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>
2021-10-28 18:35:01 +02:00
1 parent a4fece8c14
commit ed887b5976
@@ -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": []
    }
  ]
-}
+}
@@ -140,7 +140,7 @@ class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
            yield next(self.iterator)


-class _RepeatSampler(object):
+class _RepeatSampler:
    """ Sampler that repeats forever

    Args:
@@ -287,7 +287,7 @@ class LoadStreams:
        self.stride = stride

        if os.path.isfile(sources):
-            with open(sources, 'r') as f:
+            with open(sources) as f:
                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
        else:
            sources = [sources]
@@ -398,14 +398,14 @@ class LoadImagesAndLabels(Dataset):
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                    # f = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
-                    with open(p, 'r') as t:
+                    with open(p) as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                else:
                    raise Exception(f'{prefix}{p} does not exist')
-            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS])
+            self.img_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert self.img_files, f'{prefix}No images found'
        except Exception as e:
@@ -681,7 +681,7 @@ def load_mosaic(self, index):
    # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
    labels4, segments4 = [], []
    s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
+    yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
    random.shuffle(indices)
    for i, index in enumerate(indices):
@@ -767,7 +767,7 @@ def load_mosaic9(self, index):
            c = s - w, s + h0 - hp - h, s, s + h0 - hp

        padx, pady = c[:2]
-        x1, y1, x2, y2 = [max(x, 0) for x in c]  # allocate coords
+        x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords

        # Labels
        labels, segments = self.labels[index].copy(), self.segments[index].copy()
@@ -782,7 +782,7 @@ def load_mosaic9(self, index):
        hp, wp = h, w  # height, width previous

    # Offset
-    yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border]  # mosaic center x, y
+    yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y
    img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]

    # Concat/clip labels
@@ -838,7 +838,7 @@ def extract_boxes(path='../datasets/coco128'):  # from utils.datasets import *;
            # labels
            lb_file = Path(img2label_paths([str(im_file)])[0])
            if Path(lb_file).exists():
-                with open(lb_file, 'r') as f:
+                with open(lb_file) as f:
                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels

                for j, x in enumerate(lb):
@@ -866,7 +866,7 @@ def autosplit(path='../datasets/coco128/images', weights=(0.9, 0.1, 0.0), annota
        annotated_only:  Only use images with an annotated txt file
    """
    path = Path(path)  # images dir
-    files = sorted([x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS])  # image files only
+    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
    n = len(files)  # number of files
    random.seed(0)  # for reproducibility
    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
@@ -902,7 +902,7 @@ def verify_image_label(args):
        # verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
-            with open(lb_file, 'r') as f:
+            with open(lb_file) as f:
                l = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any([len(x) > 8 for x in l]):  # is segment
                    classes = np.array([x[0] for x in l], dtype=np.float32)
@@ -944,7 +944,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profil

    def round_labels(labels):
        # Update labels to integer class and 6 decimal place floats
-        return [[int(c), *[round(x, 4) for x in points]] for c, *points in labels]
+        return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]

    def unzip(path):
        # Unzip data.zip TODO: CONSTRAINT: path/to/abc.zip MUST unzip to 'path/to/abc/'
@@ -1019,7 +1019,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profil
            with open(file, 'w') as f:
                json.dump(stats, f)  # save stats *.json
            t2 = time.time()
-            with open(file, 'r') as f:
+            with open(file) as f:
                x = json.load(f)  # load hyps dict
            print(f'stats.json times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write')

@@ -136,7 +136,7 @@ def is_writeable(dir, test=False):
                pass
            file.unlink()  # remove file
            return True
-        except IOError:
+        except OSError:
            return False
    else:  # method 2
        return os.access(dir, os.R_OK)  # possible issues on Windows
@@ -355,7 +355,7 @@ def check_dataset(data, autodownload=True):
    assert 'nc' in data, "Dataset 'nc' key missing."
    if 'names' not in data:
        data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missing
-    train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')]
+    train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
    if val:
        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
        if not all(x.exists() for x in val):
@@ -11,4 +11,4 @@ manual_scaling:
 resources:
  cpu: 1
  memory_gb: 4
-  disk_size_gb: 20
+  disk_size_gb: 20
@@ -135,7 +135,7 @@ class Loggers():
        # Callback runs on training end
        if plots:
            plot_results(file=self.save_dir / 'results.csv')  # save results.png
-        files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
+        files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
        files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter

        if self.tb:
@@ -61,10 +61,10 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
-   
+
 ![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
 </details>
-  
+
 <h3> 2: Train and Log Evaluation simultaneousy </h3>
   This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
   Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
@@ -72,31 +72,31 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data ..  --upload_data </code>
-   
+
 ![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
 </details>
-  
+
 <h3> 3: Train using dataset artifact </h3>
-   When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that 
+   When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
   can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml </code>
-   
+
 ![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
 </details>
-  
+
   <h3> 4: Save model checkpoints as artifacts </h3>
-  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. 
+  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
  You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged

 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python train.py --save_period 1 </code>
-   
+
 ![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
 </details>
-  
+
 </details>

 <h3> 5: Resume runs from checkpoint artifacts. </h3>
@@ -105,28 +105,28 @@ Any run can be resumed using artifacts if the <code>--resume</code> argument sta
 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-  
+
 ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
 </details>
- 
+
  <h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
 <b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
- The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or 
+ The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
 train from <code>_wandb.yaml</code> file and set <code>--save_period</code>

 <details>
  <summary> <b>Usage</b> </summary>
   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-  
+
 ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
 </details>
-  
+
 </details>


 <h3> Reports </h3>
 W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
- 
+
 <img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">


@@ -1,17 +1,17 @@
 # Hyperparameters for training
-# To set range- 
+# To set range-
 # Provide min and max values as:
 #      parameter:
-#         
+#
 #         min: scalar
 #         max: scalar
 # OR
 #
 # Set a specific list of search space-
-#     parameter: 
+#     parameter:
 #         values: [scalar1, scalar2, scalar3...]
-#         
-# You can use grid, bayesian and hyperopt search strategy 
+#
+# You can use grid, bayesian and hyperopt search strategy
 # For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration

 program: utils/loggers/wandb/sweep.py
@@ -5,6 +5,7 @@ import os
 import sys
 from contextlib import contextmanager
 from pathlib import Path
+from typing import Dict

 import pkg_resources as pkg
 import yaml
@@ -25,7 +26,7 @@ try:
    assert hasattr(wandb, '__version__')  # verify package import not local dir
 except (ImportError, AssertionError):
    wandb = None
-    
+
 RANK = int(os.getenv('RANK', -1))
 WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'

@@ -127,7 +128,7 @@ class WandbLogger():
        arguments:
        opt (namespace) -- Commandline arguments for this run
        run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run 
+        job_type (str) -- To set the job_type for this run

       """
        # Pre-training routine --
@@ -142,7 +143,8 @@ class WandbLogger():
        self.max_imgs_to_log = 16
        self.wandb_artifact_data_dict = None
        self.data_dict = None
-        # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
+        # It's more elegant to stick to 1 wandb.init call,
+        #  but useful config data is overwritten in the WandbLogger's wandb.init call
        if isinstance(opt.resume, str):  # checks resume from artifact
            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
@@ -212,7 +214,7 @@ class WandbLogger():
        Setup the necessary processes for training YOLO models:
          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval 
+          - Setup log_dict, initialize bbox_interval

        arguments:
        opt (namespace) -- commandline arguments for this run
@@ -301,7 +303,7 @@ class WandbLogger():
        path (Path)   -- Path of directory containing the checkpoints
        opt (namespace) -- Command line arguments for this run
        epoch (int)  -- Current epoch number
-        fitness_score (float) -- fitness score for current epoch 
+        fitness_score (float) -- fitness score for current epoch
        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
        """
        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
@@ -325,7 +327,7 @@ class WandbLogger():
        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
        single_class (boolean)  -- train multi-class data as single-class
        project (str) -- project name. Used to construct the artifact path
-        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new 
+        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
        file with _wandb postfix. Eg -> data_wandb.yaml

        returns:
@@ -371,14 +373,14 @@ class WandbLogger():
        for i, data in enumerate(tqdm(self.val_table.data)):
            self.val_table_path_map[data[3]] = data[0]

-    def create_dataset_table(self, dataset, class_to_id, name='dataset'):
+    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = 'dataset'):
        """
        Create and return W&B artifact containing W&B Table of the dataset.

        arguments:
-        dataset (LoadImagesAndLabels) -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
-        class_to_id (dict(int, str)) -- hash map that maps class ids to labels
-        name (str) -- name of the artifact
+        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
+        class_to_id -- hash map that maps class ids to labels
+        name -- name of the artifact

        returns:
        dataset artifact to be logged or used
@@ -419,7 +421,7 @@ class WandbLogger():

        arguments:
        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image 
+        path (str): local path of the current evaluation image
        names (dict(int, str)): hash map that maps class ids to labels
        """
        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
@@ -430,7 +432,7 @@ class WandbLogger():
                box_data.append(
                    {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                     "class_id": int(cls),
-                     "box_caption": "%s %.3f" % (names[cls], conf),
+                     "box_caption": f"{names[cls]} {conf:.3f}",
                     "scores": {"class_score": conf},
                     "domain": "pixel"})
                total_conf += conf
@@ -450,7 +452,7 @@ class WandbLogger():
        arguments:
        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image 
+        path (str): local path of the current evaluation image
        """
        if self.val_table and self.result_table:  # Log Table if Val dataset is uploaded as artifact
            self.log_training_progress(predn, path, names)
@@ -459,7 +461,7 @@ class WandbLogger():
            if self.current_epoch % self.bbox_interval == 0:
                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                             "class_id": int(cls),
-                             "box_caption": "%s %.3f" % (names[cls], conf),
+                             "box_caption": f"{names[cls]} {conf:.3f}",
                             "scores": {"class_score": conf},
                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
@@ -18,7 +18,7 @@ def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#iss
 class BCEBlurWithLogitsLoss(nn.Module):
    # BCEwithLogitLoss() with reduced missing label effects.
    def __init__(self, alpha=0.05):
-        super(BCEBlurWithLogitsLoss, self).__init__()
+        super().__init__()
        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
        self.alpha = alpha

@@ -35,7 +35,7 @@ class BCEBlurWithLogitsLoss(nn.Module):
 class FocalLoss(nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super(FocalLoss, self).__init__()
+        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
@@ -65,7 +65,7 @@ class FocalLoss(nn.Module):
 class QFocalLoss(nn.Module):
    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super(QFocalLoss, self).__init__()
+        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
@@ -250,7 +250,7 @@ def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
    ax = ax.ravel()
    for i in range(4):
-        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
+        ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
        ax[i].legend()
        ax[i].set_title(s[i])
    plt.savefig('targets.jpg', dpi=200)
@@ -363,7 +363,7 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
                else:
                    a.remove()
        except Exception as e:
-            print('Warning: Plotting error for %s; %s' % (f, e))
+            print(f'Warning: Plotting error for {f}; {e}')
    ax[1].legend()
    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)

@@ -384,10 +384,10 @@ def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *;
        plt.subplot(6, 5, i + 1)
        plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
        plt.plot(mu, f.max(), 'k+', markersize=15)
-        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
+        plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9})  # limit to 40 characters
        if i % 5 != 0:
            plt.yticks([])
-        print('%15s: %.3g' % (k, mu))
+        print(f'{k:>15}: {mu:.3g}')
    f = evolve_csv.with_suffix('.png')  # filename
    plt.savefig(f, dpi=200)
    plt.close()
@@ -123,7 +123,7 @@ def profile(input, ops, n=10, device=None):
                    y = m(x)
                    t[1] = time_sync()
                    try:
-                        _ = (sum([yi.sum() for yi in y]) if isinstance(y, list) else y).sum().backward()
+                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
                        t[2] = time_sync()
                    except Exception as e:  # no backward method
                        # print(e)  # for debug
@@ -223,7 +223,7 @@ def model_info(model, verbose=False, img_size=640):
    n_p = sum(x.numel() for x in model.parameters())  # number parameters
    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
    if verbose:
-        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace('module_list.', '')
            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
@@ -270,7 +270,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
        s = (int(h * ratio), int(w * ratio))  # new size
        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
        if not same_shape:  # pad/crop img
-            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
+            h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean