ultralytics 8.0.47 Docker and reformat updates (#1153)

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

.github/workflows/ci.yaml

@@ -38,7 +38,7 @@ jobs:
           if [ "${{ matrix.os }}" == "macos-latest" ]; then
               pip install -e . coremltools openvino-dev tensorflow-macos --extra-index-url https://download.pytorch.org/whl/cpu
             else
-              pip install -e . coremltools openvino-dev tensorflow-cpu paddlepaddle x2paddle --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
           fi
           yolo export format=tflite
       - name: Check environment
@@ -66,7 +66,7 @@ jobs:
         shell: python
         run: |
           from ultralytics.yolo.utils.benchmarks import benchmark
-          benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.70)
+          benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.60)
       - name: Benchmark Summary
         run: cat benchmarks.log
 

README.zh-CN.md

@@ -223,10 +223,11 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
 
 ## <div align="center">License</div>
 
-- YOLOv8 在两种不同的 License 下可用：
+YOLOv8 在两种不同的 License 下可用：
-  - **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。
+
-  - **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI
+- **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件的详细信息。
-    模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
+- **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI
+  模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
 
 ## <div align="center">联系我们</div>
 

docker/Dockerfile

@@ -29,9 +29,8 @@ WORKDIR /usr/src/ultralytics
 RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 
 # Install pip packages
-COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics[export] albumentations comet gsutil notebook
+RUN pip install --no-cache '.[export]' albumentations comet gsutil notebook
 
 # Set environment variables
 ENV OMP_NUM_THREADS=1

docker/Dockerfile-arm64

@@ -24,9 +24,8 @@ WORKDIR /usr/src/ultralytics
 RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 
 # Install pip packages
-COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics albumentations gsutil notebook
+RUN pip install --no-cache . albumentations gsutil notebook
 
 # Cleanup
 ENV DEBIAN_FRONTEND teletype

docker/Dockerfile-cpu

@@ -24,9 +24,8 @@ WORKDIR /usr/src/ultralytics
 RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 
 # Install pip packages
-COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics[export] albumentations gsutil notebook \
+RUN pip install --no-cache '.[export]' albumentations gsutil notebook \
         --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Cleanup

docs/predict.md

@@ -97,8 +97,8 @@ Class reference documentation for `Results` module and its components can be fou
 
 ## Plotting results
 
-You can use `plot()` function of `Result` object to plot results on in image object. 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,
-classification logits, etc) found in the results object
+masks, classification logits, etc) found in the results object
 
 ```python
 res = model(img)

docs/tasks/tracking.md

@@ -42,11 +42,15 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
 
         ```
 
-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.
+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).
+
+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"
@@ -65,7 +69,10 @@ Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More
         ```
 
 ### 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.
+
+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"
@@ -82,5 +89,7 @@ We also support using a modified tracker config file, just copy a config file i.
         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. 
+
+Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg)
+page. 
 

requirements.txt

@@ -37,7 +37,6 @@ seaborn>=0.11.0
 # Extras --------------------------------------
 psutil  # system utilization
 thop>=0.1.1  # FLOPs computation
-wheel>=0.38.0  # Snyk vulnerability fix
 # ipython  # interactive notebook
 # albumentations>=1.0.3
 # pycocotools>=2.0.6  # COCO mAP

setup.cfg

@@ -25,17 +25,19 @@ verbose = 2
 # https://pep8.readthedocs.io/en/latest/intro.html#error-codes
 format = pylint
 # see: https://www.flake8rules.com/
-ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403
+ignore = E731,F405,E402,W504,E501
     # E731: Do not assign a lambda expression, use a def
     # F405: name may be undefined, or defined from star imports: module
     # E402: module level import not at top of file
-    # F401: module imported but unused
     # W504: line break after binary operator
-    # E127: continuation line over-indented for visual indent
-    # E231: missing whitespace after ‘,’, ‘;’, or ‘:’
     # E501: line too long
+    # removed:
+    # F401: module imported but unused
+    # E231: missing whitespace after ‘,’, ‘;’, or ‘:’
+    # E127: continuation line over-indented for visual indent
     # F403: ‘from module import *’ used; unable to detect undefined names
 
+
 [isort]
 # https://pycqa.github.io/isort/docs/configuration/options.html
 line_length = 120
@@ -48,7 +50,7 @@ spaces_before_comment = 2
 COLUMN_LIMIT = 120
 COALESCE_BRACKETS = True
 SPACES_AROUND_POWER_OPERATOR = True
-SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
+SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = True
 SPLIT_BEFORE_CLOSING_BRACKET = False
 SPLIT_BEFORE_FIRST_ARGUMENT = False
 # EACH_DICT_ENTRY_ON_SEPARATE_LINE = False

setup.py

@@ -59,7 +59,7 @@ setup(
         'Topic :: Scientific/Engineering :: Image Recognition',
         'Operating System :: POSIX :: Linux',
         'Operating System :: MacOS',
-        'Operating System :: Microsoft :: Windows',],
+        'Operating System :: Microsoft :: Windows', ],
     keywords='machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics',
     entry_points={
         'console_scripts': ['yolo = ultralytics.yolo.cfg:entrypoint', 'ultralytics = ultralytics.yolo.cfg:entrypoint']})

tests/test_cli.py

@@ -22,7 +22,7 @@ def test_special_modes():
 
 # Train checks ---------------------------------------------------------------------------------------------------------
 def test_train_det():
-    run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1')
+    run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1 v5loader')
 
 
 def test_train_seg():
@@ -48,7 +48,7 @@ def test_val_classify():
 
 # Predict checks -------------------------------------------------------------------------------------------------------
 def test_predict_detect():
-    run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save")
+    run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
     if checks.check_online():
         run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
         run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')

tests/test_python.py

@@ -162,9 +162,8 @@ def test_workflow():
 
 
 def test_predict_callback_and_setup():
-
+    # test callback addition for prediction
-    def on_predict_batch_end(predictor):
+    def on_predict_batch_end(predictor):  # results -> List[batch_size]
-        # results -> List[batch_size]
         path, _, im0s, _, _ = predictor.batch
         # print('on_predict_batch_end', im0s[0].shape)
         im0s = im0s if isinstance(im0s, list) else [im0s]

ultralytics/__init__.py

@@ -1,8 +1,8 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.46'
+__version__ = '8.0.47'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks
 
-__all__ = ['__version__', 'YOLO', 'checks']  # allow simpler import
+__all__ = '__version__', 'YOLO', 'checks'  # allow simpler import

ultralytics/hub/utils.py

@@ -154,11 +154,12 @@ class Traces:
             'python': platform.python_version(),
             'release': __version__,
             'environment': ENVIRONMENT}
-        self.enabled = SETTINGS['sync'] and \
+        self.enabled = \
-                       RANK in {-1, 0} and \
+            SETTINGS['sync'] and \
-                       check_online() and \
+            RANK in {-1, 0} and \
-                       not TESTS_RUNNING and \
+            check_online() and \
-                       (is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
+            not TESTS_RUNNING and \
+            (is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
 
     def __call__(self, cfg, all_keys=False, traces_sample_rate=1.0):
         """

ultralytics/nn/autobackend.py

@@ -136,7 +136,7 @@ class AutoBackend(nn.Module):
             batch_dim = get_batch(network)
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
+            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for NCS2
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
             import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
@@ -176,6 +176,8 @@ class AutoBackend(nn.Module):
             LOGGER.info(f'Loading {w} for CoreML inference...')
             import coremltools as ct
             model = ct.models.MLModel(w)
+            names, stride, task = (model.user_defined_metadata.get(k) for k in ('names', 'stride', 'task'))
+            names, stride = eval(names), int(stride)
         elif saved_model:  # TF SavedModel
             LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
             import tensorflow as tf
@@ -185,18 +187,13 @@ class AutoBackend(nn.Module):
             LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
             import tensorflow as tf
 
+            from ultralytics.yolo.engine.exporter import gd_outputs
+
             def wrap_frozen_graph(gd, inputs, outputs):
                 x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), [])  # wrapped
                 ge = x.graph.as_graph_element
                 return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
 
-            def gd_outputs(gd):
-                name_list, input_list = [], []
-                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
-                    name_list.append(node.name)
-                    input_list.extend(node.input)
-                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
-
             gd = tf.Graph().as_graph_def()  # TF GraphDef
             with open(w, 'rb') as f:
                 gd.ParseFromString(f.read())
@@ -319,10 +316,17 @@ class AutoBackend(nn.Module):
             self.context.execute_v2(list(self.binding_addrs.values()))
             y = [self.bindings[x].data for x in sorted(self.output_names)]
         elif self.coreml:  # CoreML
-            im = im.cpu().numpy()
+            im = im[0].cpu().numpy()
-            im = Image.fromarray((im[0] * 255).astype('uint8'))
+            if self.task == 'classify':
+                from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD
+
+                # im_pil = Image.fromarray(((im / 6 + 0.5) * 255).astype('uint8'))
+                for i in range(3):
+                    im[..., i] *= IMAGENET_STD[i]
+                    im[..., i] += IMAGENET_MEAN[i]
+            im_pil = Image.fromarray((im * 255).astype('uint8'))
             # im = im.resize((192, 320), Image.ANTIALIAS)
-            y = self.model.predict({'image': im})  # coordinates are xywh normalized
+            y = self.model.predict({'image': im_pil})  # coordinates are xywh normalized
             if 'confidence' in y:
                 box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
                 conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)

ultralytics/nn/tasks.py

@@ -11,7 +11,7 @@ import torch.nn as nn
 from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
                                     Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
                                     GhostBottleneck, GhostConv, Segment)
-from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, yaml_load
+from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
 from ultralytics.yolo.utils.checks import check_requirements, check_yaml
 from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
                                                 intersect_dicts, make_divisible, model_info, scale_img, time_sync)
@@ -76,7 +76,7 @@ class BaseModel(nn.Module):
             None
         """
         c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
         t = time_sync()
         for _ in range(10):
             m(x.clone() if c else x)
@@ -339,14 +339,20 @@ def torch_safe_load(weight):
     file = attempt_download_asset(weight)  # search online if missing locally
     try:
         return torch.load(file, map_location='cpu'), file  # load
-    except ModuleNotFoundError as e:
+    except ModuleNotFoundError as e:  # e.name is missing module name
-        if e.name == 'omegaconf':  # e.name is missing module name
+        if e.name == 'models':
-            LOGGER.warning(f'WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements.'
+            raise TypeError(
-                           f'\nAutoInstall will run now for {e.name} but this feature will be removed in the future.'
+                emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained '
-                           f'\nRecommend fixes are to train a new model using updated ultralytics package or to '
+                       f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with '
-                           f'download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0')
+                       f'YOLOv8 at https://github.com/ultralytics/ultralytics.'
-        if e.name != 'models':
+                       f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
-            check_requirements(e.name)  # install missing module
+                       f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")) from e
+        LOGGER.warning(f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
+                       f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
+                       f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
+                       f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")
+        check_requirements(e.name)  # install missing module
+
         return torch.load(file, map_location='cpu'), file  # load
 
 
@@ -437,22 +443,21 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
                 args[j] = eval(a) if isinstance(a, str) else a  # eval strings
 
         n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in {
+        if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
-                Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
+                 BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
-                BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
             c1, c2 = ch[f], args[0]
             if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                 c2 = make_divisible(c2 * gw, 8)
 
             args = [c1, c2, *args[1:]]
-            if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
+            if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x):
                 args.insert(2, n)  # number of repeats
                 n = 1
         elif m is nn.BatchNorm2d:
             args = [ch[f]]
         elif m is Concat:
             c2 = sum(ch[x] for x in f)
-        elif m in {Detect, Segment}:
+        elif m in (Detect, Segment):
             args.append([ch[x] for x in f])
             if m is Segment:
                 args[2] = make_divisible(args[2] * gw, 8)
@@ -490,11 +495,11 @@ def guess_model_task(model):
     def cfg2task(cfg):
         # Guess from YAML dictionary
         m = cfg['head'][-1][-2].lower()  # output module name
-        if m in ['classify', 'classifier', 'cls', 'fc']:
+        if m in ('classify', 'classifier', 'cls', 'fc'):
             return 'classify'
-        if m in ['detect']:
+        if m == 'detect':
             return 'detect'
-        if m in ['segment']:
+        if m == 'segment':
             return 'segment'
 
     # Guess from model cfg

ultralytics/tracker/__init__.py

@@ -2,3 +2,5 @@
 
 from .track import register_tracker
 from .trackers import BOTSORT, BYTETracker
+
+__all__ = 'register_tracker', 'BOTSORT', 'BYTETracker'  # allow simpler import

ultralytics/tracker/trackers/__init__.py

@@ -2,3 +2,5 @@
 
 from .bot_sort import BOTSORT
 from .byte_tracker import BYTETracker
+
+__all__ = 'BOTSORT', 'BYTETracker'  # allow simpler import

ultralytics/tracker/utils/gmc.py

@@ -213,7 +213,7 @@ class GMC:
                 prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
                 curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
                 curr_pt[0] += W
-                color = np.random.randint(0, 255, (3,))
+                color = np.random.randint(0, 255, 3)
                 color = (int(color[0]), int(color[1]), int(color[2]))
 
                 matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)

ultralytics/yolo/__init__.py

@@ -2,4 +2,4 @@
 
 from . import v8
 
-__all__ = ['v8']
+__all__ = 'v8',  # tuple or list

ultralytics/yolo/cfg/__init__.py

@@ -269,6 +269,11 @@ def entrypoint(debug=''):
         checks.check_yolo()
         return
 
+    # Task
+    task = overrides.get('task')
+    if task and task not in TASKS:
+        raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
+
     # Model
     model = overrides.pop('model', DEFAULT_CFG.model)
     if model is None:
@@ -276,15 +281,11 @@ def entrypoint(debug=''):
         LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
     from ultralytics.yolo.engine.model import YOLO
     overrides['model'] = model
-    model = YOLO(model)
+    model = YOLO(model, task=task)
 
-    # Task
+    # Task Update
-    task = overrides.get('task', model.task)
+    task = task or model.task
-    if task is not None:
+    overrides['task'] = task
-        if task not in TASKS:
-            raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
-        else:
-            model.task = task
 
     # Mode
     if mode in {'predict', 'track'} and 'source' not in overrides:

ultralytics/yolo/data/__init__.py

@@ -5,12 +5,5 @@ from .build import build_classification_dataloader, build_dataloader, load_infer
 from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
 from .dataset_wrappers import MixAndRectDataset
 
-__all__ = [
+__all__ = ('BaseDataset', 'ClassificationDataset', 'MixAndRectDataset', 'SemanticDataset', 'YOLODataset',
-    'BaseDataset',
+           'build_classification_dataloader', 'build_dataloader', 'load_inference_source')
-    'ClassificationDataset',
-    'MixAndRectDataset',
-    'SemanticDataset',
-    'YOLODataset',
-    'build_classification_dataloader',
-    'build_dataloader',
-    'load_inference_source',]

ultralytics/yolo/data/augment.py

@@ -564,7 +564,7 @@ class Albumentations:
                 A.CLAHE(p=0.01),
                 A.RandomBrightnessContrast(p=0.0),
                 A.RandomGamma(p=0.0),
-                A.ImageCompression(quality_lower=75, p=0.0),]  # transforms
+                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
             self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
 
             LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
@@ -671,14 +671,14 @@ def v8_transforms(dataset, imgsz, hyp):
             shear=hyp.shear,
             perspective=hyp.perspective,
             pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
-        ),])
+        )])
     return Compose([
         pre_transform,
         MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
         Albumentations(p=1.0),
         RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
         RandomFlip(direction='vertical', p=hyp.flipud),
-        RandomFlip(direction='horizontal', p=hyp.fliplr),])  # transforms
+        RandomFlip(direction='horizontal', p=hyp.fliplr)])  # transforms
 
 
 # Classification augmentations -----------------------------------------------------------------------------------------
@@ -719,8 +719,8 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, saturation, 0 hue
+                    jitter = float(jitter)
-                    T += [A.ColorJitter(*color_jitter, 0)]
+                    T += [A.ColorJitter(jitter, jitter, jitter, 0)]  # brightness, contrast, saturation, 0 hue
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
         T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor

ultralytics/yolo/data/base.py

@@ -24,20 +24,18 @@ class BaseDataset(Dataset):
         label_path (str): label path, this can also be an ann_file or other custom label path.
     """
 
-    def __init__(
+    def __init__(self,
-        self,
+                 img_path,
-        img_path,
+                 imgsz=640,
-        imgsz=640,
+                 cache=False,
-        cache=False,
+                 augment=True,
-        augment=True,
+                 hyp=None,
-        hyp=None,
+                 prefix='',
-        prefix='',
+                 rect=False,
-        rect=False,
+                 batch_size=None,
-        batch_size=None,
+                 stride=32,
-        stride=32,
+                 pad=0.5,
-        pad=0.5,
+                 single_cls=False):
-        single_cls=False,
-    ):
         super().__init__()
         self.img_path = img_path
         self.imgsz = imgsz

ultralytics/yolo/data/dataloaders/v5augmentations.py

@@ -335,8 +335,8 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    jitter = float(jitter)
-                    T += [A.ColorJitter(*color_jitter, 0)]
+                    T += [A.ColorJitter(jitter, jitter, jitter, 0)]  # brightness, contrast, satuaration, 0 hue
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
         T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor

ultralytics/yolo/data/dataset.py

@@ -4,13 +4,16 @@ from itertools import repeat
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
+import cv2
+import numpy as np
+import torch
 import torchvision
 from tqdm import tqdm
 
 from ..utils import NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable
-from .augment import *
+from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
 from .base import BaseDataset
-from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image_label
+from .utils import HELP_URL, LOCAL_RANK, LOGGER, get_hash, img2label_paths, verify_image_label
 
 
 class YOLODataset(BaseDataset):

ultralytics/yolo/engine/exporter.py

@@ -50,7 +50,6 @@ TensorFlow.js:
 import json
 import os
 import platform
-import re
 import subprocess
 import time
 import warnings
@@ -90,9 +89,9 @@ def export_formats():
         ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
         ['TensorFlow GraphDef', 'pb', '.pb', True, True],
         ['TensorFlow Lite', 'tflite', '.tflite', True, False],
-        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
+        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', True, False],
-        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
+        ['TensorFlow.js', 'tfjs', '_web_model', True, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+        ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -100,6 +99,15 @@ EXPORT_FORMATS_LIST = list(export_formats()['Argument'][1:])
 EXPORT_FORMATS_TABLE = str(export_formats())
 
 
+def gd_outputs(gd):
+    # TensorFlow GraphDef model output node names
+    name_list, input_list = [], []
+    for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
+        name_list.append(node.name)
+        input_list.extend(node.input)
+    return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
+
+
 def try_export(inner_func):
     # YOLOv8 export decorator, i..e @try_export
     inner_args = get_default_args(inner_func)
@@ -164,10 +172,10 @@ class Exporter:
         # Checks
         model.names = check_class_names(model.names)
         self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
-        if model.task == 'classify':
-            self.args.nms = self.args.agnostic_nms = False
         if self.args.optimize:
             assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
+        if edgetpu and not LINUX:
+            raise SystemError('Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler/')
 
         # Input
         im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
@@ -208,7 +216,7 @@ class Exporter:
         self.file = file
         self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
         self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
-        description = f'Ultralytics {self.pretty_name} model' + f'trained on {Path(self.args.data).name}' \
+        description = f'Ultralytics {self.pretty_name} model ' + f'trained on {Path(self.args.data).name}' \
             if self.args.data else '(untrained)'
         self.metadata = {
             'description': description,
@@ -239,8 +247,7 @@ class Exporter:
                            'Please consider contributing to the effort if you have TF expertise. Thank you!')
             nms = False
             self.args.int8 |= edgetpu
-            f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
+            f[5], s_model = self._export_saved_model()
-                                                     agnostic_nms=self.args.agnostic_nms or tfjs)
             if pb or tfjs:  # pb prerequisite to tfjs
                 f[6], _ = self._export_pb(s_model)
             if tflite:
@@ -386,7 +393,7 @@ class Exporter:
         check_requirements('coremltools>=6.0')
         import coremltools as ct  # noqa
 
-        class iOSModel(torch.nn.Module):
+        class iOSDetectModel(torch.nn.Module):
             # Wrap an Ultralytics YOLO model for iOS export
             def __init__(self, model, im):
                 super().__init__()
@@ -405,29 +412,36 @@ class Exporter:
         LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
         f = self.file.with_suffix('.mlmodel')
 
+        bias = [0.0, 0.0, 0.0]
+        scale = 1 / 255
+        classifier_config = None
         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())) if self.args.nms else None
-        else:
+            model = self.model
-            bias = [0.0, 0.0, 0.0]
+        elif self.model.task == 'detect':
-            scale = 1 / 255
+            model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
-            classifier_config = None
+        elif self.model.task == 'segment':
-        model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
+            # TODO CoreML Segmentation model pipelining
-        ts = torch.jit.trace(model, self.im, strict=False)  # TorchScript model
+            model = self.model
+
+        ts = torch.jit.trace(model.eval(), self.im, strict=False)  # TorchScript model
         ct_model = ct.convert(ts,
                               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:
             ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
-        if self.args.nms:
+        if self.args.nms and self.model.task == 'detect':
             ct_model = self._pipeline_coreml(ct_model)
 
-        ct_model.short_description = self.metadata['description']
+        m = self.metadata  # metadata dict
-        ct_model.author = self.metadata['author']
+        ct_model.short_description = m['description']
-        ct_model.license = self.metadata['license']
+        ct_model.author = m['author']
-        ct_model.version = self.metadata['version']
+        ct_model.license = m['license']
+        ct_model.version = m['version']
+        ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items() if k in ('stride', 'task', 'names')})
         ct_model.save(str(f))
         return f, ct_model
 
@@ -497,14 +511,7 @@ class Exporter:
         return f, None
 
     @try_export
-    def _export_saved_model(self,
+    def _export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):
-                            nms=False,
-                            agnostic_nms=False,
-                            topk_per_class=100,
-                            topk_all=100,
-                            iou_thres=0.45,
-                            conf_thres=0.25,
-                            prefix=colorstr('TensorFlow SavedModel:')):
 
         # YOLOv8 TensorFlow SavedModel export
         try:
@@ -562,6 +569,9 @@ class Exporter:
     @try_export
     def _export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
         # YOLOv8 TensorFlow Lite export
+        import tensorflow as tf  # noqa
+
+        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         saved_model = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
         if self.args.int8:
             f = saved_model / (self.file.stem + 'yolov8n_integer_quant.tflite')  # fp32 in/out
@@ -572,9 +582,6 @@ class Exporter:
         return str(f), None  # noqa
 
         # OLD VERSION BELOW ---------------------------------------------------------------
-        import tensorflow as tf  # noqa
-
-        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
         batch_size, ch, *imgsz = list(self.im.shape)  # BCHW
         f = str(self.file).replace(self.file.suffix, '-fp16.tflite')
 
@@ -619,7 +626,9 @@ class Exporter:
             LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
             sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
             for c in (
-                    'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
+                    # 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',  # errors
+                    'wget --no-check-certificate -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | '
+                    'sudo apt-key add -',
                     'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | '  # no comma
                     'sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
                     'sudo apt-get update',
@@ -639,30 +648,36 @@ class Exporter:
     def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
         # YOLOv8 TensorFlow.js export
         check_requirements('tensorflowjs')
+        import tensorflow as tf
         import tensorflowjs as tfjs  # noqa
 
         LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
         f = str(self.file).replace(self.file.suffix, '_web_model')  # js dir
         f_pb = self.file.with_suffix('.pb')  # *.pb path
-        f_json = Path(f) / 'model.json'  # *.json path
 
-        cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
+        gd = tf.Graph().as_graph_def()  # TF GraphDef
-              f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
+        with open(f_pb, 'rb') as file:
+            gd.ParseFromString(file.read())
+        outputs = ','.join(gd_outputs(gd))
+        LOGGER.info(f'\n{prefix} output node names: {outputs}')
+
+        cmd = f'tensorflowjs_converter --input_format=tf_frozen_model --output_node_names={outputs} {f_pb} {f}'
         subprocess.run(cmd.split(), check=True)
 
-        with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
+        # f_json = Path(f) / 'model.json'  # *.json path
-            subst = re.sub(
+        # with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
-                r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
+        #     subst = re.sub(
-                r'"Identity.?.?": {"name": "Identity.?.?"}, '
+        #         r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'"Identity.?.?": {"name": "Identity.?.?"}, '
+        #         r'"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'"Identity.?.?": {"name": "Identity.?.?"}}}',
+        #         r'"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'{"outputs": {"Identity": {"name": "Identity"}, '
+        #         r'"Identity.?.?": {"name": "Identity.?.?"}}}',
-                r'"Identity_1": {"name": "Identity_1"}, '
+        #         r'{"outputs": {"Identity": {"name": "Identity"}, '
-                r'"Identity_2": {"name": "Identity_2"}, '
+        #         r'"Identity_1": {"name": "Identity_1"}, '
-                r'"Identity_3": {"name": "Identity_3"}}}',
+        #         r'"Identity_2": {"name": "Identity_2"}, '
-                f_json.read_text(),
+        #         r'"Identity_3": {"name": "Identity_3"}}}',
-            )
+        #         f_json.read_text(),
-            j.write(subst)
+        #     )
+        #     j.write(subst)
         yaml_save(Path(f) / 'metadata.yaml', self.metadata)  # add metadata.yaml
         return f, None
 
@@ -680,7 +695,7 @@ class Exporter:
         model_meta.license = self.metadata['license']
 
         # Label file
-        tmp_file = file.parent / 'temp_meta.txt'
+        tmp_file = Path(file).parent / 'temp_meta.txt'
         with open(tmp_file, 'w') as f:
             f.write(str(self.metadata))
 
@@ -718,7 +733,7 @@ class Exporter:
         b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
         metadata_buf = b.Output()
 
-        populator = _metadata.MetadataPopulator.with_model_file(file)
+        populator = _metadata.MetadataPopulator.with_model_file(str(file))
         populator.load_metadata_buffer(metadata_buf)
         populator.load_associated_files([str(tmp_file)])
         populator.populate()

ultralytics/yolo/engine/model.py

@@ -2,7 +2,6 @@
 
 import sys
 from pathlib import Path
-from typing import List
 
 from ultralytics import yolo  # noqa
 from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight,
@@ -68,7 +67,7 @@ class YOLO:
         list(ultralytics.yolo.engine.results.Results): The prediction results.
     """
 
-    def __init__(self, model='yolov8n.pt') -> None:
+    def __init__(self, model='yolov8n.pt', task=None) -> None:
         """
         Initializes the YOLO model.
 
@@ -91,9 +90,9 @@ class YOLO:
         if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
             model, suffix = Path(model).with_suffix('.pt'), '.pt'  # add suffix, i.e. yolov8n -> yolov8n.pt
         if suffix == '.yaml':
-            self._new(model)
+            self._new(model, task)
         else:
-            self._load(model)
+            self._load(model, task)
 
     def __call__(self, source=None, stream=False, **kwargs):
         return self.predict(source, stream, **kwargs)
@@ -102,17 +101,18 @@ class YOLO:
         name = self.__class__.__name__
         raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
 
-    def _new(self, cfg: str, verbose=True):
+    def _new(self, cfg: str, task=None, verbose=True):
         """
         Initializes a new model and infers the task type from the model definitions.
 
         Args:
             cfg (str): model configuration file
+            task (str) or (None): model task
             verbose (bool): display model info on load
         """
         self.cfg = check_yaml(cfg)  # check YAML
         cfg_dict = yaml_load(self.cfg, append_filename=True)  # model dict
-        self.task = guess_model_task(cfg_dict)
+        self.task = task or guess_model_task(cfg_dict)
         self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1)  # build model
         self.overrides['model'] = self.cfg
 
@@ -121,12 +121,13 @@ class YOLO:
         self.model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model
         self.model.task = self.task
 
-    def _load(self, weights: str, task=''):
+    def _load(self, weights: str, task=None):
         """
         Initializes a new model and infers the task type from the model head.
 
         Args:
             weights (str): model checkpoint to be loaded
+            task (str) or (None): model task
         """
         suffix = Path(weights).suffix
         if suffix == '.pt':
@@ -137,7 +138,7 @@ class YOLO:
         else:
             weights = check_file(weights)
             self.model, self.ckpt = weights, None
-            self.task = guess_model_task(weights)
+            self.task = task or guess_model_task(weights)
             self.ckpt_path = weights
         self.overrides['model'] = weights
 

ultralytics/yolo/engine/predictor.py

@@ -32,7 +32,6 @@ from collections import defaultdict
 from pathlib import Path
 
 import cv2
-import torch
 
 from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.cfg import get_cfg

ultralytics/yolo/engine/trainer.py

@@ -242,7 +242,7 @@ class BaseTrainer:
             metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
             self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))  # TODO: init metrics for plot_results()?
             self.ema = ModelEMA(self.model)
-            if self.args.plots:
+            if self.args.plots and not self.args.v5loader:
                 self.plot_training_labels()
         self.resume_training(ckpt)
         self.scheduler.last_epoch = self.start_epoch - 1  # do not move

ultralytics/yolo/utils/__init__.py

@@ -18,7 +18,6 @@ from typing import Union
 import cv2
 import numpy as np
 import pandas as pd
-import requests
 import torch
 import yaml
 
@@ -517,10 +516,7 @@ def set_sentry():
             ((is_pip_package() and not is_git_dir()) or
              (get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git' and get_git_branch() == 'main')):
 
-        import hashlib
-
         import sentry_sdk  # noqa
-
         sentry_sdk.init(
             dsn='https://f805855f03bb4363bc1e16cb7d87b654@o4504521589325824.ingest.sentry.io/4504521592406016',
             debug=False,

ultralytics/yolo/utils/benchmarks.py

@@ -30,14 +30,14 @@ import pandas as pd
 
 from ultralytics import YOLO
 from ultralytics.yolo.engine.exporter import export_formats
-from ultralytics.yolo.utils import LOGGER, ROOT, SETTINGS
+from ultralytics.yolo.utils import LINUX, LOGGER, ROOT, SETTINGS
 from ultralytics.yolo.utils.checks import check_yolo
 from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.torch_utils import select_device
 
 
-def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=0.30):
+def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=False):
     device = select_device(device, verbose=False)
     if isinstance(model, (str, Path)):
         model = YOLO(model)
@@ -45,11 +45,10 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
     y = []
     t0 = time.time()
     for i, (name, format, suffix, cpu, gpu) in export_formats().iterrows():  # index, (name, format, suffix, CPU, GPU)
+        emoji = '❌'  # indicates export failure
         try:
-            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
+            assert i != 11, 'paddle exports coming soon'
-            assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
+            assert i != 9 or LINUX, 'Edge TPU export only supported on Linux'
-            assert i != 11 or model.task != 'classify', 'paddle-classify bug'
-
             if 'cpu' in device.type:
                 assert cpu, 'inference not supported on CPU'
             if 'cuda' in device.type:
@@ -61,13 +60,16 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
                 export = model  # PyTorch format
             else:
                 filename = model.export(imgsz=imgsz, format=format, half=half, device=device)  # all others
-                export = YOLO(filename)
+                export = YOLO(filename, task=model.task)
                 assert suffix in str(filename), 'export failed'
+            emoji = '❎'  # indicates export succeeded
 
             # Predict
+            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
+            assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
             if not (ROOT / 'assets/bus.jpg').exists():
                 download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
-            export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)  # test
+            export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
 
             # Validate
             if model.task == 'detect':
@@ -84,17 +86,16 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
             if hard_fail:
                 assert type(e) is AssertionError, f'Benchmark hard_fail for {name}: {e}'
             LOGGER.warning(f'ERROR ❌️ Benchmark failure for {name}: {e}')
-            y.append([name, '❌', None, None, None])  # mAP, t_inference
+            y.append([name, emoji, None, None, None])  # mAP, t_inference
 
     # Print results
     check_yolo(device=device)  # print system info
-    c = ['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)']
+    df = pd.DataFrame(y, columns=['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'])
-    df = pd.DataFrame(y, columns=c)
 
     name = Path(model.ckpt_path).name
     s = f'\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n'
     LOGGER.info(s)
-    with open('benchmarks.log', 'a') as f:
+    with open('benchmarks.log', 'a', errors='ignore', encoding='utf-8') as f:
         f.write(s)
 
     if hard_fail and isinstance(hard_fail, float):

ultralytics/yolo/utils/callbacks/__init__.py

@@ -1,5 +1,3 @@
 from .base import add_integration_callbacks, default_callbacks
 
-__all__ = [
+__all__ = 'add_integration_callbacks', 'default_callbacks'
-    'add_integration_callbacks',
-    'default_callbacks',]

ultralytics/yolo/utils/checks.py

@@ -137,7 +137,6 @@ def check_latest_pypi_version(package_name='ultralytics'):
 def check_pip_update():
     from ultralytics import __version__
     latest = check_latest_pypi_version()
-    latest = '9.0.0'
     if pkg.parse_version(__version__) < pkg.parse_version(latest):
         LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
                     f"Update with 'pip install -U ultralytics'")
@@ -239,7 +238,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
             LOGGER.warning(f'{prefix} ❌ {e}')
 
 
-def check_suffix(file='yolov8n.pt', suffix=('.pt',), msg=''):
+def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
     # Check file(s) for acceptable suffix
     if file and suffix:
         if isinstance(suffix, str):

ultralytics/yolo/utils/instance.py

@@ -10,9 +10,8 @@ import numpy as np
 from .ops import ltwh2xywh, ltwh2xyxy, resample_segments, xywh2ltwh, xywh2xyxy, xyxy2ltwh, xyxy2xywh
 
 
-# From PyTorch internals
 def _ntuple(n):
-
+    # From PyTorch internals
     def parse(x):
         return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))
 
@@ -26,7 +25,7 @@ to_4tuple = _ntuple(4)
 # `ltwh` means left top and width, height(coco format)
 _formats = ['xyxy', 'xywh', 'ltwh']
 
-__all__ = ['Bboxes']
+__all__ = 'Bboxes',  # tuple or list
 
 
 class Bboxes:

ultralytics/yolo/utils/plotting.py

@@ -207,8 +207,7 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
 
 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)
+    b = xyxy2xywh(xyxy.view(-1, 4))  # boxes
-    b = xyxy2xywh(xyxy)  # boxes
     if square:
         b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
     b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad

ultralytics/yolo/utils/torch_utils.py

@@ -195,7 +195,7 @@ def get_flops(model, imgsz=640):
         p = next(model.parameters())
         stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
         im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
-        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
+        flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2  # stride GFLOPs
         imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
         flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
         return flops
@@ -374,7 +374,7 @@ def profile(input, ops, n=10, device=None):
             m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
             tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
-                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
+                flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1E9 * 2  # GFLOPs
             except Exception:
                 flops = 0
 

ultralytics/yolo/v8/__init__.py

@@ -2,4 +2,4 @@
 
 from ultralytics.yolo.v8 import classify, detect, segment
 
-__all__ = ['classify', 'segment', 'detect']
+__all__ = 'classify', 'segment', 'detect'

ultralytics/yolo/v8/classify/__init__.py

@@ -4,4 +4,4 @@ from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predic
 from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
 from ultralytics.yolo.v8.classify.val import ClassificationValidator, val
 
-__all__ = ['ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val']
+__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'

ultralytics/yolo/v8/detect/__init__.py

@@ -4,4 +4,4 @@ from .predict import DetectionPredictor, predict
 from .train import DetectionTrainer, train
 from .val import DetectionValidator, val
 
-__all__ = ['DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val']
+__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'

ultralytics/yolo/v8/segment/__init__.py

@@ -4,4 +4,4 @@ from .predict import SegmentationPredictor, predict
 from .train import SegmentationTrainer, train
 from .val import SegmentationValidator, val
 
-__all__ = ['SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val']
+__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'