Update

.pre-commit-config.yaml

@@ -1,35 +1,60 @@
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - id: check-shebang-scripts-are-executable
-  - id: check-toml
-  - id: check-yaml
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: mixed-line-ending
-    args: ['--fix=lf']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.12.0
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.9.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.4.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.8.5
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

.vscode/settings.json

@@ -0,0 +1,30 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.lint.args": [
+        "--line-length=119"
+    ],
+    "notebook.output.scrolling": true,
+    "notebook.formatOnCellExecution": true,
+    "notebook.formatOnSave.enabled": true,
+        "notebook.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+}

app.py

@@ -10,11 +10,11 @@ from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions
 from transformers import AutoImageProcessor, DetaForObjectDetection
 
-DESCRIPTION = '# DETA (Detection Transformers with Assignment)'
+DESCRIPTION = "# DETA (Detection Transformers with Assignment)"
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-MODEL_ID = 'jozhang97/deta-swin-large'
+MODEL_ID = "jozhang97/deta-swin-large"
 image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
 model = DetaForObjectDetection.from_pretrained(MODEL_ID)
 model.to(device)
@@ -23,50 +23,44 @@ model.to(device)
 @torch.inference_mode()
 def run(image_path: str, threshold: float) -> np.ndarray:
     image = PIL.Image.open(image_path)
-    inputs = image_processor(images=image, return_tensors='pt').to(device)
+    inputs = image_processor(images=image, return_tensors="pt").to(device)
     outputs = model(**inputs)
     target_sizes = torch.tensor([image.size[::-1]])
-    results = image_processor.post_process_object_detection(
-        outputs, threshold=threshold, target_sizes=target_sizes)[0]
+    results = image_processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[0]
 
-    boxes = results['boxes'].cpu().numpy()
-    scores = results['scores'].cpu().numpy()
-    cat_ids = results['labels'].cpu().numpy().tolist()
+    boxes = results["boxes"].cpu().numpy()
+    scores = results["scores"].cpu().numpy()
+    cat_ids = results["labels"].cpu().numpy().tolist()
 
     preds = []
     for box, score, cat_id in zip(boxes, scores, cat_ids):
         box = np.round(box).astype(int)
         cat_label = model.config.id2label[cat_id]
-        pred = ObjectPrediction(bbox=box,
-                                category_id=cat_id,
-                                category_name=cat_label,
-                                score=score)
+        pred = ObjectPrediction(bbox=box, category_id=cat_id, category_name=cat_label, score=score)
         preds.append(pred)
 
-    res = visualize_object_predictions(np.asarray(image), preds)['image']
+    res = visualize_object_predictions(np.asarray(image), preds)["image"]
     return res
 
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input image', type='filepath')
-            threshold = gr.Slider(label='Score threshold',
-                                  minimum=0,
-                                  maximum=1,
-                                  value=0.1,
-                                  step=0.01)
-            run_button = gr.Button('Run')
-        result = gr.Image(label='Result', type='numpy')
+            image = gr.Image(label="Input image", type="filepath")
+            threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, value=0.1, step=0.01)
+            run_button = gr.Button("Run")
+        result = gr.Image(label="Result", type="numpy")
 
     with gr.Row():
-        paths = sorted(pathlib.Path('images').glob('*.jpg'))
-        gr.Examples(examples=[[path.as_posix(), 0.1] for path in paths],
-                    inputs=[image, threshold],
-                    outputs=result,
-                    fn=run,
-                    cache_examples=True)
+        paths = sorted(pathlib.Path("images").glob("*.jpg"))
+        gr.Examples(
+            examples=[[path.as_posix(), 0.1] for path in paths],
+            inputs=[image, threshold],
+            outputs=result,
+            fn=run,
+            cache_examples=True,
+        )
 
     run_button.click(fn=run, inputs=[image, threshold], outputs=result)