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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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