182 lines
6.7 KiB
Python
182 lines
6.7 KiB
Python
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"""
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train_Yolo
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Общая задача: обнаружение объекта (Object detection)
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Реализуемая функция: обучение нейросетевой модели YoloV8 по заданному BOP-датасету
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python3 $PYTHON_TRAIN --path /Users/idontsudo/webservice/server/build/public/7065d6b6-c8a3-48c5-9679-bb8f3a690296/datasets \
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--name test123 --datasetName ds213 --outpath /Users/idontsudo/webservice/server/build/public/7065d6b6-c8a3-48c5-9679-bb8f3a690296/weights
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27.04.2024 @shalenikol release 0.1
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"""
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import os
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import shutil
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import json
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import yaml
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from ultralytics import YOLO
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# from ultralytics.utils.metrics import DetMetrics
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FILE_BASEMODEL = "yolov8n.pt"
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FILE_RBS_INFO = "rbs_info.json"
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FILE_RBS_TRAIN = "rbs_train.yaml"
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FILE_GT_COCO = "scene_gt_coco.json"
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FILE_L_TRAIN = "i_train.txt"
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FILE_L_VAL = "i_val.txt"
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FILE_TRAIN_RES = "weights/last.pt"
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DIR_ROOT_DS = "datasets"
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DIR_COCO_DS = "rbs_coco"
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DIR_RGB_DS = "images"
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DIR_LABELS_DS = "labels"
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SZ_SERIES = 15 # number of train images per validation images
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nn_image = 0
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f1 = f2 = None
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def convert2relative(height, width, bbox):
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""" YOLO format use relative coordinates for annotation """
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x, y, w, h = bbox
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x += w/2
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y += h/2
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return x/width, y/height, w/width, h/height
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def gt_parse(path: str, out_dir: str):
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global nn_image, f1, f2
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with open(os.path.join(path, FILE_GT_COCO), "r") as fh:
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coco_data = json.load(fh)
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for img in coco_data["images"]:
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rgb_file = os.path.join(path, img["file_name"])
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if os.path.isfile(rgb_file):
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ext = os.path.splitext(rgb_file)[1] # only ext
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f = f"{nn_image:06}"
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out_img = os.path.join(out_dir, DIR_RGB_DS, f + ext)
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shutil.copy2(rgb_file, out_img)
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# заполним файлы с метками bbox
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img_id = img["id"]
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with open(os.path.join(out_dir, DIR_LABELS_DS, f + ".txt"), "w") as fh:
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for i in coco_data["annotations"]:
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if i["image_id"] == img_id:
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cat_id = i["category_id"]
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if cat_id < 999:
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bbox = i["bbox"]
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im_h = i["height"]
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im_w = i["width"]
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rel = convert2relative(im_h,im_w,bbox)
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# формат: <target> <x-center> <y-center> <width> <height>
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fh.write(f"{cat_id-1} {rel[0]} {rel[1]} {rel[2]} {rel[3]}\n") # category from 0
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nn_image += 1
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line = os.path.join("./", DIR_RGB_DS, f + ext) + "\n"
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if nn_image % SZ_SERIES == 0:
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f2.write(line)
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else:
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f1.write(line)
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def explore(path: str, res_dir: str):
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if not os.path.isdir(path):
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return
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folders = [
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os.path.join(path, o)
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for o in os.listdir(path)
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if os.path.isdir(os.path.join(path, o))
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]
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for path_entry in folders:
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if os.path.isfile(os.path.join(path_entry,FILE_GT_COCO)):
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gt_parse(path_entry, res_dir)
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else:
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explore(path_entry, res_dir)
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def BOP2Yolo_dataset(dpath: str, out_dir: str, lname: list) -> str:
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""" Convert BOP-dataset to YOLO format for train """
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cfg_yaml = os.path.join(out_dir, FILE_RBS_TRAIN)
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p = os.path.join(out_dir, DIR_ROOT_DS, DIR_COCO_DS)
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cfg_data = {"path": p, "train": FILE_L_TRAIN, "val": FILE_L_VAL}
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cfg_data["names"] = {i:x for i,x in enumerate(lname)}
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with open(cfg_yaml, "w") as fh:
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yaml.dump(cfg_data, fh)
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res_dir = os.path.join(out_dir, DIR_ROOT_DS)
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if not os.path.isdir(res_dir):
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os.mkdir(res_dir)
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res_dir = os.path.join(res_dir, DIR_COCO_DS)
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if not os.path.isdir(res_dir):
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os.mkdir(res_dir)
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p = os.path.join(res_dir, DIR_RGB_DS)
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if not os.path.isdir(p):
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os.mkdir(p)
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p = os.path.join(res_dir, DIR_LABELS_DS)
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if not os.path.isdir(p):
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os.mkdir(p)
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global f1, f2
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f1 = open(os.path.join(res_dir, FILE_L_TRAIN), "w")
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f2 = open(os.path.join(res_dir, FILE_L_VAL), "w")
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explore(dpath, res_dir)
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f1.close()
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f2.close()
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return out_dir
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def train_YoloV8(path:str, wname:str, dname:str, outpath:str, epochs:int, pretrain: bool):
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""" Main procedure for train YOLOv8 model """
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if not os.path.isdir(outpath):
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print(f"Invalid output path '{outpath}'")
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exit(-1)
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out_dir = os.path.join(outpath, wname)
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if pretrain:
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# продолжить обучение
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if not os.path.isdir(out_dir):
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print(f"No dir '{out_dir}'")
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exit(-2)
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dpath = out_dir
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model_path = os.path.join(dpath, wname + ".pt")
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else:
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# обучение сначала
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if not os.path.isdir(out_dir):
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os.mkdir(out_dir)
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ds_path = os.path.join(path, dname)
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rbs_info = os.path.join(ds_path, FILE_RBS_INFO)
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if not os.path.isfile(rbs_info):
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print(f"{rbs_info} : no dataset description file")
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exit(-3)
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with open(rbs_info, "r") as fh:
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y = json.load(fh)
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# список имён объектов
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list_name = list(map(lambda x: x["name"], y))
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dpath = BOP2Yolo_dataset(ds_path, out_dir, list_name)
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if len(dpath) == 0:
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print(f"Error in convert dataset '{ds_path}' to '{outpath}'")
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exit(-4)
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model_path = os.path.join(dpath, FILE_BASEMODEL)
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model = YOLO(model_path)
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results = model.train(data=os.path.join(dpath, FILE_RBS_TRAIN), epochs=epochs, project=out_dir)
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wf = os.path.join(results.save_dir, FILE_TRAIN_RES)
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if not os.path.isfile(wf):
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print(f"Error in train: no result file '{wf}'")
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exit(-5)
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shutil.copy2(wf, os.path.join(dpath, wname + ".pt"))
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shutil.rmtree(results.save_dir)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--path", required=True, help="Path for dataset")
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parser.add_argument("--name", required=True, help="String with result weights name")
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parser.add_argument("--datasetName", required=True, help="String with dataset name")
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parser.add_argument("--outpath", default="weights", help="Output path for weights")
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parser.add_argument("--epoch", default=3, type=int, help="How many training epochs")
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parser.add_argument('--pretrain', action="store_true", help="Use pretraining")
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args = parser.parse_args()
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train_YoloV8(args.path, args.name, args.datasetName, args.outpath, args.epoch, args.pretrain)
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