公開日：2022年3月10日
最終更新日：2022年9月10日
はじめに
AutoGluonに導入されている「MultiModalPredictor」を使って画像やテキストを含む表データの分類を行います。以前も同様のことを行っていますがその時にはMultiModalPredictorは使用していません。touch-sp.hatenablog.com
環境
Ubuntu 20.04 on WSL2 (Windows 11) CUDA Toolkit 11.3.1
Python 3.8.10
インストール
PyTorchとAutoGluonをpipでインストールするだけです。MXNetのインストールは不要です。
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchtext --extra-index-url https://download.pytorch.org/whl/cu113 pip install autogluon
実行
データのダウンロード
from autogluon.core.utils.loaders import load_zip download_dir = './ag_petfinder_tutorial' zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_for_tutorial.zip' load_zip.unzip(zip_file, unzip_dir=download_dir)
学習スクリプト
import warnings warnings.filterwarnings('ignore') import os import pandas as pd from autogluon.multimodal import MultiModalPredictor download_dir = './ag_petfinder_tutorial' dataset_path = download_dir + '/petfinder_for_tutorial' train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0) label_col = 'AdoptionSpeed' image_col = 'Images' train_data[image_col] = train_data[image_col].apply(lambda x: os.path.abspath(os.path.join(dataset_path, x.split(';')[0]))) predictor = MultiModalPredictor(label=label_col) predictor.fit(train_data=train_data) predictor.save('my_saved_dir')
学習時の出力
Global seed set to 123 Auto select gpus: [0] Using 16bit native Automatic Mixed Precision (AMP) GPU available: True, used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] | Name | Type | Params ---------------------------------------------------------- 0 | model | MultimodalFusionMLP | 349 M 1 | validation_metric | AUROC | 0 2 | loss_func | CrossEntropyLoss | 0 ---------------------------------------------------------- 349 M Trainable params 0 Non-trainable params 349 M Total params 699.890 Total estimated model params size (MB) Epoch 0: 50%|████████████████████████████████████████████████████████████▌ | 45/90 [00:09<00:09, 4.97it/s, loss=1.25, v_num=Epoch 0, global step 1: 'val_roc_auc' reached 0.58528 (best 0.58528), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=0-step=1.ckpt' as top 3 Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:23<00:00, 3.82it/s, loss=1.74, v_num=Epoch 0, global step 4: 'val_roc_auc' reached 0.70139 (best 0.70139), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=0-step=4.ckpt' as top 3 Epoch 1: 50%|████████████████████████████████████████████████████████████▌ | 45/90 [00:08<00:08, 5.08it/s, loss=1.02, v_num=Epoch 1, global step 5: 'val_roc_auc' reached 0.73111 (best 0.73111), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=1-step=5.ckpt' as top 3 Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:48<00:00, 1.85it/s, loss=0.746, v_num=Epoch 1, global step 8: 'val_roc_auc' reached 0.76333 (best 0.76333), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=1-step=8.ckpt' as top 3 Epoch 2: 50%|████████████████████████████████████████████████████████████▌ | 45/90 [00:13<00:13, 3.22it/s, loss=0.97, v_num=Epoch 2, global step 9: 'val_roc_auc' reached 0.76944 (best 0.76944), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=2-step=9.ckpt' as top 3 Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [01:04<00:00, 1.40it/s, loss=0.657, v_num=Epoch 2, global step 12: 'val_roc_auc' reached 0.77917 (best 0.77917), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=2-step=12.ckpt' as top 3 ・ ・ ・ Epoch 8: 50%|████████████████████████████████████████████████████████████ | 45/90 [00:08<00:08, 5.21it/s, loss=0.929, v_num=Epoch 8, global step 33: 'val_roc_auc' reached 0.80639 (best 0.80639), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=8-step=33.ckpt' as top 3 Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:57<00:00, 1.55it/s, loss=0.266, v_num=Epoch 8, global step 36: 'val_roc_auc' reached 0.80694 (best 0.80694), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=8-step=36.ckpt' as top 3 Epoch 9: 50%|████████████████████████████████████████████████████████████ | 45/90 [00:11<00:11, 4.06it/s, loss=0.904, v_num=Epoch 9, global step 37: 'val_roc_auc' reached 0.80667 (best 0.80694), saving model to '/mnt/wsl/PHYSICALDRIVE0p1/autogluon_works/multimodal/AutogluonModels/ag-20220910_052227/epoch=9-step=37.ckpt' as top 3 Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:51<00:00, 1.76it/s, loss=0.241, v_num=Epoch 9, global step 40: 'val_roc_auc' was not in top 3 Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [01:02<00:00, 1.44it/s, loss=0.241, v_num=] Start to fuse 3 checkpoints via the greedy soup algorithm. Predicting DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 4.04it/s] Predicting DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 4.26it/s] Predicting DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 4.44it/s]
評価スクリプト
import warnings warnings.filterwarnings('ignore') import os import pandas as pd from autogluon.multimodal import MultiModalPredictor download_dir = './ag_petfinder_tutorial' dataset_path = download_dir + '/petfinder_for_tutorial' test_data = pd.read_csv(f'{dataset_path}/test.csv', index_col=0) image_col = 'Images' test_data[image_col] = test_data[image_col].apply(lambda x: os.path.abspath(os.path.join(dataset_path, x.split(';')[0]))) predictor = MultiModalPredictor.load('my_saved_dir') scores = predictor.evaluate(test_data, metrics=["roc_auc"]) print(scores)
評価の結果
Load pretrained checkpoint: my_saved_dir/model.ckpt
Predicting DataLoader 0: 100%|████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:05<00:00, 1.41s/it]
{'roc_auc': 0.9084}
