add save_dir arg to plot_lr_scheduler, default to current dir.

train.py

@@ -148,7 +148,7 @@ def train(hyp):
     scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
     scheduler.last_epoch = start_epoch - 1  # do not move
     # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
-    # plot_lr_scheduler(optimizer, scheduler, epochs)
+    plot_lr_scheduler(optimizer, scheduler, epochs, save_dir = log_dir)
 
     # Initialize distributed training
     if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():

utils/utils.py

@@ -1005,7 +1005,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
     return mosaic
 
 
-def plot_lr_scheduler(optimizer, scheduler, epochs=300):
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir='./'):
     # Plot LR simulating training for full epochs
     optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
     y = []