### Setup FLAVA Repository and Install Dependencies

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

This snippet outlines the steps to clone the FLAVA repository, install the multimodal library, and set up project-specific requirements using pip. It assumes a conda environment is already set up.

```bash
git clone https://github.com/facebookresearch/multimodal.git
cd multimodal
pip install -e .
cd examples
pip install -r flava/requirements.txt
```

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

### Install PyTorch and Multimodal Package

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/omnivore/README.md

Installs necessary PyTorch versions and the multimodal package from source. Also installs specific versions of scipy and av libraries required for certain functionalities.

```bash
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch-nightly
cd <torchmultimodal root folder>
pip install -e .
pip install scipy==1.8.1 av==9.2.0
```

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

### Install Pre-commit Hooks (Shell)

Source: https://github.com/facebookresearch/multimodal/blob/main/CONTRIBUTING.md

Installs pre-commit hooks to maintain code style consistency and prevent common errors before each commit. This is a one-time setup for the development environment.

```shell
pre-commit install
```

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

### Run Omnivore Training Command

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/omnivore/README.md

Example command to initiate the training process for the Omnivore model across image, video, and RGB-D modalities. It specifies distributed training parameters, data paths, learning rates, epochs, and various augmentation techniques.

```bash
torchrun --nproc_per_node=8 --nnodes=8  -m omnivore.train \
    --batch-size=128 --workers=6 --extra-video-dataloader-workers=6 \
    --cache-video-dataset --eval-every-num-epoch=10 --model="omnivore_swin_t" \
    --lr=0.002 --lr-warmup-epochs=25 --lr-warmup-method=linear \
    --lr-scheduler="cosineannealinglr" --lr-min=0.0000001 \
    --epochs=500 --weight-decay=0.05 \
    --label-smoothing=0.1 --mixup-alpha=0.8 --cutmix-alpha=1.0 \
    --train-crop-size=224 --val-resize-size=224 \
    --opt="adamw" --random-erase=0.25 \
    --color-jitter-factor 0.4 0.4 0.4 0.4 \
    --model-ema --model-ema-steps=1 --model-ema-decay=0.99998 \
    --video-grad-accum-iter=32 \
    --modalities image video rgbd \
    --val-data-sampling-factor 1 1 1 \
    --train-data-sampling-factor 1 1 10 \
    --imagenet-data-path="${IMAGENET_PATH}" \
    --kinetics-data-path="${KINETICS_PATH}" \
    --sunrgbd-data-path="${SUNRGBD_PATH}" \

```

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

### Install MUGEN Dependencies

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/README.md

Installs the necessary PyTorch and project-specific dependencies required to run MUGEN multimodal models. This assumes a Conda environment is already active.

```bash
conda install -c pytorch torchtext
pip install -r examples/mugen/requirements.txt
```

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

### Setup MDETR Repository

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/LoadAndComparePretrainedWeights.ipynb

Clones the official MDETR GitHub repository into a specified directory. This is necessary to access the MDETR classes and functions required for model loading.

```bash
# Install MDETR repo 
!git clone https://github.com/ashkamath/mdetr.git $repo_dir
```

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

### Run FLAVA Training on a Single Node with Torchrun

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/native/README.md

Provides the command to initiate FLAVA model training on a single compute node using torchrun. This example specifies the number of GPUs to use per node and points to the main training script and configuration file.

```bash
NUM_GPUS=8; torchrun --nproc_per_node=$NUM_GPUS -m flava.native.train config=flava/native/configs/pretrain_debug.yaml
```

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

### Run Linters and Formatters (Python/Shell)

Source: https://github.com/facebookresearch/multimodal/blob/main/CONTRIBUTING.md

Installs development requirements and runs flake8 for linting and ufmt for code formatting on specified directories. It also shows how to run these tools on modified files only.

```python
pip install -r dev-requirements.txt
flake8 (examples|test|torchmultimodal)
ufmt format (examples|test|torchmultimodal)
```

```shell
flake8 `git diff main --name-only`
ufmt format `git diff main --name-only`
```

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

### Download and Setup Flickr30k Dataset for MDETR

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/README.md

This snippet outlines the steps to download and prepare the Flickr30k dataset, including images, annotations, and split mappings, required for MDETR phrase grounding tasks. It also shows how to create symbolic links for dataset splits and configure paths in a JSON file.

```bash
tar -xvzf flickr30k-images.tar.gz

# Note that MDETR will expect separate directories for each dataset split, but you can just create symlinks.
ln -s flickr30k-images flickr30k-images/train
ln -s flickr30k-images flickr30k-images/val
ln -s flickr30k-images flickr30k-images/test

wget https://github.com/BryanPlummer/flickr30k_entities/blob/master/annotations.zip
unzip annotations.zip
wget https://github.com/BryanPlummer/flickr30k_entities/blob/master/train.txt
wget https://github.com/BryanPlummer/flickr30k_entities/blob/master/val.txt
wget https://github.com/BryanPlummer/flickr30k_entities/blob/master/test.txt

wget https://zenodo.org/record/4729015/files/mdetr_annotations.tar.gz?download=1
tar -xvzf 'mdetr_annotations.tar.gz?download=1'
```

```json
{
    "combine_datasets": ["flickr"],
    "combine_datasets_val": ["flickr"],
    "GT_type" : "separate",
    "flickr_img_path" : "/data/flickr30k-images",
   "flickr_dataset_path" : "/data/flickr30k/",
   "flickr_ann_path" : "/data/OpenSource"
  }
```

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

### Install Development Dependencies (Python)

Source: https://github.com/facebookresearch/multimodal/blob/main/CONTRIBUTING.md

Installs the necessary development dependencies for the TorchMultimodal project using pip. This command ensures all required packages for development are available.

```python
pip install -e ".[dev]"
```

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

### GET /models/mdetr/phrase-grounding

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Initializes the MDETR model for phrase grounding tasks and loads pretrained weights from a remote checkpoint.

```APIDOC
## GET /models/mdetr/phrase-grounding

### Description
Initializes the MDETR model specifically configured for phrase grounding and loads the pretrained ResNet101 weights.

### Method
GET

### Endpoint
/models/mdetr/phrase-grounding

### Parameters
#### Query Parameters
- **checkpoint_url** (string) - Required - The URL to the pretrained model weights (e.g., https://pytorch.s3.amazonaws.com/models/multimodal/mdetr/pretrained_resnet101_checkpoint.pth)

### Request Example
{
  "checkpoint_url": "https://pytorch.s3.amazonaws.com/models/multimodal/mdetr/pretrained_resnet101_checkpoint.pth"
}

### Response
#### Success Response (200)
- **model** (object) - The initialized MDETR model instance loaded with weights.

#### Response Example
{
  "status": "success",
  "message": "Model loaded successfully"
}
```

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

### Override FLAVA Pretraining Configuration

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

Example of how to override specific parameters in the FLAVA pretraining configuration using dot notation. This demonstrates changing the maximum training steps.

```python
python -m flava.train config=flava/configs/pretraining/debug.yaml training.lightning.max_steps=1000
```

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

### Masked Autoencoder (MAE) Initialization and Usage (Python)

Source: https://context7.com/facebookresearch/multimodal/llms.txt

Illustrates the use of Masked Autoencoders (MAE) for vision transformers, including both image and audio variants. The code shows how to initialize an image MAE model, calculate reconstruction loss, and use a pretrained encoder for downstream tasks. It also includes an example for the audio MAE.

```python
import torch
from torchmultimodal.models.masked_auto_encoder.model import (
    vit_l_16_image_mae,
    vit_b_16_image_mae_encoder,
    vit_b_16_audio_mae
)
from torchmultimodal.modules.losses.reconstruction_loss import ReconstructionLoss

# Create Image MAE model
mae_model = vit_l_16_image_mae()
mae_loss = ReconstructionLoss(normalize_target=True)

# Training mode - masks 75% of patches
mae_model.train()
images = torch.randn(8, 3, 224, 224)

output = mae_model(images)
# output.encoder_output: encoder representations
# output.decoder_pred: reconstructed patches
# output.label_patches: original patchified image
# output.mask: which patches were masked (1=masked, 0=visible)

loss = mae_loss(output.decoder_pred, output.label_patches, output.mask)
print(f"Reconstruction loss: {loss.item()}")

# Use pretrained encoder for downstream tasks
encoder = vit_b_16_image_mae_encoder(pretrained=True)
encoder.eval()

with torch.no_grad():
    features = encoder(images)
    # features.last_hidden_state: (batch, num_patches+1, hidden_dim)
    cls_token = features.last_hidden_state[:, 0]  # CLS token for classification

# Audio MAE for spectrograms
audio_mae = vit_b_16_audio_mae()
spectrograms = torch.randn(4, 1, 1024, 128)  # (batch, channels, time, freq)
audio_output = audio_mae(spectrograms)
```

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

### Initialize ALBEF Model and Environment

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/albef/retrieval_with_albef.ipynb

Sets up the necessary environment, downloads retrieval data, and initializes the ALBEF model using a configuration file and pre-trained checkpoints.

```python
import json
import torch
from PIL import Image
from ruamel import yaml
from examples.albef.model import albef_model_for_retrieval
from examples.albef.data.transforms import ALBEFTextTransform, testing_image_transform

data_dir = "./retrieval_data/"
!wget https://download.pytorch.org/torchmultimodal/examples/albef/assets/retrieval_data.zip
!unzip retrieval_data.zip

config = yaml.load(open("./configs/retrieval.yaml", "r"), Loader=yaml.Loader)
model = albef_model_for_retrieval(config)

checkpoint_url = "https://download.pytorch.org/models/multimodal/albef/finetuned_retrieval_checkpoint.pt"
checkpoint = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
model.load_state_dict(checkpoint)
```

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

### Initialize and Configure MDETR Model

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Demonstrates how to swap components like the text encoder and class embedding, and how to instantiate the MDETR model with custom parameters.

```python
from transformers import DistilBertModel
distilbert_text_encoder = DistilBertModel.from_pretrained("distilbert-base-uncased")
mlp_class_embed = MLP(transformer_dim, num_classes + 1, [transformer_dim] * 2, activation=nn.GELU, dropout=0.1)
modified_mdetr = MDETR(
    image_backbone = image_backbone,
    text_encoder = distilbert_text_encoder, 
    transformer = transformer,
    pos_embed = pos_embed, 
    text_projection = text_projection,
    image_projection = image_projection, 
    query_embed = query_embed,
    bbox_embed = bbox_embed,
    class_embed = class_embed,
)
```

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

### Launch FLAVA Finetuning

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

Command to launch the finetuning process for the FLAVA model using a specified configuration file and enabling pretrained weights.

```python
python -m flava.finetune config=flava/configs/finetuning/qnli.yaml model.pretrained=True
```

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

### Initialize ALBEF VQA Environment

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/albef/vqa_with_albef.ipynb

Imports necessary libraries and downloads the required dataset for the VQA task.

```python
import json
import torch
from PIL import Image
from ruamel import yaml
from examples.albef.model import albef_model_for_vqa
from examples.albef.data.transforms import ALBEFTextTransform, testing_image_transform

data_dir = "./vqa_data/"
!wget https://download.pytorch.org/torchmultimodal/examples/albef/assets/vqa_data.zip
!unzip vqa_data.zip
```

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

### Custom Question Inference Example

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

This Python example shows how to perform VQA inference with a custom-generated question. It calls `plot_qa_inference_results` with a specific question about an animal in a backpack and provides the answer ID for 'dog'. This demonstrates the flexibility of the function in handling user-defined queries.

```python
plot_qa_inference_results(img, "What animal is inside the backpack?", torch.tensor(dataset.answer2id["dog"])))
```

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

### Generate video from text using MUGEN

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/README.md

This snippet demonstrates how to initialize a pre-trained MUGEN model and use the GenerationUtil class to sample video sequences from text prompts. It requires a configured model and device context to perform auto-regressive generation.

```python
from torchmultimodal.utils.generate import GenerationUtil
from examples.mugen.generation.text_video_gpt import text_video_gpt

model = text_video_gpt(video_seq_len=32, pretrained_text_video_gpt_model_key="mugen_L32")
generator = GenerationUtil(model)

output = generator.sample(
    ['Mugen moves left to right on a cliff and picks up a gem.'],
    max_seq_len=512,
    use_cache=True,
    causal=True,
    device=<current_device>,
)
samples = output.decoded
```

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

### VQA Inference Example with Dataset

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

This Python code snippet demonstrates how to use the `plot_qa_inference_results` function with a dataset. It iterates through the dataset, extracts an image, question, and answer, and then calls the plotting function to display the inference results. This is a basic usage example for performing VQA on sample data.

```python
i = 0
img = dataset[i][0]
question = dataset[i][1]["caption"]
answer = dataset[i][1]["answer"]
plot_qa_inference_results(img, question, answer)
```

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

### Instantiate and Load VideoCLIP Model

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/retrieval/tutorial.ipynb

Instantiates the VideoCLIP model and loads pre-trained weights specifically finetuned on the MUGEN dataset from a provided URL. The `text_pretrained` and `video_pretrained` flags are set to False as they are intended for generic pretraining, not finetuning.

```python
# Instantiate the VideoCLIP model and load the weights from URL

model = videoclip(text_pretrained=False, video_pretrained=False)
load_module_from_url(model, "https://pytorch.s3.amazonaws.com/models/multimodal/mugen/videoclip_mugen.pt")
```

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

### Vision Transformer Implementation and Configuration

Source: https://context7.com/facebookresearch/multimodal/llms.txt

Demonstrates how to instantiate pre-built Vision Transformer models like ViT-B/16 and configure custom ViT architectures. It also shows how to apply global average pooling and handle patch masking for MAE-style training.

```python
import torch
from torchmultimodal.modules.encoders.vision_transformer import (
	vision_transformer,
	vit_b_16,
	GlobalAveragePooler
)

pooler = GlobalAveragePooler(input_dim=768, output_dim=1000)
vit = vit_b_16(pooler=pooler)
images = torch.randn(4, 3, 224, 224)
output = vit(images)

custom_vit = vision_transformer(
	patch_size=16, hidden_dim=512, n_layer=8, n_head=8, image_size=256
)
mask = torch.randint(0, 2, (4, 256))
output_masked = custom_vit(images, image_patches_mask=mask)
```

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

### Setup Diffusion Hybrid Loss

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Initializes the DiffusionHybridLoss function based on a predefined schedule to measure model performance during training.

```python
h_loss = DiffusionHybridLoss(schedule)
```

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

### Load MUGEN Checkpoint - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Loads a pre-trained Video VQVAE checkpoint from MUGEN into a PyTorch model. It sets up the system path and uses torch.load to load the checkpoint, mapping it to the CPU.

```python
import sys
import os
sys.path.append(home_dir)

import torch
from torch import nn
import mugen

ckpt = torch.load(
    os.path.join(checkpoint_dir, 'generation/video_vqvae/L32/epoch=54-step=599999.ckpt'), 
    map_location=torch.device('cpu')
)
```

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

### Execute MDETR Validation Test

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/LoadAndComparePretrainedWeights.ipynb

A script to instantiate the test class and run the full validation pipeline including setup, inference, and result comparison.

```python
tester = TestMDETR()
tester.setUp()
tester.run_mdetr()
tester.run_mm_mdetr()
tester.compare_results()
```

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

### Create TorchMultimodal Video VQVAE - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Initializes an instance of TorchMultimodal's Video VQVAE model. The 'pretrained_model_key' is set to None, indicating a fresh model instance.

```python
from examples.mugen.generation.video_vqvae import video_vqvae_mugen

vv_torchmm = video_vqvae_mugen(pretrained_model_key=None)
```

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

### Apply Transforms and Get Model Output

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/retrieval/tutorial.ipynb

Applies text and video transforms to the data batch and passes it through the VideoCLIP model in evaluation mode. This step generates embeddings for both text and video modalities.

```python
text_transform = BertTextTransform()
video_transform = VideoTransform()

model.eval()
with torch.no_grad():
    output = model(
        text_transform(sample_batch['text']),
        video_transform(sample_batch['video'])
    )
```

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

### CoCa Model Initialization and Forward Pass (Python)

Source: https://context7.com/facebookresearch/multimodal/llms.txt

Shows how to set up the CoCa (Contrastive Captioner) model for pre-training, combining contrastive learning and generative captioning. It includes creating the base model, wrapping it for pre-training, and performing a forward pass to obtain contrastive and captioning losses. Inference usage is also demonstrated.

```python
import torch
from torchmultimodal.models.coca.coca_model import coca_vit_b_32, CoCaForPretraining

# Create CoCa model
model = coca_vit_b_32()

# For pretraining with contrastive and captioning losses
pretrain_model = CoCaForPretraining(model, pad_idx=0)

# Prepare inputs
images = torch.randn(4, 3, 224, 224)
texts = torch.randint(0, 49408, (4, 77))  # CLIP vocab size
text_padding_mask = torch.ones(4, 77, dtype=torch.bool)

# Forward pass
losses = pretrain_model(images, texts, text_padding_mask)

print(f"Contrastive loss: {losses['contrastive'].item()}")
print(f"Captioning loss: {losses['captioning'].item()}")

# For inference, use the base model
model.eval()
with torch.no_grad():
    output = model(images, texts, text_padding_mask)
    # output.image_pooled_output: contrastive image embeddings
    # output.text_pooled_output: contrastive text embeddings
    # output.multimodal_embeddings: captioning logits
```

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

### Train Masked Autoencoder (MAE)

Source: https://github.com/facebookresearch/multimodal/blob/main/README.md

Shows the basic training loop for a Masked Autoencoder model. It covers model instantiation, loss function setup, optimizer configuration, and the training iteration process.

```python
import torch
from torch.utils.data import DataLoader
from torchmultimodal.models.masked_auto_encoder.model import vit_l_16_image_mae
from torchmultimodal.models.masked_auto_encoder.utils import CosineWithWarmupAndLRScaling
from torchmultimodal.modules.losses.reconstruction_loss import ReconstructionLoss
from torchmultimodal.transforms.mae_transform import ImagePretrainTransform

mae_transform = ImagePretrainTransform()
dataset = MyDatasetClass(transforms=mae_transform)
dataloader = DataLoader(dataset, batch_size=8)

mae_model = vit_l_16_image_mae()
mae_loss = ReconstructionLoss()

optimizer = torch.optim.AdamW(mae_model.parameters())
lr_scheduler = CosineWithWarmupAndLRScaling(optimizer, max_iters=1000, warmup_iters=100)

for batch in dataloader:
    model_out = mae_model(batch["images"])
    loss = mae_loss(model_out.decoder_pred, model_out.label_patches, model_out.mask)
    loss.backward()
    optimizer.step()
    lr_scheduler.step()
```

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

### BLIP-2 Model Initialization and Forward Pass (Python)

Source: https://context7.com/facebookresearch/multimodal/llms.txt

Demonstrates how to initialize the BLIP-2 model, which uses a Querying Transformer (Q-Former) to connect frozen image encoders and language models. It includes setting up the vision encoder, Q-former, and the main BLIP-2 model, followed by a forward pass with dummy image and text inputs.

```python
import torch
from torchmultimodal.models.blip2.blip2 import BLIP2
from torchmultimodal.models.blip2.qformer_model import QformerForCLM
from torchmultimodal.modules.encoders.vision_transformer import vit_b_16

# Build BLIP-2 components
vision_encoder = vit_b_16()
qformer = QformerForCLM(
    dim_q=768,
    dim_kv=768,
    dim_feedforward=3072,
    num_heads=12,
    num_layers=12,
    vocab_size=30522,
    cross_attention_freq=2,
)

# Initialize BLIP-2 model
model = BLIP2(
    qformer=qformer,
    vision_encoder=vision_encoder,
    dim_q=768,
    image_encoder_embedding_dim=768,
    freeze_vision_encoder=True,
    embedding_dim=256,
    num_query_token=32
)

# Forward pass with image and text
images = torch.randn(2, 3, 224, 224)
input_ids = torch.randint(0, 30522, (2, 32))
attention_mask = torch.ones(2, 32)

output = model(
    image=images,
    input_ids=input_ids,
    attention_mask=attention_mask
)

print(f"Image embeddings: {output.image_embeddings.shape}")  # (2, 197, 768)
print(f"Image features: {output.image_features.shape}")  # (2, 32, 256)
print(f"Text features: {output.text_features.shape}")  # (2, 256)
```

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

### Prepare Dataset and Tokenization

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Configures the tokenizer and image transformations, then initializes the ModulatedDetection dataset for phrase grounding tasks.

```python
from examples.mdetr.data.dataset import ModulatedDetection
import torchvision.transforms as T
from transformers import RobertaTokenizerFast

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
img_transform = T.Compose([
    T.Resize(800),
    T.ToTensor(),
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dataset = ModulatedDetection(
        img_dir,
        os.path.join(asset_dir, "flickr30k_sample_annotations.json"),
        transforms=None,
        return_tokens=True,
        tokenizer=tokenizer,
        is_train=False,
    )
```

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

### Get Top 5 Predictions - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/omnivore/omnivore_inference_demo.ipynb

This snippet takes a model's output and retrieves the top 5 predictions. It uses the model to predict based on the prepared input and then extracts the values and indices of the top predictions. It also maps these indices to human-readable labels using a predefined class list.

```python
# Assuming model, sunrgbd_classes, and input_depth are defined
preds = model(input_depth, "rgbd")
top5_values, top5_indices = preds[0].topk(5)
top5_labels = [sunrgbd_classes[index] for index in top5_indices.tolist()]
# The correct label is kitchen
```

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

### Launch FLAVA Pretraining with Pretrained Model

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

Command to launch FLAVA pretraining, enabling the use of a pretrained model by setting `model.pretrained=True` in the configuration.

```python
python -m flava.train config=flava/configs/pretraining/debug.yaml model.pretrained=True
```

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

### Download MDETR Checkpoint

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/LoadAndComparePretrainedWeights.ipynb

Downloads the pretrained ResNet101 checkpoint file from a given URL and saves it to a specified directory. This is a prerequisite for loading the model weights.

```python
download_dir = "/data/home/ebs/data/mdetr"
repo_dir = "/data/home/ebs"
!wget https://zenodo.org/record/4721981/files/pretrained_resnet101_checkpoint.pth?download=1 -P $download_dir
```

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

### Configure Optimizer and Mixed Precision Scaler

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Moves models to the target device, initializes the AdamW optimizer, and sets up a GradScaler for mixed-precision training.

```python
encoder.to(device)
decoder.to(device)
optimizer = torch.optim.AdamW([{"params": encoder.parameters()}, {"params": decoder.parameters()}], lr=0.0001)
scaler = torch.cuda.amp.GradScaler()
```

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

### Configure MDETR for VQA Tasks

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Demonstrates how to initialize an MDETR model for VQA using the TorchMultimodal builder, including loading pretrained weights and customizing classification heads for specific datasets.

```python
from torchmultimodal.models.mdetr.model import mdetr_for_vqa
import torch
import torch.nn as nn

mdetr_vqa = mdetr_for_vqa()
other_vqa_heads = nn.ModuleDict({"head1": nn.Linear(256, 3), "head2": nn.Linear(256,12)})
mdetr_other_vqa_dataset = mdetr_for_vqa(vqa_heads=other_vqa_heads)

checkpoint = torch.hub.load_state_dict_from_url("https://pytorch.s3.amazonaws.com/models/multimodal/mdetr/gqa_resnet101_checkpoint.pth", map_location="cpu", check_hash=True)
mdetr_vqa.load_state_dict(checkpoint["model_ema"], strict=False)
mdetr_vqa.eval()
```

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

### Set Local Directories - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Defines local directory paths for checkpoints, the MUGEN repository, and the home directory. Users should replace these with their specific paths.

```python
checkpoint_dir = '/Users/rafiayub/checkpoints/'
repo_dir = '/Users/rafiayub/mugen/'
home_dir = '/Users/rafiayub/'
```

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

### Load MUGEN Dataset and DataModule

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/retrieval/tutorial.ipynb

Initializes the MUGENDataset and MUGENDataModule for loading and batching data from the MUGEN dataset. It configures dataset arguments such as text description retrieval and game frame inclusion, and sets the batch size for data loading.

```python
mugen_args = MUGENDatasetArgs(
    get_text_desc=True,
    get_game_frame=True,
    get_audio=False, 
    get_seg_map=False, 
    use_manual_annotation=True,
    use_auto_annotation=False,
)

datamodule = MUGENDataModule(
    mugen_args, 
    batch_size=8,
    shuffle=False,
)
```

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

### Launch FLAVA Debug Pretraining (GPU)

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

Command to launch a debug pretraining job for the FLAVA model on a GPU. This uses a specified configuration file and defaults to GPU acceleration.

```python
python -m flava.train config=flava/configs/pretraining/debug.yaml
```

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

### Initialize DDPModule Sampler

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Initializes the DDPModule sampler, which orchestrates the denoising process. It uses the defined decoder, schedule, predictor, and a set of evaluation steps to generate a denoised image.

```python
eval_steps = torch.linspace(0, 999, 250, dtype=torch.int)
decoder = DDPModule(decoder, schedule, predictor, eval_steps)

```

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

### Override Training Configuration via Command Line

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/native/README.md

Demonstrates how to override default configuration settings for FLAVA model training directly from the command line. This allows for dynamic adjustment of parameters like batch size, activation checkpointing, and distributed training strategy.

```bash
python -m flava.native.train config=flava/native/configs/pretrain_debug.yaml training.batch_size=8 training.enable_amp=True training.activation_checkpointing=True training.strategy=fsdp
```

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

### Retrieve and Display Video from Text Query

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/retrieval/tutorial.ipynb

This sequence demonstrates how to select a text query from a batch, identify the most similar video using a precomputed similarity matrix, and display the result by saving it as a temporary file. It assumes the existence of helper functions like retrieve_video_from_text and save_and_display_video.

```python
text_idx = 6
text = sample_batch['text'][text_idx]
video_idx = retrieve_video_from_text(similarity_matrix, text_idx)
video = sample_batch['video'][video_idx]
video_filename = f"video_{video_idx}.mp4"
save_and_display_video(video_filename, video)
!rm "{video_filename}"
```

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

### Run FLAVA Training on Multiple Nodes using SLURM

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/native/README.md

Illustrates how to configure and run FLAVA model training across multiple compute nodes using SLURM job scheduler. It includes a sample SLURM script (`run.slurm`) that sets up distributed training parameters and the command to submit the job.

```bash
RDZV_ENDPOINT=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
srun torchrun --nnodes=$SLURM_NNODES --nproc_per_node=$SLURM_GPUS_PER_TASK --rdzv_id=$SLURM_JOB_ID --rdzv_backend=c10d --rdzv_endpoint=$RDZV_ENDPOINT --max_restarts 0  -m flava.native.train config=flava/native/configs/pretrain_debug.yaml
$@
```

```bash
sbatch --partition=[PARTITION] --nodes=[NUM_NODES] --gpus-per-task=[NUM_GPUS_PER_NODE] run.slurm
```

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

### Download VQA Sample Assets

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Downloads sample images and JSON annotation files from a remote S3 bucket into a local directory structure. It uses the wget utility to ensure files are retrieved and organized for dataset processing.

```python
import os
asset_dir = "assets/vqa"
img_dir = os.path.join(asset_dir, "images")
os.makedirs(img_dir, exist_ok=True)
!wget -nc "https://pytorch.s3.amazonaws.com/torchmultimodal/examples/mdetr/assets/gqa/images/n216553.jpg" -P $img_dir
!wget -nc "https://pytorch.s3.amazonaws.com/torchmultimodal/examples/mdetr/assets/gqa/images/n411121.jpg" -P $img_dir
!wget -nc "https://pytorch.s3.amazonaws.com/torchmultimodal/examples/mdetr/assets/gqa/gqa_sample_annotations.json" -P $asset_dir
!wget -nc "https://pytorch.s3.amazonaws.com/torchmultimodal/examples/mdetr/assets/gqa/gqa_answer2id.json" -P $asset_dir
!wget -nc "https://pytorch.s3.amazonaws.com/torchmultimodal/examples/mdetr/assets/gqa/gqa_answer2id_by_type.json" -P $asset_dir
```

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

### Initialize GQA Dataset

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Constructs an instance of the GQADataset class for multimodal inference. It requires paths to image directories and annotation files, and supports optional tokenization and custom transforms.

```python
from examples.mdetr.data.dataset import GQADataset
from pathlib import Path

dataset = GQADataset(
            img_dir,
            os.path.join(asset_dir, "gqa_sample_annotations.json"),
            transforms=None,
            return_tokens=True,
            tokenizer=tokenizer,
            ann_folder=Path(asset_dir),
        )
```

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

### Build MDETR Model with Builder Function

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

This code shows the simplest way to instantiate an MDETR model using the `mdetr_resnet101` builder function. It allows for easy creation of a prebuilt model with a ResNet101 backbone and provides options to modify default parameters like the number of object queries and transformer feed-forward network dimensions.

```python
# Simplest entry point: model builder function.
# Provides prebuilt model from the paper with ResNet101 backbone.
from torchmultimodal.models.mdetr.model import mdetr_resnet101
mdetr_rn101 = mdetr_resnet101()

# The builder function contains sensible default values but can also be easily modified.
# E.g. we can modify the number of object queries and change the transformer FFN dimension
modified_mdetr_rn101 = mdetr_resnet101(num_queries=100, transformer_dim_feedforward=1024)
```

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

### Launch FLAVA Debug Pretraining (CPU)

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/flava/README.md

Command to launch a debug pretraining job for the FLAVA model on a CPU. This explicitly sets the accelerator to CPU and disables GPU usage.

```python
python -m flava.train config=flava/configs/pretraining/debug.yaml training.lightning.accelerator=cpu training.lightning.gpus=0 training.lightning.strategy=null
```

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

### Initialize and Use CLIP Model for Zero-Shot Classification

Source: https://context7.com/facebookresearch/multimodal/llms.txt

This snippet demonstrates how to load a pretrained CLIP model, apply necessary image and text transforms, and compute similarity scores between image and text embeddings to perform zero-shot classification.

```python
import torch
from PIL import Image
from torchmultimodal.models.clip.model import clip_vit_b16, clip_vit_l14
from torchmultimodal.transforms.clip_transform import CLIPTransform

# Initialize CLIP model with pretrained weights
model = clip_vit_b16(pretrained=True)
model.eval()

# Create transforms for image and text
transform = CLIPTransform(image_size=224, is_train=False)

# Load and transform an image
image = Image.open("cat.jpg")
text_labels = ["a photo of a cat", "a photo of a dog", "a photo of a bird"]

# Apply transforms
image_tensor, text_tensor = transform(image, text_labels)

# Get embeddings
with torch.no_grad():
    output = model(image_tensor.unsqueeze(0), text_tensor)

    # Calculate similarity scores
    similarity = output.embeddings_a @ output.embeddings_b.T
    probs = torch.softmax(similarity * 100, dim=-1)

print(f"Predicted label: {text_labels[probs.argmax()]}")
```

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

### Load ALBEF Model and Checkpoint

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/albef/vqa_with_albef.ipynb

Loads the model configuration from a YAML file and initializes the model with a pre-trained checkpoint.

```python
config = yaml.load(open("./configs/vqa.yaml", "r"), Loader=yaml.Loader)
model = albef_model_for_vqa(config)

checkpoint_url = "https://download.pytorch.org/models/multimodal/albef/finetuned_vqa_checkpoint.pt"
checkpoint = torch.hub.load_state_dict_from_url(checkpoint_url, map_location='cpu')
model.load_state_dict(checkpoint)
```

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

### Load Pretrained MDETR for Phrase Grounding

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

Shows how to load a task-specific MDETR model for phrase grounding and initialize it with pretrained weights from a URL.

```python
import torch
from torchmultimodal.models.mdetr.model import mdetr_for_phrase_grounding

mdetr_pg = mdetr_for_phrase_grounding()
checkpoint = torch.hub.load_state_dict_from_url("https://pytorch.s3.amazonaws.com/models/multimodal/mdetr/pretrained_resnet101_checkpoint.pth")
mdetr_pg.load_state_dict(checkpoint["model_ema"])
mdetr_pg.eval()
```

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

### ALBEF Model Initialization (Python)

Source: https://context7.com/facebookresearch/multimodal/llms.txt

Shows the initialization of the ALBEF (Align Before Fuse) model, which aligns image and text representations before fusing them. The code outlines the construction of the vision encoder, text encoder, multimodal encoder, and the final ALBEF model, preparing for a forward pass with sample inputs.

```python
import torch
from torchmultimodal.models.albef.model import ALBEFModel, ALBEFModelWithSimilarity
from torchmultimodal.models.albef.image_encoder import albef_image_encoder
from torchmultimodal.models.albef.multimodal_encoder import albef_multimodal_encoder
from torchmultimodal.modules.encoders.bert_text_encoder import bert_text_encoder

# Build ALBEF components
vision_encoder = albef_image_encoder()
text_encoder = bert_text_encoder(hidden_size=768, num_hidden_layers=6)
multimodal_encoder = albef_multimodal_encoder()

# Create ALBEF model
model = ALBEFModel(
    vision_encoder=vision_encoder,
    text_encoder=text_encoder,
    multimodal_encoder=multimodal_encoder,
    momentum=0.995
)

# Prepare inputs
images = torch.randn(4, 3, 384, 384)
text_ids = torch.randint(0, 30522, (4, 64))
text_atts = torch.ones(4, 64)
```

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

### Load State Dict into MUGEN Model - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Loads the state dictionary from the checkpoint into the initialized MUGEN VQVAE model. This step ensures the model has the pre-trained weights.

```python
vv_mugen.load_state_dict(ckpt['state_dict'])
```

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

### Instantiate MDETR Model from Components

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mdetr/MDETRTutorial.ipynb

This snippet illustrates how to construct an MDETR model by assembling its modular components. It details the core parts like the image backbone, text encoder, and multimodal transformer, along with auxiliary components such as positional embeddings, projection layers, and prediction heads. This approach offers flexibility for customization and experimentation.

```python
# mdetr_resnet101 is an instantiation of the MDETR model class.
# This class is composed of modular components which can be swapped out for easy experimentation.
from torchmultimodal.models.mdetr.model import MDETR

# Other imports we will use
import math
from torch import nn
from torchmultimodal.modules.layers.mlp import MLP
from torchmultimodal.models.mdetr.model import FeatureResizer
from torchmultimodal.models.mdetr.image_encoder import mdetr_resnet101_backbone, MaskedIntermediateLayer, PositionEmbedding2D
from torchmultimodal.models.mdetr.text_encoder import mdetr_roberta_text_encoder
from torchmultimodal.models.mdetr.transformer import mdetr_transformer

# Set some dimensions as constant
image_embedding_dim = 2048
text_embedding_dim = 768
transformer_dim = 256
num_queries = 100
num_classes = 255

# The core components of the MDETR model class are:
#   - image backbone 
#   - text encoder 
#   - multimodal transformer

# Encoder and transformer components also have builder functions.
image_backbone = mdetr_resnet101_backbone()
text_encoder = mdetr_roberta_text_encoder()
transformer = mdetr_transformer()


# Other components include
#   - positional embeddings: 2D embeddings added to image backbone outputs
#   - text/image projections (between unimodal encoders and multimodal transformer)
#   - query embeddings: learned object query embeddings, used in multimodal transformer decoder
#   - bounding box embeddings: map multimodal transformer outputs to bounding box coordinates
#   - class embeddings: map multimodal transformer outputs to class logits 

pos_embed = PositionEmbedding2D(num_pos_feats=128, scale=2 * math.pi)
text_projection = FeatureResizer(text_embedding_dim, transformer_dim)
image_projection = nn.Conv2d(image_embedding_dim, transformer_dim, kernel_size=1)
query_embed = nn.Embedding(num_queries, transformer_dim)
bbox_embed = MLP(transformer_dim, 4, [transformer_dim] * 2, dropout=0.0)
class_embed = nn.Linear(transformer_dim, num_classes + 1)

# This will give us the original ResNet101 MDETR model
mdetr = MDETR(
    image_backbone = image_backbone,
    text_encoder = text_encoder, 
    transformer = transformer,
    pos_embed = pos_embed, 
    text_projection = text_projection,
    image_projection = image_projection, 
    query_embed = query_embed,
    bbox_embed = bbox_embed,
    class_embed = class_embed,
)
```

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

### Initialize Omnivore Model

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/omnivore/omnivore_inference_demo.ipynb

Loads the pretrained Omnivore Swin Transformer model and sets it to evaluation mode for inference.

```python
import torchmultimodal.models.omnivore as omnivore
model = omnivore.omnivore_swin_t(pretrained=True)
model = model.eval()
```

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

### Import Diffusion Libraries

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Imports necessary libraries for building and running diffusion models, including PyTorch, torchvision, and specific modules from torchmultimodal for U-Net, adapters, losses, samplers, predictors, schedules, and transforms.

```python
import torch
import torchvision
import torchvision.transforms.functional as F

from torch import nn
from tqdm import tqdm
from torchmultimodal.diffusion_labs.models.adm_unet.adm import adm_unet
from torchmultimodal.diffusion_labs.modules.adapters.cfguidance import CFGuidance
from torchmultimodal.diffusion_labs.modules.losses.diffusion_hybrid_loss import DiffusionHybridLoss
from torchmultimodal.diffusion_labs.samplers.ddpm import DDPModule
from torchmultimodal.diffusion_labs.predictors.noise_predictor import NoisePredictor
from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import linear_beta_schedule, DiscreteGaussianSchedule
from torchmultimodal.diffusion_labs.transforms.diffusion_transform import RandomDiffusionSteps

device = "cuda"

```

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

### Define MUGEN VQVAE Arguments - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Defines the arguments for MUGEN's Video VQVAE model using a Namespace class. These arguments are derived from MUGEN's training scripts.

```python
class Namespace:
    def __init__(self, **kwargs):
        self.__dict__.update(kwargs)


vqvae_args=Namespace(
    embedding_dim=256,
    n_codes=2048,
    n_hiddens=240,
    n_res_layers=4,
    lr=0.0003,
    downsample=(4, 32, 32),
    kernel_size=3,
    sequence_length=16,
    resolution=256,
)

vv_mugen = mugen.VQVAE(vqvae_args)
```

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

### Apply CFGuidance Adapter

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Applies the CFGuidance adapter to the U-Net model. This technique enhances image-prompt alignment in conditional generative models by adjusting the guidance strength. It takes the base model and a dictionary of conditional inputs with their sizes.

```python
decoder = CFGuidance(unet,
                    {"digit": 768},
                    guidance=2.0)

```

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

### Initialize and Use FLAVA Model for Zero-Shot Classification

Source: https://context7.com/facebookresearch/multimodal/llms.txt

This snippet shows how to initialize the FLAVA model, process inputs using specific image and text transforms, and perform zero-shot classification by comparing projected image and text features.

```python
import torch
from PIL import Image
from torchmultimodal.models.flava.model import flava_model
from torchmultimodal.transforms.flava_transform import FLAVAImageTransform
from torchmultimodal.transforms.bert_text_transform import BertTextTransform

# Initialize FLAVA model with pretrained weights
model = flava_model(pretrained=True)
model.eval()

# Create transforms
image_transform = FLAVAImageTransform(is_train=False)
text_transform = BertTextTransform()

# Process image and text
image = Image.open("sample.jpg")
image_input = image_transform(image)["image"].unsqueeze(0)
text_input = text_transform(["a dog playing in the park"])

# Zero-shot classification
labels = ["dog", "cat", "bird", "car"]
text_tokens = text_transform(labels)

with torch.no_grad():
    _, image_features = model.encode_image(image_input, projection=True)
    _, text_features = model.encode_text(text_tokens, projection=True)

    # Calculate similarity
    scores = image_features @ text_features.t()
    probs = torch.nn.Softmax(dim=-1)(scores)
    predicted_label = labels[torch.argmax(probs)]

print(f"Predicted: {predicted_label}, Probabilities: {probs}")
```

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

### Apply Transforms and Prepare Input - Python

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/omnivore/omnivore_inference_demo.ipynb

This code applies a predefined transformation preset for depth classification evaluation to an RGB-D tensor. It resizes and crops the input to a specified size and adds a batch dimension, preparing it for model inference.

```python
# Assuming presets and tensor_rgbd are defined
depth_val_presets = presets.DepthClassificationPresetEval(crop_size=224, resize_size=224)
input_depth = depth_val_presets(tensor_rgbd)
# Add batch dimension
input_depth = input_depth.unsqueeze(0)
```

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

### Download and Unzip Checkpoints - Shell

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/generation/LoadAndComparePretrainedVQVAE.ipynb

Downloads the MUGEN checkpoint zip file from a provided URL and saves it to the specified checkpoint directory. It then unzips the downloaded file.

```shell
!wget https://dl.noahmt.com/creativity/data/MUGEN_release/checkpoints.zip -P $checkpoint_dir
```

```shell
import os

# Unzip checkpoints
zip_location = os.path.join(checkpoint_dir, 'checkpoints.zip')
!unzip $zip_location -d $checkpoint_dir
```

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

### Initialize MNIST DataLoader

Source: https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/diffusion_labs/mnist_training.ipynb

Loads the MNIST dataset and configures a DataLoader with specific batching, shuffling, and worker settings for efficient training.

```python
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
train_dataset = MNIST("mnist", train=True, download=True, transform=transform)
train_dataloader = DataLoader(train_dataset, batch_size=192, shuffle=True, num_workers=2, pin_memory=True)
```

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

### Prepare Data and Perform Inference

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/albef/vqa_with_albef.ipynb

Transforms input images and text, then runs the model to predict answer IDs for the given questions.

```python
image_transform = testing_image_transform()
question_transform = ALBEFTextTransform(add_end_token=False)
answer_transform = ALBEFTextTransform(do_pre_process=False)

vqa_data = json.load(open(data_dir + "vqa_data.json", "r"))
answer_list = json.load(open(data_dir + "answer_list.json", "r"))

image_paths = [sample["image"] for sample in vqa_data]
questions = [sample["question"] for sample in vqa_data]

images = [Image.open(data_dir + path).convert("RGB") for path in image_paths]
image_input = [image_transform(image) for image in images]
image_input = torch.stack(image_input, dim=0)
question_input = question_transform(questions)
question_atts = (question_input != 0).type(torch.long)
answer_input = answer_transform(answer_list)
answer_atts = (answer_input != 0).type(torch.long)

answer_ids, _ = model(
    image_input, 
    question_input,
    question_atts,
    answer_input,
    answer_atts,
    k=1,
    is_train=False,
)
```

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

### Importing Libraries for Video-Text Retrieval

Source: https://github.com/facebookresearch/multimodal/blob/main/examples/mugen/retrieval/tutorial.ipynb

Imports necessary libraries from PyTorch, torchvision, and the multimodal library for video processing, model loading, and data handling. These are essential for setting up the video-text retrieval pipeline.

```python
import torch
from torchvision.io import write_video
from IPython.core.display import Video
from examples.mugen.retrieval.video_clip import videoclip
from examples.mugen.data.mugen_dataset import MUGENDatasetArgs
from examples.mugen.data.mugen_datamodules import MUGENDataModule
from torchmultimodal.utils.common import load_module_from_url
from examples.mugen.data.bert_text_transform import BertTextTransform
from torchmultimodal.transforms.video_transform import VideoTransform
```