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Refactor Python code (#13448)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-06-09 02:32:17 +02:00 • committed by GitHub
parent 6a234f3639
commit 1b26838def
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22 changed files with 81 additions and 101 deletions
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2
ultralytics/models/fastsam/prompt.py
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@ -25,7 +25,7 @@ class FastSAMPrompt:
def __init__(self, source, results, device="cuda") -> None:
"""Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
if isinstance(source, (str, Path)) and os.path.isdir(source):
raise ValueError(f"FastSAM only accepts image paths and PIL Image sources, not directories.")
raise ValueError("FastSAM only accepts image paths and PIL Image sources, not directories.")
self.device = device
self.results = results
self.source = source

2
ultralytics/models/nas/val.py
View file

@ -17,7 +17,7 @@ class NASValidator(DetectionValidator):
ultimately producing the final detections.
Attributes:
args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU thresholds.
args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU.
lb (torch.Tensor): Optional tensor for multilabel NMS.
Example:

16
ultralytics/models/yolo/detect/val.py
View file

@ -300,22 +300,22 @@ class DetectionValidator(BaseValidator):
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")
val = COCOeval(anno, pred, "bbox")
else:
from lvis import LVIS, LVISEval
anno = LVIS(str(anno_json)) # init annotations api
pred = anno._load_json(str(pred_json)) # init predictions api (must pass string, not Path)
eval = LVISEval(anno, pred, "bbox")
eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
eval.evaluate()
eval.accumulate()
eval.summarize()
val = LVISEval(anno, pred, "bbox")
val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
val.evaluate()
val.accumulate()
val.summarize()
if self.is_lvis:
eval.print_results() # explicitly call print_results
val.print_results() # explicitly call print_results
# update mAP50-95 and mAP50
stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = (
eval.stats[:2] if self.is_coco else [eval.results["AP50"], eval.results["AP"]]
val.stats[:2] if self.is_coco else [val.results["AP50"], val.results["AP"]]
)
except Exception as e:
LOGGER.warning(f"{pkg} unable to run: {e}")

21
ultralytics/models/yolo/world/train_world.py
View file

@ -54,16 +54,15 @@ class WorldTrainerFromScratch(WorldTrainer):
batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
"""
gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
if mode == "train":
dataset = [
build_yolo_dataset(self.args, im_path, batch, self.data, stride=gs, multi_modal=True)
if isinstance(im_path, str)
else build_grounding(self.args, im_path["img_path"], im_path["json_file"], batch, stride=gs)
for im_path in img_path
]
return YOLOConcatDataset(dataset) if len(dataset) > 1 else dataset[0]
else:
if mode != "train":
return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs)
dataset = [
build_yolo_dataset(self.args, im_path, batch, self.data, stride=gs, multi_modal=True)
if isinstance(im_path, str)
else build_grounding(self.args, im_path["img_path"], im_path["json_file"], batch, stride=gs)
for im_path in img_path
]
return YOLOConcatDataset(dataset) if len(dataset) > 1 else dataset[0]
def get_dataset(self):
"""
@ -71,7 +70,7 @@ class WorldTrainerFromScratch(WorldTrainer):
Returns None if data format is not recognized.
"""
final_data = dict()
final_data = {}
data_yaml = self.args.data
assert data_yaml.get("train", False) # object365.yaml
assert data_yaml.get("val", False) # lvis.yaml
@ -88,7 +87,7 @@ class WorldTrainerFromScratch(WorldTrainer):
grounding_data = data_yaml[s].get("grounding_data")
if grounding_data is None:
continue
grounding_data = [grounding_data] if not isinstance(grounding_data, list) else grounding_data
grounding_data = grounding_data if isinstance(grounding_data, list) else [grounding_data]
for g in grounding_data:
assert isinstance(g, dict), f"Grounding data should be provided in dict format, but got {type(g)}"
final_data[s] += grounding_data