Update .pre-commit-config.yaml (ultralytics#11009)

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update __init__.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Pre-commit updates

* Pre-commit updates

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -1,8 +1,5 @@
-# Define hooks for code formations
-# Will be applied on any updated commit files if a user has installed and linked commit hook
-
-default_language_version:
-  python: python3.8
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
 
 exclude: 'docs/'
 # Define bot property if installed via https://github.com/marketplace/pre-commit-ci
@@ -16,13 +13,13 @@ repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.4.0
     hooks:
-      # - id: end-of-file-fixer
+      - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-case-conflict
       - id: check-yaml
-      - id: check-toml
-      - id: pretty-format-json
       - id: check-docstring-first
+      - id: double-quote-string-fixer
+      - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
     rev: v3.3.1
@@ -31,11 +28,11 @@ repos:
         name: Upgrade code
         args: [--py37-plus]
 
-  # - repo: https://github.com/PyCQA/isort
-  #   rev: 5.11.4
-  #   hooks:
-  #     - id: isort
-  #       name: Sort imports
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        name: Sort imports
 
   - repo: https://github.com/google/yapf
     rev: v0.32.0
@@ -59,12 +56,12 @@ repos:
       - id: flake8
         name: PEP8
 
-  #- repo: https://github.com/codespell-project/codespell
-  #  rev: v2.2.2
-  #  hooks:
-  #    - id: codespell
-  #      args:
-  #        - --ignore-words-list=crate,nd
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.2
+    hooks:
+      - id: codespell
+        args:
+          - --ignore-words-list=crate,nd,strack,dota
 
   #- repo: https://github.com/asottile/yesqa
   #  rev: v1.4.0

benchmarks.py

@@ -164,6 +164,6 @@ def main(opt):
     test(**vars(opt)) if opt.test else run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

classify/predict.py

@@ -179,7 +179,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms')
 
     # Print results
     t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
@@ -221,6 +221,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

classify/train.py

@@ -78,7 +78,7 @@ def train(opt, device):
             LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
             t = time.time()
             if str(data) == 'imagenet':
-                subprocess.run(["bash", str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True)
+                subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True)
             else:
                 url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
                 download(url, dir=data_dir.parent)
@@ -220,11 +220,11 @@ def train(opt, device):
 
             # Log
             metrics = {
-                "train/loss": tloss,
-                f"{val}/loss": vloss,
-                "metrics/accuracy_top1": top1,
-                "metrics/accuracy_top5": top5,
-                "lr/0": optimizer.param_groups[0]['lr']}  # learning rate
+                'train/loss': tloss,
+                f'{val}/loss': vloss,
+                'metrics/accuracy_top1': top1,
+                'metrics/accuracy_top5': top5,
+                'lr/0': optimizer.param_groups[0]['lr']}  # learning rate
             logger.log_metrics(metrics, epoch)
 
             # Save model
@@ -251,19 +251,19 @@ def train(opt, device):
     if RANK in {-1, 0} and final_epoch:
         LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
                     f"\nResults saved to {colorstr('bold', save_dir)}"
-                    f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
-                    f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
-                    f"\nExport:          python export.py --weights {best} --include onnx"
+                    f'\nPredict:         python classify/predict.py --weights {best} --source im.jpg'
+                    f'\nValidate:        python classify/val.py --weights {best} --data {data_dir}'
+                    f'\nExport:          python export.py --weights {best} --include onnx'
                     f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
-                    f"\nVisualize:       https://netron.app\n")
+                    f'\nVisualize:       https://netron.app\n')
 
         # Plot examples
         images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
         pred = torch.max(ema.ema(images.to(device)), 1)[1]
         file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
 
         # Log results
-        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
+        meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()}
         logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
         logger.log_model(best, epochs, metadata=meta)
 
@@ -310,7 +310,7 @@ def main(opt):
         assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
         torch.cuda.set_device(LOCAL_RANK)
         device = torch.device('cuda', LOCAL_RANK)
-        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
+        dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
 
     # Parameters
     opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
@@ -328,6 +328,6 @@ def run(**kwargs):
     return opt
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

classify/tutorial.ipynb

@@ -1477,4 +1477,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

classify/val.py

@@ -100,7 +100,7 @@ def run(
     pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
     n = len(dataloader)  # number of batches
     action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
-    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}'
     bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
     with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
         for images, labels in bar:
@@ -123,14 +123,14 @@ def run(
     top1, top5 = acc.mean(0).tolist()
 
     if pbar:
-        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+        pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}'
     if verbose:  # all classes
         LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
         LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
         for i, c in model.names.items():
             acc_i = acc[targets == i]
             top1i, top5i = acc_i.mean(0).tolist()
-            LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+            LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}')
 
         # Print results
         t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image
@@ -165,6 +165,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

detect.py

@@ -256,6 +256,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

export.py

@@ -120,7 +120,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
     f = file.with_suffix('.torchscript')
 
     ts = torch.jit.trace(model, im, strict=False)
-    d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
+    d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names}
     extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
     if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
         optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
@@ -195,13 +195,13 @@ def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
     f = str(file).replace('.pt', f'_openvino_model{os.sep}')
 
     args = [
-        "mo",
-        "--input_model",
+        'mo',
+        '--input_model',
         str(file.with_suffix('.onnx')),
-        "--output_dir",
+        '--output_dir',
         f,
-        "--data_type",
-        ("FP16" if half else "FP32"),]
+        '--data_type',
+        ('FP16' if half else 'FP32'),]
     subprocess.run(args, check=True, env=os.environ)  # export
     yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
     return f, None
@@ -237,7 +237,7 @@ def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
     if bits < 32:
         if MACOS:  # quantization only supported on macOS
             with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
+                warnings.filterwarnings('ignore', category=DeprecationWarning)  # suppress numpy==1.20 float warning
                 ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
         else:
             print(f'{prefix} quantization only supported on macOS, skipping...')
@@ -293,7 +293,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
 
     if dynamic:
         if im.shape[0] <= 1:
-            LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
+            LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
         profile = builder.create_optimization_profile()
         for inp in inputs:
             profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
@@ -403,7 +403,7 @@ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=c
         converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
 
     tflite_model = converter.convert()
-    open(f, "wb").write(tflite_model)
+    open(f, 'wb').write(tflite_model)
     return f, None
 
 
@@ -618,14 +618,14 @@ def run(
         det &= not seg  # segmentation models inherit from SegmentationModel(DetectionModel)
         dir = Path('segment' if seg else 'classify' if cls else '')
         h = '--half' if half else ''  # --half FP16 inference arg
-        s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
-            "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
+        s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \
+            '# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else ''
         LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
                     f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
                     f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
                     f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
                     f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
-                    f"\nVisualize:       https://netron.app")
+                    f'\nVisualize:       https://netron.app')
     return f  # return list of exported files/dirs
 
 
@@ -667,6 +667,6 @@ def main(opt):
         run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)

models/common.py

@@ -380,11 +380,11 @@ def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False,
                 w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
             network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
             if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout("NCHW"))
+                network.get_parameters()[0].set_layout(Layout('NCHW'))
             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 Intel NCS2
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -431,7 +431,7 @@ def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False,
             import tensorflow as tf
 
             def wrap_frozen_graph(gd, inputs, outputs):
-                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                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))
 
@@ -445,7 +445,7 @@ def gd_outputs(gd):
             gd = tf.Graph().as_graph_def()  # TF GraphDef
             with open(w, 'rb') as f:
                 gd.ParseFromString(f.read())
-            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
+            frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
         elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
             try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                 from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -467,9 +467,9 @@ def gd_outputs(gd):
             output_details = interpreter.get_output_details()  # outputs
             # load metadata
             with contextlib.suppress(zipfile.BadZipFile):
-                with zipfile.ZipFile(w, "r") as model:
+                with zipfile.ZipFile(w, 'r') as model:
                     meta_file = model.namelist()[0]
-                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
                     stride, names = int(meta['stride']), meta['names']
         elif tfjs:  # TF.js
             raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
@@ -491,7 +491,7 @@ def gd_outputs(gd):
             check_requirements('tritonclient[all]')
             from utils.triton import TritonRemoteModel
             model = TritonRemoteModel(url=w)
-            nhwc = model.runtime.startswith("tensorflow")
+            nhwc = model.runtime.startswith('tensorflow')
         else:
             raise NotImplementedError(f'ERROR: {w} is not a supported format')
 
@@ -608,7 +608,7 @@ def _model_type(p='path/to/model.pt'):
         url = urlparse(p)  # if url may be Triton inference server
         types = [s in Path(p).name for s in sf]
         types[8] &= not types[9]  # tflite &= not edgetpu
-        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
+        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
         return types + [triton]
 
     @staticmethod

models/segment/yolov5m-seg.yaml

@@ -45,4 +45,4 @@ head:
    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
    [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]],  # Detect(P3, P4, P5)
-  ]
+  ]

models/segment/yolov5s-seg.yaml

@@ -45,4 +45,4 @@ head:
    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
    [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]],  # Detect(P3, P4, P5)
-  ]
+  ]