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: v4.5.0
+    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: v1.8.0
+    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: 24.2.0
+    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: 1.7.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

.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"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🌍
 colorFrom: green
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -9,14 +9,9 @@ import shlex
 import subprocess
 import tarfile
 
-if os.getenv('SYSTEM') == 'spaces':
-    subprocess.run(
-        shlex.split(
-            'pip install git+https://github.com/facebookresearch/[email protected]'
-        ))
-    subprocess.run(
-        shlex.split(
-            'pip install git+https://github.com/aim-uofa/AdelaiDet@7bf9d87'))
+if os.getenv("SYSTEM") == "spaces":
+    subprocess.run(shlex.split("pip install git+https://github.com/facebookresearch/[email protected]"))
+    subprocess.run(shlex.split("pip install git+https://github.com/aim-uofa/AdelaiDet@7bf9d87"))
 
 import gradio as gr
 import huggingface_hub
@@ -27,26 +22,24 @@ from detectron2.data.detection_utils import read_image
 from detectron2.engine.defaults import DefaultPredictor
 from detectron2.utils.visualizer import Visualizer
 
-DESCRIPTION = '# [Yet-Another-Anime-Segmenter](https://github.com/zymk9/Yet-Another-Anime-Segmenter)'
+DESCRIPTION = "# [Yet-Another-Anime-Segmenter](https://github.com/zymk9/Yet-Another-Anime-Segmenter)"
 
-MODEL_REPO = 'public-data/Yet-Another-Anime-Segmenter'
+MODEL_REPO = "public-data/Yet-Another-Anime-Segmenter"
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
+    image_dir = pathlib.Path("images")
     if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset')
+        dataset_repo = "hysts/sample-images-TADNE"
+        path = huggingface_hub.hf_hub_download(dataset_repo, "images.tar.gz", repo_type="dataset")
         with tarfile.open(path) as f:
             f.extractall()
-    return sorted(image_dir.glob('*'))
+    return sorted(image_dir.glob("*"))
 
 
 def load_model(device: torch.device) -> DefaultPredictor:
-    config_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'SOLOv2.yaml')
-    model_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'SOLOv2.pth')
+    config_path = huggingface_hub.hf_hub_download(MODEL_REPO, "SOLOv2.yaml")
+    model_path = huggingface_hub.hf_hub_download(MODEL_REPO, "SOLOv2.pth")
     cfg = get_cfg()
     cfg.merge_from_file(config_path)
     cfg.MODEL.WEIGHTS = model_path
@@ -55,14 +48,14 @@ def load_model(device: torch.device) -> DefaultPredictor:
     return DefaultPredictor(cfg)
 
 
-def predict(image_path: str, class_score_threshold: float,
-            mask_score_threshold: float,
-            model: DefaultPredictor) -> tuple[np.ndarray, np.ndarray]:
+def predict(
+    image_path: str, class_score_threshold: float, mask_score_threshold: float, model: DefaultPredictor
+) -> tuple[np.ndarray, np.ndarray]:
     model.score_threshold = class_score_threshold
     model.mask_threshold = mask_score_threshold
-    image = read_image(image_path, format='BGR')
+    image = read_image(image_path, format="BGR")
     preds = model(image)
-    instances = preds['instances'].to('cpu')
+    instances = preds["instances"].to("cpu")
 
     visualizer = Visualizer(image[:, :, ::-1])
     vis = visualizer.draw_instance_predictions(predictions=instances)
@@ -78,37 +71,38 @@ def predict(image_path: str, class_score_threshold: float,
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 0.1, 0.5] for path in image_paths]
 
-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 = load_model(device)
 
 fn = functools.partial(predict, model=model)
 
-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', type='filepath')
-            class_score_threshold = gr.Slider(label='Score Threshold',
-                                              minimum=0,
-                                              maximum=1,
-                                              step=0.05,
-                                              value=0.1)
-            mask_score_threshold = gr.Slider(label='Mask Score Threshold',
-                                             minimum=0,
-                                             maximum=1,
-                                             step=0.05,
-                                             value=0.5)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="filepath")
+            class_score_threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.05, value=0.1)
+            mask_score_threshold = gr.Slider(label="Mask Score Threshold", minimum=0, maximum=1, step=0.05, value=0.5)
+            run_button = gr.Button("Run")
         with gr.Column():
-            result_instances = gr.Image(label='Instances')
-            result_masked = gr.Image(label='Masked')
+            result_instances = gr.Image(label="Instances")
+            result_masked = gr.Image(label="Masked")
 
     inputs = [image, class_score_threshold, mask_score_threshold]
     outputs = [result_instances, result_masked]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=outputs,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=inputs, outputs=outputs, api_name='predict')
-demo.queue(max_size=15).launch()
+    gr.Examples(
+        examples=examples,
+        inputs=inputs,
+        outputs=outputs,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=inputs,
+        outputs=outputs,
+        api_name="predict",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=15).launch()

style.css

@@ -1,3 +1,11 @@
 h1 {
   text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }