Source code for monai.losses.contrastive

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from typing import Union

import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss

from monai.utils import LossReduction

[docs]class ContrastiveLoss(_Loss): """ Compute the Contrastive loss defined in: Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International conference on machine learning. PMLR, 2020. ( Adapted from: """
[docs] def __init__( self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM ) -> None: """ Args: temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5. Raises: AssertionError: When an input of dimension length > 2 is passed AssertionError: When input and target are of different shapes """ super().__init__(reduction=LossReduction(reduction).value) self.batch_size = batch_size self.temperature = temperature
[docs] def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Args: input: the shape should be B[F]. target: the shape should be B[F]. Raises: ValueError: When ``self.reduction`` is not one of ["sum", "none"]. """ if len(target.shape) > 2 or len(input.shape) > 2: raise AssertionError( f"Either target or input has dimensions greater than 2 where target " f"shape is ({target.shape}) and input shape is ({input.shape})" ) if target.shape != input.shape: raise AssertionError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})") temperature_tensor = torch.tensor(self.temperature).to(input.device) norm_i = F.normalize(input, dim=1) norm_j = F.normalize(target, dim=1) negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool) negatives_mask = torch.tensor(negatives_mask, dtype=torch.float) negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device) repr =[norm_i, norm_j], dim=0) sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2) sim_ij = torch.diag(sim_matrix, self.batch_size) sim_ji = torch.diag(sim_matrix, -self.batch_size) positives =[sim_ij, sim_ji], dim=0) nominator = torch.exp(positives / temperature_tensor) denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor) loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1)) if self.reduction == LossReduction.SUM.value: return torch.sum(loss_partial) / (2 * self.batch_size) raise ValueError(f"Unsupported reduction: {self.reduction}, " f'available options are ["mean", "sum", "none"].')