prototorch_models/examples/glvq_mnist.py

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2021-04-21 12:54:14 +00:00
import pytorch_lightning as pl
import torchvision
from matplotlib import pyplot as plt
from prototorch.functions.initializers import stratified_mean
from prototorch.models.glvq import ImageGLVQ
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from torchvision.datasets import MNIST
def plot_protos(protos, shape=(-1, 1, 28, 28), nrow=2):
grid = torchvision.utils.make_grid(protos.reshape(*shape), nrow=nrow)
grid = grid.permute((1, 2, 0))
plt.imshow(grid)
if __name__ == "__main__":
dataset = MNIST("./datasets",
train=True,
download=True,
transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=1024)
val_loader = DataLoader(mnist_val, batch_size=1024)
model = ImageGLVQ(input_dim=28 * 28, nclasses=10, prototypes_per_class=2)
# Warm-start prototypes
prototypes, prototype_labels = stratified_mean(
x_train,
y_train,
prototype_distribution=self.prototype_distribution,
one_hot=one_hot_labels,
)
trainer = pl.Trainer(gpus=0, max_epochs=3)
trainer.fit(model, train_loader, val_loader)
protos = model.proto_layer.prototypes.detach().cpu()
plot_protos(protos, shape=(-1, 1, 28, 28), nrow=4)
plt.show(block=True)