From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/network_kernels.cu | 1411 ++++++++++++++++++++++++++++++----------------------------
1 files changed, 734 insertions(+), 677 deletions(-)
diff --git a/lib/detecter_tools/darknet/network_kernels.cu b/lib/detecter_tools/darknet/network_kernels.cu
index d2049eb..f25e39a 100644
--- a/lib/detecter_tools/darknet/network_kernels.cu
+++ b/lib/detecter_tools/darknet/network_kernels.cu
@@ -1,677 +1,734 @@
-#include "dark_cuda.h"
-
-#include <stdio.h>
-#include <time.h>
-#include <assert.h>
-
-#include "network.h"
-#include "image.h"
-#include "data.h"
-#include "utils.h"
-#include "parser.h"
-
-#include "crop_layer.h"
-#include "connected_layer.h"
-#include "rnn_layer.h"
-#include "gru_layer.h"
-#include "crnn_layer.h"
-#include "detection_layer.h"
-#include "region_layer.h"
-#include "convolutional_layer.h"
-#include "activation_layer.h"
-#include "maxpool_layer.h"
-#include "reorg_layer.h"
-#include "avgpool_layer.h"
-#include "normalization_layer.h"
-#include "batchnorm_layer.h"
-#include "cost_layer.h"
-#include "local_layer.h"
-#include "softmax_layer.h"
-#include "dropout_layer.h"
-#include "route_layer.h"
-#include "shortcut_layer.h"
-#include "blas.h"
-
-//#ifdef OPENCV
-//#include <opencv2/highgui/highgui_c.h>
-//#endif
-
-#include "http_stream.h"
-
-float * get_network_output_gpu_layer(network net, int i);
-float * get_network_delta_gpu_layer(network net, int i);
-float * get_network_output_gpu(network net);
-
-typedef struct time_benchmark_layers {
- float time;
- int layer_id, layer_type;
-} time_benchmark_layers;
-
-int time_comparator(const void *pa, const void *pb)
-{
- time_benchmark_layers a = *(time_benchmark_layers *)pa;
- time_benchmark_layers b = *(time_benchmark_layers *)pb;
- float diff = a.time - b.time;
- if (diff < 0) return 1;
- else if (diff > 0) return -1;
- return 0;
-}
-
-void forward_network_gpu(network net, network_state state)
-{
- static time_benchmark_layers *avg_time_per_layer = NULL;
- static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
- double start_time, end_time;
- if (net.benchmark_layers) {
- if (!avg_time_per_layer) {
- avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
- sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
- }
- cudaDeviceSynchronize();
- }
-
- //printf("\n");
- state.workspace = net.workspace;
- int i;
- for(i = 0; i < net.n; ++i){
- state.index = i;
- layer l = net.layers[i];
- if(l.delta_gpu && state.train){
- fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
- }
-
- if (net.benchmark_layers) {
- start_time = get_time_point();
- }
-
- l.forward_gpu(l, state);
-
- if (net.benchmark_layers) {
- CHECK_CUDA(cudaDeviceSynchronize());
- end_time = get_time_point();
- const double took_time = (end_time - start_time) / 1000;
- const double alpha = 0.9;
- if (avg_time_per_layer[i].time == 0) {
- avg_time_per_layer[i].layer_id = i;
- avg_time_per_layer[i].layer_type = l.type;
- avg_time_per_layer[i].time = took_time;
- }
- else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
-
- sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
- 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);
- }
-
- if(net.wait_stream)
- cudaStreamSynchronize(get_cuda_stream());
- state.input = l.output_gpu;
- //cudaDeviceSynchronize();
-
- /*
- cuda_pull_array(l.output_gpu, l.output, l.outputs);
- cudaStreamSynchronize(get_cuda_stream());
- float avg_val = 0;
- int k;
- for (k = 0; k < l.outputs; ++k) avg_val += l.output[k];
- printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs);
- */
-
-/*
- cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
- if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) {
- int j;
- for (j = 0; j < l.out_c; ++j) {
- image img = make_image(l.out_w, l.out_h, 3);
- memcpy(img.data, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
- 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));
- 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));
- char buff[256];
- sprintf(buff, "layer-%d slice-%d", i, j);
- show_image(img, buff);
- save_image(img, buff);
- }
- cvWaitKey(0); // wait press-key in console
- cvDestroyAllWindows();
- }
-*/
- }
-
- if (net.benchmark_layers) {
- printf("\n\nSorted by time (forward):\n");
- qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
- for (i = 0; i < net.n; ++i) {
- //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);
- 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);
- }
- }
-
- //cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions
- //cudaDeviceSynchronize();
-}
-
-void backward_network_gpu(network net, network_state state)
-{
- static time_benchmark_layers *avg_time_per_layer = NULL;
- static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
- double start_time, end_time;
- if (net.benchmark_layers) {
- if (!avg_time_per_layer) {
- avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
- sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
- }
- cudaDeviceSynchronize();
- }
-
- state.workspace = net.workspace;
- int i;
- float * original_input = state.input;
- float * original_delta = state.delta;
- for(i = net.n-1; i >= 0; --i){
- state.index = i;
- layer l = net.layers[i];
- if (l.stopbackward == 1) break;
- if (l.stopbackward > get_current_iteration(net)) break;
- if(i == 0){
- state.input = original_input;
- state.delta = original_delta;
- }else{
- layer prev = net.layers[i-1];
- state.input = prev.output_gpu;
- state.delta = prev.delta_gpu;
- if (net.optimized_memory && !prev.keep_delta_gpu) {
- state.delta = net.state_delta_gpu;
- }
- }
- if (l.onlyforward) continue;
-
- if (net.benchmark_layers) {
- start_time = get_time_point();
- }
-
- l.backward_gpu(l, state);
-
- if (net.benchmark_layers) {
- CHECK_CUDA(cudaDeviceSynchronize());
- end_time = get_time_point();
- const double took_time = (end_time - start_time) / 1000;
- const double alpha = 0.9;
- if (avg_time_per_layer[i].time == 0) {
- avg_time_per_layer[i].layer_id = i;
- avg_time_per_layer[i].layer_type = l.type;
- avg_time_per_layer[i].time = took_time;
- }
- else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
-
- sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
- 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);
- }
-
- if (i != 0) {
- layer prev = net.layers[i - 1];
- if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) {
- if (prev.delta_gpu != state.delta) simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu);
- fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1);
- }
- }
-
- /*
- if(i != 0)
- {
- layer l = net.layers[i - 1];
- int state_delta_nan_inf = is_nan_or_inf(state.delta, l.outputs * l.batch);
- int state_input_nan_inf = is_nan_or_inf(state.input, l.outputs * l.batch);
- printf("\n i - %d is_nan_or_inf(s.delta) = %d \n", i, state_delta_nan_inf);
- printf(" i - %d is_nan_or_inf(s.input) = %d \n", i, state_input_nan_inf);
- if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); getchar(); }
- }
- */
- }
-
- if (net.adversarial && net.attention)
- {
- int img_size = net.w * net.h * net.c;
- float *original_input_cpu = (float *)xcalloc(img_size, sizeof(float));
- float *original_delta_cpu = (float *)xcalloc(img_size, sizeof(float));
- cuda_pull_array(original_input, original_input_cpu, img_size);
- cuda_pull_array(original_delta, original_delta_cpu, img_size);
-
- image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c);
- show_image(attention_img, "attention_img");
-
- free_image(attention_img);
-
- free(original_input_cpu);
- free(original_delta_cpu);
- }
- if (net.adversarial) {
- int x_size = get_network_input_size(net)*net.batch;
- printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %f \n",
- x_size, original_delta, original_input, net.learning_rate);
- axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1);
- constrain_min_max_ongpu(x_size, 0, 1, original_input, 1);
- }
-
- if (net.benchmark_layers) {
- printf("\n\nSorted by time (backward):\n");
- qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
- for (i = 0; i < net.n; ++i) {
- //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);
- 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);
- }
- }
-}
-
-void update_network_gpu(network net)
-{
- cuda_set_device(net.gpu_index);
- const int iteration_num = (*net.seen) / (net.batch * net.subdivisions);
- int i;
- int update_batch = net.batch*net.subdivisions * get_sequence_value(net);
- float rate = get_current_rate(net);
- for(i = 0; i < net.n; ++i){
- layer l = net.layers[i];
- l.t = get_current_batch(net);
- if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0;
- if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue;
- if (l.train_only_bn) continue;
-
- if(l.update_gpu && l.dont_update < iteration_num){
- l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
- }
- }
-}
-
-void forward_backward_network_gpu(network net, float *x, float *y)
-{
- network_state state;
- state.index = 0;
- state.net = net;
- int x_size = get_network_input_size(net)*net.batch;
- int y_size = get_network_output_size(net)*net.batch;
- if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch;
- if(!*net.input_gpu){
- *net.input_gpu = cuda_make_array(x, x_size);
- *net.truth_gpu = cuda_make_array(y, y_size);
- }else{
- cuda_push_array(*net.input_gpu, x, x_size);
- cuda_push_array(*net.truth_gpu, y, y_size);
- }
- state.input = *net.input_gpu;
- state.delta = 0;
- if (net.adversarial) {
- state.train = 0;
- state.delta = cuda_make_array(NULL, x_size);
- }
- state.truth = *net.truth_gpu;
- state.train = 1;
-#if defined(CUDNN_HALF) && defined(CUDNN)
- int i;
- for (i = 0; i < net.n; ++i) {
- layer l = net.layers[i];
- if (net.cudnn_half){
- if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) {
- assert((l.nweights) > 0);
- cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16);
- }
- else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) {
- assert((l.input_layer->c*l.input_layer->n*l.input_layer->size*l.input_layer->size) > 0);
- cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16);
- cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16);
- cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16);
- }
- else if (l.type == CONV_LSTM && l.wf->weights_gpu && l.wf->weights_gpu16) {
- assert((l.wf->c * l.wf->n * l.wf->size * l.wf->size) > 0);
- if (l.peephole) {
- cuda_convert_f32_to_f16(l.vf->weights_gpu, l.vf->nweights, l.vf->weights_gpu16);
- cuda_convert_f32_to_f16(l.vi->weights_gpu, l.vi->nweights, l.vi->weights_gpu16);
- cuda_convert_f32_to_f16(l.vo->weights_gpu, l.vo->nweights, l.vo->weights_gpu16);
- }
- cuda_convert_f32_to_f16(l.wf->weights_gpu, l.wf->nweights, l.wf->weights_gpu16);
- cuda_convert_f32_to_f16(l.wi->weights_gpu, l.wi->nweights, l.wi->weights_gpu16);
- cuda_convert_f32_to_f16(l.wg->weights_gpu, l.wg->nweights, l.wg->weights_gpu16);
- cuda_convert_f32_to_f16(l.wo->weights_gpu, l.wo->nweights, l.wo->weights_gpu16);
- cuda_convert_f32_to_f16(l.uf->weights_gpu, l.uf->nweights, l.uf->weights_gpu16);
- cuda_convert_f32_to_f16(l.ui->weights_gpu, l.ui->nweights, l.ui->weights_gpu16);
- cuda_convert_f32_to_f16(l.ug->weights_gpu, l.ug->nweights, l.ug->weights_gpu16);
- cuda_convert_f32_to_f16(l.uo->weights_gpu, l.uo->nweights, l.uo->weights_gpu16);
- }
- }
- }
-#endif
- forward_network_gpu(net, state);
- //cudaStreamSynchronize(get_cuda_stream());
- backward_network_gpu(net, state);
-
- if (net.adversarial) {
- cuda_free(state.delta);
- cuda_pull_array(*net.input_gpu, x, x_size);
- }
-}
-
-float train_network_datum_gpu(network net, float *x, float *y)
-{
- *net.seen += net.batch;
- if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) {
- net.adversarial = 1;
- float lr_old = net.learning_rate;
- float scale = 1.0 - (get_current_iteration(net) / ((float)net.max_batches));
- net.learning_rate = net.adversarial_lr * scale;
- layer l = net.layers[net.n - 1];
- int y_size = get_network_output_size(net)*net.batch;
- if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch;
- float *truth_cpu = (float *)xcalloc(y_size, sizeof(float));
-
- printf("\n adversarial training, adversarial_lr = %f \n", net.adversarial_lr);
-
- forward_backward_network_gpu(net, x, truth_cpu);
-
- image im;
- im.w = net.w;
- im.h = net.h;
- im.c = net.c;
- im.data = x;
- //show_image(im, "adversarial data augmentation");
-
- free(truth_cpu);
- net.learning_rate = lr_old;
- net.adversarial = 0;
- }
- forward_backward_network_gpu(net, x, y);
- float error = get_network_cost(net);
- //if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
- const int sequence = get_sequence_value(net);
- //if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net);
-
- return error;
-}
-
-typedef struct {
- network net;
- data d;
- float *err;
-} train_args;
-
-void *train_thread(void *ptr)
-{
- train_args args = *(train_args*)ptr;
- free(ptr);
- cuda_set_device(args.net.gpu_index);
- *args.err = train_network(args.net, args.d);
- return 0;
-}
-
-pthread_t train_network_in_thread(network net, data d, float *err)
-{
- pthread_t thread;
- train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
- ptr->net = net;
- ptr->d = d;
- ptr->err = err;
- if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
- return thread;
-}
-
-void pull_updates(layer l)
-{
- if(l.type == CONVOLUTIONAL){
- cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
- cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
- if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n);
- } else if(l.type == CONNECTED){
- cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
- cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
- }
-}
-
-void push_updates(layer l)
-{
- if(l.type == CONVOLUTIONAL){
- cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
- cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
- if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n);
- } else if(l.type == CONNECTED){
- cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
- cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
- }
-}
-
-void update_layer(layer l, network net)
-{
- int update_batch = net.batch*net.subdivisions;
- float rate = get_current_rate(net);
- l.t = get_current_batch(net);
- if(l.update_gpu){
- l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
- }
-}
-
-void merge_weights(layer l, layer base)
-{
- if (l.type == CONVOLUTIONAL) {
- 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) 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));
- cuda_push_array(state.input, net.input_pinned_cpu, size);
- state.truth = 0;
- state.train = 0;
- state.delta = 0;
- forward_network_gpu(net, state);
- float *out = get_network_output_gpu(net);
- //cuda_free(state.input); // will be freed in the free_network()
- return out;
-}
+#include "dark_cuda.h"
+
+#include <stdio.h>
+#include <time.h>
+#include <assert.h>
+
+#include "network.h"
+#include "image.h"
+#include "data.h"
+#include "utils.h"
+#include "parser.h"
+
+#include "crop_layer.h"
+#include "connected_layer.h"
+#include "rnn_layer.h"
+#include "gru_layer.h"
+#include "crnn_layer.h"
+#include "detection_layer.h"
+#include "region_layer.h"
+#include "convolutional_layer.h"
+#include "activation_layer.h"
+#include "maxpool_layer.h"
+#include "reorg_layer.h"
+#include "avgpool_layer.h"
+#include "normalization_layer.h"
+#include "batchnorm_layer.h"
+#include "cost_layer.h"
+#include "local_layer.h"
+#include "softmax_layer.h"
+#include "dropout_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
+#include "blas.h"
+
+//#ifdef OPENCV
+//#include <opencv2/highgui/highgui_c.h>
+//#endif
+
+#include "http_stream.h"
+
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu(network net);
+
+typedef struct time_benchmark_layers {
+ float time;
+ int layer_id, layer_type;
+} time_benchmark_layers;
+
+int time_comparator(const void *pa, const void *pb)
+{
+ time_benchmark_layers a = *(time_benchmark_layers *)pa;
+ time_benchmark_layers b = *(time_benchmark_layers *)pb;
+ float diff = a.time - b.time;
+ if (diff < 0) return 1;
+ else if (diff > 0) return -1;
+ return 0;
+}
+
+void forward_network_gpu(network net, network_state state)
+{
+ static time_benchmark_layers *avg_time_per_layer = NULL;
+ static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
+ double start_time, end_time;
+ if (net.benchmark_layers) {
+ if (!avg_time_per_layer) {
+ avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
+ sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
+ }
+ cudaDeviceSynchronize();
+ }
+
+ //printf("\n");
+ state.workspace = net.workspace;
+ int i;
+ for(i = 0; i < net.n; ++i){
+ state.index = i;
+ layer l = net.layers[i];
+ if(l.delta_gpu && state.train){
+ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
+ }
+
+ if (net.benchmark_layers) {
+ start_time = get_time_point();
+ }
+
+ l.forward_gpu(l, state);
+
+ if (net.benchmark_layers) {
+ CHECK_CUDA(cudaDeviceSynchronize());
+ end_time = get_time_point();
+ const double took_time = (end_time - start_time) / 1000;
+ const double alpha = 0.9;
+ if (avg_time_per_layer[i].time == 0) {
+ avg_time_per_layer[i].layer_id = i;
+ avg_time_per_layer[i].layer_type = l.type;
+ avg_time_per_layer[i].time = took_time;
+ }
+ else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
+
+ sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
+ 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);
+ }
+
+ if(net.wait_stream)
+ cudaStreamSynchronize(get_cuda_stream());
+ state.input = l.output_gpu;
+ //cudaDeviceSynchronize();
+
+ /*
+ cuda_pull_array(l.output_gpu, l.output, l.outputs);
+ cudaStreamSynchronize(get_cuda_stream());
+ float avg_val = 0;
+ int k;
+ for (k = 0; k < l.outputs; ++k) avg_val += l.output[k];
+ printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs);
+ */
+
+/*
+ cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
+ if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) {
+ int j;
+ for (j = 0; j < l.out_c; ++j) {
+ image img = make_image(l.out_w, l.out_h, 3);
+ memcpy(img.data, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
+ 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));
+ 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));
+ char buff[256];
+ sprintf(buff, "layer-%d slice-%d", i, j);
+ show_image(img, buff);
+ save_image(img, buff);
+ }
+ cvWaitKey(0); // wait press-key in console
+ cvDestroyAllWindows();
+ }
+*/
+ }
+
+ if (net.benchmark_layers) {
+ printf("\n\nSorted by time (forward):\n");
+ qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
+ for (i = 0; i < net.n; ++i) {
+ //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);
+ 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);
+ }
+ }
+
+ //cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions
+ //cudaDeviceSynchronize();
+}
+
+void backward_network_gpu(network net, network_state state)
+{
+ static time_benchmark_layers *avg_time_per_layer = NULL;
+ static time_benchmark_layers *sorted_avg_time_per_layer = NULL;
+ double start_time, end_time;
+ if (net.benchmark_layers) {
+ if (!avg_time_per_layer) {
+ avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
+ sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers));
+ }
+ cudaDeviceSynchronize();
+ }
+
+ state.workspace = net.workspace;
+ int i;
+ float * original_input = state.input;
+ float * original_delta = state.delta;
+ for(i = net.n-1; i >= 0; --i){
+ state.index = i;
+ layer l = net.layers[i];
+ if (l.stopbackward == 1) break;
+ if (l.stopbackward > get_current_iteration(net)) break;
+ if(i == 0){
+ state.input = original_input;
+ state.delta = original_delta;
+ }else{
+ layer prev = net.layers[i-1];
+ state.input = prev.output_gpu;
+ state.delta = prev.delta_gpu;
+ if (net.optimized_memory && !prev.keep_delta_gpu) {
+ state.delta = net.state_delta_gpu;
+ }
+ }
+ if (l.onlyforward) continue;
+
+ if (net.benchmark_layers) {
+ start_time = get_time_point();
+ }
+
+ l.backward_gpu(l, state);
+
+ if (net.benchmark_layers) {
+ CHECK_CUDA(cudaDeviceSynchronize());
+ end_time = get_time_point();
+ const double took_time = (end_time - start_time) / 1000;
+ const double alpha = 0.9;
+ if (avg_time_per_layer[i].time == 0) {
+ avg_time_per_layer[i].layer_id = i;
+ avg_time_per_layer[i].layer_type = l.type;
+ avg_time_per_layer[i].time = took_time;
+ }
+ else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha);
+
+ sorted_avg_time_per_layer[i] = avg_time_per_layer[i];
+ 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);
+ }
+
+ if (i != 0) {
+ layer prev = net.layers[i - 1];
+ if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) {
+ if (prev.delta_gpu != state.delta) simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu);
+ fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1);
+ }
+ }
+
+ /*
+ if(i != 0)
+ {
+ layer l = net.layers[i - 1];
+ int state_delta_nan_inf = is_nan_or_inf(state.delta, l.outputs * l.batch);
+ int state_input_nan_inf = is_nan_or_inf(state.input, l.outputs * l.batch);
+ printf("\n i - %d is_nan_or_inf(s.delta) = %d \n", i, state_delta_nan_inf);
+ printf(" i - %d is_nan_or_inf(s.input) = %d \n", i, state_input_nan_inf);
+ if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); getchar(); }
+ }
+ */
+ }
+
+ if (net.adversarial && net.attention)
+ {
+ int img_size = net.w * net.h * net.c;
+ float *original_input_cpu = (float *)xcalloc(img_size, sizeof(float));
+ float *original_delta_cpu = (float *)xcalloc(img_size, sizeof(float));
+ cuda_pull_array(original_input, original_input_cpu, img_size);
+ cuda_pull_array(original_delta, original_delta_cpu, img_size);
+
+ image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c);
+ show_image(attention_img, "attention_img");
+ resize_window_cv("attention_img", 500, 500);
+
+ free_image(attention_img);
+
+ free(original_input_cpu);
+ free(original_delta_cpu);
+ }
+ if (net.adversarial) {
+ int x_size = get_network_input_size(net)*net.batch;
+ printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %f \n",
+ x_size, original_delta, original_input, net.learning_rate);
+ axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1);
+ constrain_min_max_ongpu(x_size, 0, 1, original_input, 1);
+ }
+
+ if (net.benchmark_layers) {
+ printf("\n\nSorted by time (backward):\n");
+ qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator);
+ for (i = 0; i < net.n; ++i) {
+ //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);
+ 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);
+ }
+ }
+}
+
+void update_network_gpu(network net)
+{
+ cuda_set_device(net.gpu_index);
+ const int iteration_num = (*net.seen) / (net.batch * net.subdivisions);
+ int i;
+ int update_batch = net.batch*net.subdivisions * get_sequence_value(net);
+ float rate = get_current_rate(net);
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ l.t = get_current_batch(net);
+ if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0;
+ if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue;
+ if (l.train_only_bn) continue;
+
+ if(l.update_gpu && l.dont_update < iteration_num){
+ l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
+ }
+ }
+}
+
+void forward_backward_network_gpu(network net, float *x, float *y)
+{
+ network_state state;
+ state.index = 0;
+ state.net = net;
+ int x_size = get_network_input_size(net)*net.batch;
+ int y_size = get_network_output_size(net)*net.batch;
+ if(net.layers[net.n-1].truths) y_size = net.layers[net.n-1].truths*net.batch;
+ if(!*net.input_gpu){
+ *net.input_gpu = cuda_make_array(x, x_size);
+ *net.truth_gpu = cuda_make_array(y, y_size);
+ }else{
+ cuda_push_array(*net.input_gpu, x, x_size);
+ cuda_push_array(*net.truth_gpu, y, y_size);
+ }
+ state.input = *net.input_gpu;
+ state.delta = 0;
+ if (net.adversarial) {
+ state.delta = cuda_make_array(NULL, x_size);
+ }
+ state.truth = *net.truth_gpu;
+ state.train = 1;
+#if defined(CUDNN_HALF) && defined(CUDNN)
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ layer l = net.layers[i];
+ if (net.cudnn_half){
+ if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) {
+ assert((l.nweights) > 0);
+ cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16);
+ }
+ else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) {
+ assert((l.input_layer->c*l.input_layer->n*l.input_layer->size*l.input_layer->size) > 0);
+ cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16);
+ cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16);
+ cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16);
+ }
+ else if (l.type == CONV_LSTM && l.wf->weights_gpu && l.wf->weights_gpu16) {
+ assert((l.wf->c * l.wf->n * l.wf->size * l.wf->size) > 0);
+ if (l.peephole) {
+ cuda_convert_f32_to_f16(l.vf->weights_gpu, l.vf->nweights, l.vf->weights_gpu16);
+ cuda_convert_f32_to_f16(l.vi->weights_gpu, l.vi->nweights, l.vi->weights_gpu16);
+ cuda_convert_f32_to_f16(l.vo->weights_gpu, l.vo->nweights, l.vo->weights_gpu16);
+ }
+ cuda_convert_f32_to_f16(l.wf->weights_gpu, l.wf->nweights, l.wf->weights_gpu16);
+ if (!l.bottleneck) {
+ cuda_convert_f32_to_f16(l.wi->weights_gpu, l.wi->nweights, l.wi->weights_gpu16);
+ cuda_convert_f32_to_f16(l.wg->weights_gpu, l.wg->nweights, l.wg->weights_gpu16);
+ cuda_convert_f32_to_f16(l.wo->weights_gpu, l.wo->nweights, l.wo->weights_gpu16);
+ }
+ cuda_convert_f32_to_f16(l.uf->weights_gpu, l.uf->nweights, l.uf->weights_gpu16);
+ cuda_convert_f32_to_f16(l.ui->weights_gpu, l.ui->nweights, l.ui->weights_gpu16);
+ cuda_convert_f32_to_f16(l.ug->weights_gpu, l.ug->nweights, l.ug->weights_gpu16);
+ cuda_convert_f32_to_f16(l.uo->weights_gpu, l.uo->nweights, l.uo->weights_gpu16);
+ }
+ }
+ }
+#endif
+ forward_network_gpu(net, state);
+ //cudaStreamSynchronize(get_cuda_stream());
+ backward_network_gpu(net, state);
+
+ if (net.adversarial) {
+ cuda_free(state.delta);
+ cuda_pull_array(*net.input_gpu, x, x_size);
+ }
+ if(*(state.net.total_bbox) > 0)
+ fprintf(stderr, " total_bbox = %d, rewritten_bbox = %f %% \n", *(state.net.total_bbox), 100 * (float)*(state.net.rewritten_bbox) / *(state.net.total_bbox));
+}
+
+float train_network_datum_gpu(network net, float *x, float *y)
+{
+ *net.seen += net.batch;
+ if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) {
+ net.adversarial = 1;
+ float lr_old = net.learning_rate;
+ float scale = (get_current_iteration(net) / ((float)net.max_batches));
+ //scale = sin(scale * M_PI);
+ net.learning_rate = net.adversarial_lr * scale;
+ layer l = net.layers[net.n - 1];
+ int y_size = get_network_output_size(net)*net.batch;
+ if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch;
+ float *truth_cpu = (float *)xcalloc(y_size, sizeof(float));
+
+ const int img_size = net.w*net.h*net.c;
+ float *old_input = (float *)xcalloc(img_size*net.batch, sizeof(float));
+ memcpy(old_input, x, img_size*net.batch * sizeof(float));
+
+ printf("\n adversarial training, adversarial_lr = %f \n", net.adversarial_lr * scale);
+
+ forward_backward_network_gpu(net, x, truth_cpu);
+
+ int b;
+ for (b = 0; b < net.batch; ++b) {
+ if (b % 2 == 1 && net.contrastive) {
+ //printf(" b = %d old img, ", b);
+ memcpy(x + img_size*b, old_input + img_size*b, img_size * sizeof(float));
+ }
+ }
+
+ image im;
+ im.w = net.w;
+ im.h = net.h;
+ im.c = net.c;
+ im.data = x;
+ show_image(im, "adversarial data augmentation");
+ resize_window_cv("adversarial data augmentation", 500, 500);
+ wait_key_cv(1);
+
+ free(old_input);
+ free(truth_cpu);
+ net.learning_rate = lr_old;
+ net.adversarial = 0;
+ }
+ forward_backward_network_gpu(net, x, y);
+ float error = get_network_cost(net);
+ //if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
+ const int sequence = get_sequence_value(net);
+ //if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net);
+
+ return error;
+}
+
+typedef struct {
+ network net;
+ data d;
+ float *err;
+} train_args;
+
+void *train_thread(void *ptr)
+{
+ train_args args = *(train_args*)ptr;
+ free(ptr);
+ cuda_set_device(args.net.gpu_index);
+ *args.err = train_network(args.net, args.d);
+ return 0;
+}
+
+pthread_t train_network_in_thread(network net, data d, float *err)
+{
+ pthread_t thread;
+ train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
+ ptr->net = net;
+ ptr->d = d;
+ ptr->err = err;
+ if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
+ return thread;
+}
+
+void pull_updates(layer l)
+{
+ if(l.type == CONVOLUTIONAL){
+ cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n);
+ cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+ if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+ cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
+ }
+}
+
+void push_updates(layer l)
+{
+ if(l.type == CONVOLUTIONAL){
+ cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
+ cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights);
+ if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n);
+ } else if(l.type == CONNECTED){
+ cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
+ cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs);
+ }
+}
+
+void update_layer(layer l, network net)
+{
+ int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
+ l.t = get_current_batch(net);
+ if(l.update_gpu){
+ l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale);
+ }
+}
+
+void merge_weights(layer l, layer base)
+{
+ if (l.type == CONVOLUTIONAL) {
+ 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;
+}
--
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