Add files

.gitmodules

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+[submodule "CutLER"]
+	path = CutLER
+	url = https://github.com/facebookresearch/CutLER

.pre-commit-config.yaml

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+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: 5.10.1
+  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

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+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

CutLER

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+Subproject commit 077938c626341723050a1971107af552a6ca6697

Dockerfile

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+FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y --no-install-recommends \
+    git \
+    wget \
+    curl \
+    # python build dependencies \
+    build-essential \
+    libssl-dev \
+    zlib1g-dev \
+    libbz2-dev \
+    libreadline-dev \
+    libsqlite3-dev \
+    libncursesw5-dev \
+    xz-utils \
+    tk-dev \
+    libxml2-dev \
+    libxmlsec1-dev \
+    libffi-dev \
+    liblzma-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:${PATH}
+WORKDIR ${HOME}/app
+
+RUN curl https://pyenv.run | bash
+ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
+ARG PYTHON_VERSION=3.10.9
+RUN pyenv install ${PYTHON_VERSION} && \
+    pyenv global ${PYTHON_VERSION} && \
+    pyenv rehash && \
+    pip install --no-cache-dir -U pip setuptools wheel
+
+RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
+RUN pip install --no-cache-dir \
+    git+https://github.com/facebookresearch/detectron2.git@58e472e \
+    git+https://github.com/cocodataset/panopticapi.git@7bb4655 \
+    git+https://github.com/mcordts/cityscapesScripts.git@8da5dd0
+RUN pip install --no-cache-dir -U \
+    numpy==1.23.5 \
+    scikit-image==0.19.2 \
+    opencv-python-headless==4.6.0.66 \
+    colored==1.4.4
+RUN pip install --no-cache-dir -U gradio==3.16.2
+
+COPY --chown=1000 . ${HOME}/app
+ENV PYTHONPATH=${HOME}/app \
+	PYTHONUNBUFFERED=1 \
+	GRADIO_ALLOW_FLAGGING=never \
+	GRADIO_NUM_PORTS=1 \
+	GRADIO_SERVER_NAME=0.0.0.0 \
+	GRADIO_THEME=huggingface \
+	SYSTEM=spaces
+CMD ["python", "app.py"]

app.py

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+#!/usr/bin/env
+
+import pathlib
+
+import gradio as gr
+
+from model import FULLY_SUPERVISED_MODELS, SEMI_SUPERVISED_MODELS, Model
+
+DESCRIPTION = '''# CutLER
+
+This is an unofficial demo for [https://github.com/facebookresearch/CutLER](https://github.com/facebookresearch/CutLER).
+'''
+
+model = Model()
+paths = sorted(pathlib.Path('CutLER/cutler/demo/imgs').glob('*.jpg'))
+
+
+def create_unsupervised_demo():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(label='Input image', type='filepath')
+                model_name = gr.Text(label='Model',
+                                     value='Unsupervised',
+                                     visible=False)
+                score_threshold = gr.Slider(label='Score threshold',
+                                            minimum=0,
+                                            maximum=1,
+                                            value=0.5,
+                                            step=0.05)
+                run_button = gr.Button('Run')
+            with gr.Column():
+                result = gr.Image(label='Result', type='numpy')
+        with gr.Row():
+            gr.Examples(examples=[[path.as_posix()] for path in paths],
+                        inputs=[image])
+
+        run_button.click(fn=model,
+                         inputs=[
+                             image,
+                             model_name,
+                             score_threshold,
+                         ],
+                         outputs=result)
+
+    return demo
+
+
+def create_supervised_demo():
+    model_names = list(SEMI_SUPERVISED_MODELS.keys()) + list(
+        FULLY_SUPERVISED_MODELS.keys())
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(label='Input image', type='filepath')
+                model_name = gr.Dropdown(label='Model',
+                                         choices=model_names,
+                                         value=model_names[-1])
+                score_threshold = gr.Slider(label='Score threshold',
+                                            minimum=0,
+                                            maximum=1,
+                                            value=0.5,
+                                            step=0.05)
+                run_button = gr.Button('Run')
+            with gr.Column():
+                result = gr.Image(label='Result', type='numpy')
+        with gr.Row():
+            gr.Examples(examples=[[path.as_posix()] for path in paths],
+                        inputs=[image])
+
+        run_button.click(fn=model,
+                         inputs=[
+                             image,
+                             model_name,
+                             score_threshold,
+                         ],
+                         outputs=result)
+
+    return demo
+
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tabs():
+        with gr.TabItem('Zero-shot unsupervised'):
+            create_unsupervised_demo()
+        with gr.TabItem('Semi/Fully-supervised'):
+            create_supervised_demo()
+demo.queue().launch()

model.py

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+# This file is adapted from https://github.com/facebookresearch/CutLER/blob/077938c626341723050a1971107af552a6ca6697/cutler/demo/demo.py
+# The original license file is the file named LICENSE.CutLER in this repo.
+
+import argparse
+import multiprocessing as mp
+import pathlib
+import shlex
+import subprocess
+import sys
+
+import numpy as np
+import torch
+from detectron2.config import get_cfg
+from detectron2.data.detection_utils import read_image
+
+sys.path.append('CutLER/cutler/')
+sys.path.append('CutLER/cutler/demo')
+
+from config import add_cutler_config
+from predictor import VisualizationDemo
+
+mp.set_start_method('spawn', force=True)
+
+UNSUPERVISED_MODELS = {
+    'Unsupervised': {
+        'config_path':
+        'CutLER/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml',
+        'weight_url':
+        'http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth',
+    }
+}
+SEMI_SUPERVISED_MODELS = {
+    f'Semi-supervised with COCO ({perc}%)': {
+        'config_path':
+        f'CutLER/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_{perc}perc.yaml',
+        'weight_url':
+        f'http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_semi_{perc}perc.pth',
+    }
+    for perc in [1, 2, 5, 10, 20, 30, 40, 50, 60, 80]
+}
+FULLY_SUPERVISED_MODELS = {
+    'Fully-supervised with COCO': {
+        'config_path':
+        f'CutLER/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_100perc.yaml',
+        'weight_url':
+        f'http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_fully_100perc.pth',
+    }
+}
+
+
+def setup_cfg(args):
+    # load config from file and command-line arguments
+    cfg = get_cfg()
+    add_cutler_config(cfg)
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    # Disable the use of SyncBN normalization when running on a CPU
+    # SyncBN is not supported on CPU and can cause errors, so we switch to BN instead
+    if cfg.MODEL.DEVICE == 'cpu' and cfg.MODEL.RESNETS.NORM == 'SyncBN':
+        cfg.MODEL.RESNETS.NORM = 'BN'
+        cfg.MODEL.FPN.NORM = 'BN'
+    # Set score_threshold for builtin models
+    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
+    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
+    cfg.freeze()
+    return cfg
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description='Detectron2 demo for builtin configs')
+    parser.add_argument(
+        '--config-file',
+        default=
+        'model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml',
+        metavar='FILE',
+        help='path to config file',
+    )
+    parser.add_argument('--webcam',
+                        action='store_true',
+                        help='Take inputs from webcam.')
+    parser.add_argument('--video-input', help='Path to video file.')
+    parser.add_argument(
+        '--input',
+        nargs='+',
+        help='A list of space separated input images; '
+        "or a single glob pattern such as 'directory/*.jpg'",
+    )
+    parser.add_argument(
+        '--output',
+        help='A file or directory to save output visualizations. '
+        'If not given, will show output in an OpenCV window.',
+    )
+
+    parser.add_argument(
+        '--confidence-threshold',
+        type=float,
+        default=0.35,
+        help='Minimum score for instance predictions to be shown',
+    )
+    parser.add_argument(
+        '--opts',
+        help="Modify config options using the command-line 'KEY VALUE' pairs",
+        default=[],
+        nargs=argparse.REMAINDER,
+    )
+    return parser
+
+
+class Model:
+    MODEL_DICT = UNSUPERVISED_MODELS | SEMI_SUPERVISED_MODELS | FULLY_SUPERVISED_MODELS
+
+    def __init__(self):
+        self.model_dir = pathlib.Path('checkpoints')
+        self.model_dir.mkdir(exist_ok=True)
+
+    def load_model(self, model_name: str,
+                   score_threshold: float) -> VisualizationDemo:
+        model_info = self.MODEL_DICT[model_name]
+        weight_url = model_info['weight_url']
+        weight_path = self.model_dir / weight_url.split('/')[-1]
+        if not weight_path.exists():
+            weight_path.parent.mkdir(exist_ok=True)
+            subprocess.run(shlex.split(f'wget {weight_url} -O {weight_path}'))
+
+        arg_list = [
+            '--config-file', model_info['config_path'],
+            '--confidence-threshold',
+            str(score_threshold), '--opts', 'MODEL.WEIGHTS',
+            weight_path.as_posix(), 'MODEL.DEVICE',
+            'cuda:0' if torch.cuda.is_available() else 'cpu'
+        ]
+        args = get_parser().parse_args(arg_list)
+        cfg = setup_cfg(args)
+        return VisualizationDemo(cfg)
+
+    def __call__(self,
+                 image_path: str,
+                 model_name: str,
+                 score_threshold: float = 0.5) -> np.ndarray:
+        model = self.load_model(model_name, score_threshold)
+        image = read_image(image_path, format='BGR')
+        _, res = model.run_on_image(image)
+        return res.get_image()

style.css

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+h1 {
+  text-align: center;
+}