对于分割网络，如果当成一个黑箱就是：输入一个3x1024x1024 输出4x1024x1024。
我没有使用二分类，直接使用了四分类。
分类网络使用了SegNet，没有加载预训练模型，参数也是默认初始化。为了加快训练，1024输入进网络后直接通过
pooling缩小到256的尺寸，等到输出层，直接使用bilinear放大4倍，相当于直接在256的尺寸上训练。
import os
import urllib
import torch
import torch.nn as nn
import torch.nn.functional as F
#import torch.utils.model_zoo as model_zoo
from torchvision import models
#https://raw.githubusercontent.com/delta-onera/delta_tb/master/deltatb/networks/net_segnet_bn_relu.py
class SegNet_BN_ReLU(nn.Module):
# Unet network
@staticmethod
def weight_init(m):
if isinstance(m, nn.Linear):
torch.nn.init.kaiming_normal(m.weight.data)
def __init__(self, in_channels, out_channels):
super(SegNet_BN_ReLU, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.pool = nn.MaxPool2d(2, return_indices=True)
self.unpool = nn.MaxUnpool2d(2)
self.conv1_1 = nn.Conv2d(in_channels, 64, 3, padding=1)
self.conv1_1_bn = nn.BatchNorm2d(64)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv1_2_bn = nn.BatchNorm2d(64)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.conv2_1_bn = nn.BatchNorm2d(128)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.conv2_2_bn = nn.BatchNorm2d(128)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.conv3_1_bn = nn.BatchNorm2d(256)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_2_bn = nn.BatchNorm2d(256)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_3_bn = nn.BatchNorm2d(256)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.conv4_1_bn = nn.BatchNorm2d(512)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_2_bn = nn.BatchNorm2d(512)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_3_bn = nn.BatchNorm2d(512)
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_1_bn = nn.BatchNorm2d(512)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_2_bn = nn.BatchNorm2d(512)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_3_bn = nn.BatchNorm2d(512)
self.conv5_3_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_3_D_bn = nn.BatchNorm2d(512)
self.conv5_2_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_2_D_bn = nn.BatchNorm2d(512)
self.conv5_1_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv5_1_D_bn = nn.BatchNorm2d(512)
self.conv4_3_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_3_D_bn = nn.BatchNorm2d(512)
self.conv4_2_D = nn.Conv2d(512, 512, 3, padding=1)
self.conv4_2_D_bn = nn.BatchNorm2d(512)
self.conv4_1_D = nn.Conv2d(512, 256, 3, padding=1)
self.conv4_1_D_bn = nn.BatchNorm2d(256)
self.conv3_3_D = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_3_D_bn = nn.BatchNorm2d(256)
self.conv3_2_D = nn.Conv2d(256, 256, 3, padding=1)
self.conv3_2_D_bn = nn.BatchNorm2d(256)
self.conv3_1_D = nn.Conv2d(256, 128, 3, padding=1)
self.conv3_1_D_bn = nn.BatchNorm2d(128)
self.conv2_2_D = nn.Conv2d(128, 128, 3, padding=1)
self.conv2_2_D_bn = nn.BatchNorm2d(128)
self.conv2_1_D = nn.Conv2d(128, 64, 3, padding=1)
self.conv2_1_D_bn = nn.BatchNorm2d(64)
self.conv1_2_D = nn.Conv2d(64, 64, 3, padding=1)
self.conv1_2_D_bn = nn.BatchNorm2d(64)
self.conv1_1_D = nn.Conv2d(64, out_channels, 3, padding=1)
self.apply(self.weight_init)
def forward(self, x):
# Encoder block 1
x =F.avg_pool2d(x,4)
#print(x.shape)
x = self.conv1_1_bn(F.relu(self.conv1_1(x)))
x1 = self.conv1_2_bn(F.relu(self.conv1_2(x)))
size1 = x.size()
x, mask1 = self.pool(x1)
# Encoder block 2
x = self.conv2_1_bn(F.relu(self.conv2_1(x)))
#x = self.drop2_1(x)
x2 = self.conv2_2_bn(F.relu(self.conv2_2(x)))
size2 = x.size()
x, mask2 = self.pool(x2)
# Encoder block 3
x = self.conv3_1_bn(F.relu(self.conv3_1(x)))
x = self.conv3_2_bn(F.relu(self.conv3_2(x)))
x3 = self.conv3_3_bn(F.relu(self.conv3_3(x)))
size3 = x.size()
x, mask3 = self.pool(x3)
# Encoder block 4
x = self.conv4_1_bn(F.relu(self.conv4_1(x)))
x = self.conv4_2_bn(F.relu(self.conv4_2(x)))
x4 = self.conv4_3_bn(F.relu(self.conv4_3(x)))
size4 = x.size()
x, mask4 = self.pool(x4)
# Encoder block 5
x = self.conv5_1_bn(F.relu(self.conv5_1(x)))
x = self.conv5_2_bn(F.relu(self.conv5_2(x)))
x = self.conv5_3_bn(F.relu(self.conv5_3(x)))
size5 = x.size()
x, mask5 = self.pool(x)
# Decoder block 5
x = self.unpool(x, mask5, output_size = size5)
x = self.conv5_3_D_bn(F.relu(self.conv5_3_D(x)))
x = self.conv5_2_D_bn(F.relu(self.conv5_2_D(x)))
x = self.conv5_1_D_bn(F.relu(self.conv5_1_D(x)))
# Decoder block 4
x = self.unpool(x, mask4, output_size = size4)
x = self.conv4_3_D_bn(F.relu(self.conv4_3_D(x)))
x = self.conv4_2_D_bn(F.relu(self.conv4_2_D(x)))
x = self.conv4_1_D_bn(F.relu(self.conv4_1_D(x)))
# Decoder block 3
x = self.unpool(x, mask3, output_size = size3)
x = self.conv3_3_D_bn(F.relu(self.conv3_3_D(x)))
x = self.conv3_2_D_bn(F.relu(self.conv3_2_D(x)))
x = self.conv3_1_D_bn(F.relu(self.conv3_1_D(x)))
# Decoder block 2
x = self.unpool(x, mask2, output_size = size2)
x = self.conv2_2_D_bn(F.relu(self.conv2_2_D(x)))
x = self.conv2_1_D_bn(F.relu(self.conv2_1_D(x)))
# Decoder block 1
x = self.unpool(x, mask1, output_size = size1)
x = self.conv1_2_D_bn(F.relu(self.conv1_2_D(x)))
x = self.conv1_1_D(x)
#print(x.shape)
return F.interpolate(x,mode='bilinear',scale_factor=4)
def load_pretrained_weights(self):
#vgg16_weights = model_zoo.load_url("https://download.pytorch.org/models/vgg16_bn-6c64b313.pth")
vgg16_weights=models.vgg16_bn(True).state_dict()
count_vgg = 0
count_this = 0
vggkeys = list(vgg16_weights.keys())
thiskeys = list(self.state_dict().keys())
corresp_map = []
while(True):
vggkey = vggkeys[count_vgg]
thiskey = thiskeys[count_this]
if "classifier" in vggkey:
break
while vggkey.split(".")[-1] not in thiskey:
count_this += 1
thiskey = thiskeys[count_this]
corresp_map.append([vggkey, thiskey])
count_vgg+=1
count_this += 1
mapped_weights = self.state_dict()
for k_vgg, k_segnet in corresp_map:
if (self.in_channels != 3) and "features" in k_vgg and "conv1_1." not in k_segnet:
mapped_weights[k_segnet] = vgg16_weights[k_vgg]
elif (self.in_channels == 3) and "features" in k_vgg:
mapped_weights[k_segnet] = vgg16_weights[k_vgg]
try:
self.load_state_dict(mapped_weights)
print("Loaded VGG-16 weights in Segnet !")
except:
print("Error VGG-16 weights in Segnet !")
raise
def load_from_filename(self, model_path):
"""Load weights from filename."""
th = torch.load(model_path) # load the weigths
self.load_state_dict(th)
def segnet_bn_relu(in_channels, out_channels, pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = SegNet_BN_ReLU(in_channels, out_channels)
if pretrained:
model.load_pretrained_weights()
return model
if __name__=='__main__':
net=segnet_bn_relu(3,4,False)
print(net)
x=torch.rand((1,3,1024,1024))
print(net.forward(x).shape)
训练网络的代码：
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from farmdataset import FarmDataset
from segnet import segnet_bn_relu as Unet
import time
from PIL import Image
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
#print(target.shape)
optimizer.zero_grad()
output = model(data)
#print('output size',output.size(),output)
output = F.log_softmax(output, dim=1)
loss=nn.NLLLoss2d(weight=torch.Tensor([0.1,0.5,0.5,0.2]).to('cuda'))(output,target)
loss.backward()
optimizer.step()
#time.sleep(0.6)#make gpu sleep
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
if epoch%2==0:
imgd=output.detach()[0,:,:,:].cpu()
img=torch.argmax(imgd,0).byte().numpy()
imgx=Image.fromarray(img).convert('L')
imgxx=Image.fromarray(target.detach()[0,:,:].cpu().byte().numpy()*255).convert('L')
imgx.save("./tmp/predict{}.bmp".format(epoch))
imgxx.save('./tmp/real{}.bmp'.format(epoch))
def test(args, model, device, testdataset,issave=False):
model.eval()
test_loss = 0
correct = 0
evalid=[i+7 for i in range(0,2100,15)]
maxbatch=len(evalid)
with torch.no_grad():
for idx in evalid:
data, target=testdataset[idx]
data, target = data.unsqueeze(0).to(device), target.unsqueeze(0).to(device)
#print(target.shape)
target=target[:,:1472,:1472]
output = model(data[:,:,:1472,:1472])
output = F.log_softmax(output, dim=1)
loss=nn.NLLLoss2d().to('cuda')(output,target)
test_loss+=loss
r=torch.argmax(output[0],0).byte()
tg=target.byte().squeeze(0)
tmp=0
count=0
for i in range(1,4):
mp=r==i
tr=tg==i
tp=mp*tr==1
t=(mp+tr-tp).sum().item()
if t==0:
continue
else:
tmp+=tp.sum().item()/t
count+=1
if count>0:
correct+=tmp/count
if issave:
Image.fromarray(r.cpu().numpy()).save('predict.png')
Image.fromarray(tg.cpu().numpy()).save('target.png')
input()
print('Test Loss is {:.6f}, mean precision is: {:.4f}%'.format(test_loss/maxbatch,correct))
def main():
# Training settings
parser = argparse.ArgumentParser(description='Scratch segmentation Example')
parser.add_argument('--batch-size', type=int, default=8, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=8, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=30, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
print('my device is :',device)
kwargs = {'num_workers': 0, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader( FarmDataset(istrain=True),batch_size=args.batch_size, shuffle=True,drop_last=True, **kwargs)
startepoch=0
model =torch.load('./tmp/model{}'.format(startepoch)) if startepoch else Unet(3,4).to(device)
args.epochs=50
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(startepoch, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
if epoch %3==0:
print(epoch)
test(args, model, device, FarmDataset(istrain=True,isaug=False),issave=False)
torch.save(model,'./tmp/model{}'.format(epoch))
if __name__ == '__main__':
main()
训练代码每隔三轮，评测一次训练精度，测试数据仍然使用训练数据，只是抽样了。可以根据该精度选择使用什么时刻的网络作为预测节点。
训练精度可以达到0.4，但是这时候貌似过学习了。提交结果并不好。
到现在感觉，只要改进一点，分数就会高一点。如果要继续提高成绩，感觉可以从以下几个方面改进：
样本的不均衡
损失函数
模型结构的设计 可以参考PSP，UNET，deeplab，或者GAN的pix2pix。
总之，感觉只要进行一点改进，功夫就不会白费。
整个从数据切割，数据集准备，数据增强，预测结果保存，深度分割网络 和网络训练，全部代码到此分享完毕，
做完这些你的结果就能到0.2以上。 也是折腾了好几天才到现在，希望这能成为一个基线，看到更精彩的模型思路。
（完）
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