Automatic Formating.

examples/cbc_circle.py

@@ -4,26 +4,24 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-from prototorch.models.cbc import CBC, rescaled_cosine_similarity, euclidean_similarity
-from prototorch.models.glvq import GLVQ
 from sklearn.datasets import make_circles
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
 
-
+from prototorch.datasets.abstract import NumpyDataset
-class NumpyDataset(TensorDataset):
+from prototorch.models.callbacks.visualization import VisPointProtos
-    def __init__(self, *arrays):
+from prototorch.models.cbc import CBC, euclidean_similarity
-        # tensors = [torch.from_numpy(arr) for arr in arrays]
+from prototorch.models.glvq import GLVQ
-        tensors = [torch.Tensor(arr) for arr in arrays]
-        super().__init__(*tensors)
 
 
 class VisualizationCallback(pl.Callback):
-    def __init__(self,
+    def __init__(
+        self,
         x_train,
         y_train,
         prototype_model=True,
         title="Prototype Visualization",
-                 cmap="viridis"):
+        cmap="viridis",
+    ):
         super().__init__()
         self.x_train = x_train
         self.y_train = y_train
@@ -38,20 +36,22 @@ class VisualizationCallback(pl.Callback):
             color = pl_module.prototype_labels
         else:
             protos = pl_module.components
-            color = 'k'
+            color = "k"
         ax = self.fig.gca()
         ax.cla()
         ax.set_title(self.title)
         ax.set_xlabel("Data dimension 1")
         ax.set_ylabel("Data dimension 2")
         ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(protos[:, 0],
+        ax.scatter(
+            protos[:, 0],
             protos[:, 1],
             c=color,
             cmap=self.cmap,
             edgecolor="k",
             marker="D",
-                   s=50)
+            s=50,
+        )
         x = np.vstack((x_train, protos))
         x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
         y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
@@ -95,7 +95,7 @@ if __name__ == "__main__":
         similarity=euclidean_similarity,
     )
 
-    #model = GLVQ(hparams, data=[x_train, y_train])
+    model = GLVQ(hparams, data=[x_train, y_train])
 
     # Fix the component locations
     # model.proto_layer.requires_grad_(False)
@@ -107,13 +107,21 @@ if __name__ == "__main__":
     print(model)
 
     # Callbacks
-    vis = VisualizationCallback(x_train, y_train, prototype_model=False)
+    dvis = VisPointProtos(
+        data=(x_train, y_train),
+        save=True,
+        snap=False,
+        voronoi=True,
+        resolution=50,
+        pause_time=0.1,
+        make_gif=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer(
-        max_epochs=500,
+        max_epochs=10,
         callbacks=[
-            vis,
+            dvis,
         ],
     )
 

examples/cbc_iris.py

@@ -4,16 +4,11 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-from prototorch.models.cbc import CBC
 from sklearn.datasets import load_iris
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
 
-
+from prototorch.datasets.abstract import NumpyDataset
-class NumpyDataset(TensorDataset):
+from prototorch.models.cbc import CBC
-    def __init__(self, *arrays):
-        # tensors = [torch.from_numpy(arr) for arr in arrays]
-        tensors = [torch.Tensor(arr) for arr in arrays]
-        super().__init__(*tensors)
 
 
 class VisualizationCallback(pl.Callback):
@@ -47,7 +42,8 @@ class VisualizationCallback(pl.Callback):
             cmap=self.cmap,
             edgecolor="k",
             marker="D",
-            s=50)
+            s=50,
+        )
         x = np.vstack((x_train, protos))
         x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
         y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
@@ -73,11 +69,13 @@ if __name__ == "__main__":
     train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
 
     # Hyperparameters
-    hparams = dict(input_dim=x_train.shape[1],
+    hparams = dict(
+        input_dim=x_train.shape[1],
         nclasses=3,
         prototypes_per_class=3,
         prototype_initializer="stratified_mean",
-                   lr=0.01)
+        lr=0.01,
+    )
 
     # Initialize the model
     model = CBC(hparams, data=[x_train, y_train])

examples/cbc_mnist.py

@@ -7,12 +7,12 @@ import argparse
 
 import pytorch_lightning as pl
 import torchvision
-from matplotlib import pyplot as plt
-from prototorch.models.cbc import ImageCBC, euclidean_similarity, rescaled_cosine_similarity
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from torchvision.datasets import MNIST
 
+from prototorch.models.cbc import CBC, ImageCBC, euclidean_similarity
+
 
 class VisualizationCallback(pl.Callback):
     def __init__(self, to_shape=(-1, 1, 28, 28), nrow=2):
@@ -89,8 +89,8 @@ if __name__ == "__main__":
     )
 
     # Dataloaders
-    train_loader = DataLoader(mnist_train, batch_size=1024)
+    train_loader = DataLoader(mnist_train, batch_size=32)
-    test_loader = DataLoader(mnist_test, batch_size=1024)
+    test_loader = DataLoader(mnist_test, batch_size=32)
 
     # Grab the full dataset to warm-start prototypes
     x, y = next(iter(DataLoader(mnist_train, batch_size=len(mnist_train))))
@@ -102,12 +102,12 @@ if __name__ == "__main__":
         nclasses=10,
         prototypes_per_class=args.ppc,
         prototype_initializer="randn",
-        lr=1,
+        lr=0.01,
         similarity=euclidean_similarity,
     )
 
     # Initialize the model
-    model = ImageCBC(hparams, data=[x, y])
+    model = CBC(hparams, data=[x, y])
     # Model summary
     print(model)
 

examples/cbc_spiral.py

@@ -0,0 +1,135 @@
+"""CBC example using the Iris dataset."""
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from matplotlib import pyplot as plt
+from torch.utils.data import DataLoader
+
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.cbc import CBC
+
+
+class VisualizationCallback(pl.Callback):
+    def __init__(
+        self,
+        x_train,
+        y_train,
+        prototype_model=True,
+        title="Prototype Visualization",
+        cmap="viridis",
+    ):
+        super().__init__()
+        self.x_train = x_train
+        self.y_train = y_train
+        self.title = title
+        self.fig = plt.figure(self.title)
+        self.cmap = cmap
+        self.prototype_model = prototype_model
+
+    def on_epoch_end(self, trainer, pl_module):
+        if self.prototype_model:
+            protos = pl_module.prototypes
+            color = pl_module.prototype_labels
+        else:
+            protos = pl_module.components
+            color = "k"
+        ax = self.fig.gca()
+        ax.cla()
+        ax.set_title(self.title)
+        ax.set_xlabel("Data dimension 1")
+        ax.set_ylabel("Data dimension 2")
+        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+        ax.scatter(
+            protos[:, 0],
+            protos[:, 1],
+            c=color,
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="D",
+            s=50,
+        )
+        x = np.vstack((x_train, protos))
+        x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
+        y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
+        xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
+                             np.arange(y_min, y_max, 1 / 50))
+        mesh_input = np.c_[xx.ravel(), yy.ravel()]
+        y_pred = pl_module.predict(torch.Tensor(mesh_input))
+        y_pred = y_pred.reshape(xx.shape)
+
+        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
+        ax.set_xlim(left=x_min + 0, right=x_max - 0)
+        ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
+        plt.pause(0.1)
+
+
+def make_spirals(n_samples=500, noise=0.3):
+    def get_samples(n, delta_t):
+        points = []
+        for i in range(n):
+            r = i / n_samples * 5
+            t = 1.75 * i / n * 2 * np.pi + delta_t
+            x = r * np.sin(t) + np.random.rand(1) * noise
+            y = r * np.cos(t) + np.random.rand(1) * noise
+            points.append([x, y])
+        return points
+
+    n = n_samples // 2
+    positive = get_samples(n=n, delta_t=0)
+    negative = get_samples(n=n, delta_t=np.pi)
+    x = np.concatenate(
+        [np.array(positive).reshape(n, -1),
+         np.array(negative).reshape(n, -1)],
+        axis=0)
+    y = np.concatenate([np.zeros(n), np.ones(n)])
+    return x, y
+
+
+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
+    model_class = CBC
+    model = model_class(hparams, data=[x_train, y_train])
+
+    # Pure-positive reasonings
+    new_reasoning = torch.zeros_like(
+        model.reasoning_layer.reasoning_probabilities)
+    for i, label in enumerate(model.proto_layer.prototype_labels):
+        new_reasoning[0][0][i][int(label)] = 1.0
+
+    model.reasoning_layer.reasoning_probabilities.data = new_reasoning
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = VisualizationCallback(x_train,
+                                y_train,
+                                prototype_model=hasattr(model, "prototypes"))
+
+    # Setup trainer
+    trainer = pl.Trainer(
+        max_epochs=500,
+        callbacks=[
+            vis,
+        ],
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/cbc_spiral_with_GLVQ_start.py

@@ -0,0 +1,142 @@
+"""CBC example using the Iris dataset."""
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from matplotlib import pyplot as plt
+from torch.utils.data import DataLoader
+
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.cbc import CBC
+from prototorch.models.glvq import GLVQ
+
+
+class VisualizationCallback(pl.Callback):
+    def __init__(
+        self,
+        x_train,
+        y_train,
+        prototype_model=True,
+        title="Prototype Visualization",
+        cmap="viridis",
+    ):
+        super().__init__()
+        self.x_train = x_train
+        self.y_train = y_train
+        self.title = title
+        self.fig = plt.figure(self.title)
+        self.cmap = cmap
+        self.prototype_model = prototype_model
+
+    def on_epoch_end(self, trainer, pl_module):
+        if self.prototype_model:
+            protos = pl_module.prototypes
+            color = pl_module.prototype_labels
+        else:
+            protos = pl_module.components
+            color = "k"
+        ax = self.fig.gca()
+        ax.cla()
+        ax.set_title(self.title)
+        ax.set_xlabel("Data dimension 1")
+        ax.set_ylabel("Data dimension 2")
+        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+        ax.scatter(
+            protos[:, 0],
+            protos[:, 1],
+            c=color,
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="D",
+            s=50,
+        )
+        x = np.vstack((x_train, protos))
+        x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
+        y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
+        xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
+                             np.arange(y_min, y_max, 1 / 50))
+        mesh_input = np.c_[xx.ravel(), yy.ravel()]
+        y_pred = pl_module.predict(torch.Tensor(mesh_input))
+        y_pred = y_pred.reshape(xx.shape)
+
+        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
+        ax.set_xlim(left=x_min + 0, right=x_max - 0)
+        ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
+        plt.pause(0.1)
+
+
+def make_spirals(n_samples=500, noise=0.3):
+    def get_samples(n, delta_t):
+        points = []
+        for i in range(n):
+            r = i / n_samples * 5
+            t = 1.75 * i / n * 2 * np.pi + delta_t
+            x = r * np.sin(t) + np.random.rand(1) * noise
+            y = r * np.cos(t) + np.random.rand(1) * noise
+            points.append([x, y])
+        return points
+
+    n = n_samples // 2
+    positive = get_samples(n=n, delta_t=0)
+    negative = get_samples(n=n, delta_t=np.pi)
+    x = np.concatenate(
+        [np.array(positive).reshape(n, -1),
+         np.array(negative).reshape(n, -1)],
+        axis=0)
+    y = np.concatenate([np.zeros(n), np.ones(n)])
+    return x, y
+
+
+def train(model, x_train, y_train, train_loader, epochs=100):
+    # Callbacks
+    vis = VisualizationCallback(x_train,
+                                y_train,
+                                prototype_model=hasattr(model, "prototypes"))
+    # Setup trainer
+    trainer = pl.Trainer(
+        max_epochs=epochs,
+        callbacks=[
+            vis,
+        ],
+    )
+    # Training loop
+    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.proto_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.proto_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)

examples/glvq_iris.py

@@ -6,15 +6,11 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-from prototorch.models.glvq import GLVQ
 from sklearn.datasets import load_iris
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
 
-
+from prototorch.datasets.abstract import NumpyDataset
-class NumpyDataset(TensorDataset):
+from prototorch.models.glvq import GLVQ
-    def __init__(self, *arrays):
-        tensors = [torch.from_numpy(arr) for arr in arrays]
-        super().__init__(*tensors)
 
 
 class GLVQIris(GLVQ):
@@ -56,13 +52,15 @@ class VisualizationCallback(pl.Callback):
         ax.set_xlabel("Data dimension 1")
         ax.set_ylabel("Data dimension 2")
         ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(protos[:, 0],
+        ax.scatter(
+            protos[:, 0],
             protos[:, 1],
             c=plabels,
             cmap=self.cmap,
             edgecolor="k",
             marker="D",
-                   s=50)
+            s=50,
+        )
         x = np.vstack((x_train, protos))
         x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
         y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
@@ -105,8 +103,8 @@ if __name__ == "__main__":
         parser,
         max_epochs=10,
         callbacks=[
-            vis,  # comment this line out to disable the visualization
+            vis,
-        ],
+        ],  # comment this line out to disable the visualization
     )
     # trainer.tune(model)
 

examples/glvq_iris_v1.py

@@ -4,15 +4,11 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-from prototorch.models.glvq import GLVQ
 from sklearn.datasets import load_iris
-from torch.utils.data import DataLoader, TensorDataset
+from torch.utils.data import DataLoader
 
-
+from prototorch.datasets.abstract import NumpyDataset
-class NumpyDataset(TensorDataset):
+from prototorch.models.glvq import GLVQ
-    def __init__(self, *arrays):
-        tensors = [torch.from_numpy(arr) for arr in arrays]
-        super().__init__(*tensors)
 
 
 class VisualizationCallback(pl.Callback):
@@ -37,13 +33,15 @@ class VisualizationCallback(pl.Callback):
         ax.set_xlabel("Data dimension 1")
         ax.set_ylabel("Data dimension 2")
         ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(protos[:, 0],
+        ax.scatter(
+            protos[:, 0],
             protos[:, 1],
             c=plabels,
             cmap=self.cmap,
             edgecolor="k",
             marker="D",
-                   s=50)
+            s=50,
+        )
         x = np.vstack((x_train, protos))
         x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
         y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
@@ -69,11 +67,13 @@ if __name__ == "__main__":
     train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
 
     # Hyperparameters
-    hparams = dict(input_dim=x_train.shape[1],
+    hparams = dict(
+        input_dim=x_train.shape[1],
         nclasses=3,
         prototypes_per_class=3,
         prototype_initializer="stratified_mean",
-                   lr=0.1)
+        lr=0.1,
+    )
 
     # Initialize the model
     model = GLVQ(hparams, data=[x_train, y_train])

examples/glvq_mnist.py

@@ -11,13 +11,12 @@ import argparse
 
 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
 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):
@@ -31,10 +30,12 @@ class VisualizationCallback(pl.Callback):
         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",
+        tb.add_image(
+            tag="MNIST Prototypes",
             img_tensor=grid,
             global_step=trainer.current_epoch,
-                     dataformats="CHW")
+            dataformats="CHW",
+        )
 
 
 if __name__ == "__main__":
@@ -91,11 +92,13 @@ if __name__ == "__main__":
     x = x.view(len(mnist_train), -1)
 
     # Initialize the model
-    model = ImageGLVQ(input_dim=28 * 28,
+    model = ImageGLVQ(
+        input_dim=28 * 28,
         nclasses=10,
         prototypes_per_class=args.ppc,
         prototype_initializer="stratified_mean",
-                      data=[x, y])
+        data=[x, y],
+    )
     # Model summary
     print(model)
 

prototorch/models/__init__.py

@@ -1,4 +1,4 @@
-from importlib.metadata import version, PackageNotFoundError
+from importlib.metadata import PackageNotFoundError, version
 
 VERSION_FALLBACK = "uninstalled_version"
 try:

prototorch/models/cbc.py

@@ -1,13 +1,9 @@
-import argparse
-
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-from prototorch.functions.competitions import wtac
+
 from prototorch.functions.distances import euclidean_distance
 from prototorch.functions.similarities import cosine_similarity
-from prototorch.functions.initializers import get_initializer
-from prototorch.functions.losses import glvq_loss
 from prototorch.modules.prototypes import Prototypes1D
 
 
@@ -64,9 +60,6 @@ class ReasoningLayer(torch.nn.Module):
         super().__init__()
         self.n_replicas = n_replicas
         self.n_classes = n_classes
-        # probabilities_init = torch.zeros(2, self.n_replicas, n_components,
-        #                                  self.n_classes)
-        # probabilities_init = torch.zeros(2, n_components, self.n_classes)
         probabilities_init = torch.zeros(2, 1, n_components, self.n_classes)
         probabilities_init.uniform_(0.4, 0.6)
         self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
@@ -75,35 +68,26 @@ class ReasoningLayer(torch.nn.Module):
     def reasonings(self):
         pk = self.reasoning_probabilities[0]
         nk = (1 - pk) * self.reasoning_probabilities[1]
-        ik = (1 - pk - nk)
+        ik = 1 - pk - nk
-        # pk is of shape (1, n_components, n_classes)
         img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
-        return img.unsqueeze(1)  # (n_components, 1, 3, n_classes)
+        return img.unsqueeze(1)
 
     def forward(self, detections):
         pk = self.reasoning_probabilities[0].clamp(0, 1)
         nk = (1 - pk) * self.reasoning_probabilities[1].clamp(0, 1)
         epsilon = torch.finfo(pk.dtype).eps
-        # print(f"{detections.shape=}")
-        # print(f"{pk.shape=}")
-        # print(f"{detections.min()=}")
-        # print(f"{detections.max()=}")
         numerator = (detections @ (pk - nk)) + nk.sum(1)
-        # probs = numerator / (pk + nk).sum(1).clamp(min=epsilon)
         probs = numerator / (pk + nk).sum(1)
-        # probs = probs.squeeze(0)
         probs = probs.squeeze(0)
         return probs
 
 
 class CBC(pl.LightningModule):
     """Classification-By-Components."""
-    def __init__(
+    def __init__(self,
-            self,
                  hparams,
                  margin=0.1,
                  backbone_class=torch.nn.Identity,
-            # similarity=rescaled_cosine_similarity,
                  similarity=euclidean_similarity,
                  **kwargs):
         super().__init__()
@@ -142,15 +126,11 @@ class CBC(pl.LightningModule):
 
     def forward(self, x):
         self.sync_backbones()
-        protos = self.proto_layer.prototypes
+        protos, _ = self.proto_layer()
-        # protos, _ = self.proto_layer()
 
         latent_x = self.backbone(x)
         latent_protos = self.backbone_dependent(protos)
 
-        # print(f"{latent_x.dtype=}")
-        # print(f"{latent_protos.dtype=}")
-
         detections = self.similarity(latent_x, latent_protos)
         probs = self.reasoning_layer(detections)
         return probs
@@ -159,20 +139,10 @@ class CBC(pl.LightningModule):
         x, y = train_batch
         x = x.view(x.size(0), -1)
         y_pred = self(x)
-        # print(f"{y_pred.min()=}")
-        # print(f"{y_pred.max()=}")
         nclasses = self.reasoning_layer.n_classes
-        # y_true = torch.nn.functional.one_hot(y, num_classes=nclasses)
-        # y_true = torch.eye(nclasses)[y.long()]
         y_true = torch.nn.functional.one_hot(y.long(), num_classes=nclasses)
         loss = MarginLoss(self.margin)(y_pred, y_true).mean(dim=0)
         self.log("train_loss", loss)
-        # with torch.no_grad():
-        #     preds = torch.argmax(y_pred, dim=1)
-        # # self.train_acc.update(preds.int(), y.int())
-        # self.train_acc(
-        #     preds.int(),
-        #     y.int())  # FloatTensors are assumed to be class probabilities
         self.train_acc(y_pred, y_true)
         self.log(
             "acc",
@@ -184,17 +154,8 @@ class CBC(pl.LightningModule):
         )
         return loss
 
-    #def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
-    #    self.reasoning_layer.reasoning_probabilities.data.clamp_(0., 1.)
-
-    # def training_epoch_end(self, outs):
-    #     # Calling `self.train_acc.compute()` is
-    #     # automatically done by setting `on_epoch=True` when logging in `self.training_step(...)`
-    #     self.log("train_acc_epoch", self.train_acc.compute())
-
     def predict(self, x):
         with torch.no_grad():
-            # model.eval()  # ?!
             y_pred = self(x)
             y_pred = torch.argmax(y_pred, dim=1)
         return y_pred.numpy()
@@ -205,5 +166,5 @@ class ImageCBC(CBC):
     clamping after updates.
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
-        #super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
+        # super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
         self.proto_layer.prototypes.data.clamp_(0.0, 1.0)

prototorch/models/glvq.py

@@ -1,11 +1,9 @@
-import argparse
-
 import pytorch_lightning as pl
 import torch
 import torchmetrics
+
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import euclidean_distance
-from prototorch.functions.initializers import get_initializer
 from prototorch.functions.losses import glvq_loss
 from prototorch.modules.prototypes import Prototypes1D
 
@@ -54,12 +52,14 @@ class GLVQ(pl.LightningModule):
         self.train_acc(
             preds.int(),
             y.int())  # FloatTensors are assumed to be class probabilities
-        self.log("acc",
+        self.log(
+            "acc",
             self.train_acc,
             on_step=False,
             on_epoch=True,
             prog_bar=True,
-                 logger=True)
+            logger=True,
+        )
         return loss
 
     # def training_epoch_end(self, outs):
@@ -81,4 +81,4 @@ class ImageGLVQ(GLVQ):
     clamping after updates.
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
-        self.proto_layer.prototypes.data.clamp_(0., 1.)
+        self.proto_layer.prototypes.data.clamp_(0.0, 1.0)

setup.py

@@ -9,8 +9,7 @@
 ProtoTorch models Plugin Package
 """
 from pkg_resources import safe_name
-from setuptools import setup
+from setuptools import find_namespace_packages, setup
-from setuptools import find_namespace_packages
 
 PLUGIN_NAME = "models"
 
@@ -28,7 +27,8 @@ ALL = EXAMPLES + TESTS
 setup(
     name=safe_name("prototorch_" + PLUGIN_NAME),
     use_scm_version=True,
-    descripion="Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning.",
+    descripion=
+    "Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning.",
     long_description=long_description,
     author="Alexander Engelsberger",
     author_email="engelsbe@hs-mittweida.de",