From c123f320fcc2212f1d3b59a4d3d2ae6be71cf1e6 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Sun, 04 Mar 2018 20:41:23 +0000
Subject: [PATCH] Show avg-loss chart during Training, if compiled with OpenCV. Use -dont_show to disable.
---
src/parser.c | 156 +++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 124 insertions(+), 32 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 44ba1c4..1adcd16 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -2,33 +2,35 @@
#include <string.h>
#include <stdlib.h>
-#include "blas.h"
-#include "parser.h"
-#include "assert.h"
-#include "activations.h"
-#include "crop_layer.h"
-#include "cost_layer.h"
-#include "convolutional_layer.h"
#include "activation_layer.h"
-#include "normalization_layer.h"
-#include "batchnorm_layer.h"
-#include "connected_layer.h"
-#include "rnn_layer.h"
-#include "gru_layer.h"
-#include "crnn_layer.h"
-#include "maxpool_layer.h"
-#include "reorg_layer.h"
-#include "softmax_layer.h"
-#include "dropout_layer.h"
-#include "detection_layer.h"
-#include "region_layer.h"
+#include "activations.h"
+#include "assert.h"
#include "avgpool_layer.h"
+#include "batchnorm_layer.h"
+#include "blas.h"
+#include "connected_layer.h"
+#include "convolutional_layer.h"
+#include "cost_layer.h"
+#include "crnn_layer.h"
+#include "crop_layer.h"
+#include "detection_layer.h"
+#include "dropout_layer.h"
+#include "gru_layer.h"
+#include "list.h"
#include "local_layer.h"
+#include "maxpool_layer.h"
+#include "normalization_layer.h"
+#include "option_list.h"
+#include "parser.h"
+#include "region_layer.h"
+#include "reorg_layer.h"
+#include "reorg_old_layer.h"
+#include "rnn_layer.h"
#include "route_layer.h"
#include "shortcut_layer.h"
-#include "list.h"
-#include "option_list.h"
+#include "softmax_layer.h"
#include "utils.h"
+#include <stdint.h>
typedef struct{
char *type;
@@ -59,6 +61,7 @@
if (strcmp(type, "[max]")==0
|| strcmp(type, "[maxpool]")==0) return MAXPOOL;
if (strcmp(type, "[reorg]")==0) return REORG;
+ if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD;
if (strcmp(type, "[avg]")==0
|| strcmp(type, "[avgpool]")==0) return AVGPOOL;
if (strcmp(type, "[dropout]")==0) return DROPOUT;
@@ -237,25 +240,50 @@
int coords = option_find_int(options, "coords", 4);
int classes = option_find_int(options, "classes", 20);
int num = option_find_int(options, "num", 1);
+ int max_boxes = option_find_int_quiet(options, "max", 30);
- params.w = option_find_int(options, "side", params.w);
- params.h = option_find_int(options, "side", params.h);
-
- layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
+ layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes);
assert(l.outputs == params.inputs);
l.log = option_find_int_quiet(options, "log", 0);
l.sqrt = option_find_int_quiet(options, "sqrt", 0);
+ l.small_object = option_find_int_quiet(options, "small_object", 0);
l.softmax = option_find_int(options, "softmax", 0);
- l.max_boxes = option_find_int_quiet(options, "max",30);
+ //l.max_boxes = option_find_int_quiet(options, "max",30);
l.jitter = option_find_float(options, "jitter", .2);
l.rescore = option_find_int_quiet(options, "rescore",0);
+ l.thresh = option_find_float(options, "thresh", .5);
+ l.classfix = option_find_int_quiet(options, "classfix", 0);
+ l.absolute = option_find_int_quiet(options, "absolute", 0);
+ l.random = option_find_int_quiet(options, "random", 0);
+
l.coord_scale = option_find_float(options, "coord_scale", 1);
l.object_scale = option_find_float(options, "object_scale", 1);
l.noobject_scale = option_find_float(options, "noobject_scale", 1);
l.class_scale = option_find_float(options, "class_scale", 1);
+ l.bias_match = option_find_int_quiet(options, "bias_match",0);
+
+ char *tree_file = option_find_str(options, "tree", 0);
+ if (tree_file) l.softmax_tree = read_tree(tree_file);
+ char *map_file = option_find_str(options, "map", 0);
+ if (map_file) l.map = read_map(map_file);
+
+ char *a = option_find_str(options, "anchors", 0);
+ if(a){
+ int len = strlen(a);
+ int n = 1;
+ int i;
+ for(i = 0; i < len; ++i){
+ if (a[i] == ',') ++n;
+ }
+ for(i = 0; i < n; ++i){
+ float bias = atof(a);
+ l.biases[i] = bias;
+ a = strchr(a, ',')+1;
+ }
+ }
return l;
}
detection_layer parse_detection(list *options, size_params params)
@@ -319,6 +347,7 @@
layer parse_reorg(list *options, size_params params)
{
int stride = option_find_int(options, "stride",1);
+ int reverse = option_find_int_quiet(options, "reverse",0);
int batch,h,w,c;
h = params.h;
@@ -327,10 +356,27 @@
batch=params.batch;
if(!(h && w && c)) error("Layer before reorg layer must output image.");
- layer layer = make_reorg_layer(batch,w,h,c,stride);
+ layer layer = make_reorg_layer(batch,w,h,c,stride,reverse);
return layer;
}
+layer parse_reorg_old(list *options, size_params params)
+{
+ printf("\n reorg_old \n");
+ int stride = option_find_int(options, "stride", 1);
+ int reverse = option_find_int_quiet(options, "reverse", 0);
+
+ int batch, h, w, c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch = params.batch;
+ if (!(h && w && c)) error("Layer before reorg layer must output image.");
+
+ layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse);
+ return layer;
+}
+
maxpool_layer parse_maxpool(list *options, size_params params)
{
int stride = option_find_int(options, "stride",1);
@@ -507,6 +553,7 @@
net->saturation = option_find_float_quiet(options, "saturation", 1);
net->exposure = option_find_float_quiet(options, "exposure", 1);
net->hue = option_find_float_quiet(options, "hue", 0);
+ net->power = option_find_float_quiet(options, "power", 4);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
@@ -546,7 +593,7 @@
net->gamma = option_find_float(options, "gamma", 1);
net->step = option_find_int(options, "step", 1);
} else if (net->policy == POLY || net->policy == RANDOM){
- net->power = option_find_float(options, "power", 1);
+ //net->power = option_find_float(options, "power", 1);
}
net->max_batches = option_find_int(options, "max_batches", 0);
}
@@ -559,6 +606,11 @@
network parse_network_cfg(char *filename)
{
+ return parse_network_cfg_custom(filename, 0);
+}
+
+network parse_network_cfg_custom(char *filename, int batch)
+{
list *sections = read_cfg(filename);
node *n = sections->front;
if(!n) error("Config file has no sections");
@@ -575,6 +627,7 @@
params.w = net.w;
params.c = net.c;
params.inputs = net.inputs;
+ if (batch > 0) net.batch = batch;
params.batch = net.batch;
params.time_steps = net.time_steps;
params.net = net;
@@ -583,9 +636,10 @@
n = n->next;
int count = 0;
free_section(s);
+ fprintf(stderr, "layer filters size input output\n");
while(n){
params.index = count;
- fprintf(stderr, "%d: ", count);
+ fprintf(stderr, "%5d ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
@@ -622,7 +676,9 @@
}else if(lt == MAXPOOL){
l = parse_maxpool(options, params);
}else if(lt == REORG){
- l = parse_reorg(options, params);
+ l = parse_reorg(options, params); }
+ else if (lt == REORG_OLD) {
+ l = parse_reorg_old(options, params);
}else if(lt == AVGPOOL){
l = parse_avgpool(options, params);
}else if(lt == ROUTE){
@@ -640,6 +696,8 @@
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
+ l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
+ l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
l.dontload = option_find_int_quiet(options, "dontload", 0);
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
option_unused(options);
@@ -673,6 +731,8 @@
return net;
}
+
+
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
@@ -759,6 +819,10 @@
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
fwrite(l.weights, sizeof(float), num, fp);
+ if(l.adam){
+ fwrite(l.m, sizeof(float), num, fp);
+ fwrite(l.v, sizeof(float), num, fp);
+ }
}
void save_batchnorm_weights(layer l, FILE *fp)
@@ -797,7 +861,7 @@
}
#endif
fprintf(stderr, "Saving weights to %s\n", filename);
- FILE *fp = fopen(filename, "w");
+ FILE *fp = fopen(filename, "wb");
if(!fp) file_error(filename);
int major = 0;
@@ -942,8 +1006,27 @@
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
+ if(0){
+ int i;
+ for(i = 0; i < l.n; ++i){
+ printf("%g, ", l.rolling_mean[i]);
+ }
+ printf("\n");
+ for(i = 0; i < l.n; ++i){
+ printf("%g, ", l.rolling_variance[i]);
+ }
+ printf("\n");
+ }
+ if(0){
+ fill_cpu(l.n, 0, l.rolling_mean, 1);
+ fill_cpu(l.n, 0, l.rolling_variance, 1);
+ }
}
fread(l.weights, sizeof(float), num, fp);
+ if(l.adam){
+ fread(l.m, sizeof(float), num, fp);
+ fread(l.v, sizeof(float), num, fp);
+ }
//if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
if (l.flipped) {
transpose_matrix(l.weights, l.c*l.size*l.size, l.n);
@@ -975,7 +1058,16 @@
fread(&major, sizeof(int), 1, fp);
fread(&minor, sizeof(int), 1, fp);
fread(&revision, sizeof(int), 1, fp);
- fread(net->seen, sizeof(int), 1, fp);
+ if ((major * 10 + minor) >= 2) {
+ printf("\n seen 64 \n");
+ uint64_t iseen = 0;
+ fread(&iseen, sizeof(uint64_t), 1, fp);
+ *net->seen = iseen;
+ }
+ else {
+ printf("\n seen 32 \n");
+ fread(net->seen, sizeof(int), 1, fp);
+ }
int transpose = (major > 1000) || (minor > 1000);
int i;
--
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