45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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import pytorch_lightning as pl
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import torchvision
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from matplotlib import pyplot as plt
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from prototorch.functions.initializers import stratified_mean
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from prototorch.models.glvq import ImageGLVQ
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from torch.utils.data import DataLoader, random_split
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from torchvision import transforms
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from torchvision.datasets import MNIST
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def plot_protos(protos, shape=(-1, 1, 28, 28), nrow=2):
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grid = torchvision.utils.make_grid(protos.reshape(*shape), nrow=nrow)
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grid = grid.permute((1, 2, 0))
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plt.imshow(grid)
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if __name__ == "__main__":
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dataset = MNIST("./datasets",
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train=True,
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download=True,
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transform=transforms.ToTensor())
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mnist_train, mnist_val = random_split(dataset, [55000, 5000])
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train_loader = DataLoader(mnist_train, batch_size=1024)
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val_loader = DataLoader(mnist_val, batch_size=1024)
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model = ImageGLVQ(input_dim=28 * 28, nclasses=10, prototypes_per_class=2)
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# Warm-start prototypes
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prototypes, prototype_labels = stratified_mean(
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x_train,
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y_train,
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prototype_distribution=self.prototype_distribution,
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one_hot=one_hot_labels,
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)
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trainer = pl.Trainer(gpus=0, max_epochs=3)
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trainer.fit(model, train_loader, val_loader)
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protos = model.proto_layer.prototypes.detach().cpu()
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plot_protos(protos, shape=(-1, 1, 28, 28), nrow=4)
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plt.show(block=True)
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