Upload caption_map.ipynb

caption_map.ipynb

@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dd1298de3c84ea3ab8ed31b2a0b2888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from multiprocessing import set_start_method\n",
+    "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# Load BLIP-2 model and processor\n",
+    "processor = Blip2Processor.from_pretrained(\"Salesforce/blip2-opt-2.7b\")\n",
+    "model = Blip2ForConditionalGeneration.from_pretrained(\"Salesforce/blip2-opt-2.7b\", torch_dtype=torch.float16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gpu_computation(batch, rank):\n",
+    "    device = f\"cuda:{(rank or 0) % torch.cuda.device_count()}\"\n",
+    "    model.to(device)\n",
+    "    inputs = processor(images=batch[\"image\"], return_tensors=\"pt\").to(device, torch.float16)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        generated_ids = model.generate(**inputs, max_length=51)\n",
+    "    \n",
+    "    batch[\"caption\"] = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61fe62d696904a7c894bd2c6f082b426",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import multiprocessing\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Check if start method is already set\n",
+    "    try:\n",
+    "        multiprocessing.get_start_method()\n",
+    "    except RuntimeError:\n",
+    "        multiprocessing.set_start_method(\"spawn\")\n",
+    "\n",
+    "    # Load your dataset\n",
+    "    dataset = load_dataset(\"visual-layer/oxford-iiit-pet-vl-enriched\", split=\"train\")\n",
+    "    dataset = dataset.select(range(10))\n",
+    "\n",
+    "    updated_dataset = dataset.map(\n",
+    "        gpu_computation,\n",
+    "        batched=True,\n",
+    "        batch_size=4,  # Adjust based on your GPU memory\n",
+    "        with_rank=True,\n",
+    "        num_proc=torch.cuda.device_count(),  # one process per GPU\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a cat walking on grass\\n',\n",
+       " 'a white dog playing with a ball\\n',\n",
+       " 'a dog sitting in the grass\\n',\n",
+       " 'a dog laying in the grass\\n',\n",
+       " 'a dog standing in the snow\\n',\n",
+       " 'a dog laying in the grass\\n',\n",
+       " 'a dog laying on a brick sidewalk\\n',\n",
+       " 'a man holding a black dog\\n',\n",
+       " 'a large dog standing in the grass\\n',\n",
+       " 'a pug dog with its tongue out standing on a tiled floor\\n']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "updated_dataset['caption']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}