# -*- coding: utf-8 -*-

'''

@time: 2019/9/12 15:16

@ author: javis

'''

import torch

import numpy as np

import time,os

from sklearn.metrics import f1_score

from torch import nn

def mkdirs(path):

    if not os.path.exists(path):

        os.makedirs(path)

#计算F1score

def calc_f1(y_true, y_pre, threshold=0.5):

    y_true = y_true.view(-1).cpu().detach().numpy().astype(np.int)

    y_pre = y_pre.view(-1).cpu().detach().numpy() > threshold

    return f1_score(y_true, y_pre)

#打印时间

def print_time_cost(since):

    time_elapsed = time.time() - since

    return '{:.0f}m{:.0f}s\n'.format(time_elapsed // 60, time_elapsed % 60)

# 调整学习率

def adjust_learning_rate(optimizer, lr):

    for param_group in optimizer.param_groups:

        param_group['lr'] = lr

    return lr

#多标签使用类别权重

class WeightedMultilabel(nn.Module):

    def __init__(self, weights: torch.Tensor):

        super(WeightedMultilabel, self).__init__()

        self.cerition = nn.BCEWithLogitsLoss(reduction='none')

        self.weights = weights

    def forward(self, outputs, targets):

        loss = self.cerition(outputs, targets)

        return (loss * self.weights).mean()