Fixed LR finder

3 files changed, 26 insertions, 22 deletions

diff --git a/train_dreambooth.py b/train_dreambooth.py
index 1e49474..218018b 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -634,7 +634,7 @@ def main():
         )
 
     if args.find_lr:
-        args.learning_rate = 1e2
+        args.learning_rate = 1e-4
 
     # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
     if args.use_8bit_adam:
@@ -901,7 +901,7 @@ def main():
             on_train=lambda: tokenizer.set_use_vector_shuffle(args.vector_shuffle),
             on_eval=lambda: tokenizer.set_use_vector_shuffle(False)
         )
-        lr_finder.run(min_lr=1e-4)
+        lr_finder.run(end_lr=1e2)
 
         plt.savefig(basepath.joinpath("lr.png"))
         plt.close()
diff --git a/train_ti.py b/train_ti.py
index 2b3f017..102c0fa 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -584,7 +584,7 @@ def main():
         )
 
     if args.find_lr:
-        args.learning_rate = 1e2
+        args.learning_rate = 1e-4
 
     # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
     if args.use_8bit_adam:
@@ -853,9 +853,9 @@ def main():
             on_train=lambda: tokenizer.set_use_vector_shuffle(args.vector_shuffle),
             on_eval=lambda: tokenizer.set_use_vector_shuffle(False)
         )
-        lr_finder.run(min_lr=1e-4)
+        lr_finder.run(end_lr=1e2)
 
-        plt.savefig(basepath.joinpath("lr.png"))
+        plt.savefig(basepath.joinpath("lr.png"), dpi=300)
         plt.close()
 
         quit()
diff --git a/training/lr.py b/training/lr.py
index fe166ed..acc01a2 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -1,6 +1,7 @@
 import math
 import copy
 from typing import Callable
+from functools import partial
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -41,7 +42,7 @@ class LRFinder():
 
     def run(
         self,
-        min_lr,
+        end_lr,
         skip_start: int = 10,
         skip_end: int = 5,
         num_epochs: int = 100,
@@ -57,7 +58,7 @@ class LRFinder():
         losses = []
         accs = []
 
-        lr_scheduler = get_exponential_schedule(self.optimizer, min_lr, num_epochs)
+        lr_scheduler = get_exponential_schedule(self.optimizer, end_lr, num_epochs)
 
         steps = min(num_train_batches, len(self.train_dataloader))
         steps += min(num_val_batches, len(self.val_dataloader))
@@ -152,29 +153,30 @@ class LRFinder():
                 print("Stopping early, the loss has diverged")
                 break
 
-        if skip_end == 0:
-            lrs = lrs[skip_start:]
-            losses = losses[skip_start:]
-            accs = accs[skip_start:]
-        else:
-            lrs = lrs[skip_start:-skip_end]
-            losses = losses[skip_start:-skip_end]
-            accs = accs[skip_start:-skip_end]
-
         fig, ax_loss = plt.subplots()
+        ax_acc = ax_loss.twinx()
 
         ax_loss.plot(lrs, losses, color='red')
         ax_loss.set_xscale("log")
-        ax_loss.set_xlabel("Learning rate")
+        ax_loss.set_xlabel(f"Learning rate")
         ax_loss.set_ylabel("Loss")
 
-        ax_acc = ax_loss.twinx()
         ax_acc.plot(lrs, accs, color='blue')
+        ax_acc.set_xscale("log")
         ax_acc.set_ylabel("Accuracy")
 
         print("LR suggestion: steepest gradient")
         min_grad_idx = None
 
+        if skip_end == 0:
+            lrs = lrs[skip_start:]
+            losses = losses[skip_start:]
+            accs = accs[skip_start:]
+        else:
+            lrs = lrs[skip_start:-skip_end]
+            losses = losses[skip_start:-skip_end]
+            accs = accs[skip_start:-skip_end]
+
         try:
             min_grad_idx = (np.gradient(np.array(losses))).argmin()
         except ValueError:
@@ -196,8 +198,10 @@ class LRFinder():
             ax_loss.legend()
 
 
-def get_exponential_schedule(optimizer, min_lr, num_epochs, last_epoch=-1):
-    def lr_lambda(current_epoch: int):
-        return min_lr + ((current_epoch / num_epochs) ** 10) * (1 - min_lr)
+def get_exponential_schedule(optimizer, end_lr: float, num_epochs: int, last_epoch: int = -1):
+    def lr_lambda(base_lr: float, current_epoch: int):
+        return (end_lr / base_lr) ** (current_epoch / num_epochs)
+
+    lr_lambdas = [partial(lr_lambda, group["lr"]) for group in optimizer.param_groups]
 
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
+    return LambdaLR(optimizer, lr_lambdas, last_epoch)