AutoGluon 多模态 - 快速入门¶
AutoGluon 的 MultiModalPredictor 是一个深度学习模型库的模型库，可以自动构建最先进的深度学习模型，用于处理包括图像、文本和表格数据在内的输入。将您的数据转换为 AutoGluon 的多模态数据框格式，MultiModalPredictor 就可以根据其他特征预测某一列的值。
首先确保已安装 AutoGluon，然后导入所需的模块。
!python -m pip install --upgrade pip
!python -m pip install autogluon
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Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/ci/opt/venv/lib/python3.11/site-packages (from fsspec[http]>=2021.05.0->evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.3.1b20250508->autogluon) (3.11.18)
Requirement already satisfied: setuptools in /home/ci/opt/venv/lib/python3.11/site-packages (from lightning-utilities<2.0,>=0.10.0->lightning<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (65.5.0)
Requirement already satisfied: contourpy>=1.0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (1.3.2)
Requirement already satisfied: cycler>=0.10 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (4.57.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (1.4.8)
Requirement already satisfied: pyparsing>=2.3.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (3.2.3)
Requirement already satisfied: numba in /home/ci/opt/venv/lib/python3.11/site-packages (from mlforecast<0.14,>0.13->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (0.61.2)
Requirement already satisfied: optuna in /home/ci/opt/venv/lib/python3.11/site-packages (from mlforecast<0.14,>0.13->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (4.3.0)
Requirement already satisfied: window-ops in /home/ci/opt/venv/lib/python3.11/site-packages (from mlforecast<0.14,>0.13->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (0.0.15)
Requirement already satisfied: gdown>=4.0.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.3.1b20250508->autogluon) (5.2.0)
Requirement already satisfied: click in /home/ci/opt/venv/lib/python3.11/site-packages (from nltk<3.9,>=3.4.5->autogluon.multimodal==1.3.1b20250508->autogluon) (8.1.8)
Requirement already satisfied: regex>=2021.8.3 in /home/ci/opt/venv/lib/python3.11/site-packages (from nltk<3.9,>=3.4.5->autogluon.multimodal==1.3.1b20250508->autogluon) (2024.11.6)
Requirement already satisfied: antlr4-python3-runtime==4.9.* in /home/ci/opt/venv/lib/python3.11/site-packages (from omegaconf<2.4.0,>=2.1.1->autogluon.multimodal==1.3.1b20250508->autogluon) (4.9.3)
Requirement already satisfied: colorama in /home/ci/opt/venv/lib/python3.11/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.3.1b20250508->autogluon) (0.4.4)
Requirement already satisfied: model-index in /home/ci/opt/venv/lib/python3.11/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.3.1b20250508->autogluon) (0.1.11)
Requirement already satisfied: opendatalab in /home/ci/opt/venv/lib/python3.11/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.3.1b20250508->autogluon) (0.0.10)
Requirement already satisfied: rich in /home/ci/opt/venv/lib/python3.11/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.3.1b20250508->autogluon) (14.0.0)
Requirement already satisfied: tabulate in /home/ci/opt/venv/lib/python3.11/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.3.1b20250508->autogluon) (0.9.0)
Requirement already satisfied: pytz>=2020.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from pandas<2.3.0,>=2.0.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in /home/ci/opt/venv/lib/python3.11/site-packages (from pandas<2.3.0,>=2.0.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (2025.2)
Requirement already satisfied: annotated-types>=0.6.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (0.7.0)
Requirement already satisfied: pydantic-core==2.33.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (2.33.2)
Requirement already satisfied: typing-inspection>=0.4.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (0.4.0)
Requirement already satisfied: filelock in /home/ci/opt/venv/lib/python3.11/site-packages (from ray<2.45,>=2.10.0->ray[default]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (3.18.0)
Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray<2.45,>=2.10.0->ray[default]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (1.1.0)
Requirement already satisfied: protobuf!=3.19.5,>=3.15.3 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray<2.45,>=2.10.0->ray[default]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (3.20.2)
Requirement already satisfied: aiosignal in /home/ci/opt/venv/lib/python3.11/site-packages (from ray<2.45,>=2.10.0->ray[default]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (1.3.2)
Requirement already satisfied: frozenlist in /home/ci/opt/venv/lib/python3.11/site-packages (from ray<2.45,>=2.10.0->ray[default]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (1.6.0)
Requirement already satisfied: aiohttp_cors in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (0.8.1)
Requirement already satisfied: colorful in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (0.5.6)
Requirement already satisfied: py-spy>=0.2.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (0.4.0)
Requirement already satisfied: grpcio>=1.42.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (1.71.0)
Requirement already satisfied: opencensus in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (0.11.4)
Requirement already satisfied: prometheus_client>=0.7.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (0.21.1)
Requirement already satisfied: smart_open in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (7.1.0)
Requirement already satisfied: virtualenv!=20.21.1,>=20.0.24 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (20.31.2)
Requirement already satisfied: tensorboardX>=1.9 in /home/ci/opt/venv/lib/python3.11/site-packages (from ray[default,tune]<2.45,>=2.10.0; extra == "all"->autogluon.core[all]==1.3.1b20250508->autogluon) (2.6.2.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/ci/opt/venv/lib/python3.11/site-packages (from requests->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (3.4.2)
Requirement already satisfied: idna<4,>=2.5 in /home/ci/opt/venv/lib/python3.11/site-packages (from requests->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (3.10)
Requirement already satisfied: certifi>=2017.4.17 in /home/ci/opt/venv/lib/python3.11/site-packages (from requests->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (2025.4.26)
Requirement already satisfied: imageio!=2.35.0,>=2.33 in /home/ci/opt/venv/lib/python3.11/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.3.1b20250508->autogluon) (2.37.0)
Requirement already satisfied: tifffile>=2022.8.12 in /home/ci/opt/venv/lib/python3.11/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.3.1b20250508->autogluon) (2025.3.30)
Requirement already satisfied: lazy-loader>=0.4 in /home/ci/opt/venv/lib/python3.11/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.3.1b20250508->autogluon) (0.4)
Requirement already satisfied: threadpoolctl>=3.1.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from scikit-learn<1.7.0,>=1.4.0->autogluon.core==1.3.1b20250508->autogluon.core[all]==1.3.1b20250508->autogluon) (3.6.0)
Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (3.0.12)
Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.0.5)
Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.0.12)
Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (2.0.11)
Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (3.0.9)
Requirement already satisfied: thinc<8.4.0,>=8.3.4 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (8.3.6)
Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.1.3)
Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (2.5.1)
Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (2.0.10)
Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (0.4.1)
Requirement already satisfied: typer<1.0.0,>=0.3.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (0.15.3)
Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (3.5.0)
Requirement already satisfied: language-data>=1.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.3.0)
Requirement already satisfied: statsmodels>=0.13.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from statsforecast<2.0.2,>=1.7.0->autogluon.timeseries==1.3.1b20250508->autogluon.timeseries[all]==1.3.1b20250508->autogluon) (0.14.4)
Requirement already satisfied: absl-py>=0.4 in /home/ci/opt/venv/lib/python3.11/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.3.1b20250508->autogluon) (2.2.2)
Requirement already satisfied: markdown>=2.6.8 in /home/ci/opt/venv/lib/python3.11/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.3.1b20250508->autogluon) (3.8)
Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.3.1b20250508->autogluon) (0.7.2)
Requirement already satisfied: werkzeug>=1.0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.3.1b20250508->autogluon) (3.1.3)
Requirement already satisfied: blis<1.4.0,>=1.3.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from thinc<8.4.0,>=8.3.4->spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.3.0)
Requirement already satisfied: confection<1.0.0,>=0.0.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from thinc<8.4.0,>=8.3.4->spacy<3.9->autogluon.tabular[all]==1.3.1b20250508->autogluon) (0.1.5)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.127)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.127)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.127)
Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (9.1.0.70)
Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.5.8)
Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (11.2.1.3)
Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (10.3.5.147)
Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (11.6.1.9)
Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.3.1.170)
Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (0.6.2)
Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (2.21.5)
Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.127)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (12.4.127)
Requirement already satisfied: triton==3.2.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (3.2.0)
Requirement already satisfied: sympy==1.13.1 in /home/ci/opt/venv/lib/python3.11/site-packages (from torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (1.13.1)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from sympy==1.13.1->torch<2.7,>=2.2->autogluon.multimodal==1.3.1b20250508->autogluon) (1.3.0)
Requirement already satisfied: tokenizers<0.22,>=0.21 in /home/ci/opt/venv/lib/python3.11/site-packages (from transformers<4.50,>=4.38.0->transformers[sentencepiece]<4.50,>=4.38.0->autogluon.multimodal==1.3.1b20250508->autogluon) (0.21.1)
Requirement already satisfied: hf-xet<2.0.0,>=1.1.0 in /home/ci/opt/venv/lib/python3.11/site-packages (from huggingface_hub[torch]->autogluon.tabular[all]==1.3.1b20250508->autogluon) (1.1.0)
Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /home/ci/opt/venv/lib/python3.11/site-packages (from transformers[sentencepiece]<4.50,>=4.38.0->autogluon.multimodal==1.3.1b20250508->autogluon) (0.2.0)
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import os
import warnings
import numpy as np
warnings.filterwarnings('ignore')
np.random.seed(123)
示例数据¶
在本教程中，我们使用 PetFinder 数据集 的简化子采样版本。目标是根据宠物的领养资料预测领养率。在这个简化版本中，领养速度被分为两类：0（慢）和 1（快）。我们首先下载包含 petfinder 数据集的 zip 文件，并在当前工作目录中解压它们。
from autogluon.core.utils.loaders import load_zip
download_dir = './ag_multimodal_tutorial'
zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_for_tutorial.zip'
load_zip.unzip(zip_file, unzip_dir=download_dir)
Downloading ./ag_multimodal_tutorial/file.zip from https://automl-mm-bench.s3.amazonaws.com/petfinder_for_tutorial.zip...
0%| | 0.00/18.8M [00:00<?, ?iB/s]
45%|████▍ | 8.38M/18.8M [00:00<00:00, 58.0MiB/s]
89%|████████▉ | 16.8M/18.8M [00:00<00:00, 53.3MiB/s]
100%|██████████| 18.8M/18.8M [00:00<00:00, 57.0MiB/s]
接下来，我们使用 pandas 将数据集的 CSV 文件读入 DataFrames，并注意我们感兴趣的预测列是“AdoptionSpeed”。
import pandas as pd
dataset_path = f'{download_dir}/petfinder_for_tutorial'
train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0)
test_data = pd.read_csv(f'{dataset_path}/test.csv', index_col=0)
label_col = 'AdoptionSpeed'
PetFinder 数据集附带一个图像目录，数据中的某些记录关联了多张图像。AutoGluon 的多模态数据框格式要求图像列包含一个字符串，其值为单个图像文件的路径。对于本示例，我们将图像特征列限制为仅包含第一张图像，并且需要进行一些路径操作才能为当前目录结构正确设置一切。
image_col = 'Images'
train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0])
test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])
def path_expander(path, base_folder):
path_l = path.split(';')
return ';'.join([os.path.abspath(os.path.join(base_folder, path)) for path in path_l])
train_data[image_col] = train_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
test_data[image_col] = test_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
每只动物的领养资料都包含图片、文字描述以及年龄、品种、名称、颜色等各种表格特征。让我们看一下某个示例数据行的图片和描述。
example_row = train_data.iloc[0]
example_image = example_row[image_col]
from IPython.display import Image, display
pil_img = Image(filename=example_image)
display(pil_img)
example_row['Description']
"I rescued Yumi Hamasaki at a food stall far away in Kelantan. At that time i was on my way back to KL, she was suffer from stomach problem and looking very2 sick.. I send her to vet & get the treatment + vaccinated and right now she's very2 healthy.. About yumi : - love to sleep with ppl - she will keep on meowing if she's hugry - very2 active, always seeking for people to accompany her playing - well trained (poo+pee in her own potty) - easy to bathing - I only feed her with these brands : IAMS, Kittenbites, Pro-formance Reason why i need someone to adopt Yumi: I just married and need to move to a new house where no pets are allowed :( As Yumi is very2 special to me, i will only give her to ppl that i think could take care of her just like i did (especially on her foods things).."
训练¶
现在数据已采用合适的格式，我们可以将 MultiModalPredictor 应用于训练数据。在此，我们为这个快速演示设置了一个严格的训练时间预算。更多的训练时间将带来更好的预测性能，但在短时间内我们也能获得出人意料的好性能。
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor(label=label_col).fit(
train_data=train_data,
time_limit=120
)
显示代码单元格输出
No path specified. Models will be saved in: "AutogluonModels/ag-20250508_210057"
=================== System Info ===================
AutoGluon Version: 1.3.1b20250508
Python Version: 3.11.9
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Wed Mar 12 14:53:59 UTC 2025
CPU Count: 8
Pytorch Version: 2.6.0+cu124
CUDA Version: 12.4
Memory Avail: 28.40 GB / 30.95 GB (91.8%)
Disk Space Avail: 182.44 GB / 255.99 GB (71.3%)
===================================================
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [np.int64(0), np.int64(1)]
If 'binary' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
AutoMM starts to create your model. ✨✨✨
To track the learning progress, you can open a terminal and launch Tensorboard:
```shell
# Assume you have installed tensorboard
tensorboard --logdir /home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057
```
INFO: Seed set to 0
GPU Count: 1
GPU Count to be Used: 1
INFO: Using 16bit Automatic Mixed Precision (AMP)
INFO: GPU available: True (cuda), used: True
INFO: TPU available: False, using: 0 TPU cores
INFO: HPU available: False, using: 0 HPUs
INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
INFO:
| Name | Type | Params | Mode
------------------------------------------------------------------
0 | model | MultimodalFusionMLP | 207 M | train
1 | validation_metric | BinaryAUROC | 0 | train
2 | loss_func | CrossEntropyLoss | 0 | train
------------------------------------------------------------------
207 M Trainable params
0 Non-trainable params
207 M Total params
828.307 Total estimated model params size (MB)
1171 Modules in train mode
0 Modules in eval mode
INFO: Epoch 0, global step 1: 'val_roc_auc' reached 0.56250 (best 0.56250), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=0-step=1.ckpt' as top 3
INFO: Epoch 0, global step 4: 'val_roc_auc' reached 0.76083 (best 0.76083), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=0-step=4.ckpt' as top 3
INFO: Epoch 1, global step 5: 'val_roc_auc' reached 0.78056 (best 0.78056), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=1-step=5.ckpt' as top 3
INFO: Epoch 1, global step 8: 'val_roc_auc' reached 0.79000 (best 0.79000), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=1-step=8.ckpt' as top 3
INFO: Epoch 2, global step 9: 'val_roc_auc' reached 0.78972 (best 0.79000), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=2-step=9.ckpt' as top 3
INFO: Time limit reached. Elapsed time is 0:02:00. Signaling Trainer to stop.
INFO: Epoch 2, global step 10: 'val_roc_auc' reached 0.79167 (best 0.79167), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057/epoch=2-step=10.ckpt' as top 3
Start to fuse 3 checkpoints via the greedy soup algorithm.
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
AutoMM has created your model. 🎉🎉🎉
To load the model, use the code below:
```python
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor.load("/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/AutogluonModels/ag-20250508_210057")
```
If you are not satisfied with the model, try to increase the training time,
adjust the hyperparameters (https://autogluon.cn/stable/tutorials/multimodal/advanced_topics/customization.html),
or post issues on GitHub (https://github.com/autogluon/autogluon/issues).
在内部，MultiModalPredictor 会自动推断问题类型（分类或回归）、检测特征模态、从多模态模型池中选择模型并训练选定的模型。如果使用了多个骨干网络，MultiModalPredictor 会在它们之上附加一个后期融合模型（MLP 或 transformer）。
预测¶
拟合模型后，我们希望使用它来预测保留的测试数据集中的标签。
predictions = predictor.predict(test_data.drop(columns=label_col))
predictions[:5]
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
8 1
70 1
82 1
28 0
63 1
Name: AdoptionSpeed, dtype: int64
对于分类任务，我们可以同样轻松地获取每个输出类别的预测概率。
probs = predictor.predict_proba(test_data.drop(columns=label_col))
probs[:5]
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
| 0 | 1 | |
|---|---|---|
| 8 | 0.441312 | 0.558688 |
| 70 | 0.123683 | 0.876317 |
| 82 | 0.018797 | 0.981203 |
| 28 | 0.899298 | 0.100702 |
| 63 | 0.138098 | 0.861902 |
评估¶
最后，我们可以使用其他性能指标（在本例中为 roc_auc）在保留的测试数据集上评估预测器。
scores = predictor.evaluate(test_data, metrics=["roc_auc"])
scores
INFO: Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.
{'roc_auc': np.float64(0.8844)}
结论¶
在本快速入门教程中，我们看到了 AutoGluon MultiModalPredictor 的基本拟合和预测功能，但这只是其功能的皮毛。请查阅深度教程，了解 AutoGluon MultiModalPredictor 的其他功能，例如嵌入提取、蒸馏、模型微调、文本或图像预测以及语义匹配。