APIs

1. MMSA_run

This is the main function of this project. It runs MSA experiments on datasets and models specified in the parameters.

Definition:

def MMSA_run(
    model_name: str, dataset_name: str, config_file: str = "",
    config: dict = None, seeds: list = [], is_tune: bool = False,
    tune_times: int = 50, feature_T: str = "", feature_A: str = "",
    feature_V: str = "", model_save_dir: str = "", res_save_dir: str = "",
    log_dir: str = "", gpu_ids: list = [0], num_workers: int = 4,
    verbose_level: int = 1
)

Args:

• model_name(required): Name of MSA model, see Supported Models for details.
• dataset_name(required): Name of MSA dataset, see Supported Datasets for details.
• config_file: Path to config file. Default config files will be used if not specified. See Config Files for details.
• config: Config in the format of Python dict. Used to override arguments in config_file. Ignored in tune mode.
• seeds: List of seeds. Default: [1111, 1112, 1113, 1114, 1115]
• is_tune: Tuning mode switch. See Tuning Mode for details. Default: False
• tune_times: # Sets of hyper parameters to tune. Default: 50
• feature_T: Path to text feature file. Provide an empty string to use default BERT features. Default: ""
• feature_A: Path to audio feature file. Provide an empty string to use default features provided by dataset creators. Default: ""
• feature_V: Path to video feature file. Provide an empty string to use default features provided by dataset creators. Default: ""
• model_save_dir: Path to save trained models. Default: ~/MMSA/saved_models
• res_save_dir: Path to save csv results. Default: ~/MMSA/results
• log_dir: Path to save log files. Default: ~/MMSA/logs
• gpu_ids: GPUs to use. Will assign the most memory-free gpu if an empty list is provided. Default: [0]. Currently only supports single gpu.
• num_workers: Number of workers used to load data. Default: 4
• verbose_level: Verbose level of stdout. 0 for error, 1 for info, 2 for debug. Default: 1

Example Usage:

from MMSA import MMSA_run

# run lmf on mosi with default params
MMSA_run('lmf', 'mosi')

# tune mult on mosei with default param ranges
MMSA_run('mult', 'mosi', is_tune=True, seeds=[1111])

2. get_config_regression

Retrieves config dict of given dataset and model from a config file, so that it can be viewed or altered easily.

Definition:

def get_config_regression(
    model_name: str, dataset_name: str, config_file: str = ""
) -> dict:

Args:

• model_name*(required)*: Name of model.
• dataset_name*(required)*: Name of dataset.
• config_file: Path to config file, if given an empty string, will use default config file. Default: ""

Returns:

Config of the given dataset and model in the format of Python dict.

Example Usage:

from MMSA import MMSA_run, get_config_regression

# get default config of mult on sims
config = get_config_regression('mult', 'sims')
# alter the default config
config['nlevels'] = 4
config['conv1d_kernel_size_l'] = 3
config['conv1d_kernel_size_a'] = 3
config['conv1d_kernel_size_v'] = 3
# check config
print(config)
# run with altered config
MMSA_run('mult', 'sims', config=config)

3. get_config_tune

Get the tuning config of given dataset and model from a config file. If random_choice is True, the returning config will contain only one set of randomly selected parameters.

Definition:

def get_config_tune(
    model_name: str, dataset_name: str, config_file: str = "",
    random_choice: bool = True
) -> dict

Args:

• model_name*(required)*: Name of model.
• dataset_name*(required)*: Name of dataset.
• config_file: Path to config file, if given an empty string, will use default tuning config file. Default: ""
• random_choice: If True, will randomly select one parameter for each of the tuning parameters. Default: True

Returns:

Config of the given dataset and model in the format of Python dict.

Example Usage:

from MMSA import get_config_tune, MMSA_run

# get default tuning parameters of misa on mosi
config = get_config_tune('misa', 'mosi', random_choice=False)
print(config)

# random select one set of default tuning parameters and run
config = get_config_tune('misa', 'mosi')
MMSA_run('misa', 'mosi', config=config)

4. get_config_all

Get all default configs. This function is used to export default config file.

Definition:

def get_config_all(config_file: str) -> dict

Args:

• config_file: "regression" or "tune".

Returns:

Default regression or tuning config for all models in the format of Python dict.

Example Usage:

from MMSA import get_config_all

# get default regression config and dump to file
config = get_config_all("regression")
with open("./config.json", "w") as f:
    json.dump(config, f)

5. get_citations (WIP)

Get paper titles and citations for models and datasets.

Definition:

def get_citations() -> dict

Returns:

cites: {
    models: {
        tfn: {
            title: "xxx",
            paper_url: "xxx",
            citation: "xxx",
            description: "xxx"
        },
        ...
    },
    datasets: {
        ...
    },
}

Example Usage:

from MMSA import get_citations

# get citations of MOSI dataset
citations = get_citations()
print(citations["mosi"])