Implementation of "MORPHEUS-1" from Prophetic AI and "The world’s first multi-modal generative ultrasonic transformer designed to induce and stabilize lucid dreams. "
pip install morpheus-torch
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The input is FRMI and EEG tensors.
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FRMI shape is (batch_size, in_channels, D, H, W)
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EEG Embedding is [batch_size, channels, time_samples]
# Importing the torch library
import torch
# Importing the Morpheus model from the morpheus_torch package
from morpheus_torch.model import Morpheus
# Creating an instance of the Morpheus model with specified parameters
model = Morpheus(
dim=128, # Dimension of the model
heads=4, # Number of attention heads
depth=2, # Number of transformer layers
dim_head=32, # Dimension of each attention head
dropout=0.1, # Dropout rate
num_channels=32, # Number of input channels
conv_channels=32, # Number of channels in convolutional layers
kernel_size=3, # Kernel size for convolutional layers
in_channels=1, # Number of input channels for convolutional layers
out_channels=32, # Number of output channels for convolutional layers
stride=1, # Stride for convolutional layers
padding=1, # Padding for convolutional layers
ff_mult=4, # Multiplier for feed-forward layer dimension
scatter = False, # Whether to scatter to 4d representing spatial dimensions
)
# Creating random tensors for input data
frmi = torch.randn(1, 1, 32, 32, 32) # Random tensor for FRMI data
eeg = torch.randn(1, 32, 128) # Random tensor for EEG data
# Passing the input data through the model to get the output
output = model(frmi, eeg)
# Printing the shape of the output tensor
print(output.shape)
make style
to format the codemake check_code_quality
to check code quality (PEP8 basically)black .
ruff . --fix
MIT
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Implement the scatter in the end of the decoder to output spatial outputs which are 4d?
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Implement a full model with the depth of the decoder layers
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Change all the MHAs to Multi Query Attentions
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Double check popular brain scan EEG and FRMI AI papers to double check tensor shape