#include "darknet.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 "blas.h"
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#include "crop_layer.h"
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#include "connected_layer.h"
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#include "gru_layer.h"
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#include "rnn_layer.h"
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#include "crnn_layer.h"
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#include "conv_lstm_layer.h"
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#include "local_layer.h"
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#include "convolutional_layer.h"
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#include "activation_layer.h"
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#include "detection_layer.h"
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#include "region_layer.h"
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#include "normalization_layer.h"
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#include "batchnorm_layer.h"
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#include "maxpool_layer.h"
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#include "reorg_layer.h"
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#include "reorg_old_layer.h"
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#include "avgpool_layer.h"
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#include "cost_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 "scale_channels_layer.h"
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#include "sam_layer.h"
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#include "yolo_layer.h"
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#include "gaussian_yolo_layer.h"
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#include "upsample_layer.h"
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#include "parser.h"
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load_args get_base_args(network *net)
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{
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load_args args = { 0 };
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args.w = net->w;
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args.h = net->h;
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args.size = net->w;
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args.min = net->min_crop;
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args.max = net->max_crop;
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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.center = net->center;
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args.saturation = net->saturation;
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args.hue = net->hue;
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return args;
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}
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int64_t get_current_iteration(network net)
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{
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return *net.cur_iteration;
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}
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int get_current_batch(network net)
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{
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int batch_num = (*net.seen)/(net.batch*net.subdivisions);
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return batch_num;
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}
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/*
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void reset_momentum(network net)
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{
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if (net.momentum == 0) return;
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net.learning_rate = 0;
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net.momentum = 0;
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net.decay = 0;
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#ifdef GPU
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//if(net.gpu_index >= 0) update_network_gpu(net);
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#endif
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}
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*/
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void reset_network_state(network *net, int b)
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{
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int i;
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for (i = 0; i < net->n; ++i) {
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#ifdef GPU
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layer l = net->layers[i];
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if (l.state_gpu) {
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fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
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}
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if (l.h_gpu) {
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fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1);
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}
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#endif
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}
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}
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void reset_rnn(network *net)
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{
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reset_network_state(net, 0);
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}
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float get_current_seq_subdivisions(network net)
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{
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int sequence_subdivisions = net.init_sequential_subdivisions;
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if (net.num_steps > 0)
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{
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int batch_num = get_current_batch(net);
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int i;
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for (i = 0; i < net.num_steps; ++i) {
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if (net.steps[i] > batch_num) break;
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sequence_subdivisions *= net.seq_scales[i];
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}
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}
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if (sequence_subdivisions < 1) sequence_subdivisions = 1;
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if (sequence_subdivisions > net.subdivisions) sequence_subdivisions = net.subdivisions;
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return sequence_subdivisions;
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}
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int get_sequence_value(network net)
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{
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int sequence = 1;
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if (net.sequential_subdivisions != 0) sequence = net.subdivisions / net.sequential_subdivisions;
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if (sequence < 1) sequence = 1;
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return sequence;
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}
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float get_current_rate(network net)
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{
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int batch_num = get_current_batch(net);
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int i;
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float rate;
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if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
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switch (net.policy) {
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case CONSTANT:
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return net.learning_rate;
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case STEP:
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return net.learning_rate * pow(net.scale, batch_num/net.step);
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case STEPS:
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rate = net.learning_rate;
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for(i = 0; i < net.num_steps; ++i){
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if(net.steps[i] > batch_num) return rate;
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rate *= net.scales[i];
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//if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net);
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}
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return rate;
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case EXP:
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return net.learning_rate * pow(net.gamma, batch_num);
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case POLY:
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return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
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//if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
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//return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
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case RANDOM:
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return net.learning_rate * pow(rand_uniform(0,1), net.power);
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case SIG:
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return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
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case SGDR:
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{
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int last_iteration_start = 0;
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int cycle_size = net.batches_per_cycle;
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while ((last_iteration_start + cycle_size) < batch_num)
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{
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last_iteration_start += cycle_size;
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cycle_size *= net.batches_cycle_mult;
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}
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rate = net.learning_rate_min +
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0.5*(net.learning_rate - net.learning_rate_min)
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* (1. + cos((float)(batch_num - last_iteration_start)*3.14159265 / cycle_size));
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return rate;
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}
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default:
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fprintf(stderr, "Policy is weird!\n");
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return net.learning_rate;
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}
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}
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char *get_layer_string(LAYER_TYPE a)
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{
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switch(a){
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case CONVOLUTIONAL:
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return "convolutional";
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case ACTIVE:
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return "activation";
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case LOCAL:
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return "local";
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case DECONVOLUTIONAL:
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return "deconvolutional";
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case CONNECTED:
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return "connected";
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case RNN:
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return "rnn";
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case GRU:
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return "gru";
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case LSTM:
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return "lstm";
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case CRNN:
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return "crnn";
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case MAXPOOL:
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return "maxpool";
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case REORG:
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return "reorg";
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case AVGPOOL:
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return "avgpool";
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case SOFTMAX:
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return "softmax";
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case DETECTION:
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return "detection";
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case REGION:
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return "region";
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case YOLO:
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return "yolo";
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case GAUSSIAN_YOLO:
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return "Gaussian_yolo";
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case DROPOUT:
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return "dropout";
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case CROP:
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return "crop";
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case COST:
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return "cost";
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case ROUTE:
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return "route";
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case SHORTCUT:
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return "shortcut";
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case SCALE_CHANNELS:
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return "scale_channels";
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case SAM:
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return "sam";
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case NORMALIZATION:
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return "normalization";
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case BATCHNORM:
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return "batchnorm";
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default:
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break;
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}
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return "none";
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}
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network make_network(int n)
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{
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network net = {0};
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net.n = n;
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net.layers = (layer*)xcalloc(net.n, sizeof(layer));
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net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t));
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net.cuda_graph_ready = (int*)xcalloc(1, sizeof(int));
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net.badlabels_reject_threshold = (float*)xcalloc(1, sizeof(float));
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net.delta_rolling_max = (float*)xcalloc(1, sizeof(float));
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net.delta_rolling_avg = (float*)xcalloc(1, sizeof(float));
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net.delta_rolling_std = (float*)xcalloc(1, sizeof(float));
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net.cur_iteration = (int*)xcalloc(1, sizeof(int));
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net.total_bbox = (int*)xcalloc(1, sizeof(int));
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net.rewritten_bbox = (int*)xcalloc(1, sizeof(int));
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*net.rewritten_bbox = *net.total_bbox = 0;
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#ifdef GPU
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net.input_gpu = (float**)xcalloc(1, sizeof(float*));
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net.truth_gpu = (float**)xcalloc(1, sizeof(float*));
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net.input16_gpu = (float**)xcalloc(1, sizeof(float*));
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net.output16_gpu = (float**)xcalloc(1, sizeof(float*));
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net.max_input16_size = (size_t*)xcalloc(1, sizeof(size_t));
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net.max_output16_size = (size_t*)xcalloc(1, sizeof(size_t));
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#endif
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return net;
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}
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void forward_network(network net, network_state state)
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{
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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 && state.train){
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scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
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}
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//double time = get_time_point();
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l.forward(l, state);
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//printf("%d - Predicted in %lf milli-seconds.\n", i, ((double)get_time_point() - time) / 1000);
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state.input = l.output;
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/*
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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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}
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void update_network(network net)
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{
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int i;
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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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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.update){
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l.update(l, update_batch, rate, net.momentum, net.decay);
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}
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}
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}
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float *get_network_output(network net)
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{
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#ifdef GPU
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if (gpu_index >= 0) return get_network_output_gpu(net);
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#endif
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int i;
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for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
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return net.layers[i].output;
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}
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float get_network_cost(network net)
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{
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int i;
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float sum = 0;
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int count = 0;
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for(i = 0; i < net.n; ++i){
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if(net.layers[i].cost){
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sum += net.layers[i].cost[0];
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++count;
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}
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}
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return sum/count;
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}
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int get_predicted_class_network(network net)
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{
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float *out = get_network_output(net);
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int k = get_network_output_size(net);
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return max_index(out, k);
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}
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void backward_network(network net, network_state state)
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{
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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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state.workspace = net.workspace;
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for(i = net.n-1; i >= 0; --i){
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state.index = i;
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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;
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state.delta = prev.delta;
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}
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layer l = net.layers[i];
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if (l.stopbackward) break;
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if (l.onlyforward) continue;
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l.backward(l, state);
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}
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}
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float train_network_datum(network net, float *x, float *y)
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{
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#ifdef GPU
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if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
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#endif
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network_state state={0};
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*net.seen += net.batch;
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state.index = 0;
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state.net = net;
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state.input = x;
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state.delta = 0;
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state.truth = y;
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state.train = 1;
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forward_network(net, state);
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backward_network(net, state);
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float error = get_network_cost(net);
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//if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net);
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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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return error;
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}
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float train_network_sgd(network net, data d, int n)
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{
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int batch = net.batch;
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float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float));
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float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_random_batch(d, batch, X, y);
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net.current_subdivision = i;
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float err = train_network_datum(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network(network net, data d)
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{
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return train_network_waitkey(net, d, 0);
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}
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float train_network_waitkey(network net, data d, int wait_key)
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{
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assert(d.X.rows % net.batch == 0);
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int batch = net.batch;
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int n = d.X.rows / batch;
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float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float));
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float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_next_batch(d, batch, i*batch, X, y);
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net.current_subdivision = i;
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float err = train_network_datum(net, X, y);
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sum += err;
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if(wait_key) wait_key_cv(5);
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}
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(*net.cur_iteration) += 1;
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#ifdef GPU
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update_network_gpu(net);
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#else // GPU
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update_network(net);
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#endif // GPU
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int ema_start_point = net.max_batches / 2;
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if (net.ema_alpha && (*net.cur_iteration) >= ema_start_point)
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{
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int ema_period = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);
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int ema_apply_point = net.max_batches - 1000;
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if (!is_ema_initialized(net))
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{
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ema_update(net, 0); // init EMA
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printf(" EMA initialization \n");
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}
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if ((*net.cur_iteration) == ema_apply_point)
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{
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ema_apply(net); // apply EMA (BN rolling mean/var recalculation is required)
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printf(" ema_apply() \n");
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}
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else
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if ((*net.cur_iteration) < ema_apply_point)// && (*net.cur_iteration) % ema_period == 0)
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{
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ema_update(net, net.ema_alpha); // update EMA
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printf(" ema_update(), ema_alpha = %f \n", net.ema_alpha);
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}
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}
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int reject_stop_point = net.max_batches*3/4;
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if ((*net.cur_iteration) < reject_stop_point &&
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net.weights_reject_freq &&
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(*net.cur_iteration) % net.weights_reject_freq == 0)
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{
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float sim_threshold = 0.4;
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reject_similar_weights(net, sim_threshold);
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network_batch(network net, data d, int n)
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{
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int i,j;
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network_state state={0};
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state.index = 0;
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state.net = net;
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state.train = 1;
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state.delta = 0;
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float sum = 0;
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int batch = 2;
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for(i = 0; i < n; ++i){
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for(j = 0; j < batch; ++j){
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int index = random_gen()%d.X.rows;
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state.input = d.X.vals[index];
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state.truth = d.y.vals[index];
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forward_network(net, state);
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backward_network(net, state);
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sum += get_network_cost(net);
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}
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update_network(net);
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}
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return (float)sum/(n*batch);
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}
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int recalculate_workspace_size(network *net)
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{
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#ifdef GPU
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cuda_set_device(net->gpu_index);
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if (gpu_index >= 0) cuda_free(net->workspace);
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#endif
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int i;
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size_t workspace_size = 0;
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for (i = 0; i < net->n; ++i) {
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layer l = net->layers[i];
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//printf(" %d: layer = %d,", i, l.type);
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if (l.type == CONVOLUTIONAL) {
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l.workspace_size = get_convolutional_workspace_size(l);
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}
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else if (l.type == CONNECTED) {
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l.workspace_size = get_connected_workspace_size(l);
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}
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if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
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net->layers[i] = l;
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}
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#ifdef GPU
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if (gpu_index >= 0) {
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printf("\n try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
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net->workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
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printf(" CUDA allocate done! \n");
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}
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else {
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free(net->workspace);
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net->workspace = (float*)xcalloc(1, workspace_size);
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}
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#else
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free(net->workspace);
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net->workspace = (float*)xcalloc(1, workspace_size);
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#endif
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//fprintf(stderr, " Done!\n");
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return 0;
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}
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void set_batch_network(network *net, int b)
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{
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net->batch = b;
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int i;
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for(i = 0; i < net->n; ++i){
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net->layers[i].batch = b;
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#ifdef CUDNN
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if(net->layers[i].type == CONVOLUTIONAL){
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cudnn_convolutional_setup(net->layers + i, cudnn_fastest, 0);
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}
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else if (net->layers[i].type == MAXPOOL) {
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cudnn_maxpool_setup(net->layers + i);
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}
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#endif
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}
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recalculate_workspace_size(net); // recalculate workspace size
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}
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int resize_network(network *net, int w, int h)
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{
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#ifdef GPU
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cuda_set_device(net->gpu_index);
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if(gpu_index >= 0){
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cuda_free(net->workspace);
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if (net->input_gpu) {
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cuda_free(*net->input_gpu);
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*net->input_gpu = 0;
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cuda_free(*net->truth_gpu);
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*net->truth_gpu = 0;
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}
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if (net->input_state_gpu) cuda_free(net->input_state_gpu);
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if (net->input_pinned_cpu) {
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if (net->input_pinned_cpu_flag) cudaFreeHost(net->input_pinned_cpu);
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else free(net->input_pinned_cpu);
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}
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}
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#endif
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int i;
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//if(w == net->w && h == net->h) return 0;
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net->w = w;
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net->h = h;
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int inputs = 0;
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size_t workspace_size = 0;
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//fprintf(stderr, "Resizing to %d x %d...\n", w, h);
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//fflush(stderr);
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for (i = 0; i < net->n; ++i){
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layer l = net->layers[i];
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//printf(" (resize %d: layer = %d) , ", i, l.type);
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if(l.type == CONVOLUTIONAL){
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resize_convolutional_layer(&l, w, h);
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}
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else if (l.type == CRNN) {
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resize_crnn_layer(&l, w, h);
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}else if (l.type == CONV_LSTM) {
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resize_conv_lstm_layer(&l, w, h);
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}else if(l.type == CROP){
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resize_crop_layer(&l, w, h);
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}else if(l.type == MAXPOOL){
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resize_maxpool_layer(&l, w, h);
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}else if (l.type == LOCAL_AVGPOOL) {
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resize_maxpool_layer(&l, w, h);
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}else if (l.type == BATCHNORM) {
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resize_batchnorm_layer(&l, w, h);
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}else if(l.type == REGION){
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resize_region_layer(&l, w, h);
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}else if (l.type == YOLO) {
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resize_yolo_layer(&l, w, h);
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}else if (l.type == GAUSSIAN_YOLO) {
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resize_gaussian_yolo_layer(&l, w, h);
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}else if(l.type == ROUTE){
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resize_route_layer(&l, net);
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}else if (l.type == SHORTCUT) {
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resize_shortcut_layer(&l, w, h, net);
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}else if (l.type == SCALE_CHANNELS) {
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resize_scale_channels_layer(&l, net);
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}else if (l.type == SAM) {
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resize_sam_layer(&l, w, h);
|
}else if (l.type == DROPOUT) {
|
resize_dropout_layer(&l, inputs);
|
l.out_w = l.w = w;
|
l.out_h = l.h = h;
|
l.output = net->layers[i - 1].output;
|
l.delta = net->layers[i - 1].delta;
|
#ifdef GPU
|
l.output_gpu = net->layers[i-1].output_gpu;
|
l.delta_gpu = net->layers[i-1].delta_gpu;
|
#endif
|
}else if (l.type == UPSAMPLE) {
|
resize_upsample_layer(&l, w, h);
|
}else if(l.type == REORG){
|
resize_reorg_layer(&l, w, h);
|
} else if (l.type == REORG_OLD) {
|
resize_reorg_old_layer(&l, w, h);
|
}else if(l.type == AVGPOOL){
|
resize_avgpool_layer(&l, w, h);
|
}else if(l.type == NORMALIZATION){
|
resize_normalization_layer(&l, w, h);
|
}else if(l.type == COST){
|
resize_cost_layer(&l, inputs);
|
}else{
|
fprintf(stderr, "Resizing type %d \n", (int)l.type);
|
error("Cannot resize this type of layer");
|
}
|
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
|
inputs = l.outputs;
|
net->layers[i] = l;
|
//if(l.type != DROPOUT)
|
{
|
w = l.out_w;
|
h = l.out_h;
|
}
|
//if(l.type == AVGPOOL) break;
|
}
|
#ifdef GPU
|
const int size = get_network_input_size(*net) * net->batch;
|
if(gpu_index >= 0){
|
printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
|
net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
|
net->input_state_gpu = cuda_make_array(0, size);
|
if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))
|
net->input_pinned_cpu_flag = 1;
|
else {
|
cudaGetLastError(); // reset CUDA-error
|
net->input_pinned_cpu = (float*)xcalloc(size, sizeof(float));
|
net->input_pinned_cpu_flag = 0;
|
}
|
printf(" CUDA allocate done! \n");
|
}else {
|
free(net->workspace);
|
net->workspace = (float*)xcalloc(1, workspace_size);
|
if(!net->input_pinned_cpu_flag)
|
net->input_pinned_cpu = (float*)xrealloc(net->input_pinned_cpu, size * sizeof(float));
|
}
|
#else
|
free(net->workspace);
|
net->workspace = (float*)xcalloc(1, workspace_size);
|
#endif
|
//fprintf(stderr, " Done!\n");
|
return 0;
|
}
|
|
int get_network_output_size(network net)
|
{
|
int i;
|
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
|
return net.layers[i].outputs;
|
}
|
|
int get_network_input_size(network net)
|
{
|
return net.layers[0].inputs;
|
}
|
|
detection_layer get_network_detection_layer(network net)
|
{
|
int i;
|
for(i = 0; i < net.n; ++i){
|
if(net.layers[i].type == DETECTION){
|
return net.layers[i];
|
}
|
}
|
fprintf(stderr, "Detection layer not found!!\n");
|
detection_layer l = { (LAYER_TYPE)0 };
|
return l;
|
}
|
|
image get_network_image_layer(network net, int i)
|
{
|
layer l = net.layers[i];
|
if (l.out_w && l.out_h && l.out_c){
|
return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
|
}
|
image def = {0};
|
return def;
|
}
|
|
layer* get_network_layer(network* net, int i)
|
{
|
return net->layers + i;
|
}
|
|
image get_network_image(network net)
|
{
|
int i;
|
for(i = net.n-1; i >= 0; --i){
|
image m = get_network_image_layer(net, i);
|
if(m.h != 0) return m;
|
}
|
image def = {0};
|
return def;
|
}
|
|
void visualize_network(network net)
|
{
|
image *prev = 0;
|
int i;
|
char buff[256];
|
for(i = 0; i < net.n; ++i){
|
sprintf(buff, "Layer %d", i);
|
layer l = net.layers[i];
|
if(l.type == CONVOLUTIONAL){
|
prev = visualize_convolutional_layer(l, buff, prev);
|
}
|
}
|
}
|
|
void top_predictions(network net, int k, int *index)
|
{
|
int size = get_network_output_size(net);
|
float *out = get_network_output(net);
|
top_k(out, size, k, index);
|
}
|
|
// A version of network_predict that uses a pointer for the network
|
// struct to make the python binding work properly.
|
float *network_predict_ptr(network *net, float *input)
|
{
|
return network_predict(*net, input);
|
}
|
|
float *network_predict(network net, float *input)
|
{
|
#ifdef GPU
|
if(gpu_index >= 0) return network_predict_gpu(net, input);
|
#endif
|
|
network_state state = {0};
|
state.net = net;
|
state.index = 0;
|
state.input = input;
|
state.truth = 0;
|
state.train = 0;
|
state.delta = 0;
|
forward_network(net, state);
|
float *out = get_network_output(net);
|
return out;
|
}
|
|
int num_detections(network *net, float thresh)
|
{
|
int i;
|
int s = 0;
|
for (i = 0; i < net->n; ++i) {
|
layer l = net->layers[i];
|
if (l.type == YOLO) {
|
s += yolo_num_detections(l, thresh);
|
}
|
if (l.type == GAUSSIAN_YOLO) {
|
s += gaussian_yolo_num_detections(l, thresh);
|
}
|
if (l.type == DETECTION || l.type == REGION) {
|
s += l.w*l.h*l.n;
|
}
|
}
|
return s;
|
}
|
|
int num_detections_batch(network *net, float thresh, int batch)
|
{
|
int i;
|
int s = 0;
|
for (i = 0; i < net->n; ++i) {
|
layer l = net->layers[i];
|
if (l.type == YOLO) {
|
s += yolo_num_detections_batch(l, thresh, batch);
|
}
|
if (l.type == DETECTION || l.type == REGION) {
|
s += l.w*l.h*l.n;
|
}
|
}
|
return s;
|
}
|
|
detection *make_network_boxes(network *net, float thresh, int *num)
|
{
|
int i;
|
layer l = net->layers[net->n - 1];
|
for (i = 0; i < net->n; ++i) {
|
layer l_tmp = net->layers[i];
|
if (l_tmp.type == YOLO || l_tmp.type == GAUSSIAN_YOLO || l_tmp.type == DETECTION || l_tmp.type == REGION) {
|
l = l_tmp;
|
break;
|
}
|
}
|
|
int nboxes = num_detections(net, thresh);
|
if (num) *num = nboxes;
|
detection* dets = (detection*)xcalloc(nboxes, sizeof(detection));
|
for (i = 0; i < nboxes; ++i) {
|
dets[i].prob = (float*)xcalloc(l.classes, sizeof(float));
|
// tx,ty,tw,th uncertainty
|
if(l.type == GAUSSIAN_YOLO) dets[i].uc = (float*)xcalloc(4, sizeof(float)); // Gaussian_YOLOv3
|
else dets[i].uc = NULL;
|
|
if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float));
|
else dets[i].mask = NULL;
|
|
if(l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float));
|
else dets[i].embeddings = NULL;
|
dets[i].embedding_size = l.embedding_size;
|
}
|
return dets;
|
}
|
|
detection *make_network_boxes_batch(network *net, float thresh, int *num, int batch)
|
{
|
int i;
|
layer l = net->layers[net->n - 1];
|
for (i = 0; i < net->n; ++i) {
|
layer l_tmp = net->layers[i];
|
if (l_tmp.type == YOLO || l_tmp.type == GAUSSIAN_YOLO || l_tmp.type == DETECTION || l_tmp.type == REGION) {
|
l = l_tmp;
|
break;
|
}
|
}
|
|
int nboxes = num_detections_batch(net, thresh, batch);
|
assert(num != NULL);
|
*num = nboxes;
|
detection* dets = (detection*)calloc(nboxes, sizeof(detection));
|
for (i = 0; i < nboxes; ++i) {
|
dets[i].prob = (float*)calloc(l.classes, sizeof(float));
|
// tx,ty,tw,th uncertainty
|
if (l.type == GAUSSIAN_YOLO) dets[i].uc = (float*)xcalloc(4, sizeof(float)); // Gaussian_YOLOv3
|
else dets[i].uc = NULL;
|
|
if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float));
|
else dets[i].mask = NULL;
|
|
if (l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float));
|
else dets[i].embeddings = NULL;
|
dets[i].embedding_size = l.embedding_size;
|
}
|
return dets;
|
}
|
|
void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter)
|
{
|
box* boxes = (box*)xcalloc(l.w * l.h * l.n, sizeof(box));
|
float** probs = (float**)xcalloc(l.w * l.h * l.n, sizeof(float*));
|
int i, j;
|
for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float));
|
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
|
for (j = 0; j < l.w*l.h*l.n; ++j) {
|
dets[j].classes = l.classes;
|
dets[j].bbox = boxes[j];
|
dets[j].objectness = 1;
|
for (i = 0; i < l.classes; ++i) {
|
dets[j].prob[i] = probs[j][i];
|
}
|
}
|
|
free(boxes);
|
free_ptrs((void **)probs, l.w*l.h*l.n);
|
|
//correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative);
|
correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter);
|
}
|
|
void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
|
{
|
int prev_classes = -1;
|
int j;
|
for (j = 0; j < net->n; ++j) {
|
layer l = net->layers[j];
|
if (l.type == YOLO) {
|
int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
|
dets += count;
|
if (prev_classes < 0) prev_classes = l.classes;
|
else if (prev_classes != l.classes) {
|
printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
|
prev_classes, l.classes);
|
}
|
}
|
if (l.type == GAUSSIAN_YOLO) {
|
int count = get_gaussian_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
|
dets += count;
|
}
|
if (l.type == REGION) {
|
custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
|
//get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
|
dets += l.w*l.h*l.n;
|
}
|
if (l.type == DETECTION) {
|
get_detection_detections(l, w, h, thresh, dets);
|
dets += l.w*l.h*l.n;
|
}
|
}
|
}
|
|
void fill_network_boxes_batch(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter, int batch)
|
{
|
int prev_classes = -1;
|
int j;
|
for (j = 0; j < net->n; ++j) {
|
layer l = net->layers[j];
|
if (l.type == YOLO) {
|
int count = get_yolo_detections_batch(l, w, h, net->w, net->h, thresh, map, relative, dets, letter, batch);
|
dets += count;
|
if (prev_classes < 0) prev_classes = l.classes;
|
else if (prev_classes != l.classes) {
|
printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
|
prev_classes, l.classes);
|
}
|
}
|
if (l.type == REGION) {
|
custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
|
//get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
|
dets += l.w*l.h*l.n;
|
}
|
if (l.type == DETECTION) {
|
get_detection_detections(l, w, h, thresh, dets);
|
dets += l.w*l.h*l.n;
|
}
|
}
|
}
|
|
detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter)
|
{
|
detection *dets = make_network_boxes(net, thresh, num);
|
fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter);
|
return dets;
|
}
|
|
void free_detections(detection *dets, int n)
|
{
|
int i;
|
for (i = 0; i < n; ++i) {
|
free(dets[i].prob);
|
if (dets[i].uc) free(dets[i].uc);
|
if (dets[i].mask) free(dets[i].mask);
|
if (dets[i].embeddings) free(dets[i].embeddings);
|
}
|
free(dets);
|
}
|
|
void free_batch_detections(det_num_pair *det_num_pairs, int n)
|
{
|
int i;
|
for(i=0; i<n; ++i)
|
free_detections(det_num_pairs[i].dets, det_num_pairs[i].num);
|
free(det_num_pairs);
|
}
|
|
// JSON format:
|
//{
|
// "frame_id":8990,
|
// "objects":[
|
// {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070},
|
// {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497}
|
// ]
|
//},
|
|
char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename)
|
{
|
const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold
|
|
char *send_buf = (char *)calloc(1024, sizeof(char));
|
if (!send_buf) return 0;
|
if (filename) {
|
sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename);
|
}
|
else {
|
sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"objects\": [ \n", frame_id);
|
}
|
|
int i, j;
|
int class_id = -1;
|
for (i = 0; i < nboxes; ++i) {
|
for (j = 0; j < classes; ++j) {
|
int show = strncmp(names[j], "dont_show", 9);
|
if (dets[i].prob[j] > thresh && show)
|
{
|
if (class_id != -1) strcat(send_buf, ", \n");
|
class_id = j;
|
char *buf = (char *)calloc(2048, sizeof(char));
|
if (!buf) return 0;
|
//sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}",
|
// image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);
|
|
sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}",
|
j, names[j], dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);
|
|
int send_buf_len = strlen(send_buf);
|
int buf_len = strlen(buf);
|
int total_len = send_buf_len + buf_len + 100;
|
send_buf = (char *)realloc(send_buf, total_len * sizeof(char));
|
if (!send_buf) {
|
if (buf) free(buf);
|
return 0;// exit(-1);
|
}
|
strcat(send_buf, buf);
|
free(buf);
|
}
|
}
|
}
|
strcat(send_buf, "\n ] \n}");
|
return send_buf;
|
}
|
|
|
float *network_predict_image(network *net, image im)
|
{
|
//image imr = letterbox_image(im, net->w, net->h);
|
float *p;
|
if(net->batch != 1) set_batch_network(net, 1);
|
if (im.w == net->w && im.h == net->h) {
|
// Input image is the same size as our net, predict on that image
|
p = network_predict(*net, im.data);
|
}
|
else {
|
// Need to resize image to the desired size for the net
|
image imr = resize_image(im, net->w, net->h);
|
p = network_predict(*net, imr.data);
|
free_image(imr);
|
}
|
return p;
|
}
|
|
det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
|
{
|
network_predict(*net, im.data);
|
det_num_pair *pdets = (struct det_num_pair *)calloc(batch_size, sizeof(det_num_pair));
|
int num;
|
int batch;
|
for(batch=0; batch < batch_size; batch++){
|
detection *dets = make_network_boxes_batch(net, thresh, &num, batch);
|
fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);
|
pdets[batch].num = num;
|
pdets[batch].dets = dets;
|
}
|
return pdets;
|
}
|
|
float *network_predict_image_letterbox(network *net, image im)
|
{
|
//image imr = letterbox_image(im, net->w, net->h);
|
float *p;
|
if (net->batch != 1) set_batch_network(net, 1);
|
if (im.w == net->w && im.h == net->h) {
|
// Input image is the same size as our net, predict on that image
|
p = network_predict(*net, im.data);
|
}
|
else {
|
// Need to resize image to the desired size for the net
|
image imr = letterbox_image(im, net->w, net->h);
|
p = network_predict(*net, imr.data);
|
free_image(imr);
|
}
|
return p;
|
}
|
|
int network_width(network *net) { return net->w; }
|
int network_height(network *net) { return net->h; }
|
|
matrix network_predict_data_multi(network net, data test, int n)
|
{
|
int i,j,b,m;
|
int k = get_network_output_size(net);
|
matrix pred = make_matrix(test.X.rows, k);
|
float* X = (float*)xcalloc(net.batch * test.X.rows, sizeof(float));
|
for(i = 0; i < test.X.rows; i += net.batch){
|
for(b = 0; b < net.batch; ++b){
|
if(i+b == test.X.rows) break;
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
}
|
for(m = 0; m < n; ++m){
|
float *out = network_predict(net, X);
|
for(b = 0; b < net.batch; ++b){
|
if(i+b == test.X.rows) break;
|
for(j = 0; j < k; ++j){
|
pred.vals[i+b][j] += out[j+b*k]/n;
|
}
|
}
|
}
|
}
|
free(X);
|
return pred;
|
}
|
|
matrix network_predict_data(network net, data test)
|
{
|
int i,j,b;
|
int k = get_network_output_size(net);
|
matrix pred = make_matrix(test.X.rows, k);
|
float* X = (float*)xcalloc(net.batch * test.X.cols, sizeof(float));
|
for(i = 0; i < test.X.rows; i += net.batch){
|
for(b = 0; b < net.batch; ++b){
|
if(i+b == test.X.rows) break;
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
}
|
float *out = network_predict(net, X);
|
for(b = 0; b < net.batch; ++b){
|
if(i+b == test.X.rows) break;
|
for(j = 0; j < k; ++j){
|
pred.vals[i+b][j] = out[j+b*k];
|
}
|
}
|
}
|
free(X);
|
return pred;
|
}
|
|
void print_network(network net)
|
{
|
int i,j;
|
for(i = 0; i < net.n; ++i){
|
layer l = net.layers[i];
|
float *output = l.output;
|
int n = l.outputs;
|
float mean = mean_array(output, n);
|
float vari = variance_array(output, n);
|
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
|
if(n > 100) n = 100;
|
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
|
if(n == 100)fprintf(stderr,".....\n");
|
fprintf(stderr, "\n");
|
}
|
}
|
|
void compare_networks(network n1, network n2, data test)
|
{
|
matrix g1 = network_predict_data(n1, test);
|
matrix g2 = network_predict_data(n2, test);
|
int i;
|
int a,b,c,d;
|
a = b = c = d = 0;
|
for(i = 0; i < g1.rows; ++i){
|
int truth = max_index(test.y.vals[i], test.y.cols);
|
int p1 = max_index(g1.vals[i], g1.cols);
|
int p2 = max_index(g2.vals[i], g2.cols);
|
if(p1 == truth){
|
if(p2 == truth) ++d;
|
else ++c;
|
}else{
|
if(p2 == truth) ++b;
|
else ++a;
|
}
|
}
|
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
|
float num = pow((abs(b - c) - 1.), 2.);
|
float den = b + c;
|
printf("%f\n", num/den);
|
}
|
|
float network_accuracy(network net, data d)
|
{
|
matrix guess = network_predict_data(net, d);
|
float acc = matrix_topk_accuracy(d.y, guess,1);
|
free_matrix(guess);
|
return acc;
|
}
|
|
float *network_accuracies(network net, data d, int n)
|
{
|
static float acc[2];
|
matrix guess = network_predict_data(net, d);
|
acc[0] = matrix_topk_accuracy(d.y, guess, 1);
|
acc[1] = matrix_topk_accuracy(d.y, guess, n);
|
free_matrix(guess);
|
return acc;
|
}
|
|
float network_accuracy_multi(network net, data d, int n)
|
{
|
matrix guess = network_predict_data_multi(net, d, n);
|
float acc = matrix_topk_accuracy(d.y, guess,1);
|
free_matrix(guess);
|
return acc;
|
}
|
|
void free_network_ptr(network* net)
|
{
|
free_network(*net);
|
}
|
|
void free_network(network net)
|
{
|
int i;
|
for (i = 0; i < net.n; ++i) {
|
free_layer(net.layers[i]);
|
}
|
free(net.layers);
|
|
free(net.seq_scales);
|
free(net.scales);
|
free(net.steps);
|
free(net.seen);
|
free(net.cuda_graph_ready);
|
free(net.badlabels_reject_threshold);
|
free(net.delta_rolling_max);
|
free(net.delta_rolling_avg);
|
free(net.delta_rolling_std);
|
free(net.cur_iteration);
|
free(net.total_bbox);
|
free(net.rewritten_bbox);
|
|
#ifdef GPU
|
if (gpu_index >= 0) cuda_free(net.workspace);
|
else free(net.workspace);
|
free_pinned_memory();
|
if (net.input_state_gpu) cuda_free(net.input_state_gpu);
|
if (net.input_pinned_cpu) { // CPU
|
if (net.input_pinned_cpu_flag) cudaFreeHost(net.input_pinned_cpu);
|
else free(net.input_pinned_cpu);
|
}
|
if (*net.input_gpu) cuda_free(*net.input_gpu);
|
if (*net.truth_gpu) cuda_free(*net.truth_gpu);
|
if (net.input_gpu) free(net.input_gpu);
|
if (net.truth_gpu) free(net.truth_gpu);
|
|
if (*net.input16_gpu) cuda_free(*net.input16_gpu);
|
if (*net.output16_gpu) cuda_free(*net.output16_gpu);
|
if (net.input16_gpu) free(net.input16_gpu);
|
if (net.output16_gpu) free(net.output16_gpu);
|
if (net.max_input16_size) free(net.max_input16_size);
|
if (net.max_output16_size) free(net.max_output16_size);
|
#else
|
free(net.workspace);
|
#endif
|
}
|
|
static float relu(float src) {
|
if (src > 0) return src;
|
return 0;
|
}
|
|
static float lrelu(float src) {
|
const float eps = 0.001;
|
if (src > eps) return src;
|
return eps;
|
}
|
|
void fuse_conv_batchnorm(network net)
|
{
|
int j;
|
for (j = 0; j < net.n; ++j) {
|
layer *l = &net.layers[j];
|
|
if (l->type == CONVOLUTIONAL) {
|
//printf(" Merges Convolutional-%d and batch_norm \n", j);
|
|
if (l->share_layer != NULL) {
|
l->batch_normalize = 0;
|
}
|
|
if (l->batch_normalize) {
|
int f;
|
for (f = 0; f < l->n; ++f)
|
{
|
l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .00001));
|
|
double precomputed = l->scales[f] / (sqrt((double)l->rolling_variance[f] + .00001));
|
|
const size_t filter_size = l->size*l->size*l->c / l->groups;
|
int i;
|
for (i = 0; i < filter_size; ++i) {
|
int w_index = f*filter_size + i;
|
|
l->weights[w_index] *= precomputed;
|
}
|
}
|
|
free_convolutional_batchnorm(l);
|
l->batch_normalize = 0;
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
push_convolutional_layer(*l);
|
}
|
#endif
|
}
|
}
|
else if (l->type == SHORTCUT && l->weights && l->weights_normalization)
|
{
|
if (l->nweights > 0) {
|
//cuda_pull_array(l.weights_gpu, l.weights, l.nweights);
|
int i;
|
for (i = 0; i < l->nweights; ++i) printf(" w = %f,", l->weights[i]);
|
printf(" l->nweights = %d, j = %d \n", l->nweights, j);
|
}
|
|
// nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w)
|
const int layer_step = l->nweights / (l->n + 1); // 1 or l.c or (l.c * l.h * l.w)
|
|
int chan, i;
|
for (chan = 0; chan < layer_step; ++chan)
|
{
|
float sum = 1, max_val = -FLT_MAX;
|
|
if (l->weights_normalization == SOFTMAX_NORMALIZATION) {
|
for (i = 0; i < (l->n + 1); ++i) {
|
int w_index = chan + i * layer_step;
|
float w = l->weights[w_index];
|
if (max_val < w) max_val = w;
|
}
|
}
|
|
const float eps = 0.0001;
|
sum = eps;
|
|
for (i = 0; i < (l->n + 1); ++i) {
|
int w_index = chan + i * layer_step;
|
float w = l->weights[w_index];
|
if (l->weights_normalization == RELU_NORMALIZATION) sum += lrelu(w);
|
else if (l->weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val);
|
}
|
|
for (i = 0; i < (l->n + 1); ++i) {
|
int w_index = chan + i * layer_step;
|
float w = l->weights[w_index];
|
if (l->weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum;
|
else if (l->weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
|
l->weights[w_index] = w;
|
}
|
}
|
|
l->weights_normalization = NO_NORMALIZATION;
|
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
push_shortcut_layer(*l);
|
}
|
#endif
|
}
|
else {
|
//printf(" Fusion skip layer type: %d \n", l->type);
|
}
|
}
|
}
|
|
void forward_blank_layer(layer l, network_state state) {}
|
|
void calculate_binary_weights(network net)
|
{
|
int j;
|
for (j = 0; j < net.n; ++j) {
|
layer *l = &net.layers[j];
|
|
if (l->type == CONVOLUTIONAL) {
|
//printf(" Merges Convolutional-%d and batch_norm \n", j);
|
|
if (l->xnor) {
|
//printf("\n %d \n", j);
|
//l->lda_align = 256; // 256bit for AVX2 // set in make_convolutional_layer()
|
//if (l->size*l->size*l->c >= 2048) l->lda_align = 512;
|
|
binary_align_weights(l);
|
|
if (net.layers[j].use_bin_output) {
|
l->activation = LINEAR;
|
}
|
|
#ifdef GPU
|
// fuse conv_xnor + shortcut -> conv_xnor
|
if ((j + 1) < net.n && net.layers[j].type == CONVOLUTIONAL) {
|
layer *sc = &net.layers[j + 1];
|
if (sc->type == SHORTCUT && sc->w == sc->out_w && sc->h == sc->out_h && sc->c == sc->out_c)
|
{
|
l->bin_conv_shortcut_in_gpu = net.layers[net.layers[j + 1].index].output_gpu;
|
l->bin_conv_shortcut_out_gpu = net.layers[j + 1].output_gpu;
|
|
net.layers[j + 1].type = BLANK;
|
net.layers[j + 1].forward_gpu = forward_blank_layer;
|
}
|
}
|
#endif // GPU
|
}
|
}
|
}
|
//printf("\n calculate_binary_weights Done! \n");
|
|
}
|
|
void copy_cudnn_descriptors(layer src, layer *dst)
|
{
|
#ifdef CUDNN
|
dst->normTensorDesc = src.normTensorDesc;
|
dst->normDstTensorDesc = src.normDstTensorDesc;
|
dst->normDstTensorDescF16 = src.normDstTensorDescF16;
|
|
dst->srcTensorDesc = src.srcTensorDesc;
|
dst->dstTensorDesc = src.dstTensorDesc;
|
|
dst->srcTensorDesc16 = src.srcTensorDesc16;
|
dst->dstTensorDesc16 = src.dstTensorDesc16;
|
#endif // CUDNN
|
}
|
|
void copy_weights_net(network net_train, network *net_map)
|
{
|
int k;
|
for (k = 0; k < net_train.n; ++k) {
|
layer *l = &(net_train.layers[k]);
|
layer tmp_layer;
|
copy_cudnn_descriptors(net_map->layers[k], &tmp_layer);
|
net_map->layers[k] = net_train.layers[k];
|
copy_cudnn_descriptors(tmp_layer, &net_map->layers[k]);
|
|
if (l->type == CRNN) {
|
layer tmp_input_layer, tmp_self_layer, tmp_output_layer;
|
copy_cudnn_descriptors(*net_map->layers[k].input_layer, &tmp_input_layer);
|
copy_cudnn_descriptors(*net_map->layers[k].self_layer, &tmp_self_layer);
|
copy_cudnn_descriptors(*net_map->layers[k].output_layer, &tmp_output_layer);
|
net_map->layers[k].input_layer = net_train.layers[k].input_layer;
|
net_map->layers[k].self_layer = net_train.layers[k].self_layer;
|
net_map->layers[k].output_layer = net_train.layers[k].output_layer;
|
//net_map->layers[k].output_gpu = net_map->layers[k].output_layer->output_gpu; // already copied out of if()
|
|
copy_cudnn_descriptors(tmp_input_layer, net_map->layers[k].input_layer);
|
copy_cudnn_descriptors(tmp_self_layer, net_map->layers[k].self_layer);
|
copy_cudnn_descriptors(tmp_output_layer, net_map->layers[k].output_layer);
|
}
|
else if(l->input_layer) // for AntiAliasing
|
{
|
layer tmp_input_layer;
|
copy_cudnn_descriptors(*net_map->layers[k].input_layer, &tmp_input_layer);
|
net_map->layers[k].input_layer = net_train.layers[k].input_layer;
|
copy_cudnn_descriptors(tmp_input_layer, net_map->layers[k].input_layer);
|
}
|
net_map->layers[k].batch = 1;
|
net_map->layers[k].steps = 1;
|
}
|
}
|
|
|
// combine Training and Validation networks
|
network combine_train_valid_networks(network net_train, network net_map)
|
{
|
network net_combined = make_network(net_train.n);
|
layer *old_layers = net_combined.layers;
|
net_combined = net_train;
|
net_combined.layers = old_layers;
|
net_combined.batch = 1;
|
|
int k;
|
for (k = 0; k < net_train.n; ++k) {
|
layer *l = &(net_train.layers[k]);
|
net_combined.layers[k] = net_train.layers[k];
|
net_combined.layers[k].batch = 1;
|
|
if (l->type == CONVOLUTIONAL) {
|
#ifdef CUDNN
|
net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc;
|
net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc;
|
net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16;
|
|
net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc;
|
net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc;
|
|
net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16;
|
net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16;
|
#endif // CUDNN
|
}
|
}
|
return net_combined;
|
}
|
|
void free_network_recurrent_state(network net)
|
{
|
int k;
|
for (k = 0; k < net.n; ++k) {
|
if (net.layers[k].type == CONV_LSTM) free_state_conv_lstm(net.layers[k]);
|
if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]);
|
}
|
}
|
|
void randomize_network_recurrent_state(network net)
|
{
|
int k;
|
for (k = 0; k < net.n; ++k) {
|
if (net.layers[k].type == CONV_LSTM) randomize_state_conv_lstm(net.layers[k]);
|
if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]);
|
}
|
}
|
|
|
void remember_network_recurrent_state(network net)
|
{
|
int k;
|
for (k = 0; k < net.n; ++k) {
|
if (net.layers[k].type == CONV_LSTM) remember_state_conv_lstm(net.layers[k]);
|
//if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]);
|
}
|
}
|
|
void restore_network_recurrent_state(network net)
|
{
|
int k;
|
for (k = 0; k < net.n; ++k) {
|
if (net.layers[k].type == CONV_LSTM) restore_state_conv_lstm(net.layers[k]);
|
if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]);
|
}
|
}
|
|
|
int is_ema_initialized(network net)
|
{
|
int i;
|
for (i = 0; i < net.n; ++i) {
|
layer l = net.layers[i];
|
if (l.type == CONVOLUTIONAL) {
|
int k;
|
if (l.weights_ema) {
|
for (k = 0; k < l.nweights; ++k) {
|
if (l.weights_ema[k] != 0) return 1;
|
}
|
}
|
}
|
}
|
|
return 0;
|
}
|
|
void ema_update(network net, float ema_alpha)
|
{
|
int i;
|
for (i = 0; i < net.n; ++i) {
|
layer l = net.layers[i];
|
if (l.type == CONVOLUTIONAL) {
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
pull_convolutional_layer(l);
|
}
|
#endif
|
int k;
|
if (l.weights_ema) {
|
for (k = 0; k < l.nweights; ++k) {
|
l.weights_ema[k] = ema_alpha * l.weights_ema[k] + (1 - ema_alpha) * l.weights[k];
|
}
|
}
|
|
for (k = 0; k < l.n; ++k) {
|
if (l.biases_ema) l.biases_ema[k] = ema_alpha * l.biases_ema[k] + (1 - ema_alpha) * l.biases[k];
|
if (l.scales_ema) l.scales_ema[k] = ema_alpha * l.scales_ema[k] + (1 - ema_alpha) * l.scales[k];
|
}
|
}
|
}
|
}
|
|
|
void ema_apply(network net)
|
{
|
int i;
|
for (i = 0; i < net.n; ++i) {
|
layer l = net.layers[i];
|
if (l.type == CONVOLUTIONAL) {
|
int k;
|
if (l.weights_ema) {
|
for (k = 0; k < l.nweights; ++k) {
|
l.weights[k] = l.weights_ema[k];
|
}
|
}
|
|
for (k = 0; k < l.n; ++k) {
|
if (l.biases_ema) l.biases[k] = l.biases_ema[k];
|
if (l.scales_ema) l.scales[k] = l.scales_ema[k];
|
}
|
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
push_convolutional_layer(l);
|
}
|
#endif
|
}
|
}
|
}
|
|
|
|
void reject_similar_weights(network net, float sim_threshold)
|
{
|
int i;
|
for (i = 0; i < net.n; ++i) {
|
layer l = net.layers[i];
|
if (i == 0) continue;
|
if (net.n > i + 1) if (net.layers[i + 1].type == YOLO) continue;
|
if (net.n > i + 2) if (net.layers[i + 2].type == YOLO) continue;
|
if (net.n > i + 3) if (net.layers[i + 3].type == YOLO) continue;
|
|
if (l.type == CONVOLUTIONAL && l.activation != LINEAR) {
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
pull_convolutional_layer(l);
|
}
|
#endif
|
int k, j;
|
float max_sim = -1000;
|
int max_sim_index = 0;
|
int max_sim_index2 = 0;
|
int filter_size = l.size*l.size*l.c;
|
for (k = 0; k < l.n; ++k)
|
{
|
for (j = k+1; j < l.n; ++j)
|
{
|
int w1 = k;
|
int w2 = j;
|
|
float sim = cosine_similarity(&l.weights[filter_size*w1], &l.weights[filter_size*w2], filter_size);
|
if (sim > max_sim) {
|
max_sim = sim;
|
max_sim_index = w1;
|
max_sim_index2 = w2;
|
}
|
}
|
}
|
|
printf(" reject_similar_weights: i = %d, l.n = %d, w1 = %d, w2 = %d, sim = %f, thresh = %f \n",
|
i, l.n, max_sim_index, max_sim_index2, max_sim, sim_threshold);
|
|
if (max_sim > sim_threshold) {
|
printf(" rejecting... \n");
|
float scale = sqrt(2. / (l.size*l.size*l.c / l.groups));
|
|
for (k = 0; k < filter_size; ++k) {
|
l.weights[max_sim_index*filter_size + k] = scale*rand_uniform(-1, 1);
|
}
|
if (l.biases) l.biases[max_sim_index] = 0.0f;
|
if (l.scales) l.scales[max_sim_index] = 1.0f;
|
}
|
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
push_convolutional_layer(l);
|
}
|
#endif
|
}
|
}
|
}
|
