import logging
file_logger = logging.getLogger(__name__)
from delira import get_backends
if "TORCH" in get_backends():
import torch.nn as nn
import torch.nn.functional as F
from .classification_network import ClassificationNetworkBasePyTorch
[docs] class VGG3DClassificationNetworkPyTorch(ClassificationNetworkBasePyTorch):
"""
Exemplaric VGG Network for 3D Classification
Notes
-----
The original network has been adjusted to fit for 3D data
References
----------
https://arxiv.org/abs/1409.1556
See Also
--------
:class:`ClassificationNetworkBasePyTorch`
"""
def __init__(self, in_channels: int, n_outputs: int, **kwargs):
"""
Parameters
----------
in_channels : int
number of input channels
n_outputs : int
number of outputs
**kwargs :
additional keyword arguments
"""
super().__init__(in_channels, n_outputs, **kwargs)
[docs] @staticmethod
def _build_model(in_channels: int, n_outputs: int, **kwargs):
"""
Helper Function to build the actual model
Parameters
----------
in_channels : int
number of input channels
n_outputs : int
number of outputs
**kwargs :
additional keyword arguments
Returns
-------
torch.nn.Module
ensembeled model
"""
class VGGlike3D(nn.Module):
def __init__(self, in_channels=3, n_outputs=2):
super().__init__()
self.conv1 = nn.Conv3d(
in_channels, 64, 3, stride=2, padding=0)
self.conv2 = nn.Conv3d(64, 64, 3, stride=1, padding=0)
self.bn1 = nn.BatchNorm3d(64)
self.conv3 = nn.Conv3d(64, 128, 3, stride=2, padding=0)
self.conv4 = nn.Conv3d(128, 128, 3, stride=1, padding=0)
self.bn2 = nn.BatchNorm3d(128)
self.conv5 = nn.Conv3d(128, 256, 3, stride=2, padding=0)
self.conv6 = nn.Conv3d(256, 256, (1, 3, 3), stride=1,
padding=0)
self.bn3 = nn.BatchNorm3d(256)
self.pool = nn.AdaptiveMaxPool3d((1, 16, 16))
self.fc1 = nn.Linear(in_features=65536, out_features=1024)
self.dropout1 = nn.Dropout(p=0.5, inplace=True)
self.fc2 = nn.Linear(1024, 64)
self.dropout2 = nn.Dropout(p=0.1, inplace=True)
self.fc3 = nn.Linear(64, n_outputs)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.bn1(x)
x = F.leaky_relu(x, inplace=True)
x = self.conv3(x)
x = self.conv4(x)
x = self.bn2(x)
x = F.leaky_relu(x, inplace=True)
x = self.conv5(x)
x = self.conv6(x)
x = self.bn3(x)
x = F.leaky_relu(x, inplace=True)
x = self.pool(x)
x = x.view(x.size(0), -1)
x = F.leaky_relu(self.dropout1(self.fc1(x)), inplace=True)
x = F.leaky_relu(self.dropout2(self.fc2(x)), inplace=True)
x = F.softmax(self.fc3(x), dim=1)
return x
_model = VGGlike3D(in_channels=in_channels, n_outputs=n_outputs)
return _model