FCN是全卷积网络，用于做图像语义分割。通常将一般卷积网络最后的全连接层换成上采样或者反卷积网络，对图像的每个像素做分类，从而完成图像分割任务。
网络结构如下：
这里并没有完全按照原始网络结构实现，而是尝试upsample和convTranspose2d结合的方式，看看有什么效果。
下面代码是用VOC数据集做的语义分割，一共2000多张图片，21种类别，还是有一些效果的。
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms
import os
from PIL import Image
import numpy as np
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
colormap = [[0,0,0],[128,0,0],[0,128,0], [128,128,0], [0,0,128],
[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],
[64,128,0],[192,128,0],[64,0,128],[192,0,128],
[64,128,128],[192,128,128],[0,64,0],[128,64,0],
[0,192,0],[128,192,0],[0,64,128]]
class VOCData(Dataset):
def __init__(self, root):
super(VOCData, self).__init__()
self.lab_path = root + 'VOC2012/SegmentationClass/'
self.img_path = root + 'VOC2012/JPEGImages/'
self.lab_names = self.get_file_names(self.lab_path)
self.img_names=[]
for file in self.lab_names:
self.img_names.append(file.replace('.png', '.jpg'))
self.cm2lbl = np.zeros(256**3)
for i,cm in enumerate(colormap):
self.cm2lbl[cm[0]*256*256+cm[1]*256+cm[2]] = i
self.image = []
self.label = []
for i in range(len(self.lab_names)):
image = Image.open(self.img_path+self.img_names[i]).convert('RGB')
image = transform(image)
label = Image.open(self.lab_path+self.lab_names[i]).convert('RGB').resize((256,256))
label = torch.from_numpy(self.image2label(label))
self.image.append(image)
self.label.append(label)
def __len__(self):
return len(self.image)
def __getitem__(self, idx):
return self.image[idx], self.label[idx]
def get_file_names(self,directory):
file_names = []
for file_name in os.listdir(directory):
if os.path.isfile(os.path.join(directory, file_name)):
file_names.append(file_name)
return file_names
def image2label(self,im):
data = np.array(im, dtype='int32')
idx = data[:, :, 0] * 256 * 256 + data[:, :, 1] * 256 + data[:, :, 2]
return np.array(self.cm2lbl[idx], dtype='int64')
class convblock(nn.Module):
def __init__(self, in_channels, out_channels):
super(convblock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu2 = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.maxpool(x)
return x
class Fcn32s(nn.Module):
def __init__(self, num_classes):
super(Fcn32s, self).__init__()
self.conv_block1 = convblock(3,64)
self.conv_block2 = convblock(64,128)
self.conv_block3 = convblock(128,256)
self.conv_block4 = convblock(256,512)
self.conv_block5 = convblock(512,512)
self.conv = nn.Conv2d(512,4096,kernel_size=1)
self.up16x = nn.Upsample(scale_factor=16)
self.convTrans2x = nn.ConvTranspose2d(4096, num_classes, kernel_size=4, stride=2, padding=1)
def forward(self, x):
x = self.conv_block1(x)
x = self.conv_block2(x)
x = self.conv_block3(x)
x = self.conv_block4(x)
x = self.conv_block5(x)
x = self.conv(x)
x = self.up16x(x)
x = self.convTrans2x(x)
return x
class Fcn16s(nn.Module):
def __init__(self, num_classes):
super(Fcn16s, self).__init__()
self.conv_block1 = convblock(3,64)
self.conv_block2 = convblock(64,128)
self.conv_block3 = convblock(128,256)
self.conv_block4 = convblock(256,512)
self.conv_block5 = convblock(512,512)
self.conv1 = nn.Conv2d(512, num_classes, kernel_size=1)
self.conv2 = nn.Conv2d(512,4096,kernel_size=1)
self.convTrans2x = nn.ConvTranspose2d(4096, num_classes, kernel_size=4, stride=2, padding=1)
self.up8x = nn.Upsample(scale_factor=8)
self.convTrans2x2 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, padding=1)
def forward(self, x):
x = self.conv_block1(x)
x = self.conv_block2(x)
x = self.conv_block3(x)
x1 = self.conv_block4(x)
x2 = self.conv_block5(x1)
x1 = self.conv1(x1)
x2 = self.conv2(x2)
x2 = self.convTrans2x(x2)
x = x1+x2
x = self.up8x(x)
x = self.convTrans2x2(x)
return x
class Fcn8s(nn.Module):
def __init__(self, num_classes):
super(Fcn8s, self).__init__()
self.conv_block1 = convblock(3,64)
self.conv_block2 = convblock(64,128)
self.conv_block3 = convblock(128,256)
self.conv_block4 = convblock(256,512)
self.conv_block5 = convblock(512,512)
self.conv1 = nn.Conv2d(256, num_classes, kernel_size=1)
self.conv2 = nn.Conv2d(512, num_classes, kernel_size=1)
self.conv3 = nn.Conv2d(512,4096,kernel_size=1)
self.upsample2x1 = nn.ConvTranspose2d(4096, num_classes, kernel_size=4, stride=2, padding=1)
self.upsample2x2 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, padding=1)
self.up = nn.Upsample(scale_factor=4)
self.upsample2x3 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, padding=1)
def forward(self, x):
x = self.conv_block1(x)
x = self.conv_block2(x)
x1 = self.conv_block3(x)
x2 = self.conv_block4(x1)
x3 = self.conv_block5(x2)
x1 = self.conv1(x1)
x2 = self.conv2(x2)
x3 = self.conv3(x3)
x3 = self.upsample2x1(x3)
x3 = x2 + x3
x3 = self.upsample2x2(x3)
x3 = x1 + x3
x3 = self.up(x3)
x = self.upsample2x3(x3)
return x
def train():
train_dataset = VOCData(root='./VOCdevkit/')
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
#net = Fcn32s(21)
#net = Fcn16s(21)
net = Fcn8s(21)
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss()
net.to(device)
net.train()
num_epochs = 100
for epoch in range(num_epochs):
loss_sum = 0
img_sum = 0
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_sum += loss.item()
img_sum += inputs.shape[0]
print('epochs:',epoch,loss_sum / img_sum )
torch.save(net.state_dict(), 'my_fcn.pth')
def val():
net = Fcn8s(21)
net.load_state_dict(torch.load('my_fcn.pth'))
net.to(device)
net.eval()
image = Image.open('./VOCdevkit/VOC2012/JPEGImages/2007_009794.jpg').convert('RGB')
image = transform(image).unsqueeze(0).to(device)
out = net(image).squeeze(0)
ToPIL= transforms.ToPILImage()
maxind = torch.argmax(out,dim=0)
outimg = torch.zeros([3,256,256])
for y in range(256):
for x in range(256):
outimg[:,x,y] = torch.from_numpy(np.array(colormap[maxind[x,y]]))
re = ToPIL(outimg)
re.show()
if __name__ == "__main__":
train()
val()
标签：__,nn,conv,self,学习,深度,128,256,FCN
From： https://www.cnblogs.com/tiandsp/p/18415257