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ultralytics 8.0.21 Windows, segments, YAML fixes (#655)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: corey-nm <109536191+corey-nm@users.noreply.github.com>
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Glenn Jocher 2023-01-26 23:07:27 +01:00 • committed by GitHub
parent dc9502c700
commit 6c44ce21d9
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16 changed files with 147 additions and 146 deletions
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20
ultralytics/yolo/engine/predictor.py
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@ -88,7 +88,7 @@ class BasePredictor:
self.vid_path, self.vid_writer = None, None
self.annotator = None
self.data_path = None
self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()}) # add callbacks
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
callbacks.add_integration_callbacks(self)
def preprocess(self, img):
@ -151,19 +151,19 @@ class BasePredictor:
self.bs = bs
@smart_inference_mode()
def __call__(self, source=None, model=None, verbose=False, stream=False):
def __call__(self, source=None, model=None, stream=False):
if stream:
return self.stream_inference(source, model, verbose)
return self.stream_inference(source, model)
else:
return list(self.stream_inference(source, model, verbose)) # merge list of Result into one
return list(self.stream_inference(source, model)) # merge list of Result into one
def predict_cli(self):
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
gen = self.stream_inference(verbose=True)
gen = self.stream_inference()
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
pass
def stream_inference(self, source=None, model=None, verbose=False):
def stream_inference(self, source=None, model=None):
self.run_callbacks("on_predict_start")
# setup model
@ -201,7 +201,7 @@ class BasePredictor:
p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
p = Path(p)
if verbose or self.args.save or self.args.save_txt or self.args.show:
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
s += self.write_results(i, self.results, (p, im, im0))
if self.args.show:
@ -214,11 +214,11 @@ class BasePredictor:
yield from self.results
# Print time (inference-only)
if verbose:
if self.args.verbose:
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
# Print results
if verbose and self.seen:
if self.args.verbose and self.seen:
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
f'{(1, 3, *self.imgsz)}' % t)
@ -243,7 +243,7 @@ class BasePredictor:
if isinstance(source, (str, int, Path)): # int for local usb carame
source = str(source)
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://'))
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
screenshot = source.lower().startswith('screen')
if is_url and is_file: