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 <[email protected]>

.github/ISSUE_TEMPLATE/feature-request.md

@@ -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

.github/workflows/ci-testing.yml

@@ -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
 

.github/workflows/code-format.yml

@@ -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/[email protected]
+        # 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 }}

.github/workflows/codeql-analysis.yml

@@ -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"

.github/workflows/greetings.yml

@@ -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.
-            

.gitignore

@@ -20,6 +20,7 @@
 *.data
 *.json
 *.cfg
+!setup.cfg
 !cfg/yolov3*.cfg
 
 storage.googleapis.com

.pre-commit-config.yaml

@@ -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

LICENSE

@@ -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>.

README.md

@@ -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>

data/Objects365.yaml

@@ -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())]

data/coco128.yaml

@@ -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

data/hyps/hyp.scratch-high.yaml

@@ -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)

data/hyps/hyp.scratch-low.yaml

@@ -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)

models/common.py

@@ -79,7 +79,7 @@ def __init__(self, c1, c2, num_heads, num_layers):
         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 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         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 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         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

models/experimental.py

@@ -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]:

models/hub/yolov5-bifpn.yaml

@@ -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