Unclutter the examples folder
This commit is contained in:
Jensun Ravichandran
parent
e87663d10c
commit
f2541acde9
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"""CBC example using the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from sklearn.datasets import make_circles
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from torch.utils.data import DataLoader
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from prototorch.models.cbc import CBC, euclidean_similarity
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class VisualizationCallback(pl.Callback):
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def __init__(
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self,
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x_train,
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y_train,
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prototype_model=True,
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title="Prototype Visualization",
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cmap="viridis",
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):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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self.prototype_model = prototype_model
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def on_epoch_end(self, trainer, pl_module):
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if self.prototype_model:
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protos = pl_module.components
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color = pl_module.prototype_labels
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else:
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protos = pl_module.components
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color = "k"
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=color,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = make_circles(n_samples=300,
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shuffle=True,
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noise=0.05,
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random_state=None,
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factor=0.5)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=len(np.unique(y_train)),
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num_components=5,
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component_initializer=cinit.RandomInitializer(x_train.shape[1]),
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lr=0.01,
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)
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# Initialize the model
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model = CBC(
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hparams,
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data=[x_train, y_train],
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similarity=euclidean_similarity,
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)
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# Callbacks
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dvis = VisualizationCallback(x_train,
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y_train,
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prototype_model=False,
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title="CBC Circle Example")
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[
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dvis,
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],
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)
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# Training loop
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trainer.fit(model, train_loader)
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"""CBC 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 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.cbc import CBC, ImageCBC, euclidean_similarity
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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: ImageCBC):
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tb = pl_module.logger.experiment
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# components
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components = pl_module.components
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components_img = components.reshape(self.to_shape)
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grid = torchvision.utils.make_grid(components_img, nrow=self.nrow)
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tb.add_image(
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tag="MNIST Components",
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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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# Reasonings
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reasonings = pl_module.reasonings
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tb.add_images(
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tag="MNIST Reasoning",
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img_tensor=reasonings,
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global_step=trainer.current_epoch,
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dataformats="NCHW",
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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=32)
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test_loader = DataLoader(mnist_test, batch_size=32)
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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=args.ppc,
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prototype_initializer="randn",
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lr=0.01,
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similarity=euclidean_similarity,
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)
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# Initialize the model
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model = CBC(hparams, data=[x, y])
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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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track_grad_norm=2,
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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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"""CBC example using the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.datasets.abstract import NumpyDataset
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from torch.utils.data import DataLoader
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from prototorch.models.cbc import CBC
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class VisualizationCallback(pl.Callback):
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def __init__(
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self,
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x_train,
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y_train,
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prototype_model=True,
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title="Prototype Visualization",
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cmap="viridis",
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):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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self.prototype_model = prototype_model
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def on_epoch_end(self, trainer, pl_module):
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if self.prototype_model:
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protos = pl_module.prototypes
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color = pl_module.prototype_labels
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else:
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protos = pl_module.components
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color = "k"
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=color,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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def make_spirals(n_samples=500, noise=0.3):
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def get_samples(n, delta_t):
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points = []
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for i in range(n):
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r = i / n_samples * 5
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t = 1.75 * i / n * 2 * np.pi + delta_t
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x = r * np.sin(t) + np.random.rand(1) * noise
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y = r * np.cos(t) + np.random.rand(1) * noise
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points.append([x, y])
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return points
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n = n_samples // 2
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positive = get_samples(n=n, delta_t=0)
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negative = get_samples(n=n, delta_t=np.pi)
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x = np.concatenate(
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[np.array(positive).reshape(n, -1),
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np.array(negative).reshape(n, -1)],
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axis=0)
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y = np.concatenate([np.zeros(n), np.ones(n)])
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return x, y
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = make_spirals(n_samples=1000, noise=0.3)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=2,
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prototypes_per_class=40,
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prototype_initializer="stratified_random",
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lr=0.05,
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)
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# Initialize the model
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model_class = CBC
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model = model_class(hparams, data=[x_train, y_train])
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# Pure-positive reasonings
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new_reasoning = torch.zeros_like(
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model.reasoning_layer.reasoning_probabilities)
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for i, label in enumerate(model.component_layer.prototype_labels):
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new_reasoning[0][0][i][int(label)] = 1.0
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model.reasoning_layer.reasoning_probabilities.data = new_reasoning
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(x_train,
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y_train,
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prototype_model=hasattr(model, "prototypes"))
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=500,
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callbacks=[
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vis,
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],
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)
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# Training loop
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trainer.fit(model, train_loader)
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@ -1,147 +0,0 @@
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"""CBC example using the spirals dataset.
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This example shows how to jump start a model by transferring weights from
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another more stable model.
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"""
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.datasets.abstract import NumpyDataset
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from torch.utils.data import DataLoader
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from prototorch.models.cbc import CBC
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from prototorch.models.glvq import GLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(
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self,
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x_train,
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y_train,
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prototype_model=True,
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title="Prototype Visualization",
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cmap="viridis",
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):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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self.prototype_model = prototype_model
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def on_epoch_end(self, trainer, pl_module):
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if self.prototype_model:
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protos = pl_module.prototypes
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color = pl_module.prototype_labels
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else:
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protos = pl_module.components
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color = "k"
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=color,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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def make_spirals(n_samples=500, noise=0.3):
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def get_samples(n, delta_t):
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points = []
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for i in range(n):
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r = i / n_samples * 5
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t = 1.75 * i / n * 2 * np.pi + delta_t
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x = r * np.sin(t) + np.random.rand(1) * noise
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y = r * np.cos(t) + np.random.rand(1) * noise
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points.append([x, y])
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return points
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n = n_samples // 2
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positive = get_samples(n=n, delta_t=0)
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negative = get_samples(n=n, delta_t=np.pi)
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x = np.concatenate(
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[np.array(positive).reshape(n, -1),
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np.array(negative).reshape(n, -1)],
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axis=0)
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y = np.concatenate([np.zeros(n), np.ones(n)])
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return x, y
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def train(model, x_train, y_train, train_loader, epochs=100):
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# Callbacks
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vis = VisualizationCallback(x_train,
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y_train,
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prototype_model=hasattr(model, "prototypes"))
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=epochs,
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callbacks=[
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vis,
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],
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)
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# Training loop
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trainer.fit(model, train_loader)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Dataset
|
||||
x_train, y_train = make_spirals(n_samples=1000, noise=0.3)
|
||||
train_ds = NumpyDataset(x_train, y_train)
|
||||
|
||||
# Dataloaders
|
||||
train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
|
||||
|
||||
# Hyperparameters
|
||||
hparams = dict(
|
||||
input_dim=x_train.shape[1],
|
||||
nclasses=2,
|
||||
prototypes_per_class=40,
|
||||
prototype_initializer="stratified_random",
|
||||
lr=0.05,
|
||||
)
|
||||
|
||||
# Initialize the model
|
||||
glvq_model = GLVQ(hparams, data=[x_train, y_train])
|
||||
cbc_model = CBC(hparams, data=[x_train, y_train])
|
||||
|
||||
# Train GLVQ
|
||||
train(glvq_model, x_train, y_train, train_loader, epochs=10)
|
||||
|
||||
# Transfer Prototypes
|
||||
cbc_model.component_layer.load_state_dict(
|
||||
glvq_model.proto_layer.state_dict())
|
||||
# Pure-positive reasonings
|
||||
new_reasoning = torch.zeros_like(
|
||||
cbc_model.reasoning_layer.reasoning_probabilities)
|
||||
for i, label in enumerate(cbc_model.component_layer.prototype_labels):
|
||||
new_reasoning[0][0][i][int(label)] = 1.0
|
||||
new_reasoning[1][0][i][1 - int(label)] = 1.0
|
||||
|
||||
cbc_model.reasoning_layer.reasoning_probabilities.data = new_reasoning
|
||||
|
||||
# Train CBC
|
||||
train(cbc_model, x_train, y_train, train_loader, epochs=50)
|
||||
@ -1,119 +0,0 @@
|
||||
"""GLVQ example using the MNIST dataset.
|
||||
|
||||
This script also shows how to use Tensorboard for visualizing the prototypes.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
||||
import pytorch_lightning as pl
|
||||
import torchvision
|
||||
from prototorch.components import initializers as cinit
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision import transforms
|
||||
from torchvision.datasets import MNIST
|
||||
|
||||
from prototorch.models.glvq import ImageGLVQ
|
||||
|
||||
|
||||
class VisualizationCallback(pl.Callback):
|
||||
def __init__(self, to_shape=(-1, 1, 28, 28), nrow=2):
|
||||
super().__init__()
|
||||
self.to_shape = to_shape
|
||||
self.nrow = nrow
|
||||
|
||||
def on_epoch_end(self, trainer, pl_module):
|
||||
protos = pl_module.proto_layer.prototypes.detach().cpu()
|
||||
protos_img = protos.reshape(self.to_shape)
|
||||
grid = torchvision.utils.make_grid(protos_img, nrow=self.nrow)
|
||||
# grid = grid.permute((1, 2, 0))
|
||||
tb = pl_module.logger.experiment
|
||||
tb.add_image(
|
||||
tag="MNIST Prototypes",
|
||||
img_tensor=grid,
|
||||
global_step=trainer.current_epoch,
|
||||
dataformats="CHW",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--epochs",
|
||||
type=int,
|
||||
default=10,
|
||||
help="Epochs to train.")
|
||||
parser.add_argument("--lr",
|
||||
type=float,
|
||||
default=0.001,
|
||||
help="Learning rate.")
|
||||
parser.add_argument("--batch_size",
|
||||
type=int,
|
||||
default=256,
|
||||
help="Batch size.")
|
||||
parser.add_argument("--gpus",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Number of GPUs to use.")
|
||||
parser.add_argument("--ppc",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Prototypes-Per-Class.")
|
||||
args = parser.parse_args()
|
||||
|
||||
# Dataset
|
||||
mnist_train = MNIST(
|
||||
"./datasets",
|
||||
train=True,
|
||||
download=True,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307, ), (0.3081, ))
|
||||
]),
|
||||
)
|
||||
mnist_test = MNIST(
|
||||
"./datasets",
|
||||
train=False,
|
||||
download=True,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307, ), (0.3081, ))
|
||||
]),
|
||||
)
|
||||
|
||||
# Dataloaders
|
||||
train_loader = DataLoader(mnist_train, batch_size=1024)
|
||||
test_loader = DataLoader(mnist_test, batch_size=1024)
|
||||
|
||||
# Grab the full dataset to warm-start prototypes
|
||||
x, y = next(iter(DataLoader(mnist_train, batch_size=len(mnist_train))))
|
||||
x = x.view(len(mnist_train), -1)
|
||||
|
||||
# Hyperparameters
|
||||
hparams = dict(
|
||||
input_dim=28 * 28,
|
||||
nclasses=10,
|
||||
prototypes_per_class=1,
|
||||
prototype_initializer=cinit.StratifiedMeanInitializer(x, y),
|
||||
lr=args.lr,
|
||||
)
|
||||
|
||||
# Initialize the model
|
||||
model = ImageGLVQ(hparams)
|
||||
|
||||
# Model summary
|
||||
print(model)
|
||||
|
||||
# Callbacks
|
||||
vis = VisualizationCallback(to_shape=(-1, 1, 28, 28), nrow=args.ppc)
|
||||
|
||||
# Setup trainer
|
||||
trainer = pl.Trainer(
|
||||
gpus=args.gpus, # change to use GPUs for training
|
||||
max_epochs=args.epochs,
|
||||
callbacks=[vis],
|
||||
# accelerator="ddp_cpu", # DEBUG-ONLY
|
||||
# num_processes=2, # DEBUG-ONLY
|
||||
)
|
||||
|
||||
# Training loop
|
||||
trainer.fit(model, train_loader, test_loader)
|
||||
@ -1,62 +0,0 @@
|
||||
"""GMLVQ example using all four dimensions of the Iris dataset."""
|
||||
|
||||
import pytorch_lightning as pl
|
||||
import torch
|
||||
from prototorch.components import initializers as cinit
|
||||
from prototorch.datasets.abstract import NumpyDataset
|
||||
from sklearn.datasets import load_iris
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
|
||||
from prototorch.models.glvq import GRLVQ
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
|
||||
class PrintRelevanceCallback(pl.Callback):
|
||||
def on_epoch_end(self, trainer, pl_module: GRLVQ):
|
||||
print(pl_module.relevance_profile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Dataset
|
||||
x_train, y_train = load_iris(return_X_y=True)
|
||||
x_train = x_train[:, [0, 2]]
|
||||
scaler = StandardScaler()
|
||||
scaler.fit(x_train)
|
||||
x_train = scaler.transform(x_train)
|
||||
train_ds = NumpyDataset(x_train, y_train)
|
||||
|
||||
# Dataloaders
|
||||
train_loader = DataLoader(train_ds,
|
||||
num_workers=0,
|
||||
batch_size=50,
|
||||
shuffle=True)
|
||||
|
||||
# Hyperparameters
|
||||
hparams = dict(
|
||||
nclasses=3,
|
||||
prototypes_per_class=1,
|
||||
#prototype_initializer=cinit.SMI(torch.Tensor(x_train),
|
||||
# torch.Tensor(y_train)),
|
||||
prototype_initializer=cinit.UniformInitializer(2),
|
||||
input_dim=x_train.shape[1],
|
||||
lr=0.1,
|
||||
#transfer_function="sigmoid_beta",
|
||||
)
|
||||
|
||||
# Initialize the model
|
||||
model = GRLVQ(hparams)
|
||||
|
||||
# Model summary
|
||||
print(model)
|
||||
|
||||
# Callbacks
|
||||
vis = VisSiameseGLVQ2D(x_train, y_train)
|
||||
debug = PrintRelevanceCallback()
|
||||
|
||||
# Setup trainer
|
||||
trainer = pl.Trainer(max_epochs=200, callbacks=[vis, debug])
|
||||
|
||||
# Training loop
|
||||
trainer.fit(model, train_loader)
|
||||
@ -1,57 +0,0 @@
|
||||
"""GMLVQ example using all four dimensions of the Iris dataset."""
|
||||
|
||||
import pytorch_lightning as pl
|
||||
import torch
|
||||
from prototorch.components import initializers as cinit
|
||||
from prototorch.datasets.abstract import NumpyDataset
|
||||
from sklearn.datasets import load_iris
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
|
||||
from prototorch.models.glvq import GRLVQ
|
||||
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
from prototorch.datasets.spiral import make_spiral
|
||||
|
||||
|
||||
class PrintRelevanceCallback(pl.Callback):
|
||||
def on_epoch_end(self, trainer, pl_module: GRLVQ):
|
||||
print(pl_module.relevance_profile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Dataset
|
||||
x_train, y_train = make_spiral(n_samples=1000, noise=0.3)
|
||||
train_ds = NumpyDataset(x_train, y_train)
|
||||
|
||||
# Dataloaders
|
||||
train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
|
||||
|
||||
# Hyperparameters
|
||||
hparams = dict(
|
||||
nclasses=2,
|
||||
prototypes_per_class=20,
|
||||
prototype_initializer=cinit.SSI(torch.Tensor(x_train),
|
||||
torch.Tensor(y_train)),
|
||||
#prototype_initializer=cinit.UniformInitializer(2),
|
||||
input_dim=x_train.shape[1],
|
||||
lr=0.1,
|
||||
#transfer_function="sigmoid_beta",
|
||||
)
|
||||
|
||||
# Initialize the model
|
||||
model = GRLVQ(hparams)
|
||||
|
||||
# Model summary
|
||||
print(model)
|
||||
|
||||
# Callbacks
|
||||
vis = VisSiameseGLVQ2D(x_train, y_train)
|
||||
debug = PrintRelevanceCallback()
|
||||
|
||||
# Setup trainer
|
||||
trainer = pl.Trainer(max_epochs=200, callbacks=[vis, debug])
|
||||
|
||||
# Training loop
|
||||
trainer.fit(model, train_loader)
|
||||
Loading…
Reference in New Issue
Block a user