EOS/losses.py at main · dd1github/EOS · GitHub

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"""
code adapted from: https://github.com/kaidic/LDAM-DRW
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

def focal_loss(input_values, gamma):
    """Computes the focal loss"""
    p = torch.exp(-input_values)
    loss = (1 - p) ** gamma * input_values
    return loss.mean()

class FocalLoss(nn.Module):
    def __init__(self, weight=None, gamma=0.):
        super(FocalLoss, self).__init__()
        assert gamma >= 0
        self.gamma = gamma
        self.weight = weight

    def forward(self, input, target):
        return focal_loss(F.cross_entropy(input, target, reduction='none',
                                          weight=self.weight), self.gamma)


class LDAMLoss(nn.Module):

    def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30):
        super(LDAMLoss, self).__init__()
        m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list))
        m_list = m_list * (max_m / np.max(m_list))
        m_list = torch.cuda.FloatTensor(m_list)
        self.m_list = m_list
        assert s > 0
        self.s = s
        self.weight = weight

    def forward(self, x, target):
        index = torch.zeros_like(x, dtype=torch.uint8)
        index.scatter_(1, target.data.view(-1, 1), 1)

        index_float = index.type(torch.cuda.FloatTensor)
        batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1))
        batch_m = batch_m.view((-1, 1))
        x_m = x - batch_m

        output = torch.where(index, x_m, x)
        return F.cross_entropy(self.s*output, target, weight=self.weight)


class ASLSingleLabel(nn.Module):
    '''
    code adpated from: https://github.com/Alibaba-MIIL/ASL
    This loss is intended for single-label classification problems
    '''
    def __init__(self, gamma_pos=0, gamma_neg=4, eps: float = 0.1, reduction='mean'):
        super(ASLSingleLabel, self).__init__()

        self.eps = eps
        self.logsoftmax = nn.LogSoftmax(dim=-1)
        self.targets_classes = []
        self.gamma_pos = gamma_pos
        self.gamma_neg = gamma_neg
        self.reduction = reduction

    def forward(self, inputs, target):
        '''
        "input" dimensions: - (batch_size,number_classes)
        "target" dimensions: - (batch_size)
        '''
        num_classes = inputs.size()[-1]
        log_preds = self.logsoftmax(inputs)
        self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)

        # ASL weights
        targets = self.targets_classes
        anti_targets = 1 - targets
        xs_pos = torch.exp(log_preds)
        xs_neg = 1 - xs_pos
        xs_pos = xs_pos * targets
        xs_neg = xs_neg * anti_targets
        asymmetric_w = torch.pow(1 - xs_pos - xs_neg,
                                 self.gamma_pos * targets + self.gamma_neg * anti_targets)
        log_preds = log_preds * asymmetric_w

        if self.eps > 0:  # label smoothing
            self.targets_classes = self.targets_classes.mul(1 - self.eps).add(self.eps / num_classes)

        # loss calculation
        loss = - self.targets_classes.mul(log_preds)

        loss = loss.sum(dim=-1)
        if self.reduction == 'mean':
            loss = loss.mean()

        return loss