【21】FCN网络训练及理解
2024-05-06 03:19:56
准备工作
代码地址：https://github.com/bat67/pytorch-FCN-easiest-demo
论文参考：全卷积网络 FCN 详解
FCN详解与pytorch简单实现（附详细代码解读）
【1】FCN网络简介
FCN网络实现了端到端的图像分割，去除了卷积网络的全连接层，采用反卷积的方式，实现像素级别的分割，并加入skip方式，提升了分割性能。
【2】训练步骤
文件目录展示
【2.1】打开python -m visdom.server
[2.1.1]激活深度学习环境（此处的环境名称为tensorflow，之前创建的）：
activate tensorflow [2.1.2]激活visdom
python -m visdom.server[2.1.3]结果展示
【2.2】打开python解释器，运行train.py
【3】训练结果展示
训练第1个epoch
train result
train label
train loss function
train loss
训练第99个epoch
train result
test result
【4】FCN网络模型代码
文件夹介绍
主要.py文件已经在下方展示代码，其中图中位置1为训练的图像，位置2为模型保存的路径
关于网络模型中的FCN32S表示特征图缩小到1/32，随后在变大到32倍。
关于网络模型中的FCN16S表示特征图缩小到1/16，随后在变大到16倍。
关于网络模型中的FCN8S表示特征图缩小到1/8，随后在变大到8倍。
性能：FCN8S>FCN16S>FCN32S
fcn.py
import torch
import torch.nn as nn
from torchvision import models
from torchvision.models.vgg import VGGclass FCN32s(nn.Module):def __init__(self, pretrained_net, n_class):super().__init__()self.n_class = n_classself.pretrained_net = pretrained_netself.relu = nn.ReLU(inplace=True)self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn1 = nn.BatchNorm2d(512)self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn2 = nn.BatchNorm2d(256)self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn3 = nn.BatchNorm2d(128)self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn4 = nn.BatchNorm2d(64)self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn5 = nn.BatchNorm2d(32)self.classifier = nn.Conv2d(32, n_class, kernel_size=1)def forward(self, x):output = self.pretrained_net(x)x5 = output['x5']score = self.bn1(self.relu(self.deconv1(x5))) score = self.bn2(self.relu(self.deconv2(score))) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCN16s(nn.Module):def __init__(self, pretrained_net, n_class):super().__init__()self.n_class = n_classself.pretrained_net = pretrained_netself.relu = nn.ReLU(inplace=True)self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn1 = nn.BatchNorm2d(512)self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn2 = nn.BatchNorm2d(256)self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn3 = nn.BatchNorm2d(128)self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn4 = nn.BatchNorm2d(64)self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn5 = nn.BatchNorm2d(32)self.classifier = nn.Conv2d(32, n_class, kernel_size=1)def forward(self, x):output = self.pretrained_net(x)x5 = output['x5'] x4 = output['x4'] score = self.relu(self.deconv1(x5)) score = self.bn1(score + x4) score = self.bn2(self.relu(self.deconv2(score))) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCN8s(nn.Module):def __init__(self, pretrained_net, n_class):super().__init__()self.n_class = n_classself.pretrained_net = pretrained_netself.relu = nn.ReLU(inplace=True)self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn1 = nn.BatchNorm2d(512)self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn2 = nn.BatchNorm2d(256)self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn3 = nn.BatchNorm2d(128)self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn4 = nn.BatchNorm2d(64)self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn5 = nn.BatchNorm2d(32)self.classifier = nn.Conv2d(32, n_class, kernel_size=1)def forward(self, x):output = self.pretrained_net(x)x5 = output['x5'] x4 = output['x4'] x3 = output['x3'] score = self.relu(self.deconv1(x5)) score = self.bn1(score + x4) score = self.relu(self.deconv2(score)) score = self.bn2(score + x3) score = self.bn3(self.relu(self.deconv3(score))) score = self.bn4(self.relu(self.deconv4(score))) score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class FCNs(nn.Module):def __init__(self, pretrained_net, n_class):super().__init__()#设置基本参数self.n_class = n_classself.pretrained_net = pretrained_netself.relu = nn.ReLU(inplace=True)self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn1 = nn.BatchNorm2d(512)self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn2 = nn.BatchNorm2d(256)self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn3 = nn.BatchNorm2d(128)self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn4 = nn.BatchNorm2d(64)self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)self.bn5 = nn.BatchNorm2d(32)self.classifier = nn.Conv2d(32, n_class, kernel_size=1) # classifier is 1x1 conv, to reduce channels from 32 to n_classdef forward(self, x):output = self.pretrained_net(x)x5 = output['x5'] x4 = output['x4'] x3 = output['x3'] x2 = output['x2'] x1 = output['x1'] score = self.bn1(self.relu(self.deconv1(x5))) score = score + x4 score = self.bn2(self.relu(self.deconv2(score))) score = score + x3 score = self.bn3(self.relu(self.deconv3(score))) score = score + x2 score = self.bn4(self.relu(self.deconv4(score))) score = score + x1 score = self.bn5(self.relu(self.deconv5(score))) score = self.classifier(score) return score class VGGNet(VGG):def __init__(self, pretrained=True, model='vgg16', requires_grad=True, remove_fc=True, show_params=False):super().__init__(make_layers(cfg[model]))self.ranges = ranges[model]if pretrained:exec("self.load_state_dict(models.%s(pretrained=True).state_dict())" % model)if not requires_grad:for param in super().parameters():param.requires_grad = False# delete redundant fully-connected layer params, can save memory# 去掉vgg最后的全连接层(classifier)if remove_fc: del self.classifierif show_params:for name, param in self.named_parameters():print(name, param.size())def forward(self, x):output = {}# get the output of each maxpooling layer (5 maxpool in VGG net)for idx, (begin, end) in enumerate(self.ranges):#self.ranges = ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31)) (vgg16 examples)for layer in range(begin, end):x = self.features[layer](x)output["x%d"%(idx+1)] = xreturn output#字典类型，键值对
ranges = {'vgg11': ((0, 3), (3, 6), (6, 11), (11, 16), (16, 21)),'vgg13': ((0, 5), (5, 10), (10, 15), (15, 20), (20, 25)),'vgg16': ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31)),'vgg19': ((0, 5), (5, 10), (10, 19), (19, 28), (28, 37))
}# Vgg-Net config
# Vgg网络结构配置
cfg = {'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}# make layers using Vgg-Net config(cfg)
# 由cfg构建vgg-Net
def make_layers(cfg, batch_norm=False):layers = []in_channels = 3for v in cfg:if v == 'M':layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)if batch_norm:layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]else:layers += [conv2d, nn.ReLU(inplace=True)]in_channels = vreturn nn.Sequential(*layers)'''
VGG-16网络参数Sequential((0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): ReLU(inplace)(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(3): ReLU(inplace)(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(6): ReLU(inplace)(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(8): ReLU(inplace)(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(11): ReLU(inplace)(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(13): ReLU(inplace)(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(15): ReLU(inplace)(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(18): ReLU(inplace)(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(20): ReLU(inplace)(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(22): ReLU(inplace)(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(25): ReLU(inplace)(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(27): ReLU(inplace)(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(29): ReLU(inplace)(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)'''if __name__ == "__main__":passtrain.py
from datetime import datetimeimport matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import visdomfrom BagData import test_dataloader, train_dataloader
from FCN import FCN8s, FCN16s, FCN32s, FCNs, VGGNetdef train(epo_num=50, show_vgg_params=False):vis = visdom.Visdom()device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')vgg_model = VGGNet(requires_grad=True, show_params=show_vgg_params)fcn_model = FCNs(pretrained_net=vgg_model, n_class=2)fcn_model = fcn_model.to(device)criterion = nn.BCELoss().to(device)optimizer = optim.SGD(fcn_model.parameters(), lr=1e-2, momentum=0.7)all_train_iter_loss = []all_test_iter_loss = []# start timingprev_time = datetime.now()for epo in range(epo_num):train_loss = 0fcn_model.train()for index, (bag, bag_msk) in enumerate(train_dataloader):# bag.shape is torch.Size([4, 3, 160, 160])# bag_msk.shape is torch.Size([4, 2, 160, 160])bag = bag.to(device)bag_msk = bag_msk.to(device)optimizer.zero_grad()output = fcn_model(bag)output = torch.sigmoid(output) # output.shape is torch.Size([4, 2, 160, 160])loss = criterion(output, bag_msk)loss.backward()iter_loss = loss.item()all_train_iter_loss.append(iter_loss)train_loss += iter_lossoptimizer.step()output_np = output.cpu().detach().numpy().copy() # output_np.shape = (4, 2, 160, 160) output_np = np.argmin(output_np, axis=1)bag_msk_np = bag_msk.cpu().detach().numpy().copy() # bag_msk_np.shape = (4, 2, 160, 160) bag_msk_np = np.argmin(bag_msk_np, axis=1)#每隔15次打印一下结果if np.mod(index, 15) == 0:print('epoch {}, {}/{},train loss is {}'.format(epo, index, len(train_dataloader), iter_loss))# vis.close()vis.images(output_np[:, None, :, :], win='train_pred', opts=dict(title='train prediction')) vis.images(bag_msk_np[:, None, :, :], win='train_label', opts=dict(title='label'))vis.line(all_train_iter_loss, win='train_iter_loss',opts=dict(title='train iter loss'))# plt.subplot(1, 2, 1) # plt.imshow(np.squeeze(bag_msk_np[0, ...]), 'gray')# plt.subplot(1, 2, 2) # plt.imshow(np.squeeze(output_np[0, ...]), 'gray')# plt.pause(0.5)test_loss = 0fcn_model.eval()with torch.no_grad():for index, (bag, bag_msk) in enumerate(test_dataloader):bag = bag.to(device)bag_msk = bag_msk.to(device)optimizer.zero_grad()output = fcn_model(bag)output = torch.sigmoid(output) # output.shape is torch.Size([4, 2, 160, 160])loss = criterion(output, bag_msk)iter_loss = loss.item()all_test_iter_loss.append(iter_loss)test_loss += iter_lossoutput_np = output.cpu().detach().numpy().copy() # output_np.shape = (4, 2, 160, 160) output_np = np.argmin(output_np, axis=1)bag_msk_np = bag_msk.cpu().detach().numpy().copy() # bag_msk_np.shape = (4, 2, 160, 160) bag_msk_np = np.argmin(bag_msk_np, axis=1)if np.mod(index, 15) == 0:print(r'Testing... Open http://localhost:8097/ to see test result.')# vis.close()vis.images(output_np[:, None, :, :], win='test_pred', opts=dict(title='test prediction')) vis.images(bag_msk_np[:, None, :, :], win='test_label', opts=dict(title='label'))vis.line(all_test_iter_loss, win='test_iter_loss', opts=dict(title='test iter loss'))# plt.subplot(1, 2, 1) # plt.imshow(np.squeeze(bag_msk_np[0, ...]), 'gray')# plt.subplot(1, 2, 2) # plt.imshow(np.squeeze(output_np[0, ...]), 'gray')# plt.pause(0.5)cur_time = datetime.now()h, remainder = divmod((cur_time - prev_time).seconds, 3600)m, s = divmod(remainder, 60)time_str = "Time %02d:%02d:%02d" % (h, m, s)prev_time = cur_timeprint('epoch train loss = %f, epoch test loss = %f, %s'%(train_loss/len(train_dataloader), test_loss/len(test_dataloader), time_str))if np.mod(epo, 5) == 0:torch.save(fcn_model, 'checkpoints/fcn_model_{}.pt'.format(epo))print('saveing checkpoints/fcn_model_{}.pt'.format(epo))if __name__ == "__main__":train(epo_num=100, show_vgg_params=False)
onehot.py
import numpy as npdef onehot(data, n):buf = np.zeros(data.shape + (n, ))#ravel()将多维数组降为一维nmsk = np.arange(data.size)*n + data.ravel()buf.ravel()[nmsk-1] = 1return buf'''one-hot是比较常用的文本特征特征提取的方法。
one-hot编码，又称“独热编码”。其实就是用N位状态寄存器编码N个状态，
每个状态都有独立的寄存器位，且这些寄存器位中只有一位有效，说白了就是只能有一个状态。参考文献：
https://blog.csdn.net/Dorothy_Xue/article/details/84641417?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-4.control&dist_request_id=&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-4.control
'''BagData.py
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : BagData.py
@Time : 2021/04/18 11:32:40
@Author : Jian Song
@Contact : 1248975661@qq.com
@Desc : None
'''# here put the import libimport os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset, random_split
from torchvision import transformsimport cv2
from onehot import onehot#张量转换，设置三个通道的均值和方差
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])class BagDataset(Dataset):def __init__(self, transform=None):self.transform = transformdef __len__(self):return len(os.listdir('bag_data'))def __getitem__(self, idx):img_name = os.listdir('bag_data')[idx] #获取原始图像imgA = cv2.imread('bag_data/'+img_name)imgA = cv2.resize(imgA, (160, 160))#获取研磨图像imgB = cv2.imread('bag_data_msk/'+img_name, 0)imgB = cv2.resize(imgB, (160, 160))imgB = imgB/255imgB = imgB.astype('uint8')imgB = onehot(imgB, 2)#根据轴的索引进行转置,imgB = imgB.transpose(2,0,1)#类型转换，uint8转换为torchTensorimgB = torch.FloatTensor(imgB)#print(imgB.shape)#如果设置了图像预处理if self.transform:imgA = self.transform(imgA) return imgA, imgBbag = BagDataset(transform)#%90数据用于训练，10%数据用于测试
train_size = int(0.9 * len(bag))
test_size = len(bag) - train_size
train_dataset, test_dataset = random_split(bag, [train_size, test_size])train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4)
test_dataloader = DataLoader(test_dataset, batch_size=4, shuffle=True, num_workers=4)if __name__ =='__main__':for train_batch in train_dataloader:print(train_batch)for test_batch in test_dataloader:print(test_batch)
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