### Install CMU Multimodal SDK via Pip

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Installs the CMU Multimodal SDK in development mode using pip. This involves cloning the repository and then running the pip install command.

```bash
git clone git@github.com:A2Zadeh/CMU-MultimodalSDK.git
cd CMU-MultimodalSDK
pip install -e .
```

--------------------------------

### Initialize and Use DynamicFusionGraph Model

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Illustrates the setup and application of the DynamicFusionGraph module for interpretable dynamic fusion. It involves defining a pattern model and an efficacy model, then initializing the graph with these components and sample multimodal inputs to learn efficacy-weighted connections.

```python
import torch
from torch import nn
from torch.autograd import Variable
import numpy
from mmsdk.mmmodelsdk.fusion.dynamic_fusion_graph import DynamicFusionGraph

# Define input dimensions for 3 modalities
language_dim, visual_dim, acoustic_dim = 40, 12, 20
output_dim = 20
batch_size = 32

# Define pattern model (core network for each fusion node)
pattern_model = nn.Sequential(
    nn.Linear(100, 64),
    nn.ReLU(),
    nn.Linear(64, output_dim)
)

# Define efficacy model (learns connection weights)
efficacy_model = nn.Sequential(
    nn.Linear(100, 64),
    nn.ReLU()
)

# Initialize Dynamic Fusion Graph
fusion_model = DynamicFusionGraph(
    pattern_model=pattern_model,
    in_dimensions=[language_dim, visual_dim, acoustic_dim],
    out_dimension=output_dim,
    efficacy_model=efficacy_model
)

# Create sample inputs
language_input = Variable(torch.randn(batch_size, language_dim), requires_grad=True)
visual_input = Variable(torch.randn(batch_size, visual_dim), requires_grad=True)
acoustic_input = Variable(torch.randn(batch_size, acoustic_dim), requires_grad=True)

# Perform fusion
modalities = [language_input, visual_input, acoustic_input]
final_output, intermediate_outputs, efficacies = fusion_model(modalities)

print(final_output.shape)  # torch.Size([32, 20])
print(efficacies.shape)  # Learned efficacy weights for all connections
```

--------------------------------

### Python: Get Dataset Citation Information

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/README.md

This Python code snippet demonstrates how to retrieve citation information for datasets and computational sequences processed by the CMU-Multimodal SDK. It writes the citations to specified .bib files.

```python
>>> mydataset.bib_citations(open('mydataset.bib','w'))
>>> mycompseq.bib_citations(open('mycompseq.bib','w'))
```

--------------------------------

### Initialize and Manage Datasets with mmdatasdk.mmdataset

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Demonstrates how to initialize an mmdatasdk.mmdataset object, download datasets, add computational sequences (like labels), and access data. It shows how to load data from URLs or local folders and inspect features and intervals.

```python
from mmsdk import mmdatasdk

# Download CMU-MOSI dataset with high-level features (GloVe, FACET, COVAREP)
cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')

# Add sentiment labels computational sequence
cmumosi.add_computational_sequences(mmdatasdk.cmu_mosi.labels, 'cmumosi/')

# Access computational sequence keys
print(cmumosi.keys())
# Output: dict_keys(['glove_vectors', 'FACET_4.1', 'FACET_4.2', 'OpenSmile-emobase2010',
#                    'OpenSMILE', 'OpenFace_1', 'OpenFace_2', 'COVAREP', 'Opinion Segment Labels'])

# Access data for a specific video in a computational sequence
video_data = cmumosi['glove_vectors']['video_id']
print(video_data['features'].shape)  # (num_segments, feature_dim)
print(video_data['intervals'].shape)  # (num_segments, 2)

# Load dataset from local folder containing .csd files
local_dataset = mmdatasdk.mmdataset('./my_dataset_folder/')
```

--------------------------------

### Load and Initialize mmdatasdk

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/README.md

This snippet demonstrates how to import the mmdatasdk and initialize an mmdatasdk object with a specified dataset and local directory. It's the first step for data acquisition.

```python
from mmsdk import mmdatasdk
cmumosi_highlevel=mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel,'cmumosi/')
```

--------------------------------

### Initialize and Use LSTHM Module

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Shows how to initialize the Long Short-Term Hybrid Memory (LSTHM) module and process a sequence of inputs step-by-step with hybrid cross-modal information.

```python
from mmsdk.mmmodelsdk.modules.LSTHM import LSTHM
lsthm = LSTHM(cell_size=128, in_size=300, hybrid_in_size=109)
c_t = torch.zeros(32, 128)
h_t = torch.zeros(32, 128)
for t in range(25):
    c_t, h_t = lsthm.step(language_seq[t], c_t, h_t, hybrid_seq[t])
```

--------------------------------

### Configure Standard Multimodal Datasets

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Retrieves pre-configured high-level features and labels for standard datasets like CMU-MOSI, CMU-MOSEI, and POM.

```python
from mmsdk import mmdatasdk
mosei_dataset = mmdatasdk.mmdataset(mmdatasdk.cmu_mosei.highlevel, 'cmumosei/')
mosei_dataset.add_computational_sequences(mmdatasdk.cmu_mosei.labels, 'cmumosei/')
```

--------------------------------

### Perform Multimodal Fusion

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Demonstrates how to pass multiple modality inputs into a fusion model and inspect the resulting dimension-reduced output shapes.

```python
modalities = [language_input, visual_input, acoustic_input]
dim_reduced, attention_outputs = fusion_model(modalities)
print(dim_reduced[0].shape)
print(dim_reduced[1].shape)
print(dim_reduced[2].shape)
```

--------------------------------

### Initialize and Use MultipleAttentionFusion Model

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Shows how to initialize and use the MultipleAttentionFusion module, which implements multi-attention recurrent fusion. It involves defining an attention model and dimension reduction networks for each modality, then creating the fusion model and preparing sample inputs.

```python
import torch
from torch import nn
from torch.autograd import Variable
import numpy
from mmsdk.mmmodelsdk.fusion.multiple_attention import MultipleAttentionFusion

# Define input dimensions
language_dim, visual_dim, acoustic_dim = 40, 12, 20
total_dim = language_dim + visual_dim + acoustic_dim
num_attentions = 4
batch_size = 32

# Attention model: outputs attention weights for all modalities
attention_model = nn.Sequential(
    nn.Linear(total_dim, total_dim * num_attentions),
    nn.Tanh()
)

# Dimension reduction networks for each modality after attention
dim_reduce_nets = [
    nn.Sequential(nn.Linear(language_dim * num_attentions, 32)),
    nn.Sequential(nn.Linear(visual_dim * num_attentions, 16)),
    nn.Sequential(nn.Linear(acoustic_dim * num_attentions, 24))
]

# Initialize Multiple Attention Fusion
fusion_model = MultipleAttentionFusion(
    attention_model=attention_model,
    dim_reduce_nets=dim_reduce_nets,
    num_atts=num_attentions
)

# Create sample inputs
language_input = Variable(torch.randn(batch_size, language_dim), requires_grad=True)
visual_input = Variable(torch.randn(batch_size, visual_dim), requires_grad=True)
acoustic_input = Variable(torch.randn(batch_size, acoustic_dim), requires_grad=True)

# Note: The code snippet provided in the prompt was incomplete for this section. 
# A full example would typically include a call to fusion_model(modalities).
```

--------------------------------

### Initialize and Use RecurrentFusion Model

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Demonstrates the initialization and usage of the RecurrentFusion module, which employs LSTM cells for iterative multimodal fusion. It shows how to create the model, provide sample inputs, and perform fusion over multiple steps, outputting fused features, hidden states, and cell states.

```python
import torch
from torch.autograd import Variable
import numpy
from mmsdk.mmmodelsdk.fusion.recurrent_fusion import RecurrentFusion

# Define input dimensions and LSTM cell size
language_dim, visual_dim, acoustic_dim = 300, 35, 74
cell_size = 128
batch_size = 32
fusion_steps = 5

# Initialize Recurrent Fusion model
fusion_model = RecurrentFusion(
    in_dimensions=[language_dim, visual_dim, acoustic_dim],
    cell_size=cell_size
)

# Create sample inputs
language_input = Variable(torch.randn(batch_size, language_dim), requires_grad=True)
visual_input = Variable(torch.randn(batch_size, visual_dim), requires_grad=True)
acoustic_input = Variable(torch.randn(batch_size, acoustic_dim), requires_grad=True)

# Perform recurrent fusion with 5 iterations
modalities = [language_input, visual_input, acoustic_input]
outputs, hidden_state, cell_state = fusion_model(modalities, steps=fusion_steps)

print(outputs.shape)  # torch.Size([5, 32, 128]) - (steps, batch, cell_size)
print(hidden_state.shape)  # torch.Size([1, 32, 128])
print(cell_state.shape)  # torch.Size([1, 32, 128])
```

--------------------------------

### Deploy and Load Datasets with mmdataset.deploy

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Saves processed computational sequences to disk as .csd files. This allows for efficient reuse of aligned datasets without needing to re-process or re-download data.

```python
from mmsdk import mmdatasdk

cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')
cmumosi.add_computational_sequences(mmdatasdk.cmu_mosi.labels, 'cmumosi/')
cmumosi.align('Opinion Segment Labels')

deploy_filenames = {key: key for key in cmumosi.keys()}
cmumosi.deploy('./deployed/', deploy_filenames)

aligned_dataset = mmdatasdk.mmdataset('./deployed/')
```

--------------------------------

### Build End-to-End Training Pipeline

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

A complete workflow for downloading data, performing word-level alignment, imputing missing values, and preparing PyTorch DataLoaders.

```python
cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')
cmumosi.align('glove_vectors', collapse_functions=[myavg])
cmumosi.impute('glove_vectors')
tensors = cmumosi.get_tensors(seq_len=50, non_sequences=['Opinion Segment Labels'], folds=folds)
train_loader = create_loader(tensors[0])
```

--------------------------------

### Implement Training Loop and Dataset Verification

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

This snippet demonstrates the standard iteration pattern for multimodal data loaders in PyTorch and provides a mechanism to print the count of samples for each data split. It assumes the existence of a train_loader and data dictionaries containing glove_vectors.

```python
for batch_idx, (language, visual, labels) in enumerate(train_loader):
    # language.shape: (batch, seq_len, 300)
    # visual.shape: (batch, seq_len, 74)
    # labels.shape: (batch,)
    pass

print("Training pipeline ready!")
print(f"Train samples: {len(train_data['glove_vectors'])}")
print(f"Valid samples: {len(valid_data['glove_vectors'])}")
print(f"Test samples: {len(test_data['glove_vectors'])}")
```

--------------------------------

### Implement Tensor Fusion Network

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Initializes the Tensor Fusion module for multimodal analysis. It computes outer products between modalities to capture inter-modality dynamics.

```python
import torch
from torch.autograd import Variable
import numpy
from mmsdk.mmmodelsdk.fusion.tensor_fusion import TensorFusion

language_dim, visual_dim, acoustic_dim = 300, 35, 74
output_dim = 128
batch_size = 32
```

--------------------------------

### Initialize and Use TensorFusion Model

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Initializes the TensorFusion model with specified input dimensions and performs a fusion operation on sample multimodal inputs. It demonstrates how to create the model and pass concatenated modality inputs for fusion.

```python
from torch.autograd import Variable
import torch
from mmsdk.mmmodelsdk.fusion.tensor_fusion import TensorFusion

# Define dimensions
language_dim, visual_dim, acoustic_dim = 300, 35, 74
output_dim = 128
batch_size = 32

# Initialize Tensor Fusion model
fusion_model = TensorFusion(
    in_dimensions=[language_dim, visual_dim, acoustic_dim],
    out_dimension=output_dim
)

# Create sample inputs for each modality
language_input = Variable(torch.randn(batch_size, language_dim), requires_grad=True)
visual_input = Variable(torch.randn(batch_size, visual_dim), requires_grad=True)
acoustic_input = Variable(torch.randn(batch_size, acoustic_dim), requires_grad=True)

# Perform fusion
modalities = [language_input, visual_input, acoustic_input]
fused_output = fusion_model(modalities)

print(fused_output.shape)  # torch.Size([32, 128])
```

--------------------------------

### Word-Level Alignment with Collapsing

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/README.md

This snippet illustrates performing word-level alignment using 'glove_vectors' as the primary alignment sequence and then aligning to 'Opinion Segment Labels'. It also demonstrates collapsing other modalities using a custom average function.

```python
from mmsdk import mmdatasdk
cmumosi_highlevel=mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel,'cmumosi/')
cmumosi_highlevel.align('glove_vectors',collapse_functions=[myavg])
cmumosi_highlevel.add_computational_sequences(mmdatasdk.cmu_mosi.labels,'cmumosi/')
cmumosi_highlevel.align('Opinion Segment Labels')
```

--------------------------------

### Temporally Align Computational Sequences with mmdataset.align

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Explains how to use the `align` method to synchronize computational sequences based on time intervals. It supports custom functions for feature collapsing and can perform word-level alignment by aligning to specific reference sequences.

```python
from mmsdk import mmdatasdk
import numpy

# Define collapse function for averaging features across time intervals
def myavg(intervals, features):
    return numpy.average(features, axis=0)

# Download dataset
cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')

# Word-level alignment: align all modalities to glove_vectors with averaging
cmumosi.align('glove_vectors', collapse_functions=[myavg])

# After alignment, entries have segment indices like 'video_id[0]', 'video_id[1]', etc.
# Each segment corresponds to one word

# Add labels and align to opinion segments
cmumosi.add_computational_sequences(mmdatasdk.cmu_mosi.labels, 'cmumosi/')
cmumosi.align('Opinion Segment Labels')

# Access aligned data with segment index
segment_data = cmumosi['glove_vectors']['video_id[0]']
print(segment_data['features'].shape)  # Features for first opinion segment
print(segment_data['intervals'].shape)  # Time intervals for first segment
```

--------------------------------

### Convert Datasets to Tensors with mmdataset.get_tensors

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Converts aligned computational sequences into numpy arrays for machine learning. It supports sequence padding, fold-based partitioning, and handling non-sequential data labels.

```python
from mmsdk import mmdatasdk

cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')
cmumosi.add_computational_sequences(mmdatasdk.cmu_mosi.labels, 'cmumosi/')

def myavg(intervals, features):
    import numpy
    return numpy.average(features, axis=0)

cmumosi.align('glove_vectors', collapse_functions=[myavg])
cmumosi.align('Opinion Segment Labels')
cmumosi.hard_unify()

train_fold = mmdatasdk.cmu_mosi.standard_folds.standard_train_fold
valid_fold = mmdatasdk.cmu_mosi.standard_folds.standard_valid_fold
test_fold = mmdatasdk.cmu_mosi.standard_folds.standard_test_fold

tensors = cmumosi.get_tensors(
    seq_len=25,
    non_sequences=['Opinion Segment Labels'],
    direction=False,
    folds=[train_fold, valid_fold, test_fold]
)

train_tensors, valid_tensors, test_tensors = tensors[0], tensors[1], tensors[2]
```

--------------------------------

### Add and Align Computational Sequences

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/README.md

This code shows how to add additional computational sequences (like labels) to an existing mmdatasdk object and then align all sequences to a reference sequence, such as 'Opinion Segment Labels'.

```python
cmumosi_highlevel.add_computational_sequences(mmdatasdk.cmu_mosi.labels,'cmumosi/')
cmumosi_highlevel.align('Opinion Segment Labels')
```

--------------------------------

### Custom Average Function for Collapsing

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/README.md

Defines a custom Python function 'myavg' that takes intervals and features as input and returns the average of the features, suitable for use with the collapse_functions parameter in the align method.

```python
import numpy
def myavg(intervals,features):
        return numpy.average(features,axis=0)
```

--------------------------------

### Unify Datasets Across Sequences with mmdataset.unify and hard_unify

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Details the `unify` and `hard_unify` methods for ensuring data consistency across computational sequences. `unify` removes entire entries (e.g., videos) not present in all sequences, while `hard_unify` operates on aligned data to remove segment-level mismatches.

```python
from mmsdk import mmdatasdk

cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')

# Remove videos that don't exist in all computational sequences
cmumosi.unify(active=True)  # active=True removes violators, False just reports them

# After alignment, use hard_unify for segment-level consistency
cmumosi.add_computational_sequences(mmdatasdk.cmu_mosi.labels, 'cmumosi/')
cmumosi.align('Opinion Segment Labels')
cmumosi.hard_unify(active=True)  # Ensures all segment IDs like 'video[0]' exist in all sequences
```

--------------------------------

### Impute Missing Data with mmdataset.impute

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Shows how to use the `impute` method to fill in missing segments within computational sequences. It can use default zero imputation or a custom function (like `numpy.ones`) for filling missing values, based on a reference sequence's intervals.

```python
from mmsdk import mmdatasdk
import numpy

cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')

# Align to word vectors
def myavg(intervals, features):
    return numpy.average(features, axis=0)

cmumosi.align('glove_vectors', collapse_functions=[myavg])

# Impute missing entries using zeros (default)
cmumosi.impute('glove_vectors')

# Impute with custom function (e.g., ones instead of zeros)
cmumosi.impute('glove_vectors', imputation_fn=numpy.ones)
```

--------------------------------

### Generate BibTeX Citations with mmdataset.bib_citations

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Outputs academic citations for the datasets and sequences used in a project. This ensures proper attribution for the multimodal data sources.

```python
from mmsdk import mmdatasdk

cmumosi = mmdatasdk.mmdataset(mmdatasdk.cmu_mosi.highlevel, 'cmumosi/')
cmumosi.bib_citations()

with open('citations.bib', 'w') as f:
    cmumosi.bib_citations(f)
```

--------------------------------

### Revert Dataset Alignment with mmdataset.revert

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Undoes alignment operations by stacking segmented data back into original video-level entries. This is useful for restoring the original data structure after alignment.

```python
from mmsdk import mmdatasdk

aligned_dataset = mmdatasdk.mmdataset('./deployed/')
aligned_dataset.revert(replace=True)
reverted_dataset = aligned_dataset.revert(replace=False)
```

--------------------------------

### Manage Computational Sequences with mmdatasdk.computational_sequence

Source: https://context7.com/cmu-multicomp-lab/cmu-multimodalsdk/llms.txt

Defines a hierarchical structure for modality data, containing features and intervals. It supports manual data construction, disk deployment with compression, and loading.

```python
from mmsdk import mmdatasdk
import numpy

compseq = mmdatasdk.computational_sequence("my_modality")
data = {}
video_ids = ["video1", "video2", "video3"]

for vid in video_ids:
    num_segments = numpy.random.randint(10, 50)
    data[vid] = {
        "features": numpy.random.randn(num_segments, 128).astype(numpy.float32),
        "intervals": numpy.column_stack([numpy.arange(num_segments), numpy.arange(1, num_segments + 1)]).astype(numpy.float32)
    }

compseq.setData(data, "my_modality")
compseq.deploy("my_modality.csd", compression="gzip", compression_opts=9)
loaded_compseq = mmdatasdk.computational_sequence("my_modality.csd")
```

--------------------------------

### Binarize CMU-MOSEI Sentiment Scores (Python)

Source: https://github.com/cmu-multicomp-lab/cmu-multimodalsdk/blob/main/mmsdk/mmdatasdk/dataset/standard_datasets/CMU_MOSEI/README.md

This function rounds sentiment scores to the nearest integer within the CMU-MOSEI dataset's defined ranges. It handles the mapping from continuous sentiment values to discrete integer labels.

```python
def cmumosei_round(a):
        if a < -2:
                res = -3
        if -2 <= a and a < -1:
                res = -2
        if -1 <= a and a < 0:
                res = -1
        if 0 <= a and a <= 0:
                res = 0
        if 0 < a and a <= 1:
                res = 1
        if 1 < a and a <= 2:
                res = 2
        if a > 2:
                res = 3
        return res
```

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