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"]

.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"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🐢
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -18,29 +18,27 @@ import torch.nn as nn
 import torchvision
 import torchvision.transforms as T
 
-sys.path.insert(0, 'bizarre-pose-estimator')
+sys.path.insert(0, "bizarre-pose-estimator")
 
 from _util.twodee_v0 import I as ImageWrapper
 
-DESCRIPTION = '# [ShuhongChen/bizarre-pose-estimator (segmenter)](https://github.com/ShuhongChen/bizarre-pose-estimator)'
+DESCRIPTION = (
+    "# [ShuhongChen/bizarre-pose-estimator (segmenter)](https://github.com/ShuhongChen/bizarre-pose-estimator)"
+)
 
 
 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) -> tuple[torch.nn.Module, torch.nn.Module]:
-    path = huggingface_hub.hf_hub_download(
-        'public-data/bizarre-pose-estimator-models', 'segmenter.pth')
+def load_model(device: torch.device) -> tuple[torch.nn.Module, torch.nn.Module]:
+    path = huggingface_hub.hf_hub_download("public-data/bizarre-pose-estimator-models", "segmenter.pth")
     ckpt = torch.load(path)
 
     model = torchvision.models.segmentation.deeplabv3_resnet101()
@@ -62,8 +60,8 @@ def load_model(
         nn.LeakyReLU(),
         nn.Conv2d(8, 2, kernel_size=1, stride=1),
     )
-    model.load_state_dict(ckpt['model'])
-    final_head.load_state_dict(ckpt['final_head'])
+    model.load_state_dict(ckpt["model"])
+    final_head.load_state_dict(ckpt["final_head"])
     model.to(device)
     model.eval()
     final_head.to(device)
@@ -72,20 +70,29 @@ def load_model(
 
 
 @torch.inference_mode()
-def predict(image: PIL.Image.Image, score_threshold: float,
-            transform: Callable, device: torch.device, model: torch.nn.Module,
-            final_head: torch.nn.Module) -> np.ndarray:
-    data = ImageWrapper(image).resize_min(256).convert('RGBA').alpha_bg(
-        1).convert('RGB').pil()
+def predict(
+    image: PIL.Image.Image,
+    score_threshold: float,
+    transform: Callable,
+    device: torch.device,
+    model: torch.nn.Module,
+    final_head: torch.nn.Module,
+) -> np.ndarray:
+    data = ImageWrapper(image).resize_min(256).convert("RGBA").alpha_bg(1).convert("RGB").pil()
     data = torchvision.transforms.functional.to_tensor(data)
     data = transform(data)
     data = data.to(device).unsqueeze(0)
 
-    out = model(data)['out']
-    out_fin = final_head(torch.cat([
-        out,
-        data,
-    ], dim=1))
+    out = model(data)["out"]
+    out_fin = final_head(
+        torch.cat(
+            [
+                out,
+                data,
+            ],
+            dim=1,
+        )
+    )
     probs = torch.softmax(out_fin, dim=1)[0]
     probs = probs[1]  # foreground
     probs = PIL.Image.fromarray(probs.cpu().numpy()).resize(image.size)
@@ -103,35 +110,36 @@ def predict(image: PIL.Image.Image, score_threshold: float,
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 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, final_head = load_model(device)
 transform = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
-fn = functools.partial(predict,
-                       transform=transform,
-                       device=device,
-                       model=model,
-                       final_head=final_head)
+fn = functools.partial(predict, transform=transform, device=device, model=model, final_head=final_head)
 
-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='pil')
-            threshold = gr.Slider(label='Score Threshold',
-                                  minimum=0,
-                                  maximum=1,
-                                  step=0.05,
-                                  value=0.5)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="pil")
+            threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.05, value=0.5)
+            run_button = gr.Button("Run")
         with gr.Column():
-            result = gr.Image(label='Masked')
+            result = gr.Image(label="Masked")
 
     inputs = [image, threshold]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+    gr.Examples(
+        examples=examples,
+        inputs=inputs,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=inputs,
+        outputs=result,
+        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;
 }