Add partial metric/hparam features [BROKEN STATE]

prototorch/models/glvq.py

@@ -1,5 +1,6 @@
 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
@@ -9,10 +10,11 @@ from prototorch.modules.prototypes import Prototypes1D
 
 class GLVQ(pl.LightningModule):
     """Generalized Learning Vector Quantization."""
-    def __init__(self, lr=1e-3, **kwargs):
+    def __init__(self, hparams):
         super().__init__()
-        self.lr = lr
+        self.lr = hparams.lr
         self.proto_layer = Prototypes1D(**kwargs)
+        self.train_acc = torchmetrics.Accuracy()
 
     @property
     def prototypes(self):
@@ -39,10 +41,21 @@ class GLVQ(pl.LightningModule):
         mu = glvq_loss(dis, y, prototype_labels=plabels)
         loss = mu.sum(dim=0)
         self.log("train_loss", loss)
+        with torch.no_grad():
+            preds = wtac(dis, plabels)
+        # self.train_acc.update(preds.int(), y.int())
+        self.train_acc(preds.int(), y.int())  # FloatTensors are assumed to be class probabilities
+        self.log("Training Accuracy", self.train_acc, on_step=False, on_epoch=True)
         return loss
 
+    # 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()  # ?!
             d = self(x)
             plabels = self.proto_layer.prototype_labels
             y_pred = wtac(d, plabels)