Source code for anomalib.models.reverse_distillation.loss

"""Loss function for Reverse Distillation."""

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from typing import List

import torch
from torch import Tensor


[docs]class ReverseDistillationLoss: """Loss function for Reverse Distillation."""
[docs] def __call__(self, encoder_features: List[Tensor], decoder_features: List[Tensor]) -> Tensor: """Computes cosine similarity loss based on features from encoder and decoder. Args: encoder_features (List[Tensor]): List of features extracted from encoder decoder_features (List[Tensor]): List of features extracted from decoder Returns: Tensor: Cosine similarity loss """ cos_loss = torch.nn.CosineSimilarity() losses = list(map(cos_loss, encoder_features, decoder_features)) loss_sum = 0 for loss in losses: loss_sum += torch.mean(1 - loss) # mean of cosine distance return loss_sum