Enable OpenVINO™ Optimization for GroundingDINO

In the ever-evolving landscape of biopharmaceutical technology and drug development, a recent effort in the field of Cell Analytics for Monoclonal Antibody Production has shed light on the crucial role of Edge AI Technology in navigating complex challenges of scaling and producing solutions.  

In this 2-part blog series, we will explore the use of Intel Edge AI Technology in biopharma and drug development, addressing challenges and providing insights into the development of AI pipelines for cell segmentation and analysis.

Intel has been involved in this process with a variety of partners. One of Intel’s contributions to the cell image project centers around processing brightfield¹ images using an AI pipeline containing multiple deep learning models. The pipeline's purpose is to identify cells and other biological components and provide feedback on dynamic biological characteristics such as cell morphology, viability, and phenotypic changes, among others. Throughout this process, working on cell-AI projects usually brings a unique set of challenges to the forefront.  

First, it is an interdisciplinary field and the knowledge gap between data scientists and biopharma experts requires more back-and-forth clear communications for planning and validity checks. Frequently when attempting to implement AI solutions in the laboratory, data scientists and bench scientists struggle to fully grasp the nature and needs of each other’s role. This lack of mutual understanding can also hinder the usability and scalability of an AI solution needing to be integrated into diverse lab environments.

The second challenge is instrument variability. Different plate reader² microscopes have different hardware, optics, and apertures which cause their produced images not to be consistent. This adds an extra layer of work to assess and address these inconsistencies along the way (like regular tracked calibration and adjustment). Additionally, equipment vendor-to-vendor differences, culture temperature, medium conditions, and genetic modifications can all affect the variability of data and the inherent transferability of the deep learning pipeline. This would drive the need to monitor the performance of DL models at the edge and cloud ML ops components.    

The third challenge is obtaining peer-review labels because the process is based on supervised Machine Learning and obtaining clean accurate labels is very costly and time-consuming.  

And the last challenge is about the model deployment. In most cases, cloud deployment is not an option due to data size and data privacy. Produced images from plate reader microscopes are huge and transferring data to the cloud and sending the results back would create high latency because a huge amount of data must be streamed (30Gb per hour). And more importantly, laboratories are usually not willing to share the data. Due to these two constraints, cloud deployments are not usually an option, and the pipeline must be deployed at the edge.  

Now, let’s talk about a specific application of this technology: the CHO Cell Segmentation Use Case.

CHO Cell Segmentation Use Case

CHO cells, or Chinese Hamster Ovary cells, are a cornerstone in the production of complex protein molecules such as monoclonal antibodies, fusion proteins, hormones, and coagulation factors. Unlike stem cells or CAR-T cells, where the cells themselves are the therapeutic product, in CHO cells, it is the proteins they produce that are of paramount importance. Monitoring the health, viability, and production capability of these cells is a critical step in commercial protein production.

Traditionally, assessing the condition of CHO cells involves a multi-step process that is not only time-consuming but also requires the use of expensive reagents and chemicals. Depending on the process, the workflow can be something like below.  

1. Culture cells  

2. Fix cells – wash in expensive reagents to remove the culture medium.  

3. Permeabilization – wash in more expensive chemicals to permeabilize the cell membrane (to stain for intercellular proteins).

4. Blocking – incubate cells in another expensive reagent to prevent binding of no specific antibodies.

5. Primary Antibody Incubation – antibody specifically to bind to a protein that is being produced.

6. Washing – removing unbound Primary Antibodies using more expensive chemicals.

7. Nuclear staining – use nuclear stain like DAPI to visualize cell nuclei then wash with the same chemicals from the washing step

8. Mounting – get ready to read in the microscope (plate reader¹)

9. Imaging – Stained cells …. count them up and determine the state in the protein production cycle and relative cell health (eventually they peter out and stop producing and the batch needs to be flushed. (Cell count, viability number, etc. are the output not the image)

From culturing to imaging, each step plays a vital role in ensuring the quality of the protein product. However, with the advent of AI and deep learning, there is an opportunity to streamline this workflow significantly. Using an AI pipeline including multiple Deep Learning models and data pre and post-processing, we can go from Step 1 directly to Step 9, removing the majority of the labor and latency in getting actionable results out of a staining workflow and bypassing expensive specialty chemicals requirement. Intel has put together a reference implementation for deploying said pipeline and inferencing of these images on the edge as part of the Cell Image project https://www.cellimage.ie/. OpenVINO Toolkit, OpenVINO Model Server, and AI Connect for Scientific Data are used in this design. Let’s briefly talk about each of these wonderful SW packages in part 2 of this article series. Stay tuned!

Conclusion

In conclusion, the integration of Intel Edge AI Technology into the biopharmaceutical sector represents a transformative step towards more efficient and scalable drug development processes. As we have seen in this first installment of our blog series, the deployment of AI pipelines for cell segmentation and analysis in monoclonal antibody production is not without its challenges. These include bridging the interdisciplinary knowledge gap, managing instrument variability, acquiring peer-reviewed labels, and overcoming the hurdles associated with model deployment.

Despite these challenges, the potential benefits of Edge AI in biopharma are substantial. By leveraging Intel's advanced AI technologies, we can significantly reduce the time and cost associated with traditional cell analysis methods, while also enhancing the accuracy and reliability of the results. The use of edge computing addresses the concerns of data size and privacy, allowing for real-time processing and analysis without the need for cloud transfer.

As we move forward in this blog series, we will delve deeper into the specifics of Intel's Edge AI solutions, including the OpenVINO toolkit, OpenVINO Model Server, and AI Connect for Scientific Data. We will explore how these tools are being applied in real-world scenarios to drive innovation and improve outcomes in the realm of biopharma and drug development in the next part of this series.

Reach out to Intel's Health and Life Sciences team at health.lifesciences@intel.com or learn more about what we do at https://www.intel.com/health.

We'd like to hear from you! Let us know in the comments or discuss – which AI use cases in health and life sciences do you think will have the greatest impact on global health?

If you enjoyed hearing from the Health and Life Sciences team and want to hear more, give this post a like and ensure you subscribe to get the latest updates from the team. 

About the Author

Nooshin Nabizadeh has Ph.D. in Electrical and Computer Engineering from the University of Miami and works at Intel Corporation as AI Solutions Architect. She enjoys photography, writing poetry, reading about psychology and philosophy, and optimizing solutions to run as fast as possible on a given piece of hardware. Connect with her on LinkedIn https://www.linkedin.com/in/nooshin-nabizadeh/ by mentioning this blog.

1. Brightfield microscopy is a widely used technique for observing the morphology of cells and tissues.

2. A plate reader is a laboratory instrument used to obtain images from samples in microtiter plates. The reader shines a specific calibrated frequency of light (UV, visible, fluorescence, etc.) through the samples in the wells of the plate. Plate reader microscopy data sets have inherent variability which drives the requirement of regular tracked calibration and adjustment.

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Introduction

Latent Consistency Models (LCMs) is the next generation of generative models after Latent Diffusion Models (LDMs). While Latent Diffusion Models (LDMs) like Stable Diffusion are capable of achieving the outstanding quality of generation, they often suffer from the slowness of the iterative image denoising process. LCM is an optimized version of LDM. Inspired by Consistency Models (CM), Latent Consistency Models (LCMs) enabled swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion. The Consistency Models is a new family of generative models that enables one-step or few-step generation. More details about the proposed approach and models can be found using the following resources: project page, paper, original repository.

This article will demonstrate a C++ application of the LCM model with Intel’s OpenVINO™ C++ API on Linux systems. For model inference performance and accuracy, the C++ pipeline is well aligned with the Python implementation.

The full implementation of the LCM C++ demo described in this post is available on the GitHub: openvino.genai/lcm_dreamshaper_v7.

Model Conversion

To leverage efficient inference with OpenVINO™ runtime on Intel platforms, the original model should be converted to OpenVINO™ Intermediate Representation (IR).

LCM model

Optimum Intel can be used to load SimianLuo/LCM_Dreamshaper_v7 model from Hugging Face Hub and convert PyTorch checkpoint to the OpenVINO™ IR on-the-fly, by setting export=True when loading the model, like:

from optimum.intel.openvino import OVLatentConsistencyModelPipeline

model = OVLatentConsistencyModelPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", export=True)
model.save_pretrained("ov_lcm_model")

Tokenizer

OpenVINO Tokenizers is an extension that adds text processing operations to OpenVINO Inference Engine. In addition, the OpenVINO Tokenizers project has a tool to convert a HuggingFace tokenizer into OpenVINO IR model tokenizer and detokenizer: it provides the convert_tokenizer function that accepts a tokenizer Python object and returns an OpenVINO Model object:

from transformers import AutoTokenizer
from openvino_tokenizers import convert_tokenizer
from openvino import compile_model, save_model

hf_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
ov_tokenizer_encoder = convert_tokenizer(hf_tokenizer)
save_model(ov_tokenizer_encoder, "ov_tokenizer.xml")

Note: Currently OpenVINO Tokenizers can be inferred on CPU devices only.

Conversion step

You can find the full script for model conversion at the original repo.

Note: The tutorial assumes that the current working directory is and <openvino.genai repo>/image_generation/lcm_ dreamshaper_v7/cpp all paths are relative to this folder.

Let’s prepare a Python environment and install dependencies:

conda create -n openvino_lcm_cpp python==3.10
conda activate openvino_lcm_cpp
conda install -c conda-forge 'openvino>=2023.3.0'
python -m pip install -r scripts/requirements.txt
python -m pip install ../../../thirdparty/openvino_contrib/modules/custom_operations/[transformers]

Now we can use the script scripts/convert_model.py to download and convert models:

cd scripts
python convert_model.py -lcm "SimianLuo/LCM_Dreamshaper_v7" -t FP16

C++ Pipeline

Pipeline flow

Let’s now talk about the logical structure of the LCM model pipeline.

Just like the classic Stable Diffusion pipeline, the LCM pipeline consists of three important parts:
- A text encoder to create a condition to generate an image from a text prompt.
- U-Net for step-by-step denoising the latent image representation.
- Autoencoder (VAE) for decoding the latent space to an image.

The pipeline takes a latent image representation and a text prompt transformed to text embedding via CLIP’s text encoder as an input. The initial latent image representation is generated using random noise generator. LCM uses a guidance scale for getting time step conditional embeddings as input for the diffusion process, while in Stable Diffusion, it used for scaling output latents.

Next, the U-Net iteratively denoises the random latent image representations while being conditioned on the text embeddings. The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm. LCM introduces its own scheduling algorithm that extends the denoising procedure introduced by denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. The denoising process is repeated for a given number of times to step-by-step retrieve better latent image representations. When complete, the latent image representation is decoded by the decoder part of the variational auto encoder.

The C++ implementations of the scheduler algorithm and LCM pipeline are available at the following links: LCM Scheduler, LCM Pipeline.

LoRA support

LoRA (Low-Rank Adaptation) is a training technique for fine-tuning Stable Diffusion models. There are various LoRA models available on https://civitai.com/tag/lora.

The main idea for LoRA weights enabling, is to append weights onto the OpenVINO LCM models at runtime before compiling the Unet/text_encoder model. The method is to extract LoRA weights from safetensors file, find the corresponding weights in Unet/text_encoder model and insert the LoRA bias weights. The common approach to add LoRA weights looks like:

The original LoRA safetensor model is loaded via safetensors.h. The layer name and weight of LoRA are modified with Eigen Lib and inserted into Unet/text_encoder OpenVINO model using ov::pass::MatcherPass - you can see the implementation in the file common/diffusers/src/lora.cpp.

To run the LCM demo with the LoRA model, first download LoRA, for example: LoRa/Soulcard.

Build and Run LCM demo

Let’s start with the dependencies installation:‍

conda activate openvino_lcm_cpp
conda install -c conda-forge eigen c-compiler cxx-compiler make

Now we can build the application:

cmake -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build --config Release --parallel
cd build

‍‍And finally we’re ready to run the LCM demo. By default the positive prompt is set to: “a beautiful pink unicorn”.

Please note, that the quality of the resulting image depends on the quality of the random noise generator, so there is a difference for output images generated by the C++ noise generator and the PyTorch generator. Use oprion -r to read the PyTorch generated noise from the provided textfiles for the alignment with Python pipeline.
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Note: Run ./lcm_dreamshaper -h to see all the available demo options

Let’s try to run the application in a few modes:

Read the numpy latent input and noise for scheduler instead of C++ std lib for the alignment with Python pipeline: ./lcm_dreamshaper -r‍

Generate image with C++ std lib generated latent and noise : ./lcm_dreamshaper

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Generate image with Soulcard LoRa and C++ generated latent and noise: ./lcm_dreamshaper -r -l path/to/soulcard.safetensors

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See Also

1. Optimizing Latent Consistency Model for Image Generation with OpenVINO™ and NNCF
2. Image generation with Latent Consistency Model and OpenVINO
3. C++ Pipeline for Stable Diffusion v1.5 with Pybind for Lora Enabling
4. Enable LoRA weights with Stable Diffusion Controlnet Pipeline

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Deploying deep-learning capabilities to edge devices can present security challenges like ensuring inference integrity, or providing copyright protection of your deep-learning models. OpenVINO provide a simple method with crypto algorithm to protect model in disk. Model encryption, decryption and authentication are not provided by OpenVINO but can be implemented with third-party tools (i.e., OpenSSL). In this example, we use AES-128-cbc algorithm in OpenSSL to demonstrate the model cryptography.

As you can see the mechanism in below image, there are two part to process:

1. First is to encrypt your plain IR model into encrypted model.
2. The second part is to use the same password key and IV which used for encryption before to decrypt model at model loading runtime.

Step 1: Encrypt model

Make sure you install the OpenSSL and boost, for example in Ubuntu:

$ sudo apt install openssl libboost-dev

Then use command line to do model encryption by OpenSSL AES-128-CBC algorithm. In this simply example, I use same password for Key and IV, it is hexadecimal of string "openvino encrypt". You can use some online str2hex tool to generate hex representation of your string password.

$ openssl enc -aes-128-cbc -in openvino_model.xml -out openvino_model_enc.xml -K 6f70656e76696e6f20656e6372797074 -iv 6f70656e76696e6f20656e6372797074
$ openssl enc -aes-128-cbc -in openvino_model.bin -out openvino_model_enc.bin -K 6f70656e76696e6f20656e6372797074 -iv 6f70656e76696e6f20656e6372797074

Step 2: Decrypt model

Here provide the sample code to read encrypted model into buffer and decrypt to plain model binary. Then read and compile model.

#include <fstream>
#include <iostream>
#include <vector>
#include <cmath>
#include <cctype>
#include <string>
#include <openvino/runtime/core.hpp>
#include <openssl/aes.h>
#include <boost/algorithm/hex.hpp>

using namespace std;

vector<unsigned char> aes_128_cbc_decrypt(
    vector<unsigned char> &cipher,
    std::vector<unsigned char> &key,
    std::vector<unsigned char> iv) {

    AES_KEY ctx;
    AES_set_decrypt_key(key.data(), 128, &ctx);
    std::vector<uint8_t> plain;
    //cipherLen = clearLen + 16 - (clearLen mod 16)
    int plain_size = ceil(cipher.size()/16)*16; //make sure alloc buffer is enough to plain_size
    plain.resize(plain_size);
    std::cout << "AES_cbc_encrypt start:" << std::endl;
    AES_cbc_encrypt(cipher.data(), plain.data(), plain.size(), &ctx, iv.data(), AES_DECRYPT);
    std::cout << "AES_cbc_encrypt done" << std::endl;
    return plain;
}

void decrypt_file(std::ifstream & stream,
                  std::vector<unsigned char> & key,
                  std::vector<unsigned char> & iv,
                  std::vector<uint8_t> & result) {
    std::vector<unsigned char> cipher((std::istreambuf_iterator<char>(stream)),  std::istreambuf_iterator<char>());
    std::cout << "aes_128_cbc_decrypt" << std::endl;
    std::vector<unsigned char> decrypt_model = aes_128_cbc_decrypt(cipher, key, iv);
    result = decrypt_model;

}

int main() {
    std::string key_hex = "6f70656e76696e6f20656e6372797074";
    std::string iv_hex = "6f70656e76696e6f20656e6372797074";
    std::vector<unsigned char> key_bytes;
    std::vector<unsigned char> iv_bytes;
    boost::algorithm::unhex(key_hex, std::back_inserter(key_bytes));
    boost::algorithm::unhex(iv_hex, std::back_inserter(iv_bytes));
    std::vector<uint8_t> model_data, weights_data;
    std::ifstream model_file("openvino_model_enc.xml",std::ios::in | std::ios::binary), weights_file("openvino_model_enc.bin",std::ios::in | std::ios::binary);
    // Read model files and decrypt them into temporary memory block
    std::cout << "decrypt file" << std::endl;
    decrypt_file(model_file, key_bytes, iv_bytes, model_data); //key & iv is the same
    decrypt_file(weights_file, key_bytes, iv_bytes, weights_data);
    ov::Core core;
    // Load model from temporary memory block
    std::string str_model(model_data.begin(), model_data.end());
    std::unique_ptr<ov::InferRequest> infer_request= std::make_unique<ov::InferRequest>(core.compile_model(str_model,ov::Tensor(ov::element::u8, {weights_data.size()}, weights_data.data()),"CPU").create_infer_request());
    std::cout << "compile success" << std::endl;
    return 0;
}

CMakeLists.txt file like below for compiling:

cmake_minimum_required(VERSION 3.5)
set(CMAKE_CXX_STANDARD 23)
set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
add_compile_options(-O3 -march=native -Wall)

find_package(OpenVINO REQUIRED)
find_package(OpenSSL REQUIRED)
find_package(Boost REQUIRED)

add_executable(model_crypto main.cpp)
target_include_directories(model_crypto PRIVATE ${OV_INCLUDE_DIR} )
target_link_libraries(model_crypto PRIVATE openvino::runtime OpenSSL::SSL Boost::headers)

This blog just provide an example of model encryption by OpenSSL. This method can only protect you model in disk, for total memory crypto, you can refer technologies like OpenVINO™ Security Add-on in virtual machine to provide an isolated environment for security sensitive operations, and use Intel® SGX (Software Guard Extensions) which allows developers to split a computer's memory into private, predefined, highly secure areas called enclaves, which better protect sensitive information.

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Reference:

1. OpenVINO model protection: https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_protecting_model_guide.html
2. OpenVINO™ Security Add-on: https://docs.openvino.ai/2023.1/ovsa_get_started.html
3. OpenSSL official website: https://www.openssl.org/