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Fix model re-fuse() in inference loops (#466)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Glenn Jocher
2023-01-18 20:32:36 +01:00
committed by GitHub
parent cc3c774bde
commit a86218b767
22 changed files with 135 additions and 66 deletions
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5
ultralytics/yolo/v8/classify/predict.py
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@ -5,7 +5,7 @@ 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 import DEFAULT_CONFIG, ROOT, is_git_directory
from ultralytics.yolo.utils.plotting import Annotator
@ -67,7 +67,8 @@ 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.source = cfg.source if cfg.source is not None else ROOT / "assets"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
else "https://ultralytics.com/images/bus.jpg"
predictor = ClassificationPredictor(cfg)
predictor.predict_cli()

11
ultralytics/yolo/v8/classify/train.py
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@ -140,10 +140,13 @@ class ClassificationTrainer(BaseTrainer):
def train(cfg):
cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
cfg.data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist")
cfg.lr0 = 0.1
cfg.weight_decay = 5e-5
cfg.label_smoothing = 0.1
cfg.warmup_epochs = 0.0
# Reproduce ImageNet results
# cfg.lr0 = 0.1
# cfg.weight_decay = 5e-5
# cfg.label_smoothing = 0.1
# cfg.warmup_epochs = 0.0
cfg.device = cfg.device if cfg.device is not None else ''
# trainer = ClassificationTrainer(cfg)
# trainer.train()

5
ultralytics/yolo/v8/detect/predict.py
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@ -5,7 +5,7 @@ import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
@ -84,7 +84,8 @@ class DetectionPredictor(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.pt"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
else "https://ultralytics.com/images/bus.jpg"
predictor = DetectionPredictor(cfg)
predictor.predict_cli()

6
ultralytics/yolo/v8/segment/predict.py
View File

@ -4,7 +4,7 @@ import hydra
import torch
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory, ops
from ultralytics.yolo.utils.plotting import colors, save_one_box
from ultralytics.yolo.v8.detect.predict import DetectionPredictor
@ -101,8 +101,8 @@ class SegmentationPredictor(DetectionPredictor):
@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-seg.pt"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
else "https://ultralytics.com/images/bus.jpg"
predictor = SegmentationPredictor(cfg)
predictor.predict_cli()

6
ultralytics/yolo/v8/segment/val.py
View File

@ -45,6 +45,7 @@ class SegmentationValidator(DetectionValidator):
self.jdict = []
self.stats = []
if self.args.save_json:
check_requirements('pycocotools>=2.0.6')
self.process = ops.process_mask_upsample # more accurate
else:
self.process = ops.process_mask # faster
@ -189,8 +190,9 @@ class SegmentationValidator(DetectionValidator):
self.plot_masks.clear()
def pred_to_json(self, predn, filename, pred_masks):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
from pycocotools.mask import encode
# Save one JSON result
# Example result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
from pycocotools.mask import encode # noqa
def single_encode(x):
rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]