`imgsz` warning fix, download function consolidation (#681)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: HaeJin Lee <seareale@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.22"
+__version__ = "8.0.23"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/nn/autobackend.py

@@ -14,7 +14,7 @@ from PIL import Image
 
 from ultralytics.yolo.utils import LOGGER, ROOT, yaml_load
 from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_version
-from ultralytics.yolo.utils.downloads import attempt_download, is_url
+from ultralytics.yolo.utils.downloads import attempt_download_asset, is_url
 from ultralytics.yolo.utils.ops import xywh2xyxy
 
 
@@ -58,7 +58,7 @@ class AutoBackend(nn.Module):
         model = None  # TODO: resolves ONNX inference, verify effect on other backends
         cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
         if not (pt or triton or nn_module):
-            w = attempt_download(w)  # download if not local
+            w = attempt_download_asset(w)  # download if not local
 
         # NOTE: special case: in-memory pytorch model
         if nn_module:

ultralytics/nn/tasks.py

@@ -325,9 +325,9 @@ def torch_safe_load(weight):
     Returns:
         The loaded PyTorch model.
     """
-    from ultralytics.yolo.utils.downloads import attempt_download
+    from ultralytics.yolo.utils.downloads import attempt_download_asset
 
-    file = attempt_download(weight)  # search online if missing locally
+    file = attempt_download_asset(weight)  # search online if missing locally
     try:
         return torch.load(file, map_location='cpu')  # load
     except ModuleNotFoundError as e:

ultralytics/yolo/cfg/__init__.py

@@ -90,7 +90,7 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, override
 
     # Type checks
     for k in 'project', 'name':
-        if isinstance(cfg[k], (int, float)):
+        if k in cfg and isinstance(cfg[k], (int, float)):
             cfg[k] = str(cfg[k])
 
     # Return instance
@@ -176,7 +176,7 @@ def entrypoint(debug=False):
         'version': lambda: LOGGER.info(__version__),
         'settings': lambda: yaml_print(USER_CONFIG_DIR / 'settings.yaml'),
         'cfg': lambda: yaml_print(DEFAULT_CFG_PATH),
-        'copy-cfg': copy_default_config}
+        'copy-cfg': copy_default_cfg}
 
     overrides = {}  # basic overrides, i.e. imgsz=320
     for a in merge_equals_args(args):  # merge spaces around '=' sign
@@ -221,7 +221,7 @@ def entrypoint(debug=False):
     task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='mnist160')
 
     # Mode
-    mode = overrides['mode']
+    mode = overrides.get('mode', None)
     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}'.")
@@ -266,7 +266,7 @@ def entrypoint(debug=False):
 
 
 # Special modes --------------------------------------------------------------------------------------------------------
-def copy_default_config():
+def copy_default_cfg():
     new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
     shutil.copy2(DEFAULT_CFG_PATH, new_file)
     LOGGER.info(f"{PREFIX}{DEFAULT_CFG_PATH} copied to {new_file}\n"

ultralytics/yolo/data/augment.py

@@ -44,8 +44,20 @@ class Compose:
         self.transforms = transforms
 
     def __call__(self, data):
+        mosaic_p = None
+        mosaic_imgsz = None
+
         for t in self.transforms:
+            if isinstance(t, Mosaic):
+                temp = t(data)
+                mosaic_p = False if temp == data else True
+                mosaic_imgsz = t.imgsz
+                data = temp
+            else:
+                if isinstance(t, RandomPerspective):
+                    t.border = [-mosaic_imgsz // 2, -mosaic_imgsz // 2] if mosaic_p else [0, 0]
                 data = t(data)
+
         return data
 
     def append(self, transform):

ultralytics/yolo/data/base.py

@@ -120,7 +120,8 @@ class BaseDataset(Dataset):
                 im = np.load(fn)
             else:  # read image
                 im = cv2.imread(f)  # BGR
-                assert im is not None, f"Image Not Found {f}"
+                if im is None:
+                    raise FileNotFoundError(f"Image Not Found {f}")
             h0, w0 = im.shape[:2]  # orig hw
             r = self.imgsz / max(h0, w0)  # ratio
             if r != 1:  # if sizes are not equal

ultralytics/yolo/data/build.py

@@ -65,7 +65,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
     assert mode in ["train", "val"]
     shuffle = mode == "train"
     if cfg.rect and shuffle:
-        LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
+        LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = YOLODataset(

ultralytics/yolo/data/datasets/Argoverse.yaml

@@ -64,7 +64,7 @@ download: |
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
-  download(urls, dir=dir, delete=False)
+  download(urls, dir=dir)
 
   # Convert
   annotations_dir = 'Argoverse-HD/annotations/'

ultralytics/yolo/data/datasets/Objects365.yaml

@@ -411,12 +411,12 @@ download: |
       # Download
       url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
       if split == 'train':
-          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir)  # annotations json
-          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8)
       elif split == 'val':
-          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir)  # annotations json
-          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8)
-          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8)
 
       # Move
       for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):

ultralytics/yolo/data/datasets/SKU-110K.yaml

@@ -34,7 +34,7 @@ download: |
   dir = Path(yaml['path'])  # dataset root dir
   parent = Path(dir.parent)  # download dir
   urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
-  download(urls, dir=parent, delete=False)
+  download(urls, dir=parent)
 
   # Rename directories
   if dir.exists():

ultralytics/yolo/data/datasets/VOC.yaml

@@ -81,7 +81,7 @@ download: |
   urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
           f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
           f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+  download(urls, dir=dir / 'images', curl=True, threads=3)
 
   # Convert
   path = dir / 'images/VOCdevkit'

ultralytics/yolo/data/datasets/xView.yaml

@@ -138,7 +138,7 @@ download: |
   # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
   #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
   #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
-  # download(urls, dir=dir, delete=False)
+  # download(urls, dir=dir)
 
   # Convert labels
   convert_labels(dir / 'xView_train.geojson')

ultralytics/yolo/data/utils.py

@@ -237,11 +237,7 @@ def check_det_dataset(dataset, autodownload=True):
                 raise FileNotFoundError(msg)
             t = time.time()
             if s.startswith('http') and s.endswith('.zip'):  # URL
-                f = Path(s).name  # filename
+                safe_download(url=s, dir=DATASETS_DIR, delete=True)
-                safe_download(file=f, url=s)
-                Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True)  # create root
-                unzip_file(f, path=DATASETS_DIR)  # unzip
-                Path(f).unlink()  # remove zip
                 r = None  # success
             elif s.startswith('bash '):  # bash script
                 LOGGER.info(f'Running {s} ...')
@@ -251,7 +247,7 @@ def check_det_dataset(dataset, autodownload=True):
             dt = f'({round(time.time() - t, 1)}s)'
             s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
             LOGGER.info(f"Dataset download {s}")
-    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
+    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf')  # download fonts
 
     return data  # dictionary
 

ultralytics/yolo/engine/exporter.py

@@ -125,7 +125,7 @@ class Exporter:
         Initializes the Exporter 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/utils/__init__.py

@@ -28,7 +28,6 @@ DEFAULT_CFG_PATH = ROOT / "yolo/cfg/default.yaml"
 RANK = int(os.getenv('RANK', -1))
 NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
 AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
-FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
 VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true'  # global verbose mode
 TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}'  # tqdm bar format
 LOGGING_NAME = 'ultralytics'
@@ -328,6 +327,20 @@ def get_git_origin_url():
     return None  # if not git dir or on error
 
 
+def get_git_branch():
+    """
+    Returns the current git branch name. If not in a git repository, returns None.
+
+    Returns:
+        (str) or (None): The current git branch name.
+    """
+    if is_git_dir():
+        with contextlib.suppress(subprocess.CalledProcessError):
+            origin = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+            return origin.decode().strip()
+    return None  # if not git dir or on error
+
+
 def get_default_args(func):
     # Get func() default arguments
     signature = inspect.signature(func)
@@ -466,7 +479,8 @@ def set_sentry():
     if SETTINGS['sync'] and \
             not is_pytest_running() and \
             not is_github_actions_ci() and \
-            (is_pip_package() or get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git"):
+            (is_pip_package() or
+             (get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git" and get_git_branch() == "main")):
         import sentry_sdk  # noqa
 
         import ultralytics

ultralytics/yolo/utils/autobatch.py

@@ -28,7 +28,7 @@ def autobatch(model, imgsz=640, fraction=0.7, batch_size=16):
 
     # Check device
     prefix = colorstr('AutoBatch: ')
-    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
+    LOGGER.info(f'{prefix}Computing optimal batch size for imgsz={imgsz}')
     device = next(model.parameters()).device  # get model device
     if device.type == 'cpu':
         LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')

ultralytics/yolo/utils/checks.py

@@ -17,9 +17,10 @@ import pkg_resources as pkg
 import psutil
 import torch
 from IPython import display
+from matplotlib import font_manager
 
-from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
+from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis,
-                                    emojis, is_colab, is_docker, is_jupyter)
+                                    is_colab, is_docker, is_jupyter)
 
 
 def is_ascii(s) -> bool:
@@ -57,15 +58,14 @@ def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
     stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
 
     # Convert image size to list if it is an integer
-    if isinstance(imgsz, int):
+    imgsz = [imgsz] if isinstance(imgsz, int) else list(imgsz)
-        imgsz = [imgsz]
 
     # Make image size a multiple of the stride
     sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]
 
     # Print warning message if image size was updated
     if sz != imgsz:
-        LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {stride}, updating to {sz}')
+        LOGGER.warning(f'WARNING ⚠️ imgsz={imgsz} must be multiple of max stride {stride}, updating to {sz}')
 
     # Add missing dimensions if necessary
     sz = [sz[0], sz[0]] if min_dim == 2 and len(sz) == 1 else sz[0] if min_dim == 1 and len(sz) == 1 else sz
@@ -104,26 +104,33 @@ def check_version(current: str = "0.0.0",
     return result
 
 
-def check_font(font: str = FONT, progress: bool = False) -> None:
+def check_font(font='Arial.ttf'):
     """
-    Download font file to the user's configuration directory if it does not already exist.
+    Find font locally or download to user's configuration directory if it does not already exist.
 
     Args:
-        font (str): Path to font file.
+        font (str): Path or name of font.
-        progress (bool): If True, display a progress bar during the download.
 
     Returns:
-        None
+        file (Path): Resolved font file path.
     """
-    font = Path(font)
+    name = Path(font).name
+
+    # Check USER_CONFIG_DIR
+    file = USER_CONFIG_DIR / name
+    if file.exists():
+        return file
 
-    # Destination path for the font file
+    # Check system fonts
-    file = USER_CONFIG_DIR / font.name
+    matches = [s for s in font_manager.findSystemFonts() if font in s]
+    if any(matches):
+        return matches[0]
 
-    # Check if font file exists at the source or destination path
+    # Download to USER_CONFIG_DIR if missing
-    if not font.exists() and not file.exists():
+    url = f'https://ultralytics.com/assets/{name}'
-        # Download font file
+    if downloads.is_url(url):
-        downloads.safe_download(file=file, url=f'https://ultralytics.com/assets/{font.name}', progress=progress)
+        downloads.safe_download(url=url, file=file)
+        return file
 
 
 def check_online() -> bool:
@@ -213,7 +220,7 @@ def check_file(file, suffix=''):
         if Path(file).is_file():
             LOGGER.info(f'Found {url} locally at {file}')  # file already exists
         else:
-            downloads.safe_download(file=file, url=url)
+            downloads.safe_download(url=url, file=file)
         return file
     else:  # search
         files = []

ultralytics/yolo/utils/downloads.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import logging
+import contextlib
 import os
 import subprocess
 import urllib
@@ -15,27 +15,6 @@ import torch
 from ultralytics.yolo.utils import LOGGER
 
 
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg='', progress=True):
-    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
-    file = Path(file)
-    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
-    try:  # url1
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=progress and LOGGER.level <= logging.INFO)
-        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
-    except Exception as e:  # url2
-        if file.exists():
-            file.unlink()  # remove partial downloads
-        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        os.system(f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
-    finally:
-        if not file.exists() or file.stat().st_size < min_bytes:  # check
-            if file.exists():
-                file.unlink()  # remove partial downloads
-            LOGGER.warning(f"ERROR: {assert_msg}\n{error_msg}")
-        LOGGER.info('')
-
-
 def is_url(url, check=True):
     # Check if string is URL and check if URL exists
     try:
@@ -47,7 +26,71 @@ def is_url(url, check=True):
         return False
 
 
-def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
+def safe_download(url,
+                  file=None,
+                  dir=None,
+                  unzip=True,
+                  delete=False,
+                  curl=False,
+                  retry=3,
+                  min_bytes=1E0,
+                  progress=True):
+    """
+    Function for downloading files from a URL, with options for retrying, unzipping, and deleting the downloaded file.
+
+    Args:
+        url: str: The URL of the file to be downloaded.
+        file: str, optional: The filename of the downloaded file.
+            If not provided, the file will be saved with the same name as the URL.
+        dir: str, optional: The directory to save the downloaded file.
+            If not provided, the file will be saved in the current working directory.
+        unzip: bool, optional: Whether to unzip the downloaded file. Default: True.
+        delete: bool, optional: Whether to delete the downloaded file after unzipping. Default: False.
+        curl: bool, optional: Whether to use curl command line tool for downloading. Default: False.
+        retry: int, optional: The number of times to retry the download in case of failure. Default: 3.
+        min_bytes: float, optional: The minimum number of bytes that the downloaded file should have, to be considered
+            a successful download. Default: 1E0.
+        progress: bool, optional: Whether to display a progress bar during the download. Default: True.
+    """
+    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
+        assert dir or file, 'dir or file required for download'
+        f = dir / Path(url).name if dir else Path(file)
+        LOGGER.info(f'Downloading {url} to {f}...')
+        f.parent.mkdir(parents=True, exist_ok=True)  # make directory if missing
+        for i in range(retry + 1):
+            try:
+                if curl or i > 0:  # curl download with retry, continue
+                    s = 'sS' * (not progress)  # silent
+                    r = os.system(f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -')
+                else:  # torch download
+                    r = torch.hub.download_url_to_file(url, f, progress=progress)
+                assert r in {0, None}
+            except Exception as e:
+                if i >= retry:
+                    raise ConnectionError(f'❌  Download failure for {url}') from e
+                LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
+                continue
+
+            if f.exists():
+                if f.stat().st_size > min_bytes:
+                    break  # success
+                f.unlink()  # remove partial downloads
+
+    if unzip and f.exists() and f.suffix in {'.zip', '.tar', '.gz'}:
+        LOGGER.info(f'Unzipping {f}...')
+        if f.suffix == '.zip':
+            ZipFile(f).extractall(path=f.parent)  # unzip
+        elif f.suffix == '.tar':
+            os.system(f'tar xf {f} --directory {f.parent}')  # unzip
+        elif f.suffix == '.gz':
+            os.system(f'tar xfz {f} --directory {f.parent}')  # unzip
+        if delete:
+            f.unlink()  # remove zip
+
+
+def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
     # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
     from ultralytics.yolo.utils import SETTINGS
 
@@ -73,7 +116,7 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
             if Path(file).is_file():
                 LOGGER.info(f'Found {url} locally at {file}')  # file already exists
             else:
-                safe_download(file=file, url=url, min_bytes=1E5)
+                safe_download(url=url, file=file, min_bytes=1E5)
             return file
 
         # GitHub assets
@@ -91,61 +134,23 @@ 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:
-            safe_download(file,
+            safe_download(url=f'https://github.com/{repo}/releases/download/{tag}/{name}', file=file, min_bytes=1E5)
-                          url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
-                          min_bytes=1E5,
-                          error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}')
 
         return str(file)
 
 
-def download(url, dir=Path.cwd(), unzip=True, delete=True, curl=False, threads=1, retry=3):
+def download(url, dir=Path.cwd(), unzip=True, delete=False, curl=False, threads=1, retry=3):
     # Multithreaded file download and unzip function, used in data.yaml for autodownload
-    def download_one(url, dir):
-        # Download 1 file
-        success = True
-        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
-            LOGGER.info(f'Downloading {url} to {f}...')
-            for i in range(retry + 1):
-                if curl:  # curl download with retry, continue
-                    s = 'sS' * (threads > 1)  # silent
-                    r = os.system(f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -')
-                    success = r == 0
-                else:  # torch download
-                    torch.hub.download_url_to_file(url, f, progress=threads == 1)
-                    success = f.is_file()
-                if success:
-                    break
-                elif i < retry:
-                    LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
-                else:
-                    LOGGER.warning(f'❌ Failed to download {url}...')
-
-        if unzip and success and f.suffix in ('.zip', '.tar', '.gz'):
-            LOGGER.info(f'Unzipping {f}...')
-            if f.suffix == '.zip':
-                ZipFile(f).extractall(path=dir)  # unzip
-            elif f.suffix == '.tar':
-                os.system(f'tar xf {f} --directory {f.parent}')  # unzip
-            elif f.suffix == '.gz':
-                os.system(f'tar xfz {f} --directory {f.parent}')  # unzip
-            if delete:
-                f.unlink()  # remove zip
-
     dir = Path(dir)
     dir.mkdir(parents=True, exist_ok=True)  # make directory
     if threads > 1:
-        # pool = ThreadPool(threads)
-        # pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multithreaded
-        # pool.close()
-        # pool.join()
         with ThreadPool(threads) as pool:
-            pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multithreaded
+            pool.map(
+                lambda x: safe_download(
+                    url=x[0], dir=x[1], unzip=unzip, delete=delete, curl=curl, retry=retry, progress=threads <= 1),
+                zip(url, repeat(dir)))
             pool.close()
             pool.join()
     else:
         for u in [url] if isinstance(url, (str, Path)) else url:
-            download_one(u, dir)
+            safe_download(url=u, dir=dir, unzip=unzip, delete=delete, curl=curl, retry=retry)

ultralytics/yolo/utils/plotting.py

@@ -3,7 +3,6 @@
 import contextlib
 import math
 from pathlib import Path
-from urllib.error import URLError
 
 import cv2
 import matplotlib.pyplot as plt
@@ -12,9 +11,9 @@ import pandas as pd
 import torch
 from PIL import Image, ImageDraw, ImageFont
 
-from ultralytics.yolo.utils import FONT, USER_CONFIG_DIR, threaded
+from ultralytics.yolo.utils import threaded
 
-from .checks import check_font, check_requirements, is_ascii
+from .checks import check_font, is_ascii
 from .files import increment_path
 from .ops import clip_coords, scale_image, xywh2xyxy, xyxy2xywh
 
@@ -49,14 +48,20 @@ class Annotator:
         if self.pil:  # use PIL
             self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
             self.draw = ImageDraw.Draw(self.im)
-            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
+            try:
-                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
+                font = check_font('Arial.Unicode.ttf' if non_ascii else font)
+                size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
+                self.font = ImageFont.truetype(str(font), size)
+            except Exception:
+                self.font = ImageFont.load_default()
         else:  # use cv2
             self.im = im
         self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
 
     def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
         # Add one xyxy box to image with label
+        if isinstance(box, torch.Tensor):
+            box = box.tolist()
         if self.pil or not is_ascii(label):
             self.draw.rectangle(box, width=self.lw, outline=color)  # box
             if label:
@@ -139,22 +144,6 @@ class Annotator:
         return np.asarray(self.im)
 
 
-def check_pil_font(font=FONT, size=10):
-    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-    font = Path(font)
-    font = font if font.exists() else (USER_CONFIG_DIR / font.name)
-    try:
-        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception:  # download if missing
-        try:
-            check_font(font)
-            return ImageFont.truetype(str(font), size)
-        except TypeError:
-            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
-        except URLError:  # not online
-            return ImageFont.load_default()
-
-
 def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
     # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
     xyxy = torch.tensor(xyxy).view(-1, 4)

ultralytics/yolo/utils/torch_utils.py

@@ -85,8 +85,8 @@ def select_device(device='', batch=0, newline=False):
         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 > 0 and batch % n != 0:  # check batch_size is divisible by device_count
-            raise ValueError(f'batch={batch} is not multiple of GPU count {n}.\n'
+            raise ValueError(f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
-                             f'Try batch={batch // n} or batch={batch // n + 1}')
+                             f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}.")
         space = ' ' * (len(s) + 1)
         for i, d in enumerate(devices):
             p = torch.cuda.get_device_properties(i)

ultralytics/yolo/v8/classify/predict.py

@@ -74,7 +74,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model)(**args)
     else:
-        predictor = ClassificationPredictor(args)
+        predictor = ClassificationPredictor(overrides=args)
         predictor.predict_cli()
 
 

ultralytics/yolo/v8/classify/train.py

@@ -146,7 +146,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model).train(**args)
     else:
-        trainer = ClassificationTrainer(args)
+        trainer = ClassificationTrainer(overrides=args)
         trainer.train()
 
 

ultralytics/yolo/v8/detect/predict.py

@@ -92,7 +92,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model)(**args)
     else:
-        predictor = DetectionPredictor(args)
+        predictor = DetectionPredictor(overrides=args)
         predictor.predict_cli()
 
 

ultralytics/yolo/v8/detect/train.py

@@ -204,7 +204,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model).train(**args)
     else:
-        trainer = DetectionTrainer(args)
+        trainer = DetectionTrainer(overrides=args)
         trainer.train()
 
 

ultralytics/yolo/v8/segment/predict.py

@@ -110,7 +110,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model)(**args)
     else:
-        predictor = SegmentationPredictor(args)
+        predictor = SegmentationPredictor(overrides=args)
         predictor.predict_cli()
 
 

ultralytics/yolo/v8/segment/train.py

@@ -150,7 +150,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
         from ultralytics import YOLO
         YOLO(model).train(**args)
     else:
-        trainer = SegmentationTrainer(args)
+        trainer = SegmentationTrainer(overrides=args)
         trainer.train()