ultralytics 8.0.47 Docker and reformat updates (ultralytics#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：
-  - **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
-    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) 。
+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
+  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,
-classification logits, etc) found in the results object
+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)

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 @@ def get_version():
         '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():
-
-    def on_predict_batch_end(predictor):
-        # results -> List[batch_size]
+    # test callback addition for prediction
+    def on_predict_batch_end(predictor):  # 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 @@ def __init__(self):
             'python': platform.python_version(),
             'release': __version__,
             'environment': ENVIRONMENT}
-        self.enabled = SETTINGS['sync'] and \
-                       RANK in {-1, 0} and \
-                       check_online() and \
-                       not TESTS_RUNNING and \
-                       (is_pip_package() or get_git_origin_url() == 'https://github.com/ultralytics/ultralytics.git')
+        self.enabled = \
+            SETTINGS['sync'] and \
+            RANK in {-1, 0} and \
+            check_online() and \
+            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 @@ def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False,
             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 @@ def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False,
             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 @@ def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False,
             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 @@ def forward(self, im, augment=False, visualize=False):
             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 = Image.fromarray((im[0] * 255).astype('uint8'))
+            im = im[0].cpu().numpy()
+            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 @@
 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 @@ def _profile_one_layer(self, m, x, dt):
             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:
-        if e.name == 'omegaconf':  # e.name is missing module name
-            LOGGER.warning(f'WARNING ⚠️ {weight} requires {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 updated ultralytics package or to '
-                           f'download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0')
-        if e.name != 'models':
-            check_requirements(e.name)  # install missing module
+    except ModuleNotFoundError as e:  # e.name is missing module name
+        if e.name == 'models':
+            raise TypeError(
+                emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained '
+                       f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with '
+                       f'YOLOv8 at https://github.com/ultralytics/ultralytics.'
+                       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'")) 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 {
-                Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
-                BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
+        if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
+                 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