train.py

from utils.logger import setup_logger
from datasets import make_dataloader
from model import make_model
from solver import make_optimizer
from solver.scheduler_factory import create_scheduler
from loss import make_loss
from processor import do_train
import random
import torch
import numpy as np
import os
import argparse
# from timm.scheduler import create_scheduler
from config import cfg


def set_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True


if __name__ == '__main__':

    parser = argparse.ArgumentParser(description="ReID Baseline Training")
    parser.add_argument("--config_file",
                        default="",
                        help="path to config file",
                        type=str)

    parser.add_argument("opts",
                        help="Modify config options using the command-line",
                        default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument("--local_rank", default=0, type=int)
    args = parser.parse_args()

    if args.config_file != "":
        cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    set_seed(cfg.SOLVER.SEED)


    output_dir = cfg.OUTPUT_DIR
    if output_dir and not os.path.exists(output_dir):
        os.makedirs(output_dir)

    logger = setup_logger("transreid", output_dir, if_train=True)
    logger.info("Saving model in the path :{}".format(cfg.OUTPUT_DIR))
    logger.info(args)

    if args.config_file != "":
        logger.info("Loaded configuration file {}".format(args.config_file))
        with open(args.config_file, 'r') as cf:
            config_str = "\n" + cf.read()
            logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))


    os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
    train_loader, train_loader_normal, val_loader, corrupted_val_loader, corrupted_query_loader, corrupted_gallery_loader, num_query, num_classes, camera_num, view_num = make_dataloader(
        cfg)

    model = make_model(cfg,
                       num_class=num_classes,
                       camera_num=camera_num,
                       view_num=view_num)

    loss_func, center_criterion = make_loss(cfg, num_classes=num_classes)

    optimizer, optimizer_center = make_optimizer(cfg, model, center_criterion)

    scheduler = create_scheduler(cfg, optimizer)

    do_train(cfg, model, center_criterion, train_loader, val_loader, optimizer,
             optimizer_center, scheduler, loss_func, num_query,
             args.local_rank)