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 = "",
}
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
Download datasets
Inside ./datasets
folder, run ./download_datasets.sh
to download MOSI and MOSEI datasets
Training MAG-BERT / MAG-XLNet on MOSI
First, install python dependancies using pip install -r requirements.txt
Training scripts:
python multimodal_driver.py --model bert-base-uncased
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
.
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
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)