Source code for monai.losses.contrastive

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# Licensed under the Apache License, Version 2.0 (the "License");
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from __future__ import annotations

from warnings import warn

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

[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) -> None: """ Args: temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5. Raises: ValueError: When an input of dimension length > 2 is passed ValueError: When input and target are of different shapes """ super().__init__() self.temperature = temperature if batch_size != -1: warn("batch_size is no longer required to be set. It will be estimated dynamically in the forward call")
[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]. """ if len(target.shape) > 2 or len(input.shape) > 2: raise ValueError( 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 ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})") temperature_tensor = torch.as_tensor(self.temperature).to(input.device) batch_size = input.shape[0] negatives_mask = ~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool) negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device) repr =[input, target], dim=0) sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2) sim_ij = torch.diag(sim_matrix, batch_size) sim_ji = torch.diag(sim_matrix, -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)) return torch.sum(loss_partial) / (2 * batch_size)