loss.py

import torch
import torch.nn.functional as F


def weighted_mse_loss(inputs, targets, weights=None):
    loss = (inputs - targets) ** 2
    if weights is not None:
        loss *= weights.expand_as(loss)
    loss = torch.mean(loss)
    return loss


def weighted_l1_loss(inputs, targets, weights=None):
    loss = F.l1_loss(inputs, targets, reduction='none')
    if weights is not None:
        loss *= weights.expand_as(loss)
    loss = torch.mean(loss)
    return loss


def weighted_focal_mse_loss(inputs, targets, weights=None, activate='sigmoid', beta=.2, gamma=1):
    loss = (inputs - targets) ** 2
    loss *= (torch.tanh(beta * torch.abs(inputs - targets))) ** gamma if activate == 'tanh' else \
        (2 * torch.sigmoid(beta * torch.abs(inputs - targets)) - 1) ** gamma
    if weights is not None:
        loss *= weights.expand_as(loss)
    loss = torch.mean(loss)
    return loss


def weighted_focal_l1_loss(inputs, targets, weights=None, activate='sigmoid', beta=.2, gamma=1):
    loss = F.l1_loss(inputs, targets, reduction='none')
    loss *= (torch.tanh(beta * torch.abs(inputs - targets))) ** gamma if activate == 'tanh' else \
        (2 * torch.sigmoid(beta * torch.abs(inputs - targets)) - 1) ** gamma
    if weights is not None:
        loss *= weights.expand_as(loss)
    loss = torch.mean(loss)
    return loss


def weighted_huber_loss(inputs, targets, weights=None, beta=1.):
    l1_loss = torch.abs(inputs - targets)
    cond = l1_loss < beta
    loss = torch.where(cond, 0.5 * l1_loss ** 2 / beta, l1_loss - 0.5 * beta)
    if weights is not None:
        loss *= weights.expand_as(loss)
    loss = torch.mean(loss)
    return loss