Getting Error in running Training Script

[ronit450]

I have coded a training script for starting training for Object detection. I am having problem in setting up the environment.

The error I am encountering is this
AssertionError: CocoDataset: Incompatible version of pycocotools is installed. Run pip uninstall pycocotools first. Then run pip install mmpycocotools to install open-mmlab forked pycocotools.

I have tried uninstalling it and then installing it but nothing seems to work.

here is the detailed script

import json
import mmcv
import numpy as np
import os.path as osp
from mmdet.apis import train_detector, set_random_seed
from mmdet.datasets import build_dataset
from mmdet.models import build_detector

class ObjectDetector:
    def __init__(self, config_path):
        with open(config_path, 'r') as file:
            self.config = json.load(file)
        
        self.cfg = mmcv.Config.fromfile('./configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py')  # Update this path to your actual config file
        self._update_config()

    def _update_config(self):
        # Dataset configuration
        self.cfg.dataset_type = 'CocoDataset'
        self.cfg.data_root = 'C:/Users/User/Desktop/Ronit-Projects/Detr_Train/dataset'

        self.cfg.data.train.type = self.cfg.dataset_type
        self.cfg.data.train.data_root = self.config['train_folder']
        self.cfg.data.train.ann_file = self.config['train_json']
        self.cfg.data.train.img_prefix = ''

        self.cfg.data.val.type = self.cfg.dataset_type
        self.cfg.data.val.data_root = self.config['val_folder']
        self.cfg.data.val.ann_file = self.config['val_json']
        self.cfg.data.val.img_prefix = ''

        self.cfg.data.test.type = self.cfg.dataset_type
        self.cfg.data.test.data_root = self.config['test_folder']
        self.cfg.data.test.ann_file = self.config['test_json']
        self.cfg.data.test.img_prefix = ''

        # Model settings
        self.cfg.model.roi_head.bbox_head.num_classes = self.config['num_classes']

        # Pre-trained model
        self.cfg.load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' 


        # Training settings
        self.cfg.runner.max_epochs = self.config['epochs']
        self.cfg.work_dir = './your_training_output_directory'
        self.cfg.optimizer.lr = 0.02 / 8  # Adjust as necessary
        self.cfg.lr_config.warmup = None
        self.cfg.log_config.interval = 10

        self.cfg.evaluation.metric = 'mAP'
        self.cfg.evaluation.interval = 12
        self.cfg.checkpoint_config.interval = 12

        # Seed for reproducibility
        self.cfg.seed = 0
        set_random_seed(0, deterministic=False)
        self.cfg.gpu_ids = range(1)  # Adjust the number of GPUs

    def train(self):
        # Build dataset and model
        datasets = [build_dataset(self.cfg.data.train)]
        model = build_detector(self.cfg.model, train_cfg=self.cfg.train_cfg, test_cfg=self.cfg.test_cfg)
        model.CLASSES = datasets[0].CLASSES

        # Create directory for work
        mmcv.mkdir_or_exist(osp.abspath(self.cfg.work_dir))
        train_detector(model, datasets, self.cfg, distributed=False, validate=True)

# Usage
config_path = 'config.json'
detector = ObjectDetector(config_path)
detector.train()