2 files changed, 15 insertions, 9 deletions

diff --git a/training/lr.py b/training/lr.py
index c1fa3a0..c0e9b3f 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -19,8 +19,8 @@ class LRFinder():
         self.val_dataloader = val_dataloader
         self.loss_fn = loss_fn
 
-        self.model_state = copy.deepcopy(model.state_dict())
-        self.optimizer_state = copy.deepcopy(optimizer.state_dict())
+        # self.model_state = copy.deepcopy(model.state_dict())
+        # self.optimizer_state = copy.deepcopy(optimizer.state_dict())
 
     def run(self, min_lr, skip_start=10, skip_end=5, num_epochs=100, num_train_batches=1, num_val_batches=math.inf, smooth_f=0.05, diverge_th=5):
         best_loss = None
@@ -109,8 +109,8 @@ class LRFinder():
                 "lr": lr,
             })
 
-            self.model.load_state_dict(self.model_state)
-            self.optimizer.load_state_dict(self.optimizer_state)
+            # self.model.load_state_dict(self.model_state)
+            # self.optimizer.load_state_dict(self.optimizer_state)
 
             if loss > diverge_th * best_loss:
                 print("Stopping early, the loss has diverged")
@@ -127,12 +127,14 @@ class LRFinder():
 
         fig, ax_loss = plt.subplots()
 
-        ax_loss.plot(lrs, losses, color='red', label='Loss')
+        ax_loss.plot(lrs, losses, color='red')
         ax_loss.set_xscale("log")
         ax_loss.set_xlabel("Learning rate")
+        ax_loss.set_ylabel("Loss")
 
         # ax_acc = ax_loss.twinx()
-        # ax_acc.plot(lrs, accs, color='blue', label='Accuracy')
+        # ax_acc.plot(lrs, accs, color='blue')
+        # ax_acc.set_ylabel("Accuracy")
 
         print("LR suggestion: steepest gradient")
         min_grad_idx = None
diff --git a/training/optimization.py b/training/optimization.py
index 3809f3b..a0c8673 100644
--- a/training/optimization.py
+++ b/training/optimization.py
@@ -6,7 +6,7 @@ from diffusers.utils import logging
 logger = logging.get_logger(__name__)
 
 
-def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_lr=0.01, mid_point=0.4, last_epoch=-1):
+def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_lr=0.04, mid_point=0.3, last_epoch=-1):
     """
     Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
     a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
@@ -35,8 +35,12 @@ def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_l
 
             progress = float(num_training_steps - current_step) / float(max(1, num_training_steps - thresh_down))
             return max(0.0, progress) * min_lr
-        else:
-            progress = float(current_step - thresh_up) / float(max(1, num_training_steps - thresh_up))
+
+        progress = float(current_step - thresh_up) / float(max(1, num_training_steps - thresh_up))
+
+        if annealing == "half_cos":
             return max(0.0, 1.0 + math.cos(math.pi * (0.5 + 0.5 * progress)))
 
+        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
+
     return LambdaLR(optimizer, lr_lambda, last_epoch)