tools.py

import numpy as np
from pathlib import Path
import json
import random
import torch
import os


def save_json(data, file_path):
    '''
    save json
    :param data:
    :param json_path:
    :param file_name:
    :return:
    '''
    if not isinstance(file_path, Path):
        file_path = Path(file_path)
    # if isinstance(data,dict):
    #     data = json.dumps(data)
    with open(str(file_path), 'w') as f:
        json.dump(data, f)


def load_json(file_path):
    '''
    load json
    :param json_path:
    :param file_name:
    :return:
    '''
    if not isinstance(file_path, Path):
        file_path = Path(file_path)
    with open(str(file_path), 'r') as f:
        data = json.load(f)
    return data

class AverageMeter(object):
    '''
    # computes and stores the average and current value
    # Example:
    #     >>> loss = AverageMeter()
    #     >>> for step,batch in enumerate(train_data):
    #     >>>     pred = self.model(batch)
    #     >>>     raw_loss = self.metrics(pred,target)
    #     >>>     loss.update(raw_loss.item(),n = 1)
    #     >>> cur_loss = loss.avg
    # '''

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def seed_everything(seed=1029):
    '''
    :param seed:
    :param device:
    :return:
    '''
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # some cudnn methods can be random even after fixing the seed
    # unless you tell it to be deterministic
    torch.backends.cudnn.deterministic = True