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from typing import Dict, List, Any
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from PIL import Image
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from io import BytesIO
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from transformers import AutoModelForSemanticSegmentation, AutoFeatureExtractor
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import base64
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import torch
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from torch import nn
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class EndpointHandler():
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def __init__(self, path="."):
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.model = AutoModelForSemanticSegmentation.from_pretrained(path).to(self.device).eval()
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self.feature_extractor = AutoFeatureExtractor.from_pretrained(path)
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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data args:
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images (:obj:`PIL.Image`)
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candiates (:obj:`list`)
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Return:
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A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
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"""
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inputs = data.pop("inputs", data)
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image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
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encoding = self.feature_extractor(images=image, return_tensors="pt")
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pixel_values = encoding["pixel_values"].to(self.device)
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with torch.no_grad():
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outputs = self.model(pixel_values=pixel_values)
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logits = outputs.logits
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upsampled_logits = nn.functional.interpolate(logits,
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size=image.size[::-1],
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mode="bilinear",
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align_corners=False,)
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pred_seg = upsampled_logits.argmax(dim=1)[0]
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return pred_seg.tolist()
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