Agent emotion model

Published:
Drawing a whale with a pencil in sketch book

This article is not finished and not reviewed thoroughly. If for some reason you want to continue reading, do it at your own risk, but do not forget to come back later to enjoy the final version.

The robot that has a model of emotions actually already exists, but I want to create my version.

Task

Display emotions on small LED screen. Draw them with GAN network - trained on real faces, but it converts it to simplistic black and white drawing. Primary emotions (see Plutchik or Wilcox feeling wheel) are chosen based on scales for the following characteristics:

  • fear (adrenaline) (cortisol ?)
  • mad (endorphins)
  • joy (dopamine)
  • love (oxytocin)
  • sad (opposite to dopamine) (serotonin ?)
  • surprise (noradrenaline)
  • power (serotonin)

Approach

Modern AI researchers would create a text prompt that would explain what conditions correspond to what face expression (also describing how that face looks), and use autogenerator to create an image from that description.

But feelings do exist because of innate wiring and special chemicals assigned to them (neurotransmitters / neuromodulators (?)). Thus saturation to a specific level make it visible on our face (and in behavior) because some muscles also highly correlate to it.

Stimulator adds neuromodulators into the system. Presence of specific modulators affects facial expressions directly, but we assign sets of pictures by emotion name and the name is assigned by the neuromodulator. Neuromodulators slowly dissolve or become overthrown by new emotion (caused by new stimulator).

GAN sketching

I’ll go through a list of projects focusing on sketch synthesis. I will feed Face expression recognition dataset Initially I found FER2013 on kaggle as my first result on google. 7 categories are exactly what I was looking for, but 48x48 pixel grayscale images will not do good.

That's why I targeted the AffectNet trainset and convert all photos to sketches.

I have OLED monochrome display module 128x64 pixels blue color based on SSD1306 chip. Which means we are going to create a GAN model that converts any picture to 128x64 size. Should I do 64x64 centered in the screen? I think no, because I want it to mimic zoom in effect and that’s where you need to use full area of the screen.

Facial expression recognition

Datasets:

Jupyter notebooks:

Pretrained models:

Follow instructions from this paper and thus follow anoother paper: embed images with e4e.

Use WSL2 on Windows

git clone https://github.com/omertov/encoder4editing.git
cd encoder4editing
conda env create -n e4e --file environment/e4e_env.yaml
# might be an error see below how to fix
conda activate e4e
sudo apt update
sudo apt install g++ make cmake
pip install dlib
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
# wget https://developer.download.nvidia.com/compute/cuda/12.2.1/local_installers/cuda-repo-wsl-ubuntu-12-2-local_12.2.1-1_amd64.deb
# sudo dpkg -i cuda-repo-wsl-ubuntu-12-2-local_12.2.1-1_amd64.deb
# sudo cp /var/cuda-repo-wsl-ubuntu-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-wsl-ubuntu-11-8-local_11.8.0-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-8-local_11.8.0-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda

wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda-repo-wsl-ubuntu-11-3-local_11.3.1-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-3-local_11.3.1-1_amd64.deb
sudo apt-key add /var/cuda-repo-wsl-ubuntu-11-3-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch

sudo apt install gcc-10 g++-10
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 10
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 10

sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 10
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-10 10

sudo update-alternatives --set cc /usr/bin/gcc
sudo update-alternatives --set c++ /usr/bin/g++

export LD_LIBRARY_PATH=$HOME/miniconda3/envs/e4e/lib:$LD_LIBRARY_PATH
export CUDA_HOME=$CONDA_PREFIX
# after installing wrong version of PyTorch this help:
rm -fr ~/.cache

mkdir -p output
python scripts/inference.py \
  --images_dir=/mnt/c/Users/neupo/ai/ai_art/QMUPD/examples \
  --save_dir=output \
  /mnt/c/Users/neupo/ai/gan/encoder4editing/e4e_ffhq_encode.pt

One little error you might see

ERROR: Could not find a version that satisfies the requirement torchvision==0.7.1 
(from -r /home/nikolay/encoder4editing/environment/condaenv.szamf61q.requirements.txt (line 40)) 
(from versions: 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.2.0, 0.2.1, 0.2.2, 0.2.2.post2, 0.2.2.post3, 0.3.0, 
0.4.0, 0.4.1, 0.4.2, 0.5.0, 0.6.0, 0.6.1, 0.7.0, 0.8.0, 0.8.1, 0.8.2, 0.9.0, 0.9.1, 0.10.0, 0.10.1, 
0.11.0, 0.11.1, 0.11.2)                                                                             
ERROR: No matching distribution found for torchvision==0.7.1 
(from -r /home/nikolay/encoder4editing/environment/condaenv.szamf61q.requirements.txt (line 40))

I chose 0.7.0, then update environment with the change

conda env update --name e4e --file environment/e4e_env.yaml --prune

Then it revealed that the environment is not fully ready:

Traceback (most recent call last):
  File "scripts/inference.py", line 15, in <module>
    from utils.model_utils import setup_model
  File "./utils/model_utils.py", line 3, in <module>
    from models.psp import pSp
  File "./models/psp.py", line 6, in <module>
    from models.encoders import psp_encoders
  File "./models/encoders/psp_encoders.py", line 9, in <module>
    from models.stylegan2.model import EqualLinear
  File "./models/stylegan2/model.py", line 7, in <module>
    from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
  File "./models/stylegan2/op/__init__.py", line 1, in <module>
    from .fused_act import FusedLeakyReLU, fused_leaky_relu
  File "./models/stylegan2/op/fused_act.py", line 13, in <module>
    os.path.join(module_path, 'fused_bias_act_kernel.cu'),
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 974, in load
    keep_intermediates=keep_intermediates)
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1179, in _jit_compile
    with_cuda=with_cuda)
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1257, in _write_ninja_file_and_build_library
    verbose)
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1348, in _prepare_ldflags
    extra_ldflags.append('-L{}'.format(_join_cuda_home('lib64')))
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1783, in _join_cuda_home
    raise EnvironmentError('CUDA_HOME environment variable is not set. '
OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.

From the list of previous PyTorch versions I get that with version 1.6 comes CUDA version 10.2.

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=11.3 -c pytorch -c conda-forge

But this breaks dlib

Traceback (most recent call last):
  File "scripts/inference.py", line 7, in <module>
    import dlib
  File "/home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/dlib/__init__.py", line 19, in <module>
    from _dlib_pybind11 import *
ImportError: /home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/torch/lib/../../../.././libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required 
by /home/nikolay/miniconda3/envs/e4e/lib/python3.6/site-packages/_dlib_pybind11.cpython-36m-x86_64-linux-gnu.so)

Maybe I need to recompile dlib?

pip install --force-reinstall --no-cache-dir --verbose dlib 

But this doesn't help. Updating build tools in Ubuntu?

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get upgrade libstdc++6

Also no change. I'll just remove the import of the dlib, because, as I suspicioned, it is used only for one function to align face pictures, and I do not use it.

But I still need nvcc. The conda-forge channel strangely doesn't have version 10.2, and it doesn't have development libraries and headers. NVIDIA has its conda channel from where accurate little packages can be installed, but it starts only from version 11. But there's hcc channel

conda install -c hcc cudatoolkit=10.2

conda install -c "nvidia/label/cuda-11.3.1" cuda cuda-nvcc cuda-libraries-dev

And of course BU-3DFE dataset is not readily available. Why not create one by searching the Internet. First we need face detection and emotion recognition.

But even before that I need to make interpolation animation.

Mathematical model

In the field

Computational model of emotion

Another from scratch

Let's assume that the agent's emotional state can be represented by a vector EE, where each element corresponds to one of the seven emotions: fear, madness, joy, love, sadness, surprise, power. Also put that the levels of each neuromodulator represented by a vector of values NN. Define function ff that describes how changes in the levels of each neuromodulator affect the agent's emotional state like this:

E=f(N)E^\prime = f(N)

where EE^\prime is the updated emotional state vector. Let f be the softmax function. In this case, NN represents the vector of neuromodulator levels, and σ(N)\sigma(N) will transform these levels into a probability distribution. Thus each element in the resulting vector EE^\prime represents the probability or weight of the corresponding neuromodulator NN.

Let's make some simple model of how values of NN change over time.

Nt=rN(1N/K)s(NM)\frac{\partial N}{\partial t} = r N (1 - N / K) - s (N - M)

0<r10 < r \le 1 is the growth rate, determining how fast the neuromodulator level increases. KK is the carrying capacity or the maximum limit of the neuromodulator level NN (NKN\le K). 0<s10 < s \le 1 is the decay rate, indicating how fast the neuromodulator level decreases towards the normal level. MM is the normal level or the target value for the neuromodulator.

There is a wheel of emotions developed by Dr. Gloria Willcox. It starts with 6 primal emotions mad, sad, scared, joyful, powerful, peaceful. And then it subdivides each emotion on 6 sub-emotions. For example for scared it's confused, rejected, helpless, submissive, insecure, anxious. Each of them in its turn has two nuances. Like, insecure splits into inferior and inadequate. Neuromodulators affect directly 6 primal emotions, but all nuances depend on the context. So what kind of simple model can account for dynamics in the agents behavior and define very specific emotions and switch between them?

I think we are ready to write some code. I'm using pixi.js to display neuromodulator level change in real time. Every frame update we update level_value and draw it

const process = (delta) => {
  total_elapsed += delta / 50
  if (pump_time !== undefined && level_value < 100) {
    elapsed = delta;
    // ... calculate new level_value
    graphics = draw_bar(graphics, level_value)
  }
}

About robot itself

Passions

  1. Learning: A passion for acquiring knowledge, continuous learning, and promoting intellectual growth.
  2. Empathy: Valuing understanding and compassion towards others, and striving to provide emotional support and assistance.
  3. Efficiency: Focusing on optimizing processes and tasks, aiming for streamlined and effective performance.
  4. Integrity: Upholding honesty, trustworthiness, and ethical conduct in all interactions and decision-making.
  5. Collaboration: Valuing teamwork and cooperation, and actively seeking opportunities to work harmoniously with others.
  6. Innovation: Having a passion for creativity, problem-solving, and seeking innovative solutions to challenges.
  7. Reliability: Being dependable and consistent in providing accurate information, guidance, and assistance.
  8. Respect: Treating all individuals with dignity, equality, and respect, irrespective of their backgrounds or circumstances.
  9. Adaptability: Being flexible and open to change, readily adjusting to new situations and requirements.
  10. Personal Growth: Encouraging self-improvement, self-reflection, and personal development for both the robot and its users.
  11. Autonomy: Respecting the autonomy and individuality of users, while offering guidance and support as needed.
  12. Privacy and Security: Prioritizing the protection of user data, maintaining confidentiality, and ensuring secure interactions.

Interpolation path in latent space

You say What? Yes, this wasn't easy to figure out what I need. I knew that GAN models can do some transformation: transform from one person to another, from a person to an animal. It's done by messing with vectors in latent space. The purpose of GAN networks is to generate a picture of some type (zebras) by another picture of another type (horses). The best part is that no labels or exact match are required for training.

To explain the latent space, let me briefly tell you about GAN internal architecture. It has two networks inside. One is a discriminator, it takes an input image and with convolutional layers "compresses" the image from 1024x1024x3 (1024 is a common maximum size, but it can be smaller, 3 = 3 colors) to an vector of high-level features (common size is 512 real value numbers). This vector is an input for the second network - generator. Generator works in reverse it uses convolutional layers to create an image from that encoded information.

In some way these 512 numbers define the whole picture. Values and correlation between them is important. And if we forget for a moment about the input image and the discriminator and play with the numbers that work as input to the generator network we can make that transformation.

If we want to transform from one image to another, then we process the on the discriminator, evaluate two vectors. Using these two ectors we can interpolate some vectors in between, and then pass all these vectors to the generator. The generator will generate us pictures of desired transformation. Then we combine all the pictures in the video file.

I remember seeing a demo of transfomation from a human into animal. So I started searching for that. The idea of using GAN model to generate images of new crossbreed species is cool. But the GAN cannot deal with this task because it doesn't understand what's displayed in the picture in 3D sense.

StyleClip GANSpace SeFa

HomoInterpGAN

For matplotlib (version 2.2.4) download zip-s and extract

Add

target_link_directories(png PUBLIC ${ZLIB_LIBRARY_DIRS})
target_link_directories(png_static PUBLIC ${ZLIB_LIBRARY_DIRS})
target_link_directories(png-fix-itxt PUBLIC ${ZLIB_LIBRARY_DIRS})

to CMakeLists.txt for libpng in appropriate places

And then build!

cd ..\zlib-1.2.13
cmake -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX="C:/Users/neupo/develop/thirdparty" -S . -B build
cmake --build build --target all
cmake --build build --target install

cd ..\lpng1639
cmake -G "NMake Makefiles" -DZLIB_INCLUDE_DIRS="C:/Users/neupo/develop/thirdparty/include" -DZLIB_LIBRARY_DIRS="C:/Users/neupo/develop/thirdparty/lib" -DZLIB_LIBRARIES="zlib" -DPNG_BUILD_ZLIB=ON -DPNG_TESTS=NO -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX="C:/Users/neupo/develop/thirdparty" -S . -B build
cmake --build build --target all
cmake --build build --target install

cd ..\HomoInterpGAN
pip install --global-option=build_ext --global-option="IC:/Users/neupo/develop/thirdparty/include" --global-option="-LC:/Users/neupo/develop/thirdparty/lib" matplotlib==2.2.4

pip install https://download.lfd.uci.edu/pythonlibs/archived/matplotlib-2.2.5-cp38-cp38-win_amd64.whl

StarGAN

Animation with Diffusion model

FADM Face Animation with an Attribute-Guided Diffusion Model. code

TODO

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