utils.py

import shutil
import torch
import sys
import os
import json
import numpy as np
from config import config
from torch import nn
import torch.nn.functional as F
def save_checkpoint(state, is_best,fold):
    filename = config.weights + config.model_name + os.sep +str(fold) + os.sep + "_checkpoint.pth.tar"
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, config.best_models + config.model_name+ os.sep +str(fold)  + os.sep + 'model_best.pth.tar')

class AverageMeter(object):
    """Computes and stores the average and current value"""
    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 adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 3 epochs"""
    lr = config.lr * (0.1 ** (epoch // 3))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def schedule(current_epoch, current_lrs, **logs):
        lrs = [1e-3, 1e-4, 0.5e-4, 1e-5, 0.5e-5]
        epochs = [0, 1, 6, 8, 12]
        for lr, epoch in zip(lrs, epochs):
            if current_epoch >= epoch:
                current_lrs[5] = lr
                if current_epoch >= 2:
                    current_lrs[4] = lr * 1
                    current_lrs[3] = lr * 1
                    current_lrs[2] = lr * 1
                    current_lrs[1] = lr * 1
                    current_lrs[0] = lr * 0.1
        return current_lrs

def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

class Logger(object):
    def __init__(self):
        self.terminal = sys.stdout  #stdout
        self.file = None

    def open(self, file, mode=None):
        if mode is None: mode ='w'
        self.file = open(file, mode)

    def write(self, message, is_terminal=1, is_file=1 ):
        if '\r' in message: is_file=0

        if is_terminal == 1:
            self.terminal.write(message)
            self.terminal.flush()
            #time.sleep(1)

        if is_file == 1:
            self.file.write(message)
            self.file.flush()

    def flush(self):
        # this flush method is needed for python 3 compatibility.
        # this handles the flush command by doing nothing.
        # you might want to specify some extra behavior here.
        pass

def get_learning_rate(optimizer):
    lr=[]
    for param_group in optimizer.param_groups:
       lr +=[ param_group['lr'] ]

    #assert(len(lr)==1) #we support only one param_group
    lr = lr[0]

    return lr


def time_to_str(t, mode='min'):
    if mode=='min':
        t  = int(t)/60
        hr = t//60
        min = t%60
        return '%2d hr %02d min'%(hr,min)

    elif mode=='sec':
        t   = int(t)
        min = t//60
        sec = t%60
        return '%2d min %02d sec'%(min,sec)


    else:
        raise NotImplementedError


class FocalLoss(nn.Module):

    def __init__(self, focusing_param=2, balance_param=0.25):
        super(FocalLoss, self).__init__()

        self.focusing_param = focusing_param
        self.balance_param = balance_param

    def forward(self, output, target):

        cross_entropy = F.cross_entropy(output, target)
        cross_entropy_log = torch.log(cross_entropy)
        logpt = - F.cross_entropy(output, target)
        pt    = torch.exp(logpt)

        focal_loss = -((1 - pt) ** self.focusing_param) * logpt

        balanced_focal_loss = self.balance_param * focal_loss

        return balanced_focal_loss

class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return super(MyEncoder, self).default(obj)