xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update .pre-commit-config.yaml (#1026)

Browse Source
This commit is contained in:
Glenn Jocher
2023-02-17 22:26:40 +01:00
committed by GitHub
parent 9047d737f4
commit edd3ff1669
76 changed files with 928 additions and 935 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
ultralytics/yolo/v8/classify/__init__.py
View File

@ -4,4 +4,4 @@ from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predic
from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
from ultralytics.yolo.v8.classify.val import ClassificationValidator, val
__all__ = ["ClassificationPredictor", "predict", "ClassificationTrainer", "train", "ClassificationValidator", "val"]
__all__ = ['ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val']

10
ultralytics/yolo/v8/classify/predict.py
View File

@ -28,7 +28,7 @@ class ClassificationPredictor(BasePredictor):
def write_results(self, idx, results, batch):
p, im, im0 = batch
log_string = ""
log_string = ''
if len(im.shape) == 3:
im = im[None] # expand for batch dim
self.seen += 1
@ -65,9 +65,9 @@ class ClassificationPredictor(BasePredictor):
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
else "https://ultralytics.com/images/bus.jpg"
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
args = dict(model=model, source=source)
if use_python:
@ -78,5 +78,5 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
predictor.predict_cli()
if __name__ == "__main__":
if __name__ == '__main__':
predict()

34
ultralytics/yolo/v8/classify/train.py
View File

@ -16,14 +16,14 @@ class ClassificationTrainer(BaseTrainer):
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
if overrides is None:
overrides = {}
overrides["task"] = "classify"
overrides['task'] = 'classify'
super().__init__(cfg, overrides)
def set_model_attributes(self):
self.model.names = self.data["names"]
self.model.names = self.data['names']
def get_model(self, cfg=None, weights=None, verbose=True):
model = ClassificationModel(cfg, nc=self.data["nc"], verbose=verbose and RANK == -1)
model = ClassificationModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
if weights:
model.load(weights)
@ -53,11 +53,11 @@ class ClassificationTrainer(BaseTrainer):
model = str(self.model)
# Load a YOLO model locally, from torchvision, or from Ultralytics assets
if model.endswith(".pt"):
if model.endswith('.pt'):
self.model, _ = attempt_load_one_weight(model, device='cpu')
for p in self.model.parameters():
p.requires_grad = True # for training
elif model.endswith(".yaml"):
elif model.endswith('.yaml'):
self.model = self.get_model(cfg=model)
elif model in torchvision.models.__dict__:
pretrained = True
@ -67,15 +67,15 @@ class ClassificationTrainer(BaseTrainer):
return # dont return ckpt. Classification doesn't support resume
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
loader = build_classification_dataloader(path=dataset_path,
imgsz=self.args.imgsz,
batch_size=batch_size if mode == "train" else (batch_size * 2),
augment=mode == "train",
batch_size=batch_size if mode == 'train' else (batch_size * 2),
augment=mode == 'train',
rank=rank,
workers=self.args.workers)
# Attach inference transforms
if mode != "train":
if mode != 'train':
if is_parallel(self.model):
self.model.module.transforms = loader.dataset.torch_transforms
else:
@ -83,8 +83,8 @@ class ClassificationTrainer(BaseTrainer):
return loader
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device)
batch["cls"] = batch["cls"].to(self.device)
batch['img'] = batch['img'].to(self.device)
batch['cls'] = batch['cls'].to(self.device)
return batch
def progress_string(self):
@ -96,7 +96,7 @@ class ClassificationTrainer(BaseTrainer):
return v8.classify.ClassificationValidator(self.test_loader, self.save_dir, logger=self.console)
def criterion(self, preds, batch):
loss = torch.nn.functional.cross_entropy(preds, batch["cls"], reduction='sum') / self.args.nbs
loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / self.args.nbs
loss_items = loss.detach()
return loss, loss_items
@ -112,12 +112,12 @@ class ClassificationTrainer(BaseTrainer):
# else:
# return keys
def label_loss_items(self, loss_items=None, prefix="train"):
def label_loss_items(self, loss_items=None, prefix='train'):
"""
Returns a loss dict with labelled training loss items tensor
"""
# Not needed for classification but necessary for segmentation & detection
keys = [f"{prefix}/{x}" for x in self.loss_names]
keys = [f'{prefix}/{x}' for x in self.loss_names]
if loss_items is None:
return keys
loss_items = [round(float(loss_items), 5)]
@ -140,8 +140,8 @@ class ClassificationTrainer(BaseTrainer):
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist")
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
data = cfg.data or 'mnist160' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
@ -153,5 +153,5 @@ def train(cfg=DEFAULT_CFG, use_python=False):
trainer.train()
if __name__ == "__main__":
if __name__ == '__main__':
train()

16
ultralytics/yolo/v8/classify/val.py
View File

@ -21,14 +21,14 @@ class ClassificationValidator(BaseValidator):
self.targets = []
def preprocess(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True)
batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
batch["cls"] = batch["cls"].to(self.device)
batch['img'] = batch['img'].to(self.device, non_blocking=True)
batch['img'] = batch['img'].half() if self.args.half else batch['img'].float()
batch['cls'] = batch['cls'].to(self.device)
return batch
def update_metrics(self, preds, batch):
self.pred.append(preds.argsort(1, descending=True)[:, :5])
self.targets.append(batch["cls"])
self.targets.append(batch['cls'])
def get_stats(self):
self.metrics.process(self.targets, self.pred)
@ -42,12 +42,12 @@ class ClassificationValidator(BaseValidator):
def print_results(self):
pf = '%22s' + '%11.3g' * len(self.metrics.keys) # print format
self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5))
self.logger.info(pf % ('all', self.metrics.top1, self.metrics.top5))
def val(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
data = cfg.data or "mnist160"
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
data = cfg.data or 'mnist160'
args = dict(model=model, data=data)
if use_python:
@ -58,5 +58,5 @@ def val(cfg=DEFAULT_CFG, use_python=False):
validator(model=args['model'])
if __name__ == "__main__":
if __name__ == '__main__':
val()