HongChuang/ultralytics-ascend
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Simplify argument names (#141)

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Glenn Jocher 2023-01-03 19:47:56 +01:00 • committed by GitHub
parent d74de2582c
commit 3c18aa5278
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9 changed files with 22 additions and 24 deletions
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8
ultralytics/yolo/engine/validator.py
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@ -87,18 +87,18 @@ class BaseValidator:
callbacks.add_integration_callbacks(self)
self.run_callbacks('on_val_start')
assert model is not None, "Either trainer or model is needed for validation"
self.device = select_device(self.args.device, self.args.batch_size)
self.device = select_device(self.args.device, self.args.batch)
self.args.half &= self.device.type != 'cpu'
model = AutoBackend(model, device=self.device, dnn=self.args.dnn, fp16=self.args.half)
self.model = model
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_imgsz(self.args.imgsz, stride=stride)
if engine:
self.args.batch_size = model.batch_size
self.args.batch = model.batch_size
else:
self.device = model.device
if not pt and not jit:
self.args.batch_size = 1 # export.py models default to batch-size 1
self.args.batch = 1 # export.py models default to batch-size 1
self.logger.info(
f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
@ -110,7 +110,7 @@ class BaseValidator:
if self.device.type == 'cpu':
self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading
self.dataloader = self.dataloader or \
self.get_dataloader(data.get("val") or data.set("test"), self.args.batch_size)
self.get_dataloader(data.get("val") or data.set("test"), self.args.batch)
self.data = data
model.eval()