From 23cb35e6c8eae8b59fab161036ae3f417a55c8db Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 30 Mar 2018 11:46:51 +0000
Subject: [PATCH] Changed small_object
---
src/network.c | 366 +++++++++++++++++++++++++++++++++++++++------------
1 files changed, 278 insertions(+), 88 deletions(-)
diff --git a/src/network.c b/src/network.c
index d823c15..175c102 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,5 +1,6 @@
#include <stdio.h>
#include <time.h>
+#include <assert.h>
#include "network.h"
#include "image.h"
#include "data.h"
@@ -8,17 +9,25 @@
#include "crop_layer.h"
#include "connected_layer.h"
+#include "gru_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
+#include "local_layer.h"
#include "convolutional_layer.h"
-#include "deconvolutional_layer.h"
+#include "activation_layer.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "normalization_layer.h"
+#include "batchnorm_layer.h"
#include "maxpool_layer.h"
+#include "reorg_layer.h"
#include "avgpool_layer.h"
#include "cost_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "route_layer.h"
+#include "shortcut_layer.h"
+#include "yolo_layer.h"
int get_current_batch(network net)
{
@@ -26,18 +35,46 @@
return batch_num;
}
+void reset_momentum(network net)
+{
+ if (net.momentum == 0) return;
+ net.learning_rate = 0;
+ net.momentum = 0;
+ net.decay = 0;
+ #ifdef GPU
+ //if(net.gpu_index >= 0) update_network_gpu(net);
+ #endif
+}
+
float get_current_rate(network net)
{
int batch_num = get_current_batch(net);
+ int i;
+ float rate;
+ if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
switch (net.policy) {
case CONSTANT:
return net.learning_rate;
case STEP:
- return net.learning_rate * pow(net.gamma, batch_num/net.step);
+ return net.learning_rate * pow(net.scale, batch_num/net.step);
+ case STEPS:
+ rate = net.learning_rate;
+ for(i = 0; i < net.num_steps; ++i){
+ if(net.steps[i] > batch_num) return rate;
+ rate *= net.scales[i];
+ //if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net);
+ }
+ return rate;
case EXP:
return net.learning_rate * pow(net.gamma, batch_num);
case POLY:
- return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ //if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
+ //return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ case RANDOM:
+ return net.learning_rate * pow(rand_uniform(0,1), net.power);
+ case SIG:
+ return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
default:
fprintf(stderr, "Policy is weird!\n");
return net.learning_rate;
@@ -49,12 +86,24 @@
switch(a){
case CONVOLUTIONAL:
return "convolutional";
+ case ACTIVE:
+ return "activation";
+ case LOCAL:
+ return "local";
case DECONVOLUTIONAL:
return "deconvolutional";
case CONNECTED:
return "connected";
+ case RNN:
+ return "rnn";
+ case GRU:
+ return "gru";
+ case CRNN:
+ return "crnn";
case MAXPOOL:
return "maxpool";
+ case REORG:
+ return "reorg";
case AVGPOOL:
return "avgpool";
case SOFTMAX:
@@ -71,8 +120,12 @@
return "cost";
case ROUTE:
return "route";
+ case SHORTCUT:
+ return "shortcut";
case NORMALIZATION:
return "normalization";
+ case BATCHNORM:
+ return "batchnorm";
default:
break;
}
@@ -88,45 +141,26 @@
#ifdef GPU
net.input_gpu = calloc(1, sizeof(float *));
net.truth_gpu = calloc(1, sizeof(float *));
+
+ net.input16_gpu = calloc(1, sizeof(float *));
+ net.output16_gpu = calloc(1, sizeof(float *));
+ net.max_input16_size = calloc(1, sizeof(size_t));
+ net.max_output16_size = calloc(1, sizeof(size_t));
#endif
return net;
}
void forward_network(network net, network_state state)
{
+ state.workspace = net.workspace;
int i;
for(i = 0; i < net.n; ++i){
+ state.index = i;
layer l = net.layers[i];
if(l.delta){
scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
}
- if(l.type == CONVOLUTIONAL){
- forward_convolutional_layer(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- forward_deconvolutional_layer(l, state);
- } else if(l.type == NORMALIZATION){
- forward_normalization_layer(l, state);
- } else if(l.type == DETECTION){
- forward_detection_layer(l, state);
- } else if(l.type == REGION){
- forward_region_layer(l, state);
- } else if(l.type == CONNECTED){
- forward_connected_layer(l, state);
- } else if(l.type == CROP){
- forward_crop_layer(l, state);
- } else if(l.type == COST){
- forward_cost_layer(l, state);
- } else if(l.type == SOFTMAX){
- forward_softmax_layer(l, state);
- } else if(l.type == MAXPOOL){
- forward_maxpool_layer(l, state);
- } else if(l.type == AVGPOOL){
- forward_avgpool_layer(l, state);
- } else if(l.type == DROPOUT){
- forward_dropout_layer(l, state);
- } else if(l.type == ROUTE){
- forward_route_layer(l, net);
- }
+ l.forward(l, state);
state.input = l.output;
}
}
@@ -138,18 +172,17 @@
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer(l, rate, net.momentum, net.decay);
- } else if(l.type == CONNECTED){
- update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
+ if(l.update){
+ l.update(l, update_batch, rate, net.momentum, net.decay);
}
}
}
float *get_network_output(network net)
{
+#ifdef GPU
+ if (gpu_index >= 0) return get_network_output_gpu(net);
+#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
@@ -161,15 +194,7 @@
float sum = 0;
int count = 0;
for(i = 0; i < net.n; ++i){
- if(net.layers[i].type == COST){
- sum += net.layers[i].output[0];
- ++count;
- }
- if(net.layers[i].type == DETECTION){
- sum += net.layers[i].cost[0];
- ++count;
- }
- if(net.layers[i].type == REGION){
+ if(net.layers[i].cost){
sum += net.layers[i].cost[0];
++count;
}
@@ -189,7 +214,9 @@
int i;
float *original_input = state.input;
float *original_delta = state.delta;
+ state.workspace = net.workspace;
for(i = net.n-1; i >= 0; --i){
+ state.index = i;
if(i == 0){
state.input = original_input;
state.delta = original_delta;
@@ -199,41 +226,20 @@
state.delta = prev.delta;
}
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- backward_convolutional_layer(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- backward_deconvolutional_layer(l, state);
- } else if(l.type == NORMALIZATION){
- backward_normalization_layer(l, state);
- } else if(l.type == MAXPOOL){
- if(i != 0) backward_maxpool_layer(l, state);
- } else if(l.type == AVGPOOL){
- backward_avgpool_layer(l, state);
- } else if(l.type == DROPOUT){
- backward_dropout_layer(l, state);
- } else if(l.type == DETECTION){
- backward_detection_layer(l, state);
- } else if(l.type == REGION){
- backward_region_layer(l, state);
- } else if(l.type == SOFTMAX){
- if(i != 0) backward_softmax_layer(l, state);
- } else if(l.type == CONNECTED){
- backward_connected_layer(l, state);
- } else if(l.type == COST){
- backward_cost_layer(l, state);
- } else if(l.type == ROUTE){
- backward_route_layer(l, net);
- }
+ if (l.stopbackward) break;
+ l.backward(l, state);
}
}
float train_network_datum(network net, float *x, float *y)
{
- *net.seen += net.batch;
#ifdef GPU
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
network_state state;
+ *net.seen += net.batch;
+ state.index = 0;
+ state.net = net;
state.input = x;
state.delta = 0;
state.truth = y;
@@ -265,6 +271,7 @@
float train_network(network net, data d)
{
+ assert(d.X.rows % net.batch == 0);
int batch = net.batch;
int n = d.X.rows / batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
@@ -282,10 +289,13 @@
return (float)sum/(n*batch);
}
+
float train_network_batch(network net, data d, int n)
{
int i,j;
network_state state;
+ state.index = 0;
+ state.net = net;
state.train = 1;
state.delta = 0;
float sum = 0;
@@ -310,35 +320,100 @@
int i;
for(i = 0; i < net->n; ++i){
net->layers[i].batch = b;
+#ifdef CUDNN
+ if(net->layers[i].type == CONVOLUTIONAL){
+ cudnn_convolutional_setup(net->layers + i, cudnn_fastest);
+ /*
+ layer *l = net->layers + i;
+ cudnn_convolutional_setup(l, cudnn_fastest);
+ // check for excessive memory consumption
+ size_t free_byte;
+ size_t total_byte;
+ check_error(cudaMemGetInfo(&free_byte, &total_byte));
+ if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) {
+ printf(" used slow CUDNN algo without Workspace! \n");
+ cudnn_convolutional_setup(l, cudnn_smallest);
+ l->workspace_size = get_workspace_size(*l);
+ }
+ */
+ }
+#endif
}
}
int resize_network(network *net, int w, int h)
{
+#ifdef GPU
+ cuda_set_device(net->gpu_index);
+ if(gpu_index >= 0){
+ cuda_free(net->workspace);
+ if (net->input_gpu) {
+ cuda_free(*net->input_gpu);
+ *net->input_gpu = 0;
+ cuda_free(*net->truth_gpu);
+ *net->truth_gpu = 0;
+ }
+ }
+#endif
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
- //fprintf(stderr, "Resizing to %d x %d...", w, h);
+ int inputs = 0;
+ size_t workspace_size = 0;
+ //fprintf(stderr, "Resizing to %d x %d...\n", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
+ //printf(" %d: layer = %d,", i, l.type);
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
+ }else if(l.type == CROP){
+ resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
+ }else if(l.type == REGION){
+ resize_region_layer(&l, w, h);
+ }else if (l.type == YOLO) {
+ resize_yolo_layer(&l, w, h);
+ }else if(l.type == ROUTE){
+ resize_route_layer(&l, net);
+ }else if (l.type == SHORTCUT) {
+ resize_shortcut_layer(&l, w, h);
+ }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 == AVGPOOL){
resize_avgpool_layer(&l, w, h);
- break;
}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;
w = l.out_w;
h = l.out_h;
+ if(l.type == AVGPOOL) break;
}
+#ifdef GPU
+ if(gpu_index >= 0){
+ printf(" try to allocate workspace = %zu * sizeof(float), ", (workspace_size - 1) / sizeof(float) + 1);
+ net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+ printf(" CUDA allocate done! \n");
+ }else {
+ free(net->workspace);
+ net->workspace = calloc(1, workspace_size);
+ }
+#else
+ free(net->workspace);
+ net->workspace = calloc(1, workspace_size);
+#endif
//fprintf(stderr, " Done!\n");
return 0;
}
@@ -418,6 +493,8 @@
#endif
network_state state;
+ state.net = net;
+ state.index = 0;
state.input = input;
state.truth = 0;
state.train = 0;
@@ -427,6 +504,109 @@
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 == DETECTION || l.type == REGION) {
+ s += l.w*l.h*l.n;
+ }
+ }
+ return s;
+}
+
+detection *make_network_boxes(network *net, float thresh, int *num)
+{
+ layer l = net->layers[net->n - 1];
+ int i;
+ int nboxes = num_detections(net, thresh);
+ if (num) *num = nboxes;
+ detection *dets = calloc(nboxes, sizeof(detection));
+ for (i = 0; i < nboxes; ++i) {
+ dets[i].prob = calloc(l.classes, sizeof(float));
+ if (l.coords > 4) {
+ dets[i].mask = calloc(l.coords - 4, sizeof(float));
+ }
+ }
+ 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 = calloc(l.w*l.h*l.n, sizeof(box));
+ float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
+ int i, j;
+ for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(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);
+}
+
+void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
+{
+ 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 (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].mask) free(dets[i].mask);
+ }
+ free(dets);
+}
+
+float *network_predict_image(network *net, image im)
+{
+ image imr = letterbox_image(im, net->w, net->h);
+ set_batch_network(net, 1);
+ float *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;
@@ -525,17 +705,16 @@
return acc;
}
-float *network_accuracies(network net, data d)
+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,5);
+ 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);
@@ -546,15 +725,26 @@
void free_network(network net)
{
- int i;
- for(i = 0; i < net.n; ++i){
- free_layer(net.layers[i]);
- }
- free(net.layers);
- #ifdef GPU
- 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);
- #endif
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ free_layer(net.layers[i]);
+ }
+ free(net.layers);
+#ifdef GPU
+ if (gpu_index >= 0) cuda_free(net.workspace);
+ else free(net.workspace);
+ 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
}
--
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