120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
"""GLVQ example using the MNIST dataset.
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This script also shows how to use Tensorboard for visualizing the prototypes.
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"""
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import argparse
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import pytorch_lightning as pl
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import torchvision
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from prototorch.components import initializers as cinit
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from torch.utils.data import DataLoader
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from torchvision import transforms
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from torchvision.datasets import MNIST
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from prototorch.models.glvq import ImageGLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(self, to_shape=(-1, 1, 28, 28), nrow=2):
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super().__init__()
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self.to_shape = to_shape
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self.nrow = nrow
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.proto_layer.prototypes.detach().cpu()
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protos_img = protos.reshape(self.to_shape)
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grid = torchvision.utils.make_grid(protos_img, nrow=self.nrow)
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# grid = grid.permute((1, 2, 0))
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tb = pl_module.logger.experiment
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tb.add_image(
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tag="MNIST Prototypes",
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img_tensor=grid,
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global_step=trainer.current_epoch,
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dataformats="CHW",
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)
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if __name__ == "__main__":
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# Arguments
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parser = argparse.ArgumentParser()
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parser.add_argument("--epochs",
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type=int,
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default=10,
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help="Epochs to train.")
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parser.add_argument("--lr",
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type=float,
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default=0.001,
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help="Learning rate.")
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parser.add_argument("--batch_size",
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type=int,
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default=256,
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help="Batch size.")
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parser.add_argument("--gpus",
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type=int,
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default=0,
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help="Number of GPUs to use.")
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parser.add_argument("--ppc",
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type=int,
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default=1,
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help="Prototypes-Per-Class.")
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args = parser.parse_args()
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# Dataset
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mnist_train = MNIST(
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"./datasets",
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307, ), (0.3081, ))
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]),
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)
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mnist_test = MNIST(
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"./datasets",
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307, ), (0.3081, ))
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]),
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)
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# Dataloaders
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train_loader = DataLoader(mnist_train, batch_size=1024)
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test_loader = DataLoader(mnist_test, batch_size=1024)
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# Grab the full dataset to warm-start prototypes
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x, y = next(iter(DataLoader(mnist_train, batch_size=len(mnist_train))))
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x = x.view(len(mnist_train), -1)
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# Hyperparameters
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hparams = dict(
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input_dim=28 * 28,
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nclasses=10,
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prototypes_per_class=1,
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prototype_initializer=cinit.StratifiedMeanInitializer(x, y),
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lr=args.lr,
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)
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# Initialize the model
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model = ImageGLVQ(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(to_shape=(-1, 1, 28, 28), nrow=args.ppc)
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# Setup trainer
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trainer = pl.Trainer(
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gpus=args.gpus, # change to use GPUs for training
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max_epochs=args.epochs,
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callbacks=[vis],
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# accelerator="ddp_cpu", # DEBUG-ONLY
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# num_processes=2, # DEBUG-ONLY
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)
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# Training loop
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trainer.fit(model, train_loader, test_loader)
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