Add partial cbc implementation
This commit is contained in:
Jensun Ravichandran
116
examples/cbc_iris.py
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116
examples/cbc_iris.py
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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.models.cbc import CBC
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader, TensorDataset
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class NumpyDataset(TensorDataset):
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def __init__(self, *arrays):
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# tensors = [torch.from_numpy(arr) for arr in arrays]
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tensors = [torch.Tensor(arr) for arr in arrays]
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super().__init__(*tensors)
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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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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def on_epoch_end(self, trainer, pl_module):
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# protos = pl_module.prototypes
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protos = pl_module.components
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# plabels = pl_module.prototype_labels
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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=plabels,
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c="k",
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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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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 = load_iris(return_X_y=True)
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x_train = x_train[:, [0, 2]]
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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(input_dim=x_train.shape[1],
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nclasses=3,
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prototypes_per_class=3,
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prototype_initializer="stratified_mean",
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lr=0.01)
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# Initialize the model
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model = CBC(hparams, data=[x_train, y_train])
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# Fix the component locations
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# model.proto_layer.requires_grad_(False)
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# Pure-positive reasonings
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ncomps = 3
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nclasses = 3
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rmat = torch.stack(
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[0.9 * torch.eye(ncomps),
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torch.zeros(ncomps, nclasses)], dim=0)
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# model.reasoning_layer.load_state_dict({"reasoning_probabilities": rmat},
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# strict=True)
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print(model.reasoning_layer.reasoning_probabilities)
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# import sys
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# sys.exit()
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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, y_train)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=100,
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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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205
prototorch/models/cbc.py
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205
prototorch/models/cbc.py
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import argparse
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import pytorch_lightning as pl
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import torch
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import torchmetrics
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from prototorch.functions.competitions import wtac
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from prototorch.functions.distances import euclidean_distance
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from prototorch.functions.similarities import cosine_similarity
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from prototorch.functions.initializers import get_initializer
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from prototorch.functions.losses import glvq_loss
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from prototorch.modules.prototypes import Prototypes1D
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def rescaled_cosine_similarity(x, y):
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"""Cosine Similarity rescaled to [0, 1]."""
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similarities = cosine_similarity(x, y)
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return (similarities + 1.0) / 2.0
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def shift_activation(x):
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return (x + 1.0) / 2.0
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def euclidean_similarity(x, y):
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d = euclidean_distance(x, y)
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return torch.exp(-d * 3)
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class CosineSimilarity(torch.nn.Module):
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def __init__(self, activation=shift_activation):
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super().__init__()
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self.activation = activation
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def forward(self, x, y):
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epsilon = torch.finfo(x.dtype).eps
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normed_x = (x/ x.pow(2) \
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.sum(dim=tuple(range(1, x.ndim)), keepdim=True) \
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.clamp(min=epsilon) \
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.sqrt()).flatten(start_dim=1)
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normed_y = (y / y.pow(2) \
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.sum(dim=tuple(range(1, y.ndim)), keepdim=True) \
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.clamp(min=epsilon) \
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.sqrt()).flatten(start_dim=1)
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# normed_x = (x / torch.linalg.norm(x, dim=1))
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diss = torch.inner(normed_x, normed_y)
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return self.activation(diss)
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class MarginLoss(torch.nn.modules.loss._Loss):
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def __init__(self,
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margin=0.3,
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size_average=None,
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reduce=None,
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reduction="mean"):
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super().__init__(size_average, reduce, reduction)
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self.margin = margin
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def forward(self, input_, target):
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dp = torch.sum(target * input_, dim=-1)
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dm = torch.max(input_ - target, dim=-1).values
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return torch.nn.functional.relu(dm - dp + self.margin)
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class ReasoningLayer(torch.nn.Module):
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def __init__(self, n_components, n_classes, n_replicas=1):
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super().__init__()
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self.n_replicas = n_replicas
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self.n_classes = n_classes
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# probabilities_init = torch.zeros(2, self.n_replicas, n_components,
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# self.n_classes)
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# probabilities_init = torch.zeros(2, n_components, self.n_classes)
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probabilities_init = torch.zeros(2, 1, n_components, self.n_classes)
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probabilities_init.uniform_(0.4, 0.6)
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self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
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# @property
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# def reasonings(self):
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# pk = self.reasoning_probabilities[0]
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# nk = (1 - pk) * self.reasoning_probabilities[1]
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# ik = (1 - pk - nk)
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# # pk is of shape (1, n_components, n_classes)
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# img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
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# return img.unsqueeze(1) # (n_components, 1, 3, n_classes)
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def forward(self, detections):
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pk = self.reasoning_probabilities[0].clamp(0, 1)
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nk = (1 - pk) * self.reasoning_probabilities[1].clamp(0, 1)
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epsilon = torch.finfo(pk.dtype).eps
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# print(f"{detections.shape=}")
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# print(f"{pk.shape=}")
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# print(f"{detections.min()=}")
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# print(f"{detections.max()=}")
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numerator = (detections @ (pk - nk)) + nk.sum(1)
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# probs = numerator / (pk + nk).sum(1).clamp(min=epsilon)
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probs = numerator / (pk + nk).sum(1)
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# probs = probs.squeeze(0)
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probs = probs.squeeze(0)
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return probs
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class CBC(pl.LightningModule):
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"""Classification-By-Components."""
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def __init__(
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self,
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hparams,
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margin=0.1,
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backbone_class=torch.nn.Identity,
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# similarity=rescaled_cosine_similarity,
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similarity=euclidean_similarity,
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**kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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self.margin = margin
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self.proto_layer = Prototypes1D(
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input_dim=self.hparams.input_dim,
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nclasses=self.hparams.nclasses,
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prototypes_per_class=self.hparams.prototypes_per_class,
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prototype_initializer=self.hparams.prototype_initializer,
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**kwargs)
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# self.similarity = CosineSimilarity()
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self.similarity = similarity
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self.backbone = backbone_class()
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self.backbone_dependent = backbone_class().requires_grad_(False)
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n_components = self.components.shape[0]
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self.reasoning_layer = ReasoningLayer(n_components=n_components,
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n_classes=self.hparams.nclasses)
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self.train_acc = torchmetrics.Accuracy()
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@property
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def components(self):
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return self.proto_layer.prototypes.detach().numpy()
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
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return optimizer
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def sync_backbones(self):
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master_state = self.backbone.state_dict()
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self.backbone_dependent.load_state_dict(master_state, strict=True)
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def forward(self, x):
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self.sync_backbones()
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# protos = self.proto_layer.prototypes
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protos, _ = self.proto_layer()
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latent_x = self.backbone(x)
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latent_protos = self.backbone_dependent(protos)
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# print(f"{latent_x.dtype=}")
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# print(f"{latent_protos.dtype=}")
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detections = self.similarity(latent_x, latent_protos)
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probs = self.reasoning_layer(detections)
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return probs
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def training_step(self, train_batch, batch_idx):
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x, y = train_batch
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x = x.view(x.size(0), -1)
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y_pred = self(x)
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# print(f"{y_pred.min()=}")
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# print(f"{y_pred.max()=}")
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nclasses = self.reasoning_layer.n_classes
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# y_true = torch.nn.functional.one_hot(y, num_classes=nclasses)
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# y_true = torch.eye(nclasses)[y.long()]
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y_true = torch.nn.functional.one_hot(y.long(), num_classes=nclasses)
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loss = MarginLoss(self.margin)(y_pred, y_true).sum(dim=0)
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self.log("train_loss", loss)
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# with torch.no_grad():
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# preds = torch.argmax(y_pred, dim=1)
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# # self.train_acc.update(preds.int(), y.int())
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# self.train_acc(
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# preds.int(),
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# y.int()) # FloatTensors are assumed to be class probabilities
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self.train_acc(y_pred, y_true)
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self.log("acc",
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self.train_acc,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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logger=True)
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return loss
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# def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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# self.reasoning_layer.reasoning_probabilities.data.clamp_(0., 1.)
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# def training_epoch_end(self, outs):
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# # Calling `self.train_acc.compute()` is
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# # automatically done by setting `on_epoch=True` when logging in `self.training_step(...)`
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# self.log("train_acc_epoch", self.train_acc.compute())
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def predict(self, x):
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with torch.no_grad():
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# model.eval() # ?!
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y_pred = self(x)
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y_pred = torch.argmax(y_pred, dim=1)
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return y_pred.numpy()
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class ImageCBC(CBC):
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"""CBC model that constrains the components to the range [0, 1] by
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clamping after updates.
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"""
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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super().on_train_batch_end(outputs, batch, batch_idx, dataload_idx)
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self.proto_layer.prototypes.data.clamp_(0., 1.)
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