
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int x_size = get_network_input_size(net)*net.batch;
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printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %f \n",
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x_size, original_delta, original_input, net.learning_rate);
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axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1);
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constrain_min_max_ongpu(x_size, 0, 1, original_input, 1);
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}
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if (net.benchmark_layers) {
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printf("\n\nSorted by time (backward):\n");
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qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
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for (i = 0; i < net.n; ++i) {
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//printf("layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time);
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printf("%d - bw-sort-layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time);
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}
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}
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}
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void update_network_gpu(network net)
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{
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cuda_set_device(net.gpu_index);
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const int iteration_num = (*net.seen) / (net.batch * net.subdivisions);
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int i;
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int update_batch = net.batch*net.subdivisions * get_sequence_value(net);
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float rate = get_current_rate(net);
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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l.t = get_current_batch(net);
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if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0;
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if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue;
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if (l.train_only_bn) continue;
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if(l.update_gpu && l.dont_update < iteration_num){
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l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
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}
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}
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}
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void forward_backward_network_gpu(network net, float *x, float *y)
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{
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network_state state;
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state.index = 0;
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state.net = net;
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch;
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if(!*net.input_gpu){
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*net.input_gpu = cuda_make_array(x, x_size);
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*net.truth_gpu = cuda_make_array(y, y_size);
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}else{
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cuda_push_array(*net.input_gpu, x, x_size);
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cuda_push_array(*net.truth_gpu, y, y_size);
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}
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state.input = *net.input_gpu;
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state.delta = 0;
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if (net.adversarial) {
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state.delta = cuda_make_array(NULL, x_size);
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}
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state.truth = *net.truth_gpu;
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state.train = 1;
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#if defined(CUDNN_HALF) && defined(CUDNN)
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int i;
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for (i = 0; i < net.n; ++i) {
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layer l = net.layers[i];
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if (net.cudnn_half){
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if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) {
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assert((l.nweights) > 0);
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cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16);
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}
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else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) {
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assert((l.input_layer->c*l.input_layer->n*l.input_layer->size*l.input_layer->size) > 0);
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cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16);
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cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16);
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cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16);
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}
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else if (l.type == CONV_LSTM && l.wf->weights_gpu && l.wf->weights_gpu16) {
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assert((l.wf->c * l.wf->n * l.wf->size * l.wf->size) > 0);
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if (l.peephole) {
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cuda_convert_f32_to_f16(l.vf->weights_gpu, l.vf->nweights, l.vf->weights_gpu16);
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cuda_convert_f32_to_f16(l.vi->weights_gpu, l.vi->nweights, l.vi->weights_gpu16);
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cuda_convert_f32_to_f16(l.vo->weights_gpu, l.vo->nweights, l.vo->weights_gpu16);
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}
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cuda_convert_f32_to_f16(l.wf->weights_gpu, l.wf->nweights, l.wf->weights_gpu16);
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if (!l.bottleneck) {
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cuda_convert_f32_to_f16(l.wi->weights_gpu, l.wi->nweights, l.wi->weights_gpu16);
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cuda_convert_f32_to_f16(l.wg->weights_gpu, l.wg->nweights, l.wg->weights_gpu16);
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cuda_convert_f32_to_f16(l.wo->weights_gpu, l.wo->nweights, l.wo->weights_gpu16);
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}
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cuda_convert_f32_to_f16(l.uf->weights_gpu, l.uf->nweights, l.uf->weights_gpu16);
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cuda_convert_f32_to_f16(l.ui->weights_gpu, l.ui->nweights, l.ui->weights_gpu16);
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cuda_convert_f32_to_f16(l.ug->weights_gpu, l.ug->nweights, l.ug->weights_gpu16);
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cuda_convert_f32_to_f16(l.uo->weights_gpu, l.uo->nweights, l.uo->weights_gpu16);
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}
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}
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}
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#endif
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forward_network_gpu(net, state);
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//cudaStreamSynchronize(get_cuda_stream());
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backward_network_gpu(net, state);
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if (net.adversarial) {
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cuda_free(state.delta);
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cuda_pull_array(*net.input_gpu, x, x_size);
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}
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if(*(state.net.total_bbox) > 0)
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fprintf(stderr, " total_bbox = %d, rewritten_bbox = %f %% \n", *(state.net.total_bbox), 100 * (float)*(state.net.rewritten_bbox) / *(state.net.total_bbox));
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}
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float train_network_datum_gpu(network net, float *x, float *y)
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{
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*net.seen += net.batch;
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if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) {
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net.adversarial = 1;
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float lr_old = net.learning_rate;
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float scale = (get_current_iteration(net) / ((float)net.max_batches));
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//scale = sin(scale * M_PI);
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net.learning_rate = net.adversarial_lr * scale;
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layer l = net.layers[net.n - 1];
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int y_size = get_network_output_size(net)*net.batch;
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if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch;
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float *truth_cpu = (float *)xcalloc(y_size, sizeof(float));
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const int img_size = net.w*net.h*net.c;
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float *old_input = (float *)xcalloc(img_size*net.batch, sizeof(float));
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memcpy(old_input, x, img_size*net.batch * sizeof(float));
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printf("\n adversarial training, adversarial_lr = %f \n", net.adversarial_lr * scale);
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forward_backward_network_gpu(net, x, truth_cpu);
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int b;
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for (b = 0; b < net.batch; ++b) {
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if (b % 2 == 1 && net.contrastive) {
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//printf(" b = %d old img, ", b);
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memcpy(x + img_size*b, old_input + img_size*b, img_size * sizeof(float));
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}
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}
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image im;
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im.w = net.w;
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im.h = net.h;
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im.c = net.c;
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im.data = x;
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show_image(im, "adversarial data augmentation");
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resize_window_cv("adversarial data augmentation", 500, 500);
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wait_key_cv(1);
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free(old_input);
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free(truth_cpu);
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net.learning_rate = lr_old;
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net.adversarial = 0;
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}
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forward_backward_network_gpu(net, x, y);
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float error = get_network_cost(net);
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//if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
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const int sequence = get_sequence_value(net);
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//if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net);
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return error;
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}
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typedef struct {
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network net;
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data d;
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float *err;
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} train_args;
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void *train_thread(void *ptr)
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{
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train_args args = *(train_args*)ptr;
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free(ptr);
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cuda_set_device(args.net.gpu_index);
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*args.err = train_network(args.net, args.d);
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return 0;
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}
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pthread_t train_network_in_thread(network net, data d, float *err)
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{
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pthread_t thread;
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train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
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ptr->net = net;
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ptr->d = d;
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ptr->err = err;
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if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
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return thread;
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}
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void pull_updates(layer l)
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{
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if(l.type == CONVOLUTIONAL){
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cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
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cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
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if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n);
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} else if(l.type == CONNECTED){
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cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
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cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
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}
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}
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void push_updates(layer l)
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{
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if(l.type == CONVOLUTIONAL){
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cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
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cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
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if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n);
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} else if(l.type == CONNECTED){
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cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
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cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
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}
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}
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void update_layer(layer l, network net)
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{
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int update_batch = net.batch*net.subdivisions;
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float rate = get_current_rate(net);
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l.t = get_current_batch(net);
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if(l.update_gpu){
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l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
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}
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}
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void merge_weights(layer l, layer base)
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{
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if (l.type == CONVOLUTIONAL) {
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axpy_cpu(l.n, 1, l.biases, 1, base.biases, 1);
|
axpy_cpu(l.nweights, 1, l.weights, 1, base.weights, 1);
|
if (l.scales) {
|
axpy_cpu(l.n, 1, l.scales, 1, base.scales, 1);
|
}
|
} else if(l.type == CONNECTED) {
|
axpy_cpu(l.outputs, 1, l.biases, 1, base.biases, 1);
|
axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, base.weights, 1);
|
}
|
}
|
|
void scale_weights(layer l, float s)
|
{
|
if (l.type == CONVOLUTIONAL) {
|
scal_cpu(l.n, s, l.biases, 1);
|
scal_cpu(l.nweights, s, l.weights, 1);
|
if (l.scales) {
|
scal_cpu(l.n, s, l.scales, 1);
|
}
|
} else if(l.type == CONNECTED) {
|
scal_cpu(l.outputs, s, l.biases, 1);
|
scal_cpu(l.outputs*l.inputs, s, l.weights, 1);
|
}
|
}
|
|
|
void pull_weights(layer l)
|
{
|
if(l.type == CONVOLUTIONAL){
|
cuda_pull_array(l.biases_gpu, l.biases, l.n);
|
cuda_pull_array(l.weights_gpu, l.weights, l.nweights);
|
if(l.scales) cuda_pull_array(l.scales_gpu, l.scales, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
|
cuda_pull_array(l.weights_gpu, l.weights, l.outputs*l.inputs);
|
}
|
}
|
|
void push_weights(layer l)
|
{
|
if(l.type == CONVOLUTIONAL){
|
cuda_push_array(l.biases_gpu, l.biases, l.n);
|
cuda_push_array(l.weights_gpu, l.weights, l.nweights);
|
if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_push_array(l.biases_gpu, l.biases, l.outputs);
|
cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs);
|
}
|
}
|
|
void distribute_weights(layer l, layer base)
|
{
|
if(l.type == CONVOLUTIONAL){
|
cuda_push_array(l.biases_gpu, base.biases, l.n);
|
cuda_push_array(l.weights_gpu, base.weights, l.nweights);
|
if(base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_push_array(l.biases_gpu, base.biases, l.outputs);
|
cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs);
|
}
|
}
|
|
|
void merge_updates(layer l, layer base)
|
{
|
if (l.type == CONVOLUTIONAL) {
|
axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1);
|
axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1);
|
if (l.scale_updates) {
|
axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1);
|
}
|
} else if(l.type == CONNECTED) {
|
axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1);
|
axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1);
|
}
|
}
|
|
void distribute_updates(layer l, layer base)
|
{
|
if(l.type == CONVOLUTIONAL){
|
cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n);
|
cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights);
|
if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n);
|
} else if(l.type == CONNECTED){
|
cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs);
|
cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs);
|
}
|
}
|
|
void sync_layer(network *nets, int n, int j)
|
{
|
//printf("Syncing layer %d\n", j);
|
int i;
|
network net = nets[0];
|
layer base = net.layers[j];
|
cuda_set_device(net.gpu_index);
|
pull_weights(base);
|
for (i = 1; i < n; ++i) {
|
cuda_set_device(nets[i].gpu_index);
|
layer l = nets[i].layers[j];
|
pull_weights(l);
|
merge_weights(l, base);
|
}
|
scale_weights(base, 1./n);
|
for (i = 0; i < n; ++i) {
|
cuda_set_device(nets[i].gpu_index);
|
layer l = nets[i].layers[j];
|
distribute_weights(l, base);
|
}
|
//printf("Done syncing layer %d\n", j);
|
}
|
|
typedef struct{
|
network *nets;
|
int n;
|
int j;
|
} sync_args;
|
|
void *sync_layer_thread(void *ptr)
|
{
|
sync_args args = *(sync_args*)ptr;
|
sync_layer(args.nets, args.n, args.j);
|
free(ptr);
|
return 0;
|
}
|
|
pthread_t sync_layer_in_thread(network *nets, int n, int j)
|
{
|
pthread_t thread;
|
sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args));
|
ptr->nets = nets;
|
ptr->n = n;
|
ptr->j = j;
|
if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed");
|
return thread;
|
}
|
|
void sync_nets(network *nets, int n, int interval)
|
{
|
int j;
|
int layers = nets[0].n;
|
pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t));
|
|
*nets[0].seen += interval * (n-1) * nets[0].batch * nets[0].subdivisions;
|
for (j = 0; j < n; ++j){
|
*nets[j].seen = *nets[0].seen;
|
}
|
for (j = 0; j < layers; ++j) {
|
threads[j] = sync_layer_in_thread(nets, n, j);
|
}
|
for (j = 0; j < layers; ++j) {
|
pthread_join(threads[j], 0);
|
}
|
free(threads);
|
}
|
|
float train_networks(network *nets, int n, data d, int interval)
|
{
|
int i;
|
#ifdef _DEBUG
|
int batch = nets[0].batch;
|
int subdivisions = nets[0].subdivisions;
|
assert(batch * subdivisions * n == d.X.rows);
|
#endif
|
pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t));
|
float *errors = (float *) calloc(n, sizeof(float));
|
|
float sum = 0;
|
for(i = 0; i < n; ++i){
|
data p = get_data_part(d, i, n);
|
threads[i] = train_network_in_thread(nets[i], p, errors + i);
|
}
|
for(i = 0; i < n; ++i){
|
pthread_join(threads[i], 0);
|
//printf("%f\n", errors[i]);
|
sum += errors[i];
|
}
|
//cudaDeviceSynchronize();
|
*nets[0].cur_iteration += (n - 1);
|
*nets[0].seen = nets[0].batch * nets[0].subdivisions * get_current_iteration(nets[0]); // remove this line, when you will save to weights-file both: seen & cur_iteration
|
if (get_current_iteration(nets[0]) % interval == 0)
|
{
|
printf("Syncing... ");
|
fflush(stdout);
|
sync_nets(nets, n, interval);
|
printf("Done!\n");
|
}
|
//cudaDeviceSynchronize();
|
free(threads);
|
free(errors);
|
return (float)sum/(n);
|
}
|
|
float *get_network_output_layer_gpu(network net, int i)
|
{
|
layer l = net.layers[i];
|
if(l.type != REGION && l.type != YOLO && (*net.cuda_graph_ready) == 0) cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
|
return l.output;
|
}
|
|
float *get_network_output_gpu(network net)
|
{
|
int i;
|
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
|
return get_network_output_layer_gpu(net, i);
|
}
|
|
float *network_predict_gpu(network net, float *input)
|
{
|
if (net.gpu_index != cuda_get_device())
|
cuda_set_device(net.gpu_index);
|
int size = get_network_input_size(net) * net.batch;
|
network_state state;
|
state.index = 0;
|
state.net = net;
|
//state.input = cuda_make_array(input, size); // memory will be allocated in the parse_network_cfg_custom()
|
state.input = net.input_state_gpu;
|
memcpy(net.input_pinned_cpu, input, size * sizeof(float));
|
state.truth = 0;
|
state.train = 0;
|
state.delta = 0;
|
|
//cudaGraphExec_t instance = (cudaGraphExec_t)net.cuda_graph_exec;
|
static cudaGraphExec_t instance;
|
|
if ((*net.cuda_graph_ready) == 0) {
|
static cudaGraph_t graph;
|
if (net.use_cuda_graph == 1) {
|
int i;
|
for (i = 0; i < 16; ++i) switch_stream(i);
|
|
cudaStream_t stream0 = switch_stream(0);
|
CHECK_CUDA(cudaDeviceSynchronize());
|
printf("Try to capture graph... \n");
|
//cudaGraph_t graph = (cudaGraph_t)net.cuda_graph;
|
//CHECK_CUDA(cudaStreamBeginCapture(stream0, cudaStreamCaptureModeGlobal));
|
}
|
|
cuda_push_array(state.input, net.input_pinned_cpu, size);
|
forward_network_gpu(net, state);
|
|
if (net.use_cuda_graph == 1) {
|
cudaStream_t stream0 = switch_stream(0);
|
CHECK_CUDA(cudaStreamEndCapture(stream0, &graph));
|
CHECK_CUDA(cudaGraphInstantiate(&instance, graph, NULL, NULL, 0));
|
(*net.cuda_graph_ready) = 1;
|
printf(" graph is captured... \n");
|
CHECK_CUDA(cudaDeviceSynchronize());
|
}
|
CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream()));
|
}
|
else {
|
cudaStream_t stream0 = switch_stream(0);
|
//printf(" cudaGraphLaunch \n");
|
CHECK_CUDA( cudaGraphLaunch(instance, stream0) );
|
CHECK_CUDA( cudaStreamSynchronize(stream0) );
|
//printf(" ~cudaGraphLaunch \n");
|
}
|
|
float *out = get_network_output_gpu(net);
|
reset_wait_stream_events();
|
//cuda_free(state.input); // will be freed in the free_network()
|
return out;
|
}
|
