loss.py

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

def Entropy(input_):
    bs = input_.size(0)
    epsilon = 1e-5
    entropy = -input_ * torch.log(input_ + epsilon)
    entropy = torch.sum(entropy, dim=1)
    return entropy 
def soft_CE(softout, soft_label):
    bs = softout.size(0)
    epsilon = 1e-5
    loss = -soft_label * torch.log(softout + epsilon)
    total_loss = torch.sum(loss, dim=1)
    return total_loss
def grl_hook(coeff):
    def fun1(grad):
        return -coeff*grad.clone()
    return fun1

def CDAN(input_list, ad_net, entropy=None, coeff=None, random_layer=None):
    softmax_output = input_list[1].detach()
    feature = input_list[0]
    if random_layer is None:
        op_out = torch.bmm(softmax_output.unsqueeze(2), feature.unsqueeze(1))
        ad_out = ad_net(op_out.view(-1, softmax_output.size(1) * feature.size(1)))
    else:
        random_out = random_layer.forward([feature, softmax_output])
        ad_out = ad_net(random_out.view(-1, random_out.size(1)))       
    batch_size = softmax_output.size(0) // 2
    dc_target = torch.from_numpy(np.array([[1]] * batch_size + [[0]] * batch_size)).float().cuda()
    if entropy is not None:
        entropy.register_hook(grl_hook(coeff))
        entropy = 1.0+torch.exp(-entropy)
        source_mask = torch.ones_like(entropy)
        source_mask[feature.size(0)//2:] = 0
        source_weight = entropy*source_mask
        target_mask = torch.ones_like(entropy)
        target_mask[0:feature.size(0)//2] = 0
        target_weight = entropy*target_mask
        weight = source_weight / torch.sum(source_weight).detach().item() + \
                 target_weight / torch.sum(target_weight).detach().item()
        return torch.sum(weight.view(-1, 1) * nn.BCELoss(reduction='none')(ad_out, dc_target)) / torch.sum(weight).detach().item()
    else:
        return nn.BCELoss()(ad_out, dc_target) 

def DANN(features, ad_net):
    ad_out = ad_net(features)
    batch_size = ad_out.size(0) // 2
    dc_target = torch.from_numpy(np.array([[1]] * batch_size + [[0]] * batch_size)).float().cuda()
    return nn.BCELoss()(ad_out, dc_target)


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, reduction=True):
        super(CrossEntropyLabelSmooth, self).__init__()
        self.num_classes = num_classes
        self.epsilon = epsilon
        self.use_gpu = use_gpu
        self.reduction = reduction
        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).cpu(), 1)
        if self.use_gpu: targets = targets.cuda()
        targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
        loss = (- targets * log_probs).sum(dim=1)
        if self.reduction:
            return loss.mean()
        else:
            return loss
        return loss

class SupConLoss(nn.Module):
    """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
    It also supports the unsupervised contrastive loss in SimCLR"""
    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07):
        super(SupConLoss, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature

    def forward(self, features, labels=None, mask=None):
        """Compute loss for model. If both `labels` and `mask` are None,
        it degenerates to SimCLR unsupervised loss:
        https://arxiv.org/pdf/2002.05709.pdf
        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: ground truth of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
                has the same class as sample i. Can be asymmetric.
        Returns:
            A loss scalar.
        """
        device = (torch.device('cuda')
                  if features.is_cuda
                  else torch.device('cpu'))

        if len(features.shape) < 3:
            features=features.unsqueeze(dim=1)
            # raise ValueError('`features` needs to be [bsz, n_views, ...],'
            #                  'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and mask is None:
            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None:
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
            mask = torch.eq(labels, labels.T).float().to(device)
        else:
            mask = mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        mask = mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        mask = mask * logits_mask

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)

        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
        loss = loss.view(anchor_count, batch_size).mean()

        return loss
class SCELoss(torch.nn.Module):
    def __init__(self, alpha, beta, num_classes=10):
        super(SCELoss, self).__init__()
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.alpha = alpha
        self.beta = beta
        self.num_classes = num_classes
        self.cross_entropy = torch.nn.CrossEntropyLoss()

    def forward(self, pred, labels):
        # CCE
        ce = self.cross_entropy(pred, labels)

        # RCE
        pred = F.softmax(pred, dim=1)
        pred = torch.clamp(pred, min=1e-7, max=1.0)
        label_one_hot = torch.nn.functional.one_hot(labels, self.num_classes).float().to(self.device)
        label_one_hot = torch.clamp(label_one_hot, min=1e-4, max=1.0)
        rce = (-1*torch.sum(pred * torch.log(label_one_hot), dim=1))

        # Loss
        loss = self.alpha * ce + self.beta * rce.mean()
        return loss


class KnowledgeDistillationLoss(nn.Module):
    def __init__(self, reduction='mean', alpha=-1.0):
        super().__init__()
        self.reduction = reduction
        self.alpha = alpha

    def forward(self, inputs, targets, mask=None):
        inputs = inputs.narrow(1, 0, targets.shape[1])

        outputs = torch.log_softmax(inputs, dim=1)
        labels = torch.softmax(targets * self.alpha, dim=1)
        #labels = targets*self.alpha

        loss = (outputs * labels).mean(dim=1)

        if mask is not None:
            loss = loss * mask.float()

        if self.reduction == 'mean':
            outputs = -torch.mean(loss)
        elif self.reduction == 'sum':
            outputs = -torch.sum(loss)
        else:
            outputs = -loss

        return outputs

class HLoss(nn.Module):
    def __init__(self):
        super(HLoss, self).__init__()

    def forward(self, x):
        b = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
        b = -1.0 * b.sum()
        return b


def SoftCrossEntropyLoss(logit, soft_pseudo_label):   # Checked and is correct
    p = F.log_softmax(logit, dim=1)
    # print('sofmax logit', p.shape)
    # print('pseudo labels (GT)', soft_pseudo_label.shape)
#   w_labels = self.weights*y
    # labels = y
    # print(soft_pseudo_label*p)
    loss = -(soft_pseudo_label*p).sum(dim=1)

    # print('Sum of loss', loss.shape)
    return loss