Update

1 files changed, 31 insertions, 2 deletions

diff --git a/training/lr.py b/training/lr.py
index 3abd2f2..fe166ed 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -1,5 +1,6 @@
 import math
 import copy
+from typing import Callable
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,19 +11,45 @@ from tqdm.auto import tqdm
 from training.util import AverageMeter
 
 
+def noop():
+    pass
+
+
 class LRFinder():
-    def __init__(self, accelerator, model, optimizer, train_dataloader, val_dataloader, loss_fn):
+    def __init__(
+        self,
+        accelerator,
+        model,
+        optimizer,
+        train_dataloader,
+        val_dataloader,
+        loss_fn,
+        on_train: Callable[[], None] = noop,
+        on_eval: Callable[[], None] = noop
+    ):
         self.accelerator = accelerator
         self.model = model
         self.optimizer = optimizer
         self.train_dataloader = train_dataloader
         self.val_dataloader = val_dataloader
         self.loss_fn = loss_fn
+        self.on_train = on_train
+        self.on_eval = on_eval
 
         # 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):
+    def run(
+        self,
+        min_lr,
+        skip_start: int = 10,
+        skip_end: int = 5,
+        num_epochs: int = 100,
+        num_train_batches: int = 1,
+        num_val_batches: int = math.inf,
+        smooth_f: float = 0.05,
+        diverge_th: int = 5
+    ):
         best_loss = None
         best_acc = None
 
@@ -50,6 +77,7 @@ class LRFinder():
             avg_acc = AverageMeter()
 
             self.model.train()
+            self.on_train()
 
             for step, batch in enumerate(self.train_dataloader):
                 if step >= num_train_batches:
@@ -67,6 +95,7 @@ class LRFinder():
                     progress_bar.update(1)
 
             self.model.eval()
+            self.on_eval()
 
             with torch.inference_mode():
                 for step, batch in enumerate(self.val_dataloader):