Update

.pre-commit-config.yaml

@@ -0,0 +1,35 @@
+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']

.style.yapf

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

README.md

@@ -4,35 +4,7 @@ emoji: 🌍
 colorFrom: green
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.0.5
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

app.py

@@ -2,20 +2,21 @@
 
 from __future__ import annotations
 
-import argparse
 import functools
 import os
 import pathlib
+import shlex
 import subprocess
 import tarfile
 
-if os.environ.get('SYSTEM') == 'spaces':
+if os.getenv('SYSTEM') == 'spaces':
     subprocess.call(
-        'pip install git+https://github.com/facebookresearch/detectron2@v0.6'.
-        split())
-    subprocess.call(
-        'pip install git+https://github.com/aim-uofa/AdelaiDet@7bf9d87'.split(
+        shlex.split(
+            'pip install git+https://github.com/facebookresearch/detectron2@v0.6'
         ))
+    subprocess.call(
+        shlex.split(
+            'pip install git+https://github.com/aim-uofa/AdelaiDet@7bf9d87'))
 
 import gradio as gr
 import huggingface_hub
@@ -26,34 +27,15 @@ from detectron2.data.detection_utils import read_image
 from detectron2.engine.defaults import DefaultPredictor
 from detectron2.utils.visualizer import Visualizer
 
-TITLE = 'zymk9/Yet-Another-Anime-Segmenter'
+TITLE = 'Yet-Another-Anime-Segmenter'
 DESCRIPTION = 'This is an unofficial demo for https://github.com/zymk9/Yet-Another-Anime-Segmenter.'
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.yet-another-anime-segmenter" alt="visitor badge"/></center>'
 
-TOKEN = os.environ['TOKEN']
+HF_TOKEN = os.getenv('HF_TOKEN')
 MODEL_REPO = 'hysts/Yet-Another-Anime-Segmenter'
 MODEL_FILENAME = 'SOLOv2.pth'
 CONFIG_FILENAME = 'SOLOv2.yaml'
 
 
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--class-score-slider-step', type=float, default=0.05)
-    parser.add_argument('--class-score-threshold', type=float, default=0.1)
-    parser.add_argument('--mask-score-slider-step', type=float, default=0.05)
-    parser.add_argument('--mask-score-threshold', type=float, default=0.5)
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
-
-
 def load_sample_image_paths() -> list[pathlib.Path]:
     image_dir = pathlib.Path('images')
     if not image_dir.exists():
@@ -61,7 +43,7 @@ def load_sample_image_paths() -> list[pathlib.Path]:
         path = huggingface_hub.hf_hub_download(dataset_repo,
                                                'images.tar.gz',
                                                repo_type='dataset',
-                                               use_auth_token=TOKEN)
+                                               use_auth_token=HF_TOKEN)
         with tarfile.open(path) as f:
             f.extractall()
     return sorted(image_dir.glob('*'))
@@ -70,10 +52,10 @@ def load_sample_image_paths() -> list[pathlib.Path]:
 def load_model(device: torch.device) -> DefaultPredictor:
     config_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                   CONFIG_FILENAME,
-                                                  use_auth_token=TOKEN)
+                                                  use_auth_token=HF_TOKEN)
     model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                  MODEL_FILENAME,
-                                                 use_auth_token=TOKEN)
+                                                 use_auth_token=HF_TOKEN)
     cfg = get_cfg()
     cfg.merge_from_file(config_path)
     cfg.MODEL.WEIGHTS = model_path
@@ -82,11 +64,12 @@ def load_model(device: torch.device) -> DefaultPredictor:
     return DefaultPredictor(cfg)
 
 
-def predict(image, class_score_threshold: float, mask_score_threshold: float,
+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.name, format='BGR')
+    image = read_image(image_path, format='BGR')
     preds = model(image)
     instances = preds['instances'].to('cpu')
 
@@ -101,52 +84,34 @@ def predict(image, class_score_threshold: float, mask_score_threshold: float,
     return vis, masked
 
 
-def main():
-    args = parse_args()
-    device = torch.device(args.device)
-
-    image_paths = load_sample_image_paths()
-    examples = [[
-        path.as_posix(), args.class_score_threshold, args.mask_score_threshold
-    ] for path in image_paths]
-
-    model = load_model(device)
-
-    func = functools.partial(predict, model=model)
-    func = functools.update_wrapper(func, predict)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.class_score_slider_step,
-                             default=args.class_score_threshold,
-                             label='Class Score Threshold'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.mask_score_slider_step,
-                             default=args.mask_score_threshold,
-                             label='Mask Score Threshold'),
-        ],
-        [
-            gr.outputs.Image(label='Instances'),
-            gr.outputs.Image(label='Masked'),
-        ],
-        examples=examples,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
+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')
+model = load_model(device)
+
+func = functools.partial(predict, model=model)
+
+gr.Interface(
+    fn=func,
+    inputs=[
+        gr.Image(label='Input', type='filepath'),
+        gr.Slider(label='Class Score Threshold',
+                  minimum=0,
+                  maximum=1,
+                  step=0.05,
+                  value=0.1),
+        gr.Slider(label='Mask Score Threshold',
+                  minimum=0,
+                  maximum=1,
+                  step=0.05,
+                  default=0.5),
+    ],
+    outputs=[
+        gr.Image(label='Instances'),
+        gr.Image(label='Masked'),
+    ],
+    examples=examples,
+    title=TITLE,
+    description=DESCRIPTION,
+).queue().launch(show_api=False)

requirements.txt

@@ -1,3 +1,3 @@
-opencv-python-headless>=4.5.5.62
-torch>=1.10.1
-torchvision>=0.11.2
+opencv-python-headless==4.5.5.62
+torch==1.10.1
+torchvision==0.11.2