`ultralytics 8.0.21` Windows, segments, YAML fixes (#655)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: corey-nm <109536191+corey-nm@users.noreply.github.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -51,9 +51,9 @@ body:
       label: Environment
       description: Please specify the software and hardware you used to produce the bug.
       placeholder: |
-        - YOLO: YOLOv8 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
+        - YOLO: Ultralytics YOLOv8.0.21 🚀 Python-3.8.10 torch-1.13.1+cu117 CUDA:0 (A100-SXM-80GB, 81251MiB)
         - OS: Ubuntu 20.04
-        - Python: 3.9.0
+        - Python: 3.8.10
     validations:
       required: false
 

tests/test_cli.py

@@ -35,28 +35,29 @@ def test_train_cls():
 
 # Val checks -----------------------------------------------------------------------------------------------------------
 def test_val_detect():
-    run(f'yolo val detect model={MODEL}.pt data=coco8.yaml imgsz=32 epochs=1')
+    run(f'yolo val detect model={MODEL}.pt data=coco8.yaml imgsz=32')
 
 
 def test_val_segment():
-    run(f'yolo val segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32 epochs=1')
+    run(f'yolo val segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32')
 
 
 def test_val_classify():
-    pass
+    run(f'yolo val classify model={MODEL}-cls.pt data=mnist160 imgsz=32')
 
 
 # Predict checks -------------------------------------------------------------------------------------------------------
 def test_predict_detect():
-    run(f"yolo predict detect model={MODEL}.pt source={ROOT / 'assets'} imgsz=320 conf=0.25")
+    run(f"yolo predict detect model={MODEL}.pt source={ROOT / 'assets'} imgsz=32")
+    run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32")
 
 
 def test_predict_segment():
-    run(f"yolo predict segment model={MODEL}-seg.pt source={ROOT / 'assets'}")
+    run(f"yolo predict segment model={MODEL}-seg.pt source={ROOT / 'assets'} imgsz=32")
 
 
 def test_predict_classify():
-    pass
+    run(f"yolo predict segment model={MODEL}-cls.pt source={ROOT / 'assets'} imgsz=32")
 
 
 # Export checks --------------------------------------------------------------------------------------------------------

tests/test_python.py

@@ -111,9 +111,11 @@ def test_export_coreml():
     model.export(format='coreml')
 
 
-def test_export_paddle():
-    model = YOLO(MODEL)
-    model.export(format='paddle')
+def test_export_paddle(enabled=False):
+    # Paddle protobuf requirements conflicting with onnx protobuf requirements
+    if enabled:
+        model = YOLO(MODEL)
+        model.export(format='paddle')
 
 
 def test_all_model_yamls():

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.20"
+__version__ = "8.0.21"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/yolo/cfg/__init__.py

@@ -9,8 +9,8 @@ from types import SimpleNamespace
 from typing import Dict, List, Union
 
 from ultralytics import __version__
-from ultralytics.yolo.utils import (DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, PREFIX, ROOT, USER_CONFIG_DIR,
-                                    IterableSimpleNamespace, colorstr, yaml_load, yaml_print)
+from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, PREFIX, ROOT,
+                                    USER_CONFIG_DIR, IterableSimpleNamespace, colorstr, emojis, yaml_load, yaml_print)
 from ultralytics.yolo.utils.checks import check_yolo
 
 CLI_HELP_MSG = \
@@ -69,7 +69,7 @@ def cfg2dict(cfg):
     return cfg
 
 
-def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace], overrides: Dict = None):
+def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, overrides: Dict = None):
     """
     Load and merge configuration data from a file or dictionary.
 
@@ -214,17 +214,19 @@ def entrypoint(debug=False):
     # Mode
     mode = overrides.pop('mode', None)
     model = overrides.pop('model', None)
-    if mode == 'checks':
+    if mode is None:
+        mode = DEFAULT_CFG.mode or 'predict'
+        LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
+    elif mode not in modes:
+        if mode != 'checks':
+            raise ValueError(emojis(f"ERROR ❌ Invalid 'mode={mode}'. Valid modes are {modes}."))
         LOGGER.warning("WARNING ⚠️ 'yolo mode=checks' is deprecated. Use 'yolo checks' instead.")
         check_yolo()
         return
-    elif mode is None:
-        mode = DEFAULT_CFG_DICT['mode'] or 'predict'
-        LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.")
 
     # Model
     if model is None:
-        model = DEFAULT_CFG_DICT['model'] or 'yolov8n.pt'
+        model = DEFAULT_CFG.model or 'yolov8n.pt'
         LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
     from ultralytics.yolo.engine.model import YOLO
     model = YOLO(model)
@@ -232,21 +234,21 @@ def entrypoint(debug=False):
 
     # Task
     if mode == 'predict' and 'source' not in overrides:
-        overrides['source'] = DEFAULT_CFG_DICT['source'] or ROOT / "assets" if (ROOT / "assets").exists() \
+        overrides['source'] = DEFAULT_CFG.source or ROOT / "assets" if (ROOT / "assets").exists() \
             else "https://ultralytics.com/images/bus.jpg"
         LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
     elif mode in ('train', 'val'):
         if 'data' not in overrides:
-            overrides['data'] = DEFAULT_CFG_DICT['data'] or 'mnist160' if task == 'classify' \
+            overrides['data'] = DEFAULT_CFG.data or 'mnist160' if task == 'classify' \
                 else 'coco128-seg.yaml' if task == 'segment' else 'coco128.yaml'
             LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
     elif mode == 'export':
         if 'format' not in overrides:
-            overrides['format'] = DEFAULT_CFG_DICT['format'] or 'torchscript'
+            overrides['format'] = DEFAULT_CFG.format or 'torchscript'
             LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
 
     # Run command in python
-    getattr(model, mode)(verbose=True, **overrides)
+    getattr(model, mode)(**overrides)
 
 
 # Special modes --------------------------------------------------------------------------------------------------------

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -44,7 +44,8 @@ class LoadStreams:
                 assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
                 assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'
             cap = cv2.VideoCapture(s)
-            assert cap.isOpened(), f'{st}Failed to open {s}'
+            if not cap.isOpened():
+                raise ConnectionError(f'{st}Failed to open {s}')
             w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
             h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
             fps = cap.get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
@@ -188,8 +189,9 @@ class LoadImages:
             self._new_video(videos[0])  # new video
         else:
             self.cap = None
-        assert self.nf > 0, f'No images or videos found in {p}. ' \
-                            f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
+        if self.nf == 0:
+            raise FileNotFoundError(f'No images or videos found in {p}. '
+                                    f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}')
 
     def __iter__(self):
         self.count = 0
@@ -223,7 +225,8 @@ class LoadImages:
             # Read image
             self.count += 1
             im0 = cv2.imread(path)  # BGR
-            assert im0 is not None, f'Image Not Found {path}'
+            if im0 is None:
+                raise FileNotFoundError(f'Image Not Found {path}')
             s = f'image {self.count}/{self.nf} {path}: '
 
         if self.transforms:

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -23,14 +23,13 @@ import numpy as np
 import psutil
 import torch
 import torchvision
-import yaml
 from PIL import ExifTags, Image, ImageOps
 from torch.utils.data import DataLoader, Dataset, dataloader, distributed
 from tqdm import tqdm
 
 from ultralytics.yolo.data.utils import check_det_dataset, unzip_file
 from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, is_colab, is_dir_writeable,
-                                    is_kaggle)
+                                    is_kaggle, yaml_load)
 from ultralytics.yolo.utils.checks import check_requirements, check_yaml
 from ultralytics.yolo.utils.ops import clean_str, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn
 from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
@@ -1056,10 +1055,9 @@ class HUBDatasetStats():
         # Initialize class
         zipped, data_dir, yaml_path = self._unzip(Path(path))
         try:
-            with open(check_yaml(yaml_path), errors='ignore') as f:
-                data = yaml.safe_load(f)  # data dict
-                if zipped:
-                    data['path'] = data_dir
+            data = yaml_load(check_yaml(yaml_path))  # data dict
+            if zipped:
+                data['path'] = data_dir
         except Exception as e:
             raise Exception("error/HUB/dataset_stats/yaml_load") from e
 

ultralytics/yolo/engine/exporter.py

@@ -129,7 +129,7 @@ class Exporter:
             overrides (dict, optional): Configuration overrides. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)
-        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
         callbacks.add_integration_callbacks(self)
 
     @smart_inference_mode()

ultralytics/yolo/engine/model.py

@@ -61,8 +61,8 @@ class YOLO:
         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)
+    def __call__(self, source=None, stream=False, **kwargs):
+        return self.predict(source, stream, **kwargs)
 
     def _new(self, cfg: str, verbose=True):
         """
@@ -118,7 +118,7 @@ class YOLO:
         self.model.fuse()
 
     @smart_inference_mode()
-    def predict(self, source=None, stream=False, verbose=False, **kwargs):
+    def predict(self, source=None, stream=False, **kwargs):
         """
         Perform prediction using the YOLO model.
 
@@ -126,7 +126,6 @@ class YOLO:
             source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
                           Accepts all source types accepted by the YOLO model.
             stream (bool): Whether to stream the predictions or not. Defaults to False.
-            verbose (bool): Whether to print verbose information or not. Defaults to False.
             **kwargs : Additional keyword arguments passed to the predictor.
                        Check the 'configuration' section in the documentation for all available options.
 
@@ -143,7 +142,7 @@ class YOLO:
             self.predictor.setup_model(model=self.model)
         else:  # only update args if predictor is already setup
             self.predictor.args = get_cfg(self.predictor.args, overrides)
-        return self.predictor(source=source, stream=stream, verbose=verbose)
+        return self.predictor(source=source, stream=stream)
 
     @smart_inference_mode()
     def val(self, data=None, **kwargs):
@@ -234,7 +233,8 @@ class YOLO:
         """
         return self.model.names
 
-    def add_callback(self, event: str, func):
+    @staticmethod
+    def add_callback(event: str, func):
         """
         Add callback
         """
@@ -242,16 +242,8 @@ class YOLO:
 
     @staticmethod
     def _reset_ckpt_args(args):
-        args.pop("project", None)
-        args.pop("name", None)
-        args.pop("exist_ok", None)
-        args.pop("resume", None)
-        args.pop("batch", None)
-        args.pop("epochs", None)
-        args.pop("cache", None)
-        args.pop("save_json", None)
-        args.pop("half", None)
-        args.pop("v5loader", None)
-
-        # set device to '' to prevent from auto DDP usage
-        args["device"] = ''
+        for arg in 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', 'save_json', \
+                'half', 'v5loader':
+            args.pop(arg, None)
+
+        args["device"] = ''  # set device to '' to prevent auto-DDP usage

ultralytics/yolo/engine/predictor.py

@@ -88,7 +88,7 @@ class BasePredictor:
         self.vid_path, self.vid_writer = None, None
         self.annotator = None
         self.data_path = None
-        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
         callbacks.add_integration_callbacks(self)
 
     def preprocess(self, img):
@@ -151,19 +151,19 @@ class BasePredictor:
         self.bs = bs
 
     @smart_inference_mode()
-    def __call__(self, source=None, model=None, verbose=False, stream=False):
+    def __call__(self, source=None, model=None, stream=False):
         if stream:
-            return self.stream_inference(source, model, verbose)
+            return self.stream_inference(source, model)
         else:
-            return list(self.stream_inference(source, model, verbose))  # merge list of Result into one
+            return list(self.stream_inference(source, model))  # merge list of Result into one
 
     def predict_cli(self):
         # Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
-        gen = self.stream_inference(verbose=True)
+        gen = self.stream_inference()
         for _ in gen:  # running CLI inference without accumulating any outputs (do not modify)
             pass
 
-    def stream_inference(self, source=None, model=None, verbose=False):
+    def stream_inference(self, source=None, model=None):
         self.run_callbacks("on_predict_start")
 
         # setup model
@@ -201,7 +201,7 @@ class BasePredictor:
                 p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
                 p = Path(p)
 
-                if verbose or self.args.save or self.args.save_txt or self.args.show:
+                if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
                     s += self.write_results(i, self.results, (p, im, im0))
 
                 if self.args.show:
@@ -214,11 +214,11 @@ class BasePredictor:
             yield from self.results
 
             # Print time (inference-only)
-            if verbose:
+            if self.args.verbose:
                 LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
 
         # Print results
-        if verbose and self.seen:
+        if self.args.verbose and self.seen:
             t = tuple(x.t / self.seen * 1E3 for x in self.dt)  # speeds per image
             LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
                         f'{(1, 3, *self.imgsz)}' % t)
@@ -243,7 +243,7 @@ class BasePredictor:
         if isinstance(source, (str, int, Path)):  # int for local usb carame
             source = str(source)
             is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-            is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+            is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://'))
             webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
             screenshot = source.lower().startswith('screen')
             if is_url and is_file:

ultralytics/yolo/engine/trainer.py

@@ -85,7 +85,6 @@ class BaseTrainer:
         self.console = LOGGER
         self.validator = None
         self.model = None
-        self.callbacks = defaultdict(list)
         init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
 
         # Dirs
@@ -141,7 +140,7 @@ class BaseTrainer:
         self.plot_idx = [0, 1, 2]
 
         # Callbacks
-        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
         if RANK in {0, -1}:
             callbacks.add_integration_callbacks(self)
 

ultralytics/yolo/engine/validator.py

@@ -70,7 +70,7 @@ class BaseValidator:
         if self.args.conf is None:
             self.args.conf = 0.001  # default conf=0.001
 
-        self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()})  # add callbacks
+        self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
 
     @smart_inference_mode()
     def __call__(self, trainer=None, model=None):

ultralytics/yolo/utils/__init__.py

@@ -5,6 +5,7 @@ import inspect
 import logging.config
 import os
 import platform
+import re
 import subprocess
 import sys
 import tempfile
@@ -113,12 +114,66 @@ class IterableSimpleNamespace(SimpleNamespace):
         return getattr(self, key, default)
 
 
+def yaml_save(file='data.yaml', data=None):
+    """
+    Save YAML data to a file.
+
+    Args:
+        file (str, optional): File name. Default is 'data.yaml'.
+        data (dict, optional): Data to save in YAML format. Default is None.
+
+    Returns:
+        None: Data is saved to the specified file.
+    """
+    file = Path(file)
+    if not file.parent.exists():
+        # Create parent directories if they don't exist
+        file.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(file, 'w') as f:
+        # Dump data to file in YAML format, converting Path objects to strings
+        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
+
+
+def yaml_load(file='data.yaml', append_filename=False):
+    """
+    Load YAML data from a file.
+
+    Args:
+        file (str, optional): File name. Default is 'data.yaml'.
+        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.
+
+    Returns:
+        dict: YAML data and file name.
+    """
+    with open(file, errors='ignore', encoding='utf-8') as f:
+        # Add YAML filename to dict and return
+        s = f.read()  # string
+        if not s.isprintable():  # remove special characters
+            s = re.sub(r'[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+', '', s)
+        return {**yaml.safe_load(s), 'yaml_file': str(file)} if append_filename else yaml.safe_load(s)
+
+
+def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
+    """
+    Pretty prints a yaml file or a yaml-formatted dictionary.
+
+    Args:
+        yaml_file: The file path of the yaml file or a yaml-formatted dictionary.
+
+    Returns:
+        None
+    """
+    yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
+    dump = yaml.dump(yaml_dict, default_flow_style=False)
+    LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")
+
+
 # Default configuration
-with open(DEFAULT_CFG_PATH, errors='ignore') as f:
-    DEFAULT_CFG_DICT = yaml.safe_load(f)
-    for k, v in DEFAULT_CFG_DICT.items():
-        if isinstance(v, str) and v.lower() == 'none':
-            DEFAULT_CFG_DICT[k] = None
+DEFAULT_CFG_DICT = yaml_load(DEFAULT_CFG_PATH)
+for k, v in DEFAULT_CFG_DICT.items():
+    if isinstance(v, str) and v.lower() == 'none':
+        DEFAULT_CFG_DICT[k] = None
 DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
 DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)
 
@@ -393,58 +448,6 @@ def threaded(func):
     return wrapper
 
 
-def yaml_save(file='data.yaml', data=None):
-    """
-    Save YAML data to a file.
-
-    Args:
-        file (str, optional): File name. Default is 'data.yaml'.
-        data (dict, optional): Data to save in YAML format. Default is None.
-
-    Returns:
-        None: Data is saved to the specified file.
-    """
-    file = Path(file)
-    if not file.parent.exists():
-        # Create parent directories if they don't exist
-        file.parent.mkdir(parents=True, exist_ok=True)
-
-    with open(file, 'w') as f:
-        # Dump data to file in YAML format, converting Path objects to strings
-        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
-
-
-def yaml_load(file='data.yaml', append_filename=False):
-    """
-    Load YAML data from a file.
-
-    Args:
-        file (str, optional): File name. Default is 'data.yaml'.
-        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.
-
-    Returns:
-        dict: YAML data and file name.
-    """
-    with open(file, errors='ignore') as f:
-        # Add YAML filename to dict and return
-        return {**yaml.safe_load(f), 'yaml_file': str(file)} if append_filename else yaml.safe_load(f)
-
-
-def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
-    """
-    Pretty prints a yaml file or a yaml-formatted dictionary.
-
-    Args:
-        yaml_file: The file path of the yaml file or a yaml-formatted dictionary.
-
-    Returns:
-        None
-    """
-    yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
-    dump = yaml.dump(yaml_dict, default_flow_style=False)
-    LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")
-
-
 def set_sentry():
     """
     Initialize the Sentry SDK for error tracking and reporting if pytest is not currently running.

ultralytics/yolo/utils/checks.py

@@ -207,9 +207,9 @@ def check_file(file, suffix=''):
     # Search/download file (if necessary) and return path
     check_suffix(file, suffix)  # optional
     file = str(file)  # convert to str()
-    if Path(file).is_file() or not file:  # exists
+    if not file or ('://' not in file and Path(file).is_file()):  # exists ('://' check required in Windows Python<3.10)
         return file
-    elif file.startswith(('http:/', 'https:/')):  # download
+    elif file.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://')):  # download
         url = file  # warning: Pathlib turns :// -> :/
         file = Path(urllib.parse.unquote(file).split('?')[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth
         if Path(file).is_file():
@@ -276,7 +276,7 @@ def git_describe(path=ROOT):  # path must be a directory
     try:
         assert (Path(path) / '.git').is_dir()
         return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
-    except Exception:
+    except AssertionError:
         return ''
 
 

ultralytics/yolo/utils/downloads.py

@@ -104,7 +104,7 @@ def download(url, dir=Path.cwd(), unzip=True, delete=True, curl=False, threads=1
     def download_one(url, dir):
         # Download 1 file
         success = True
-        if Path(url).is_file():
+        if '://' not in str(url) and Path(url).is_file():  # exists ('://' check required in Windows Python<3.10)
             f = Path(url)  # filename
         else:  # does not exist
             f = dir / Path(url).name

ultralytics/yolo/utils/torch_utils.py

@@ -17,11 +17,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.parallel import DistributedDataParallel as DDP
 
-import ultralytics
 from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER
-from ultralytics.yolo.utils.checks import git_describe
-
-from .checks import check_version
+from ultralytics.yolo.utils.checks import check_version
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
@@ -60,8 +57,8 @@ def DDP_model(model):
 
 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__} '
+    from ultralytics import __version__
+    s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
     device = str(device).lower()
     for remove in 'cuda:', 'none', '(', ')', '[', ']', "'", ' ':
         device = device.replace(remove, '')  # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
@@ -247,6 +244,7 @@ class ModelEMA:
     """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
     Keeps a moving average of everything in the model state_dict (parameters and buffers)
     For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    To disable EMA set the `enabled` attribute to `False`.
     """
 
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):
@@ -256,22 +254,25 @@ class ModelEMA:
         self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
         for p in self.ema.parameters():
             p.requires_grad_(False)
+        self.enabled = True
 
     def update(self, model):
         # Update EMA parameters
-        self.updates += 1
-        d = self.decay(self.updates)
+        if self.enabled:
+            self.updates += 1
+            d = self.decay(self.updates)
 
-        msd = de_parallel(model).state_dict()  # model state_dict
-        for k, v in self.ema.state_dict().items():
-            if v.dtype.is_floating_point:  # true for FP16 and FP32
-                v *= d
-                v += (1 - d) * msd[k].detach()
-        # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'
+            msd = de_parallel(model).state_dict()  # model state_dict
+            for k, v in self.ema.state_dict().items():
+                if v.dtype.is_floating_point:  # true for FP16 and FP32
+                    v *= d
+                    v += (1 - d) * msd[k].detach()
+                    # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype},  model {msd[k].dtype}'
 
     def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
         # Update EMA attributes
-        copy_attr(self.ema, model, include, exclude)
+        if self.enabled:
+            copy_attr(self.ema, model, include, exclude)
 
 
 def strip_optimizer(f='best.pt', s=''):
@@ -285,8 +286,8 @@ def strip_optimizer(f='best.pt', s=''):
             strip_optimizer(f)
 
     Args:
-        f (str): file path to model state to strip the optimizer from. Default is 'best.pt'.
-        s (str): file path to save the model with stripped optimizer to. Default is ''. If not provided, the original file will be overwritten.
+        f (str): file path to model to strip the optimizer from. Default is 'best.pt'.
+        s (str): file path to save the model with stripped optimizer to. If not provided, 'f' will be overwritten.
 
     Returns:
         None
@@ -364,12 +365,12 @@ class EarlyStopping:
     Early stopping class that stops training when a specified number of epochs have passed without improvement.
     """
 
-    def __init__(self, patience=30):
+    def __init__(self, patience=50):
         """
         Initialize early stopping object
 
         Args:
-            patience (int, optional): Number of epochs to wait after fitness stops improving before stopping. Default is 30.
+            patience (int, optional): Number of epochs to wait after fitness stops improving before stopping.
         """
         self.best_fitness = 0.0  # i.e. mAP
         self.best_epoch = 0