Code Refactor ruff check --fix --extend-select I (#56)

.github/ISSUE_TEMPLATE/--bug-report.md

@@ -1,8 +1,8 @@
-______________________________________________________________________
+---
 
 name: "\\U0001F41BBug report" about: Create a report to help us improve title: '' labels: bug assignees: ''
 
-______________________________________________________________________
+---
 
 Before submitting a bug report, please ensure that you are using the latest versions of:
 

.github/ISSUE_TEMPLATE/--feature-request.md

@@ -1,8 +1,8 @@
-______________________________________________________________________
+---
 
 name: "\\U0001F680Feature request" about: Suggest an idea for this project title: '' labels: enhancement assignees: ''
 
-______________________________________________________________________
+---
 
 ## 🚀 Feature
 

.github/workflows/format.yml

@@ -6,9 +6,9 @@ name: Ultralytics Actions
 
 on:
   push:
-    branches: [main,master]
+    branches: [main]
   pull_request:
-    branches: [main,master]
+    branches: [main]
 
 jobs:
   format:

.github/workflows/greetings.yml

@@ -6,21 +6,20 @@ jobs:
   greeting:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/first-interaction@v1
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        pr-message: 'Hello @${{ github.actor }}, thank you for submitting a PR! We will respond as soon as possible.'
-        issue-message: |
-          Hello @${{ github.actor }}, thank you for your interest in our work! **Ultralytics has publicly released YOLOv5** at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
+      - uses: actions/first-interaction@v1
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          pr-message: "Hello @${{ github.actor }}, thank you for submitting a PR! We will respond as soon as possible."
+          issue-message: |
+            Hello @${{ github.actor }}, thank you for your interest in our work! **Ultralytics has publicly released YOLOv5** at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
 
-          <a href="https://apps.apple.com/app/id1452689527" target="_blank">
-          <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg" width="800"></a>
+            <a href="https://apps.apple.com/app/id1452689527" target="_blank">
+            <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg" width="800"></a>
 
-          <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_plot.png" width="800">
+            <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_plot.png" width="800">
 
-          If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
-          
-          If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
-          
-          For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
+            If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
 
+            If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
+
+            For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

.github/workflows/stale.yml

@@ -10,9 +10,9 @@ jobs:
       - uses: actions/stale@v9
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
-          stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
-          stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
+          stale-issue-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
+          stale-pr-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
           days-before-stale: 30
           days-before-close: 5
-          exempt-issue-labels: 'documentation,tutorial'
-          operations-per-run: 100  # The maximum number of operations per run, used to control rate limiting.
+          exempt-issue-labels: "documentation,tutorial"
+          operations-per-run: 100 # The maximum number of operations per run, used to control rate limiting.

detect.py

@@ -92,8 +92,8 @@ def detect(opt):
     for batch_i, (img_paths, img) in enumerate(dataloader):
         print("\n", batch_i, img.shape, end=" ")
 
-        img_ud = np.ascontiguousarray(np.flip(img, axis=1))
-        img_lr = np.ascontiguousarray(np.flip(img, axis=2))
+        np.ascontiguousarray(np.flip(img, axis=1))
+        np.ascontiguousarray(np.flip(img, axis=2))
 
         preds = []
         length = opt.img_size

models.py

@@ -110,7 +110,7 @@ def __init__(self, anchors, nC, img_dim, anchor_idxs):
     def forward(self, p, targets=None, requestPrecision=False, weight=None, epoch=None):
         """Processes input tensor `p`, optional targets for precision calculation; returns loss, precision, or both."""
         FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
-        device = torch.device("cuda:0" if p.is_cuda else "cpu")
+        torch.device("cuda:0" if p.is_cuda else "cpu")
         # weight = xview_class_weights(range(60)).to(device)
 
         bs = p.shape[0]

scoring/evaluation.py

@@ -142,7 +142,6 @@ def compute_average_precision_recall(groundtruth_coordinates, coordinates, iou_t
     # Start to build up the Matching instances for each of the image_id_*, which
     # is to hold the IOU computation between the rectangle pairs for the same
     # image_id_*.
-    matchings = {}
     if (len(groundtruth_coordinates) % 4 != 0) or (len(coordinates) % 4 != 0):
         raise ValueError("groundtruth_info_dict and test_info_dict should hold " "only 4 * N numbers.")
 

scoring/score.py

@@ -265,7 +265,6 @@ def score(path_predictions, path_groundtruth, path_output, iou_threshold=0.5):
         average_precision_per_class[i] = ap
 
     # metric splits
-    metric_keys = ["map", "map/small", "map/medium", "map/large", "map/common", "map/rare"]
 
     splits = {
         "map/small": [17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 32, 41, 60, 62, 63, 64, 65, 66, 91],
@@ -436,9 +435,9 @@ def score(path_predictions, path_groundtruth, path_output, iou_threshold=0.5):
     vals["map_score"] = np.nanmean(per_class_p)
     vals["mar_score"] = np.nanmean(per_class_r)
 
-    a = np.concatenate(
-        (average_precision_per_class, per_class_p, per_class_r, per_class_rcount, num_gt_per_cls)
-    ).reshape(5, 100)
+    np.concatenate((average_precision_per_class, per_class_p, per_class_r, per_class_rcount, num_gt_per_cls)).reshape(
+        5, 100
+    )
 
     for i in splits:
         vals[i] = np.nanmean(average_precision_per_class[splits[i]])