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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>
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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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segment/train.py
+7 -7
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@@ -139,7 +139,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# Batch size
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
batch_size = check_train_batch_size(model, imgsz, amp)
logger.update_params({"batch_size": batch_size})
logger.update_params({'batch_size': batch_size})
# loggers.on_params_update({"batch_size": batch_size})
# Optimizer
@@ -341,10 +341,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# Mosaic plots
if plots:
if ni < 3:
plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")
plot_images_and_masks(imgs, targets, masks, paths, save_dir / f'train_batch{ni}.jpg')
if ni == 10:
files = sorted(save_dir.glob('train*.jpg'))
logger.log_images(files, "Mosaics", epoch)
logger.log_images(files, 'Mosaics', epoch)
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
@@ -454,8 +454,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
logger.log_images(files, "Results", epoch + 1)
logger.log_images(sorted(save_dir.glob('val*.jpg')), "Validation", epoch + 1)
logger.log_images(files, 'Results', epoch + 1)
logger.log_images(sorted(save_dir.glob('val*.jpg')), 'Validation', epoch + 1)
torch.cuda.empty_cache()
return results
@@ -548,7 +548,7 @@ def main(opt, callbacks=Callbacks()):
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')
# Train
if not opt.evolve:
@@ -659,6 +659,6 @@ def run(**kwargs):
return opt
if __name__ == "__main__":
if __name__ == '__main__':
opt = parse_opt()
main(opt)