xdobro23/YOLOv8-16bit
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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/engine/model.py
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@ -111,11 +111,11 @@ class YOLO:
predictor = self.PredictorClass(overrides=kwargs)
# check size type
sz = predictor.args.img_size
sz = predictor.args.imgsz
if type(sz) != int: # recieved listConfig
predictor.args.img_size = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand
predictor.args.imgsz = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand
else:
predictor.args.img_size = [sz, sz]
predictor.args.imgsz = [sz, sz]
predictor.setup(model=self.model, source=source)
predictor()

10
ultralytics/yolo/engine/predictor.py
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@ -39,7 +39,7 @@ from ultralytics.yolo.utils.configs import get_config
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
from ultralytics.yolo.utils.plotting import Annotator
from ultralytics.yolo.utils.torch_utils import check_img_size, select_device, smart_inference_mode
from ultralytics.yolo.utils.torch_utils import check_imgsz, select_device, smart_inference_mode
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
@ -99,18 +99,18 @@ class BasePredictor:
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half) # NOTE: not passing data
stride, pt = model.stride, model.pt
imgsz = check_img_size(self.args.img_size, s=stride) # check image size
imgsz = check_imgsz(self.args.imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
if webcam:
self.view_img = check_imshow(warn=True)
self.dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
bs = len(self.dataset)
elif screenshot:
self.dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
self.dataset = LoadScreenshots(source, imgsz=imgsz, stride=stride, auto=pt)
else:
self.dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
self.dataset = LoadImages(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
self.vid_path, self.vid_writer = [None] * bs, [None] * bs
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup

4
ultralytics/yolo/engine/validator.py
View File

@ -12,7 +12,7 @@ from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.modeling import get_model
from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import check_img_size, de_parallel, select_device
from ultralytics.yolo.utils.torch_utils import check_imgsz, de_parallel, select_device
class BaseValidator:
@ -55,7 +55,7 @@ class BaseValidator:
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_img_size(self.args.img_size, s=stride)
imgsz = check_imgsz(self.args.imgsz, s=stride)
if engine:
self.args.batch_size = model.batch_size
else: