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Embodied Family Code Base

We will update the instructions for this codebase as soon as possible.

Installation

See INSTALLATION.md

Data Preparation

  1. Download the EgoCOT dataset.
  2. Download the COCO-2017 dataset.

Download the Pretrained Model

Download the testing model Embodied_family_7btiny.

Prepare the Text Data Paired with Video and Image

  • Unzip datasets_share.zip, which contains the text part of the multi-modal dataset, to the ./datasets/ directory.

🏠 Overview

image

🎁 Major Features

image

Usage

This repo can be used in conjunction with PyTorch's Dataset and DataLoader for training models on heterogeneous data. Here's a brief overview of the classes and their functionalities:

BaseDataset

The BaseDataset class extends PyTorch's Dataset and is designed to handle different media types (images, videos, and text). It includes a transformation process to standardize the input data and a processor to handle the data specific to the task.

Example

from robohusky.base_dataset_uni import BaseDataset

# Initialize the dataset with the required parameters
dataset = BaseDataset(
    dataset,  # Your dataset here
    processor,  # Your processor here
    image_path="path/to/images",
    input_size=224,
    num_segments=8,
    norm_type="openai",
    media_type="image"
)

# Use the dataset with a PyTorch DataLoader
from torch.utils.data import DataLoader

data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

WeightedConcatDataset

The WeightedConcatDataset class extends PyTorch's ConcatDataset and allows for the creation of a unified dataset by concatenating multiple datasets with specified weights.

Example

from robohusky.base_dataset_uni import WeightedConcatDataset

# Assume we have multiple datasets for different tasks
dataset1 = BaseDataset(...)
dataset2 = BaseDataset(...)
dataset3 = BaseDataset(...)

# Define the weights for each dataset
weights = [0.5, 0.3, 0.2]

# Create a weighted concatenated dataset
weighted_dataset = WeightedConcatDataset([dataset1, dataset2, dataset3], weights=weights)

# Use the weighted dataset with a PyTorch DataLoader
data_loader = DataLoader(weighted_dataset, batch_size=32, shuffle=True)

Customization

The package is designed to be flexible and customizable. You can implement your own transformation and processing logic by subclassing BaseDataset and overriding the necessary methods.

🎫 License

This project is released under the Apache 2.0 license.

🖊️ Citation

If you find this project useful in your research, please consider cite:

@article{mu2024embodiedgpt,
  title={Embodiedgpt: Vision-language pre-training via embodied chain of thought},
  author={Mu, Yao and Zhang, Qinglong and Hu, Mengkang and Wang, Wenhai and Ding, Mingyu and Jin, Jun and Wang, Bin and Dai, Jifeng and Qiao, Yu and Luo, Ping},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}