Update

.pre-commit-config.yaml

@@ -21,11 +21,11 @@ repos:
   - id: docformatter
     args: ['--in-place']
 - repo: https://github.com/pycqa/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
     - id: isort
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.812
+  rev: v0.991
   hooks:
     - id: mypy
       args: ['--ignore-missing-imports']
@@ -34,13 +34,3 @@ repos:
   hooks:
   - id: yapf
     args: ['--parallel', '--in-place']
-- repo: https://github.com/kynan/nbstripout
-  rev: 0.5.0
-  hooks:
-    - id: nbstripout
-      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
-- repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.3.1
-  hooks:
-    - id: nbqa-isort
-    - id: nbqa-yapf

README.md

@@ -5,7 +5,7 @@ colorFrom: pink
 colorTo: purple
 sdk: gradio
 python_version: 3.9.13
-sdk_version: 3.0.11
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import argparse
 import os
 import pathlib
 import subprocess
@@ -29,7 +28,6 @@ DESCRIPTION = '''# MMDetection
 This is an unofficial demo for [https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection).
 <img id="overview" alt="overview" src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png" />
 '''
-FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.mmdetection" alt="visitor badge" />'
 
 DEFAULT_MODEL_TYPE = 'detection'
 DEFAULT_MODEL_NAMES = {
@@ -40,18 +38,6 @@ DEFAULT_MODEL_NAMES = {
 DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE]
 
 
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    return parser.parse_args()
-
-
 def extract_tar() -> None:
     if pathlib.Path('mmdet_configs/configs').exists():
         return
@@ -87,94 +73,82 @@ def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
 
 
-def main():
-    args = parse_args()
-    extract_tar()
-    model = AppModel(DEFAULT_MODEL_NAME, args.device)
+extract_tar()
+model = AppModel(DEFAULT_MODEL_NAME)
 
-    with gr.Blocks(theme=args.theme, css='style.css') as demo:
-        gr.Markdown(DESCRIPTION)
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
 
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    input_image = gr.Image(label='Input Image', type='numpy')
-                with gr.Group():
-                    with gr.Row():
-                        model_type = gr.Radio(list(DEFAULT_MODEL_NAMES.keys()),
-                                              value=DEFAULT_MODEL_TYPE,
-                                              label='Model Type')
-                    with gr.Row():
-                        model_name = gr.Dropdown(list(
-                            model.DETECTION_MODEL_DICT.keys()),
-                                                 value=DEFAULT_MODEL_NAME,
-                                                 label='Model')
-                with gr.Row():
-                    run_button = gr.Button(value='Run')
-                    prediction_results = gr.Variable()
-            with gr.Column():
-                with gr.Row():
-                    visualization = gr.Image(label='Result', type='numpy')
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_image = gr.Image(label='Input Image', type='numpy')
+            with gr.Group():
                 with gr.Row():
-                    visualization_score_threshold = gr.Slider(
-                        0,
-                        1,
-                        step=0.05,
-                        value=0.3,
-                        label='Visualization Score Threshold')
+                    model_type = gr.Radio(list(DEFAULT_MODEL_NAMES.keys()),
+                                          value=DEFAULT_MODEL_TYPE,
+                                          label='Model Type')
                 with gr.Row():
-                    redraw_button = gr.Button(value='Redraw')
-
-        with gr.Row():
-            paths = sorted(pathlib.Path('images').rglob('*.jpg'))
-            example_images = gr.Dataset(components=[input_image],
-                                        samples=[[path.as_posix()]
-                                                 for path in paths])
-
-        gr.Markdown(FOOTER)
-
-        input_image.change(fn=update_input_image,
-                           inputs=input_image,
-                           outputs=input_image)
-
-        model_type.change(fn=update_model_name,
-                          inputs=model_type,
-                          outputs=model_name)
-        model_type.change(fn=update_visualization_score_threshold,
-                          inputs=model_type,
-                          outputs=visualization_score_threshold)
-        model_type.change(fn=update_redraw_button,
-                          inputs=model_type,
-                          outputs=redraw_button)
-
-        model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
-        run_button.click(fn=model.run,
-                         inputs=[
-                             model_name,
-                             input_image,
-                             visualization_score_threshold,
-                         ],
-                         outputs=[
-                             prediction_results,
-                             visualization,
-                         ])
-        redraw_button.click(fn=model.visualize_detection_results,
-                            inputs=[
-                                input_image,
-                                prediction_results,
-                                visualization_score_threshold,
-                            ],
-                            outputs=visualization)
-        example_images.click(fn=set_example_image,
-                             inputs=example_images,
-                             outputs=input_image)
-
-    demo.launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
+                    model_name = gr.Dropdown(list(
+                        model.DETECTION_MODEL_DICT.keys()),
+                                             value=DEFAULT_MODEL_NAME,
+                                             label='Model')
+            with gr.Row():
+                run_button = gr.Button(value='Run')
+                prediction_results = gr.Variable()
+        with gr.Column():
+            with gr.Row():
+                visualization = gr.Image(label='Result', type='numpy')
+            with gr.Row():
+                visualization_score_threshold = gr.Slider(
+                    0,
+                    1,
+                    step=0.05,
+                    value=0.3,
+                    label='Visualization Score Threshold')
+            with gr.Row():
+                redraw_button = gr.Button(value='Redraw')
+
+    with gr.Row():
+        paths = sorted(pathlib.Path('images').rglob('*.jpg'))
+        example_images = gr.Dataset(components=[input_image],
+                                    samples=[[path.as_posix()]
+                                             for path in paths])
+
+    input_image.change(fn=update_input_image,
+                       inputs=input_image,
+                       outputs=input_image)
+
+    model_type.change(fn=update_model_name,
+                      inputs=model_type,
+                      outputs=model_name)
+    model_type.change(fn=update_visualization_score_threshold,
+                      inputs=model_type,
+                      outputs=visualization_score_threshold)
+    model_type.change(fn=update_redraw_button,
+                      inputs=model_type,
+                      outputs=redraw_button)
+
+    model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
+    run_button.click(fn=model.run,
+                     inputs=[
+                         model_name,
+                         input_image,
+                         visualization_score_threshold,
+                     ],
+                     outputs=[
+                         prediction_results,
+                         visualization,
+                     ])
+    redraw_button.click(fn=model.visualize_detection_results,
+                        inputs=[
+                            input_image,
+                            prediction_results,
+                            visualization_score_threshold,
+                        ],
+                        outputs=visualization)
+    example_images.click(fn=set_example_image,
+                         inputs=example_images,
+                         outputs=input_image)
+
+demo.queue().launch(show_api=False)

model.py

@@ -6,7 +6,7 @@ import huggingface_hub
 import numpy as np
 import torch
 import torch.nn as nn
-import yaml
+import yaml  # type: ignore
 from mmdet.apis import inference_detector, init_detector
 
 
@@ -48,8 +48,9 @@ class Model:
         'model_dict/panoptic_segmentation.yaml')
     MODEL_DICT = DETECTION_MODEL_DICT | INSTANCE_SEGMENTATION_MODEL_DICT | PANOPTIC_SEGMENTATION_MODEL_DICT
 
-    def __init__(self, model_name: str, device: str | torch.device):
-        self.device = torch.device(device)
+    def __init__(self, model_name: str):
+        self.device = torch.device(
+            'cuda:0' if torch.cuda.is_available() else 'cpu')
         self._load_all_models_once()
         self.model_name = model_name
         self.model = self._load_model(model_name)