ultralytics 8.0.50 AMP check and YOLOv5u YAMLs (#1263)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Troy <wudashuo@vip.qq.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -574,7 +574,7 @@ class Exporter:
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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saved_model = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
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if self.args.int8:
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f = saved_model / (self.file.stem + 'yolov8n_integer_quant.tflite') # fp32 in/out
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f = saved_model / (self.file.stem + '_integer_quant.tflite') # fp32 in/out
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elif self.args.half:
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f = saved_model / (self.file.stem + '_float16.tflite')
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else:
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@ -863,18 +863,6 @@ def export(cfg=DEFAULT_CFG):
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cfg.model = cfg.model or 'yolov8n.yaml'
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cfg.format = cfg.format or 'torchscript'
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# exporter = Exporter(cfg)
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#
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# model = None
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# if isinstance(cfg.model, (str, Path)):
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# if Path(cfg.model).suffix == '.yaml':
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# model = DetectionModel(cfg.model)
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# elif Path(cfg.model).suffix == '.pt':
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# model = attempt_load_weights(cfg.model, fuse=True)
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# else:
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# TypeError(f'Unsupported model type {cfg.model}')
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# exporter(model=model)
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from ultralytics import YOLO
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model = YOLO(cfg.model)
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model.export(**vars(cfg))
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@ -203,6 +203,8 @@ class YOLO:
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if source is None:
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source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
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LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
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is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and \
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('predict' in sys.argv or 'mode=predict' in sys.argv)
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overrides = self.overrides.copy()
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overrides['conf'] = 0.25
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@ -213,10 +215,9 @@ class YOLO:
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if not self.predictor:
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self.task = overrides.get('task') or self.task
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self.predictor = TASK_MAP[self.task][3](overrides=overrides)
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self.predictor.setup_model(model=self.model)
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self.predictor.setup_model(model=self.model, verbose=is_cli)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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is_cli = sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')
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return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
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def track(self, source=None, stream=False, **kwargs):
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@ -183,6 +183,8 @@ class BasePredictor:
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'preprocess': self.dt[0].dt * 1E3 / n,
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'inference': self.dt[1].dt * 1E3 / n,
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'postprocess': self.dt[2].dt * 1E3 / n}
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if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
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continue
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p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \
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else (path, im0s.copy())
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p = Path(p)
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@ -218,11 +220,16 @@ class BasePredictor:
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self.run_callbacks('on_predict_end')
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def setup_model(self, model):
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device = select_device(self.args.device)
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def setup_model(self, model, verbose=True):
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device = select_device(self.args.device, verbose=verbose)
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model = model or self.args.model
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self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
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self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half)
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self.model = AutoBackend(model,
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device=device,
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dnn=self.args.dnn,
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data=self.args.data,
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fp16=self.args.half,
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verbose=verbose)
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self.device = device
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self.model.eval()
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@ -25,8 +25,8 @@ from tqdm import tqdm
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from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
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from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
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colorstr, emojis, yaml_save)
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from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, ONLINE, RANK, ROOT, SETTINGS, TQDM_BAR_FORMAT, __version__,
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callbacks, colorstr, emojis, yaml_save)
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from ultralytics.yolo.utils.autobatch import check_train_batch_size
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
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from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
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@ -111,8 +111,6 @@ class BaseTrainer:
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print_args(vars(self.args))
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# Device
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self.amp = self.device.type != 'cpu'
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self.scaler = amp.GradScaler(enabled=self.amp)
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if self.device.type == 'cpu':
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self.args.workers = 0 # faster CPU training as time dominated by inference, not dataloading
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@ -126,7 +124,7 @@ class BaseTrainer:
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if 'yaml_file' in self.data:
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self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
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except Exception as e:
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raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
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raise RuntimeError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
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self.trainset, self.testset = self.get_dataset(self.data)
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self.ema = None
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@ -204,6 +202,8 @@ class BaseTrainer:
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ckpt = self.setup_model()
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self.model = self.model.to(self.device)
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self.set_model_attributes()
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self.amp = check_amp(self.model)
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self.scaler = amp.GradScaler(enabled=self.amp)
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if world_size > 1:
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self.model = DDP(self.model, device_ids=[rank])
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# Check imgsz
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@ -597,3 +597,31 @@ class BaseTrainer:
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LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
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f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias')
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return optimizer
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def check_amp(model):
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# Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation
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device = next(model.parameters()).device # get model device
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if device.type in ('cpu', 'mps'):
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return False # AMP only used on CUDA devices
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def amp_allclose(m, im):
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# All close FP32 vs AMP results
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a = m(im, device=device, verbose=False)[0].boxes.boxes # FP32 inference
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with torch.cuda.amp.autocast(True):
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b = m(im, device=device, verbose=False)[0].boxes.boxes # AMP inference
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return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.1) # close to 10% absolute tolerance
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f = ROOT / 'assets/bus.jpg' # image to check
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im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
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prefix = colorstr('AMP: ')
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try:
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from ultralytics import YOLO
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LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
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assert amp_allclose(YOLO('yolov8n.pt'), im)
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LOGGER.info(f'{prefix}checks passed ✅')
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return True
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except AssertionError:
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LOGGER.warning(f'{prefix}checks failed ❌. Anomalies were detected with AMP on your system that may lead to '
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f'NaN losses or zero-mAP results, so AMP will be disabled during training.')
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return False
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