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New YOLOv8 Results() class for prediction outputs (#314)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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This commit is contained in:
Ayush Chaurasia
2023-01-17 19:02:34 +05:30
committed by GitHub
parent 0cb87f7dd3
commit c6985da9de
32 changed files with 813 additions and 259 deletions
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25
ultralytics/yolo/v8/classify/predict.py
View File

@ -4,8 +4,8 @@ import hydra
import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.plotting import Annotator
@ -15,20 +15,27 @@ class ClassificationPredictor(BasePredictor):
return Annotator(img, example=str(self.model.names), pil=True)
def preprocess(self, img):
img = torch.Tensor(img).to(self.model.device)
img = (img if isinstance(img, torch.Tensor) else torch.Tensor(img)).to(self.model.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
return img
def write_results(self, idx, preds, batch):
def postprocess(self, preds, img, orig_img):
results = []
for i, pred in enumerate(preds):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
results.append(Results(probs=pred.softmax(0), orig_shape=shape[:2]))
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.webcam: # batch_size >= 1
if self.webcam or self.from_img: # batch_size >= 1
log_string += f'{idx}: '
frame = self.dataset.cound
frame = self.dataset.count
else:
frame = getattr(self.dataset, 'frame', 0)
@ -38,9 +45,10 @@ class ClassificationPredictor(BasePredictor):
log_string += '%gx%g ' % im.shape[2:] # print string
self.annotator = self.get_annotator(im0)
prob = preds[idx].softmax(0)
if self.return_outputs:
self.output["prob"] = prob.cpu().numpy()
result = results[idx]
if len(result) == 0:
return log_string
prob = result.probs
# Print results
top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
@ -59,7 +67,6 @@ class ClassificationPredictor(BasePredictor):
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
predictor = ClassificationPredictor(cfg)
predictor.predict_cli()

2
ultralytics/yolo/v8/classify/train.py
View File

@ -56,6 +56,8 @@ class ClassificationTrainer(BaseTrainer):
# Load a YOLO model locally, from torchvision, or from Ultralytics assets
if model.endswith(".pt"):
self.model, _ = attempt_load_one_weight(model, device='cpu')
for p in model.parameters():
p.requires_grad = True # for training
elif model.endswith(".yaml"):
self.model = self.get_model(cfg=model)
elif model in torchvision.models.__dict__: