Add plot_results save location to log_dir

train.py

@@ -356,7 +356,7 @@ def train(hyp):
                 os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None  # upload
 
     if not opt.evolve:
-        plot_results()  # save as results.png
+        plot_results(save_dir = log_dir)  # save as results.png
     print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
     dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
     torch.cuda.empty_cache()

utils/utils.py

@@ -1154,7 +1154,7 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
         fig.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
-def plot_results(start=0, stop=0, bucket='', id=(), labels=()):  # from utils.utils import *; plot_results()
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir= '.'):  # from utils.utils import *; plot_results()
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()
@@ -1164,7 +1164,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=()):  # from utils.ut
         os.system('rm -rf storage.googleapis.com')
         files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
     else:
-        files = glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')
+        files = glob.glob(os.path.join(save_dir,'results*.txt')) + glob.glob('../../Downloads/results*.txt')
     for fi, f in enumerate(files):
         try:
             results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T