Create openclip/basic.py

openclip/basic.py

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+
+#basic openclip usage
+import torch
+from PIL import Image
+import open_clip
+
+mtype='ViT-B-32'
+mname='laion2b_s34b_b79k'
+
+print("Loading",mtype,mname)
+
+model, _, preprocess = open_clip.create_model_and_transforms(mtype,
+        pretrained=mname)
+tokenizer = open_clip.get_tokenizer(mtype)
+
+#image = preprocess(Image.open("CLIP.png")).unsqueeze(0)
+text = tokenizer(["a diagram", "a dog", "a cat"])
+text = tokenizer("cat")
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+#    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    embedding=text_features[0]
+