demo.py

# ---------------------------------------------------------------------
# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# ---------------------------------------------------------------------

import torch

from qai_hub_models.models._shared.imagenet_classifier.test_utils import (
    TEST_IMAGENET_IMAGE,
)
from qai_hub_models.models.mobile_vit.app import MobileVITApp
from qai_hub_models.models.mobile_vit.model import (
    MODEL_ASSET_VERSION,
    MODEL_ID,
    MobileVIT,
)
from qai_hub_models.utils.args import (
    demo_model_from_cli_args,
    get_model_cli_parser,
    get_on_device_demo_parser,
    validate_on_device_demo_args,
)
from qai_hub_models.utils.asset_loaders import (
    CachedWebModelAsset,
    load_image,
    load_json,
)
from qai_hub_models.utils.base_model import TargetRuntime

IMAGENET_LABELS_ASSET = CachedWebModelAsset(
    "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json",
    MODEL_ID,
    MODEL_ASSET_VERSION,
    "imagenet_labels.json",
)


# Run Imagenet Classifier end-to-end on a sample image.
# The demo will print the predicted class to terminal.
def demo(
    model_cls: type[MobileVIT] = MobileVIT,
    model_id: str = MODEL_ID,
    is_test: bool = False,
    available_target_runtimes: list[TargetRuntime] = list(
        TargetRuntime.__members__.values()
    ),
):

    # Demo parameters
    parser = get_model_cli_parser(model_cls)
    parser = get_on_device_demo_parser(
        parser, available_target_runtimes=available_target_runtimes
    )
    parser.add_argument(
        "--image",
        type=str,
        default=TEST_IMAGENET_IMAGE,
        help="test image file path or URL",
    )
    args = parser.parse_args([] if is_test else None)
    validate_on_device_demo_args(args, model_id)

    model = demo_model_from_cli_args(model_cls, model_id, args)
    app = MobileVITApp(model)
    print("Model Loaded")

    image = load_image(args.image)
    # Run app

    probabilities = app.predict(image)
    top5 = torch.topk(probabilities, 5)
    if not is_test:
        labels = load_json(IMAGENET_LABELS_ASSET)
        print("Top 5 predictions for image:\n")
        for i in range(5):
            print(f"{labels[top5.indices[i]]}: {100 * top5.values[i]:.3g}%\n")


if __name__ == "__main__":
    demo()