DDP, Comet, URLError fixes, improved error handling (#658)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tungway1990 <68179274+Tungway1990@users.noreply.github.com>

README.md

@@ -52,26 +52,6 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
   </div>
 </div>
 
-## <div align="center">Ultralytics Live Session</div>
-
-<div align="center">
-
-[Ultralytics Live Session 3](https://youtu.be/IPcpYO5ITa8) ✨ is here! Join us on January 24th at 18 CET as we dive into
-the latest advancements in YOLOv8, and demonstrate how to use this cutting-edge, SOTA model to improve your object
-detection, instance segmentation, and image classification projects. See firsthand how YOLOv8's speed, accuracy, and
-ease of use make it a top choice for professionals and researchers alike.
-
-In addition to learning about the exciting new features and improvements of Ultralytics YOLOv8, you will also have the
-opportunity to ask questions and interact with our team during the live Q&A session. We encourage you to come prepared
-with any questions you may have.
-
-To join the webinar, visit our YouTube [Channel](https://www.youtube.com/@Ultralytics/streams) and turn on your
-notifications!
-
-<a align="center" href="https://youtu.be/IPcpYO5ITa8" target="_blank">
-<img width="80%" src="https://user-images.githubusercontent.com/107626595/212887899-e94b006c-5192-40fa-8b24-7b5428e065e8.png"></a>
-</div>
-
 ## <div align="center">Documentation</div>
 
 See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for

tests/test_cli.py

@@ -50,6 +50,8 @@ def test_val_classify():
 def test_predict_detect():
     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")
+    run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape.mov imgsz=32")
+    run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait.mov imgsz=32")
 
 
 def test_predict_segment():

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.21"
+__version__ = "8.0.22"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/nn/modules.py

@@ -4,7 +4,6 @@ Common modules
 """
 
 import math
-import warnings
 
 import torch
 import torch.nn as nn
@@ -155,7 +154,7 @@ class C3(nn.Module):
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c1, c_, 1, 1)
         self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n)))
 
     def forward(self, x):
         return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
@@ -274,8 +273,6 @@ class SPP(nn.Module):
 
     def forward(self, x):
         x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
         return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
 
 
@@ -290,8 +287,6 @@ class SPPF(nn.Module):
 
     def forward(self, x):
         x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
         y1 = self.m(x)
         y2 = self.m(y1)
         return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))

ultralytics/yolo/cfg/__init__.py

@@ -88,6 +88,11 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, override
         check_cfg_mismatch(cfg, overrides)
         cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)
 
+    # Type checks
+    for k in 'project', 'name':
+        if isinstance(cfg[k], (int, float)):
+            cfg[k] = str(cfg[k])
+
     # Return instance
     return IterableSimpleNamespace(**cfg)
 
@@ -211,12 +216,15 @@ def entrypoint(debug=False):
         else:
             raise argument_error(a)
 
+    # Defaults
+    task2model = dict(detect='yolov8n.pt', segment='yolov8n-seg.pt', classify='yolov8n-cls.pt')
+    task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='mnist160')
+
     # Mode
-    mode = overrides.pop('mode', None)
-    model = overrides.pop('model', None)
+    mode = overrides['mode']
     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}'.")
+        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}."))
@@ -225,27 +233,33 @@ def entrypoint(debug=False):
         return
 
     # Model
+    model = overrides.pop('model', DEFAULT_CFG.model)
+    task = overrides.pop('task', None)
     if model is None:
-        model = DEFAULT_CFG.model or 'yolov8n.pt'
-        LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
+        model = task2model.get(task, 'yolov8n.pt')
+        LOGGER.warning(f"WARNING ⚠️ 'model=' is missing. Using default 'model={model}'.")
     from ultralytics.yolo.engine.model import YOLO
+    overrides['model'] = model
     model = YOLO(model)
-    task = model.task
 
     # Task
+    if task and task != model.task:
+        LOGGER.warning(f"WARNING ⚠️ 'task={task}' conflicts with {model.task} model {overrides['model']}. "
+                       f"Inheriting 'task={model.task}' from {overrides['model']} and ignoring 'task={task}'.")
+    task = model.task
+    overrides['task'] = task
     if mode == 'predict' and 'source' not in overrides:
         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']}'.")
+        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.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']}'.")
+            overrides['data'] = task2data.get(task, DEFAULT_CFG.data)
+            LOGGER.warning(f"WARNING ⚠️ 'data=' is missing. Using {model.task} default 'data={overrides['data']}'.")
     elif mode == 'export':
         if 'format' not in overrides:
             overrides['format'] = DEFAULT_CFG.format or 'torchscript'
-            LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
+            LOGGER.warning(f"WARNING ⚠️ 'format=' is missing. Using default 'format={overrides['format']}'.")
 
     # Run command in python
     getattr(model, mode)(**overrides)

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -186,6 +186,7 @@ class LoadImages:
         self.transforms = transforms  # optional
         self.vid_stride = vid_stride  # video frame-rate stride
         if any(videos):
+            self.orientation = None  # rotation degrees
             self._new_video(videos[0])  # new video
         else:
             self.cap = None
@@ -243,8 +244,10 @@ class LoadImages:
         self.frame = 0
         self.cap = cv2.VideoCapture(path)
         self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
+        if hasattr(cv2, 'CAP_PROP_ORIENTATION_META'):  # cv2<4.6.0 compatibility
             self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees
-        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493
+            # Disable auto-orientation due to known issues in https://github.com/ultralytics/yolov5/issues/8493
+            # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)
 
     def _cv2_rotate(self, im):
         # Rotate a cv2 video manually

ultralytics/yolo/data/utils.py

@@ -16,7 +16,7 @@ from PIL import ExifTags, Image, ImageOps
 
 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.downloads import download, safe_download
 from ultralytics.yolo.utils.files import unzip_file
 from ultralytics.yolo.utils.ops import segments2boxes
 
@@ -238,8 +238,7 @@ def check_det_dataset(dataset, autodownload=True):
             t = time.time()
             if s.startswith('http') and s.endswith('.zip'):  # URL
                 f = Path(s).name  # filename
-                LOGGER.info(f'Downloading {s} to {f}...')
-                torch.hub.download_url_to_file(s, f)
+                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

ultralytics/yolo/engine/predictor.py

@@ -233,7 +233,7 @@ class BasePredictor:
         device = select_device(self.args.device)
         model = model or self.args.model
         self.args.half &= device.type != 'cpu'  # half precision only supported on CUDA
-        self.model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
+        self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half)
         self.device = device
         self.model.eval()
 

ultralytics/yolo/utils/callbacks/clearml.py

@@ -6,7 +6,7 @@ try:
     import clearml
     from clearml import Task
 
-    assert hasattr(clearml, '__version__')
+    assert clearml.__version__  # verify package is not directory
 except (ImportError, AssertionError):
     clearml = None
 

ultralytics/yolo/utils/callbacks/comet.py

@@ -5,7 +5,7 @@ from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
 try:
     import comet_ml
 
-except (ModuleNotFoundError, ImportError):
+except ImportError:
     comet_ml = None
 
 
@@ -35,7 +35,7 @@ def on_fit_epoch_end(trainer):
 
 def on_train_end(trainer):
     experiment = comet_ml.get_global_experiment()
-    experiment.log_model("YOLOv8", file_or_folder=trainer.best, file_name="best.pt", overwrite=True)
+    experiment.log_model("YOLOv8", file_or_folder=str(trainer.best), file_name="best.pt", overwrite=True)
 
 
 callbacks = {

ultralytics/yolo/utils/checks.py

@@ -18,8 +18,8 @@ import psutil
 import torch
 from IPython import display
 
-from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, emojis,
-                                    is_colab, is_docker, is_jupyter)
+from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
+                                    emojis, is_colab, is_docker, is_jupyter)
 
 
 def is_ascii(s) -> bool:
@@ -123,9 +123,7 @@ def check_font(font: str = FONT, progress: bool = False) -> None:
     # Check if font file exists at the source or destination path
     if not font.exists() and not file.exists():
         # Download font file
-        url = f'https://ultralytics.com/assets/{font.name}'
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=progress)
+        downloads.safe_download(file=file, url=f'https://ultralytics.com/assets/{font.name}', progress=progress)
 
 
 def check_online() -> bool:
@@ -215,9 +213,7 @@ def check_file(file, suffix=''):
         if Path(file).is_file():
             LOGGER.info(f'Found {url} locally at {file}')  # file already exists
         else:
-            LOGGER.info(f'Downloading {url} to {file}...')
-            torch.hub.download_url_to_file(url, file)
-            assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
+            downloads.safe_download(file=file, url=url)
         return file
     else:  # search
         files = []

ultralytics/yolo/utils/downloads.py

@@ -12,16 +12,16 @@ from zipfile import ZipFile
 import requests
 import torch
 
-from ultralytics.yolo.utils import LOGGER, SETTINGS
+from ultralytics.yolo.utils import LOGGER
 
 
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
+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=LOGGER.level <= logging.INFO)
+        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():
@@ -32,7 +32,7 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
         if not file.exists() or file.stat().st_size < min_bytes:  # check
             if file.exists():
                 file.unlink()  # remove partial downloads
-            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
+            LOGGER.warning(f"ERROR: {assert_msg}\n{error_msg}")
         LOGGER.info('')
 
 
@@ -49,6 +49,7 @@ def is_url(url, check=True):
 
 def attempt_download(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
 
     def github_assets(repository, version='latest'):
         # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov5m.pt', ...])
@@ -76,7 +77,6 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
             return file
 
         # GitHub assets
-        assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
         assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
         try:
             tag, assets = github_assets(repo, release)
@@ -110,13 +110,12 @@ def download(url, dir=Path.cwd(), unzip=True, delete=True, curl=False, threads=1
             f = dir / Path(url).name
             LOGGER.info(f'Downloading {url} to {f}...')
             for i in range(retry + 1):
-                if curl:
-                    s = 'sS' if threads > 1 else ''  # silent
-                    r = os.system(
-                        f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -')  # curl download with retry, continue
+                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.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download
+                else:  # torch download
+                    torch.hub.download_url_to_file(url, f, progress=threads == 1)
                     success = f.is_file()
                 if success:
                     break

ultralytics/yolo/utils/torch_utils.py

@@ -67,9 +67,19 @@ def select_device(device='', batch=0, newline=False):
     if cpu or mps:
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
     elif device:  # non-cpu device requested
+        visible = os.environ.get('CUDA_VISIBLE_DEVICES', None)
         os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()
         if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', ''))):
-            raise ValueError(f"Invalid CUDA 'device={device}' requested, use 'device=cpu' or pass valid CUDA device(s)")
+            LOGGER.info(s)
+            install = "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no " \
+                      "CUDA devices are seen by torch.\n" if torch.cuda.device_count() == 0 else ""
+            raise ValueError(f"Invalid CUDA 'device={device}' requested."
+                             f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
+                             f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
+                             f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
+                             f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
+                             f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
+                             f"{install}")
 
     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

ultralytics/yolo/v8/classify/predict.py

@@ -69,7 +69,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
     source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
 
-    args = dict(model=model, source=source, verbose=True)
+    args = dict(model=model, source=source)
     if use_python:
         from ultralytics import YOLO
         YOLO(model)(**args)

ultralytics/yolo/v8/classify/train.py

@@ -141,7 +141,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
     data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
     device = cfg.device if cfg.device is not None else ''
 
-    args = dict(model=model, data=data, device=device, verbose=True)
+    args = dict(model=model, data=data, device=device)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).train(**args)

ultralytics/yolo/v8/classify/val.py

@@ -50,7 +50,7 @@ def val(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     data = cfg.data or "mnist160"
 
-    args = dict(model=model, data=data, verbose=True)
+    args = dict(model=model, data=data)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).val(**args)

ultralytics/yolo/v8/detect/predict.py

@@ -87,7 +87,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
     source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
 
-    args = dict(model=model, source=source, verbose=True)
+    args = dict(model=model, source=source)
     if use_python:
         from ultralytics import YOLO
         YOLO(model)(**args)

ultralytics/yolo/v8/detect/train.py

@@ -199,7 +199,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
     data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
     device = cfg.device if cfg.device is not None else ''
 
-    args = dict(model=model, data=data, device=device, verbose=True)
+    args = dict(model=model, data=data, device=device)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).train(**args)

ultralytics/yolo/v8/detect/val.py

@@ -129,7 +129,7 @@ class DetectionValidator(BaseValidator):
                 f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
 
         # Print results per class
-        if (self.args.verbose or not self.training) and self.nc > 1 and len(self.stats):
+        if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
             for i, c in enumerate(self.metrics.ap_class_index):
                 self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
 
@@ -237,7 +237,7 @@ def val(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or "yolov8n.pt"
     data = cfg.data or "coco128.yaml"
 
-    args = dict(model=model, data=data, verbose=True)
+    args = dict(model=model, data=data)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).val(**args)

ultralytics/yolo/v8/segment/predict.py

@@ -105,7 +105,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False):
     source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
 
-    args = dict(model=model, source=source, verbose=True)
+    args = dict(model=model, source=source)
     if use_python:
         from ultralytics import YOLO
         YOLO(model)(**args)

ultralytics/yolo/v8/segment/train.py

@@ -145,7 +145,7 @@ def train(cfg=DEFAULT_CFG, use_python=False):
     data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     device = cfg.device if cfg.device is not None else ''
 
-    args = dict(model=model, data=data, device=device, verbose=True)
+    args = dict(model=model, data=data, device=device)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).train(**args)

ultralytics/yolo/v8/segment/val.py

@@ -247,7 +247,7 @@ def val(cfg=DEFAULT_CFG, use_python=False):
     model = cfg.model or "yolov8n-seg.pt"
     data = cfg.data or "coco128-seg.yaml"
 
-    args = dict(model=model, data=data, verbose=True)
+    args = dict(model=model, data=data)
     if use_python:
         from ultralytics import YOLO
         YOLO(model).val(**args)