wehub-resource-sync
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
# __serve_example_begin__
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import requests
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from io import BytesIO
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from PIL import Image
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import starlette.requests
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import torch
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from torchvision import transforms
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import torchvision.models as models
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from torchvision.models import ResNet50_Weights
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from ray import serve
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@serve.deployment(
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ray_actor_options={"num_cpus": 1},
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num_replicas="auto",
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)
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class Model:
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def __init__(self):
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self.resnet50 = (
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models.resnet50(weights=ResNet50_Weights.DEFAULT).eval().to("cpu")
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)
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self.preprocess = transforms.Compose(
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[
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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),
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]
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)
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resp = requests.get(
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"https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
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)
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self.categories = resp.content.decode("utf-8").split("\n")
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async def __call__(self, request: starlette.requests.Request) -> str:
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uri = (await request.json())["uri"]
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image_bytes = requests.get(uri).content
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image = Image.open(BytesIO(image_bytes)).convert("RGB")
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# Batch size is 1
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input_tensor = torch.cat([self.preprocess(image).unsqueeze(0)]).to("cpu")
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with torch.no_grad():
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output = self.resnet50(input_tensor)
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sm_output = torch.nn.functional.softmax(output[0], dim=0)
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ind = torch.argmax(sm_output)
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return self.categories[ind]
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app = Model.bind()
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# __serve_example_end__
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if __name__ == "__main__":
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import requests # noqa
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serve.run(app)
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resp = requests.post(
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"http://localhost:8000/",
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json={
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"uri": "https://serve-resnet-benchmark-data.s3.us-west-1.amazonaws.com/000000000019.jpeg" # noqa
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},
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) # noqa
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assert resp.text == "ox"
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