Ruff format docstring Python code (#15792)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
c1882a4327
commit
d27664216b
63 changed files with 370 additions and 374 deletions
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@ -16,8 +16,8 @@ class FastSAM(Model):
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```python
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from ultralytics import FastSAM
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model = FastSAM('last.pt')
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results = model.predict('ultralytics/assets/bus.jpg')
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model = FastSAM("last.pt")
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results = model.predict("ultralytics/assets/bus.jpg")
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```
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"""
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@ -92,8 +92,8 @@ class FastSAMPredictor(SegmentationPredictor):
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if labels.sum() == 0 # all negative points
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else torch.zeros(len(result), dtype=torch.bool, device=self.device)
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)
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for p, l in zip(points, labels):
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point_idx[torch.nonzero(masks[:, p[1], p[0]], as_tuple=True)[0]] = True if l else False
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for point, label in zip(points, labels):
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point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = True if label else False
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idx |= point_idx
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if texts is not None:
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if isinstance(texts, str):
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@ -6,8 +6,8 @@ Example:
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```python
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from ultralytics import NAS
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model = NAS('yolo_nas_s')
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results = model.predict('ultralytics/assets/bus.jpg')
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model = NAS("yolo_nas_s")
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results = model.predict("ultralytics/assets/bus.jpg")
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```
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"""
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@ -34,8 +34,8 @@ class NAS(Model):
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```python
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from ultralytics import NAS
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model = NAS('yolo_nas_s')
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results = model.predict('ultralytics/assets/bus.jpg')
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model = NAS("yolo_nas_s")
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results = model.predict("ultralytics/assets/bus.jpg")
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```
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Attributes:
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@ -22,7 +22,7 @@ class NASPredictor(BasePredictor):
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```python
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from ultralytics import NAS
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model = NAS('yolo_nas_s')
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model = NAS("yolo_nas_s")
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predictor = model.predictor
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# Assumes that raw_preds, img, orig_imgs are available
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results = predictor.postprocess(raw_preds, img, orig_imgs)
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@ -24,7 +24,7 @@ class NASValidator(DetectionValidator):
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```python
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from ultralytics import NAS
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model = NAS('yolo_nas_s')
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model = NAS("yolo_nas_s")
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validator = model.validator
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# Assumes that raw_preds are available
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final_preds = validator.postprocess(raw_preds)
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@ -21,7 +21,7 @@ class RTDETRPredictor(BasePredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.rtdetr import RTDETRPredictor
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args = dict(model='rtdetr-l.pt', source=ASSETS)
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args = dict(model="rtdetr-l.pt", source=ASSETS)
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predictor = RTDETRPredictor(overrides=args)
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predictor.predict_cli()
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```
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@ -25,7 +25,7 @@ class RTDETRTrainer(DetectionTrainer):
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```python
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from ultralytics.models.rtdetr.train import RTDETRTrainer
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args = dict(model='rtdetr-l.yaml', data='coco8.yaml', imgsz=640, epochs=3)
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args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3)
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trainer = RTDETRTrainer(overrides=args)
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trainer.train()
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```
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@ -62,7 +62,7 @@ class RTDETRValidator(DetectionValidator):
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```python
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from ultralytics.models.rtdetr import RTDETRValidator
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args = dict(model='rtdetr-l.pt', data='coco8.yaml')
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args = dict(model="rtdetr-l.pt", data="coco8.yaml")
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validator = RTDETRValidator(args=args)
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validator()
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```
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@ -41,8 +41,8 @@ class SAM(Model):
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info: Logs information about the SAM model.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> results = sam.predict('image.jpg', points=[[500, 375]])
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>>> sam = SAM("sam_b.pt")
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>>> results = sam.predict("image.jpg", points=[[500, 375]])
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>>> for r in results:
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>>> print(f"Detected {len(r.masks)} masks")
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"""
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@ -58,7 +58,7 @@ class SAM(Model):
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NotImplementedError: If the model file extension is not .pt or .pth.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> sam = SAM("sam_b.pt")
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>>> print(sam.is_sam2)
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"""
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if model and Path(model).suffix not in {".pt", ".pth"}:
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@ -78,8 +78,8 @@ class SAM(Model):
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task (str | None): Task name. If provided, it specifies the particular task the model is being loaded for.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> sam._load('path/to/custom_weights.pt')
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>>> sam = SAM("sam_b.pt")
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>>> sam._load("path/to/custom_weights.pt")
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"""
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self.model = build_sam(weights)
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@ -100,8 +100,8 @@ class SAM(Model):
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(List): The model predictions.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> results = sam.predict('image.jpg', points=[[500, 375]])
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>>> sam = SAM("sam_b.pt")
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>>> results = sam.predict("image.jpg", points=[[500, 375]])
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>>> for r in results:
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... print(f"Detected {len(r.masks)} masks")
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"""
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@ -130,8 +130,8 @@ class SAM(Model):
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(List): The model predictions, typically containing segmentation masks and other relevant information.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> results = sam('image.jpg', points=[[500, 375]])
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>>> sam = SAM("sam_b.pt")
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>>> results = sam("image.jpg", points=[[500, 375]])
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>>> print(f"Detected {len(results[0].masks)} masks")
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"""
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return self.predict(source, stream, bboxes, points, labels, **kwargs)
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@ -151,7 +151,7 @@ class SAM(Model):
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(Tuple): A tuple containing the model's information (string representations of the model).
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> sam = SAM("sam_b.pt")
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>>> info = sam.info()
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>>> print(info[0]) # Print summary information
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"""
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@ -167,7 +167,7 @@ class SAM(Model):
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class. For SAM2 models, it maps to SAM2Predictor, otherwise to the standard Predictor.
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Examples:
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>>> sam = SAM('sam_b.pt')
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>>> sam = SAM("sam_b.pt")
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>>> task_map = sam.task_map
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>>> print(task_map)
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{'segment': <class 'ultralytics.models.sam.predict.Predictor'>}
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@ -32,8 +32,9 @@ class MaskDecoder(nn.Module):
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Examples:
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>>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
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>>> masks, iou_pred = decoder(image_embeddings, image_pe, sparse_prompt_embeddings,
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... dense_prompt_embeddings, multimask_output=True)
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>>> masks, iou_pred = decoder(
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... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output=True
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... )
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>>> print(f"Predicted masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
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"""
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@ -213,7 +214,8 @@ class SAM2MaskDecoder(nn.Module):
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>>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
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>>> decoder = SAM2MaskDecoder(256, transformer)
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>>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
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... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False)
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... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
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... )
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"""
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def __init__(
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@ -345,7 +347,8 @@ class SAM2MaskDecoder(nn.Module):
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>>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
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>>> decoder = SAM2MaskDecoder(256, transformer)
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>>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
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... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False)
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... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
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... )
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"""
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masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(
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image_embeddings=image_embeddings,
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@ -417,7 +417,15 @@ class SAM2Model(torch.nn.Module):
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>>> point_inputs = {"point_coords": torch.rand(1, 2, 2), "point_labels": torch.tensor([[1, 0]])}
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>>> mask_inputs = torch.rand(1, 1, 512, 512)
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>>> results = model._forward_sam_heads(backbone_features, point_inputs, mask_inputs)
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>>> low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits = results
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>>> (
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... low_res_multimasks,
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... high_res_multimasks,
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... ious,
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... low_res_masks,
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... high_res_masks,
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... obj_ptr,
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... object_score_logits,
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... ) = results
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"""
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B = backbone_features.size(0)
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device = backbone_features.device
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@ -716,7 +716,7 @@ class BasicLayer(nn.Module):
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Examples:
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>>> layer = BasicLayer(dim=96, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
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>>> x = torch.randn(1, 56*56, 96)
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>>> x = torch.randn(1, 56 * 56, 96)
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>>> output = layer(x)
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>>> print(output.shape)
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"""
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@ -22,7 +22,7 @@ def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num
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Examples:
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>>> frame_idx = 5
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>>> cond_frame_outputs = {1: 'a', 3: 'b', 7: 'c', 9: 'd'}
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>>> cond_frame_outputs = {1: "a", 3: "b", 7: "c", 9: "d"}
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>>> max_cond_frame_num = 2
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>>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num)
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>>> print(selected)
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@ -69,8 +69,8 @@ class Predictor(BasePredictor):
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Examples:
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>>> predictor = Predictor()
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>>> predictor.setup_model(model_path='sam_model.pt')
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>>> predictor.set_image('image.jpg')
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>>> predictor.setup_model(model_path="sam_model.pt")
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>>> predictor.set_image("image.jpg")
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>>> masks, scores, boxes = predictor.generate()
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>>> results = predictor.postprocess((masks, scores, boxes), im, orig_img)
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"""
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@ -90,8 +90,8 @@ class Predictor(BasePredictor):
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Examples:
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>>> predictor = Predictor(cfg=DEFAULT_CFG)
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>>> predictor = Predictor(overrides={'imgsz': 640})
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>>> predictor = Predictor(_callbacks={'on_predict_start': custom_callback})
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>>> predictor = Predictor(overrides={"imgsz": 640})
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>>> predictor = Predictor(_callbacks={"on_predict_start": custom_callback})
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"""
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if overrides is None:
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overrides = {}
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@ -188,8 +188,8 @@ class Predictor(BasePredictor):
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Examples:
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>>> predictor = Predictor()
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>>> predictor.setup_model(model_path='sam_model.pt')
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>>> predictor.set_image('image.jpg')
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>>> predictor.setup_model(model_path="sam_model.pt")
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>>> predictor.set_image("image.jpg")
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>>> masks, scores, logits = predictor.inference(im, bboxes=[[0, 0, 100, 100]])
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"""
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# Override prompts if any stored in self.prompts
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@ -475,8 +475,8 @@ class Predictor(BasePredictor):
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Examples:
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>>> predictor = Predictor()
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>>> predictor.setup_source('path/to/images')
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>>> predictor.setup_source('video.mp4')
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>>> predictor.setup_source("path/to/images")
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>>> predictor.setup_source("video.mp4")
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>>> predictor.setup_source(None) # Uses default source if available
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Notes:
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@ -504,8 +504,8 @@ class Predictor(BasePredictor):
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Examples:
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>>> predictor = Predictor()
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>>> predictor.set_image('path/to/image.jpg')
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>>> predictor.set_image(cv2.imread('path/to/image.jpg'))
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>>> predictor.set_image("path/to/image.jpg")
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>>> predictor.set_image(cv2.imread("path/to/image.jpg"))
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Notes:
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- This method should be called before performing inference on a new image.
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@ -21,7 +21,7 @@ class ClassificationPredictor(BasePredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.classify import ClassificationPredictor
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args = dict(model='yolov8n-cls.pt', source=ASSETS)
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args = dict(model="yolov8n-cls.pt", source=ASSETS)
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predictor = ClassificationPredictor(overrides=args)
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predictor.predict_cli()
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```
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@ -22,7 +22,7 @@ class ClassificationTrainer(BaseTrainer):
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```python
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from ultralytics.models.yolo.classify import ClassificationTrainer
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args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3)
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args = dict(model="yolov8n-cls.pt", data="imagenet10", epochs=3)
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trainer = ClassificationTrainer(overrides=args)
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trainer.train()
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```
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@ -20,7 +20,7 @@ class ClassificationValidator(BaseValidator):
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```python
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from ultralytics.models.yolo.classify import ClassificationValidator
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args = dict(model='yolov8n-cls.pt', data='imagenet10')
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args = dict(model="yolov8n-cls.pt", data="imagenet10")
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validator = ClassificationValidator(args=args)
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validator()
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```
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@ -14,7 +14,7 @@ class DetectionPredictor(BasePredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.detect import DetectionPredictor
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args = dict(model='yolov8n.pt', source=ASSETS)
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args = dict(model="yolov8n.pt", source=ASSETS)
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predictor = DetectionPredictor(overrides=args)
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predictor.predict_cli()
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```
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@ -24,7 +24,7 @@ class DetectionTrainer(BaseTrainer):
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```python
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from ultralytics.models.yolo.detect import DetectionTrainer
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args = dict(model='yolov8n.pt', data='coco8.yaml', epochs=3)
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args = dict(model="yolov8n.pt", data="coco8.yaml", epochs=3)
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trainer = DetectionTrainer(overrides=args)
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trainer.train()
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```
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@ -22,7 +22,7 @@ class DetectionValidator(BaseValidator):
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```python
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from ultralytics.models.yolo.detect import DetectionValidator
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args = dict(model='yolov8n.pt', data='coco8.yaml')
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args = dict(model="yolov8n.pt", data="coco8.yaml")
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validator = DetectionValidator(args=args)
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validator()
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```
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@ -16,7 +16,7 @@ class OBBPredictor(DetectionPredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.obb import OBBPredictor
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args = dict(model='yolov8n-obb.pt', source=ASSETS)
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args = dict(model="yolov8n-obb.pt", source=ASSETS)
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predictor = OBBPredictor(overrides=args)
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predictor.predict_cli()
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```
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@ -15,7 +15,7 @@ class OBBTrainer(yolo.detect.DetectionTrainer):
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```python
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from ultralytics.models.yolo.obb import OBBTrainer
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args = dict(model='yolov8n-obb.pt', data='dota8.yaml', epochs=3)
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args = dict(model="yolov8n-obb.pt", data="dota8.yaml", epochs=3)
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trainer = OBBTrainer(overrides=args)
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trainer.train()
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```
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@ -18,9 +18,9 @@ class OBBValidator(DetectionValidator):
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```python
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from ultralytics.models.yolo.obb import OBBValidator
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args = dict(model='yolov8n-obb.pt', data='dota8.yaml')
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args = dict(model="yolov8n-obb.pt", data="dota8.yaml")
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validator = OBBValidator(args=args)
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validator(model=args['model'])
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validator(model=args["model"])
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```
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"""
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@ -14,7 +14,7 @@ class PosePredictor(DetectionPredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.pose import PosePredictor
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args = dict(model='yolov8n-pose.pt', source=ASSETS)
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args = dict(model="yolov8n-pose.pt", source=ASSETS)
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predictor = PosePredictor(overrides=args)
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predictor.predict_cli()
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```
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@ -16,7 +16,7 @@ class PoseTrainer(yolo.detect.DetectionTrainer):
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```python
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from ultralytics.models.yolo.pose import PoseTrainer
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args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
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args = dict(model="yolov8n-pose.pt", data="coco8-pose.yaml", epochs=3)
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trainer = PoseTrainer(overrides=args)
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trainer.train()
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```
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|
|
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@ -20,7 +20,7 @@ class PoseValidator(DetectionValidator):
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```python
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from ultralytics.models.yolo.pose import PoseValidator
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args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
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args = dict(model="yolov8n-pose.pt", data="coco8-pose.yaml")
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validator = PoseValidator(args=args)
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validator()
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```
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|
|
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|
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@ -14,7 +14,7 @@ class SegmentationPredictor(DetectionPredictor):
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.segment import SegmentationPredictor
|
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|
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args = dict(model='yolov8n-seg.pt', source=ASSETS)
|
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args = dict(model="yolov8n-seg.pt", source=ASSETS)
|
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predictor = SegmentationPredictor(overrides=args)
|
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predictor.predict_cli()
|
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```
|
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|
|
|
|||
|
|
@ -16,7 +16,7 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
|
|||
```python
|
||||
from ultralytics.models.yolo.segment import SegmentationTrainer
|
||||
|
||||
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
|
||||
args = dict(model="yolov8n-seg.pt", data="coco8-seg.yaml", epochs=3)
|
||||
trainer = SegmentationTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ class SegmentationValidator(DetectionValidator):
|
|||
```python
|
||||
from ultralytics.models.yolo.segment import SegmentationValidator
|
||||
|
||||
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
|
||||
args = dict(model="yolov8n-seg.pt", data="coco8-seg.yaml")
|
||||
validator = SegmentationValidator(args=args)
|
||||
validator()
|
||||
```
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ class WorldTrainer(yolo.detect.DetectionTrainer):
|
|||
```python
|
||||
from ultralytics.models.yolo.world import WorldModel
|
||||
|
||||
args = dict(model='yolov8s-world.pt', data='coco8.yaml', epochs=3)
|
||||
args = dict(model="yolov8s-world.pt", data="coco8.yaml", epochs=3)
|
||||
trainer = WorldTrainer(overrides=args)
|
||||
trainer.train()
|
||||
```
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue