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README.md

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+# MLP-Communicator
+
+We begin our work on the basis of [MISA](https://github.com/declare-lab/MISA) initially, so the whole code architecture is similar to it, including the data process, data loader and evaluation metrics. Thanks to their open source spirit for saving us a lot of time.
+
+```
+@article{hazarika2020misa,
+  title={MISA: Modality-Invariant and-Specific Representations for Multimodal Sentiment Analysis},
+  author={Hazarika, Devamanyu and Zimmermann, Roger and Poria, Soujanya},
+  journal={arXiv preprint arXiv:2005.03545},
+  year={2020}
+}
+```
+
+## Requirements
+
+- Python 3.8
+- Pytorch 1.11.0
+
+you could run the code to build the environment.
+
+```shell
+pip install requirements.txt
+```
+
+### Data Download
+
+- Install [CMU Multimodal SDK](https://github.com/A2Zadeh/CMU-MultimodalSDK). Ensure, you can perform ```from mmsdk import mmdatasdk```.    
+- Option 1: Download [pre-computed splits](https://drive.google.com/drive/folders/1IBwWNH0XjPnZWaAlP1U2tIJH6Rb3noMI?usp=sharing) and place the contents inside ```datasets``` folder.     
+- Option 2: Re-create splits by downloading data from MMSDK. For this, simply run the code as detailed next.
+
+### Running the code
+
+1. Set ```word_emb_path``` in ```config.py``` to [glove file](http://nlp.stanford.edu/data/glove.840B.300d.zip).
+2. Set ```sdk_dir``` to the path of CMU-MultimodalSDK.
+3. ```python train.py --data mosi```. Replace ```mosi``` with ```mosei```  for other datasets.
+
+The repository is updating...
+
+### Contact
+
+For any questions, please email at [zpl010720@gmail.com](zpl010720@gmail.com)

config.py

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+
+import argparse
+from datetime import datetime
+from pathlib import Path
+import pprint
+from torch import optim
+import torch.nn as nn
+
+# path to a pretrained word embedding file
+word_emb_path = ''
+assert(word_emb_path is not None)
+
+project_dir = Path(__file__).resolve().parent.parent
+sdk_dir = project_dir.joinpath('CMU-MultimodalSDK')
+data_dir = project_dir.joinpath('datasets')
+data_dict = {'mosi': data_dir.joinpath('MOSI'), 'mosei': data_dir.joinpath(
+    'MOSEI'), 'ur_funny': data_dir.joinpath('UR_FUNNY')}
+optimizer_dict = {'RMSprop': optim.RMSprop, 'Adam': optim.Adam}
+activation_dict = {'elu': nn.ELU, "hardshrink": nn.Hardshrink, "hardtanh": nn.Hardtanh,
+                   "leakyrelu": nn.LeakyReLU, "prelu": nn.PReLU, "relu": nn.ReLU, "rrelu": nn.RReLU,
+                   "tanh": nn.Tanh,"gelu":nn.GELU}
+
+
+def str2bool(v):
+    """string to boolean"""
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+class Config(object):
+    def __init__(self, **kwargs):
+        """Configuration Class: set kwargs as class attributes with setattr"""
+        if kwargs is not None:
+            for key, value in kwargs.items():
+                if key == 'optimizer':
+                    value = optimizer_dict[value]
+                if key == 'activation':
+                    value = activation_dict[value]
+                setattr(self, key, value)
+
+
+        self.dataset_dir = data_dict[self.data.lower()]
+        self.sdk_dir = sdk_dir
+        # Glove path
+        self.word_emb_path = word_emb_path
+
+        self.data_dir = self.dataset_dir
+
+    def __str__(self):
+        """Pretty-print configurations in alphabetical order"""
+        config_str = 'Configurations\n'
+        config_str += pprint.pformat(self.__dict__)
+        return config_str
+
+
+def get_config(parse=True, **optional_kwargs):
+    """
+    Get configurations as attributes of class
+    1. Parse configurations with argparse.
+    2. Create Config class initilized with parsed kwargs.
+    3. Return Config class.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Mode
+    parser.add_argument('--mode', type=str, default='train')
+
+    # parser.add_argument('--use_bert', type=str2bool, default=True)
+
+    # Train
+    time_now = datetime.now().strftime('%Y-%m-%d_%H:%M:%S')
+    parser.add_argument('--name', type=str, default=f"{time_now}")
+    parser.add_argument('--batch_size', type=int, default=128)
+    parser.add_argument('--n_epoch', type=int, default=100)
+    parser.add_argument('--patience', type=int, default=6)
+    parser.add_argument('--trials', type=int, default=3)
+
+    parser.add_argument('--learning_rate', type=float, default=1e-4)
+    parser.add_argument('--optimizer', type=str, default='Adam')
+
+    parser.add_argument('--rnncell', type=str, default='lstm')
+    parser.add_argument('--embedding_size', type=int, default=300)
+    parser.add_argument('--hidden_size', type=int, default=128)
+    parser.add_argument('--mlp_hidden_size', type=int, default=64)
+    parser.add_argument('--dropout', type=float, default=0.5)
+    parser.add_argument('--depth', type=int, default=1)
+
+    # Selectin activation from 'elu', "hardshrink", "hardtanh", "leakyrelu", "prelu", "relu", "rrelu", "tanh"
+    parser.add_argument('--activation', type=str, default='relu')
+
+    parser.add_argument('--cls_weight', type=float, default=1)
+    parser.add_argument('--polar_weight', type=float, default=0.1)
+    parser.add_argument('--scale_weight', type=float, default=0.1)
+
+    parser.add_argument('--model', type=str,
+                        default='MISA', help='one of {MISA, }')
+
+    parser.add_argument('--test_duration', type=int, default=1)
+
+    # Data
+    parser.add_argument('--data', type=str, default='mosi')
+
+    # Parse arguments
+    if parse:
+        kwargs = parser.parse_args()
+    else:
+        kwargs = parser.parse_known_args()[0]
+
+    print(kwargs.data)
+    if kwargs.data == "mosi":
+        kwargs.num_classes = 1
+        kwargs.batch_size = 128
+        kwargs.depth = 1
+    elif kwargs.data == "mosei":
+        kwargs.num_classes = 1
+        kwargs.batch_size = 64
+        kwargs.depth = 2
+    else:
+        print("No dataset mentioned")
+        exit()
+
+    # Namespace => Dictionary
+    kwargs = vars(kwargs)
+    kwargs.update(optional_kwargs)
+
+    return Config(**kwargs)