Discover the future of data transformation with OmniMorph, a cutting-edge, omni-modality embedding function designed to seamlessly handle and adapt to diverse data inputs. OmniMorph intelligently detects and optimizes embeddings for various modalities, revolutionizing data processing while saving valuable time and resources. Its unparalleled adaptability empowers users to efficiently work with multi-modal data, unlocking new possibilities for AI research and development.
- Omni-modality embedding function
- Intelligent detection and optimization of embeddings for various modalities
- Accelerated data processing and resource management
- Unlocks new possibilities for AI research and development
pip install omnimorph
import torch
from omnimorph import OmniMorph
omni_morph = OmniMorph()
text_data = torch.randint(0, 10000, (10, 50))
vision_data = torch.randn(10, 3, 224, 224)
audio_data = torch.randn(10, 1, 16000)
text_embeddings = omni_morph(text_data)
vision_embeddings = omni_morph(vision_data)
audio_embeddings = omni_morph(audio_data)
Check out the examples folder for more detailed usage and code examples.
Here's a simple example of how to use OmniMorph with text, image, and audio inputs:
import torch
from omni_morph import OmniMorph
omni_morph = OmniMorph()
text_input = torch.randint(0, 10000, (1, 50))
image_input = torch.randn(1, 3, 224, 224)
audio_input = torch.randn(1, 128, 100)
text_embedding = omni_morph(text_input, user_defined_modality='text')
image_embedding = omni_morph(image_input)
audio_embedding = omni_morph(audio_input)
You can provide your own modality detection function to handle custom data formats:
def custom_modality_detector(input_data):
# Add custom logic to detect input data modality
return "custom_modality"
embedding = omni_morph(custom_input, custom_modality_fn=custom_modality_detector)
To add a user-defined modality, register the corresponding embedding class and instantiate the embedding:
omni_morph.register_and_instantiate("custom_modality", CustomEmbeddingClass, **kwargs)
Then, you can use the custom modality when generating embeddings:
embedding = omni_morph(custom_input, user_defined_modality="custom_modality")
Register a fusion technique to combine embeddings in a specific way:
def custom_fusion(embedding):
# Add custom logic to fuse the embedding
return fused_embedding
omni_morph.register_fusion_technique("custom_fusion", custom_fusion)
Apply the registered fusion technique when generating embeddings:
fused_embedding = omni_morph(input_data, fusion_technique="custom_fusion")
For more in-depth information on how to use OmniMorph, please refer to the documentation.
We welcome all contributions to improve OmniMorph! Please check out the contributing guide for guidelines on how to proceed.
OmniMorph is released under the MIT License.
For any questions, issues, or suggestions, feel free to open an issue on our GitHub repository or reach out to us through our community forum.
Join the OmniMorph revolution and experience the future of data transformation today! π
OmniMorph is a powerful and flexible solution for working with multimodal data, making it easy to generate embeddings for various data types through a unified interface. By simplifying the process of handling different data modalities, OmniMorph enables researchers and engineers to focus on their core tasks and accelerate their work.
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