Update setup.cfg (#11814)

* Update setup.cfg

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 .pre-commit-config.yaml

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

* Update export.py

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

* Update downloads.py

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

* Update yolo.py

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

---------

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

@@ -16,7 +16,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-case-conflict
-      - id: check-yaml
+      # - id: check-yaml
       - id: check-docstring-first
       - id: double-quote-string-fixer
       - id: detect-private-key
@@ -26,7 +26,6 @@ repos:
     hooks:
       - id: pyupgrade
         name: Upgrade code
-        args: [--py37-plus]
 
   - repo: https://github.com/PyCQA/isort
     rev: 5.12.0
@@ -63,7 +62,12 @@ repos:
         args:
           - --ignore-words-list=crate,nd,strack,dota
 
-  #- repo: https://github.com/asottile/yesqa
+#  - repo: https://github.com/asottile/yesqa
-  #  rev: v1.4.0
+#    rev: v1.4.0
-  #  hooks:
+#    hooks:
-  #    - id: yesqa
+#      - id: yesqa
+
+#  - repo: https://github.com/asottile/dead
+#    rev: v1.5.0
+#    hooks:
+#    -   id: dead

export.py

@@ -110,7 +110,7 @@ def export_formats():
         ['TensorFlow Lite', 'tflite', '.tflite', True, False],
         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
         ['TensorFlow.js', 'tfjs', '_web_model', False, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+        ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -221,7 +221,7 @@ def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:
         import numpy as np
         from openvino.runtime import Core
 
-        from utils.dataloaders import create_dataloader, letterbox
+        from utils.dataloaders import create_dataloader
         core = Core()
         onnx_model = core.read_model(f_onnx)  # export
 
@@ -501,7 +501,7 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
         '10',
         '--out_dir',
         str(file.parent),
-        f_tfl,], check=True)
+        f_tfl, ], check=True)
     return f, None
 
 
@@ -522,7 +522,7 @@ def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
         '--quantize_uint8' if int8 else '',
         '--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
         str(f_pb),
-        str(f),]
+        str(f), ]
     subprocess.run([arg for arg in args if arg], check=True)
 
     json = Path(f_json).read_text()

models/tf.py

@@ -310,7 +310,7 @@ class TFDetect(keras.layers.Layer):
                 y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1)
                 z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
 
-        return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
+        return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), )
 
     @staticmethod
     def _make_grid(nx=20, ny=20):
@@ -486,7 +486,7 @@ class TFModel:
                                                             iou_thres,
                                                             conf_thres,
                                                             clip_boxes=False)
-            return (nms,)
+            return (nms, )
         return x  # output [1,6300,85] = [xywh, conf, class0, class1, ...]
         # x = x[0]  # [x(1,6300,85), ...] to x(6300,85)
         # xywh = x[..., :4]  # x(6300,4) boxes

models/yolo.py

@@ -21,8 +21,8 @@ if str(ROOT) not in sys.path:
 if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.common import *
+from models.common import *  # noqa
-from models.experimental import *
+from models.experimental import *  # noqa
 from utils.autoanchor import check_anchor_order
 from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
 from utils.plots import feature_visualization
@@ -76,7 +76,7 @@ class Detect(nn.Module):
                     y = torch.cat((xy, wh, conf), 4)
                 z.append(y.view(bs, self.na * nx * ny, self.no))
 
-        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
+        return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)
 
     def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
         d = self.anchors[i].device
@@ -126,7 +126,7 @@ class BaseModel(nn.Module):
 
     def _profile_one_layer(self, m, x, dt):
         c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
         t = time_sync()
         for _ in range(10):
             m(x.copy() if c else x)

segment/train.py

@@ -605,7 +605,7 @@ def main(opt, callbacks=Callbacks()):
                 'gsutil',
                 'cp',
                 f'gs://{opt.bucket}/evolve.csv',
-                str(evolve_csv),])
+                str(evolve_csv), ])
 
         for _ in range(opt.evolve):  # generations to evolve
             if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate

setup.cfg

@@ -3,7 +3,7 @@
 # Local usage: pip install pre-commit, pre-commit run --all-files
 
 [metadata]
-license_file = LICENSE
+license_files = LICENSE
 description_file = README.md
 
 [tool:pytest]
@@ -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

train.py

@@ -584,7 +584,7 @@ def main(opt, callbacks=Callbacks()):
                 'gsutil',
                 'cp',
                 f'gs://{opt.bucket}/evolve.csv',
-                str(evolve_csv),])
+                str(evolve_csv), ])
 
         for _ in range(opt.evolve):  # generations to evolve
             if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate

utils/augmentations.py

@@ -330,7 +330,7 @@ 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
+                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
                     T += [A.ColorJitter(*color_jitter, 0)]
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]

utils/callbacks.py

@@ -32,7 +32,7 @@ class Callbacks:
             'on_model_save': [],
             'on_train_end': [],
             'on_params_update': [],
-            'teardown': [],}
+            'teardown': [], }
         self.stop_training = False  # set True to interrupt training
 
     def register_action(self, hook, name='', callback=None):

utils/downloads.py

@@ -4,7 +4,6 @@ Download utils
 """
 
 import logging
-import os
 import subprocess
 import urllib
 from pathlib import Path
@@ -53,7 +52,7 @@ def curl_download(url, filename, *, silent: bool = False) -> bool:
         '--retry',
         '9',
         '-C',
-        '-',])
+        '-', ])
     return proc.returncode == 0
 
 

utils/general.py

@@ -139,12 +139,12 @@ def set_logging(name=LOGGING_NAME, verbose=True):
             name: {
                 'class': 'logging.StreamHandler',
                 'formatter': name,
-                'level': level,}},
+                'level': level, }},
         'loggers': {
             name: {
                 'level': level,
                 'handlers': [name],
-                'propagate': False,}}})
+                'propagate': False, }}})
 
 
 set_logging(LOGGING_NAME)  # run before defining LOGGER
@@ -416,7 +416,7 @@ def check_imshow(warn=False):
         return False
 
 
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
+def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''):
     # Check file(s) for acceptable suffix
     if file and suffix:
         if isinstance(suffix, str):

utils/loggers/comet/__init__.py

@@ -90,7 +90,7 @@ class CometLogger:
             'log_code': False,
             'log_env_gpu': True,
             'log_env_cpu': True,
-            'project_name': COMET_PROJECT_NAME,}
+            'project_name': COMET_PROJECT_NAME, }
         self.default_experiment_kwargs.update(experiment_kwargs)
         self.experiment = self._get_experiment(self.comet_mode, run_id)
         self.experiment.set_name(self.opt.name)
@@ -153,7 +153,7 @@ class CometLogger:
             'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
             'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
             'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
-            'comet_model_name': COMET_MODEL_NAME,})
+            'comet_model_name': COMET_MODEL_NAME, })
 
         # Check if running the Experiment with the Comet Optimizer
         if hasattr(self.opt, 'comet_optimizer_id'):
@@ -170,7 +170,7 @@ class CometLogger:
                     **self.default_experiment_kwargs,
                 )
 
-            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
+            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, )
 
         else:
             try:
@@ -214,7 +214,7 @@ class CometLogger:
             'fitness_score': fitness_score[-1],
             'epochs_trained': epoch + 1,
             'save_period': opt.save_period,
-            'total_epochs': opt.epochs,}
+            'total_epochs': opt.epochs, }
 
         model_files = glob.glob(f'{path}/*.pt')
         for model_path in model_files:
@@ -270,7 +270,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
         for *xyxy, conf, cls in filtered_detections.tolist():
             metadata.append({
                 'label': f'{self.class_names[int(cls)]}',
@@ -279,7 +279,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
 
         self.metadata_dict[image_name] = metadata
         self.logged_images_count += 1

utils/segment/metrics.py

@@ -196,7 +196,7 @@ KEYS = [
     'val/cls_loss',
     'x/lr0',
     'x/lr1',
-    'x/lr2',]
+    'x/lr2', ]
 
 BEST_KEYS = [
     'best/epoch',
@@ -207,4 +207,4 @@ BEST_KEYS = [
     'best/precision(M)',
     'best/recall(M)',
     'best/mAP_0.5(M)',
-    'best/mAP_0.5:0.95(M)',]
+    'best/mAP_0.5:0.95(M)', ]

utils/torch_utils.py

@@ -170,7 +170,7 @@ 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
 
@@ -284,7 +284,7 @@ def model_info(model, verbose=False, 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
         fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
     except Exception: