mednet.models.classify.cnn3d

Simple 3D convolutional neural network architecture for classification.

Classes

Conv3DNet([loss_type, loss_arguments, ...])

Simple 3D convolutional neural network architecture for classification.

class mednet.models.classify.cnn3d.Conv3DNet(loss_type=<class 'torch.nn.modules.loss.BCEWithLogitsLoss'>, loss_arguments={}, optimizer_type=<class 'torch.optim.adam.Adam'>, optimizer_arguments={}, scheduler_type=None, scheduler_arguments={}, model_transforms=[], augmentation_transforms=[], num_classes=1)

Bases: Model

Simple 3D convolutional neural network architecture for classification.

This network has a linear output. You should use losses with WithLogit instead of cross-entropy versions when training.

Parameters:

• loss_type (type[Module]) –

  The loss to be used for training and evaluation.

  Warning

  The loss should be set to always return batch averages (as opposed to the batch sum), as our logging system expects it so.

• loss_arguments (dict[str, Any]) – Arguments to the loss.

• optimizer_type (type[Optimizer]) – The type of optimizer to use for training.

• optimizer_arguments (dict[str, Any]) – Arguments to the optimizer after params.

• scheduler_type (type[LRScheduler] | None) – The type of scheduler to use for training.

• scheduler_arguments (dict[str, Any]) – Arguments to the scheduler after params.

• model_transforms (Sequence[Callable[[Tensor], Tensor]]) – An optional sequence of torch modules containing transforms to be applied on the input before it is fed into the network.

• augmentation_transforms (Sequence[Callable[[Tensor], Tensor]]) – An optional sequence of torch modules containing transforms to be applied on the input before it is fed into the network.

• num_classes (int) – Number of outputs (classes) for this model.

forward(x)

Same as torch.nn.Module.forward().

Parameters:

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output