The network architecture is illustrated in Figure 1. It consists of a contracting path (left side) and an expansive path (right side). The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each fol- lowed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a 1x1 convolution is used to map each 64- component feature vector to the desired number of classes. In total the network has 23 convolutional layers.
Encoder：左半部分，由两个3x3的卷积层（ReLU）+2x2的max polling层（stride=2）反复组成，每经过一次下采样，通道数翻倍；
Decoder：右半部分，由一个2x2的上采样卷积层（ReLU）+Concatenation（crop[3]对应的Encoder层的输出feature map然后与Decoder层的上采样结果相加）+2个3x3的卷积层（ReLU）反复构成；
最后一层通过一个1x1卷积将通道数变成期望的类别数。(Refer: https://zhuanlan.zhihu.com/p/90418337)
## Implementation using Pytorch
```python
# -*- coding: utf-8 -*-
"""Unet.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1oLnoOuSmkQjZ998vNvMzhUq_zVq81MPS
"""
import torch
import torch.nn as nn
def double_conv(in_c, out_c):
conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3),
nn.ReLU(inplace=True),
nn.Conv2d(out_c, out_c, kernel_size=3),
nn.ReLU(inplace=True)
)
return conv
def crop_img(tensor, target_tensor):
target_size = target_tensor.size()[2]
tensor_size = tensor.size()[2]
delta = tensor_size - target_size
delta = delta // 2
return tensor[:,:,delta:tensor_size-delta, delta:tensor_size-delta]
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.max_pool_2x2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.down_con_1 = double_conv(1, 64)
self.down_con_2 = double_conv(64, 128)
self.down_con_3 = double_conv(128, 256)
self.down_con_4 = double_conv(256, 512)
self.down_con_5 = double_conv(512, 1024)
self.up_trans_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=2, stride=2)
self.up_cov_1 = double_conv(1024, 512)
self.up_trans_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=2, stride=2)
self.up_cov_2 = double_conv(512, 256)
self.up_trans_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=2, stride=2)
self.up_cov_3 = double_conv(256, 128)
self.up_trans_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=2, stride=2)
self.up_cov_4 = double_conv(128, 64)
self.out = nn.Conv2d(in_channels=64, out_channels=2, kernel_size=1)
def forward(self, image):
# batch size, c, h, w
# encode
x1 = self.down_con_1(image) #
print("x1: ", x1.size())
x2 = self.max_pool_2x2(x1)
x3 = self.down_con_2(x2) #
x4 = self.max_pool_2x2(x3)
x5 = self.down_con_3(x4) #
x6 = self.max_pool_2x2(x5)
x7 = self.down_con_4(x6) #
x8 = self.max_pool_2x2(x7)
x9 = self.down_con_5(x8)
print("x9: ", x9.size())
# decoder
x = self.up_trans_1(x9)
y = crop_img(x7, x)
x = self.up_cov_1(torch.cat([x, y], 1))
x = self.up_trans_2(x)
y = crop_img(x5, x)
x = self.up_cov_2(torch.cat([x, y], 1))
x = self.up_trans_3(x)
y = crop_img(x3, x)
x = self.up_cov_3(torch.cat([x, y], 1))
x = self.up_trans_4(x)
y = crop_img(x1, x)
x = self.up_cov_4(torch.cat([x, y], 1))
y = self.out(x)
# print(x.size())
print("output:", y.size())
if __name__ == "__main__":
image = torch.rand((1, 1, 572, 572))
print("Input:", image.size())
model = UNet()
print(model(image))
```
## References
- [Implementing original U-Net from scratch using PyTorch](https://www.youtube.com/watch?v=u1loyDCoGbE)
- [paper阅读笔记 UNet](https://zhuanlan.zhihu.com/p/90418337)
U-net: replication using Pytorch [复现U-Net竟然如此简单]
## Objective
- Implement U-net use Pytorch
## Paper
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
## U-net architecture
The network architecture is illustrated in Figure 1. It consists of a contracting path (left side) and an expansive path (right side). The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each fol- lowed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a 1x1 convolution is used to map each 64- component feature vector to the desired number of classes. In total the network has 23 convolutional layers.
Encoder：左半部分，由两个3x3的卷积层（ReLU）+2x2的max polling层（stride=2）反复组成，每经过一次下采样，通道数翻倍；
Decoder：右半部分，由一个2x2的上采样卷积层（ReLU）+Concatenation（crop[3]对应的Encoder层的输出feature map然后与Decoder层的上采样结果相加）+2个3x3的卷积层（ReLU）反复构成；
最后一层通过一个1x1卷积将通道数变成期望的类别数。(Refer: https://zhuanlan.zhihu.com/p/90418337)
## Implementation using Pytorch
```python
# -*- coding: utf-8 -*-
"""Unet.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1oLnoOuSmkQjZ998vNvMzhUq_zVq81MPS
"""
import torch
import torch.nn as nn
def double_conv(in_c, out_c):
conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3),
nn.ReLU(inplace=True),
nn.Conv2d(out_c, out_c, kernel_size=3),
nn.ReLU(inplace=True)
)
return conv
def crop_img(tensor, target_tensor):
target_size = target_tensor.size()[2]
tensor_size = tensor.size()[2]
delta = tensor_size - target_size
delta = delta // 2
return tensor[:,:,delta:tensor_size-delta, delta:tensor_size-delta]
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.max_pool_2x2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.down_con_1 = double_conv(1, 64)
self.down_con_2 = double_conv(64, 128)
self.down_con_3 = double_conv(128, 256)
self.down_con_4 = double_conv(256, 512)
self.down_con_5 = double_conv(512, 1024)
self.up_trans_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=2, stride=2)
self.up_cov_1 = double_conv(1024, 512)
self.up_trans_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=2, stride=2)
self.up_cov_2 = double_conv(512, 256)
self.up_trans_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=2, stride=2)
self.up_cov_3 = double_conv(256, 128)
self.up_trans_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=2, stride=2)
self.up_cov_4 = double_conv(128, 64)
self.out = nn.Conv2d(in_channels=64, out_channels=2, kernel_size=1)
def forward(self, image):
# batch size, c, h, w
# encode
x1 = self.down_con_1(image) #
print("x1: ", x1.size())
x2 = self.max_pool_2x2(x1)
x3 = self.down_con_2(x2) #
x4 = self.max_pool_2x2(x3)
x5 = self.down_con_3(x4) #
x6 = self.max_pool_2x2(x5)
x7 = self.down_con_4(x6) #
x8 = self.max_pool_2x2(x7)
x9 = self.down_con_5(x8)
print("x9: ", x9.size())
# decoder
x = self.up_trans_1(x9)
y = crop_img(x7, x)
x = self.up_cov_1(torch.cat([x, y], 1))
x = self.up_trans_2(x)
y = crop_img(x5, x)
x = self.up_cov_2(torch.cat([x, y], 1))
x = self.up_trans_3(x)
y = crop_img(x3, x)
x = self.up_cov_3(torch.cat([x, y], 1))
x = self.up_trans_4(x)
y = crop_img(x1, x)
x = self.up_cov_4(torch.cat([x, y], 1))
y = self.out(x)
# print(x.size())
print("output:", y.size())
if __name__ == "__main__":
image = torch.rand((1, 1, 572, 572))
print("Input:", image.size())
model = UNet()
print(model(image))
```
## References
- [Implementing original U-Net from scratch using PyTorch](https://www.youtube.com/watch?v=u1loyDCoGbE)
- [paper阅读笔记 UNet](https://zhuanlan.zhihu.com/p/90418337)
The network architecture is illustrated in Figure 1. It consists of a contracting path (left side) and an expansive path (right side). The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each fol- lowed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a 1x1 convolution is used to map each 64- component feature vector to the desired number of classes. In total the network has 23 convolutional layers.
Encoder：左半部分，由两个3x3的卷积层（ReLU）+2x2的max polling层（stride=2）反复组成，每经过一次下采样，通道数翻倍；
Decoder：右半部分，由一个2x2的上采样卷积层（ReLU）+Concatenation（crop[3]对应的Encoder层的输出feature map然后与Decoder层的上采样结果相加）+2个3x3的卷积层（ReLU）反复构成；
最后一层通过一个1x1卷积将通道数变成期望的类别数。(Refer: https://zhuanlan.zhihu.com/p/90418337)
## Implementation using Pytorch
```python
# -*- coding: utf-8 -*-
"""Unet.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1oLnoOuSmkQjZ998vNvMzhUq_zVq81MPS
"""
import torch
import torch.nn as nn
def double_conv(in_c, out_c):
conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3),
nn.ReLU(inplace=True),
nn.Conv2d(out_c, out_c, kernel_size=3),
nn.ReLU(inplace=True)
)
return conv
def crop_img(tensor, target_tensor):
target_size = target_tensor.size()[2]
tensor_size = tensor.size()[2]
delta = tensor_size - target_size
delta = delta // 2
return tensor[:,:,delta:tensor_size-delta, delta:tensor_size-delta]
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.max_pool_2x2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.down_con_1 = double_conv(1, 64)
self.down_con_2 = double_conv(64, 128)
self.down_con_3 = double_conv(128, 256)
self.down_con_4 = double_conv(256, 512)
self.down_con_5 = double_conv(512, 1024)
self.up_trans_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=2, stride=2)
self.up_cov_1 = double_conv(1024, 512)
self.up_trans_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=2, stride=2)
self.up_cov_2 = double_conv(512, 256)
self.up_trans_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=2, stride=2)
self.up_cov_3 = double_conv(256, 128)
self.up_trans_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=2, stride=2)
self.up_cov_4 = double_conv(128, 64)
self.out = nn.Conv2d(in_channels=64, out_channels=2, kernel_size=1)
def forward(self, image):
# batch size, c, h, w
# encode
x1 = self.down_con_1(image) #
print("x1: ", x1.size())
x2 = self.max_pool_2x2(x1)
x3 = self.down_con_2(x2) #
x4 = self.max_pool_2x2(x3)
x5 = self.down_con_3(x4) #
x6 = self.max_pool_2x2(x5)
x7 = self.down_con_4(x6) #
x8 = self.max_pool_2x2(x7)
x9 = self.down_con_5(x8)
print("x9: ", x9.size())
# decoder
x = self.up_trans_1(x9)
y = crop_img(x7, x)
x = self.up_cov_1(torch.cat([x, y], 1))
x = self.up_trans_2(x)
y = crop_img(x5, x)
x = self.up_cov_2(torch.cat([x, y], 1))
x = self.up_trans_3(x)
y = crop_img(x3, x)
x = self.up_cov_3(torch.cat([x, y], 1))
x = self.up_trans_4(x)
y = crop_img(x1, x)
x = self.up_cov_4(torch.cat([x, y], 1))
y = self.out(x)
# print(x.size())
print("output:", y.size())
if __name__ == "__main__":
image = torch.rand((1, 1, 572, 572))
print("Input:", image.size())
model = UNet()
print(model(image))
```
## References
- [Implementing original U-Net from scratch using PyTorch](https://www.youtube.com/watch?v=u1loyDCoGbE)
- [paper阅读笔记 UNet](https://zhuanlan.zhihu.com/p/90418337)
No comments