prototorch_models/prototorch/models/abstract.py

import pytorch_lightning as pl
from torch.optim.lr_scheduler import ExponentialLR


class AbstractPrototypeModel(pl.LightningModule):
    @property
    def num_prototypes(self):
        return len(self.proto_layer.components)

    @property
    def prototypes(self):
        return self.proto_layer.components.detach().cpu()

    @property
    def components(self):
        """Only an alias for the prototypes."""
        return self.prototypes

    def configure_optimizers(self):
        optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
        scheduler = ExponentialLR(optimizer,
                                  gamma=0.99,
                                  last_epoch=-1,
                                  verbose=False)
        sch = {
            "scheduler": scheduler,
            "interval": "step",
        }  # called after each training step
        return [optimizer], [sch]


class PrototypeImageModel(pl.LightningModule):
    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
        self.proto_layer.components.data.clamp_(0.0, 1.0)

    def get_prototype_grid(self, num_columns=2, return_channels_last=True):
        from torchvision.utils import make_grid
        grid = make_grid(self.components, nrow=num_columns)
        if return_channels_last:
            grid = grid.permute((1, 2, 0))
        return grid.cpu()