prototorch_models/prototorch/models/abstract.py

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import pytorch_lightning as pl
import torch
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from torch.optim.lr_scheduler import ExponentialLR
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class AbstractPrototypeModel(pl.LightningModule):
@property
def prototypes(self):
return self.proto_layer.components.detach().cpu()
@property
def components(self):
"""Only an alias for the prototypes."""
return self.prototypes
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def configure_optimizers(self):
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optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
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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)
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def get_prototype_grid(self, num_columns=2, return_channels_last=True):
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from torchvision.utils import make_grid
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grid = make_grid(self.components, nrow=num_columns)
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if return_channels_last:
grid = grid.permute((1, 2, 0))
return grid.cpu()