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Extend vision exporter to support all models (#24351) #24351

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Merged
meta-codesync merged 1 commit into from
Nov 2, 2026
Merged

Extend vision exporter to support all models (#24351) #24351

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1 change: 0 additions & 1 deletion examples/native/vision/BUCK
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@ fbcode_target(
deps = [
"fbsource//third-party/pypi/pillow:pillow",
"//caffe2:torch",
"//executorch/examples/models:models",
"//executorch/exir/native:native",
"//pytorch/vision:torchvision", # @manual
],
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33 changes: 14 additions & 19 deletions examples/native/vision/export.py
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Expand Up @@ -17,15 +17,17 @@
from typing import TypedDict

import torch
from executorch.examples.models import MODEL_NAME_TO_MODEL
from executorch.examples.models.model_factory import EagerModelFactory
from executorch.exir.native import to_native
from PIL import Image
from torchvision.models import MobileNet_V2_Weights, ResNet50_Weights # @manual
from torchvision import models # @manual

logger: logging.Logger = logging.getLogger(__name__)

_MODEL_NAMES = ("mv2", "resnet50")
_MODEL_ALIASES = {"mv2": "mobilenet_v2", "vit": "vit_b_16"}
_WEIGHTS = {
name: models.get_model_weights(name)["DEFAULT"]
for name in models.list_models(module=models)
}


class _Expected(TypedDict):
Expand All @@ -41,22 +43,14 @@ def _default_out_dir() -> Path:


def _load_model(name: str) -> torch.nn.Module:
model, _, _, _ = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL[name])
return model.eval()
return models.get_model(name, weights=_WEIGHTS[name]).eval()


def _load_image(name: str, image_path: Path) -> tuple[torch.Tensor, list[str]]:
weights = _WEIGHTS[name]
with Image.open(image_path) as image:
rgb_image = image.convert("RGB")
if name == "mv2":
weights = MobileNet_V2_Weights.DEFAULT
image_tensor = weights.transforms()(rgb_image)
categories = list(weights.meta["categories"])
else:
weights = ResNet50_Weights.IMAGENET1K_V1
image_tensor = weights.transforms()(rgb_image)
categories = list(weights.meta["categories"])
return image_tensor.unsqueeze(0).contiguous(), categories
image_tensor = weights.transforms()(image.convert("RGB"))
return image_tensor.unsqueeze(0).contiguous(), list(weights.meta["categories"])


def _expected(
Expand Down Expand Up @@ -113,7 +107,7 @@ def _summary(
def main() -> None:
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("--model", choices=_MODEL_NAMES, default="mv2")
parser.add_argument("--model", choices=[*_WEIGHTS, *_MODEL_ALIASES], default="mv2")
parser.add_argument("--out-dir", type=Path, default=None)
parser.add_argument("--image", type=Path, required=True)
args = parser.parse_args()
Expand All @@ -129,8 +123,9 @@ def main() -> None:
"summary": output_dir / f"{args.model}.summary.txt",
}

model = _load_model(args.model)
example_input, categories = _load_image(args.model, args.image)
model_name = _MODEL_ALIASES.get(args.model, args.model)
model = _load_model(model_name)
example_input, categories = _load_image(model_name, args.image)
logits, expected = _expected(model, example_input, categories)
native_program = to_native(torch.export.export(model, (example_input,)))

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