PixelRegressionRoutine¶
- class torch_uncertainty.routines.PixelRegressionRoutine(model, output_dim, probabilistic, loss, is_ensemble=False, format_batch_fn=None, optim_recipe=None, eval_shift=False, num_image_plot=4, log_plots=False)[source]¶
Routine for training & testing on pixel regression tasks.
- Parameters:
model (nn.Module) – Model to train.
output_dim (int) – Number of outputs of the model.
probabilistic (bool) – Whether the model is probabilistic, i.e., outputs a PyTorch distribution.
loss (nn.Module) – Loss function to optimize the
model.is_ensemble (bool, optional) – Whether the model is an ensemble. Defaults to
False.optim_recipe (dict or Optimizer, optional) – The optimizer and optionally the scheduler to use. Defaults to
None.eval_shift (bool, optional) – Indicates whether to evaluate the Distribution shift performance. Defaults to
False.format_batch_fn (nn.Module, optional) – The function to format the batch. Defaults to
None.num_image_plot (int, optional) – Number of images to plot. Defaults to
4.log_plots (bool, optional) – Indicates whether to log plots from metrics. Defaults to
False.