animegan
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43 lines
1.7 KiB
43 lines
1.7 KiB
import os
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import numpy
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from pathlib import Path
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from PIL import Image as PImage
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from torchvision import transforms
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from towhee import register
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from towhee.operator import Operator, OperatorFlag
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from towhee.types import arg, to_image_color
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from towhee._types import Image
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import warnings
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warnings.filterwarnings('ignore')
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@register(output_schema=['styled_image'], flag=OperatorFlag.STATELESS | OperatorFlag.REUSEABLE,)
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class Animegan(Operator):
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"""
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PyTorch model for image embedding.
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"""
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def __init__(self, model_name: str, framework: str = 'pytorch', device: str = 'cpu') -> None:
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super().__init__()
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self._device = device
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if framework == 'pytorch':
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import importlib.util
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path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
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opname = os.path.basename(str(Path(__file__))).split('.')[0]
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spec = importlib.util.spec_from_file_location(opname, path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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self.model = module.Model(model_name, self._device)
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self.tfms = transforms.Compose([
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transforms.ToTensor()
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])
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@arg(1, to_image_color('RGB'))
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def __call__(self, image):
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img = self.tfms(image).unsqueeze(0)
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styled_image = self.model(img)
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styled_image = numpy.transpose(styled_image, (1,2,0))
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styled_image = PImage.fromarray((styled_image * 255).astype(numpy.uint8))
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styled_image = numpy.array(styled_image)
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styled_image = styled_image[:, :, ::-1].copy()
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return Image(styled_image, 'BGR')
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