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ultralytics 8.0.73 minor fixes (#1929)

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Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: joseliraGB <122470533+joseliraGB@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-04-11 01:00:09 +02:00
committed by GitHub
parent 95f96dc5bc
commit 5629ed0bb7
16 changed files with 224 additions and 198 deletions
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41
ultralytics/yolo/v8/classify/predict.py
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@ -5,14 +5,10 @@ import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
from ultralytics.yolo.utils.plotting import Annotator
class ClassificationPredictor(BasePredictor):
def get_annotator(self, img):
return Annotator(img, example=str(self.model.names), pil=True)
def preprocess(self, img):
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
@ -27,43 +23,6 @@ class ClassificationPredictor(BasePredictor):
return results
def write_results(self, idx, results, batch):
p, im, im0 = batch
log_string = ''
if len(im.shape) == 3:
im = im[None] # expand for batch dim
self.seen += 1
im0 = im0.copy()
if self.source_type.webcam or self.source_type.from_img: # batch_size >= 1
log_string += f'{idx}: '
frame = self.dataset.count
else:
frame = getattr(self.dataset, 'frame', 0)
self.data_path = p
# save_path = str(self.save_dir / p.name) # im.jpg
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string
result = results[idx]
if len(result) == 0:
return log_string
prob = result.probs
# Print results
n5 = min(len(self.model.names), 5)
top5i = prob.argsort(0, descending=True)[:n5].tolist() # top 5 indices
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
# write
if self.args.save or self.args.show: # Add bbox to image
self.plotted_img = result.plot()
if self.args.save_txt: # Write to file
text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
with open(f'{self.txt_path}.txt', 'a') as f:
f.write(text + '\n')
return log_string
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"