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這期內(nèi)容當(dāng)中小編將會給大家?guī)碛嘘P(guān)pytorch實現(xiàn)unet網(wǎng)絡(luò)的方法，文章內(nèi)容豐富且以專業(yè)的角度為大家分析和敘述，閱讀完這篇文章希望大家可以有所收獲。
設(shè)計神經(jīng)網(wǎng)絡(luò)的一般步驟：
1. 設(shè)計框架
2. 設(shè)計骨干網(wǎng)絡(luò)
Unet網(wǎng)絡(luò)設(shè)計的步驟：
1. 設(shè)計Unet網(wǎng)絡(luò)工廠模式
2. 設(shè)計編解碼結(jié)構(gòu)
3. 設(shè)計卷積模塊
4. unet實例模塊
Unet網(wǎng)絡(luò)最重要的特征：
1. 編解碼結(jié)構(gòu)。
2. 解碼結(jié)構(gòu)，比FCN更加完善，采用連接方式。
3. 本質(zhì)是一個框架，編碼部分可以使用很多圖像分類網(wǎng)絡(luò)。
示例代碼：
import torch
import torch.nn as nn
class Unet(nn.Module):
#初始化參數(shù)：Encoder,Decoder,bridge
#bridge默認(rèn)值為無，如果有參數(shù)傳入，則用該參數(shù)替換None
def __init__(self,Encoder,Decoder,bridge = None):
super(Unet,self).__init__()
self.encoder = Encoder(encoder_blocks)
self.decoder = Decoder(decoder_blocks)
self.bridge = bridge
def forward(self,x):
res = self.encoder(x)
out,skip = res[0],res[1,:]
if bridge is not None:
out = bridge(out)
out = self.decoder(out,skip)
return out
#設(shè)計編碼模塊
class Encoder(nn.Module):
def __init__(self,blocks):
super(Encoder,self).__init__()
#assert:斷言函數(shù)，避免出現(xiàn)參數(shù)錯誤
assert len(blocks) > 0
#nn.Modulelist（）：模型列表，所有的參數(shù)可以納入網(wǎng)絡(luò)，但是沒有forward函數(shù)
self.blocks = nn.Modulelist(blocks)
def forward(self,x):
skip = []
for i in range(len(self.blocks) - 1):
x = self.blocks[i](x)
skip.append(x)
res = [self.block[i+1](x)]
#列表之間可以通過+號拼接
res += skip
return res
#設(shè)計Decoder模塊
class Decoder(nn.Module):
def __init__(self,blocks):
super(Decoder, self).__init__()
assert len(blocks) > 0
self.blocks = nn.Modulelist(blocks)
def ceter_crop(self,skips,x):
_,_,height1,width2 = skips.shape()
_,_,height2,width3 = x.shape()
#對圖像進(jìn)行剪切處理，拼接的時候保持對應(yīng)size參數(shù)一致
ht,wt = min(height1,height2),min(width2,width3)
dh2 = (height1 - height2)//2 if height1 > height2 else 0
dw1 = (width2 - width3)//2 if width2 > width3 else 0
dh3 = (height2 - height1)//2 if height2 > height1 else 0
dw2 = (width3 - width2)//2 if width3 > width2 else 0
return skips[:,:,dh2:(dh2 + ht),dw1:(dw1 + wt)],\
x[:,:,dh3:(dh3 + ht),dw2 : (dw2 + wt)]
def forward(self, skips,x,reverse_skips = True):
assert len(skips) == len(blocks) - 1
if reverse_skips is True:
skips = skips[: : -1]
x = self.blocks[0](x)
for i in range(1, len(self.blocks)):
skip = skips[i-1]
x = torch.cat(skip,x,1)
x = self.blocks[i](x)
return x
#定義了一個卷積block
def unet_convs(in_channels,out_channels,padding = 0):
#nn.Sequential：與Modulelist相比，包含了forward函數(shù)
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernal_size = 3, padding = padding, bias = False),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace = True),
nn.Conv2d(in_channels, out_channels, kernal_size=3, padding=padding, bias=False),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace=True),
)
#實例化Unet模型
def unet(in_channels,out_channels):
encoder_blocks = [unet_convs(in_channels, 64),\
nn.Sequential(nn.Maxpool2d(kernal_size = 2, stride = 2, ceil_mode = True),\
unet_convs(64,128)), \
nn.Sequential(nn.Maxpool2d(kernal_size=2, stride=2, ceil_mode=True), \
unet_convs(128, 256)),
nn.Sequential(nn.Maxpool2d(kernal_size=2, stride=2, ceil_mode=True), \
unet_convs(256, 512)),
]
bridge = nn.Sequential(unet_convs(512, 1024))
decoder_blocks = [nn.conTranpose2d(1024, 512), \
nn.Sequential(unet_convs(1024, 512),
nn.conTranpose2d(512, 256)),\
nn.Sequential(unet_convs(512, 256),
nn.conTranpose2d(256, 128)), \
nn.Sequential(unet_convs(512, 256),
nn.conTranpose2d(256, 128)), \
nn.Sequential(unet_convs(256, 128),
nn.conTranpose2d(128, 64))
]
return Unet(encoder_blocks,decoder_blocks,bridge)補充知識：Pytorch搭建U-Net網(wǎng)絡(luò)
U-Net: Convolutional Networks for Biomedical Image Segmentation
import torch.nn as nn import torch from torch import autograd from torchsummary import summary class DoubleConv(nn.Module): def __init__(self, in_ch, out_ch): super(DoubleConv, self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_ch, out_ch, 3, padding=0), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True), nn.Conv2d(out_ch, out_ch, 3, padding=0), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True) ) def forward(self, input): return self.conv(input) class Unet(nn.Module): def __init__(self, in_ch, out_ch): super(Unet, self).__init__() self.conv1 = DoubleConv(in_ch, 64) self.pool1 = nn.MaxPool2d(2) self.conv2 = DoubleConv(64, 128) self.pool2 = nn.MaxPool2d(2) self.conv3 = DoubleConv(128, 256) self.pool3 = nn.MaxPool2d(2) self.conv4 = DoubleConv(256, 512) self.pool4 = nn.MaxPool2d(2) self.conv5 = DoubleConv(512, 1024) # 逆卷積，也可以使用上采樣 self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2) self.conv6 = DoubleConv(1024, 512) self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2) self.conv7 = DoubleConv(512, 256) self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2) self.conv8 = DoubleConv(256, 128) self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2) self.conv9 = DoubleConv(128, 64) self.conv10 = nn.Conv2d(64, out_ch, 1) def forward(self, x): c1 = self.conv1(x) crop1 = c1[:,:,88:480,88:480] p1 = self.pool1(c1) c2 = self.conv2(p1) crop2 = c2[:,:,40:240,40:240] p2 = self.pool2(c2) c3 = self.conv3(p2) crop3 = c3[:,:,16:120,16:120] p3 = self.pool3(c3) c4 = self.conv4(p3) crop4 = c4[:,:,4:60,4:60] p4 = self.pool4(c4) c5 = self.conv5(p4) up_6 = self.up6(c5) merge6 = torch.cat([up_6, crop4], dim=1) c6 = self.conv6(merge6) up_7 = self.up7(c6) merge7 = torch.cat([up_7, crop3], dim=1) c7 = self.conv7(merge7) up_8 = self.up8(c7) merge8 = torch.cat([up_8, crop2], dim=1) c8 = self.conv8(merge8) up_9 = self.up9(c8) merge9 = torch.cat([up_9, crop1], dim=1) c9 = self.conv9(merge9) c10 = self.conv10(c9) out = nn.Sigmoid()(c10) return out if __name__=="__main__": test_input=torch.rand(1, 1, 572, 572) model=Unet(in_ch=1, out_ch=2) summary(model, (1,572,572)) ouput=model(test_input) print(ouput.size())
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 570, 570] 640
BatchNorm2d-2 [-1, 64, 570, 570] 128
ReLU-3 [-1, 64, 570, 570] 0
Conv2d-4 [-1, 64, 568, 568] 36,928
BatchNorm2d-5 [-1, 64, 568, 568] 128
ReLU-6 [-1, 64, 568, 568] 0
DoubleConv-7 [-1, 64, 568, 568] 0
MaxPool2d-8 [-1, 64, 284, 284] 0
Conv2d-9 [-1, 128, 282, 282] 73,856
BatchNorm2d-10 [-1, 128, 282, 282] 256
ReLU-11 [-1, 128, 282, 282] 0
Conv2d-12 [-1, 128, 280, 280] 147,584
BatchNorm2d-13 [-1, 128, 280, 280] 256
ReLU-14 [-1, 128, 280, 280] 0
DoubleConv-15 [-1, 128, 280, 280] 0
MaxPool2d-16 [-1, 128, 140, 140] 0
Conv2d-17 [-1, 256, 138, 138] 295,168
BatchNorm2d-18 [-1, 256, 138, 138] 512
ReLU-19 [-1, 256, 138, 138] 0
Conv2d-20 [-1, 256, 136, 136] 590,080
BatchNorm2d-21 [-1, 256, 136, 136] 512
ReLU-22 [-1, 256, 136, 136] 0
DoubleConv-23 [-1, 256, 136, 136] 0
MaxPool2d-24 [-1, 256, 68, 68] 0
Conv2d-25 [-1, 512, 66, 66] 1,180,160
BatchNorm2d-26 [-1, 512, 66, 66] 1,024
ReLU-27 [-1, 512, 66, 66] 0
Conv2d-28 [-1, 512, 64, 64] 2,359,808
BatchNorm2d-29 [-1, 512, 64, 64] 1,024
ReLU-30 [-1, 512, 64, 64] 0
DoubleConv-31 [-1, 512, 64, 64] 0
MaxPool2d-32 [-1, 512, 32, 32] 0
Conv2d-33 [-1, 1024, 30, 30] 4,719,616
BatchNorm2d-34 [-1, 1024, 30, 30] 2,048
ReLU-35 [-1, 1024, 30, 30] 0
Conv2d-36 [-1, 1024, 28, 28] 9,438,208
BatchNorm2d-37 [-1, 1024, 28, 28] 2,048
ReLU-38 [-1, 1024, 28, 28] 0
DoubleConv-39 [-1, 1024, 28, 28] 0
ConvTranspose2d-40 [-1, 512, 56, 56] 2,097,664
Conv2d-41 [-1, 512, 54, 54] 4,719,104
BatchNorm2d-42 [-1, 512, 54, 54] 1,024
ReLU-43 [-1, 512, 54, 54] 0
Conv2d-44 [-1, 512, 52, 52] 2,359,808
BatchNorm2d-45 [-1, 512, 52, 52] 1,024
ReLU-46 [-1, 512, 52, 52] 0
DoubleConv-47 [-1, 512, 52, 52] 0
ConvTranspose2d-48 [-1, 256, 104, 104] 524,544
Conv2d-49 [-1, 256, 102, 102] 1,179,904
BatchNorm2d-50 [-1, 256, 102, 102] 512
ReLU-51 [-1, 256, 102, 102] 0
Conv2d-52 [-1, 256, 100, 100] 590,080
BatchNorm2d-53 [-1, 256, 100, 100] 512
ReLU-54 [-1, 256, 100, 100] 0
DoubleConv-55 [-1, 256, 100, 100] 0
ConvTranspose2d-56 [-1, 128, 200, 200] 131,200
Conv2d-57 [-1, 128, 198, 198] 295,040
BatchNorm2d-58 [-1, 128, 198, 198] 256
ReLU-59 [-1, 128, 198, 198] 0
Conv2d-60 [-1, 128, 196, 196] 147,584
BatchNorm2d-61 [-1, 128, 196, 196] 256
ReLU-62 [-1, 128, 196, 196] 0
DoubleConv-63 [-1, 128, 196, 196] 0
ConvTranspose2d-64 [-1, 64, 392, 392] 32,832
Conv2d-65 [-1, 64, 390, 390] 73,792
BatchNorm2d-66 [-1, 64, 390, 390] 128
ReLU-67 [-1, 64, 390, 390] 0
Conv2d-68 [-1, 64, 388, 388] 36,928
BatchNorm2d-69 [-1, 64, 388, 388] 128
ReLU-70 [-1, 64, 388, 388] 0
DoubleConv-71 [-1, 64, 388, 388] 0
Conv2d-72 [-1, 2, 388, 388] 130
================================================================
Total params: 31,042,434
Trainable params: 31,042,434
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.25
Forward/backward pass size (MB): 3280.59
Params size (MB): 118.42
Estimated Total Size (MB): 3400.26
----------------------------------------------------------------
torch.Size([1, 2, 388, 388])上述就是小編為大家分享的pytorch實現(xiàn)unet網(wǎng)絡(luò)的方法了，如果剛好有類似的疑惑，不妨參照上述分析進(jìn)行理解。如果想知道更多相關(guān)知識，歡迎關(guān)注億速云行業(yè)資訊頻道。
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