Merge pull request #2 from si-cim/dev

Use Components instead of Prototypes and refactor old examples

README.md

@@ -35,7 +35,7 @@ workon pt
 git clone git@github.com:si-cim/prototorch_models.git
 cd prototorch_models
 git checkout dev
-pip install -e .[all]  # \[all\] if you are using zsh
+pip install -e .[all]  # \[all\] if you are using zsh or MacOS
 ```
 
 To assist in the development process, you may also find it useful to install

examples/glvq_iris_v1.py

@@ -1,63 +1,14 @@
 """GLVQ example using the Iris dataset."""
 
-import numpy as np
 import pytorch_lightning as pl
 import torch
-from matplotlib import pyplot as plt
+from prototorch.components import initializers as cinit
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisGLVQ2D
+from prototorch.models.glvq import GLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.glvq import GLVQ
-
-
-class VisualizationCallback(pl.Callback):
-    def __init__(self,
-                 x_train,
-                 y_train,
-                 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
-
-    def on_epoch_end(self, trainer, pl_module):
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        x_train, y_train = self.x_train, self.y_train
-        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=plabels,
-            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)
-
-
 if __name__ == "__main__":
     # Dataset
     x_train, y_train = load_iris(return_X_y=True)
@@ -69,24 +20,21 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        input_dim=x_train.shape[1],
         nclasses=3,
-        prototypes_per_class=3,
+        prototypes_per_class=2,
-        prototype_initializer="stratified_mean",
+        prototype_initializer=cinit.StratifiedMeanInitializer(
-        lr=0.1,
+            torch.Tensor(x_train), torch.Tensor(y_train)),
+        lr=0.01,
     )
 
     # Initialize the model
     model = GLVQ(hparams, data=[x_train, y_train])
 
-    # Model summary
-    print(model)
-
-    # Callbacks
-    vis = VisualizationCallback(x_train, y_train)
-
     # Setup trainer
-    trainer = pl.Trainer(max_epochs=50, callbacks=[vis])
+    trainer = pl.Trainer(
+        max_epochs=50,
+        callbacks=[VisGLVQ2D(x_train, y_train)],
+    )
 
     # Training loop
     trainer.fit(model, train_loader)

examples/ng_iris.py

@@ -3,63 +3,13 @@
 import numpy as np
 import pytorch_lightning as pl
 from matplotlib import pyplot as plt
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisNG2D
+from prototorch.models.neural_gas import NeuralGas
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.neural_gas import NeuralGas
-
-
-class VisualizationCallback(pl.Callback):
-    def __init__(self,
-                 x_train,
-                 y_train,
-                 title="Neural Gas 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
-
-    def on_epoch_end(self, trainer, pl_module: NeuralGas):
-        protos = pl_module.proto_layer.prototypes.detach().cpu().numpy()
-        cmat = pl_module.topology_layer.cmat.cpu().numpy()
-
-        # Visualize the data and the prototypes
-        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(self.x_train[:, 0],
-                   self.x_train[:, 1],
-                   c=self.y_train,
-                   edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c="k",
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
-
-        # Draw connections
-        for i in range(len(protos)):
-            for j in range(len(protos)):
-                if cmat[i][j]:
-                    ax.plot(
-                        [protos[i, 0], protos[j, 0]],
-                        [protos[i, 1], protos[j, 1]],
-                        "k-",
-                    )
-
-        plt.pause(0.01)
-
-
 if __name__ == "__main__":
     # Dataset
     x_train, y_train = load_iris(return_X_y=True)
@@ -68,7 +18,6 @@ if __name__ == "__main__":
     scaler.fit(x_train)
     x_train = scaler.transform(x_train)
 
-    y_single_class = np.zeros_like(y_train)
     train_ds = NumpyDataset(x_train, y_train)
 
     # Dataloaders
@@ -77,20 +26,18 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         input_dim=x_train.shape[1],
-        nclasses=1,
+        num_prototypes=30,
-        prototypes_per_class=30,
+        lr=0.01,
-        prototype_initializer="rand",
-        lr=0.1,
     )
 
     # Initialize the model
-    model = NeuralGas(hparams, data=[x_train, y_single_class])
+    model = NeuralGas(hparams)
 
     # Model summary
     print(model)
 
     # Callbacks
-    vis = VisualizationCallback(x_train, y_train)
+    vis = VisNG2D(x_train, y_train)
 
     # Setup trainer
     trainer = pl.Trainer(

examples/siamese_glvq_iris.py

@@ -1,70 +1,15 @@
 """Siamese GLVQ example using all four dimensions of the Iris dataset."""
 
-import numpy as np
 import pytorch_lightning as pl
 import torch
-from matplotlib import pyplot as plt
+from prototorch.components import (StratifiedMeanInitializer,
+                                   StratifiedSelectionInitializer)
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import SiameseGLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.glvq import SiameseGLVQ
-
-
-class VisualizationCallback(pl.Callback):
-    def __init__(self,
-                 x_train,
-                 y_train,
-                 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
-
-    def on_epoch_end(self, trainer, pl_module):
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        x_train, y_train = self.x_train, self.y_train
-        x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
-        protos = pl_module.backbone(torch.Tensor(protos)).detach()
-        ax = self.fig.gca()
-        ax.cla()
-        ax.set_title(self.title)
-        ax.axis("off")
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c=plabels,
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
-        x = np.vstack((x_train, protos))
-        x_min, x_max = x[:, 0].min() - 0.2, x[:, 0].max() + 0.2
-        y_min, y_max = x[:, 1].min() - 0.2, x[:, 1].max() + 0.2
-        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_latent(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)
-        tb = pl_module.logger.experiment
-        tb.add_figure(
-            tag=f"{self.title}",
-            figure=self.fig,
-            global_step=trainer.current_epoch,
-            close=False,
-        )
-        plt.pause(0.1)
-
 
 class Backbone(torch.nn.Module):
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):
@@ -90,23 +35,24 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        input_dim=x_train.shape[1],
         nclasses=3,
         prototypes_per_class=1,
-        prototype_initializer="stratified_mean",
+        prototype_initializer=StratifiedMeanInitializer(
+            torch.Tensor(x_train), torch.Tensor(y_train)),
         lr=0.01,
     )
 
     # Initialize the model
-    model = SiameseGLVQ(hparams,
+    model = SiameseGLVQ(
+        hparams,
         backbone_module=Backbone,
-                        data=[x_train, y_train])
+    )
 
     # Model summary
     print(model)
 
     # Callbacks
-    vis = VisualizationCallback(x_train, y_train)
+    vis = VisSiameseGLVQ2D(x_train, y_train)
 
     # Setup trainer
     trainer = pl.Trainer(max_epochs=100, callbacks=[vis])

prototorch/models/callbacks/visualization.py

@@ -1,16 +1,17 @@
 import os
 
 import numpy as np
+import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
 from matplotlib.offsetbox import AnchoredText
-
 from prototorch.utils.celluloid import Camera
 from prototorch.utils.colors import color_scheme
-from prototorch.utils.utils import gif_from_dir, make_directory, prettify_string
+from prototorch.utils.utils import (gif_from_dir, make_directory,
+                                    prettify_string)
 
 
-class VisWeights(Callback):
+class VisWeights(pl.Callback):
     """Abstract weight visualization callback."""
     def __init__(
         self,
@@ -258,3 +259,155 @@ class VisPointProtos(VisWeights):
         epoch = trainer.current_epoch
         self._display_logs(self.ax, epoch, logs)
         self._show_and_save(epoch)
+
+
+class VisGLVQ2D(pl.Callback):
+    def __init__(self,
+                 x_train,
+                 y_train,
+                 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
+
+    def on_epoch_end(self, trainer, pl_module):
+        protos = pl_module.prototypes
+        plabels = pl_module.prototype_labels
+        x_train, y_train = self.x_train, self.y_train
+        ax = self.fig.gca()
+        ax.cla()
+        ax.set_title(self.title)
+        ax.axis("off")
+        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=plabels,
+            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)
+
+
+class VisSiameseGLVQ2D(pl.Callback):
+    def __init__(self,
+                 x_train,
+                 y_train,
+                 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
+
+    def on_epoch_end(self, trainer, pl_module):
+        protos = pl_module.prototypes
+        plabels = pl_module.prototype_labels
+        x_train, y_train = self.x_train, self.y_train
+        x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
+        protos = pl_module.backbone(torch.Tensor(protos)).detach()
+        ax = self.fig.gca()
+        ax.cla()
+        ax.set_title(self.title)
+        ax.axis("off")
+        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+        ax.scatter(
+            protos[:, 0],
+            protos[:, 1],
+            c=plabels,
+            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_latent(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)
+        tb = pl_module.logger.experiment
+        tb.add_figure(
+            tag=f"{self.title}",
+            figure=self.fig,
+            global_step=trainer.current_epoch,
+            close=False,
+        )
+        plt.pause(0.1)
+
+
+class VisNG2D(pl.Callback):
+    def __init__(self,
+                 x_train,
+                 y_train,
+                 title="Neural Gas 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
+
+    def on_epoch_end(self, trainer, pl_module):
+        protos = pl_module.prototypes
+        cmat = pl_module.topology_layer.cmat.cpu().numpy()
+
+        # Visualize the data and the prototypes
+        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(self.x_train[:, 0],
+                   self.x_train[:, 1],
+                   c=self.y_train,
+                   edgecolor="k")
+        ax.scatter(
+            protos[:, 0],
+            protos[:, 1],
+            c="k",
+            edgecolor="k",
+            marker="D",
+            s=50,
+        )
+
+        # Draw connections
+        for i in range(len(protos)):
+            for j in range(len(protos)):
+                if cmat[i][j]:
+                    ax.plot(
+                        [protos[i, 0], protos[j, 0]],
+                        [protos[i, 1], protos[j, 1]],
+                        "k-",
+                    )
+
+        plt.pause(0.01)

prototorch/models/glvq.py

@@ -1,14 +1,16 @@
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-
+from prototorch.components import LabeledComponents
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import euclidean_distance
 from prototorch.functions.losses import glvq_loss
 from prototorch.modules.prototypes import Prototypes1D
 
+from .abstract import AbstractPrototypeModel
 
-class GLVQ(pl.LightningModule):
+
+class GLVQ(AbstractPrototypeModel):
     """Generalized Learning Vector Quantization."""
     def __init__(self, hparams, **kwargs):
         super().__init__()
@@ -18,29 +20,18 @@ class GLVQ(pl.LightningModule):
         # Default Values
         self.hparams.setdefault("distance", euclidean_distance)
 
-        self.proto_layer = Prototypes1D(
+        self.proto_layer = LabeledComponents(
-            input_dim=self.hparams.input_dim,
+            labels=(self.hparams.nclasses, self.hparams.prototypes_per_class),
-            nclasses=self.hparams.nclasses,
+            initializer=self.hparams.prototype_initializer)
-            prototypes_per_class=self.hparams.prototypes_per_class,
-            prototype_initializer=self.hparams.prototype_initializer,
-            **kwargs)
 
         self.train_acc = torchmetrics.Accuracy()
 
-    @property
-    def prototypes(self):
-        return self.proto_layer.prototypes.detach().numpy()
-
     @property
     def prototype_labels(self):
-        return self.proto_layer.prototype_labels.detach().numpy()
+        return self.proto_layer.component_labels.detach().numpy()
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
-        return optimizer
 
     def forward(self, x):
-        protos = self.proto_layer.prototypes
+        protos, _ = self.proto_layer()
         dis = self.hparams.distance(x, protos)
         return dis
 
@@ -48,7 +39,7 @@ class GLVQ(pl.LightningModule):
         x, y = train_batch
         x = x.view(x.size(0), -1)
         dis = self(x)
-        plabels = self.proto_layer.prototype_labels
+        plabels = self.proto_layer.component_labels
         mu = glvq_loss(dis, y, prototype_labels=plabels)
         loss = mu.sum(dim=0)
         self.log("train_loss", loss)
@@ -77,7 +68,7 @@ class GLVQ(pl.LightningModule):
         # model.eval()  # ?!
         with torch.no_grad():
             d = self(x)
-            plabels = self.proto_layer.prototype_labels
+            plabels = self.proto_layer.component_labels
             y_pred = wtac(d, plabels)
         return y_pred.numpy()
 
@@ -89,7 +80,7 @@ class ImageGLVQ(GLVQ):
     clamping after updates.
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
-        self.proto_layer.prototypes.data.clamp_(0.0, 1.0)
+        self.proto_layer.components.data.clamp_(0.0, 1.0)
 
 
 class SiameseGLVQ(GLVQ):
@@ -115,7 +106,7 @@ class SiameseGLVQ(GLVQ):
 
     def forward(self, x):
         self.sync_backbones()
-        protos = self.proto_layer.prototypes
+        protos, _ = self.proto_layer()
 
         latent_x = self.backbone(x)
         latent_protos = self.backbone_dependent(protos)
@@ -126,9 +117,8 @@ class SiameseGLVQ(GLVQ):
     def predict_latent(self, x):
         # model.eval()  # ?!
         with torch.no_grad():
-            protos = self.proto_layer.prototypes
+            protos, plabels = self.proto_layer()
             latent_protos = self.backbone_dependent(protos)
             d = euclidean_distance(x, latent_protos)
-            plabels = self.proto_layer.prototype_labels
             y_pred = wtac(d, plabels)
         return y_pred.numpy()

prototorch/models/neural_gas.py

@@ -1,10 +1,13 @@
 import pytorch_lightning as pl
 import torch
-
+from prototorch.components import Components
+from prototorch.components import initializers as cinit
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules import Prototypes1D
 from prototorch.modules.losses import NeuralGasEnergy
 
+from .abstract import AbstractPrototypeModel
+
 
 class EuclideanDistance(torch.nn.Module):
     def forward(self, x, y):
@@ -34,41 +37,35 @@ class ConnectionTopology(torch.nn.Module):
         return f"agelimit: {self.agelimit}"
 
 
-class NeuralGas(pl.LightningModule):
+class NeuralGas(AbstractPrototypeModel):
     def __init__(self, hparams, **kwargs):
         super().__init__()
 
         self.save_hyperparameters(hparams)
 
         # Default Values
+        self.hparams.setdefault("input_dim", 2)
         self.hparams.setdefault("agelimit", 10)
         self.hparams.setdefault("lm", 1)
-        self.hparams.setdefault("prototype_initializer", "zeros")
+        self.hparams.setdefault("prototype_initializer",
+                                cinit.ZerosInitializer(self.hparams.input_dim))
 
-        self.proto_layer = Prototypes1D(
+        self.proto_layer = Components(
-            input_dim=self.hparams.input_dim,
+            self.hparams.num_prototypes,
-            nclasses=self.hparams.nclasses,
+            initializer=self.hparams.prototype_initializer)
-            prototypes_per_class=self.hparams.prototypes_per_class,
-            prototype_initializer=self.hparams.prototype_initializer,
-            **kwargs,
-        )
 
         self.distance_layer = EuclideanDistance()
         self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
         self.topology_layer = ConnectionTopology(
             agelimit=self.hparams.agelimit,
-            num_prototypes=len(self.proto_layer.prototypes),
+            num_prototypes=self.hparams.num_prototypes,
         )
 
     def training_step(self, train_batch, batch_idx):
-        x, _ = train_batch
+        x = train_batch[0]
-        protos, _ = self.proto_layer()
+        protos = self.proto_layer()
         d = self.distance_layer(x, protos)
         cost, order = self.energy_layer(d)
 
         self.topology_layer(d)
         return cost
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
-        return optimizer