Merge pull request #129 from point-cloud-radar/scheduler

bird_cloud_gnn/gnn_model.py

@@ -123,6 +123,9 @@ def fit_and_evaluate(
         callback=None,
         learning_rate=0.01,
         num_epochs=20,
+        sch_explr_gamma=0.99,
+        sch_multisteplr_milestones=None,
+        sch_multisteplr_gamma=0.1,
     ):
         """Fit the model while evaluating every iteraction.
 
@@ -135,9 +138,24 @@ def fit_and_evaluate(
                 Defaults to None.
             learning_rate (float, optional): Learning rate. Defaults to 0.01.
             num_epochs (int, optional): Number of training epochs. Defaults to 20.
+            sch_explr_gamma (float): The exponential decay rate of the learning rate.
+            sch_multisteplr_milestones (list): epoch numbers where the learning rate is decreased
+                by a factor of sch_multisteplr_gamma. If None this is done at epoch 100
+            sch_multisteplr_gamma (float): If a stepped decay of the learning rate is taken,
+                the multiplication factor
         """
+        if sch_multisteplr_milestones is None:
+            sch_multisteplr_milestones = [min(num_epochs, 100)]
         progress_bar = tqdm(total=num_epochs)
         optimizer = optim.Adam(self.parameters(), lr=learning_rate)
+        schedulers = [
+            optim.lr_scheduler.ExponentialLR(optimizer, gamma=sch_explr_gamma),
+            optim.lr_scheduler.MultiStepLR(
+                optimizer,
+                milestones=sch_multisteplr_milestones,
+                gamma=sch_multisteplr_gamma,
+            ),
+        ]
         epoch_values = {}
         for epoch in range(num_epochs):
             epoch_values["epoch"] = epoch
@@ -204,13 +222,18 @@ def fit_and_evaluate(
             epoch_values["Accuracy/test"] = num_correct / num_total
             epoch_values["Layer/conv1"] = self.conv1.weight.detach()
             epoch_values["Layer/conv2"] = self.conv2.weight.detach()
+            for i, pg in enumerate(optimizer.param_groups):
+                epoch_values[f"LearningRate/ParGrp{i}"] = pg["lr"]
             if self.num_classes == 2:
                 epoch_values["FalseNegativeRate/test"] = num_false_negative / num_total
                 epoch_values["FalsePositiveRate/test"] = num_false_positive / num_total
 
             progress_bar.set_postfix({"Epoch": epoch})
             progress_bar.update(1)
 
+            for scheduler in schedulers:
+                scheduler.step()
+
             if callback is not None:
                 user_request_stop = callback(epoch_values)
                 if user_request_stop is True:  # Check for explicit True