utils.general comment updates/bug fixes

Dockerfile

@@ -43,7 +43,7 @@ COPY . /usr/src/app
 # sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
 
 # Send weights to GCP
-# python -c "from utils.utils import *; strip_optimizer('runs/exp0/weights/last.pt', 'temp.pt')" && gsutil cp temp.pt gs://*
+# python -c "from utils.general import *; strip_optimizer('runs/exp0_*/weights/last.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*
 
 # Clean up
 # docker system prune -a --volumes

detect.py

@@ -138,7 +138,7 @@ def detect(save_img=False):
 
     if save_txt or save_img:
         print('Results saved to %s' % Path(out))
-        if platform == 'darwin' and not opt.update:  # MacOS
+        if platform.system() == 'Darwin' and not opt.update:  # MacOS
             os.system('open ' + save_path)
 
     print('Done. (%.3fs)' % (time.time() - t0))

tutorial.ipynb

@@ -622,7 +622,7 @@
         "colab_type": "text"
       },
       "source": [
-        "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)."
+        "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.general import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)."
       ]
     },
     {
@@ -637,7 +637,7 @@
         "outputId": "c1146425-643e-49ab-de25-73216f0dde23"
       },
       "source": [
-        "from utils.utils import plot_results; plot_results()  # plot results.txt files as results.png"
+        "from utils.general import plot_results; plot_results()  # plot results.txt files as results.png"
       ],
       "execution_count": null,
       "outputs": [

utils/general.py

@@ -670,7 +670,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False,
     return output
 
 
-def strip_optimizer(f='weights/best.pt', s=''):  # from utils.utils import *; strip_optimizer()
+def strip_optimizer(f='weights/best.pt', s=''):  # from utils.general import *; strip_optimizer()
     # Strip optimizer from 'f' to finalize training, optionally save as 's'
     x = torch.load(f, map_location=torch.device('cpu'))
     x['optimizer'] = None
@@ -695,7 +695,7 @@ def coco_class_count(path='../coco/labels/train2014/'):
         print(i, len(files))
 
 
-def coco_only_people(path='../coco/labels/train2017/'):  # from utils.utils import *; coco_only_people()
+def coco_only_people(path='../coco/labels/train2017/'):  # from utils.general import *; coco_only_people()
     # Find images with only people
     files = sorted(glob.glob('%s/*.*' % path))
     for i, file in enumerate(files):
@@ -704,7 +704,7 @@ def coco_only_people(path='../coco/labels/train2017/'):  # from utils.utils impo
             print(labels.shape[0], file)
 
 
-def crop_images_random(path='../images/', scale=0.50):  # from utils.utils import *; crop_images_random()
+def crop_images_random(path='../images/', scale=0.50):  # from utils.general import *; crop_images_random()
     # crops images into random squares up to scale fraction
     # WARNING: overwrites images!
     for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
@@ -728,7 +728,7 @@ def crop_images_random(path='../images/', scale=0.50):  # from utils.utils impor
 
 
 def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
-    # Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
+    # Makes single-class coco datasets. from utils.general import *; coco_single_class_labels()
     if os.path.exists('new/'):
         shutil.rmtree('new/')  # delete output folder
     os.makedirs('new/')  # make new output folder
@@ -763,7 +763,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
             k: kmeans evolved anchors
 
         Usage:
-            from utils.utils import *; _ = kmean_anchors()
+            from utils.general import *; _ = kmean_anchors()
     """
     thr = 1. / thr
 
@@ -986,7 +986,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
         cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
 
 
-def plot_wh_methods():  # from utils.utils import *; plot_wh_methods()
+def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
     # Compares the two methods for width-height anchor multiplication
     # https://github.com/ultralytics/yolov3/issues/168
     x = np.arange(-4.0, 4.0, .1)
@@ -1107,7 +1107,7 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
     plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
 
 
-def plot_test_txt():  # from utils.utils import *; plot_test()
+def plot_test_txt():  # from utils.general import *; plot_test()
     # Plot test.txt histograms
     x = np.loadtxt('test.txt', dtype=np.float32)
     box = xyxy2xywh(x[:, :4])
@@ -1124,7 +1124,7 @@ def plot_test_txt():  # from utils.utils import *; plot_test()
     plt.savefig('hist1d.png', dpi=200)
 
 
-def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
+def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
     # Plot targets.txt histograms
     x = np.loadtxt('targets.txt', dtype=np.float32).T
     s = ['x targets', 'y targets', 'width targets', 'height targets']
@@ -1137,7 +1137,7 @@ def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
     plt.savefig('targets.jpg', dpi=200)
 
 
-def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_study_txt()
+def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
     # Plot study.txt generated by test.py
     fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
     ax = ax.ravel()
@@ -1188,7 +1188,7 @@ def plot_labels(labels, save_dir=''):
     plt.close()
 
 
-def plot_evolution(yaml_file='runs/evolve/hyp_evolved.yaml'):  # from utils.utils import *; plot_evolution()
+def plot_evolution(yaml_file='runs/evolve/hyp_evolved.yaml'):  # from utils.general import *; plot_evolution()
     # Plot hyperparameter evolution results in evolve.txt
     with open(yaml_file) as f:
         hyp = yaml.load(f, Loader=yaml.FullLoader)
@@ -1212,7 +1212,7 @@ def plot_evolution(yaml_file='runs/evolve/hyp_evolved.yaml'):  # from utils.util
     print('\nPlot saved as evolve.png')
 
 
-def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()
+def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
     # Plot training 'results*.txt', overlaying train and val losses
     s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
     t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
@@ -1236,7 +1236,7 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
 
 
 def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
-                 save_dir=''):  # from utils.utils import *; plot_results()
+                 save_dir=''):  # from utils.general 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()