loss_adv.py

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


class LabelSmoothingCrossEntropy(nn.Module):
    """
    NLL loss with label smoothing.
    """
    def __init__(self, smoothing=0.1):
        """
        Constructor for the LabelSmoothing module.
        :param smoothing: label smoothing factor
        """
        super(LabelSmoothingCrossEntropy, self).__init__()
        assert smoothing < 1.0
        self.smoothing = smoothing
        self.confidence = 1. - smoothing

    def forward(self, x, target):
        logprobs = F.log_softmax(x, dim=-1)
        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
        nll_loss = nll_loss.squeeze(1)
        smooth_loss = -logprobs.mean(dim=-1)
        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
        return loss.mean()


class SoftTargetCrossEntropy(nn.Module):

    def __init__(self, reduce=False):
        super(SoftTargetCrossEntropy, self).__init__()
        self.reduce = reduce

    def forward(self, x, target):
        loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
        if self.reduce:
            return loss.mean()
        else:
            return loss



class DistillationLoss(torch.nn.Module):
    """
    This module wraps a standard criterion and adds an extra knowledge distillation loss by
    taking a teacher model prediction and using it as additional supervision.
    """
    def __init__(self, base_criterion: torch.nn.Module, teacher_model: torch.nn.Module,
                 distillation_type: str, alpha: float, tau: float):
        super().__init__()
        self.base_criterion = base_criterion
        self.teacher_model = teacher_model
        assert distillation_type in ['none', 'soft', 'hard']
        self.distillation_type = distillation_type
        self.alpha = alpha
        self.tau = tau

    def forward(self, inputs, outputs, labels):
        """
        Args:
            inputs: The original inputs that are feed to the teacher model
            outputs: the outputs of the model to be trained. It is expected to be
                either a Tensor, or a Tuple[Tensor, Tensor], with the original output
                in the first position and the distillation predictions as the second output
            labels: the labels for the base criterion
        """
        outputs_kd = None
        if not isinstance(outputs, torch.Tensor):
            # assume that the model outputs a tuple of [outputs, outputs_kd]
            outputs, outputs_kd = outputs
        base_loss = self.base_criterion(outputs, labels)
        if self.distillation_type == 'none':
            return base_loss

        if outputs_kd is None:
            raise ValueError("When knowledge distillation is enabled, the model is "
                             "expected to return a Tuple[Tensor, Tensor] with the output of the "
                             "class_token and the dist_token")
        # don't backprop throught the teacher
        with torch.no_grad():
            teacher_outputs = self.teacher_model(inputs)

        if self.distillation_type == 'soft':
            T = self.tau
            # taken from https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
            # with slight modifications
            distillation_loss = F.kl_div(
                F.log_softmax(outputs_kd / T, dim=1),
                #We provide the teacher's targets in log probability because we use log_target=True
                #(as recommended in pytorch https://github.com/pytorch/pytorch/blob/9324181d0ac7b4f7949a574dbc3e8be30abe7041/torch/nn/functional.py#L2719)
                #but it is possible to give just the probabilities and set log_target=False. In our experiments we tried both.
                F.log_softmax(teacher_outputs / T, dim=1),
                reduction='sum',
                log_target=True
            ) * (T * T) / outputs_kd.numel()
            #We divide by outputs_kd.numel() to have the legacy PyTorch behavior.
            #But we also experiments output_kd.size(0)
            #see issue 61(https://github.com/facebookresearch/deit/issues/61) for more details
        elif self.distillation_type == 'hard':
            distillation_loss = F.cross_entropy(outputs_kd, teacher_outputs.argmax(dim=1))

        loss = base_loss * (1 - self.alpha) + distillation_loss * self.alpha
        return loss