Initial commit

.flake8

+[flake8]
+ignore = W503, E203, E221, C901, C408, E741, C407, B017, F811, C101, EXE001, EXE002
+max-line-length = 100
+max-complexity = 18
+select = B,C,E,F,W,T4,B9
+per-file-ignores =
+  **/__init__.py:F401,F403,E402

.gitignore

+.nfs*
+
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
+*.pyd
+*.so
+*.dll
+*.egg-info/
+build/
+dist/
+wheels/
+
+# pytorch/python/numpy formats
+*.pth
+*.pkl
+*.npy
+*.ts
+model_ts*.txt
+
+# onnx models
+*.onnx
+
+# ipython/jupyter notebooks
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# editor settings
+.idea
+.vscode
+_darcs

CODE_OF_CONDUCT.md

+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@fb.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

+# Contributing to segment-anything
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints, using the `linter.sh` script in the project's root directory. Linting requires `black==23.*`, `isort==5.12.0`, `flake8`, and `mypy`.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to segment-anything, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

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+   limitations under the License.

README.md

+# Segment Anything
+
+**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
+
+[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/)
+
+[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)]
+
+![SAM design](assets/model_diagram.png?raw=true)
+
+The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.
+
+<p float="left">
+  <img src="assets/masks1.png?raw=true" width="37.25%" />
+  <img src="assets/masks2.jpg?raw=true" width="61.5%" /> 
+</p>
+
+## Installation
+
+The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
+
+Install Segment Anything:
+
+```
+pip install git+https://github.com/facebookresearch/segment-anything.git
+```
+
+or clone the repository locally and install with
+
+```
+git clone git@github.com:facebookresearch/segment-anything.git
+cd segment-anything; pip install -e .
+```
+
+The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.
+```
+pip install opencv-python pycocotools matplotlib onnxruntime onnx
+```
+
+
+## <a name="GettingStarted"></a>Getting Started
+
+First download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt:
+
+```
+from segment_anything import build_sam, SamPredictor 
+predictor = SamPredictor(build_sam(checkpoint="</path/to/model.pth>"))
+predictor.set_image(<your_image>)
+masks, _, _ = predictor.predict(<input_prompts>)
+```
+
+or generate masks for an entire image:
+
+```
+from segment_anything import build_sam, SamAutomaticMaskGenerator
+mask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint="</path/to/model.pth>"))
+masks = mask_generator_generate(<your_image>)
+```
+
+Additionally, masks can be generated for images from the command line:
+
+```
+python scripts/amg.py --checkpoint <path/to/sam/checkpoint> --input <image_or_folder> --output <output_directory>
+```
+
+See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details.
+
+<p float="left">
+  <img src="assets/notebook1.png?raw=true" width="49.1%" />
+  <img src="assets/notebook2.png?raw=true" width="48.9%" />
+</p>
+
+## ONNX Export
+
+SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with
+
+```
+python scripts/export_onnx_model.py --checkpoint <path/to/checkpoint> --output <path/to/output>
+```
+
+See the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.
+
+## <a name="Models"></a>Model Checkpoints
+
+Three model versions of the model are available with different backbone sizes. These models can be instantiated by running 
+```
+from segment_anything import sam_model_registry
+sam = sam_model_registry["<name>"](checkpoint="<path/to/checkpoint>")
+```
+Click the links below to download the checkpoint for the corresponding model name. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).
+
+* **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)**
+* `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)
+* `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
+
+## License
+The model is licensed under the [Apache 2.0 license](LICENSE).
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Contributors
+
+The Segment Anything project was made possible with the help of many contributors (alphabetical):
+
+Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom

linter.sh

+#!/bin/bash -e
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+{
+  black --version | grep -E "23\." > /dev/null
+} || {
+  echo "Linter requires 'black==23.*' !"
+  exit 1
+}
+
+ISORT_VERSION=$(isort --version-number)
+if [[ "$ISORT_VERSION" != 5.12* ]]; then
+  echo "Linter requires isort==5.12.0 !"
+  exit 1
+fi
+
+echo "Running isort ..."
+isort . --atomic
+
+echo "Running black ..."
+black -l 100 .
+
+echo "Running flake8 ..."
+if [ -x "$(command -v flake8)" ]; then
+  flake8 .
+else
+  python3 -m flake8 .
+fi
+
+echo "Running mypy..."
+
+mypy --exclude 'setup.py|notebooks' .

scripts/amg.py

+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import cv2  # type: ignore
+
+from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
+
+import argparse
+import json
+import os
+from typing import Any, Dict, List
+
+parser = argparse.ArgumentParser(
+    description=(
+        "Runs automatic mask generation on an input image or directory of images, "
+        "and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, "
+        "as well as pycocotools if saving in RLE format."
+    )
+)
+
+parser.add_argument(
+    "--input",
+    type=str,
+    required=True,
+    help="Path to either a single input image or folder of images.",
+)
+
+parser.add_argument(
+    "--output",
+    type=str,
+    required=True,
+    help=(
+        "Path to the directory where masks will be output. Output will be either a folder "
+        "of PNGs per image or a single json with COCO-style masks."
+    ),
+)
+
+parser.add_argument(
+    "--model-type",
+    type=str,
+    default="default",
+    help="The type of model to load, in ['default', 'vit_l', 'vit_b']",
+)
+
+parser.add_argument(
+    "--checkpoint",
+    type=str,
+    required=True,
+    help="The path to the SAM checkpoint to use for mask generation.",
+)
+
+parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.")
+
+parser.add_argument(
+    "--convert-to-rle",
+    action="store_true",
+    help=(
+        "Save masks as COCO RLEs in a single json instead of as a folder of PNGs. "
+        "Requires pycocotools."
+    ),
+)
+
+amg_settings = parser.add_argument_group("AMG Settings")
+
+amg_settings.add_argument(
+    "--points-per-side",
+    type=int,
+    default=None,
+    help="Generate masks by sampling a grid over the image with this many points to a side.",
+)
+
+amg_settings.add_argument(
+    "--points-per-batch",
+    type=int,
+    default=None,
+    help="How many input points to process simultaneously in one batch.",
+)
+
+amg_settings.add_argument(
+    "--pred-iou-thresh",
+    type=float,
+    default=None,
+    help="Exclude masks with a predicted score from the model that is lower than this threshold.",
+)
+
+amg_settings.add_argument(
+    "--stability-score-thresh",
+    type=float,
+    default=None,
+    help="Exclude masks with a stability score lower than this threshold.",
+)
+
+amg_settings.add_argument(
+    "--stability-score-offset",
+    type=float,
+    default=None,
+    help="Larger values perturb the mask more when measuring stability score.",
+)
+
+amg_settings.add_argument(
+    "--box-nms-thresh",
+    type=float,
+    default=None,
+    help="The overlap threshold for excluding a duplicate mask.",
+)
+
+amg_settings.add_argument(
+    "--crop-n-layers",
+    type=int,
+    default=None,
+    help=(
+        "If >0, mask generation is run on smaller crops of the image to generate more masks. "
+        "The value sets how many different scales to crop at."
+    ),
+)
+
+amg_settings.add_argument(
+    "--crop-nms-thresh",
+    type=float,
+    default=None,
+    help="The overlap threshold for excluding duplicate masks across different crops.",
+)
+
+amg_settings.add_argument(
+    "--crop-overlap-ratio",
+    type=int,
+    default=None,
+    help="Larger numbers mean image crops will overlap more.",
+)
+
+amg_settings.add_argument(
+    "--crop-n-points-downscale-factor",
+    type=int,
+    default=None,
+    help="The number of points-per-side in each layer of crop is reduced by this factor.",
+)
+
+amg_settings.add_argument(
+    "--min-mask-region-area",
+    type=int,
+    default=None,
+    help=(
+        "Disconnected mask regions or holes with area smaller than this value "
+        "in pixels are removed by postprocessing."
+    ),
+)
+
+
+def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
+    header = "id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h"  # noqa
+    metadata = [header]
+    for i, mask_data in enumerate(masks):
+        mask = mask_data["segmentation"]
+        filename = f"{i}.png"
+        cv2.imwrite(os.path.join(path, filename), mask * 255)
+        mask_metadata = [
+            str(i),
+            str(mask_data["area"]),
+            *[str(x) for x in mask_data["bbox"]],
+            *[str(x) for x in mask_data["point_coords"][0]],
+            str(mask_data["predicted_iou"]),
+            str(mask_data["stability_score"]),
+            *[str(x) for x in mask_data["crop_box"]],
+        ]
+        row = ",".join(mask_metadata)
+        metadata.append(row)
+    metadata_path = os.path.join(path, "metadata.csv")
+    with open(metadata_path, "w") as f:
+        f.write("\n".join(metadata))
+
+    return
+
+
+def get_amg_kwargs(args):
+    amg_kwargs = {
+        "points_per_side": args.points_per_side,
+        "points_per_batch": args.points_per_batch,
+        "pred_iou_thresh": args.pred_iou_thresh,
+        "stability_score_thresh": args.stability_score_thresh,
+        "stability_score_offset": args.stability_score_offset,
+        "box_nms_thresh": args.box_nms_thresh,
+        "crop_n_layers": args.crop_n_layers,
+        "crop_nms_thresh": args.crop_nms_thresh,
+        "crop_overlap_ratio": args.crop_overlap_ratio,
+        "crop_n_points_downscale_factor": args.crop_n_points_downscale_factor,
+        "min_mask_region_area": args.min_mask_region_area,
+    }
+    amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}
+    return amg_kwargs
+
+
+def main(args: argparse.Namespace) -> None:
+    print("Loading model...")
+    sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint)
+    _ = sam.to(device=args.device)
+    output_mode = "coco_rle" if args.convert_to_rle else "binary_mask"
+    amg_kwargs = get_amg_kwargs(args)
+    generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs)
+
+    if not os.path.isdir(args.input):
+        targets = [args.input]
+    else:
+        targets = [
+            f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))
+        ]
+        targets = [os.path.join(args.input, f) for f in targets]
+
+    os.makedirs(args.output, exist_ok=True)
+
+    for t in targets:
+        print(f"Processing '{t}'...")
+        image = cv2.imread(t)
+        if image is None:
+            print(f"Could not load '{t}' as an image, skipping...")
+            continue
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        masks = generator.generate(image)
+
+        base = os.path.basename(t)
+        base = os.path.splitext(base)[0]
+        save_base = os.path.join(args.output, base)
+        if output_mode == "binary_mask":
+            os.makedirs(save_base, exist_ok=False)
+            write_masks_to_folder(masks, save_base)
+        else:
+            save_file = save_base + ".json"
+            with open(save_file, "w") as f:
+                json.dump(masks, f)
+    print("Done!")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    main(args)

scripts/export_onnx_model.py

+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from segment_anything import build_sam, build_sam_vit_b, build_sam_vit_l
+from segment_anything.utils.onnx import SamOnnxModel
+
+import argparse
+import warnings
+
+try:
+    import onnxruntime  # type: ignore
+
+    onnxruntime_exists = True
+except ImportError:
+    onnxruntime_exists = False
+
+parser = argparse.ArgumentParser(
+    description="Export the SAM prompt encoder and mask decoder to an ONNX model."
+)
+
+parser.add_argument(
+    "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint."
+)
+
+parser.add_argument(
+    "--output", type=str, required=True, help="The filename to save the ONNX model to."
+)
+
+parser.add_argument(
+    "--model-type",
+    type=str,
+    default="default",
+    help="In ['default', 'vit_b', 'vit_l']. Which type of SAM model to export.",
+)
+
+parser.add_argument(
+    "--return-single-mask",
+    action="store_true",
+    help=(
+        "If true, the exported ONNX model will only return the best mask, "
+        "instead of returning multiple masks. For high resolution images "
+        "this can improve runtime when upscaling masks is expensive."
+    ),
+)
+
+parser.add_argument(
+    "--opset",
+    type=int,
+    default=17,
+    help="The ONNX opset version to use. Must be >=11",
+)
+
+parser.add_argument(
+    "--quantize-out",
+    type=str,
+    default=None,
+    help=(
+        "If set, will quantize the model and save it with this name. "
+        "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize."
+    ),
+)
+
+parser.add_argument(
+    "--gelu-approximate",
+    action="store_true",
+    help=(
+        "Replace GELU operations with approximations using tanh. Useful "
+        "for some runtimes that have slow or unimplemented erf ops, used in GELU."
+    ),
+)
+
+parser.add_argument(
+    "--use-stability-score",
+    action="store_true",
+    help=(
+        "Replaces the model's predicted mask quality score with the stability "
+        "score calculated on the low resolution masks using an offset of 1.0. "
+    ),
+)
+
+parser.add_argument(
+    "--return-extra-metrics",
+    action="store_true",
+    help=(
+        "The model will return five results: (masks, scores, stability_scores, "
+        "areas, low_res_logits) instead of the usual three. This can be "
+        "significantly slower for high resolution outputs."
+    ),
+)
+
+
+def run_export(
+    model_type: str,
+    checkpoint: str,
+    output: str,
+    opset: int,
+    return_single_mask: bool,
+    gelu_approximate: bool = False,
+    use_stability_score: bool = False,
+    return_extra_metrics=False,
+):
+    print("Loading model...")
+    if model_type == "vit_b":
+        sam = build_sam_vit_b(checkpoint)
+    elif model_type == "vit_l":
+        sam = build_sam_vit_l(checkpoint)
+    else:
+        sam = build_sam(checkpoint)
+
+    onnx_model = SamOnnxModel(
+        model=sam,
+        return_single_mask=return_single_mask,
+        use_stability_score=use_stability_score,
+        return_extra_metrics=return_extra_metrics,
+    )
+
+    if gelu_approximate:
+        for n, m in onnx_model.named_modules():
+            if isinstance(m, torch.nn.GELU):
+                m.approximate = "tanh"
+
+    dynamic_axes = {
+        "point_coords": {1: "num_points"},
+        "point_labels": {1: "num_points"},
+    }
+
+    embed_dim = sam.prompt_encoder.embed_dim
+    embed_size = sam.prompt_encoder.image_embedding_size
+    mask_input_size = [4 * x for x in embed_size]
+    dummy_inputs = {
+        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
+        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
+        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
+        "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
+        "has_mask_input": torch.tensor([1], dtype=torch.float),
+        "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
+    }
+
+    _ = onnx_model(**dummy_inputs)
+
+    output_names = ["masks", "iou_predictions", "low_res_masks"]
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
+        warnings.filterwarnings("ignore", category=UserWarning)
+        with open(output, "wb") as f:
+            print(f"Exporing onnx model to {output}...")
+            torch.onnx.export(
+                onnx_model,
+                tuple(dummy_inputs.values()),
+                f,
+                export_params=True,
+                verbose=False,
+                opset_version=opset,
+                do_constant_folding=True,
+                input_names=list(dummy_inputs.keys()),
+                output_names=output_names,
+                dynamic_axes=dynamic_axes,
+            )
+
+    if onnxruntime_exists:
+        ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}
+        ort_session = onnxruntime.InferenceSession(output)
+        _ = ort_session.run(None, ort_inputs)
+        print("Model has successfully been run with ONNXRuntime.")
+
+
+def to_numpy(tensor):
+    return tensor.cpu().numpy()
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    run_export(
+        model_type=args.model_type,
+        checkpoint=args.checkpoint,
+        output=args.output,
+        opset=args.opset,
+        return_single_mask=args.return_single_mask,
+        gelu_approximate=args.gelu_approximate,
+        use_stability_score=args.use_stability_score,
+        return_extra_metrics=args.return_extra_metrics,
+    )
+
+    if args.quantize_out is not None:
+        assert onnxruntime_exists, "onnxruntime is required to quantize the model."
+        from onnxruntime.quantization import QuantType  # type: ignore
+        from onnxruntime.quantization.quantize import quantize_dynamic  # type: ignore
+
+        print(f"Quantizing model and writing to {args.quantize_out}...")
+        quantize_dynamic(
+            model_input=args.output,
+            model_output=args.quantize_out,
+            optimize_model=True,
+            per_channel=False,
+            reduce_range=False,
+            weight_type=QuantType.QUInt8,
+        )
+        print("Done!")