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""" |
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3D Squeeze and Excitation Modules |
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***************************** |
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3D Extensions of the following 2D squeeze and excitation blocks: |
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1. `Channel Squeeze and Excitation <https://arxiv.org/abs/1709.01507>`_ |
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2. `Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_ |
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3. `Channel and Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_ |
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New Project & Excite block, designed specifically for 3D inputs |
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'quote' |
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Coded by -- Anne-Marie Rickmann (https://github.com/arickm) |
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""" |
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import torch |
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from torch import nn as nn |
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from torch.nn import functional as F |
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class ChannelSELayer3D(nn.Module): |
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""" |
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3D extension of Squeeze-and-Excitation (SE) block described in: |
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*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507* |
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*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238* |
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""" |
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def __init__(self, num_channels, reduction_ratio=2): |
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""" |
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Args: |
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num_channels (int): No of input channels |
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reduction_ratio (int): By how much should the num_channels should be reduced |
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""" |
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super(ChannelSELayer3D, self).__init__() |
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self.avg_pool = nn.AdaptiveAvgPool3d(1) |
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num_channels_reduced = num_channels // reduction_ratio |
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self.reduction_ratio = reduction_ratio |
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self.fc1 = nn.Linear(num_channels, num_channels_reduced, bias=True) |
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self.fc2 = nn.Linear(num_channels_reduced, num_channels, bias=True) |
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self.relu = nn.ReLU() |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x): |
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batch_size, num_channels, D, H, W = x.size() |
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squeeze_tensor = self.avg_pool(x) |
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fc_out_1 = self.relu(self.fc1(squeeze_tensor.view(batch_size, num_channels))) |
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fc_out_2 = self.sigmoid(self.fc2(fc_out_1)) |
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output_tensor = torch.mul(x, fc_out_2.view(batch_size, num_channels, 1, 1, 1)) |
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return output_tensor |
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class SpatialSELayer3D(nn.Module): |
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""" |
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3D extension of SE block -- squeezing spatially and exciting channel-wise described in: |
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*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018* |
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""" |
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def __init__(self, num_channels): |
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""" |
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Args: |
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num_channels (int): No of input channels |
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""" |
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super(SpatialSELayer3D, self).__init__() |
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self.conv = nn.Conv3d(num_channels, 1, 1) |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x, weights=None): |
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""" |
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Args: |
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weights (torch.Tensor): weights for few shot learning |
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x: X, shape = (batch_size, num_channels, D, H, W) |
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Returns: |
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(torch.Tensor): output_tensor |
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""" |
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batch_size, channel, D, H, W = x.size() |
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if weights: |
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weights = weights.view(1, channel, 1, 1) |
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out = F.conv2d(x, weights) |
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else: |
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out = self.conv(x) |
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squeeze_tensor = self.sigmoid(out) |
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output_tensor = torch.mul(x, squeeze_tensor.view(batch_size, 1, D, H, W)) |
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return output_tensor |
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class ChannelSpatialSELayer3D(nn.Module): |
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""" |
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3D extension of concurrent spatial and channel squeeze & excitation: |
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*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, arXiv:1803.02579* |
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""" |
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def __init__(self, num_channels, reduction_ratio=2): |
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""" |
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Args: |
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num_channels (int): No of input channels |
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reduction_ratio (int): By how much should the num_channels should be reduced |
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""" |
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super(ChannelSpatialSELayer3D, self).__init__() |
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self.cSE = ChannelSELayer3D(num_channels, reduction_ratio) |
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self.sSE = SpatialSELayer3D(num_channels) |
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def forward(self, input_tensor): |
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output_tensor = torch.max(self.cSE(input_tensor), self.sSE(input_tensor)) |
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return output_tensor |
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