Update YOLOv5 YAMLs to 'u' YAMLs (#800)

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

CONTRIBUTING.md

@@ -59,18 +59,18 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
 
 ### Docstrings
 
-Not all functions or classes require docstrings but when they do, we follow [google-stlye docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
+Not all functions or classes require docstrings but when they do, we follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
 
 ```python
 """
-   What the function does - performs nms on given detection predictions
+   What the function does. Performs NMS on given detection predictions.
 
     Args:
         arg1: The description of the 1st argument
         arg2: The description of the 2nd argument
 
     Returns:
-        What the function returns. Empty if nothing is returned
+        What the function returns. Empty if nothing is returned.
 
     Raises:
         Exception Class: When and why this exception can be raised by the function.

README.md

@@ -1,6 +1,6 @@
 <div align="center">
   <p>
-    <a align="center" href="https://ultralytics.com/yolov8" target="_blank">
+    <a href="https://ultralytics.com/yolov8" target="_blank">
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
   </p>
 
@@ -191,7 +191,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
 ## <div align="center">Integrations</div>
 
 <br>
-<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
 <br>
 <br>
@@ -220,7 +220,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on
 your iOS or Android device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
 
-<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
 
 ## <div align="center">Contribute</div>

README.zh-CN.md

@@ -1,6 +1,6 @@
 <div align="center">
   <p>
-    <a align="center" href="https://ultralytics.com/yolov8" target="_blank">
+    <a href="https://ultralytics.com/yolov8" target="_blank">
       <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
   </p>
 
@@ -167,7 +167,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 ## <div align="center">模块集成</div>
 
 <br>
-<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
 <br>
 <br>
@@ -194,7 +194,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 [Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们⭐ **新**的无代码解决方案，用于可视化数据集，训练 YOLOv8🚀 模型，并以无缝体验方式部署到现实世界。现在开始**免费**! 还可以通过下载 [Ultralytics App](https://ultralytics.com/app_install) 在你的 iOS 或 Android 设备上运行 YOLOv8 模型!
 
-<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
 
 ## <div align="center">贡献</div>

docker/Dockerfile

@@ -13,7 +13,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
 # RUN alias python=python3
 
 # Create working directory

docs/cfg.md

@@ -125,7 +125,7 @@ given task.
 | show           | False                | show results if possible                                |
 | save_txt       | False                | save results as .txt file                               |
 | save_conf      | False                | save results with confidence scores                     |
-| save_crop      | Fasle                | save cropped images with results                        |
+| save_crop      | False                | save cropped images with results                        |
 | hide_labels    | False                | hide labels                                             |
 | hide_conf      | False                | hide confidence scores                                  |
 | vid_stride     | False                | video frame-rate stride                                 |

docs/engine.md

@@ -3,12 +3,12 @@ executors. Let's take a look at the Trainer engine.
 
 ## BaseTrainer
 
-BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overidding
+BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding
 the required functions or operations as long the as correct formats are followed. For example, you can support your own
-custom model and dataloder by just overriding these functions:
+custom model and dataloader by just overriding these functions:
 
 * `get_model(cfg, weights)` - The function that builds the model to be trained
-* `get_dataloder()` - The function that builds the dataloder
+* `get_dataloder()` - The function that builds the dataloader
   More details and source code can be found in [`BaseTrainer` Reference](reference/base_trainer.md)
 
 ## DetectionTrainer
@@ -78,6 +78,6 @@ To know more about Callback triggering events and entry point, checkout our Call
 
 ## Other engine components
 
-There are other componenets that can be customized similarly like `Validators` and `Predictors`
+There are other components that can be customized similarly like `Validators` and `Predictors`
 See Reference section for more information on these.
 

docs/hub.md

@@ -80,7 +80,7 @@ training! <a href="https://colab.research.google.com/github/ultralytics/hub/blob
 Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
 models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
 
-<a align="center" href="https://ultralytics.com/app_install" target="_blank">
+<a href="https://ultralytics.com/app_install" target="_blank">
 <img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
 
 ## ❓ Issues

docs/reference/base_trainer.md

@@ -1,4 +1,4 @@
-All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimzation routine
+All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimization routine
 boilerplate. You can override any function of these Trainers to suit your needs.
 
 ---

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.27"
+__version__ = "8.0.28"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/hub/utils.py

@@ -175,7 +175,9 @@ class Traces:
             cfg = vars(cfg)  # convert type from IterableSimpleNamespace to dict
             if not all_keys:  # filter cfg
                 include_keys = {'task', 'mode'}  # always include
-                cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None) or k in include_keys}
+                cfg = {
+                    k: (v.split(os.sep)[-1] if isinstance(v, str) and os.sep in v else v)
+                    for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None) or k in include_keys}
             trace = {'uuid': SETTINGS['uuid'], 'cfg': cfg, 'metadata': self.metadata}
 
             # Send a request to the HUB API to sync analytics

ultralytics/nn/modules.py

@@ -456,4 +456,5 @@ class Classify(nn.Module):
     def forward(self, x):
         if isinstance(x, list):
             x = torch.cat(x, 1)
-        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
+        x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
+        return x if self.training else x.softmax(1)

ultralytics/yolo/engine/exporter.py

@@ -70,7 +70,7 @@ from ultralytics.nn.modules import Detect, Segment
 from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, guess_model_task
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
-from ultralytics.yolo.data.utils import check_det_dataset
+from ultralytics.yolo.data.utils import check_det_dataset, IMAGENET_MEAN, IMAGENET_STD
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
@@ -185,8 +185,8 @@ class Exporter:
         if self.args.half and not coreml and not xml:
             im, model = im.half(), model.half()  # to FP16
         shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
-        LOGGER.info(
-            f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
+        LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
+                    f"output shape {shape} ({file_size(file):.1f} MB)")
 
         # Warnings
         warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
@@ -384,12 +384,18 @@ class Exporter:
         LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
         f = self.file.with_suffix('.mlmodel')
 
-        task = self.model.task
+        if self.model.task == 'classify':
+            bias = [-x for x in IMAGENET_MEAN]
+            scale = 1 / 255 / (sum(IMAGENET_STD) / 3)
+            classifier_config = ct.ClassifierConfig(list(self.model.names.values()))
+        else:
+            bias = [0.0, 0.0, 0.0]
+            scale = 1 / 255
+            classifier_config = None
         model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
         ts = torch.jit.trace(model, self.im, strict=False)  # TorchScript model
-        classifier_config = ct.ClassifierConfig(list(model.names.values())) if task == 'classify' else None
         ct_model = ct.convert(ts,
-                              inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])],
+                              inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
                               classifier_config=classifier_config)
         bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
         if bits < 32:

ultralytics/yolo/engine/model.py

@@ -162,6 +162,8 @@ class YOLO:
         args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
         args.data = data or args.data
         args.task = self.task
+        if args.imgsz == DEFAULT_CFG.imgsz:
+            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
         args.imgsz = check_imgsz(args.imgsz, max_dim=1)
 
         validator = self.ValidatorClass(args=args)
@@ -180,6 +182,8 @@ class YOLO:
         overrides.update(kwargs)
         args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
         args.task = self.task
+        if args.imgsz == DEFAULT_CFG.imgsz:
+            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
 
         exporter = Exporter(overrides=args)
         exporter(model=self.model)

ultralytics/yolo/engine/predictor.py

@@ -120,9 +120,6 @@ class BasePredictor:
             pass
 
     def setup_source(self, source):
-        if not self.model:
-            raise Exception("Model not initialized!")
-
         self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2)  # check image size
         self.dataset = load_inference_source(source=source,
                                              transforms=getattr(self.model.model, 'transforms', None),
@@ -190,6 +187,10 @@ class BasePredictor:
             if self.args.verbose:
                 LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
 
+        # Release assets
+        if isinstance(self.vid_writer[-1], cv2.VideoWriter):
+            self.vid_writer[-1].release()  # release final video writer
+
         # Print results
         if self.args.verbose and self.seen:
             t = tuple(x.t / self.seen * 1E3 for x in self.dt)  # speeds per image

ultralytics/yolo/utils/checks.py

@@ -5,6 +5,7 @@ import inspect
 import math
 import os
 import platform
+import re
 import shutil
 import urllib
 from pathlib import Path
@@ -67,12 +68,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
                         f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
 
     # Apply max_dim
-    if max_dim == 1:
-        LOGGER.warning(f"WARNING ⚠️ 'train' and 'val' imgsz types must be integer, updating to 'imgsz={max(imgsz)}'. "
-                       f"'predict' and 'export' imgsz may be list or integer, "
-                       f"i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'")
+    if len(imgsz) > max_dim:
+        msg = "'train' and 'val' imgsz must be an integer, while 'predict' and 'export' imgsz may be a [h, w] list " \
+              "or an integer, i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'"
+        if max_dim != 1:
+            raise ValueError(f"imgsz={imgsz} is not a valid image size. {msg}")
+        LOGGER.warning(f"WARNING ⚠️ updating to 'imgsz={max(imgsz)}'. {msg}")
         imgsz = [max(imgsz)]
-
     # Make image size a multiple of the stride
     sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]
 
@@ -220,10 +222,24 @@ def check_suffix(file='yolov8n.pt', suffix=('.pt',), msg=''):
                 assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
 
 
+def check_yolov5u_filename(file: str):
+    # Replace legacy YOLOv5 filenames with updated YOLOv5u filenames
+    if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
+        original_file = file
+        file = re.sub(r"(.*yolov5([nsmlx]))\.", "\\1u.", file)  # i.e. yolov5n.pt -> yolov5nu.pt
+        file = re.sub(r"(.*yolov3(|-tiny|-spp))\.", "\\1u.", file)  # i.e. yolov3-spp.pt -> yolov3-sppu.pt
+        if file != original_file:
+            LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
+                        f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
+                        f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
+    return file
+
+
 def check_file(file, suffix=''):
     # Search/download file (if necessary) and return path
     check_suffix(file, suffix)  # optional
-    file = str(file)  # convert to str()
+    file = str(file)  # convert to string
+    file = check_yolov5u_filename(file)  # yolov5n -> yolov5nu
     if not file or ('://' not in file and Path(file).is_file()):  # exists ('://' check required in Windows Python<3.10)
         return file
     elif file.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://')):  # download

ultralytics/yolo/utils/downloads.py

@@ -1,7 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import contextlib
-import re
 import subprocess
 from itertools import repeat
 from multiprocessing.pool import ThreadPool
@@ -111,6 +110,7 @@ def safe_download(url,
 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
+    from ultralytics.yolo.utils.checks import check_yolov5u_filename
 
     def github_assets(repository, version='latest'):
         # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])
@@ -121,15 +121,7 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
 
     # YOLOv3/5u updates
     file = str(file)
-    if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
-        original_file = file
-        file = re.sub(r"(.*yolov5([nsmlx]))\.pt", "\\1u.pt", file)  # i.e. yolov5n.pt -> yolov5nu.pt
-        file = re.sub(r"(.*yolov3(|-tiny|-spp))\.pt", "\\1u.pt", file)  # i.e. yolov3-spp.pt -> yolov3-sppu.pt
-        if file != original_file:
-            LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
-                        f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
-                        f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
-
+    file = check_yolov5u_filename(file)
     file = Path(file.strip().replace("'", ''))
     if file.exists():
         return str(file)

ultralytics/yolo/v8/classify/predict.py

@@ -23,7 +23,7 @@ class ClassificationPredictor(BasePredictor):
         results = []
         for i, pred in enumerate(preds):
             shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
-            results.append(Results(probs=pred.softmax(0), orig_shape=shape[:2]))
+            results.append(Results(probs=pred, orig_shape=shape[:2]))
         return results
 
     def write_results(self, idx, results, batch):