Examples use GPUs if available.

prototorch/models/callbacks.py

@@ -0,0 +1,14 @@
+"""Callbacks for Pytorch Lighning Modules"""
+
+import pytorch_lightning as pl
+import torch
+
+
+class StopOnNaN(pl.Callback):
+    def __init__(self, param):
+        super().__init__()
+        self.param = param
+
+    def on_epoch_end(self, trainer, pl_module, logs={}):
+        if torch.isnan(self.param).any():
+            raise ValueError("NaN encountered. Stopping.")

prototorch/models/cbc.py

@@ -153,7 +153,7 @@ class CBC(pl.LightningModule):
         with torch.no_grad():
             y_pred = self(x)
             y_pred = torch.argmax(y_pred, dim=1)
-        return y_pred.numpy()
+        return y_pred
 
 
 class ImageCBC(CBC):

prototorch/models/glvq.py

@@ -84,7 +84,7 @@ class GLVQ(AbstractPrototypeModel):
             d = self(x)
             plabels = self.proto_layer.component_labels
             y_pred = wtac(d, plabels)
-        return y_pred.numpy()
+        return y_pred
 
 
 class LVQ1(GLVQ):

prototorch/models/knn.py

@@ -33,7 +33,7 @@ class KNN(AbstractPrototypeModel):
 
     @property
     def prototype_labels(self):
-        return self.proto_layer.component_labels.detach().cpu()
+        return self.proto_layer.component_labels.detach()
 
     def forward(self, x):
         protos, _ = self.proto_layer()
@@ -46,7 +46,7 @@ class KNN(AbstractPrototypeModel):
             d = self(x)
             plabels = self.proto_layer.component_labels
             y_pred = knnc(d, plabels, k=self.hparams.k)
-        return y_pred.numpy()
+        return y_pred
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         return 1

prototorch/models/vis.py

@@ -379,8 +379,10 @@ class VisGLVQ2D(Vis2DAbstract):
         self.plot_protos(ax, protos, plabels)
         x = np.vstack((x_train, protos))
         mesh_input, xx, yy = self.get_mesh_input(x)
-        y_pred = pl_module.predict(torch.Tensor(mesh_input))
-        y_pred = y_pred.reshape(xx.shape)
+        _components = pl_module.proto_layer._components
+        y_pred = pl_module.predict(
+            torch.Tensor(mesh_input).type_as(_components))
+        y_pred = y_pred.cpu().reshape(xx.shape)
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
 
         self.log_and_display(trainer, pl_module)
@@ -398,20 +400,24 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
         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()
+        x_train = pl_module.backbone(
+            torch.Tensor(x_train).to(pl_module.device)).cpu().detach()
         if self.map_protos:
-            protos = pl_module.backbone(torch.Tensor(protos)).detach()
+            protos = pl_module.backbone(
+                torch.Tensor(protos).to(pl_module.device)).cpu().detach()
         ax = self.setup_ax()
         self.plot_data(ax, x_train, y_train)
-        if self.show_protos:
-            self.plot_protos(ax, protos, plabels)
-            x = np.vstack((x_train, protos))
-            mesh_input, xx, yy = self.get_mesh_input(x)
-        else:
-            mesh_input, xx, yy = self.get_mesh_input(x_train)
-        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)
+        #if self.show_protos:
+        #    self.plot_protos(ax, protos, plabels)
+        #    x = np.vstack((x_train, protos))
+        #    mesh_input, xx, yy = self.get_mesh_input(x)
+        #else:
+        #    mesh_input, xx, yy = self.get_mesh_input(x_train)
+        #_components = pl_module.proto_layer._components
+        #y_pred = pl_module.predict(
+        #    torch.Tensor(mesh_input).type_as(_components))
+        #y_pred = y_pred.cpu().reshape(xx.shape)
+        #ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
 
         self.log_and_display(trainer, pl_module)
 
@@ -429,8 +435,10 @@ class VisCBC2D(Vis2DAbstract):
         self.plot_protos(ax, protos, "w")
         x = np.vstack((x_train, protos))
         mesh_input, xx, yy = self.get_mesh_input(x)
-        y_pred = pl_module.predict(torch.Tensor(mesh_input))
-        y_pred = y_pred.reshape(xx.shape)
+        _components = pl_module.component_layer._components
+        y_pred = pl_module.predict(
+            torch.Tensor(mesh_input).type_as(_components))
+        y_pred = y_pred.cpu().reshape(xx.shape)
 
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)