YOLO-Tutorial-v2/models/__init__.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-

import torch
from .yolov1.build import build_yolov1
from .yolov2.build import build_yolov2
from .yolov3.build import build_yolov3
from .yolov4.build import build_yolov4
from .yolov8.build import build_yolov8
from .rtdetr.build import build_rtdetr

# build object detector
def build_model(args, cfg, is_val=False):
    # ------------ build object detector ------------
    ## Modified YOLOv1
    if   'yolov1' in args.model:
        model, criterion = build_yolov1(cfg, is_val)
    ## Modified YOLOv2
    elif 'yolov2' in args.model:
        model, criterion = build_yolov2(cfg, is_val)
    ## Modified YOLOv3
    elif 'yolov3' in args.model:
        model, criterion = build_yolov3(cfg, is_val)
    ## Modified YOLOv4
    elif 'yolov4' in args.model:
        model, criterion = build_yolov4(cfg, is_val)
    ## YOLOv8
    elif 'yolov8' in args.model:
        model, criterion = build_yolov8(cfg, is_val)
    ## RT-DETR
    elif 'rtdetr' in args.model:
        model, criterion = build_rtdetr(cfg, is_val)

    if is_val:
        # ------------ Load pretrained weight ------------
        if args.pretrained is not None:
            print('Loading COCO pretrained weight ...')
            checkpoint = torch.load(args.pretrained, map_location='cpu')
            # checkpoint state dict
            checkpoint_state_dict = checkpoint.pop("model")
            # model state dict
            model_state_dict = model.state_dict()
            # check
            for k in list(checkpoint_state_dict.keys()):
                if k in model_state_dict:
                    shape_model = tuple(model_state_dict[k].shape)
                    shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
                    if shape_model != shape_checkpoint:
                        checkpoint_state_dict.pop(k)
                        print(k)
                else:
                    checkpoint_state_dict.pop(k)
                    print(k)

            model.load_state_dict(checkpoint_state_dict, strict=False)

        # ------------ Keep training from the given checkpoint ------------
        if args.resume and args.resume != "None":
            checkpoint = torch.load(args.resume, map_location='cpu')
            # checkpoint state dict
            try:
                checkpoint_state_dict = checkpoint.pop("model")
                print('Load model from the checkpoint: ', args.resume)
                model.load_state_dict(checkpoint_state_dict)
                del checkpoint, checkpoint_state_dict
            except:
                print("No model in the given checkpoint.")

        return model, criterion

    else:      
        return model