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unet3d.py
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import torch
import torch.nn as nn
import torchvision.transforms.functional as TF
class ConvBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.conv1 = nn.Conv3d(in_c, out_c, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm3d(out_c)
self.conv2 = nn.Conv3d(out_c, out_c, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm3d(out_c)
self.relu = nn.ReLU()
def forward(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x
""" Encoder block:
It consists of an ConvBlock followed by a max pooling.
Here the number of filters doubles and the height and width half after every block.
"""
class EncoderBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.conv = ConvBlock(in_c, out_c)
self.pool = nn.MaxPool3d((2, 2, 2))
def forward(self, inputs):
x = self.conv(inputs)
p = self.pool(x)
return x, p
""" Decoder block:
The decoder block begins with a transpose convolution, followed by a concatenation with the skip
connection from the encoder block. Next comes the ConvBlock.
Here the number filters decreases by half and the height and width doubles.
"""
class DecoderBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.up = nn.ConvTranspose3d(in_c, out_c, kernel_size=2, stride=2, padding=0)
self.conv = ConvBlock(out_c+out_c, out_c)
def forward(self, inputs, skip):
x = self.up(inputs)
x = torch.cat([x, skip], 1)
x = self.conv(x)
return x
class UNet3D(nn.Module):
def __init__(self):
super().__init__()
""" Encoder """
self.e1 = EncoderBlock(3, 64)
self.e2 = EncoderBlock(64, 128)
self.e3 = EncoderBlock(128, 256)
self.e4 = EncoderBlock(256, 512)
""" Bottleneck """
self.b = ConvBlock(512, 1024)
""" Decoder """
self.d1 = DecoderBlock(1024, 512)
self.d2 = DecoderBlock(512, 256)
self.d3 = DecoderBlock(256, 128)
self.d4 = DecoderBlock(128, 64)
""" Classifier """
self.outputs = nn.Conv3d(64, 1, kernel_size=1, padding=0)
def forward(self, inputs):
""" Encoder """
s1, p1 = self.e1(inputs)
s2, p2 = self.e2(p1)
s3, p3 = self.e3(p2)
s4, p4 = self.e4(p3)
""" Bottleneck """
b = self.b(p4)
""" Decoder """
d1 = self.d1(b, s4)
d2 = self.d2(d1, s3)
d3 = self.d3(d2, s2)
d4 = self.d4(d3, s1)
""" Classifier """
outputs = self.outputs(d4)
return outputs
# -----------------
class ConvBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.conv1 = nn.Conv3d(in_c, out_c, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm3d(out_c)
self.conv2 = nn.Conv3d(out_c, out_c, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm3d(out_c)
self.relu = nn.ReLU()
def forward(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x
class EncoderBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.conv = ConvBlock(in_c, out_c)
self.pool = nn.MaxPool3d((2, 2, 2))
def forward(self, inputs):
x = self.conv(inputs)
p = self.pool(x)
return x, p
class DecoderBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.up = nn.ConvTranspose3d(in_c, out_c, kernel_size=2, stride=2, padding=0)
self.conv = ConvBlock(out_c+out_c, out_c)
def forward(self, inputs, skip):
x = self.up(inputs)
x = torch.cat([x, skip], 1)
x = self.conv(x)
return x
class UNet3DVer2(nn.Module):
def __init__(self):
super().__init__()
""" Encoder """
self.e1 = EncoderBlock(3, 64)
self.e2 = EncoderBlock(64, 128)
self.e3 = EncoderBlock(128, 256)
""" Bottleneck """
self.b = ConvBlock(256, 512)
""" Decoder """
self.d1 = DecoderBlock(512, 256)
self.d2 = DecoderBlock(256, 128)
self.d3 = DecoderBlock(128, 64)
""" Classifier """
self.outputs = nn.Conv3d(64, 1, kernel_size=1, padding=0)
def forward(self, inputs):
""" Encoder """
s1, p1 = self.e1(inputs)
s2, p2 = self.e2(p1)
s3, p3 = self.e3(p2)
""" Bottleneck """
b = self.b(p3)
""" Decoder """
d1 = self.d1(b, s3)
d2 = self.d2(d1, s2)
d3 = self.d3(d2, s1)
""" Classifier """
outputs = self.outputs(d3)
return outputs