loss.py

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
import numpy as np


##############################################################################
# Classes
##############################################################################
class BCEWithLogitsLoss2d(nn.Module):

    def __init__(self, size_average=True, ignore_label=255):
        super(BCEWithLogitsLoss2d, self).__init__()
        self.size_average = size_average
        self.ignore_label = ignore_label

    def forward(self, predict, target, weight=None):
        """
            Args:
                predict:(n, 1, h, w)
                target:(n, 1, h, w)
                weight (Tensor, optional): a manual rescaling weight given to each class.
                                           If given, has to be a Tensor of size "nclasses"
        """
        assert not target.requires_grad
        assert predict.dim() == 4
        assert target.dim() == 4
        assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
        assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
        assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
        n, c, h, w = predict.size()
        target_mask = (target >= 0) * (target != self.ignore_label)
        target = target[target_mask]
        if not target.data.dim():
            return Variable(torch.zeros(1))
        predict = predict[target_mask]
        loss = F.binary_cross_entropy_with_logits(predict, target, weight=weight, size_average=self.size_average)
        return loss


class GANLoss(nn.Module):
    """Define different GAN objectives.

    The GANLoss class abstracts away the need to create the target label tensor
    that has the same size as the input.
    """

    def __init__(self, target_real_label=1.0, target_fake_label=0.0):
        """ Initialize the GANLoss class.

        Parameters:
            gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
            target_real_label (bool) - - label for a real image
            target_fake_label (bool) - - label of a fake image

        Note: Do not use sigmoid as the last layer of Discriminator.
        LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
        """
        super(GANLoss, self).__init__()
        self.register_buffer('real_label', torch.tensor(target_real_label))
        self.register_buffer('fake_label', torch.tensor(target_fake_label))
        self.loss = nn.MSELoss()


    def get_target_tensor(self, prediction, target_is_real):
        """Create label tensors with the same size as the input.

        Parameters:
            prediction (tensor) - - tpyically the prediction from a discriminator
            target_is_real (bool) - - if the ground truth label is for real images or fake images

        Returns:
            A label tensor filled with ground truth label, and with the size of the input
        """

        if target_is_real:
            target_tensor = self.real_label
        else:
            target_tensor = self.fake_label
        return target_tensor.expand_as(prediction)

    def __call__(self, prediction, target_is_real):
        """Calculate loss given Discriminator's output and grount truth labels.

        Parameters:
            prediction (tensor) - - tpyically the prediction output from a discriminator
            target_is_real (bool) - - if the ground truth label is for real images or fake images

        Returns:
            the calculated loss.
        """
        target_tensor = self.get_target_tensor(prediction, target_is_real) # Create label tensors with the same size as the input.
        loss = self.loss(prediction, target_tensor)  # Calculate the loss value, where 'self.loss = nn.MSELoss()'

        return loss
		
		
		

class CrossEntropy2d(nn.Module):

    def __init__(self, ignore_label=255):
        super(CrossEntropy2d, self).__init__()
        self.ignore_label = ignore_label

    def forward(self, predict, target, weight=None):
        """
            Args:
                predict:(n, c, h, w)
                target:(n, h, w)
                weight (Tensor, optional): a manual rescaling weight given to each class.
                                           If given, has to be a Tensor of size "nclasses"
        """
        assert not target.requires_grad
        assert predict.dim() == 4
        assert target.dim() == 3
        n, c, h, w = predict.size()
        target_mask = (target >= 0) * (target != self.ignore_label)
        target = target[target_mask]
        if not target.data.dim():
            return Variable(torch.zeros(1))
        predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
        predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
        #loss = F.cross_entropy(predict, target, weight=weight, reduction='elementwise_mean')
        loss = F.cross_entropy(predict, target, weight=weight, reduction='mean')			
        return loss