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unet.py
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unet.py
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import torch
from utils import utils
from torch import nn
class EncodingBlock(nn.Module):
"""Convolutional batch norm block with relu activation (main block used in the encoding steps)"""
def __init__(self, in_size, out_size, kernel_size=3, padding=0, stride=1, dilation=1, batch_norm=True,
dropout=False, prob=0.5):
super().__init__()
if batch_norm:
# reflection padding for same size output as input (reflection padding has shown better results than zero padding)
layers = [nn.ReflectionPad2d(padding=(kernel_size - 1) // 2),
nn.Conv2d(in_size, out_size, kernel_size=kernel_size, padding=padding, stride=stride,
dilation=dilation),
nn.PReLU(),
nn.BatchNorm2d(out_size),
nn.ReflectionPad2d(padding=(kernel_size - 1) // 2),
nn.Conv2d(out_size, out_size, kernel_size=kernel_size, padding=padding, stride=stride,
dilation=dilation),
nn.PReLU(),
nn.BatchNorm2d(out_size),
]
else:
layers = [nn.ReflectionPad2d(padding=(kernel_size - 1) // 2),
nn.Conv2d(in_size, out_size, kernel_size=kernel_size, padding=padding, stride=stride,
dilation=dilation),
nn.PReLU(),
nn.ReflectionPad2d(padding=(kernel_size - 1) // 2),
nn.Conv2d(out_size, out_size, kernel_size=kernel_size, padding=padding, stride=stride,
dilation=dilation),
nn.PReLU(), ]
if dropout:
layers.append(nn.Dropout(p=prob))
self.EncodingBlock = nn.Sequential(*layers)
def forward(self, input_data):
output = self.EncodingBlock(input_data)
return output
class DecodingBlock(nn.Module):
"""Module in the decoding section of the UNet"""
def __init__(self, in_size, out_size, batch_norm=False, upsampling=True):
super().__init__()
if upsampling:
self.up = nn.Sequential(utils.Interpolate(mode='bilinear', scale_factor=2),
nn.Conv2d(in_size, out_size, kernel_size=1))
else:
self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)
self.conv = EncodingBlock(in_size, out_size, batch_norm=batch_norm)
def forward(self, input1, input2):
output2 = self.up(input2)
output1 = nn.functional.interpolate(input1, output2.size()[2:], mode='bilinear', align_corners=True)
return self.conv(torch.cat([output1, output2], 1))
class UNet(nn.Module):
"""Main UNet architecture"""
def __init__(self, classes, in_channels, dropout=False, prob=0.5):
"""Initialize the UNet.
Args:
classes (int): number of classes for output mask
(or you can think as a number of channels of output mask).
in_channels (int): number of input channels for the model, default is 3 (RGB images).
dropout (bool, optional): spatial dropout rate in range. Defaults to False.
prob (float, optional): dropout probability. Defaults to 0.5.
"""
super().__init__()
self.conv1 = EncodingBlock(in_channels, 64, dropout=dropout, prob=prob)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = EncodingBlock(64, 128, dropout=dropout, prob=prob)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = EncodingBlock(128, 256, dropout=dropout, prob=prob)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = EncodingBlock(256, 512, dropout=dropout, prob=prob)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = EncodingBlock(512, 1024, dropout=dropout, prob=prob)
self.decode4 = DecodingBlock(1024, 512)
self.decode3 = DecodingBlock(512, 256)
self.decode2 = DecodingBlock(256, 128)
self.decode1 = DecodingBlock(128, 64)
self.final = nn.Conv2d(64, classes, kernel_size=1)
def forward(self, input_data):
"""Foward function use during trainning.
Args:
input_data (Tensor): tensor containing the image.
Returns:
Tensor: tensor containing the result from the model.
"""
conv1 = self.conv1(input_data)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
decode4 = self.decode4(conv4, center)
decode3 = self.decode3(conv3, decode4)
decode2 = self.decode2(conv2, decode3)
decode1 = self.decode1(conv1, decode2)
final = nn.functional.interpolate(self.final(decode1), input_data.size()[2:], mode='bilinear', align_corners=True)
return final
class UNetSmall(nn.Module):
"""Main UNetSmall architecture, less deep version of Unet"""
def __init__(self, classes, in_channels, dropout=False, prob=0.5):
"""Initialize the UNetSmall.
Args:
classes (int): number of classes for output mask
(or you can think as a number of channels of output mask).
in_channels (int): number of input channels for the model, default is 3 (RGB images).
dropout (bool, optional): spatial dropout rate in range. Defaults to False.
prob (float, optional): dropout probability. Defaults to 0.5.
"""
super().__init__()
self.conv1 = EncodingBlock(in_channels, 32, dropout=dropout, prob=prob)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = EncodingBlock(32, 64, dropout=dropout, prob=prob)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = EncodingBlock(64, 128, dropout=dropout, prob=prob)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.center = EncodingBlock(128, 256, dropout=dropout, prob=prob)
self.decode3 = DecodingBlock(256, 128)
self.decode2 = DecodingBlock(128, 64)
self.decode1 = DecodingBlock(64, 32)
self.final = nn.Conv2d(32, classes, kernel_size=1)
def forward(self, input_data):
"""Foward function use during trainning.
Args:
input_data (Tensor): tensor containing the image.
Returns:
Tensor: tensor containing the result from the model.
"""
conv1 = self.conv1(input_data)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
center = self.center(maxpool3)
decode3 = self.decode3(conv3, center)
decode2 = self.decode2(conv2, decode3)
decode1 = self.decode1(conv1, decode2)
final = nn.functional.interpolate(self.final(decode1), input_data.size()[2:], mode='bilinear', align_corners=True)
return final