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import json |
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import os |
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from functools import lru_cache |
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from typing import Mapping, List |
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from huggingface_hub import hf_hub_download, HfFileSystem |
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from imgutils.data import ImageTyping, load_image |
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from natsort import natsorted |
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from onnx_ import _open_onnx_model |
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from preprocess import _img_encode |
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hfs = HfFileSystem() |
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_REPO = 'deepghs/anime_classification' |
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_CLS_MODELS = natsorted([ |
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os.path.dirname(os.path.relpath(file, _REPO)) |
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for file in hfs.glob(f'{_REPO}/*/model.onnx') |
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]) |
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_DEFAULT_CLS_MODEL = 'mobilenetv3_sce_dist' |
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@lru_cache() |
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def _open_anime_classify_model(model_name): |
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return _open_onnx_model(hf_hub_download(_REPO, f'{model_name}/model.onnx')) |
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@lru_cache() |
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def _get_tags(model_name) -> List[str]: |
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with open(hf_hub_download(_REPO, f'{model_name}/meta.json'), 'r') as f: |
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return json.load(f)['labels'] |
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def _gr_classification(image: ImageTyping, model_name: str, size=384) -> Mapping[str, float]: |
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image = load_image(image, mode='RGB') |
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input_ = _img_encode(image, size=(size, size))[None, ...] |
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output, = _open_anime_classify_model(model_name).run(['output'], {'input': input_}) |
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labels = _get_tags(model_name) |
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values = dict(zip(labels, map(lambda x: x.item(), output[0]))) |
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return values |
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