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Update .pre-commit-config.yaml (#2019)

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* Update .pre-commit-config.yaml

* Update __init__.py

* Update .pre-commit-config.yaml

* Precommit updates
This commit is contained in:
Glenn Jocher
2023-02-17 21:52:12 +01:00
committed by GitHub
parent a0a4012739
commit 527ce02916
39 changed files with 383 additions and 386 deletions
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segment/predict.py
+1 -1
View File
@@ -279,6 +279,6 @@ def main(opt):
run(**vars(opt))
if __name__ == "__main__":
if __name__ == '__main__':
opt = parse_opt()
main(opt)
segment/train.py
+7 -7
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@@ -138,7 +138,7 @@ 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
@@ -340,10 +340,10 @@ 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
@@ -453,8 +453,8 @@ 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
@@ -547,7 +547,7 @@ 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:
@@ -654,6 +654,6 @@ def run(**kwargs):
return opt
if __name__ == "__main__":
if __name__ == '__main__':
opt = parse_opt()
main(opt)
segment/tutorial.ipynb
Vendored
+1 -1
View File
@@ -591,4 +591,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
segment/val.py
+8 -8
View File
@@ -70,8 +70,8 @@ def save_one_json(predn, jdict, path, class_map, pred_masks):
from pycocotools.mask import encode
def single_encode(x):
rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
rle["counts"] = rle["counts"].decode("utf-8")
rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0]
rle['counts'] = rle['counts'].decode('utf-8')
return rle
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
@@ -105,7 +105,7 @@ def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, over
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
if gt_masks.shape[1:] != pred_masks.shape[1:]:
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0]
gt_masks = gt_masks.gt_(0.5)
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
else: # boxes
@@ -231,8 +231,8 @@ def run(
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P", "R",
"mAP50", "mAP50-95)")
s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R',
'mAP50', 'mAP50-95)')
dt = Profile(), Profile(), Profile()
metrics = Metrics()
loss = torch.zeros(4, device=device)
@@ -343,7 +343,7 @@ def run(
# Print results
pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
LOGGER.info(pf % ('all', seen, nt.sum(), *metrics.mean_results()))
if nt.sum() == 0:
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
@@ -369,7 +369,7 @@ def run(
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
pred_json = str(save_dir / f"{w}_predictions.json") # predictions
pred_json = str(save_dir / f'{w}_predictions.json') # predictions
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)
@@ -468,6 +468,6 @@ def main(opt):
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
if __name__ == "__main__":
if __name__ == '__main__':
opt = parse_opt()
main(opt)