43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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# !pip install torchvision pydantic
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import base64
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import io
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import torch
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import torchvision
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from PIL import Image
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from pydantic import BaseModel
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import lightning as L
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from lightning.app.components.serve import Image as InputImage
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from lightning.app.components.serve import PythonServer
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class PyTorchServer(PythonServer):
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def setup(self):
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self._model = torchvision.models.resnet18(pretrained=True)
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self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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self._model.to(self._device)
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def predict(self, request):
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image = base64.b64decode(request.image.encode("utf-8"))
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image = Image.open(io.BytesIO(image))
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transforms = torchvision.transforms.Compose(
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[
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torchvision.transforms.Resize(224),
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torchvision.transforms.ToTensor(),
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torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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]
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)
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image = transforms(image)
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image = image.to(self._device)
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prediction = self._model(image.unsqueeze(0))
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return {"prediction": prediction.argmax().item()}
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class OutputData(BaseModel):
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prediction: int
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component = PyTorchServer(input_type=InputImage, output_type=OutputData, cloud_compute=L.CloudCompute("gpu"))
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app = L.LightningApp(component)
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