Update
Browse files- .pre-commit-config.yaml +59 -34
- .style.yapf +0 -5
- .vscode/settings.json +30 -0
- README.md +1 -1
- app.py +54 -46
- style.css +8 -0
.pre-commit-config.yaml
CHANGED
@@ -1,35 +1,60 @@
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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- repo: https://github.com/pre-commit/mirrors-mypy
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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hooks:
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- id: check-executables-have-shebangs
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- id: check-json
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- id: check-merge-conflict
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- id: check-shebang-scripts-are-executable
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- id: check-toml
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- id: check-yaml
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- id: end-of-file-fixer
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- id: mixed-line-ending
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args: ["--fix=lf"]
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- id: requirements-txt-fixer
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- id: trailing-whitespace
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- repo: https://github.com/myint/docformatter
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rev: v1.7.5
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hooks:
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- id: docformatter
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args: ["--in-place"]
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- repo: https://github.com/pycqa/isort
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rev: 5.13.2
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hooks:
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- id: isort
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args: ["--profile", "black"]
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.8.0
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hooks:
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- id: mypy
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args: ["--ignore-missing-imports"]
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additional_dependencies:
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[
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"types-python-slugify",
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"types-requests",
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"types-PyYAML",
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"types-pytz",
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]
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- repo: https://github.com/psf/black
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rev: 24.2.0
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hooks:
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- id: black
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language_version: python3.10
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args: ["--line-length", "119"]
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- repo: https://github.com/kynan/nbstripout
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rev: 0.7.1
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hooks:
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- id: nbstripout
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args:
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[
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"--extra-keys",
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"metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
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]
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- repo: https://github.com/nbQA-dev/nbQA
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rev: 1.7.1
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hooks:
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- id: nbqa-black
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- id: nbqa-pyupgrade
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args: ["--py37-plus"]
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- id: nbqa-isort
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args: ["--float-to-top"]
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.style.yapf
DELETED
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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spaces_before_comment = 2
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split_before_logical_operator = true
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.vscode/settings.json
ADDED
@@ -0,0 +1,30 @@
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{
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"editor.formatOnSave": true,
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"files.insertFinalNewline": false,
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"[python]": {
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"editor.defaultFormatter": "ms-python.black-formatter",
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"editor.formatOnType": true,
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"editor.codeActionsOnSave": {
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"source.organizeImports": "explicit"
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}
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},
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"[jupyter]": {
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"files.insertFinalNewline": false
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},
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"black-formatter.args": [
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"--line-length=119"
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],
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"isort.args": ["--profile", "black"],
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"flake8.args": [
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"--max-line-length=119"
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],
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"ruff.lint.args": [
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"--line-length=119"
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],
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"notebook.output.scrolling": true,
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"notebook.formatOnCellExecution": true,
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"notebook.formatOnSave.enabled": true,
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"notebook.codeActionsOnSave": {
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"source.organizeImports": "explicit"
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}
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}
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README.md
CHANGED
@@ -4,7 +4,7 @@ emoji: 🐢
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colorFrom: blue
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colorTo: indigo
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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---
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colorFrom: blue
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colorTo: indigo
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sdk: gradio
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sdk_version: 4.19.2
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app_file: app.py
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pinned: false
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---
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app.py
CHANGED
@@ -18,29 +18,27 @@ import torch.nn as nn
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import torchvision
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import torchvision.transforms as T
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sys.path.insert(0,
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from _util.twodee_v0 import I as ImageWrapper
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DESCRIPTION =
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def load_sample_image_paths() -> list[pathlib.Path]:
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image_dir = pathlib.Path(
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if not image_dir.exists():
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dataset_repo =
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path = huggingface_hub.hf_hub_download(dataset_repo,
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'images.tar.gz',
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repo_type='dataset')
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob(
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def load_model(
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path = huggingface_hub.hf_hub_download(
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'public-data/bizarre-pose-estimator-models', 'segmenter.pth')
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ckpt = torch.load(path)
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model = torchvision.models.segmentation.deeplabv3_resnet101()
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nn.LeakyReLU(),
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nn.Conv2d(8, 2, kernel_size=1, stride=1),
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)
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model.load_state_dict(ckpt[
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final_head.load_state_dict(ckpt[
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model.to(device)
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model.eval()
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final_head.to(device)
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@torch.inference_mode()
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def predict(
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data = torchvision.transforms.functional.to_tensor(data)
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data = transform(data)
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data = data.to(device).unsqueeze(0)
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out = model(data)[
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out_fin = final_head(
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probs = torch.softmax(out_fin, dim=1)[0]
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probs = probs[1] # foreground
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probs = PIL.Image.fromarray(probs.cpu().numpy()).resize(image.size)
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image_paths = load_sample_image_paths()
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examples = [[path.as_posix(), 0.5] for path in image_paths]
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device = torch.device(
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model, final_head = load_model(device)
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transform = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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fn = functools.partial(predict,
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transform=transform,
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device=device,
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model=model,
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final_head=final_head)
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with gr.Blocks(css=
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gr.Markdown(DESCRIPTION)
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with gr.Row():
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with gr.Column():
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image = gr.Image(label=
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threshold = gr.Slider(label=
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-
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maximum=1,
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step=0.05,
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value=0.5)
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run_button = gr.Button('Run')
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with gr.Column():
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result = gr.Image(label=
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inputs = [image, threshold]
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gr.Examples(
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-
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-
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-
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-
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-
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import torchvision
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import torchvision.transforms as T
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sys.path.insert(0, "bizarre-pose-estimator")
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from _util.twodee_v0 import I as ImageWrapper
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DESCRIPTION = (
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"# [ShuhongChen/bizarre-pose-estimator (segmenter)](https://github.com/ShuhongChen/bizarre-pose-estimator)"
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)
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def load_sample_image_paths() -> list[pathlib.Path]:
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image_dir = pathlib.Path("images")
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if not image_dir.exists():
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dataset_repo = "hysts/sample-images-TADNE"
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path = huggingface_hub.hf_hub_download(dataset_repo, "images.tar.gz", repo_type="dataset")
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob("*"))
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def load_model(device: torch.device) -> tuple[torch.nn.Module, torch.nn.Module]:
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path = huggingface_hub.hf_hub_download("public-data/bizarre-pose-estimator-models", "segmenter.pth")
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ckpt = torch.load(path)
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model = torchvision.models.segmentation.deeplabv3_resnet101()
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nn.LeakyReLU(),
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nn.Conv2d(8, 2, kernel_size=1, stride=1),
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)
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model.load_state_dict(ckpt["model"])
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final_head.load_state_dict(ckpt["final_head"])
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model.to(device)
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model.eval()
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final_head.to(device)
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@torch.inference_mode()
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def predict(
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image: PIL.Image.Image,
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score_threshold: float,
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transform: Callable,
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device: torch.device,
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model: torch.nn.Module,
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final_head: torch.nn.Module,
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) -> np.ndarray:
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data = ImageWrapper(image).resize_min(256).convert("RGBA").alpha_bg(1).convert("RGB").pil()
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data = torchvision.transforms.functional.to_tensor(data)
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data = transform(data)
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data = data.to(device).unsqueeze(0)
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out = model(data)["out"]
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out_fin = final_head(
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torch.cat(
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[
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out,
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data,
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],
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dim=1,
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)
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)
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probs = torch.softmax(out_fin, dim=1)[0]
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probs = probs[1] # foreground
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probs = PIL.Image.fromarray(probs.cpu().numpy()).resize(image.size)
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image_paths = load_sample_image_paths()
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examples = [[path.as_posix(), 0.5] for path in image_paths]
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model, final_head = load_model(device)
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transform = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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fn = functools.partial(predict, transform=transform, device=device, model=model, final_head=final_head)
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with gr.Blocks(css="style.css") as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="Input", type="pil")
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threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.05, value=0.5)
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run_button = gr.Button("Run")
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with gr.Column():
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result = gr.Image(label="Masked")
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inputs = [image, threshold]
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gr.Examples(
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examples=examples,
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inputs=inputs,
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outputs=result,
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fn=fn,
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cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
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)
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run_button.click(
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fn=fn,
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inputs=inputs,
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outputs=result,
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api_name="predict",
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)
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if __name__ == "__main__":
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demo.queue(max_size=15).launch()
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style.css
CHANGED
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h1 {
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text-align: center;
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}
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h1 {
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text-align: center;
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display: block;
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}
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#duplicate-button {
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margin: auto;
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color: #fff;
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background: #1565c0;
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border-radius: 100vh;
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}
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