From 54d72ba4a8331d822a48bad9e381b47d39598125 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Wed, 28 Dec 2022 21:00:34 +0100
Subject: Updated 1-cycle scheduler
---
training/lr.py | 14 ++++++++------
training/optimization.py | 10 +++++++---
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)
--
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