File size: 3,992 Bytes
cca9b7e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
"""
3D Squeeze and Excitation Modules
*****************************
3D Extensions of the following 2D squeeze and excitation blocks:
1. `Channel Squeeze and Excitation <https://arxiv.org/abs/1709.01507>`_
2. `Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_
3. `Channel and Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_
New Project & Excite block, designed specifically for 3D inputs
'quote'
Coded by -- Anne-Marie Rickmann (https://github.com/arickm)
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels, reduction_ratio=2):
"""
Args:
num_channels (int): No of input channels
reduction_ratio (int): By how much should the num_channels should be reduced
"""
super(ChannelSELayer3D, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool3d(1)
num_channels_reduced = num_channels // reduction_ratio
self.reduction_ratio = reduction_ratio
self.fc1 = nn.Linear(num_channels, num_channels_reduced, bias=True)
self.fc2 = nn.Linear(num_channels_reduced, num_channels, bias=True)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
batch_size, num_channels, D, H, W = x.size()
# Average along each channel
squeeze_tensor = self.avg_pool(x)
# channel excitation
fc_out_1 = self.relu(self.fc1(squeeze_tensor.view(batch_size, num_channels)))
fc_out_2 = self.sigmoid(self.fc2(fc_out_1))
output_tensor = torch.mul(x, fc_out_2.view(batch_size, num_channels, 1, 1, 1))
return output_tensor
class SpatialSELayer3D(nn.Module):
"""
3D extension of SE block -- squeezing spatially and exciting channel-wise described in:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018*
"""
def __init__(self, num_channels):
"""
Args:
num_channels (int): No of input channels
"""
super(SpatialSELayer3D, self).__init__()
self.conv = nn.Conv3d(num_channels, 1, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, weights=None):
"""
Args:
weights (torch.Tensor): weights for few shot learning
x: X, shape = (batch_size, num_channels, D, H, W)
Returns:
(torch.Tensor): output_tensor
"""
# channel squeeze
batch_size, channel, D, H, W = x.size()
if weights:
weights = weights.view(1, channel, 1, 1)
out = F.conv2d(x, weights)
else:
out = self.conv(x)
squeeze_tensor = self.sigmoid(out)
# spatial excitation
output_tensor = torch.mul(x, squeeze_tensor.view(batch_size, 1, D, H, W))
return output_tensor
class ChannelSpatialSELayer3D(nn.Module):
"""
3D extension of concurrent spatial and channel squeeze & excitation:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, arXiv:1803.02579*
"""
def __init__(self, num_channels, reduction_ratio=2):
"""
Args:
num_channels (int): No of input channels
reduction_ratio (int): By how much should the num_channels should be reduced
"""
super(ChannelSpatialSELayer3D, self).__init__()
self.cSE = ChannelSELayer3D(num_channels, reduction_ratio)
self.sSE = SpatialSELayer3D(num_channels)
def forward(self, input_tensor):
output_tensor = torch.max(self.cSE(input_tensor), self.sSE(input_tensor))
return output_tensor
|