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Multimodal Adaptation Gate (MAG)

Open source code for ACL 2020 Paper: Integrating Multimodal Information in Large Pretrained Transformers

If you use the model or results, please consider citing the research paper:

@inproceedings{rahman-etal-2020-integrating,
    title = "Integrating Multimodal Information in Large Pretrained Transformers",
    author = "Rahman, Wasifur  and
      Hasan, Md Kamrul  and
      Lee, Sangwu  and
      Bagher Zadeh, AmirAli  and
      Mao, Chengfeng  and
      Morency, Louis-Philippe  and
      Hoque, Ehsan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.214",
    doi = "10.18653/v1/2020.acl-main.214",
    pages = "2359--2369",
    abstract = "",
}

Getting started

  1. Configure global_configs.py

    global_configs.py defines global constants for runnning experiments. Dimensions of data modality (text, acoustic, visual), cpu/gpu settings, and MAG's injection position. Default configuration is set to MOSI. For running experiments on MOSEI or on custom dataset, make sure that ACOUSTIC_DIM and VISUAL_DIM are set approperiately.

    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["WANDB_PROGRAM"] = "multimodal_driver.py"

DEVICE = torch.device("cuda:0")

 # MOSI SETTING
 ACOUSTIC_DIM = 74
 VISUAL_DIM = 47
 TEXT_DIM = 768

 # MOSEI SETTING
 # ACOUSTIC_DIM = 74
 # VISUAL_DIM = 35
 # TEXT_DIM = 768

 # CUSTOM DATASET
 # ACOUSTIC_DIM = ??
 # VISUAL_DIM = ??
 # TEXT_DIM = ??

XLNET_INJECTION_INDEX = 1

  2. Download datasets Inside ./datasets folder, run ./download_datasets.sh to download MOSI and MOSEI datasets

  3. Training MAG-BERT / MAG-XLNet on MOSI

    First, install python dependancies using pip install -r requirements.txt

    Training scripts:

    • MAG-BERT python multimodal_driver.py --model bert-base-uncased
    • MAG-XLNet python multimodal_driver.py --model xlnet-base-cased

    By default, multimodal_driver.py will attempt to create a Weights and Biases (W&B) project to log your runs and results. If you wish to disable W&B logging, set environment variable to WANDB_MODE=dryrun.

  4. Model usage

    We would like to thank huggingface for providing and open-sourcing BERT / XLNet code for developing our models. Note that bert.py / xlnet.py are based on huggingface's implmentation.

    MAG

    from modeling import MAG

hidden_size, beta_shift, dropout_prob = 768, 1e-3, 0.5
multimodal_gate = MAG(hidden_size, beta_shift, dropout_prob)

fused_embedding = multimodal_gate(text_embedding, visual_embedding, acoustic_embedding)

    MAG-BERT

    from bert import MAG_BertForSequenceClassification

class MultimodalConfig(object):
    def __init__(self, beta_shift, dropout_prob):
        self.beta_shift = beta_shift
        self.dropout_prob = dropout_prob

multimodal_config = MultimodalConfig(beta_shift=1e-3, dropout_prob=0.5)
model = MAG_BertForSequenceClassification.from_pretrained(
        'bert-base-uncased', multimodal_config=multimodal_config, num_labels=1,
    )

outputs = model(input_ids, visual, acoustic, attention_mask, position_ids)
logits = outputs[0]

    MAG-XLNet

    from xlnet import MAG_XLNetForSequenceClassification

class MultimodalConfig(object):
    def __init__(self, beta_shift, dropout_prob):
        self.beta_shift = beta_shift
        self.dropout_prob = dropout_prob

multimodal_config = MultimodalConfig(beta_shift=1e-3, dropout_prob=0.5)
model = MAG_XLNet_ForSequenceClassification.from_pretrained(
        'xlnet-base-cased', multimodal_config=multimodal_config, num_labels=1,
    )

outputs = model(input_ids, visual, acoustic, attention_mask, position_ids)
logits = outputs[0]

    For MAG-BERT / MAG-XLNet usage, visual, acoustic are torch.FloatTensor of shape (batch_size, sequence_length, modality_dim).

    input_ids, attention_mask, position_ids are torch.LongTensor of shape (batch_size, sequence_length). For more details on how these tensors should be formatted / generated, please refer to multimodal_driver.py's convert_to_features method and huggingface's documentation

Dataset Format

All datasets are saved under ./datasets/ folder and is encoded as .pkl file. Format of dataset is as follows:

{
    "train": [
        (words, visual, acoustic), label_id, segment,
        ...
    ],
    "dev": [ ... ],
    "test": [ ... ]
}

Dataset is encoded as python dictionary and saved as .pkl file

import pickle as pkl

# NOTE: Use 'wb' mode
with open('data.pkl', 'wb') as f:
    pkl.dump(data, f)

Contacts