utils.py

import torch
import numpy as np
import torch.nn as nn

class MixUpLoss(nn.Module):
    """
    Mixup implementation heavily borrowed from https://github.com/fastai/fastai/blob/master/fastai/callbacks/mixup.py#L42
    Adapt the loss function `crit` to go with mixup.
    """

    def __init__(self, crit, reduction='mean'):
        super().__init__()
        if hasattr(crit, 'reduction'):
            self.crit = crit
            self.old_red = crit.reduction
            setattr(self.crit, 'reduction', 'none')
        self.reduction = reduction

    def forward(self, output, target):
        if len(target.size()) == 2:
            loss1, loss2 = self.crit(output, target[:, 0].long()), self.crit(output, target[:, 1].long())
            d = loss1 * target[:, 2] + loss2 * (1 - target[:, 2])
        else:
            d = self.crit(output, target)
        if self.reduction == 'mean':
            return d.mean()
        elif self.reduction == 'sum':
            return d.sum()
        return d

    def get_old(self):
        if hasattr(self, 'old_crit'):
            return self.old_crit
        elif hasattr(self, 'old_red'):
            setattr(self.crit, 'reduction', self.old_red)
            return self.crit


def mixup_data(x, y, alpha=0.4):

    """
    Returns mixed inputs, pairs of targets, and lambda
    """

    batch_size = x.shape[0]
    lam = np.random.beta(alpha, alpha, batch_size)
    # t = max(t, 1-t)
    lam = np.concatenate([lam[:, None], 1 - lam[:, None]], 1).max(1)
    # tensor and cuda version of lam
    lam = x.new(lam)

    shuffle = torch.randperm(batch_size).cuda()

    x1, y1 = x[shuffle], y[shuffle]
    # out_shape = [bs, 1, 1]
    out_shape = [lam.size(0)] + [1 for _ in range(len(x1.shape) - 1)]

    # [bs, temporal, sensor]
    mixed_x = (x * lam.view(out_shape) + x1 * (1 - lam).view(out_shape))
    # [bs, 3]
    y_a_y_b_lam = torch.cat([y[:, None].float(), y1[:, None].float(), lam[:, None].float()], 1)

    return mixed_x, y_a_y_b_lam

class EarlyStopping:
    """Early stops the training if validation loss doesn't improve after a given patience."""
    def __init__(self, patience=7, verbose=False, delta=0):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement. 
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
            path (str): Path for the checkpoint to be saved to.
                            Default: 'checkpoint.pt'
  
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta

    def __call__(self, val_loss, model, path, f_macro = None, f_weighted = None, log=None):

        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model, path ,f_macro, f_weighted)

        elif score < self.best_score + self.delta:
            self.counter += 1

            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')

            if self.counter >= self.patience:
                self.early_stop = True
        else:
            print("new best score!!!!")
            #print("log shi ", log)
            if log is not None:
                log.write("new best score!!!! Saving model ... \n")
                log.write("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ \n")
            self.best_score = score
            self.save_checkpoint(val_loss, model,path,f_macro, f_weighted)
            self.counter = 0
    def save_checkpoint(self, val_loss, model, path, f_macro = None, f_weighted = None):
        '''Saves model when validation loss decrease.'''
        if self.verbose:
            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
        torch.save(model.state_dict(), path+'/'+'best_vali.pth')
        self.val_loss_min = val_loss


class adjust_learning_rate_class:
    def __init__(self, args, verbose):
        self.patience = args.learning_rate_patience
        self.factor   = args.learning_rate_factor
        self.learning_rate = args.learning_rate
        self.args = args
        self.verbose = verbose
        self.val_loss_min = np.Inf
        self.counter = 0
        self.best_score = None
    def __call__(self, optimizer, val_loss):
        # val_loss 是正值，越小越好
        # 但是这里加了负值，score愈大越好
        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.counter += 1
        elif score <= self.best_score :
            self.counter += 1
            if self.verbose:
                print(f'Learning rate adjusting counter: {self.counter} out of {self.patience}')
        else:
            if self.verbose:
                print("new best score!!!!")
            self.best_score = score
            self.counter = 0
            
        if self.counter == self.patience:
            self.learning_rate = self.learning_rate * self.factor
            for param_group in optimizer.param_groups:
                param_group['lr'] = self.learning_rate
                if self.verbose:
                    print('Updating learning rate to {}'.format(self.learning_rate))
            self.counter = 0