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yolov5
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utils.general comment updates/bug fixes

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Glenn Jocher 5 years ago
parent 4949401a94
commit 9f482cbcb8
4 changed files with 16 additions and 16 deletions
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2
Dockerfile
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@ -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

2
detect.py
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@ -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))

4
tutorial.ipynb vendored
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@ -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": [

24
utils/general.py
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@ -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()

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