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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Copyright (c) Meta Platforms, Inc. and affiliates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SAM 3 Agent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows an example of how an MLLM can use SAM 3 as a tool, i.e., \"SAM 3 Agent\", to segment more complex text queries such as \"the leftmost child wearing blue vest\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Env Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First install `sam3` in your environment using the [installation instructions](https://github.com/facebookresearch/sam3?tab=readme-ov-file#installation) in the repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"# turn on tfloat32 for Ampere GPUs\n",
"# https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n",
"torch.backends.cuda.matmul.allow_tf32 = True\n",
"torch.backends.cudnn.allow_tf32 = True\n",
"\n",
"# use bfloat16 for the entire notebook. If your card doesn't support it, try float16 instead\n",
"torch.autocast(\"cuda\", dtype=torch.bfloat16).__enter__()\n",
"\n",
"# inference mode for the whole notebook. Disable if you need gradients\n",
"torch.inference_mode().__enter__()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"SAM3_ROOT = os.path.dirname(os.getcwd())\n",
"os.chdir(SAM3_ROOT)\n",
"\n",
"# setup GPU to use - A single GPU is good with the purpose of this demo\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"_ = os.system(\"nvidia-smi\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build SAM3 Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sam3\n",
"from sam3 import build_sam3_image_model\n",
"from sam3.model.sam3_image_processor import Sam3Processor\n",
"\n",
"sam3_root = os.path.join(os.path.dirname(sam3.__file__), \"..\")\n",
"bpe_path = f\"{sam3_root}/assets/bpe_simple_vocab_16e6.txt.gz\"\n",
"model = build_sam3_image_model(bpe_path=bpe_path)\n",
"processor = Sam3Processor(model, confidence_threshold=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LLM Setup\n",
"\n",
"Config which MLLM to use, it can either be a model served by vLLM that you launch from your own machine or a model is served via external API. If you want to using a vLLM model, we also provided insturctions below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"LLM_CONFIGS = {\n",
" # vLLM-served models\n",
" \"qwen3_vl_8b_thinking\": {\n",
" \"provider\": \"vllm\",\n",
" \"model\": \"Qwen/Qwen3-VL-8B-Thinking\",\n",
" }, \n",
" # models served via external APIs\n",
" # add your own\n",
"}\n",
"\n",
"model = \"qwen3_vl_8b_thinking\"\n",
"LLM_API_KEY = \"DUMMY_API_KEY\"\n",
"\n",
"llm_config = LLM_CONFIGS[model]\n",
"llm_config[\"api_key\"] = LLM_API_KEY\n",
"llm_config[\"name\"] = model\n",
"\n",
"# setup API endpoint\n",
"if llm_config[\"provider\"] == \"vllm\":\n",
" LLM_SERVER_URL = \"http://0.0.0.0:8001/v1\" # replace this with your vLLM server address as needed\n",
"else:\n",
" LLM_SERVER_URL = llm_config[\"base_url\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup vLLM server \n",
"This step is only required if you are using a model served by vLLM, skip this step if you are calling LLM using an API like Gemini and GPT.\n",
"\n",
"* Install vLLM (in a separate conda env from SAM 3 to avoid dependency conflicts).\n",
" ```bash\n",
" conda create -n vllm python=3.12\n",
" pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128\n",
" ```\n",
"* Start vLLM server on the same machine of this notebook\n",
" ```bash\n",
" # qwen 3 VL 8B thinking\n",
" vllm serve Qwen/Qwen3-VL-8B-Thinking --tensor-parallel-size 4 --allowed-local-media-path / --enforce-eager --port 8001\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run SAM3 Agent Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from functools import partial\n",
"from IPython.display import display, Image\n",
"from sam3.agent.client_llm import send_generate_request as send_generate_request_orig\n",
"from sam3.agent.client_sam3 import call_sam_service as call_sam_service_orig\n",
"from sam3.agent.inference import run_single_image_inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"output": {
"id": 689664053567678,
"loadingStatus": "loaded"
}
},
"outputs": [],
"source": [
"# prepare input args and run single image inference\n",
"image = \"assets/images/test_image.jpg\"\n",
"prompt = \"the leftmost child wearing blue vest\"\n",
"image = os.path.abspath(image)\n",
"send_generate_request = partial(send_generate_request_orig, server_url=LLM_SERVER_URL, model=llm_config[\"model\"], api_key=llm_config[\"api_key\"])\n",
"call_sam_service = partial(call_sam_service_orig, sam3_processor=processor)\n",
"output_image_path = run_single_image_inference(\n",
" image, prompt, llm_config, send_generate_request, call_sam_service, \n",
" debug=True, output_dir=\"agent_output\"\n",
")\n",
"\n",
"# display output\n",
"if output_image_path is not None:\n",
" display(Image(filename=output_image_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
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