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Rename img_size to imgsz (#86)

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This commit is contained in:
Glenn Jocher
2022-12-24 00:39:09 +01:00
committed by GitHub
parent ae2443c210
commit 6432afc5f9
25 changed files with 98 additions and 98 deletions
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6
ultralytics/yolo/v8/classify/predict.py
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@ -55,11 +55,11 @@ class ClassificationPredictor(BasePredictor):
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "squeezenet1_0"
sz = cfg.img_size
sz = cfg.imgsz
if type(sz) != int: # recieved listConfig
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
else:
cfg.img_size = [sz, sz]
cfg.imgsz = [sz, sz]
predictor = ClassificationPredictor(cfg)
predictor()

4
ultralytics/yolo/v8/classify/train.py
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@ -36,7 +36,7 @@ class ClassificationTrainer(BaseTrainer):
def get_dataloader(self, dataset_path, batch_size, rank=0, mode="train"):
return build_classification_dataloader(path=dataset_path,
imgsz=self.args.img_size,
imgsz=self.args.imgsz,
batch_size=batch_size,
rank=rank)
@ -70,7 +70,7 @@ def train(cfg):
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 img_size=224
python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 imgsz=224
TODO:
Direct cli support, i.e, yolov8 classify_train args.epochs 10

2
ultralytics/yolo/v8/classify/val.py
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@ -28,7 +28,7 @@ class ClassificationValidator(BaseValidator):
return {"top1": top1, "top5": top5, "fitness": top5}
def get_dataloader(self, dataset_path, batch_size):
return build_classification_dataloader(path=dataset_path, imgsz=self.args.img_size, batch_size=batch_size)
return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size)
@property
def metric_keys(self):

6
ultralytics/yolo/v8/detect/predict.py
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@ -84,11 +84,11 @@ class DetectionPredictor(BasePredictor):
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "n.pt"
sz = cfg.img_size
sz = cfg.imgsz
if type(sz) != int: # recieved listConfig
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
else:
cfg.img_size = [sz, sz]
cfg.imgsz = [sz, sz]
predictor = DetectionPredictor(cfg)
predictor()

4
ultralytics/yolo/v8/detect/train.py
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@ -28,7 +28,7 @@ class DetectionTrainer(BaseTrainer):
nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps)
self.args.box *= 3 / nl # scale to layers
self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
self.args.obj *= (self.args.img_size / 640) ** 2 * 3 / nl # scale to image size and layers
self.args.obj *= (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.args = self.args # attach hyperparameters to model
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
@ -223,7 +223,7 @@ def train(cfg):
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 img_size=640
python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 imgsz=640
TODO:
yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=100

6
ultralytics/yolo/v8/segment/predict.py
View File

@ -102,11 +102,11 @@ class SegmentationPredictor(DetectionPredictor):
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "n.pt"
sz = cfg.img_size
sz = cfg.imgsz
if type(sz) != int: # recieved listConfig
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
else:
cfg.img_size = [sz, sz]
cfg.imgsz = [sz, sz]
predictor = SegmentationPredictor(cfg)
predictor()

2
ultralytics/yolo/v8/segment/train.py
View File

@ -243,7 +243,7 @@ def train(cfg):
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 img_size=640
python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 imgsz=640
TODO:
Direct cli support, i.e, yolov8 classify_train args.epochs 10