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https://github.com/eshansurendra/mag-bert


https://github.com/eshansurendra/mag-bert

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README

        

# Multimodal Adaptation Gate (MAG)

Open source code for ACL 2020 Paper: [Integrating Multimodal Information in Large Pretrained Transformers](https://www.aclweb.org/anthology/2020.acl-main.214.pdf)

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.

```python
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)](https://www.wandb.com/) 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](https://huggingface.co/) 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**

```python
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**

```python
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**

```python
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](https://huggingface.co/transformers/preprocessing.html)

## Dataset Format

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

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

- words (List[str]): List of words
- visual (np.array): Numpy array of shape (sequence_len, VISUAL_DIM)
- acoustic (np.array): Numpy array of shape (seqeunce_len, ACOUSTIC_DIM)
- label_id (float): Label for data point
- segment (Any): Unique identifier for each data point

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

```python
import pickle as pkl

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

## Contacts

- Wasifur Rahman: [email protected]
- Sangwu Lee: [email protected]
- Kamrul Hasan: [email protected]