1a17193b35
* chore: update pre-commit versions * ci: remove old configurations * ci: copy workflow from prototorch * ci: run precommit for all files * ci: add examples CPU test * ci(test): failing example test * ci: fix workflow definition * ci(test): repeat failing example test * ci: fix workflow definition * ci(test): repeat failing example test II * ci: fix test command * ci: cleanup example test * ci: remove travis badge
151 lines
4.7 KiB
Python
151 lines
4.7 KiB
Python
"""Unsupervised prototype learning algorithms."""
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import numpy as np
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import torch
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from ..core.competitions import wtac
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from ..core.distances import squared_euclidean_distance
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from ..core.losses import NeuralGasEnergy
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from ..nn.wrappers import LambdaLayer
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from .abstract import NonGradientMixin, UnsupervisedPrototypeModel
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from .callbacks import GNGCallback
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from .extras import ConnectionTopology
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class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
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"""Kohonen Self-Organizing-Map.
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TODO Allow non-2D grids
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"""
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def __init__(self, hparams, **kwargs):
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h, w = hparams.get("shape")
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# Ignore `num_prototypes`
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hparams["num_prototypes"] = h * w
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distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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# Hyperparameters
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self.save_hyperparameters(hparams)
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# Default hparams
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self.hparams.setdefault("alpha", 0.3)
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self.hparams.setdefault("sigma", max(h, w) / 2.0)
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# Additional parameters
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x, y = torch.arange(h), torch.arange(w)
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grid = torch.stack(torch.meshgrid(x, y), dim=-1)
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self.register_buffer("_grid", grid)
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self._sigma = self.hparams.sigma
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self._lr = self.hparams.lr
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def predict_from_distances(self, distances):
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grid = self._grid.view(-1, 2)
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wp = wtac(distances, grid)
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return wp
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def training_step(self, train_batch, batch_idx):
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# x = train_batch
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# TODO Check if the batch has labels
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x = train_batch[0]
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d = self.compute_distances(x)
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wp = self.predict_from_distances(d)
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grid = self._grid.view(-1, 2)
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gd = squared_euclidean_distance(wp, grid)
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nh = torch.exp(-gd / self._sigma**2)
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protos = self.proto_layer()
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diff = x.unsqueeze(dim=1) - protos
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delta = self._lr * self.hparams.alpha * nh.unsqueeze(-1) * diff
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updated_protos = protos + delta.sum(dim=0)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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def training_epoch_end(self, training_step_outputs):
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self._sigma = self.hparams.sigma * np.exp(
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-self.current_epoch / self.trainer.max_epochs)
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def extra_repr(self):
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return f"(grid): (shape: {tuple(self._grid.shape)})"
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class HeskesSOM(UnsupervisedPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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def training_step(self, train_batch, batch_idx):
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# TODO Implement me!
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raise NotImplementedError()
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class NeuralGas(UnsupervisedPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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# Hyperparameters
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self.save_hyperparameters(hparams)
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# Default hparams
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self.hparams.setdefault("agelimit", 10)
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self.hparams.setdefault("lm", 1)
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self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
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self.topology_layer = ConnectionTopology(
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agelimit=self.hparams.agelimit,
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num_prototypes=self.hparams.num_prototypes,
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)
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def training_step(self, train_batch, batch_idx):
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# x = train_batch
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# TODO Check if the batch has labels
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x = train_batch[0]
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d = self.compute_distances(x)
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loss, _ = self.energy_layer(d)
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self.topology_layer(d)
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self.log("loss", loss)
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return loss
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# def training_epoch_end(self, training_step_outputs):
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# print(f"{self.trainer.lr_schedulers}")
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# print(f"{self.trainer.lr_schedulers[0]['scheduler'].optimizer}")
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class GrowingNeuralGas(NeuralGas):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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# Defaults
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self.hparams.setdefault("step_reduction", 0.5)
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self.hparams.setdefault("insert_reduction", 0.1)
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self.hparams.setdefault("insert_freq", 10)
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errors = torch.zeros(self.hparams.num_prototypes, device=self.device)
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self.register_buffer("errors", errors)
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def training_step(self, train_batch, _batch_idx):
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# x = train_batch
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# TODO Check if the batch has labels
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x = train_batch[0]
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d = self.compute_distances(x)
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loss, order = self.energy_layer(d)
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winner = order[:, 0]
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mask = torch.zeros_like(d)
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mask[torch.arange(len(mask)), winner] = 1.0
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dp = d * mask
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self.errors += torch.sum(dp * dp)
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self.errors *= self.hparams.step_reduction
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self.topology_layer(d)
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self.log("loss", loss)
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return loss
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def configure_callbacks(self):
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return [
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GNGCallback(reduction=self.hparams.insert_reduction,
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freq=self.hparams.insert_freq)
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]
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