BrainMapperUNet.py 4.13 KB
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"""Brain Mapper U-Net Architecture

Description:
-------------
This folder contains the Pytorch implementation of the core U-net architecture.
This arcitecture predicts functional connectivity rsfMRI from structural connectivity information from dMRI.

Usage
-------------
To use this module, import it and instantiate is as you wish:

    from BrainMapperUNet import BrainMapperUNet
    deep_learning_model = BrainMapperUnet(parameters)

"""

import numpy as np
import torch
import torch.nn as nn
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import utils.modules as modules
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class BrainMapperUNet(nn.Module):
    """Architecture class BrainMapper U-net.

    This class contains the pytorch implementation of the U-net architecture underpinning the BrainMapper project.

    Args:
        parameters (dict): Contains information relevant parameters
        parameters = {
            'kernel_heigth': 5
            'kernel_width': 5
            'kernel_classification': 1
            'input_channels': 1
            'output_channels': 64
            'convolution_stride': 1
            'dropout': 0.2
            'pool_kernel_size': 2
            'pool_stride': 2
            'up_mode': 'upconv'
            'number_of_classes': 1
        }
    
    Returns:
        probability_map (torch.tensor): Output forward passed tensor through the U-net block

    Raises:
        None
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    """
    
    def __init__(self, parameters):
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        super(BrainMapperUNet, self).__init__()

        # TODO: currently, architecture based on QuickNAT - need to adjust parameter values accordingly!

        self.encoderBlock1 = modules.EncoderBlock(parameters)
        parameters['input_channels'] = parameters['output_channels']
        self.encoderBlock2 = modules.EncoderBlock(parameters)
        self.encoderBlock3 = modules.EncoderBlock(parameters)
        self.encoderBlock4 = modules.EncoderBlock(parameters)

        self.bottleneck = modules.ConvolutionalBlock(parameters)

        parameters['input_channels'] = parameters['output_channels'] * 2.0
        self.decoderBlock1 = modules.DecoderBlock(parameters)
        self.decoderBlock2 = modules.DecoderBlock(parameters)
        self.decoderBlock3 = modules.DecoderBlock(parameters)
        self.decoderBlock4 = modules.DecoderBlock(parameters)

        parameters['input_channels'] = parameters['output_channels']
        self.classifier = modules.ClassifierBlock(parameters)
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    def forward(self, X):
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        """Forward pass for U-net

        Function computing the forward pass through the U-Net
        The input to the function is the dMRI map

        Args:
            X (torch.tensor): Input dMRI map, shape = (N x C x H x W) 

        Returns:
            probability_map (torch.tensor): Output forward passed tensor through the U-net block

        Raises:
            None

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        """
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        Y_encoder_1, Y_np1, pool_indices1 = self.encoderBlock1.forward(X)
        Y_encoder_2, Y_np2, pool_indices2 = self.encoderBlock2.forward(Y_encoder_1)
        Y_encoder_3, Y_np3, pool_indices3 = self.encoderBlock3.forward(Y_encoder_2)
        Y_encoder_4, Y_np4, pool_indices4 = self.encoderBlock4.forward(Y_encoder_3)

        Y_bottleNeck = self.bottleneck.forward(Y_encoder_4)

        Y_decoder_1 = self.decoderBlock1.forward(Y_bottleNeck, Y_np4, pool_indices4)
        Y_decoder_2 = self.decoderBlock2.forward(Y_decoder_1, Y_np3, pool_indices3)
        Y_decoder_3 = self.decoderBlock3.forward(Y_decoder_2, Y_np2, pool_indices2)
        Y_decoder_4 = self.decoderBlock4.forwrad(Y_decoder_3, Y_np1, pool_indices1)

        probability_map = self.classifier.forward(Y_decoder_4)
        
        return probability_map
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    def save(self, path):
        """
        Description
        """
        pass

    def predict(self, X):
        """
        Description
        """
        return None
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if __name__ == '__main__':

    parameters = {
        'kernel_heigth': 5,
        'kernel_width': 5,
        'kernel_classification': 1,
        'input_channels': 1,
        'output_channels': 64,
        'convolution_stride': 1,
        'dropout': 0.2,
        'pool_kernel_size': 2,
        'pool_stride': 2,
        'up_mode': 'upconv',
        'number_of_classes': 1
    }
    network = BrainMapperUNet(parameters)