Add clip for test inference

handler.py

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+from typing import  Dict, List, Any
+from PIL import Image
+import clip
+import torch
+import requests
+import io
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.model, self.preprocess = clip.load('ViT-B/32', device)
+        self.model.eval()
+        self.model = self.model.to(device)
+        
+
+    def __call__(self, data: Any) -> Dict[str, List[float]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing :
+                - "feature_vector": A list of floats corresponding to the image embedding.
+        """
+        inputs = data.pop("inputs", data)
+        if inputs.startswith("http") or inputs.startswith("www"):
+            response = requests.get(inputs).content
+            img = Image.open(io.BytesIO(response))
+        else:
+            img = Image.open(inputs['image'])
+        # decode base64 image to PIL
+
+        image_input = self.preprocess(img).unsqueeze(0).to(device)
+
+        # Calculate features
+        with torch.no_grad():
+            image_features = self.model.encode_image(image_input)
+        # postprocess the prediction
+        return {"feature_vector": image_features.tolist()[0]}

requirements.txt

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+ftfy
+regex
+tqdm
+git+https://github.com/openai/CLIP.git
+torch
+torchvision