def get_default_config(cls, dataset_cfgs, **args):
config = cat_config()
config.set_list("batch_sizes", args["batch_sizes"])
config.set_list("sizes", args["sizes"])
config.set("required_cnt", 6)
iconf = cat_config()
iconf.set("shuffle_flag", args.get("shuffle_flag", True))
config.add_child("iterator_config", iconf)
gconf = cls.GIVER_FN.get_default_config(**args)
config.add_child("giver_cfg", gconf)
config.add_child("data_sources", dataset_cfgs)
return config
def __init__(this):
this.weight_tree = {}
this.config_tree = cat_config()
#Comming in V3
this.submodules = []
pass
def model_testing(this,inputs,network):
cfg=cat_config();
cfg.set("network",network);
cfg.set("weight_decay",1e-5);
cfg.set("scope",this.PRFX);
score,geometry=this.MODEL_FN(cfg).model(inputs,False);
return score,geometry;
def pretrain_data_config():
config = cat_config();
config.add_child("onlineicdar", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"icdar15-a-13-idx/a.index"),"icdar",5)
);
config.add_child("onlinecoco", datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "all_tr_data/EAST_data/coco.index"), "icdar", 3)
);
config.add_child("onlinericdar", datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "all_tr_data/EAST_data/icdar_15_13_0-90.index"), "icdar", 2)
);
config.add_child("onlinesynth", datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "all_tr_data/EAST_data/synth_cat.index"), "icdar", 1)
);
config.add_child("onlineustid", datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "all_tr_data/EAST_data/ustid-mk1.index"), "icdar", 1)
);
# config.add_child("onlinemlt", datasource_cfg(
# os.path.join(pathcfg.training_dataset_root,/mlt/a.index","mlt",5)
# );
return config;
def datasource_cfg(path, type, importance,has_gt=1):
config = cat_config();
config.set("index", path);
config.set("type", type);
config.set("importance", importance);
config.set("has_gt",has_gt);
return config;
def get_default_config(cls, dataset_cfgs, **args):
config = cat_config()
# A "Dummy" value.
config.set_list("sizes", [9])
config.set_list("batch_sizes", [3])
config.set("required_cnt", 4 + 5 + 4)
iconf = cat_config()
iconf.set_item_wdef(args, "shuffle_flag", True)
dp = dataset_cfgs.get(str, "dict_path")
iconf.set("dict_path", dp)
config.add_child("iterator_config", iconf)
gconf = cls.GIVER_FN.get_default_config(dict_path=dp, **args)
config.add_child("giver_cfg", gconf)
config.add_child("data_sources", dataset_cfgs)
return config
def tencent_rects_data_config():
config = cat_config();
config.add_child("online", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"rects/a.index"),"icdar",5)
);
return config;
def data_config():
config = cat_config()
config.add_child(
"rects",
datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "rects/a.index"),
"rects", 1))
return config
def get_default_config(_, **args):
config = cat_config()
config.set("with_ohem", args.get("with_ohem", 1))
config.set("with_instance_balance", args.get("with_instance_balance",
1))
config.set("rand_neg_samples", args.get("rand_neg_samples", 1. / 32))
config.set("hard_neg_samples", args.get("hard_neg_samples", 1. / 32))
config.set("rand_reg_samples", args.get("rand_reg_samples", 1. / 128))
config.set("hard_reg_samples", args.get("hard_reg_samples", 1. / 128))
return config
def mlt_data_config():
config = cat_config();
config.add_child("onlinemlt", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"mlt/a.index"),"mlt",5)
);
# config.add_child("onlinemlt", datasource_cfg(
# os.path.join(pathcfg.training_dataset_root,/mlt/a.index","mlt",5)
# );
return config;
def get_default_config(cls, **args):
config = cat_config()
anno_config = cat_config()
anno_config.set("margin_dc", args.get("margin_dc", 0))
anno_config.set("min_text_size", args.get("min_text_size", 5))
config.add_child("anno_config", anno_config)
config.set_list("random_scale",
args.get("random_scale", [0.5, 0.8, 1, 1.5, 2]))
config.set("background_ratio", args.get("background_ratio", 0))
config.set("geometry", args.get("geometry", "RBOX"))
config.set("vis", args.get("vis", False))
config.set_list("mean", args.get("mean", [127, 127, 127]))
config.set_list("var", args.get("var", [127, 127, 127]))
config.set("featuremap_stride", args.get("featuremap_stride", 4))
config.set_list("rotate_ranges_max", args.get("rotate_ranges_max", []))
config.set_list("rotate_ranges_min", args.get("rotate_ranges_min", []))
config.set_list("rotate_ranges_freq", args.get("rotate_ranges_freq",
[]))
return config
def data_config():
config = cat_config()
config.add_child(
"onlinerctw",
datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "rctw/a.index"),
"rctw", 1))
config.add_child(
"onlinemlt",
datasource_cfg(
os.path.join(pathcfg.training_dataset_root, "mlt/a.index"), "mlt",
1))
return config
def model_training(this,inputs,DEV):
ecfg = cat_config();
ecfg.set("network", "densenet169");
ecfg.set("weight_decay", 1e-5);
ecfg.set("scope", this.PRFX);
with tf.device(DEV):
score,geometry=this.MODEL_FN(ecfg).model([inputs[0]],True);
cfg=this.LOSS_FN().get_default_config(with_instance_balance=1);
dwd={};
dwd["0xca39_scope"]=this.PRFX;
loss_fn=this.LOSS_FN().init_ret(cfg,dwd);
[loss_cls,loss_box,loss_ang]=loss_fn.call([inputs[1],score,inputs[2],geometry,inputs[3],inputs[4]],True);
return [loss_cls,loss_box,loss_ang];
def ln_data_config():
config = cat_config();
config.add_child("online", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"all_line_tr_data/td500_train.index"),"icdar",5)
);
config.add_child("online", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"all_line_tr_data/rctw.index"),"icdar",3)
);
config.add_child("online", datasource_cfg(
os.path.join(pathcfg.training_dataset_root,"all_line_tr_data/sv1k_training.index"),"icdar",2)
);
# config.add_child("onlinemlt", datasource_cfg(
# os.path.join(pathcfg.training_dataset_root,/mlt/a.index","mlt",5)
# );
return config;
def datasource_cfg(path, type, importance):
config = cat_config()
config.set("index", path)
config.set("type", type)
config.set("importance", importance)
return config
