`ultralytics 8.0.42` DDP fix and Docs updates (#1065)

Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>

.github/workflows/ci.yaml

@@ -17,7 +17,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        os: [ubuntu-latest]
         python-version: ['3.10']  # requires python<=3.9
         model: [yolov8n]
     steps:

docs/predict.md

@@ -32,10 +32,11 @@ predictor's call method.
 
 Results object consists of these component objects:
 
-- `Results.boxes` : `Boxes` object with properties and methods for manipulating bboxes
-- `Results.masks` : `Masks` object used to index masks or to get segment coordinates.
-- `Results.probs` : `torch.Tensor` containing the class probabilities/logits.
-- `Results.orig_shape` : `tuple` containing the original image size as (height, width).
+- `Results.boxes`: `Boxes` object with properties and methods for manipulating bboxes
+- `Results.masks`: `Masks` object used to index masks or to get segment coordinates.
+- `Results.probs`: `torch.Tensor` containing the class probabilities/logits.
+- `Results.orig_img`: Original image loaded in memory.
+- `Results.path`: `Path` containing the path to input image
 
 Each result is composed of torch.Tensor by default, in which you can easily use following functionality:
 
@@ -94,18 +95,18 @@ results[0].probs  # cls prob, (num_class, )
 
 Class reference documentation for `Results` module and its components can be found [here](reference/results.md)
 
-## Visualizing results
+## Plotting results
 
-You can use `visualize()` function of `Result` object to get a visualization. It plots all components(boxes, masks,
+You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes, masks,
 classification logits, etc) found in the results object
 
 ```python
-    res = model(img)
-    res_plotted = res[0].visualize()
-    cv2.imshow("result", res_plotted)
+res = model(img)
+res_plotted = res[0].plot()
+cv2.imshow("result", res_plotted)
 ```
 
-!!! example "`visualize()` arguments"
+!!! example "`plot()` arguments"
 
     `show_conf (bool)`: Show confidence
 

docs/tasks/tracking.md

@@ -0,0 +1,86 @@
+Object tracking is a task that involves identifying the location and class of objects, then assigning a unique ID to
+that detection in video streams.
+
+The output of tracker is the same as detection with an added object ID.
+
+## Available Trackers
+
+The following tracking algorithms have been implemented and can be enabled by passing `tracker=tracker_type.yaml`
+
+* [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - `botsort.yaml`
+* [ByteTrack](https://github.com/ifzhang/ByteTrack) - `bytetrack.yaml`
+
+The default tracker is BoT-SORT.
+
+## Tracking
+
+Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load an official detection model
+        model = YOLO("yolov8n-seg.pt")  # load an official segmentation model
+        model = YOLO("path/to/best.pt")  # load a custom model
+        
+        # Track with the model
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) 
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc"  # official detection model
+        yolo track model=yolov8n-seg.pt source=...   # official segmentation model
+        yolo track model=path/to/best.pt source=...  # custom model
+        yolo track model=path/to/best.pt  tracker="bytetrack.yaml" # bytetrack tracker
+
+        ```
+
+As in the above usage, we support both the detection and segmentation models for tracking and the only thing you need to do is loading the corresponding(detection or segmentation) model.
+
+## Configuration
+### Tracking
+Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer to [predict page](https://docs.ultralytics.com/cfg/#prediction).
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        model = YOLO("yolov8n.pt")
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
+
+        ```
+
+### Tracker
+We also support using a modified tracker config file, just copy a config file i.e `custom_tracker.yaml` from [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) and modify any configurations(expect the `tracker_type`) you need to.
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        model = YOLO("yolov8n.pt")
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml') 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml'
+
+        ```
+Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) page. 
+

examples/README.md

@@ -3,9 +3,10 @@ This is a list of real-world applications and walkthroughs. These can be folders
 ## Ultralytics YOLO example applications
 
 | Title                                                                    | Format             | Contributor                                         |
-| --------------------------------------------------------------- | ------------------ | --------------------------------------------------- |
-| [Yolov8/yolov5 ONNX Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
-| [YOLOv8-OpenCV-ONNX-Python](./YOLOv8-OpenCV-ONNX-Python)        | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
+| ------------------------------------------------------------------------ | ------------------ | --------------------------------------------------- |
+| [YOLO ONNX detection Inference with C++](./YOLOv8_CPP_Inference)         | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
+| [YOLO OpenCV ONNX detection Python](./YOLOv8-OpenCV-ONNX-Python)         | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
+| [YOLO .Net ONNX detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net            | [Samuel Stainback](https://github.com/sstainba)     |
 
 ## How can you contribute ?
 

examples/tutorial.ipynb

@@ -123,7 +123,7 @@
             "Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
             "100% 165k/165k [00:00<00:00, 87.4MB/s]\n",
             "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 13.3ms\n",
-            "Speed: 0.5ms pre-process, 13.3ms inference, 43.5ms postprocess per image at shape (1, 3, 640, 640)\n",
+            "Speed: 0.5ms preprocess, 13.3ms inference, 43.5ms postprocess per image at shape (1, 3, 640, 640)\n",
             "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
           ]
         }
@@ -268,7 +268,7 @@
             "              scissors        128          1          1          0      0.249     0.0746\n",
             "            teddy bear        128         21      0.877      0.333      0.591      0.394\n",
             "            toothbrush        128          5      0.743        0.6      0.638      0.374\n",
-            "Speed: 2.4ms pre-process, 7.8ms inference, 0.0ms loss, 3.3ms post-process per image\n"
+            "Speed: 2.4ms preprocess, 7.8ms inference, 0.0ms loss, 3.3ms postprocess per image\n"
           ]
         }
       ]
@@ -439,7 +439,7 @@
             "              scissors        128          1          1          0      0.142     0.0426\n",
             "            teddy bear        128         21      0.587      0.476       0.63      0.458\n",
             "            toothbrush        128          5      0.784      0.736      0.898      0.544\n",
-            "Speed: 2.0ms pre-process, 4.0ms inference, 0.0ms loss, 2.5ms post-process per image\n",
+            "Speed: 2.0ms preprocess, 4.0ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
             "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
           ]
         }

mkdocs.yml

@@ -105,6 +105,7 @@ nav:
   - Tasks:
       - Detection: tasks/detection.md
       - Segmentation: tasks/segmentation.md
+      - Multi-Object Tracking: tasks/tracking.md
       - Classification: tasks/classification.md
   - Usage:
       - CLI: cli.md

tests/test_python.py

@@ -170,15 +170,16 @@ def test_predict_callback_and_setup():
 def test_result():
     model = YOLO('yolov8n-seg.pt')
     res = model([SOURCE, SOURCE])
-    res[0].numpy()
     res[0].cpu().numpy()
-    resimg = res[0].visualize(show_conf=False)
-    print(resimg)
+    res[0].plot(show_conf=False)
+    print(res[0].path)
 
     model = YOLO('yolov8n.pt')
     res = model(SOURCE)
-    res[0].visualize()
+    res[0].plot()
+    print(res[0].path)
 
     model = YOLO('yolov8n-cls.pt')
     res = model(SOURCE)
-    res[0].visualize()
+    res[0].plot()
+    print(res[0].path)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.41'
+__version__ = '8.0.42'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks

ultralytics/nn/autoshape.py

@@ -232,6 +232,3 @@ class Detections:
 
     def __repr__(self):
         return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
-
-
-print('works')

ultralytics/nn/tasks.py

@@ -381,7 +381,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
         return ensemble[-1]
 
     # Return ensemble
-    print(f'Ensemble created with {weights}\n')
+    LOGGER.info(f'Ensemble created with {weights}\n')
     for k in 'names', 'nc', 'yaml':
         setattr(ensemble, k, getattr(ensemble[0], k))
     ensemble.stride = ensemble[torch.argmax(torch.tensor([m.stride.max() for m in ensemble])).int()].stride

ultralytics/tracker/README.md

@@ -16,7 +16,7 @@ model = YOLO("yolov8n.pt")  # or a segmentation model .i.e yolov8n-seg.pt
 model.track(
     source="video/streams",
     stream=True,
-    tracker="botsort.yaml/bytetrack.yaml",
+    tracker="botsort.yaml",  # or 'bytetrack.yaml'
     ...,
 )
 ```

ultralytics/tracker/__init__.py

@@ -1 +1,3 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 from .trackers import BOTSORT, BYTETracker

ultralytics/tracker/cfg/botsort.yaml

@@ -1,3 +1,6 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
+
 tracker_type: botsort  # tracker type, ['botsort', 'bytetrack']
 track_high_thresh: 0.5  # threshold for the first association
 track_low_thresh: 0.1  # threshold for the second association
@@ -7,7 +10,7 @@ match_thresh: 0.8  # threshold for matching tracks
 # min_box_area: 10  # threshold for min box areas(for tracker evaluation, not used for now)
 # mot20: False  # for tracker evaluation(not used for now)
 
-# Botsort settings
+# BoT-SORT settings
 cmc_method: sparseOptFlow  # method of global motion compensation
 # ReID model related thresh (not supported yet)
 proximity_thresh: 0.5

ultralytics/tracker/cfg/bytetrack.yaml

@@ -1,3 +1,6 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
+
 tracker_type: bytetrack  # tracker type, ['botsort', 'bytetrack']
 track_high_thresh: 0.5  # threshold for the first association
 track_low_thresh: 0.1  # threshold for the second association

ultralytics/tracker/track.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 import torch
 
 from ultralytics.tracker import BOTSORT, BYTETracker

ultralytics/tracker/trackers/__init__.py

@@ -1,2 +1,4 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 from .bot_sort import BOTSORT
 from .byte_tracker import BYTETracker

ultralytics/tracker/trackers/basetrack.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 from collections import OrderedDict
 
 import numpy as np

ultralytics/tracker/trackers/bot_sort.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 from collections import deque
 
 import numpy as np
@@ -97,7 +99,7 @@ class BOTSORT(BYTETracker):
         self.appearance_thresh = args.appearance_thresh
 
         if args.with_reid:
-            # haven't supported bot-sort(reid) yet
+            # haven't supported BoT-SORT(reid) yet
             self.encoder = None
         # self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
         self.gmc = GMC(method=args.cmc_method)

ultralytics/tracker/trackers/byte_tracker.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 import numpy as np
 
 from ..utils import matching

ultralytics/tracker/utils/gmc.py

@@ -1,9 +1,13 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 import copy
 
 import cv2
 import matplotlib.pyplot as plt
 import numpy as np
 
+from ultralytics.yolo.utils import LOGGER
+
 
 class GMC:
 
@@ -108,7 +112,7 @@ class GMC:
         try:
             (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria, None, 1)
         except Exception as e:
-            print(f'Warning: find transform failed. Set warp as identity {e}')
+            LOGGER.warning(f'WARNING: find transform failed. Set warp as identity {e}')
 
         return H
 
@@ -229,7 +233,7 @@ class GMC:
                 H[0, 2] *= self.downscale
                 H[1, 2] *= self.downscale
         else:
-            print('Warning: not enough matching points')
+            LOGGER.warning('WARNING: not enough matching points')
 
         # Store to next iteration
         self.prevFrame = frame.copy()
@@ -288,7 +292,7 @@ class GMC:
                 H[0, 2] *= self.downscale
                 H[1, 2] *= self.downscale
         else:
-            print('Warning: not enough matching points')
+            LOGGER.warning('WARNING: not enough matching points')
 
         # Store to next iteration
         self.prevFrame = frame.copy()

ultralytics/tracker/utils/kalman_filter.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 import numpy as np
 import scipy.linalg
 
@@ -234,7 +236,7 @@ class KalmanFilterXYAH:
 
 class KalmanFilterXYWH:
     """
-    For bot-sort
+    For BoT-SORT
     A simple Kalman filter for tracking bounding boxes in image space.
 
     The 8-dimensional state space

ultralytics/tracker/utils/matching.py

@@ -1,3 +1,5 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+
 import lap
 import numpy as np
 import scipy

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -1136,11 +1136,11 @@ class HUBDatasetStats():
         # Save, print and return
         if save:
             stats_path = self.hub_dir / 'stats.json'
-            print(f'Saving {stats_path.resolve()}...')
+            LOGGER.info(f'Saving {stats_path.resolve()}...')
             with open(stats_path, 'w') as f:
                 json.dump(self.stats, f)  # save stats.json
         if verbose:
-            print(json.dumps(self.stats, indent=2, sort_keys=False))
+            LOGGER.info(json.dumps(self.stats, indent=2, sort_keys=False))
         return self.stats
 
     def process_images(self):
@@ -1154,7 +1154,7 @@ class HUBDatasetStats():
             with ThreadPool(NUM_THREADS) as pool:
                 for _ in tqdm(pool.imap(self._hub_ops, dataset.im_files), total=total, desc=desc):
                     pass
-        print(f'Done. All images saved to {self.im_dir}')
+        LOGGER.info(f'Done. All images saved to {self.im_dir}')
         return self.im_dir
 
 

ultralytics/yolo/engine/exporter.py

@@ -75,7 +75,6 @@ from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
 from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
 
-CUDA = torch.cuda.is_available()
 ARM64 = platform.machine() in ('arm64', 'aarch64')
 
 
@@ -324,7 +323,7 @@ class Exporter:
         # Simplify
         if self.args.simplify:
             try:
-                check_requirements(('onnxsim', 'onnxruntime-gpu' if CUDA else 'onnxruntime'))
+                check_requirements(('onnxsim', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'))
                 import onnxsim
 
                 LOGGER.info(f'{prefix} simplifying with onnxsim {onnxsim.__version__}...')
@@ -506,10 +505,12 @@ class Exporter:
         try:
             import tensorflow as tf  # noqa
         except ImportError:
-            check_requirements(f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if CUDA else '-cpu'}")
+            check_requirements(
+                f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if torch.cuda.is_available() else '-cpu'}"
+            )
             import tensorflow as tf  # noqa
         check_requirements(('onnx', 'onnx2tf', 'sng4onnx', 'onnxsim', 'onnx_graphsurgeon', 'tflite_support',
-                            'onnxruntime-gpu' if CUDA else 'onnxruntime'),
+                            'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
                            cmds='--extra-index-url https://pypi.ngc.nvidia.com')
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')

ultralytics/yolo/engine/model.py

@@ -32,7 +32,7 @@ class YOLO:
         YOLO (You Only Look Once) object detection model.
 
         Args:
-            model (str or Path): Path to the model file to load or create.
+            model (str, Path): Path to the model file to load or create.
             type (str): Type/version of models to use. Defaults to "v8".
 
         Attributes:
@@ -62,7 +62,7 @@ class YOLO:
             predict(source=None, stream=False, **kwargs): Perform prediction using the YOLO model.
 
         Returns:
-            List[ultralytics.yolo.engine.results.Results]: The prediction results.
+            list(ultralytics.yolo.engine.results.Results): The prediction results.
         """
 
     def __init__(self, model='yolov8n.pt', type='v8') -> None:
@@ -114,6 +114,7 @@ class YOLO:
         self.task = guess_model_task(cfg_dict)
         self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._assign_ops_from_task()
         self.model = self.ModelClass(cfg_dict, verbose=verbose and RANK == -1)  # initialize
+        self.overrides['model'] = self.cfg
 
     def _load(self, weights: str):
         """
@@ -204,7 +205,7 @@ class YOLO:
     def track(self, source=None, stream=False, **kwargs):
         from ultralytics.tracker.track import register_tracker
         register_tracker(self)
-        # bytetrack-based method needs low confidence predictions as input
+        # ByteTrack-based method needs low confidence predictions as input
         conf = kwargs.get('conf') or 0.1
         kwargs['conf'] = conf
         kwargs['mode'] = 'track'

ultralytics/yolo/engine/predictor.py

@@ -92,6 +92,7 @@ class BasePredictor:
         self.annotator = None
         self.data_path = None
         self.source_type = None
+        self.batch = None
         self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
         callbacks.add_integration_callbacks(self)
 

ultralytics/yolo/engine/results.py

@@ -28,13 +28,14 @@ class Results:
 
         """
 
-    def __init__(self, boxes=None, masks=None, probs=None, orig_img=None, names=None) -> None:
+    def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None) -> None:
         self.orig_img = orig_img
         self.orig_shape = orig_img.shape[:2]
         self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None  # native size boxes
         self.masks = Masks(masks, self.orig_shape) if masks is not None else None  # native size or imgsz masks
         self.probs = probs if probs is not None else None
         self.names = names
+        self.path = path
         self.comp = ['boxes', 'masks', 'probs']
 
     def pandas(self):
@@ -42,7 +43,7 @@ class Results:
         # TODO masks.pandas + boxes.pandas + cls.pandas
 
     def __getitem__(self, idx):
-        r = Results(orig_img=self.orig_img)
+        r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
         for item in self.comp:
             if getattr(self, item) is None:
                 continue
@@ -58,7 +59,7 @@ class Results:
             self.probs = probs
 
     def cpu(self):
-        r = Results(orig_img=self.orig_img)
+        r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
         for item in self.comp:
             if getattr(self, item) is None:
                 continue
@@ -66,7 +67,7 @@ class Results:
         return r
 
     def numpy(self):
-        r = Results(orig_img=self.orig_img)
+        r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
         for item in self.comp:
             if getattr(self, item) is None:
                 continue
@@ -74,7 +75,7 @@ class Results:
         return r
 
     def cuda(self):
-        r = Results(orig_img=self.orig_img)
+        r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
         for item in self.comp:
             if getattr(self, item) is None:
                 continue
@@ -82,7 +83,7 @@ class Results:
         return r
 
     def to(self, *args, **kwargs):
-        r = Results(orig_img=self.orig_img)
+        r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
         for item in self.comp:
             if getattr(self, item) is None:
                 continue
@@ -123,7 +124,7 @@ class Results:
                 orig_shape (tuple, optional): Original image size.
             """)
 
-    def visualize(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
+    def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
         """
         Plots the given result on an input RGB image. Accepts cv2(numpy) or PIL Image
 
@@ -146,9 +147,9 @@ class Results:
                 annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
 
         if masks is not None:
-            im_gpu = torch.as_tensor(img, dtype=torch.float16).permute(2, 0, 1).flip(0).contiguous()
-            im_gpu = F.resize(im_gpu, masks.data.shape[1:]) / 255
-            annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im_gpu)
+            im = torch.as_tensor(img, dtype=torch.float16, device=masks.data.device).permute(2, 0, 1).flip(0)
+            im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
+            annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im)
 
         if logits is not None:
             top5i = logits.argsort(0, descending=True)[:5].tolist()  # top 5 indices
@@ -371,24 +372,3 @@ class Masks:
             Properties:
                 segments (list): A list of segments which includes x,y,w,h,label,confidence, and mask of each detection masks.
             """)
-
-
-if __name__ == '__main__':
-    # test examples
-    results = Results(boxes=torch.randn((2, 6)), masks=torch.randn((2, 160, 160)), orig_shape=[640, 640])
-    results = results.cuda()
-    print('--cuda--pass--')
-    results = results.cpu()
-    print('--cpu--pass--')
-    results = results.to('cuda:0')
-    print('--to-cuda--pass--')
-    results = results.to('cpu')
-    print('--to-cpu--pass--')
-    results = results.numpy()
-    print('--numpy--pass--')
-    # box = Boxes(boxes=torch.randn((2, 6)), orig_shape=[5, 5])
-    # box = box.cuda()
-    # box = box.cpu()
-    # box = box.numpy()
-    # for b in box:
-    #     print(b)

ultralytics/yolo/utils/metrics.py

@@ -11,7 +11,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from ultralytics.yolo.utils import TryExcept
+from ultralytics.yolo.utils import LOGGER, TryExcept
 
 
 # boxes
@@ -260,7 +260,7 @@ class ConfusionMatrix:
 
     def print(self):
         for i in range(self.nc + 1):
-            print(' '.join(map(str, self.matrix[i])))
+            LOGGER.info(' '.join(map(str, self.matrix[i])))
 
 
 def smooth(y, f=0.05):

ultralytics/yolo/utils/plotting.py

@@ -12,7 +12,7 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
 
-from ultralytics.yolo.utils import threaded
+from ultralytics.yolo.utils import LOGGER, threaded
 
 from .checks import check_font, check_version, is_ascii
 from .files import increment_path
@@ -300,7 +300,7 @@ def plot_results(file='path/to/results.csv', dir='', segment=False):
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
         except Exception as e:
-            print(f'Warning: Plotting error for {f}: {e}')
+            LOGGER.warning(f'WARNING: Plotting error for {f}: {e}')
     ax[1].legend()
     fig.savefig(save_dir / 'results.png', dpi=200)
     plt.close()

ultralytics/yolo/utils/torch_utils.py

@@ -167,10 +167,11 @@ def model_info(model, verbose=False, imgsz=640):
     n_p = get_num_params(model)
     n_g = get_num_gradients(model)  # number gradients
     if verbose:
-        print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
+        LOGGER.info(
+            f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
         for i, (name, p) in enumerate(model.named_parameters()):
             name = name.replace('module_list.', '')
-            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+            LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                         (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
 
     flops = get_flops(model, imgsz)
@@ -362,7 +363,7 @@ def profile(input, ops, n=10, device=None):
     results = []
     if not isinstance(device, torch.device):
         device = select_device(device)
-    print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
+    LOGGER.info(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
                 f"{'input':>24s}{'output':>24s}")
 
     for x in input if isinstance(input, list) else [input]:
@@ -393,10 +394,10 @@ def profile(input, ops, n=10, device=None):
                 mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
                 s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
                 p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
-                print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
+                LOGGER.info(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
                 results.append([p, flops, mem, tf, tb, s_in, s_out])
             except Exception as e:
-                print(e)
+                LOGGER.info(e)
                 results.append(None)
             torch.cuda.empty_cache()
     return results

ultralytics/yolo/v8/classify/predict.py

@@ -22,7 +22,9 @@ class ClassificationPredictor(BasePredictor):
         results = []
         for i, pred in enumerate(preds):
             orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
-            results.append(Results(probs=pred, orig_img=orig_img, names=self.model.names))
+            path, _, _, _, _ = self.batch
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
 
         return results
 

ultralytics/yolo/v8/detect/predict.py

@@ -32,7 +32,9 @@ class DetectionPredictor(BasePredictor):
             orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
             shape = orig_img.shape
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
-            results.append(Results(boxes=pred, orig_img=orig_img, names=self.model.names))
+            path, _, _, _, _ = self.batch
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
 
     def write_results(self, idx, results, batch):

ultralytics/yolo/v8/segment/predict.py

@@ -24,9 +24,10 @@ class SegmentationPredictor(DetectionPredictor):
         for i, pred in enumerate(p):
             orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
             shape = orig_img.shape
-            if not len(pred):
-                results.append(Results(boxes=pred[:, :6], orig_img=orig_img,
-                                       names=self.model.names))  # save empty boxes
+            path, _, _, _, _ = self.batch
+            img_path = path[i] if isinstance(path, list) else path
+            if not len(pred):  # save empty boxes
+                results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))
                 continue
             if self.args.retina_masks:
                 pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
@@ -34,7 +35,8 @@ class SegmentationPredictor(DetectionPredictor):
             else:
                 masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
                 pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
-            results.append(Results(boxes=pred[:, :6], masks=masks, orig_img=orig_img, names=self.model.names))
+            results.append(
+                Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results
 
     def write_results(self, idx, results, batch):