trainer.py

import sys
import logging
import copy
import torch
from utils import factory
from utils.data_manager import DataManager
from utils.toolkit import count_parameters
import os


def train(args):
    """
    Entry point for training the model.
    
    Args:
        args (dict): Configuration arguments including model settings, dataset info, device, seed list, etc.
    """
    seed_list = copy.deepcopy(args["seed"])
    device = copy.deepcopy(args["device"])

    for seed in seed_list:
        args["seed"] = seed
        args["device"] = device
        _train(args)


def _train(args):
    
    """
    Main training function to handle incremental learning across tasks.

    Args:
        args (dict): Configuration arguments including model settings, dataset info, etc.
    """
    # Initialize class counter
    init_cls = 0 if args ["init_cls"] == args["increment"] else args["init_cls"]
    logs_name = "logs/{}/{}".format(args["model_name"],args["dataset"])
    
    if not os.path.exists(logs_name):
        os.makedirs(logs_name)

    if not os.path.exists("predictions"):
        os.makedirs("predictions")

    logfilename = "logs/{}/{}/{}_{}_{}".format(
        args["model_name"],
        args["dataset"],
        args["convnet_type"],
        args["seed"],
        args["summary_file"]
    )
    logging.basicConfig(
        level=logging.INFO,
        format="[%(asctime)s: %(filename)s: %(lineno)d: %(funcName)s]=> %(message)s",
        handlers=[
            logging.FileHandler(filename=logfilename + ".log", mode="w"),
            logging.StreamHandler(sys.stdout),
        ],
    )

    _set_random()
    _set_device(args)

    # Initialize data manager for handling datasets
    data_manager = DataManager(
        args["dataset"],
        args["shuffle"],
        args["seed"],
        args["init_cls"],
        args["increment"],
    )
    model = factory.get_model(args["model_name"], args)

    # Initialize accuracy tracking variables
    cnn_curve, nme_curve = {"top1": [], "top3": []}, {"top1": [], "top3": []}

    logging.info("nb_tasks: {}".format(data_manager.nb_tasks))
    for task in range(data_manager.nb_tasks):
        
        model.incremental_train(data_manager)
        cnn_accy, nme_accy = model.eval_task()
        model.after_task()

        if nme_accy is not None:
            logging.info("CNN: {}".format(cnn_accy["grouped"]))
            logging.info("NME: {}".format(nme_accy["grouped"]))

            cnn_curve["top1"].append(cnn_accy["top1"])
            cnn_curve["top3"].append(cnn_accy["top3"])

            nme_curve["top1"].append(nme_accy["top1"])
            nme_curve["top3"].append(nme_accy["top3"])

            logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
            logging.info("CNN top3 curve: {}".format(cnn_curve["top3"]))
            logging.info("NME top1 curve: {}".format(nme_curve["top1"]))
            logging.info("NME top3 curve: {}\n".format(nme_curve["top3"]))

            print('Average Accuracy (CNN):', sum(cnn_curve["top1"])/len(cnn_curve["top1"]))
            print('Average Accuracy (NME):', sum(nme_curve["top1"])/len(nme_curve["top1"]))

            logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"])/len(cnn_curve["top1"])))
            logging.info("Average Accuracy (NME): {}".format(sum(nme_curve["top1"])/len(nme_curve["top1"])))
        else:
            logging.info("No NME accuracy.")
            logging.info("CNN: {}".format(cnn_accy["grouped"]))

            cnn_curve["top1"].append(cnn_accy["top1"])
            cnn_curve["top3"].append(cnn_accy["top3"])

            logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
            logging.info("CNN top3 curve: {}\n".format(cnn_curve["top3"]))

            print('Average Accuracy (CNN):', sum(cnn_curve["top1"])/len(cnn_curve["top1"]))
            logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"])/len(cnn_curve["top1"])))

    
def _set_device(args):
    """
    Configure device settings for the model (CPU or GPU).

    Args:
        args (dict): Configuration arguments including device settings.
    """
    device_type = args["device"]
    gpus = []

    for device in device_type:
        if device_type == -1:
            device = torch.device("cpu")
        else:
            device = torch.device("cuda:{}".format(device))

        gpus.append(device)

    args["device"] = gpus


def _set_random():
    torch.manual_seed(1)
    torch.cuda.manual_seed(1)
    torch.cuda.manual_seed_all(1)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def print_args(args):
    for key, value in args.items():
        logging.info("{}: {}".format(key, value))