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

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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
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44
ultralytics/yolo/v8/detect/train.py
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@ -20,7 +20,7 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
# BaseTrainer python usage
class DetectionTrainer(BaseTrainer):
def get_dataloader(self, dataset_path, batch_size, mode="train", rank=0):
def get_dataloader(self, dataset_path, batch_size, mode='train', rank=0):
# TODO: manage splits differently
# calculate stride - check if model is initialized
gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
@ -29,21 +29,21 @@ class DetectionTrainer(BaseTrainer):
batch_size=batch_size,
stride=gs,
hyp=vars(self.args),
augment=mode == "train",
augment=mode == 'train',
cache=self.args.cache,
pad=0 if mode == "train" else 0.5,
rect=self.args.rect or mode == "val",
pad=0 if mode == 'train' else 0.5,
rect=self.args.rect or mode == 'val',
rank=rank,
workers=self.args.workers,
close_mosaic=self.args.close_mosaic != 0,
prefix=colorstr(f'{mode}: '),
shuffle=mode == "train",
shuffle=mode == 'train',
seed=self.args.seed)[0] if self.args.v5loader else \
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
rect=mode == "val", names=self.data['names'])[0]
rect=mode == 'val', names=self.data['names'])[0]
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
batch['img'] = batch['img'].to(self.device, non_blocking=True).float() / 255
return batch
def set_model_attributes(self):
@ -51,13 +51,13 @@ class DetectionTrainer(BaseTrainer):
# self.args.box *= 3 / nl # scale to layers
# self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
# self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
self.model.nc = self.data["nc"] # attach number of classes to model
self.model.names = self.data["names"] # attach class names to model
self.model.nc = self.data['nc'] # attach number of classes to model
self.model.names = self.data['names'] # attach class names to model
self.model.args = self.args # attach hyperparameters to model
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
def get_model(self, cfg=None, weights=None, verbose=True):
model = DetectionModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
model = DetectionModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
if weights:
model.load(weights)
@ -75,12 +75,12 @@ class DetectionTrainer(BaseTrainer):
self.compute_loss = Loss(de_parallel(self.model))
return self.compute_loss(preds, batch)
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 not None:
loss_items = [round(float(x), 5) for x in loss_items] # convert tensors to 5 decimal place floats
return dict(zip(keys, loss_items))
@ -92,12 +92,12 @@ class DetectionTrainer(BaseTrainer):
(4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
def plot_training_samples(self, batch, ni):
plot_images(images=batch["img"],
batch_idx=batch["batch_idx"],
cls=batch["cls"].squeeze(-1),
bboxes=batch["bboxes"],
paths=batch["im_file"],
fname=self.save_dir / f"train_batch{ni}.jpg")
plot_images(images=batch['img'],
batch_idx=batch['batch_idx'],
cls=batch['cls'].squeeze(-1),
bboxes=batch['bboxes'],
paths=batch['im_file'],
fname=self.save_dir / f'train_batch{ni}.jpg')
def plot_metrics(self):
plot_results(file=self.csv) # save results.png
@ -169,7 +169,7 @@ class Loss:
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
# targets
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
@ -201,8 +201,8 @@ class Loss:
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or "yolov8n.pt"
data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
model = cfg.model or 'yolov8n.pt'
data = cfg.data or 'coco128.yaml' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
@ -214,5 +214,5 @@ def train(cfg=DEFAULT_CFG, use_python=False):
trainer.train()
if __name__ == "__main__":
if __name__ == '__main__':
train()