`ultralytics 8.0.19` seg/det dataset warning and DDP-cls/seg fixes (#595)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       # - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -25,14 +25,14 @@ repos:
       - id: check-docstring-first
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.37.3
+    rev: v3.3.1
     hooks:
       - id: pyupgrade
         name: Upgrade code
         args: [ --py37-plus ]
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.4
     hooks:
       - id: isort
         name: Sort imports
@@ -59,7 +59,14 @@ repos:
       - id: flake8
         name: PEP8
 
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.2
+    hooks:
+      - id: codespell
+        args:
+          - --ignore-words-list=crate,nd
+
   #- repo: https://github.com/asottile/yesqa
   #  rev: v1.4.0
   #  hooks:
-  #    - id: yesqa
+  #    - id: yesqa

docs/cli.md

@@ -183,7 +183,7 @@ Default arguments can be overriden by simply passing them as arguments in the CL
 You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments,
 i.e. `cfg=custom.yaml`.
 
-To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-config` command.
+To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-cfg` command.
 
 This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args,
 like `imgsz=320` in this example:
@@ -192,6 +192,6 @@ like `imgsz=320` in this example:
 
     === "CLI"
         ```bash
-        yolo copy-config
+        yolo copy-cfg
         yolo cfg=default_copy.yaml imgsz=320
         ```

examples/tutorial.ipynb

@@ -638,11 +638,11 @@
     {
       "cell_type": "code",
       "source": [
-        "# Load YOLOv8n-cls, train it on imagenette160 for 3 epochs and predict an image with it\n",
+        "# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n-cls.pt')  # load a pretrained YOLOv8n classification model\n",
-        "model.train(data='imagenette160', epochs=3)  # train the model\n",
+        "model.train(data='mnist160', epochs=3)  # train the model\n",
         "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {

tests/test_engine.py

@@ -3,13 +3,13 @@
 from pathlib import Path
 
 from ultralytics.yolo.cfg import get_cfg
-from ultralytics.yolo.utils import DEFAULT_CFG_PATH, ROOT, SETTINGS
+from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, SETTINGS
 from ultralytics.yolo.v8 import classify, detect, segment
 
 CFG_DET = 'yolov8n.yaml'
 CFG_SEG = 'yolov8n-seg.yaml'
 CFG_CLS = 'squeezenet1_0'
-CFG = get_cfg(DEFAULT_CFG_PATH)
+CFG = get_cfg(DEFAULT_CFG)
 MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
 SOURCE = ROOT / "assets"
 

ultralytics/nn/tasks.py

@@ -313,13 +313,39 @@ class ClassificationModel(BaseModel):
 # Functions ------------------------------------------------------------------------------------------------------------
 
 
+def torch_safe_load(weight):
+    """
+    This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it
+    catches the error, logs a warning message, and attempts to install the missing module via the check_requirements()
+    function. After installation, the function again attempts to load the model using torch.load().
+
+    Args:
+        weight (str): The file path of the PyTorch model.
+
+    Returns:
+        The loaded PyTorch model.
+    """
+    from ultralytics.yolo.utils.downloads import attempt_download
+
+    file = attempt_download(weight)  # search online if missing locally
+    try:
+        return torch.load(file, map_location='cpu')  # load
+    except ModuleNotFoundError as e:
+        if e.name == 'omegaconf':  # e.name is missing module name
+            LOGGER.warning(f"WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements."
+                           f"\nAutoInstall will run now for {e.name} but this feature will be removed in the future."
+                           f"\nRecommend fixes are to train a new model using updated ultraltyics package or to "
+                           f"download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0")
+        check_requirements(e.name)  # install missing module
+        return torch.load(file, map_location='cpu')  # load
+
+
 def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
     # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    from ultralytics.yolo.utils.downloads import attempt_download
 
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
-        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
+        ckpt = torch_safe_load(w)  # load ckpt
         args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
         ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
 
@@ -355,18 +381,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
 
 def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
     # Loads a single model weights
-    from ultralytics.yolo.utils.downloads import attempt_download
+    ckpt = torch_safe_load(weight)  # load ckpt
-
-    weight = attempt_download(weight)
-    try:
-        ckpt = torch.load(weight, map_location='cpu')  # load
-    except ModuleNotFoundError:
-        LOGGER.warning(f"WARNING ⚠️ {weight} is deprecated as it requires omegaconf, which is now removed from "
-                       "ultralytics requirements.\nAutoInstall will occur now but this feature will be removed for "
-                       "omegaconf models in the future.\nPlease train a new model or download updated models "
-                       "from https://github.com/ultralytics/assets/releases/tag/v0.0.0")
-        check_requirements('omegaconf')
-        ckpt = torch.load(weight, map_location='cpu')  # load
     args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
     model = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
 

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -611,6 +611,8 @@ class LoadImagesAndLabels(Dataset):
 
     def cache_labels(self, path=Path('./labels.cache'), prefix=''):
         # Cache dataset labels, check images and read shapes
+        if path.exists():
+            path.unlink()  # remove *.cache file if exists
         x = {}  # dict
         nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
         desc = f"{prefix}Scanning {path.parent / path.stem}..."

ultralytics/yolo/data/dataset.py

@@ -47,6 +47,8 @@ class YOLODataset(BaseDataset):
 
     def cache_labels(self, path=Path("./labels.cache")):
         # Cache dataset labels, check images and read shapes
+        if path.exists():
+            path.unlink()  # remove *.cache file if exists
         x = {"labels": []}
         nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
         desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
@@ -85,7 +87,7 @@ class YOLODataset(BaseDataset):
         x["results"] = nf, nm, ne, nc, len(self.im_files)
         x["msgs"] = msgs  # warnings
         x["version"] = self.cache_version  # cache version
-        self.im_files = [lb["im_file"] for lb in x["labels"]]
+        self.im_files = [lb["im_file"] for lb in x["labels"]]  # update im_files
         if is_dir_writeable(path.parent):
             np.save(str(path), x)  # save cache for next time
             path.with_suffix(".cache.npy").rename(path)  # remove .npy suffix
@@ -116,6 +118,17 @@ class YOLODataset(BaseDataset):
         # Read cache
         [cache.pop(k) for k in ("hash", "version", "msgs")]  # remove items
         labels = cache["labels"]
+
+        # Check if the dataset is all boxes or all segments
+        len_boxes = sum(len(lb["bboxes"]) for lb in labels)
+        len_segments = sum(len(lb["segments"]) for lb in labels)
+        if len_segments and len_boxes != len_segments:
+            LOGGER.warning(
+                f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, "
+                f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. "
+                "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.")
+            for lb in labels:
+                lb["segments"] = []
         nl = len(np.concatenate([label["cls"] for label in labels], 0))  # number of labels
         assert nl > 0, f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}"
         return labels

ultralytics/yolo/data/utils.py

@@ -14,7 +14,7 @@ import numpy as np
 import torch
 from PIL import ExifTags, Image, ImageOps
 
-from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, yaml_load
+from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, emojis, yaml_load
 from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
 from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import unzip_file
@@ -202,7 +202,10 @@ def check_det_dataset(dataset, autodownload=True):
 
     # Checks
     for k in 'train', 'val', 'names':
-        assert k in data, f"data.yaml '{k}:' field missing ❌"
+        if k not in data:
+            raise SyntaxError(
+                emojis(f"{dataset} '{k}:' key missing ❌.\n"
+                       f"'train', 'val' and 'names' are required in data.yaml files."))
     if isinstance(data['names'], (list, tuple)):  # old array format
         data['names'] = dict(enumerate(data['names']))  # convert to dict
     data['nc'] = len(data['names'])

ultralytics/yolo/engine/exporter.py

@@ -388,7 +388,7 @@ class Exporter:
     @try_export
     def _export_engine(self, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
         # YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt
-        assert self.im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `device==0`'
+        assert self.im.device.type != 'cpu', "export running on CPU but must be on GPU, i.e. use 'device=0'"
         try:
             import tensorrt as trt  # noqa
         except ImportError:

ultralytics/yolo/engine/model.py

@@ -53,7 +53,12 @@ class YOLO:
         self.overrides = {}  # overrides for trainer object
 
         # Load or create new YOLO model
-        {'.pt': self._load, '.yaml': self._new}[Path(model).suffix](model)
+        load_methods = {'.pt': self._load, '.yaml': self._new}
+        suffix = Path(model).suffix
+        if suffix in load_methods:
+            {'.pt': self._load, '.yaml': self._new}[suffix](model)
+        else:
+            raise NotImplementedError(f"'{suffix}' model loading not implemented")
 
     def __call__(self, source=None, stream=False, verbose=False, **kwargs):
         return self.predict(source, stream, verbose, **kwargs)

ultralytics/yolo/engine/predictor.py

@@ -35,7 +35,7 @@ from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
-from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, callbacks, colorstr, ops
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
@@ -61,12 +61,12 @@ class BasePredictor:
         data_path (str): Path to data.
     """
 
-    def __init__(self, cfg=DEFAULT_CFG_PATH, overrides=None):
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None):
         """
         Initializes the BasePredictor class.
 
         Args:
-            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
+            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)

ultralytics/yolo/engine/trainer.py

@@ -24,8 +24,8 @@ from ultralytics import __version__
 from ultralytics.nn.tasks import attempt_load_one_weight
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
-from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
+from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis,
-                                    emojis, yaml_save)
+                                    yaml_save)
 from ultralytics.yolo.utils.autobatch import check_train_batch_size
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
 from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
@@ -71,12 +71,12 @@ class BaseTrainer:
         csv (Path): Path to results CSV file.
     """
 
-    def __init__(self, cfg=DEFAULT_CFG_PATH, overrides=None):
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None):
         """
         Initializes the BaseTrainer class.
 
         Args:
-            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
+            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)

ultralytics/yolo/engine/validator.py

@@ -10,7 +10,7 @@ from tqdm import tqdm
 from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
-from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
 from ultralytics.yolo.utils.checks import check_imgsz
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.ops import Profile
@@ -52,7 +52,7 @@ class BaseValidator:
         self.dataloader = dataloader
         self.pbar = pbar
         self.logger = logger or LOGGER
-        self.args = args or get_cfg(DEFAULT_CFG_PATH)
+        self.args = args or get_cfg(DEFAULT_CFG)
         self.model = None
         self.data = None
         self.device = None

ultralytics/yolo/utils/__init__.py

@@ -127,8 +127,7 @@ def is_colab():
     Returns:
         bool: True if running inside a Colab notebook, False otherwise.
     """
-    # Check if the 'google.colab' module is present in sys.modules
+    return 'COLAB_RELEASE_TAG' in os.environ or 'COLAB_BACKEND_VERSION' in os.environ
-    return 'google.colab' in sys.modules
 
 
 def is_kaggle():

ultralytics/yolo/utils/checks.py

@@ -224,7 +224,7 @@ def check_file(file, suffix=''):
         for d in 'models', 'yolo/data':  # search directories
             files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True))  # find file
         if not files:
-            raise FileNotFoundError(f"{file} does not exist")
+            raise FileNotFoundError(f"'{file}' does not exist")
         elif len(files) > 1:
             raise FileNotFoundError(f"Multiple files match '{file}', specify exact path: {files}")
         return files[0]  # return file

ultralytics/yolo/utils/dist.py

@@ -10,17 +10,14 @@ from . import USER_CONFIG_DIR
 
 
 def find_free_network_port() -> int:
-    # https://github.com/Lightning-AI/lightning/blob/master/src/lightning_lite/plugins/environments/lightning.py
     """Finds a free port on localhost.
 
     It is useful in single-node training when we don't want to connect to a real main node but have to set the
     `MASTER_PORT` environment variable.
     """
-    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-    s.bind(("", 0))
+        s.bind(('127.0.0.1', 0))
-    port = s.getsockname()[1]
+        return s.getsockname()[1]  # port
-    s.close()
-    return port
 
 
 def generate_ddp_file(trainer):

ultralytics/yolo/utils/downloads.py

@@ -91,12 +91,10 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
 
         file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
         if name in assets:
-            url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl'  # backup gdrive mirror
+            safe_download(file,
-            safe_download(
+                          url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
-                file,
+                          min_bytes=1E5,
-                url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
+                          error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}')
-                min_bytes=1E5,
-                error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
 
         return str(file)
 

ultralytics/yolo/utils/torch_utils.py

@@ -58,7 +58,7 @@ def DDP_model(model):
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
 
-def select_device(device='', batch_size=0, newline=False):
+def select_device(device='', batch=0, newline=False):
     # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
     ver = git_describe() or ultralytics.__version__  # git commit or pip package version
     s = f'Ultralytics YOLOv{ver} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
@@ -71,14 +71,15 @@ def select_device(device='', batch_size=0, newline=False):
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
     elif device:  # non-cpu device requested
         os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()
-        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
+        if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', ''))):
-            f"Invalid CUDA 'device={device}' requested, use 'device=cpu' or pass valid CUDA device(s)"
+            raise ValueError(f"Invalid CUDA 'device={device}' requested, use 'device=cpu' or pass valid CUDA device(s)")
 
     if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
         devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
         n = len(devices)  # device count
-        if n > 1 and batch_size > 0:  # check batch_size is divisible by device_count
+        if n > 1 and batch > 0 and batch % n != 0:  # check batch_size is divisible by device_count
-            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
+            raise ValueError(f'batch={batch} is not multiple of GPU count {n}.\n'
+                             f'Try batch={batch // n} or batch={batch // n + 1}')
         space = ' ' * (len(s) + 1)
         for i, d in enumerate(devices):
             p = torch.cuda.get_device_properties(i)

ultralytics/yolo/v8/classify/train.py

@@ -13,11 +13,11 @@ from ultralytics.yolo.utils.torch_utils import strip_optimizer
 
 class ClassificationTrainer(BaseTrainer):
 
-    def __init__(self, config=DEFAULT_CFG, overrides=None):
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None):
         if overrides is None:
             overrides = {}
         overrides["task"] = "classify"
-        super().__init__(config, overrides)
+        super().__init__(cfg, overrides)
 
     def set_model_attributes(self):
         self.model.names = self.data["names"]

ultralytics/yolo/v8/classify/val.py

@@ -47,7 +47,7 @@ class ClassificationValidator(BaseValidator):
 
 def val(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
-    cfg.data = cfg.data or "imagenette160"
+    cfg.data = cfg.data or "mnist160"
     validator = ClassificationValidator(args=cfg)
     validator(model=cfg.model)
 

ultralytics/yolo/v8/segment/train.py

@@ -18,11 +18,11 @@ from ultralytics.yolo.v8.detect.train import Loss
 # BaseTrainer python usage
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
-    def __init__(self, config=DEFAULT_CFG, overrides=None):
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None):
         if overrides is None:
             overrides = {}
         overrides["task"] = "segment"
-        super().__init__(config, overrides)
+        super().__init__(cfg, overrides)
 
     def get_model(self, cfg=None, weights=None, verbose=True):
         model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose)