目錄
classifier.c文件中數據類型爲CLASSIFICATION_DATA
args.type = CLASSIFICATION_DATA;
data.c文件中的load_thread()函數中調用load_data_augment（）函數：
void *load_thread(void *ptr)
{
//srand(time(0));
//printf("Loading data: %d\n", random_gen());
load_args a = *(struct load_args*)ptr;
if(a.exposure == 0) a.exposure = 1;
if(a.saturation == 0) a.saturation = 1;
if(a.aspect == 0) a.aspect = 1;
if (a.type == OLD_CLASSIFICATION_DATA){
*a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
} else if (a.type == CLASSIFICATION_DATA){
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
} else if (a.type == SUPER_DATA){
load_data_augment()調用參數說明
args.paths = paths;
args.n = imgs;
args.m = train_images_num;
args.labels = labels;
args.classes = classes;
args.hierarchy = net.hierarchy;
args.flip = net.flip;
args.min = net.min_crop;
args.max = net.max_crop;
args.size = net.w > net.h ? net.w : net.h;
args.angle = net.angle;
args.aspect = net.aspect;
args.hue = net.hue;
args.saturation = net.saturation;
args.exposure = net.exposure;
a.paths,
list *options = read_data_cfg(datacfg);
char *train_list = option_find_str(options, "train", "data/train.list");
list *plist = get_paths(train_list);
char **paths = (char **)list_to_array(plist);
args.paths = paths;
train_classifier()函數調用參數說明已經知道，datacfg,數據說明文件路徑，圖像分類數據說明文件一般命名爲meta.data。
paths字符串數組，是meta.data文件夾下train後面對應list文件所包含所有圖片路徑，train默認值爲“data/train.list”。
a.n,
int subdivs = option_find_int(options, "subdivisions",1);
net->subdivisions = subdivs;
net->batch = option_find_int(options, "batch",1);
int imgs = net.batch * net.subdivisions * ngpus;
args.n = imgs;
ngpus由train指令的-ngpus命令指定，如果沒有指定該命令，ngpus默認爲1。
batch，subdivisions爲cfg文件指定batch和subdivisions。cfg文件中batch和subdivisions代表的含義，參看以前的博文：
a.m,
list *options = read_data_cfg(datacfg);
char *train_list = option_find_str(options, "train", "data/train.list");
list *plist = get_paths(train_list);
int train_images_num = plist->size;
args.m = train_images_num;
用於分類的圖像張數，該值等於datacfg路徑所指文件meta.data文件中train字段對應list中的圖片張數。
a.labels,
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
char **labels = get_labels(label_list);
args.labels = labels;
a.classes,
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 2);
args.classes = classes;
分類任務的類別個數，例如二分類任務，此處classes=2。
a.hierarchy,
}else if(lt == SOFTMAX){
l = parse_softmax(options, params);
net.hierarchy = l.softmax_tree;
。。。。。。
args.hierarchy = net.hierarchy;
cfg文件中每一層結構前有一個[]標記起來的層名，例如sofmax網絡層參數最前面會有一行[softmax]
部分cfg文件參數如下：
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=2
size=1
stride=1
pad=1
activation=linear
[avgpool]
[softmax]
groups=1
每一段代表一個網絡層，每一段第一行類似[convolutional]標識該網絡層的類別，緊接着是該網絡層的參數。[convolutional]標識下面這一段爲一個卷積層參數。
迴歸到net.hierarchy，hirerarchy中文意思爲層次關係，那麼net.hierarchy表示如果此前網絡層爲softmax層，hirerarchy指向一個softmax_tree。
a.flip,
net->flip = option_find_int_quiet(options, "flip", 1);
args.flip = net.flip;
flip就是cfg網絡結構文件中flip參數，flip中文意思翻轉開關
a.min,
net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
args.min = net.min_crop;
a.max,
net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
args.max = net.max_crop;
a.size,
net->h = option_find_int_quiet(options, "height",0);
net->w = option_find_int_quiet(options, "width",0);
args.size = net.w > net.h ? net.w : net.h;
a.angle,
net->angle = option_find_float_quiet(options, "angle", 0);
args.angle = net.angle;
角度
a.aspect,
net->aspect = option_find_float_quiet(options, "aspect", 1);
args.aspect = net.aspect;
方位
a.hue,
net->hue = option_find_float_quiet(options, "hue", 0);
args.hue = net.hue;
色調
a.saturation,
net->saturation = option_find_float_quiet(options, "saturation", 1);
args.saturation = net.saturation;
飽和度
a.exposure
net->exposure = option_find_float_quiet(options, "exposure", 1);
args.exposure = net.exposure;
曝光時間
總結
|
參數名 |
含義 |
相關文件 |
文件中變量 |
默認值 |
|
a.paths |
tain變量對應list中包含所有圖片路徑 |
Metal.data |
train |
無 |
|
a.n |
a.n=batch * subdivisions * ngpus |
*.cfg |
batch和subdivisions |
無 |
|
a.m |
tain變量對應list中包含所有圖片總數 |
metal.data |
train |
無 |
|
a.labels |
分類任務涉及類別 |
label |
無 |
|
|
a.classes |
分類任務的類別個數 |
classes |
無 |
|
|
a.hierarchy |
如果此前網絡層爲softmax層，hirerarchy指向一個softmax_tree。 |
*.cfg |
softmax |
無 |
|
a.flip |
|
flip |
1 |
|
|
a.min |
還看不出來 |
min_crop,width |
Net.w即width |
|
|
a.max |
還看不出來 |
max_crop，width |
Net.w*2 |
|
|
a.size |
a.size = net.w > net.h ? net.w : net.h |
Width,height |
0 |
|
|
a.angle |
還看不出來 |
angle |
0 |
|
|
a.aspect |
還看不出來 |
aspect |
1 |
|
|
a.hue |
還看不出來 |
hue |
0 |
|
|
a.saturation |
還看不出來 |
saturation |
1 |
|
|
a.exposure |
還看不出來 |
exposure |
1 |
load_data_augment源代碼解析
data.c中的load_data_augment()代碼：
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
{
if(m) paths = get_random_paths(paths, n, m);
data d = {0};
d.shallow = 0;
d.X = load_image_augment_paths(paths, n, use_flip, min, max, size, angle, aspect, hue, saturation, exposure);
d.y = load_labels_paths(paths, n, labels, k, hierarchy);
if(m) free(paths);
return d;
}
