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ultralytics 8.0.93 HUB docs and JSON2YOLO converter (#2431)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: 李际朝 <tubkninght@gmail.com>
Co-authored-by: Danny Kim <imbird0312@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-06 01:12:43 +02:00
committed by GitHub
parent 0ebd3f2959
commit ddb354ce5e
34 changed files with 1107 additions and 759 deletions
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20
ultralytics/yolo/engine/predictor.py
View File

@ -146,7 +146,7 @@ class BasePredictor:
log_string += result.verbose()
if self.args.save or self.args.show: # Add bbox to image
plot_args = dict(line_width=self.args.line_thickness,
plot_args = dict(line_width=self.args.line_width,
boxes=self.args.boxes,
conf=self.args.show_conf,
labels=self.args.show_labels)
@ -212,7 +212,7 @@ class BasePredictor:
self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
self.done_warmup = True
self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None
self.seen, self.windows, self.batch, profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile())
self.run_callbacks('on_predict_start')
for batch in self.dataset:
self.run_callbacks('on_predict_batch_start')
@ -222,15 +222,15 @@ class BasePredictor:
mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
# Preprocess
with self.dt[0]:
with profilers[0]:
im = self.preprocess(im0s)
# Inference
with self.dt[1]:
with profilers[1]:
preds = self.model(im, augment=self.args.augment, visualize=visualize)
# Postprocess
with self.dt[2]:
with profilers[2]:
self.results = self.postprocess(preds, im, im0s)
self.run_callbacks('on_predict_postprocess_end')
@ -238,9 +238,9 @@ class BasePredictor:
n = len(im0s)
for i in range(n):
self.results[i].speed = {
'preprocess': self.dt[0].dt * 1E3 / n,
'inference': self.dt[1].dt * 1E3 / n,
'postprocess': self.dt[2].dt * 1E3 / n}
'preprocess': profilers[0].dt * 1E3 / n,
'inference': profilers[1].dt * 1E3 / n,
'postprocess': profilers[2].dt * 1E3 / n}
if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
continue
p, im0 = path[i], im0s[i].copy()
@ -259,7 +259,7 @@ class BasePredictor:
# Print time (inference-only)
if self.args.verbose:
LOGGER.info(f'{s}{self.dt[1].dt * 1E3:.1f}ms')
LOGGER.info(f'{s}{profilers[1].dt * 1E3:.1f}ms')
# Release assets
if isinstance(self.vid_writer[-1], cv2.VideoWriter):
@ -267,7 +267,7 @@ class BasePredictor:
# Print results
if self.args.verbose and self.seen:
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
t = tuple(x.t / self.seen * 1E3 for x in profilers) # speeds per image
LOGGER.info(f'Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape '
f'{(1, 3, *self.imgsz)}' % t)
if self.args.save or self.args.save_txt or self.args.save_crop:

5
ultralytics/yolo/engine/results.py
View File

@ -197,6 +197,11 @@ class Results(SimpleClass):
conf = kwargs['show_conf']
assert type(conf) == bool, '`show_conf` should be of boolean type, i.e, show_conf=True/False'
if 'show_conf' in kwargs:
deprecation_warn('line_thickness', 'line_width')
line_width = kwargs['line_thickness']
assert type(line_width) == int, '`line_width` should be of int type, i.e, line_width=3'
names = self.names
annotator = Annotator(deepcopy(self.orig_img if img is None else img),
line_width,