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""" |
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Purpose : |
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""" |
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import torch.nn |
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import torch |
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import torch.nn as nn |
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from .attention_unet3d import AttUnet |
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from .unet3d import UNet, UNetDeepSup |
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__author__ = "Chethan Radhakrishna and Soumick Chatterjee" |
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__credits__ = ["Chethan Radhakrishna", "Soumick Chatterjee"] |
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__license__ = "GPL" |
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__version__ = "1.0.0" |
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__maintainer__ = "Chethan Radhakrishna" |
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__email__ = "chethan.radhakrishna@st.ovgu.de" |
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__status__ = "Development" |
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class WNet3dAttUNet(nn.Module): |
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""" |
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Attention Unet implementation |
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Paper: https://arxiv.org/abs/1804.03999 |
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""" |
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def __init__(self, in_ch=1, out_ch=6, init_features=64): |
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super(WNet3dAttUNet, self).__init__() |
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self.Encoder = AttUnet(in_ch=in_ch, out_ch=out_ch, init_features=init_features) |
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self.Decoder = AttUnet(in_ch=out_ch, out_ch=in_ch, init_features=init_features) |
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self.activation = torch.nn.Softmax(dim=1) |
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self.Conv = nn.Conv3d(out_ch, in_ch, kernel_size=1, stride=1, padding=0) |
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def forward(self, ip, ip_mask=None, ops="both"): |
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encoder_op = self.Encoder(ip) |
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if ip_mask is not None: |
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encoder_op = ip_mask * encoder_op |
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class_prob = self.activation(encoder_op) |
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feature_rep = self.Conv(encoder_op) |
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if ops == "enc": |
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return class_prob, feature_rep |
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reconstructed_op = self.Decoder(class_prob) |
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if ops == "dec": |
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return reconstructed_op |
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if ops == "both": |
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return class_prob, feature_rep, reconstructed_op |
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else: |
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raise ValueError('Invalid ops, ops must be in [enc, dec, both]') |
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class WNet3dUNet(nn.Module): |
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""" |
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Attention Unet implementation |
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Paper: https://arxiv.org/abs/1804.03999 |
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""" |
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def __init__(self, in_ch=1, out_ch=6, init_features=64): |
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super(WNet3dUNet, self).__init__() |
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self.Encoder = UNet(in_ch=in_ch, out_ch=out_ch, init_features=init_features) |
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self.Decoder = UNet(in_ch=out_ch, out_ch=in_ch, init_features=init_features) |
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self.activation = torch.nn.Softmax(dim=1) |
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self.Conv = nn.Conv3d(out_ch, in_ch, kernel_size=1, stride=1, padding=0) |
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def forward(self, ip, ip_mask=None, ops="both"): |
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encoder_op = self.Encoder(ip) |
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if ip_mask is not None: |
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encoder_op = ip_mask * encoder_op |
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class_prob = self.activation(encoder_op) |
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feature_rep = self.Conv(encoder_op) |
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if ops == "enc": |
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return class_prob, feature_rep |
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reconstructed_op = self.Decoder(class_prob) |
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if ops == "dec": |
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return reconstructed_op |
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if ops == "both": |
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return class_prob, feature_rep, reconstructed_op |
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else: |
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raise ValueError('Invalid ops, ops must be in [enc, dec, both]') |
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class WNet3dUNetMSS(nn.Module): |
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""" |
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Attention Unet implementation |
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Paper: https://arxiv.org/abs/1804.03999 |
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""" |
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def __init__(self, in_ch=1, out_ch=6, init_features=64): |
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super(WNet3dUNetMSS, self).__init__() |
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self.Encoder = UNetDeepSup(in_ch=in_ch, out_ch=out_ch, init_features=init_features) |
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self.Decoder = UNetDeepSup(in_ch=out_ch, out_ch=in_ch, init_features=init_features) |
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self.activation = torch.nn.Softmax(dim=1) |
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self.Conv = nn.Conv3d(out_ch, in_ch, kernel_size=1, stride=1, padding=0) |
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def forward(self, ip, ip_mask=None, ops="both"): |
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encoder_op = self.Encoder(ip) |
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if ip_mask is not None: |
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encoder_op = ip_mask * encoder_op |
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class_prob = self.activation(encoder_op) |
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feature_rep = self.Conv(encoder_op) |
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if ops == "enc": |
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return class_prob, feature_rep |
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reconstructed_op = self.Decoder(class_prob) |
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if ops == "dec": |
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return reconstructed_op |
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if ops == "both": |
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return class_prob, feature_rep, reconstructed_op |
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else: |
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raise ValueError('Invalid ops, ops must be in [enc, dec, both]') |
