added dense block Unet architecture

BrainMapperUNet.py

@@ -20,6 +20,199 @@ import torch.nn as nn
 import utils.modules as modules
 
 
+class BrainMapperResUNet3D(nn.Module):
+    """Architecture class for Residual DenseBlock BrainMapper 3D 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_depth': 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
+    """
+
+    def __init__(self, parameters):
+        super(BrainMapperResUNet3D, self).__init__()
+
+        original_input_channels = parameters['input_channels']
+        original_output_channels = parameters['output_channels']
+
+        self.encoderBlock1 = modules.DensEncoderBlock3D(parameters)
+        parameters['input_channels'] = parameters['output_channels']
+        self.encoderBlock2 = modules.DensEncoderBlock3D(parameters)
+        self.encoderBlock3 = modules.DensEncoderBlock3D(parameters)
+        self.encoderBlock4 = modules.DensEncoderBlock3D(parameters)
+
+        self.bottleneck = modules.DensBlock3D(parameters)
+
+        parameters['input_channels'] = parameters['output_channels'] * 2
+        self.decoderBlock1 = modules.DensDecoderBlock3D(parameters)
+        self.decoderBlock2 = modules.DensDecoderBlock3D(parameters)
+        self.decoderBlock3 = modules.DensDecoderBlock3D(parameters)
+        self.decoderBlock4 = modules.DensDecoderBlock3D(parameters)
+
+        parameters['input_channels'] = parameters['output_channels']
+        self.classifier = modules.DensClassifierBlock3D(parameters)
+
+        parameters['input_channels'] = original_input_channels
+        parameters['output_channels'] = original_output_channels
+
+    def forward(self, X):
+        """Forward pass for 3D U-net
+
+        Function computing the forward pass through the 3D U-Net
+        The input to the function is the dMRI map
+
+        Args:
+            X (torch.tensor): Input dMRI map, shape = (N x C x D x H x W) 
+
+        Returns:
+            probability_map (torch.tensor): Output forward passed tensor through the U-net block
+        """
+
+        Y_encoder_1, Y_np1, _ = self.encoderBlock1.forward(X)
+        Y_encoder_2, Y_np2, _ = self.encoderBlock2.forward(
+            Y_encoder_1)
+
+        del Y_encoder_1
+
+        Y_encoder_3, Y_np3, _ = self.encoderBlock3.forward(
+            Y_encoder_2)
+
+        del Y_encoder_2
+
+        Y_encoder_4, Y_np4, _ = self.encoderBlock4.forward(
+            Y_encoder_3)
+
+        del Y_encoder_3
+
+        Y_bottleNeck = self.bottleneck.forward(Y_encoder_4)
+
+        del Y_encoder_4
+
+        Y_decoder_1 = self.decoderBlock1.forward(
+            Y_bottleNeck, Y_np4)
+
+        del Y_bottleNeck, Y_np4
+
+        Y_decoder_2 = self.decoderBlock2.forward(
+            Y_decoder_1, Y_np3)
+
+        del Y_decoder_1, Y_np3
+
+        Y_decoder_3 = self.decoderBlock3.forward(
+            Y_decoder_2, Y_np2)
+
+        del Y_decoder_2, Y_np2
+
+        Y_decoder_4 = self.decoderBlock4.forward(
+            Y_decoder_3, Y_np1)
+
+        del Y_decoder_3, Y_np1
+
+        probability_map = self.classifier.forward(Y_decoder_4)
+
+        del Y_decoder_4
+
+        return probability_map
+
+    def save(self, path):
+        """Model Saver
+
+        Function saving the model with all its parameters to a given path.
+        The path must end with a *.model argument.
+
+        Args:
+            path (str): Path string
+        """
+
+        print("Saving Model... {}".format(path))
+        torch.save(self, path)
+
+    @property
+    def test_if_cuda(self):
+        """Cuda Test
+
+        This function tests if the model parameters are allocated to a CUDA enabled GPU.
+
+        Returns:
+            bool: Flag indicating True if the tensor is stored on the GPU and Flase otherwhise
+        """
+
+        return next(self.parameters()).is_cuda
+
+    def predict(self, X, device=0):
+        """Post-training Output Prediction
+
+        This function predicts the output of the of the U-net post-training
+
+        Args:
+            X (torch.tensor): input dMRI volume
+            device (int/str): Device type used for training (int - GPU id, str- CPU)
+
+        Returns:
+            prediction (ndarray): predicted output after training
+
+        """
+        self.eval()  # PyToch module setting network to evaluation mode
+
+        if type(X) is np.ndarray:
+            X = torch.tensor(X, requires_grad=False).type(torch.FloatTensor)
+        elif type(X) is torch.Tensor and not X.is_cuda:
+            X = X.type(torch.FloatTensor).cuda(device, non_blocking=True)
+
+        # .cuda() call transfers the densor from the CPU to the GPU if that is the case.
+        # Non-blocking argument lets the caller bypas synchronization when necessary
+
+        with torch.no_grad():  # Causes operations to have no gradients
+            output = self.forward(X)
+
+        _, idx = torch.max(output, 1)
+
+        # We retrieve the tensor held by idx (.data), and map it to a cpu as an ndarray
+        idx = idx.data.cpu().numpy()
+
+        prediction = np.squeeze(idx)
+
+        del X, output, idx
+
+        return prediction
+
+    def reset_parameters(self):
+        """Parameter Initialization
+
+        This function (re)initializes the parameters of the defined network.
+        This function is a wrapper for the reset_parameters() function defined for each module. 
+        More information can be found here: https://discuss.pytorch.org/t/what-is-the-default-initialization-of-a-conv2d-layer-and-linear-layer/16055 + https://discuss.pytorch.org/t/how-to-reset-model-weights-to-effectively-implement-crossvalidation/53859 
+        An alternative (re)initialization method is described here: https://discuss.pytorch.org/t/how-to-reset-variables-values-in-nn-modules/32639 
+        """
+
+        print("Initializing network parameters...")
+
+        for _, module in self.named_children():
+            for _, submodule in module.named_children():
+                for _, subsubmodule in submodule.named_children():
+                    if isinstance(subsubmodule, (torch.nn.PReLU, torch.nn.Dropout3d, torch.nn.MaxPool3d)) == False:
+                        subsubmodule.reset_parameters()
+
+        print("Initialized network parameters!")
+
+
 class BrainMapperUNet3D(nn.Module):
     """Architecture class for Traditional BrainMapper 3D U-net.
 

run.py

@@ -40,7 +40,7 @@ import torch.utils.data as data
 import numpy as np
 
 from solver import Solver
-from BrainMapperUNet import BrainMapperUNet3D
+from BrainMapperUNet import BrainMapperUNet3D, BrainMapperResUNet3D
 from utils.data_utils import get_datasets, data_test_train_validation_split, update_shuffling_flag, create_folder
 import utils.data_evaluation_utils as evaluations
 from utils.data_logging_utils import LogWriter
@@ -149,7 +149,9 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
             BrainMapperModel = torch.load(
                 training_parameters['pre_trained_path'])
         else:
-            BrainMapperModel = BrainMapperUNet3D(network_parameters)
+            # BrainMapperModel = BrainMapperUNet3D(network_parameters)
+            BrainMapperModel = BrainMapperResUNet3D(network_parameters)
+
 
         BrainMapperModel.reset_parameters()
 

utils/modules.py

@@ -19,6 +19,335 @@ import torch.nn.functional as F
 
 # TODO: Currently, it appears that we are using constant size filters. We will need to adjust this in the network architecture, to allow it to encode/decode information!
 
+# ResBlock 3D UNet:
+
+
+class DensBlock3D(nn.Module):
+    """Parent class for a 3D convolutional residual block.
+
+    This class represents a generic parent class for a convolutional residual 3D encoder or decoder block.
+    The class represents a subclass/child class of nn.Module, inheriting its functionality.
+
+    Args:
+        parameters (dict): Contains information on kernel size, number of channels, number of filters, and if convolution is strided.
+        parameters = {
+            'kernel_heigth': 5
+            'kernel_width': 5
+            'kernel_depth' : 5
+            'input_channels': 64
+            'output_channels': 64
+            'convolution_stride': 1
+            'dropout': 0.2
+        }
+
+    Returns:
+        torch.tensor: Output forward passed tensor
+    """
+
+    def __init__(self, parameters):
+        super(DensBlock3D, self).__init__()
+
+        # We first calculate the amount of zero padding required (http://cs231n.github.io/convolutional-networks/)
+        padding_heigth = int((parameters['kernel_heigth'] - 1) / 2)
+        padding_width = int((parameters['kernel_width'] - 1) / 2)
+        padding_depth = int((parameters['kernel_depth'] - 1) / 2)
+
+        convolutional_layer2_input = int(parameters['input_channels'] + parameters['output_channels'])
+        convolutional_layer3_input = int(convolutional_layer2_input + parameters['output_channels'])
+
+        self.convolutional_layer1 = nn.Sequential(
+            nn.InstanceNorm3d(num_features=parameters['input_channels']),
+            nn.PReLU(),
+            nn.Conv3d(
+                in_channels=parameters['input_channels'],
+                out_channels=parameters['output_channels'],
+                kernel_size=(parameters['kernel_depth'],
+                             parameters['kernel_heigth'],
+                             parameters['kernel_width']),
+                stride=parameters['convolution_stride'],
+                padding=(padding_depth, padding_heigth, padding_width)
+            ),
+        )
+
+        self.convolutional_layer2 = nn.Sequential(
+            nn.InstanceNorm3d(num_features=convolutional_layer2_input),
+            nn.PReLU(),
+            nn.Conv3d(
+                in_channels=convolutional_layer2_input,
+                out_channels=parameters['output_channels'],
+                kernel_size=(parameters['kernel_depth'],
+                             parameters['kernel_heigth'],
+                             parameters['kernel_width']),
+                stride=parameters['convolution_stride'],
+                padding=(padding_depth, padding_heigth, padding_width)
+            ),
+        )
+
+        self.convolutional_layer3 = nn.Sequential(
+            nn.InstanceNorm3d(num_features=convolutional_layer3_input),
+            nn.PReLU(),
+            nn.Conv3d(
+                in_channels=convolutional_layer3_input,
+                out_channels=parameters['output_channels'],
+                kernel_size=(parameters['kernel_classification'],
+                             parameters['kernel_classification'],
+                             parameters['kernel_classification']),
+                stride=parameters['convolution_stride'],
+                padding=(0, 0, 0)
+            ),
+        )
+
+        # Other activation functions which might be interesting to test:
+        # More reading: https://arxiv.org/abs/1706.02515 ; https://mlfromscratch.com/activation-functions-explained/#/
+        # self.activation = nn.SELU()
+        # self.activation = nn.ELU()
+        # self.activation = nn.ReLU()
+
+        # Instance normalisation is used to the the small batch size, and as it has shown promise during the experiments with the simple network.
+
+        if parameters['dropout'] > 0:
+            self.dropout_needed = True
+            self.dropout = nn.Dropout3d(parameters['dropout'])
+        else:
+            self.dropout_needed = False
+
+    def forward(self, X):
+        """Forward pass
+
+        Function computing the forward pass through the convolutional layer.
+        The input to the function is a torch tensor of shape N (batch size) x C (number of channels) x D (input depth) x H (input heigth) x W (input width)
+
+        Args:
+            X (torch.tensor): Input tensor, shape = (N x C x D x H x W) 
+
+        Returns:
+            torch.tensor: Output forward passed tensor
+        """
+
+        feature_map1 = self.convolutional_layer1(X)
+        feature_map2 = self.convolutional_layer2(torch.cat((X, feature_map1), dim= 1))
+        feature_map3 = self.convolutional_layer3(torch.cat((X, feature_map1, feature_map2), dim= 1))
+
+        return feature_map3
+
+
+class DensEncoderBlock3D(DensBlock3D):
+    """Forward 3D encoder path block for a U-net.
+
+    This class creates a dense encoder block following the architecture:
+        DensBlock -> Non-linear Activation -> Batch Normalisation -> MaxPool
+
+    Args:
+        parameters (dict): Contains information relevant parameters
+        parameters = {
+            'kernel_heigth': 5
+            'kernel_width': 5
+            'kernel_depth': 5
+            'input_channels': 64
+            'output_channels': 64
+            'convolution_stride': 1
+            'dropout': 0.2
+            'pool_kernel_size': 2
+            'pool_stride': 2
+        }
+
+    Returns:
+        Y (torch.tensor): Output forward passed tensor through the encoder block, with maxpool
+        Y_np (torch.tensor): Output forward passed tensor through the encoder block, with no pooling
+        pool_indices (torch.tensor): Indices for unpooling
+    """
+
+    def __init__(self, parameters):
+        super(DensEncoderBlock3D, self).__init__(parameters)
+        self.maxpool = nn.MaxPool3d(
+            kernel_size=parameters['pool_kernel_size'],
+            stride=parameters['pool_stride'],
+            # This option returns the max index along with the outputs, useful for MaxUnpool2D
+            return_indices=True
+        )
+
+    def forward(self, X):
+        """Forward pass for U-net 3D encoder block
+
+        Function computing the forward pass through the encoder block.
+        The input to the function is a torch tensor of shape N (batch size) x C (number of channels) x D (depth) x H (input heigth) x W (input width)
+
+        Args:
+            X (torch.tensor): Input tensor, shape = (N x C x D x H x W) 
+
+        Returns:
+            Y (torch.tensor): Output forward passed tensor through the encoder block, with maxpool
+            Y_np (torch.tensor): Output forward passed tensor through the encoder block, with no pooling
+            pool_indices (torch.tensor): Indices for unpooling
+
+        """
+
+        # Two convolutions are used, as per the original UNet Paper
+
+        Y_np = super(DensEncoderBlock3D, self).forward(X)
+
+        if self.dropout_needed:
+            Y_np = self.dropout(Y_np)
+
+        Y, pool_indices = self.maxpool(Y_np)
+
+        return Y, Y_np, pool_indices
+
+
+class DensDecoderBlock3D(DensBlock3D):
+    """Forward 3D decoder path block for a U-net.
+
+    This class creates a simple encoder block following the architecture:
+        Strided Convolution (or) MaxUnpool -> Convolution -> Non-linear Activation -> Batch Normalisation
+
+    Args:
+        parameters (dict): Contains information relevant parameters
+        parameters = {
+            'kernel_heigth': 5
+            'kernel_width': 5
+            'kernel_depth': 5
+            'input_channels': 64
+            'output_channels': 64
+            'convolution_stride': 1
+            'dropout': 0.2
+            'pool_kernel_size': 2
+            'pool_stride': 2
+            'up_mode': 'upconv'
+        }
+
+    Returns:
+        Y (torch.tensor): Output forward passed tensor through the decoder block
+
+    """
+
+    def __init__(self, parameters):
+        super(DensDecoderBlock3D, self).__init__(parameters)
+        self.up_mode = parameters['up_mode']
+        if self.up_mode == 'upconv':  # Attention - this will need to be checked to confirm that it is working!
+            self.up = nn.ConvTranspose3d(
+                in_channels=int(parameters['input_channels']/2),
+                out_channels=parameters['output_channels'],
+                kernel_size=parameters['pool_kernel_size'],
+                stride=parameters['pool_stride'],
+            )
+        elif self.up_mode == 'upsample':
+            self.up = nn.Sequential(
+                nn.Upsample(
+                    mode='nearest',
+                    scale_factor=2,
+                ),
+                nn.Conv3d(
+                    in_channels=parameters['input_channels'],
+                    out_channels=parameters['output_channels'],
+                    kernel_size=1,
+                )
+            )
+        elif self.up_mode == 'unpool':
+            self.up = nn.MaxUnpool3d(
+                kernel_size=parameters['pool_kernel_size'],
+                stride=parameters['pool_stride']
+            )
+
+    def forward(self, X, Y_encoder=None, pool_indices=None):
+        """Forward pass for U-net decoder block
+
+        Function computing the forward pass through the decoder block.
+        The input to the function is a torch tensor of shape N (batch size) x C (number of channels) x D (input depth) x H (input heigth) x W (input width).
+        A second input is a tensor for the skip connection, of shape (N x C x D x H x W); that defaults to None.
+        The function also takes the previous pool indices, for the unpooling operation; they aslo default to None.
+
+        Args:
+            X (torch.tensor): Input tensor, shape = (N x C x D x H x W) 
+            Y_encoder (torch.tensor): Skip-connection tensor, shape = (N x C x H x W)
+            pool_indices (torch.tensor): Indices for unpooling
+
+        Returns:
+            Y (torch.tensor): Output forward passed tensor through the decoder block
+        """
+
+        # ATTENTION: As of this code version, only "upconv" works! Debugging is ongoing for upconv and upsample!
+        # It seems that errors are generated by variable filter sizes and the unorthodox input sizes 91x109x91.
+
+        if self.up_mode == 'unpool':
+            upsampling = self.up(X, pool_indices, output_size=Y_encoder.size())
+        elif self.up_mode == 'upsample':
+            upsampling = self.up(X)
+        elif self.up_mode == 'upconv':
+            upsampling = self.up(X, output_size=Y_encoder.size())
+
+        if Y_encoder is None:
+            concatenation = upsampling
+        else:
+            concatenation = torch.cat((Y_encoder, upsampling), dim=1)
+
+        Y = super(DensDecoderBlock3D, self).forward(concatenation)
+
+        if self.dropout_needed:
+            Y = self.dropout(Y)
+
+        return Y
+
+
+class DensClassifierBlock3D(nn.Module):
+    """Classifier block for a U-net.
+
+    This class creates a simple classifier block following the architecture:
+
+    Args:
+        parameters (dict): Contains information relevant parameters
+        parameters = {
+            'kernel_heigth': 5
+            'kernel_width': 5
+            'kernel_depth': 5
+            'kernel_classification': 1
+            'input_channels': 1
+            'output_channels': 1
+            'convolution_stride': 1
+            'dropout': 0.2
+            'pool_kernel_size': 2
+            'pool_stride': 2
+            'up_mode': 'upconv'
+            'number_of_classes': 1
+        }
+
+    Returns:
+        Y (torch.tensor): Output forward passed tensor through the decoder block
+
+    """
+
+    def __init__(self, parameters):
+        super(DensClassifierBlock3D, self).__init__()
+        self.convolutional_layer = nn.Conv3d(
+            in_channels=parameters['input_channels'],
+            out_channels=parameters['number_of_classes'],
+            kernel_size=parameters['kernel_classification'],
+            stride=parameters['convolution_stride'],
+        )
+
+        # TODO: Might be wworth looking at GANS for image generation, and adding padding
+
+    def forward(self, X):
+        """Forward pass for U-net classifier block
+
+        Function computing the forward pass through the classifier block.
+        The input to the function is a torch tensor of shape N (batch size) x C (number of channels) x D (input depth) x H (input heigth) x W (input width).
+
+        Args:
+            X (torch.tensor): Input tensor, shape = (N x C x D x H x W) 
+
+        Returns:
+            logits (torch.tensor): Output logits from forward pass tensor through the classifier block
+        """
+
+        logits = self.convolutional_layer(X)
+
+        # TODO: Currently, this has no activation function. Might be worth considering adding a tanh activation function, similar to GANs
+        # For refernece : https://machinelearningmastery.com/how-to-implement-pix2pix-gan-models-from-scratch-with-keras/
+        # For refernece 2: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
+
+        return logits
+
+
 # Classical 3D UNet: