Ankle/yolov5_birdproject
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Update .pre-commit-config.yaml (#11009)

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* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update __init__.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Pre-commit updates

* Pre-commit updates

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocherandpre-commit-ci[bot] authored and GitHub committed 2023-02-18 01:06:24 +01:00
1 parent 34e1bc8ee3
commit 7a972e86c4
39 files changed
+389 -392
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classify/train.py
+13 -13
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@@ -78,7 +78,7 @@ def train(opt, device):
LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
t = time.time()
if str(data) == 'imagenet':
subprocess.run(["bash", str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True)
subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True)
else:
url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
download(url, dir=data_dir.parent)
@@ -220,11 +220,11 @@ def train(opt, device):
# Log
metrics = {
"train/loss": tloss,
f"{val}/loss": vloss,
"metrics/accuracy_top1": top1,
"metrics/accuracy_top5": top5,
"lr/0": optimizer.param_groups[0]['lr']} # learning rate
'train/loss': tloss,
f'{val}/loss': vloss,
'metrics/accuracy_top1': top1,
'metrics/accuracy_top5': top5,
'lr/0': optimizer.param_groups[0]['lr']} # learning rate
logger.log_metrics(metrics, epoch)
# Save model
@@ -251,11 +251,11 @@ def train(opt, device):
if RANK in {-1, 0} and final_epoch:
LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
f"\nResults saved to {colorstr('bold', save_dir)}"
f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
f"\nExport: python export.py --weights {best} --include onnx"
f'\nPredict: python classify/predict.py --weights {best} --source im.jpg'
f'\nValidate: python classify/val.py --weights {best} --data {data_dir}'
f'\nExport: python export.py --weights {best} --include onnx'
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
f"\nVisualize: https://netron.app\n")
f'\nVisualize: https://netron.app\n')
# Plot examples
images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
@@ -263,7 +263,7 @@ def train(opt, device):
file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
# Log results
meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()}
logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
logger.log_model(best, epochs, metadata=meta)
@@ -310,7 +310,7 @@ def main(opt):
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
torch.cuda.set_device(LOCAL_RANK)
device = torch.device('cuda', LOCAL_RANK)
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
# Parameters
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
@@ -328,6 +328,6 @@ def run(**kwargs):
return opt
if __name__ == "__main__":
if __name__ == '__main__':
opt = parse_opt()
main(opt)