load_model.py

import sys
import logging
import copy
import torch
from PIL import Image
import torchvision.transforms as transforms
from utils import factory
from utils.data_manager import DataManager
from utils.toolkit import count_parameters
import os
import numpy as np
import json
import argparse

def _set_device(args):
    device_type = args["device"]
    gpus = []

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

        gpus.append(device)

    args["device"] = gpus

def get_methods(object, spacing=20):
  methodList = []
  for method_name in dir(object):
    try:
        if callable(getattr(object, method_name)):
            methodList.append(str(method_name))
    except Exception:
        methodList.append(str(method_name))
  processFunc = (lambda s: ' '.join(s.split())) or (lambda s: s)
  for method in methodList:
    try:
        print(str(method.ljust(spacing)) + ' ' +
              processFunc(str(getattr(object, method).__doc__)[0:90]))
    except Exception:
        print(method.ljust(spacing) + ' ' + ' getattr() failed')

def load_model(args):
    _set_device(args)
    model = factory.get_model(args["model_name"], args)
    model.load_checkpoint(args["checkpoint"])
    return model
def main():
    args = setup_parser().parse_args()
    param = load_json(args.config)
    args = vars(args)  # Converting argparse Namespace to a dict.
    args.update(param)  # Add parameters from json

    load_model(args)
def load_json(settings_path):
    with open(settings_path) as data_file:
        param = json.load(data_file)

    return param


def setup_parser():
    parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorthms.')
    parser.add_argument('--config', type=str, default='./exps/finetune.json',
                        help='Json file of settings.')

    return parser
    
if __name__ == '__main__':
    main()