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"cell_type": "code", "source": [ "!pip install faiss-cpu" ], "metadata": { "id": "_4gKYSe3iUnC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"jxie/flickr8k\", split=\"train\")\n", "print(dataset[0])" ], "metadata": { "id": "XE1aP5NWeMm0" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "import numpy as np\n", "from transformers import CLIPProcessor, CLIPModel\n", "\n", "# Load CLIP model + processor\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\").to(device)\n", "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n", "\n", "# Prepare captions (flatten all 5 per image into a list)\n", "all_captions = []\n", "for row in dataset:\n", " all_captions.extend([\n", " row[\"caption_0\"],\n", " row[\"caption_1\"],\n", " row[\"caption_2\"],\n", " row[\"caption_3\"],\n", " row[\"caption_4\"],\n", " ])\n", "\n", "# Batch function for images\n", "def embed_images(images, batch_size=32):\n", " all_embeds = []\n", " for i in range(0, len(images), batch_size):\n", " batch = images[i:i+batch_size]\n", " inputs = processor(images=batch, return_tensors=\"pt\").to(device)\n", " with torch.no_grad():\n", " embeds = model.get_image_features(inputs[\"pixel_values\"])\n", " all_embeds.append(embeds.cpu())\n", " return torch.cat(all_embeds, dim=0).numpy()\n", "\n", "# Batch function for texts\n", "def embed_texts(texts, batch_size=64):\n", " all_embeds = []\n", " for i in range(0, len(texts), batch_size):\n", " batch = texts[i:i+batch_size]\n", " inputs = processor(text=batch, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n", " with torch.no_grad():\n", " embeds = model.get_text_features(**inputs)\n", " all_embeds.append(embeds.cpu())\n", " return torch.cat(all_embeds, dim=0).numpy()\n", "\n", "# Run embeddings\n", "print(\"Encoding images...\")\n", "image_embeddings = embed_images(dataset[\"image\"], batch_size=32) # Shape: (8000, 512)\n", "\n", "print(\"Encoding captions...\")\n", "text_embeddings = embed_texts(all_captions, batch_size=64) # Shape: (40000, 512)\n", "\n", "print(\"Image embeddings shape:\", image_embeddings.shape)\n", "print(\"Text embeddings shape:\", text_embeddings.shape)\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 444, "referenced_widgets": [ "d11f9cd429ab4647b6f40f2c112c5aae", "85c3446fb35a43379014d769530e9804", "904eaf55db154511897350ad2d415af7", "ef3e83c1b4a246ffb84a07d21007bce5", "755e6e55ee574eec8bf6d0345ced4a7b", "b75efbec788f4aceb3c7e5e9b3b644a8", "4090ca7fe1744fe38150835bbc03b247", "f215d383949b4dd6bfda040089db82d3", "cc0c653f02dc43c2a926058325b4b5d5", "e22c5dea1dbc4fb18f2c425306261e89", "5101d8f60e4c4c6484cf8a57010ed447", "2ba9dfaf5ece44588fa899975181854b", "891116eecafa46d1b6d5c494222e3d31", 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