Update .pre-commit-config.yaml (#1026)
This commit is contained in:
Glenn Jocher
GitHub
@ -2,4 +2,4 @@
|
||||
|
||||
from ultralytics.yolo.v8 import classify, detect, segment
|
||||
|
||||
__all__ = ["classify", "segment", "detect"]
|
||||
__all__ = ['classify', 'segment', 'detect']
|
||||
|
||||
@ -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']
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -4,4 +4,4 @@ from .predict import DetectionPredictor, predict
|
||||
from .train import DetectionTrainer, train
|
||||
from .val import DetectionValidator, val
|
||||
|
||||
__all__ = ["DetectionPredictor", "predict", "DetectionTrainer", "train", "DetectionValidator", "val"]
|
||||
__all__ = ['DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val']
|
||||
|
||||
@ -37,7 +37,7 @@ class DetectionPredictor(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
|
||||
@ -69,7 +69,7 @@ class DetectionPredictor(BasePredictor):
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
name = f"id:{int(d.id.item())} {self.model.names[c]}" if d.id is not None else self.model.names[c]
|
||||
name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]
|
||||
label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')
|
||||
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
|
||||
if self.args.save_crop:
|
||||
@ -82,9 +82,9 @@ class DetectionPredictor(BasePredictor):
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
model = cfg.model or "yolov8n.pt"
|
||||
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.pt'
|
||||
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:
|
||||
@ -95,5 +95,5 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -28,13 +28,13 @@ class DetectionValidator(BaseValidator):
|
||||
self.niou = self.iouv.numel()
|
||||
|
||||
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()) / 255
|
||||
for k in ["batch_idx", "cls", "bboxes"]:
|
||||
batch['img'] = batch['img'].to(self.device, non_blocking=True)
|
||||
batch['img'] = (batch['img'].half() if self.args.half else batch['img'].float()) / 255
|
||||
for k in ['batch_idx', 'cls', 'bboxes']:
|
||||
batch[k] = batch[k].to(self.device)
|
||||
|
||||
nb = len(batch["img"])
|
||||
self.lb = [torch.cat([batch["cls"], batch["bboxes"]], dim=-1)[batch["batch_idx"] == i]
|
||||
nb = len(batch['img'])
|
||||
self.lb = [torch.cat([batch['cls'], batch['bboxes']], dim=-1)[batch['batch_idx'] == i]
|
||||
for i in range(nb)] if self.args.save_hybrid else [] # for autolabelling
|
||||
|
||||
return batch
|
||||
@ -54,7 +54,7 @@ class DetectionValidator(BaseValidator):
|
||||
self.stats = []
|
||||
|
||||
def get_desc(self):
|
||||
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
|
||||
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)')
|
||||
|
||||
def postprocess(self, preds):
|
||||
preds = ops.non_max_suppression(preds,
|
||||
@ -69,11 +69,11 @@ class DetectionValidator(BaseValidator):
|
||||
def update_metrics(self, preds, batch):
|
||||
# Metrics
|
||||
for si, pred in enumerate(preds):
|
||||
idx = batch["batch_idx"] == si
|
||||
cls = batch["cls"][idx]
|
||||
bbox = batch["bboxes"][idx]
|
||||
idx = batch['batch_idx'] == si
|
||||
cls = batch['cls'][idx]
|
||||
bbox = batch['bboxes'][idx]
|
||||
nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions
|
||||
shape = batch["ori_shape"][si]
|
||||
shape = batch['ori_shape'][si]
|
||||
correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
|
||||
self.seen += 1
|
||||
|
||||
@ -88,16 +88,16 @@ class DetectionValidator(BaseValidator):
|
||||
if self.args.single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape,
|
||||
ratio_pad=batch["ratio_pad"][si]) # native-space pred
|
||||
ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape,
|
||||
ratio_pad=batch['ratio_pad'][si]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
height, width = batch["img"].shape[2:]
|
||||
height, width = batch['img'].shape[2:]
|
||||
tbox = ops.xywh2xyxy(bbox) * torch.tensor(
|
||||
(width, height, width, height), device=self.device) # target boxes
|
||||
ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape,
|
||||
ratio_pad=batch["ratio_pad"][si]) # native-space labels
|
||||
ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape,
|
||||
ratio_pad=batch['ratio_pad'][si]) # native-space labels
|
||||
labelsn = torch.cat((cls, tbox), 1) # native-space labels
|
||||
correct_bboxes = self._process_batch(predn, labelsn)
|
||||
# TODO: maybe remove these `self.` arguments as they already are member variable
|
||||
@ -107,7 +107,7 @@ class DetectionValidator(BaseValidator):
|
||||
|
||||
# Save
|
||||
if self.args.save_json:
|
||||
self.pred_to_json(predn, batch["im_file"][si])
|
||||
self.pred_to_json(predn, batch['im_file'][si])
|
||||
# if self.args.save_txt:
|
||||
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
|
||||
|
||||
@ -120,7 +120,7 @@ class DetectionValidator(BaseValidator):
|
||||
|
||||
def print_results(self):
|
||||
pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metrics.keys) # print format
|
||||
self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
|
||||
self.logger.info(pf % ('all', self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
|
||||
if self.nt_per_class.sum() == 0:
|
||||
self.logger.warning(
|
||||
f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
|
||||
@ -175,21 +175,21 @@ class DetectionValidator(BaseValidator):
|
||||
shuffle=False,
|
||||
seed=self.args.seed)[0] if self.args.v5loader else \
|
||||
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, names=self.data['names'],
|
||||
mode="val")[0]
|
||||
mode='val')[0]
|
||||
|
||||
def plot_val_samples(self, batch, ni):
|
||||
plot_images(batch["img"],
|
||||
batch["batch_idx"],
|
||||
batch["cls"].squeeze(-1),
|
||||
batch["bboxes"],
|
||||
paths=batch["im_file"],
|
||||
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
|
||||
plot_images(batch['img'],
|
||||
batch['batch_idx'],
|
||||
batch['cls'].squeeze(-1),
|
||||
batch['bboxes'],
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_labels.jpg',
|
||||
names=self.names)
|
||||
|
||||
def plot_predictions(self, batch, preds, ni):
|
||||
plot_images(batch["img"],
|
||||
plot_images(batch['img'],
|
||||
*output_to_target(preds, max_det=15),
|
||||
paths=batch["im_file"],
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
|
||||
names=self.names) # pred
|
||||
|
||||
@ -207,8 +207,8 @@ class DetectionValidator(BaseValidator):
|
||||
|
||||
def eval_json(self, stats):
|
||||
if self.args.save_json and self.is_coco and len(self.jdict):
|
||||
anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations
|
||||
pred_json = self.save_dir / "predictions.json" # predictions
|
||||
anno_json = self.data['path'] / 'annotations/instances_val2017.json' # annotations
|
||||
pred_json = self.save_dir / 'predictions.json' # predictions
|
||||
self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
check_requirements('pycocotools>=2.0.6')
|
||||
@ -216,7 +216,7 @@ class DetectionValidator(BaseValidator):
|
||||
from pycocotools.cocoeval import COCOeval # noqa
|
||||
|
||||
for x in anno_json, pred_json:
|
||||
assert x.is_file(), f"{x} file not found"
|
||||
assert x.is_file(), f'{x} file not found'
|
||||
anno = COCO(str(anno_json)) # init annotations api
|
||||
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
|
||||
eval = COCOeval(anno, pred, 'bbox')
|
||||
@ -232,8 +232,8 @@ class DetectionValidator(BaseValidator):
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
model = cfg.model or "yolov8n.pt"
|
||||
data = cfg.data or "coco128.yaml"
|
||||
model = cfg.model or 'yolov8n.pt'
|
||||
data = cfg.data or 'coco128.yaml'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
@ -244,5 +244,5 @@ def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
val()
|
||||
|
||||
@ -4,4 +4,4 @@ from .predict import SegmentationPredictor, predict
|
||||
from .train import SegmentationTrainer, train
|
||||
from .val import SegmentationValidator, val
|
||||
|
||||
__all__ = ["SegmentationPredictor", "predict", "SegmentationTrainer", "train", "SegmentationValidator", "val"]
|
||||
__all__ = ['SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val']
|
||||
|
||||
@ -39,7 +39,7 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
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
|
||||
@ -84,7 +84,7 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
name = f"id:{int(d.id.item())} {self.model.names[c]}" if d.id is not None else self.model.names[c]
|
||||
name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]
|
||||
label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')
|
||||
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.boxes else None
|
||||
if self.args.save_crop:
|
||||
@ -97,9 +97,9 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
|
||||
def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
model = cfg.model or "yolov8n-seg.pt"
|
||||
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-seg.pt'
|
||||
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:
|
||||
@ -110,5 +110,5 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
predict()
|
||||
|
||||
@ -20,11 +20,11 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
if overrides is None:
|
||||
overrides = {}
|
||||
overrides["task"] = "segment"
|
||||
overrides['task'] = 'segment'
|
||||
super().__init__(cfg, overrides)
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
|
||||
model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
||||
if weights:
|
||||
model.load(weights)
|
||||
|
||||
@ -43,13 +43,13 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
|
||||
return self.compute_loss(preds, batch)
|
||||
|
||||
def plot_training_samples(self, batch, ni):
|
||||
images = batch["img"]
|
||||
masks = batch["masks"]
|
||||
cls = batch["cls"].squeeze(-1)
|
||||
bboxes = batch["bboxes"]
|
||||
paths = batch["im_file"]
|
||||
batch_idx = batch["batch_idx"]
|
||||
plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg")
|
||||
images = batch['img']
|
||||
masks = batch['masks']
|
||||
cls = batch['cls'].squeeze(-1)
|
||||
bboxes = batch['bboxes']
|
||||
paths = batch['im_file']
|
||||
batch_idx = batch['batch_idx']
|
||||
plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f'train_batch{ni}.jpg')
|
||||
|
||||
def plot_metrics(self):
|
||||
plot_results(file=self.csv, segment=True) # save results.png
|
||||
@ -80,15 +80,15 @@ class SegLoss(Loss):
|
||||
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
|
||||
|
||||
# targets
|
||||
batch_idx = batch["batch_idx"].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, 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)
|
||||
|
||||
masks = batch["masks"].to(self.device).float()
|
||||
masks = batch['masks'].to(self.device).float()
|
||||
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
|
||||
masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
|
||||
masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
|
||||
|
||||
# pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
@ -135,13 +135,13 @@ class SegLoss(Loss):
|
||||
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
|
||||
# Mask loss for one image
|
||||
pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80)
|
||||
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
|
||||
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
|
||||
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
|
||||
|
||||
|
||||
def train(cfg=DEFAULT_CFG, use_python=False):
|
||||
model = cfg.model or "yolov8n-seg.pt"
|
||||
data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
|
||||
model = cfg.model or 'yolov8n-seg.pt'
|
||||
data = cfg.data or 'coco128-seg.yaml' # 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()
|
||||
|
||||
@ -24,7 +24,7 @@ class SegmentationValidator(DetectionValidator):
|
||||
|
||||
def preprocess(self, batch):
|
||||
batch = super().preprocess(batch)
|
||||
batch["masks"] = batch["masks"].to(self.device).float()
|
||||
batch['masks'] = batch['masks'].to(self.device).float()
|
||||
return batch
|
||||
|
||||
def init_metrics(self, model):
|
||||
@ -37,8 +37,8 @@ class SegmentationValidator(DetectionValidator):
|
||||
self.process = ops.process_mask # faster
|
||||
|
||||
def get_desc(self):
|
||||
return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
|
||||
"R", "mAP50", "mAP50-95)")
|
||||
return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P',
|
||||
'R', 'mAP50', 'mAP50-95)')
|
||||
|
||||
def postprocess(self, preds):
|
||||
p = ops.non_max_suppression(preds[0],
|
||||
@ -55,11 +55,11 @@ class SegmentationValidator(DetectionValidator):
|
||||
def update_metrics(self, preds, batch):
|
||||
# Metrics
|
||||
for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
|
||||
idx = batch["batch_idx"] == si
|
||||
cls = batch["cls"][idx]
|
||||
bbox = batch["bboxes"][idx]
|
||||
idx = batch['batch_idx'] == si
|
||||
cls = batch['cls'][idx]
|
||||
bbox = batch['bboxes'][idx]
|
||||
nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions
|
||||
shape = batch["ori_shape"][si]
|
||||
shape = batch['ori_shape'][si]
|
||||
correct_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
|
||||
correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
|
||||
self.seen += 1
|
||||
@ -74,23 +74,23 @@ class SegmentationValidator(DetectionValidator):
|
||||
|
||||
# Masks
|
||||
midx = [si] if self.args.overlap_mask else idx
|
||||
gt_masks = batch["masks"][midx]
|
||||
pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:])
|
||||
gt_masks = batch['masks'][midx]
|
||||
pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch['img'][si].shape[1:])
|
||||
|
||||
# Predictions
|
||||
if self.args.single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape,
|
||||
ratio_pad=batch["ratio_pad"][si]) # native-space pred
|
||||
ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape,
|
||||
ratio_pad=batch['ratio_pad'][si]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
height, width = batch["img"].shape[2:]
|
||||
height, width = batch['img'].shape[2:]
|
||||
tbox = ops.xywh2xyxy(bbox) * torch.tensor(
|
||||
(width, height, width, height), device=self.device) # target boxes
|
||||
ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape,
|
||||
ratio_pad=batch["ratio_pad"][si]) # native-space labels
|
||||
ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape,
|
||||
ratio_pad=batch['ratio_pad'][si]) # native-space labels
|
||||
labelsn = torch.cat((cls, tbox), 1) # native-space labels
|
||||
correct_bboxes = self._process_batch(predn, labelsn)
|
||||
# TODO: maybe remove these `self.` arguments as they already are member variable
|
||||
@ -112,11 +112,11 @@ class SegmentationValidator(DetectionValidator):
|
||||
|
||||
# Save
|
||||
if self.args.save_json:
|
||||
pred_masks = ops.scale_image(batch["img"][si].shape[1:],
|
||||
pred_masks = ops.scale_image(batch['img'][si].shape[1:],
|
||||
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
|
||||
shape,
|
||||
ratio_pad=batch["ratio_pad"][si])
|
||||
self.pred_to_json(predn, batch["im_file"][si], pred_masks)
|
||||
ratio_pad=batch['ratio_pad'][si])
|
||||
self.pred_to_json(predn, batch['im_file'][si], pred_masks)
|
||||
# if self.args.save_txt:
|
||||
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
|
||||
|
||||
@ -136,7 +136,7 @@ class SegmentationValidator(DetectionValidator):
|
||||
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
|
||||
@ -158,20 +158,20 @@ class SegmentationValidator(DetectionValidator):
|
||||
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
|
||||
|
||||
def plot_val_samples(self, batch, ni):
|
||||
plot_images(batch["img"],
|
||||
batch["batch_idx"],
|
||||
batch["cls"].squeeze(-1),
|
||||
batch["bboxes"],
|
||||
batch["masks"],
|
||||
paths=batch["im_file"],
|
||||
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
|
||||
plot_images(batch['img'],
|
||||
batch['batch_idx'],
|
||||
batch['cls'].squeeze(-1),
|
||||
batch['bboxes'],
|
||||
batch['masks'],
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_labels.jpg',
|
||||
names=self.names)
|
||||
|
||||
def plot_predictions(self, batch, preds, ni):
|
||||
plot_images(batch["img"],
|
||||
plot_images(batch['img'],
|
||||
*output_to_target(preds[0], max_det=15),
|
||||
torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
|
||||
paths=batch["im_file"],
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
|
||||
names=self.names) # pred
|
||||
self.plot_masks.clear()
|
||||
@ -182,8 +182,8 @@ class SegmentationValidator(DetectionValidator):
|
||||
from pycocotools.mask import encode # noqa
|
||||
|
||||
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
|
||||
|
||||
stem = Path(filename).stem
|
||||
@ -203,8 +203,8 @@ class SegmentationValidator(DetectionValidator):
|
||||
|
||||
def eval_json(self, stats):
|
||||
if self.args.save_json and self.is_coco and len(self.jdict):
|
||||
anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations
|
||||
pred_json = self.save_dir / "predictions.json" # predictions
|
||||
anno_json = self.data['path'] / 'annotations/instances_val2017.json' # annotations
|
||||
pred_json = self.save_dir / 'predictions.json' # predictions
|
||||
self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
check_requirements('pycocotools>=2.0.6')
|
||||
@ -212,7 +212,7 @@ class SegmentationValidator(DetectionValidator):
|
||||
from pycocotools.cocoeval import COCOeval # noqa
|
||||
|
||||
for x in anno_json, pred_json:
|
||||
assert x.is_file(), f"{x} file not found"
|
||||
assert x.is_file(), f'{x} file not found'
|
||||
anno = COCO(str(anno_json)) # init annotations api
|
||||
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
|
||||
for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm')]):
|
||||
@ -231,8 +231,8 @@ class SegmentationValidator(DetectionValidator):
|
||||
|
||||
|
||||
def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
model = cfg.model or "yolov8n-seg.pt"
|
||||
data = cfg.data or "coco128-seg.yaml"
|
||||
model = cfg.model or 'yolov8n-seg.pt'
|
||||
data = cfg.data or 'coco128-seg.yaml'
|
||||
|
||||
args = dict(model=model, data=data)
|
||||
if use_python:
|
||||
@ -243,5 +243,5 @@ def val(cfg=DEFAULT_CFG, use_python=False):
|
||||
validator(model=args['model'])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
val()
|
||||
|
||||
Reference in New Issue
Block a user