ultralytics 8.0.92 updates and fixes (#2361)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: introvin <vinod.4166@gmail.com>
Co-authored-by: marinmarcillat <58145636+marinmarcillat@users.noreply.github.com>
Co-authored-by: BIGBOSS-FOX <47949596+BIGBOSS-FOX@users.noreply.github.com>

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.3.2
     hooks:
       - id: pyupgrade
         name: Upgrade code
@@ -34,7 +34,7 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/google/yapf
-    rev: v0.32.0
+    rev: v0.33.0
     hooks:
       - id: yapf
         name: YAPF formatting

docs/modes/train.md

@@ -2,10 +2,6 @@
 comments: true
 ---
 
----
-comments: true
----
-
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
 
 **Train mode** is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the

docs/overrides/partials/source-file.html

@@ -0,0 +1,26 @@
+{% import "partials/language.html" as lang with context %}
+
+<!-- taken from
+https://github.com/squidfunk/mkdocs-material/blob/master/src/partials/source-file.html -->
+
+<br>
+<div class="md-source-file">
+<small>
+
+    <!-- mkdocs-git-revision-date-localized-plugin -->
+    {% if page.meta.git_revision_date_localized %}
+    📅 {{ lang.t("source.file.date.updated") }}:
+    {{ page.meta.git_revision_date_localized }}
+    {% if page.meta.git_creation_date_localized %}
+        <br />
+        🎂 {{ lang.t("source.file.date.created") }}:
+        {{ page.meta.git_creation_date_localized }}
+    {% endif %}
+
+    <!-- mkdocs-git-revision-date-plugin -->
+    {% elif page.meta.revision_date %}
+    📅 {{ lang.t("source.file.date.updated") }}:
+    {{ page.meta.revision_date }}
+    {% endif %}
+</small>
+</div>

docs/quickstart.md

@@ -96,7 +96,7 @@ CLI requires no customization or Python code. You can simply run all tasks from
 
 !!! warning "Warning"
 
-    Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` beteen arguments.
+    Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
 
     - `yolo predict model=yolov8n.pt imgsz=640 conf=0.25`   ✅
     - `yolo predict model yolov8n.pt imgsz 640 conf 0.25`   ❌

docs/reference/yolo/utils/plotting.md

@@ -32,3 +32,8 @@
 ---
 :::ultralytics.yolo.utils.plotting.output_to_target
 <br><br>
+
+# feature_visualization
+---
+:::ultralytics.yolo.utils.plotting.feature_visualization
+<br><br>

docs/tasks/classify.md

@@ -77,10 +77,16 @@ see the [Configuration](../usage/cfg.md) page.
 The YOLO classification dataset format is same as the torchvision format. Each class of images has its own folder and you have to simply pass the path of the dataset folder, i.e, `yolo classify train data="path/to/dataset"`
 ```
 dataset/
-├── class1/
+├── train/
-├── class2/
+├──── class1/
-├── class3/
+├──── class2/
-├── ...
+├──── class3/
+├──── ...
+├── val/
+├──── class1/
+├──── class2/
+├──── class3/
+├──── ...
 ```
 ## Val
 

mkdocs.yml

@@ -12,6 +12,8 @@ theme:
   custom_dir: docs/overrides
   logo: https://github.com/ultralytics/assets/raw/main/logo/Ultralytics_Logotype_Reverse.svg
   favicon: assets/favicon.ico
+  icon:
+    repo: fontawesome/brands/github
   font:
     text: Roboto
     code: Roboto Mono
@@ -55,6 +57,7 @@ copyright: <a href="https://ultralytics.com" target="_blank">Ultralytics 2023.</
 extra:
   # version:
   #   provider: mike  #  version drop-down menu
+  robots: robots.txt
   analytics:
     provider: google
     property: G-2M5EHKC0BH
@@ -91,9 +94,6 @@ extra:
 extra_css:
   - stylesheets/style.css
 
-extra_files:
-  - robots.txt
-
 markdown_extensions:
   # Div text decorators
   - admonition
@@ -289,6 +289,9 @@ nav:
 plugins:
   - mkdocstrings
   - search
+  - git-revision-date-localized:
+      type: timeago
+      enable_creation_date: true
   - redirects:
       redirect_maps:
         callbacks.md: usage/callbacks.md
@@ -338,6 +341,7 @@ plugins:
         yolov5/hyp_evolution.md: yolov5/tutorials/hyperparameter_evolution.md
         yolov5/pruning_sparsity.md: yolov5/tutorials/model_pruning_and_sparsity.md
         yolov5/comet.md: yolov5/tutorials/comet_logging_integration.md
+        yolov5/clearml.md: yolov5/tutorials/clearml_logging_integration.md
         yolov5/tta.md: yolov5/tutorials/test_time_augmentation.md
         yolov5/multi_gpu_training.md: yolov5/tutorials/multi_gpu_training.md
         yolov5/ensemble.md: yolov5/tutorials/model_ensembling.md
@@ -351,3 +355,10 @@ plugins:
         yolov5/tutorials/yolov5_neural_magic_tutorial.md: yolov5/tutorials/neural_magic_pruning_quantization.md
         yolov5/tutorials/model_ensembling_tutorial.md: yolov5/tutorials/model_ensembling.md
         yolov5/tutorials/pytorch_hub_tutorial.md: yolov5/tutorials/pytorch_hub_model_loading.md
+        yolov5/tutorials/yolov5_architecture_tutorial.md: yolov5/tutorials/architecture_description.md
+        yolov5/tutorials/multi_gpu_training_tutorial.md: yolov5/tutorials/multi_gpu_training.md
+        yolov5/tutorials/yolov5_pytorch_hub_tutorial.md: yolov5/tutorials/pytorch_hub_model_loading.md
+        yolov5/tutorials/model_export_tutorial.md: yolov5/tutorials/model_export.md
+        yolov5/tutorials/jetson_nano_tutorial.md: yolov5/tutorials/running_on_jetson_nano.md
+        yolov5/tutorials/yolov5_model_ensembling_tutorial.md: yolov5/tutorials/model_ensembling.md
+        reference/base_val.md: index.md

setup.py

@@ -39,8 +39,15 @@ setup(
     install_requires=REQUIREMENTS + PKG_REQUIREMENTS,
     extras_require={
         'dev': [
-            'check-manifest', 'pytest', 'pytest-cov', 'coverage', 'mkdocs-material', 'mkdocstrings[python]',
+            'check-manifest',
-            'mkdocs-redirects'],
+            'pytest',
+            'pytest-cov',
+            'coverage',
+            'mkdocs-material',
+            'mkdocstrings[python]',
+            'mkdocs-redirects',  # for 301 redirects
+            'mkdocs-git-revision-date-localized-plugin',  # for created/updated dates
+        ],
         'export': ['coremltools>=6.0', 'openvino-dev>=2022.3', 'tensorflowjs'],  # automatically installs tensorflow
     },
     classifiers=[

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.91'
+__version__ = '8.0.92'
 
 from ultralytics.hub import start
 from ultralytics.vit.sam import SAM

ultralytics/tracker/track.py

@@ -47,7 +47,7 @@ def on_predict_postprocess_end(predictor):
         tracks = predictor.trackers[i].update(det, im0s[i])
         if len(tracks) == 0:
             continue
-        idx = tracks[:, -1].tolist()
+        idx = tracks[:, -1].astype(int)
         predictor.results[i] = predictor.results[i][idx]
         predictor.results[i].update(boxes=torch.as_tensor(tracks[:, :-1]))
 

ultralytics/vit/sam/modules/mask_generator.py

@@ -82,8 +82,8 @@ class SamAutomaticMaskGenerator:
             memory.
         """
 
-        assert (points_per_side is None) != (point_grids is
+        assert (points_per_side is None) != (point_grids is None), \
-                                             None), 'Exactly one of points_per_side or point_grid must be provided.'
+            'Exactly one of points_per_side or point_grid must be provided.'
         if points_per_side is not None:
             self.point_grids = build_all_layer_point_grids(
                 points_per_side,

ultralytics/yolo/engine/predictor.py

@@ -115,10 +115,8 @@ class BasePredictor:
             im (torch.Tensor | List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
         """
         if not isinstance(im, torch.Tensor):
-            auto = all(x.shape == im[0].shape for x in im) and self.model.pt
+            same_shapes = all(x.shape == im[0].shape for x in im)
-            if not auto:
+            auto = same_shapes and self.model.pt
-                LOGGER.warning(
-                    'WARNING ⚠️ Source shapes differ. For optimal performance supply similarly-shaped sources.')
             im = np.stack([LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im])
             im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
             im = np.ascontiguousarray(im)  # contiguous

ultralytics/yolo/utils/__init__.py

@@ -259,13 +259,14 @@ def yaml_save(file='data.yaml', data=None):
         # Create parent directories if they don't exist
         file.parent.mkdir(parents=True, exist_ok=True)
 
+    # Convert Path objects to strings
+    for k, v in data.items():
+        if isinstance(v, Path):
+            dict[k] = str(v)
+
+    # Dump data to file in YAML format
     with open(file, 'w') as f:
-        # Dump data to file in YAML format, converting Path objects to strings
+        yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True)
-        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v
-                        for k, v in data.items()},
-                       f,
-                       sort_keys=False,
-                       allow_unicode=True)
 
 
 def yaml_load(file='data.yaml', append_filename=False):
@@ -759,7 +760,7 @@ ENVIRONMENT = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter'
 TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
 set_sentry()
 
-# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------
+# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------
 imshow_ = cv2.imshow  # copy to avoid recursion errors
 
 

ultralytics/yolo/utils/plotting.py

@@ -481,9 +481,6 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
         stage (int): Module stage within the model.
         n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
         save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
-
-    Returns:
-        None: This function does not return any value; it saves the visualization to the specified directory.
     """
     for m in ['Detect', 'Pose', 'Segment']:
         if m in module_type:

ultralytics/yolo/v8/detect/train.py

@@ -212,7 +212,6 @@ class Loss:
             pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
             anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
 
-        target_bboxes /= stride_tensor
         target_scores_sum = max(target_scores.sum(), 1)
 
         # cls loss
@@ -221,6 +220,7 @@ class Loss:
 
         # bbox loss
         if fg_mask.sum():
+            target_bboxes /= stride_tensor
             loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
                                               target_scores_sum, fg_mask)