Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -1,6 +1,6 @@
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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"""
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Train a YOLOv5 classifier model on a classification dataset
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Train a YOLOv5 classifier model on a classification dataset.
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Usage - Single-GPU training:
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$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
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@@ -40,33 +40,61 @@ from classify import val as validate
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, DetectionModel
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from utils.dataloaders import create_classification_dataloader
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from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status,
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check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
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from utils.general import (
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DATASETS_DIR,
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LOGGER,
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TQDM_BAR_FORMAT,
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WorkingDirectory,
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check_git_info,
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check_git_status,
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check_requirements,
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colorstr,
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download,
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increment_path,
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init_seeds,
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print_args,
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yaml_save,
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)
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from utils.loggers import GenericLogger
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from utils.plots import imshow_cls
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from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP,
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smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
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from utils.torch_utils import (
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ModelEMA,
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de_parallel,
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model_info,
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reshape_classifier_output,
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select_device,
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smart_DDP,
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smart_optimizer,
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smartCrossEntropyLoss,
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torch_distributed_zero_first,
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)
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv('RANK', -1))
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
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LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv("RANK", -1))
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WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
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GIT_INFO = check_git_info()
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def train(opt, device):
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init_seeds(opt.seed + 1 + RANK, deterministic=True)
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save_dir, data, bs, epochs, nw, imgsz, pretrained = \
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opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \
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opt.imgsz, str(opt.pretrained).lower() == 'true'
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cuda = device.type != 'cpu'
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save_dir, data, bs, epochs, nw, imgsz, pretrained = (
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opt.save_dir,
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Path(opt.data),
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opt.batch_size,
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opt.epochs,
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min(os.cpu_count() - 1, opt.workers),
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opt.imgsz,
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str(opt.pretrained).lower() == "true",
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)
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cuda = device.type != "cpu"
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# Directories
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wdir = save_dir / 'weights'
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wdir = save_dir / "weights"
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wdir.mkdir(parents=True, exist_ok=True) # make dir
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last, best = wdir / 'last.pt', wdir / 'best.pt'
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last, best = wdir / "last.pt", wdir / "best.pt"
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# Save run settings
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yaml_save(save_dir / 'opt.yaml', vars(opt))
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yaml_save(save_dir / "opt.yaml", vars(opt))
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# Logger
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logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
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@@ -75,51 +103,55 @@ def train(opt, device):
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with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
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data_dir = data if data.is_dir() else (DATASETS_DIR / data)
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if not data_dir.is_dir():
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LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
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LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
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t = time.time()
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if str(data) == 'imagenet':
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subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True)
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if str(data) == "imagenet":
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subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True)
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else:
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url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
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url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip"
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download(url, dir=data_dir.parent)
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s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
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LOGGER.info(s)
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# Dataloaders
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nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes
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trainloader = create_classification_dataloader(path=data_dir / 'train',
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE,
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augment=True,
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cache=opt.cache,
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rank=LOCAL_RANK,
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workers=nw)
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nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes
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trainloader = create_classification_dataloader(
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path=data_dir / "train",
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE,
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augment=True,
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cache=opt.cache,
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rank=LOCAL_RANK,
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workers=nw,
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)
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test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
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test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val
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if RANK in {-1, 0}:
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testloader = create_classification_dataloader(path=test_dir,
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE * 2,
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augment=False,
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cache=opt.cache,
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rank=-1,
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workers=nw)
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testloader = create_classification_dataloader(
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path=test_dir,
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE * 2,
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augment=False,
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cache=opt.cache,
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rank=-1,
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workers=nw,
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)
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# Model
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with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
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if Path(opt.model).is_file() or opt.model.endswith('.pt'):
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model = attempt_load(opt.model, device='cpu', fuse=False)
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if Path(opt.model).is_file() or opt.model.endswith(".pt"):
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model = attempt_load(opt.model, device="cpu", fuse=False)
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elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
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model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None)
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model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None)
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else:
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m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models
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raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
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m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models
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raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m))
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if isinstance(model, DetectionModel):
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LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
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model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
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reshape_classifier_output(model, nc) # update class count
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for m in model.modules():
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if not pretrained and hasattr(m, 'reset_parameters'):
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if not pretrained and hasattr(m, "reset_parameters"):
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m.reset_parameters()
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if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
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m.p = opt.dropout # set dropout
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@@ -135,8 +167,8 @@ def train(opt, device):
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if opt.verbose:
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LOGGER.info(model)
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images, labels = next(iter(trainloader))
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file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg')
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logger.log_images(file, name='Train Examples')
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file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg")
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logger.log_images(file, name="Train Examples")
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logger.log_graph(model, imgsz) # log model
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# Optimizer
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@@ -163,11 +195,13 @@ def train(opt, device):
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best_fitness = 0.0
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scaler = amp.GradScaler(enabled=cuda)
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val = test_dir.stem # 'val' or 'test'
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LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n'
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f'Using {nw * WORLD_SIZE} dataloader workers\n'
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f"Logging results to {colorstr('bold', save_dir)}\n"
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f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
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f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}")
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LOGGER.info(
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f'Image sizes {imgsz} train, {imgsz} test\n'
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f'Using {nw * WORLD_SIZE} dataloader workers\n'
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f"Logging results to {colorstr('bold', save_dir)}\n"
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f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
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f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
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)
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for epoch in range(epochs): # loop over the dataset multiple times
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tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
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model.train()
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@@ -198,15 +232,14 @@ def train(opt, device):
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if RANK in {-1, 0}:
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# Print
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tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses
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mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
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pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36
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mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB)
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pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36
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# Test
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if i == len(pbar) - 1: # last batch
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top1, top5, vloss = validate.run(model=ema.ema,
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dataloader=testloader,
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criterion=criterion,
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pbar=pbar) # test accuracy, loss
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top1, top5, vloss = validate.run(
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model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar
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) # test accuracy, loss
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fitness = top1 # define fitness as top1 accuracy
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# Scheduler
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@@ -220,26 +253,28 @@ def train(opt, device):
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# Log
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metrics = {
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'train/loss': tloss,
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f'{val}/loss': vloss,
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'metrics/accuracy_top1': top1,
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'metrics/accuracy_top5': top5,
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'lr/0': optimizer.param_groups[0]['lr']} # learning rate
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"train/loss": tloss,
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f"{val}/loss": vloss,
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"metrics/accuracy_top1": top1,
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"metrics/accuracy_top5": top5,
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"lr/0": optimizer.param_groups[0]["lr"],
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} # learning rate
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logger.log_metrics(metrics, epoch)
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# Save model
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final_epoch = epoch + 1 == epochs
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if (not opt.nosave) or final_epoch:
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ckpt = {
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'epoch': epoch,
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'best_fitness': best_fitness,
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'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
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'ema': None, # deepcopy(ema.ema).half(),
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'updates': ema.updates,
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'optimizer': None, # optimizer.state_dict(),
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'opt': vars(opt),
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'git': GIT_INFO, # {remote, branch, commit} if a git repo
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'date': datetime.now().isoformat()}
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"epoch": epoch,
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"best_fitness": best_fitness,
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"model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
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"ema": None, # deepcopy(ema.ema).half(),
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"updates": ema.updates,
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"optimizer": None, # optimizer.state_dict(),
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"opt": vars(opt),
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"git": GIT_INFO, # {remote, branch, commit} if a git repo
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"date": datetime.now().isoformat(),
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}
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# Save last, best and delete
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torch.save(ckpt, last)
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@@ -249,49 +284,51 @@ def train(opt, device):
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# Train complete
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if RANK in {-1, 0} and final_epoch:
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LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
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f"\nResults saved to {colorstr('bold', save_dir)}"
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f'\nPredict: python classify/predict.py --weights {best} --source im.jpg'
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f'\nValidate: python classify/val.py --weights {best} --data {data_dir}'
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f'\nExport: python export.py --weights {best} --include onnx'
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f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
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f'\nVisualize: https://netron.app\n')
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LOGGER.info(
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f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
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f"\nResults saved to {colorstr('bold', save_dir)}"
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f'\nPredict: python classify/predict.py --weights {best} --source im.jpg'
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f'\nValidate: python classify/val.py --weights {best} --data {data_dir}'
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f'\nExport: python export.py --weights {best} --include onnx'
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f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
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f'\nVisualize: https://netron.app\n'
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)
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# Plot examples
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images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
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pred = torch.max(ema.ema(images.to(device)), 1)[1]
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file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg')
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file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg")
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# Log results
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meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()}
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logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
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meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
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logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch)
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logger.log_model(best, epochs, metadata=meta)
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def parse_opt(known=False):
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
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parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...')
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parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
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parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
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parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name')
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parser.add_argument('--name', default='exp', help='save to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False')
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parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer')
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parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate')
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parser.add_argument('--decay', type=float, default=5e-5, help='weight decay')
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parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon')
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parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head')
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parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)')
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parser.add_argument('--verbose', action='store_true', help='Verbose mode')
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parser.add_argument('--seed', type=int, default=0, help='Global training seed')
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parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
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parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
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parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
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parser.add_argument("--epochs", type=int, default=10, help="total training epochs")
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parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs")
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parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)")
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parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
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parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
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parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
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parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
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parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False")
|
||||
parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer")
|
||||
parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate")
|
||||
parser.add_argument("--decay", type=float, default=5e-5, help="weight decay")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon")
|
||||
parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head")
|
||||
parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)")
|
||||
parser.add_argument("--verbose", action="store_true", help="Verbose mode")
|
||||
parser.add_argument("--seed", type=int, default=0, help="Global training seed")
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
@@ -300,17 +337,17 @@ def main(opt):
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / 'requirements.txt')
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size'
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
|
||||
assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size"
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device('cuda', LOCAL_RANK)
|
||||
dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
|
||||
device = torch.device("cuda", LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Parameters
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
|
||||
@@ -328,6 +365,6 @@ def run(**kwargs):
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
Reference in new issue
Block a user