Upload embeddings.py

embeddings.py

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+import datasets
+from transformers import CLIPProcessor, CLIPModel
+import torch
+from PIL import Image
+
+# Load the dataset
+dataset = datasets.load_dataset("metmuseum/openaccess")
+
+# Initialize the CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Set the model to use the CPU or Apple's MPS if available
+# Change this if you have a fancier computer (:
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+model.to(device)
+
+# Map to store embeddings with Object ID as key
+embeddings_map = {}
+
+# Function to create embeddings for a PIL Jpeg image
+def create_embedding(image_pil):
+    try:
+        inputs = processor(images=image_pil, return_tensors="pt", padding=True).to(device)
+        with torch.no_grad():
+            embeddings = model.get_image_features(**inputs)
+        return embeddings
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None
+
+# Loop through the dataset and process the images, and add them to a map
+# Optionally, you could add more keys here
+# Or, just add the embeddings to the full dataset
+for item in dataset['train']:
+    object_id = item['Object ID']
+    image_pil = item['jpg']
+    if image_pil:
+        embedding = create_embedding(image_pil)
+        if embedding is not None:
+            embeddings_map[object_id] = embedding.cpu().numpy()
+
+# Convert embeddings map to a new dataset
+# Note: I changed to to [embeddings][0] because the examples seemed to like [embeddings] format better
+# than [[[embeddings]]]
+# ...perhaps that's incorrect?
+embedding_dataset = datasets.Dataset.from_dict({
+    'Object ID': list(embeddings_map.keys()),
+    'Embedding': [embedding.tolist() for embedding in embeddings_map.values()][0]
+})
+
+# Save the new dataset to disk
+embedding_dataset.save_to_disk('metmuseum_embeddings')