losses.py

import torch.nn as nn
import torch.nn.functional as F
import torch

class OHEMLoss(nn.Module):
    def __init__(self, k=1):
        super(OHEMLoss, self).__init__()
        self.k = k

    def forward(self, y_pred, y_true):
        loss = F.cross_entropy(y_pred, y_true, reduction='none')
        if len(y_pred) < self.k:
            k = len(y_pred)
            _, idxs = loss.topk(k)
        else:
            _, idxs = loss.topk(self.k)
        return loss[idxs].mean()

class CrossEntropyLabelSmooth(nn.Module):
    """Cross entropy loss with label smoothing regularizer.
    Reference:
    Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
    Equation: y = (1 - epsilon) * y + epsilon / K.
    Args:
        num_classes (int): number of classes.
        epsilon (float): weight.
    """

    def __init__(self, num_classes, epsilon=0.1, use_gpu=True):
        super(CrossEntropyLabelSmooth, self).__init__()
        self.num_classes = num_classes
        self.epsilon = epsilon
        self.use_gpu = use_gpu
        self.logsoftmax = nn.LogSoftmax(dim=1)

    def forward(self, inputs, targets):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        log_probs = self.logsoftmax(inputs)
        targets = torch.zeros(log_probs.size()).scatter_(
            1, targets.unsqueeze(1).data.cpu(), 1
        )
        if self.use_gpu:
            targets = targets.cuda()

        targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
        loss = (-targets * log_probs).mean(0).sum()
        return loss