Add pre-commit CI actions (#4982)

* define pre-commit * add CI code * configure * apply pre-commit * fstring * apply MD * pre-commit * Update torch_utils.py * Update print strings * notes * Cleanup code-format.yml * Update setup.cfg * Update .pre-commit-config.yaml Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2026-05-20 11:04:37 -07:00 · 2021-10-28 18:35:01 +02:00
parent a4fece8c14
commit ed887b5976
30 changed files with 273 additions and 113 deletions
@@ -13,7 +13,7 @@ assignees: ''

 ## Motivation

-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? 
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem?
 e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->

 ## Pitch
@@ -83,7 +83,7 @@ jobs:
          # Python
          python - <<EOF
          import torch
-          # Known issue, urllib.error.HTTPError: HTTP Error 403: rate limit exceeded, will be resolved in torch==1.10.0 
+          # Known issue, urllib.error.HTTPError: HTTP Error 403: rate limit exceeded, will be resolved in torch==1.10.0
          # model = torch.hub.load('ultralytics/yolov5', 'custom', path='runs/train/exp/weights/last.pt')
          EOF

@@ -0,0 +1,47 @@
+# Run code formatting GitHub Action, can be replaced by this bot: https://github.com/marketplace/pre-commit-ci
+
+name: Code formatting
+
+on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
+  push:
+    branches: [master]
+  pull_request: {}
+
+jobs:
+  pep8-check-flake8:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@master
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install dependencies
+        run: |
+          pip install flake8
+          pip list
+        shell: bash
+      - name: PEP8
+        run: |
+          flake8 .
+
+  pre-commit-check:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v2
+        # for private repo - first is the checkout step, which needs to use unlimited fetch depth for pushing
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v2
+
+      - name: set PY
+        run: echo "PY=$(python -VV | sha256sum | cut -d' ' -f1)" >> $GITHUB_ENV
+      - uses: actions/cache@v2
+        with:
+          path: ~/.cache/pre-commit
+          key: pre-commit|${{ env.PY }}|${{ hashFiles('.pre-commit-config.yaml') }}
+
+      - uses: pre-commit/action@v2.0.3
+        # this action also provides an additional behaviour when used in private repositories
+        # when configured with a github token, the action will push back fixes to the pull request branch
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
@@ -1,4 +1,4 @@
-# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities. 
+# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
 # https://github.com/github/codeql-action

 name: "CodeQL"
@@ -57,4 +57,3 @@ jobs:
            <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>

            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
-            
@@ -20,6 +20,7 @@
 *.data
 *.json
 *.cfg
+!setup.cfg
 !cfg/yolov3*.cfg

 storage.googleapis.com
@@ -0,0 +1,67 @@
+# 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
+
+# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - 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
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.23.1
+    hooks:
+      - id: pyupgrade
+        args: [--py36-plus]
+        name: Upgrade code
+
+  # TODO
+  #- repo: https://github.com/PyCQA/isort
+  #  rev: 5.9.3
+  #  hooks:
+  #    - id: isort
+  #      name: imports
+
+  # TODO
+  #- repo: https://github.com/pre-commit/mirrors-yapf
+  #  rev: v0.31.0
+  #  hooks:
+  #    - id: yapf
+  #      name: formatting
+
+  # TODO
+  #- repo: https://github.com/executablebooks/mdformat
+  #  rev: 0.7.7
+  #  hooks:
+  #    - id: mdformat
+  #      additional_dependencies:
+  #        - mdformat-gfm
+  #        - mdformat-black
+  #        - mdformat_frontmatter
+
+  # TODO
+  #- repo: https://github.com/asottile/yesqa
+  #  rev: v1.2.3
+  #  hooks:
+  #    - id: yesqa
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+        name: PEP8
@@ -671,4 +671,4 @@ into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
@@ -46,7 +46,7 @@ YOLOv5 🚀 is a family of object detection architectures and models pretrained
 open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
 </p>

-<!-- 
+<!--
 <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
 <img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
 -->
@@ -109,7 +109,7 @@ the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and

 ```bash
 $ python detect.py --source 0  # webcam
-                            file.jpg  # image 
+                            file.jpg  # image
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
@@ -136,7 +136,7 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size

 <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">

-</details>  
+</details>

 <details open>
 <summary>Tutorials</summary>
@@ -178,7 +178,7 @@ Get started in seconds with our verified environments. Click each icon below for
    <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
    </a>
-</div>  
+</div>

 ## <div align="center">Integrations</div>

@@ -239,7 +239,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 |[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |16.8   |12.6
 |[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
 |[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.8   |111.4
-|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>- 
+|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-

 <details>
  <summary>Table Notes (click to expand)</summary>
@@ -62,21 +62,21 @@ names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gla
 download: |
  from pycocotools.coco import COCO
  from tqdm import tqdm
-  
+
  from utils.general import Path, download, np, xyxy2xywhn
-  
+
  # Make Directories
  dir = Path(yaml['path'])  # dataset root dir
  for p in 'images', 'labels':
      (dir / p).mkdir(parents=True, exist_ok=True)
      for q in 'train', 'val':
          (dir / p / q).mkdir(parents=True, exist_ok=True)
-  
+
  # Train, Val Splits
  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
      print(f"Processing {split} in {patches} patches ...")
      images, labels = dir / 'images' / split, dir / 'labels' / split
-  
+
      # Download
      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
      if split == 'train':
@@ -86,11 +86,11 @@ download: |
          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
-  
+
      # Move
      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
          f.rename(images / f.name)  # move to /images/{split}
-  
+
      # Labels
      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
@@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't


 # Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.1  # image mixup (probability)
-copy_paste: 0.1  # segment copy-paste (probability)
+copy_paste: 0.1  # segment copy-paste (probability)
@@ -31,4 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
+copy_paste: 0.0  # segment copy-paste (probability)
@@ -79,7 +79,7 @@ class TransformerBlock(nn.Module):
        if c1 != c2:
            self.conv = Conv(c1, c2)
        self.linear = nn.Linear(c2, c2)  # learnable position embedding
-        self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
+        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
        self.c2 = c2

    def forward(self, x):
@@ -114,7 +114,7 @@ class BottleneckCSP(nn.Module):
        self.cv4 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        y1 = self.cv3(self.m(self.cv1(x)))
@@ -130,7 +130,7 @@ class C3(nn.Module):
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])

    def forward(self, x):
@@ -158,7 +158,7 @@ class C3Ghost(C3):
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)])
+        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))


 class SPP(nn.Module):
@@ -362,7 +362,7 @@ class Detections:
    def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
        super().__init__()
        d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1., 1.], device=d) for im in imgs]  # normalizations
        self.imgs = imgs  # list of images as numpy arrays
        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
        self.names = names  # class names
@@ -97,7 +97,6 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
        else:
            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval())  # without layer fuse

-
    # Compatibility updates
    for m in model.modules():
        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
@@ -18,7 +18,7 @@ backbone:
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]]
+   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, C3, [1024, False]],  # 9
@@ -40,7 +40,7 @@ LOGGER = logging.getLogger(__name__)
 class TFBN(keras.layers.Layer):
    # TensorFlow BatchNormalization wrapper
    def __init__(self, w=None):
-        super(TFBN, self).__init__()
+        super().__init__()
        self.bn = keras.layers.BatchNormalization(
            beta_initializer=keras.initializers.Constant(w.bias.numpy()),
            gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
@@ -54,7 +54,7 @@ class TFBN(keras.layers.Layer):

 class TFPad(keras.layers.Layer):
    def __init__(self, pad):
-        super(TFPad, self).__init__()
+        super().__init__()
        self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])

    def call(self, inputs):
@@ -65,7 +65,7 @@ class TFConv(keras.layers.Layer):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
        # ch_in, ch_out, weights, kernel, stride, padding, groups
-        super(TFConv, self).__init__()
+        super().__init__()
        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
        assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
        # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
@@ -96,7 +96,7 @@ class TFFocus(keras.layers.Layer):
    # Focus wh information into c-space
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
        # ch_in, ch_out, kernel, stride, padding, groups
-        super(TFFocus, self).__init__()
+        super().__init__()
        self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)

    def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
@@ -110,7 +110,7 @@ class TFFocus(keras.layers.Layer):
 class TFBottleneck(keras.layers.Layer):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_in, ch_out, shortcut, groups, expansion
-        super(TFBottleneck, self).__init__()
+        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
@@ -123,7 +123,7 @@ class TFBottleneck(keras.layers.Layer):
 class TFConv2d(keras.layers.Layer):
    # Substitution for PyTorch nn.Conv2D
    def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
-        super(TFConv2d, self).__init__()
+        super().__init__()
        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
        self.conv = keras.layers.Conv2D(
            c2, k, s, 'VALID', use_bias=bias,
@@ -138,7 +138,7 @@ class TFBottleneckCSP(keras.layers.Layer):
    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
        # ch_in, ch_out, number, shortcut, groups, expansion
-        super(TFBottleneckCSP, self).__init__()
+        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
@@ -158,7 +158,7 @@ class TFC3(keras.layers.Layer):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
        # ch_in, ch_out, number, shortcut, groups, expansion
-        super(TFC3, self).__init__()
+        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
@@ -172,7 +172,7 @@ class TFC3(keras.layers.Layer):
 class TFSPP(keras.layers.Layer):
    # Spatial pyramid pooling layer used in YOLOv3-SPP
    def __init__(self, c1, c2, k=(5, 9, 13), w=None):
-        super(TFSPP, self).__init__()
+        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
@@ -186,7 +186,7 @@ class TFSPP(keras.layers.Layer):
 class TFSPPF(keras.layers.Layer):
    # Spatial pyramid pooling-Fast layer
    def __init__(self, c1, c2, k=5, w=None):
-        super(TFSPPF, self).__init__()
+        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
        self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
@@ -201,7 +201,7 @@ class TFSPPF(keras.layers.Layer):

 class TFDetect(keras.layers.Layer):
    def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detection layer
-        super(TFDetect, self).__init__()
+        super().__init__()
        self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
@@ -249,7 +249,7 @@ class TFDetect(keras.layers.Layer):

 class TFUpsample(keras.layers.Layer):
    def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
-        super(TFUpsample, self).__init__()
+        super().__init__()
        assert scale_factor == 2, "scale_factor must be 2"
        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
        # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
@@ -263,7 +263,7 @@ class TFUpsample(keras.layers.Layer):

 class TFConcat(keras.layers.Layer):
    def __init__(self, dimension=1, w=None):
-        super(TFConcat, self).__init__()
+        super().__init__()
        assert dimension == 1, "convert only NCHW to NHWC concat"
        self.d = 3

@@ -272,7 +272,7 @@ class TFConcat(keras.layers.Layer):


 def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
-    LOGGER.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
@@ -299,7 +299,7 @@ def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
-            c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
+            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
        elif m is Detect:
            args.append([ch[x + 1] for x in f])
            if isinstance(args[1], int):  # number of anchors
@@ -312,11 +312,11 @@ def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
        m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
            else tf_m(*args, w=model.model[i])  # module

-        torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
+        torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum([x.numel() for x in torch_m_.parameters()])  # number params
+        np = sum(x.numel() for x in torch_m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        LOGGER.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
+        LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10}  {t:<40}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        ch.append(c2)
@@ -325,7 +325,7 @@ def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)

 class TFModel:
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)):  # model, channels, classes
-        super(TFModel, self).__init__()
+        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
@@ -336,7 +336,7 @@ class TFModel:

        # Define model
        if nc and nc != self.yaml['nc']:
-            print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
+            print(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)

@@ -247,7 +247,7 @@ class Model(nn.Module):


 def parse_model(d, ch):  # model_dict, input_channels(3)
-    LOGGER.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
@@ -275,7 +275,7 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
-            c2 = sum([ch[x] for x in f])
+            c2 = sum(ch[x] for x in f)
        elif m is Detect:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
@@ -287,11 +287,11 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
        else:
            c2 = ch[f]

-        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
+        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum([x.numel() for x in m_.parameters()])  # number params
+        np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        LOGGER.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n_, np, t, args))  # print
+        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
@@ -0,0 +1,45 @@
+# Project-wide configuration file, can be used for package metadata and other toll configurations
+# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
+
+[metadata]
+license_file = LICENSE
+description-file = README.md
+
+
+[tool:pytest]
+norecursedirs =
+    .git
+    dist
+    build
+addopts =
+    --doctest-modules
+    --durations=25
+    --color=yes
+
+
+[flake8]
+max-line-length = 120
+exclude = .tox,*.egg,build,temp
+select = E,W,F
+doctests = True
+verbose = 2
+# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
+format = pylint
+# see: https://www.flake8rules.com/
+ignore =
+    E731  # Do not assign a lambda expression, use a def
+    F405
+    E402
+    F841
+    E741
+    F821
+    E722
+    F401
+    W504
+    E127
+    W504
+    E231
+    E501
+    F403
+    E302
+    F541
@@ -1014,4 +1014,4 @@
      "outputs": []
    }
  ]
-}
+}
--- a/Show More
+++ b/Show More