BrainMapperUNet.py 2.68 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):
        """
        Description
        """
        return None

    def enable_test_dropout(self):
        """
        Description
        """
        return None
    
    def save(self, path):
        """
        Description
        """
        pass

    def predict(self, X):
        """
        Description
        """
        return None