Losses

Loss function for DenoiSplit.

Parameters:

• model_outputs (tuple[Tensor, dict[str, Any]]) –

  Tuple containing the model predictions (shape is (B, target_ch, [Z], Y, X)) and the top-down layer data (e.g., sampled latents, KL-loss values, etc.).

• targets (Tensor) –

  The target image used to compute the reconstruction loss. Shape is (B, target_ch, [Z], Y, X).

• config (LVAELossConfig) –

  The config for loss function containing all loss hyperparameters.

• gaussian_likelihood (GaussianLikelihood, default: None ) –

  The Gaussian likelihood object.

• noise_model_likelihood (NoiseModelLikelihood, default: None ) –

  The noise model likelihood object.

Returns:

• output ( Optional[dict[str, Tensor]] ) –

  A dictionary containing the overall loss ["loss"], the reconstruction loss ["reconstruction_loss"], and the KL divergence loss ["kl_loss"].

Loss function for DenoiSplit.

Parameters:

• model_outputs (tuple[Tensor, dict[str, Any]]) –

  Tuple containing the model predictions (shape is (B, target_ch, [Z], Y, X)) and the top-down layer data (e.g., sampled latents, KL-loss values, etc.).

• targets (Tensor) –

  The target image used to compute the reconstruction loss. Shape is (B, target_ch, [Z], Y, X).

• config (LVAELossConfig) –

  The config for loss function containing all loss hyperparameters.

• gaussian_likelihood (GaussianLikelihood) –

  The Gaussian likelihood object.

• noise_model_likelihood (NoiseModelLikelihood) –

  The noise model likelihood object.

Returns:

• output ( Optional[dict[str, Tensor]] ) –

  A dictionary containing the overall loss ["loss"], the reconstruction loss ["reconstruction_loss"], and the KL divergence loss ["kl_loss"].

Loss function for HDN.

Parameters:

• model_outputs (tuple[Tensor, dict[str, Any]]) –

  Tuple containing the model predictions (shape is (B, target_ch, [Z], Y, X)) and the top-down layer data (e.g., sampled latents, KL-loss values, etc.).

• targets (Tensor) –

  The target image used to compute the reconstruction loss. In this case we use the input patch itself as target. Shape is (B, target_ch, [Z], Y, X).

• config (LVAELossConfig) –

  The config for loss function containing all loss hyperparameters.

• gaussian_likelihood (GaussianLikelihood) –

  The Gaussian likelihood object.

• noise_model_likelihood (NoiseModelLikelihood) –

  The noise model likelihood object.

Returns:

• output ( Optional[dict[str, Tensor]] ) –

  A dictionary containing the overall loss ["loss"], the reconstruction loss ["reconstruction_loss"], and the KL divergence loss ["kl_loss"].

Loss function for muSplit.

Parameters:

• model_outputs (tuple[Tensor, dict[str, Any]]) –

  Tuple containing the model predictions (shape is (B, target_ch, [Z], Y, X)) and the top-down layer data (e.g., sampled latents, KL-loss values, etc.).

• targets (Tensor) –

  The target image used to compute the reconstruction loss. Shape is (B, target_ch, [Z], Y, X).

• config (LVAELossConfig) –

  The config for loss function (e.g., KL hyperparameters, likelihood module, noise model, etc.).

• gaussian_likelihood (GaussianLikelihood) –

  The Gaussian likelihood object.

• noise_model_likelihood (Optional[NoiseModelLikelihood], default: None ) –

  The noise model likelihood object. Not used here.

Returns:

• output ( Optional[dict[str, Tensor]] ) –

  A dictionary containing the overall loss ["loss"], the reconstruction loss ["reconstruction_loss"], and the KL divergence loss ["kl_loss"].

N2V Loss function described in A Krull et al 2018.

Parameters:

• manipulated_batch (Tensor) –

  Batch after manipulation function applied.

• original_batch (Tensor) –

  Original images.

• masks (Tensor) –

  Coordinates of changed pixels.

• *args (Any, default: () ) –

  Additional arguments.

Returns:

• Tensor –

  Loss value.

Probabilistic N2V loss function described in A Krull et al., CVF (2019).

Parameters:

• samples (Tensor) –

  Predicted pixel values from the network.

• labels (Tensor) –

  Original pixel values.

• masks (Tensor) –

  Coordinates of manipulated pixels.

• noise_model (GaussianMixtureNoiseModel) –

  Noise model for computing likelihood.

Returns:

• Tensor –

  Loss value.