# Check TF version
import tensorflow as tf
print(tf.__version__)
import os
import sys
sys.path.append("MONK/Monk_Object_Detection/13_tf_obj_2/lib/")
from train_detector import Detector
gtf = Detector()
print(gtf.list_models())
train_img_dir = "COCO_CREATION/results/Train/images"
train_anno_dir = "COCO_CREATION/results/Train/annotations"
class_list_file = "COCO_CREATION/pascal-voc-classes.txt"
gtf.set_train_dataset(train_img_dir,
train_anno_dir,
class_list_file,
batch_size=24,
trainval_split=0.8)
## Output dir
output_dir = os.path.join("data_tfrecord")
gtf.create_tfrecord(data_output_dir=output_dir)
val_anno_dir = labels_dir
else:
val_root_dir = system["val_yolo_root_dir"]
val_img_dir = system["val_yolo_img_dir"]
val_anno_dir = system["val_yolo_anno_dir"]
val_classes_file = system["val_yolo_classes_file"]
from train_detector import Detector
gtf = Detector()
gtf.set_train_dataset(root_dir + "/" + img_dir,
root_dir + "/" + anno_dir,
root_dir + "/" + classes_file,
batch_size=system["batch_size"],
img_size=system["img_size"],
cache_images=system["cache_images"])
if (system["val_data"] == "yes"):
gtf.set_val_dataset(val_root_dir + "/" + val_img_dir,
val_root_dir + "/" + val_anno_dir)
gtf.set_model(model_name=system["model"])
gtf.set_hyperparams(optimizer=system["optimizer"],
lr=system["lr"],
multi_scale=system["multi_scale"],
evolve=system["evolve"],
num_generations=system["num_generations"],
mixed_precision=system["mixed_precision"],