ShuffleNet V1 对花数据集训练

目录

1. shufflenet 介绍

分组卷积

通道重排

2. ShuffleNet V1 网络

2.1 shufflenet 的结构

2.2 代码解释

2.3 shufflenet 代码

3. train 训练

4. Net performance on flower datasets

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1. shufflenet 介绍

shufflenet的亮点：分组卷积 + 通道重排

mobilenet 提出的深度可分离卷积分为两个step，第一步是深度卷积DW，也就是每一个channel都用一个单独的卷积核卷积，输出一张对应的特征图。第二步是点卷积PW，就是用1*1的卷积核对DW的结果进行通道融合

这样的做法可以有效的减少计算量，然而这样的方式对性能是有一定影响的。而后面的mobilenet 2，3是在bottleneck里面扩充了维度或者更新了激活函数防止维度丢失等等。所以，mobilenet都是在维度信息进行操作的，为了不丢失manifold of interest

分组卷积

而shufflenet 提出了一个新的思路，正常的卷积是卷积的深度=输入的channel个数。深度可分离卷积是卷积对单个channel进行响应。而shufflenet 取折中，将固定个数的channel作为一组，然后进行正常的卷积

类似于单个样本（mobilenet），batch（正常卷积），mini batch（shufflenet）

过程如下：这样相对于正常的卷积也是大大减少了参数

然而仅仅的分组卷积会落入一个类似近亲繁衍的bug中，如图中的a。这样红色的channel始终和红色的操作，失去了特征的多样性，不同channel信息之间的传递被堵塞了

 

通道重排

而解决这样的方法就是通道重排，例如上图中的b，将不同组分为相同的子块，然后按照顺序打乱。图c和图b是一样的

而通道重排可以用矩阵转置的方式实现：

• 将channel放置如下
• 然后按照不同的组(g为分组的个数)，reshape 成(g,n)的矩阵
• 将矩阵转置变成n*g的矩阵
• 最后flatten 拉平就行了

 

2. ShuffleNet V1 网络

搭建shufflenet 网络

2.1 shufflenet 的结构

shufflenet 中 bottleneck 如下所示

其中，a为正常的bottleneck块，也就是residual残差块

图b和图c全都是为shufflenet中的bottleneck，区别就是c是做下采样的bottleneck。

注：一般的bottleneck的下采样是用卷积核stride=2或者maxpooling实现的，而shufflenet中采用3*3平均池化，stride=2实现

 

下图为shufflenet V1的网络结构

 

2.2 代码解释

如下定义了一个channel shuffle 的类，因为pytorch中的传递方式是batch*channel*height*width

所以，这里先将x分解成各个部分，然后channel / group 就是每个组里面channel的个数，按照之前提到的方式。显示reshape成g * n的矩阵，然后进行转置，在flatten就行了

 

然后针对于stage中的第一步stride = 2，和上图c对应实现

 针对于stage中的第一步stride = 1，和上图b对应实现

 

具体的参考结构，可以慢慢理解，代码实现的方法还是很nice的

2.3 shufflenet 代码

代码：

import torch
import torch.nn as nn# channel shuffle
class Channel_Shuffle(nn.Module):def __init__(self,groups):super(Channel_Shuffle, self).__init__()self.groups = groupsdef forward(self,x):batch_size, channels, height, width = x.size()channels_per_group = channels // self.groupsx = x.view(batch_size,self.groups,channels_per_group,height,width)x = x.transpose(1,2).contiguous()           # 转置x = x.view(batch_size,-1,height,width)return x# bottleneck 模块
class BLOCK(nn.Module):def __init__(self,in_channels,out_channels, stride,group):super(BLOCK, self).__init__()hidden_channels = out_channels // 2     # 降维self.shortcut = nn.Sequential()self.cat = Trueif stride == 1:         # 图 b 的结构，shortcut 直接连过来self.conv = nn.Sequential(nn.Conv2d(in_channels, hidden_channels, 1, 1, groups=group),    # size不变，channel改变,1*1卷积降维nn.BatchNorm2d(hidden_channels),nn.ReLU(inplace=True),Channel_Shuffle(group),     # shuffle channelnn.Conv2d(hidden_channels,hidden_channels,3,stride,1,groups=hidden_channels),nn.BatchNorm2d(hidden_channels),nn.Conv2d(hidden_channels, out_channels, 1, 1, groups=group),nn.BatchNorm2d(out_channels))self.cat = Falseelif stride == 2:       # 图 c concat的 bottleneckself.conv = nn.Sequential(nn.Conv2d(in_channels, hidden_channels, 1, 1, groups=group),nn.BatchNorm2d(hidden_channels),nn.ReLU(inplace=True),Channel_Shuffle(group),nn.Conv2d(hidden_channels, hidden_channels, 3, stride, 1, groups=hidden_channels),nn.BatchNorm2d(hidden_channels),nn.Conv2d(hidden_channels, out_channels - in_channels, 1, 1, groups=group),nn.BatchNorm2d(out_channels - in_channels))self.shortcut = nn.Sequential(nn.AvgPool2d(kernel_size=3,stride=2,padding = 1))self.relu = nn.ReLU(inplace=True)def forward(self,x):out = self.conv(x)x = self.shortcut(x)if self.cat:x = torch.cat([out,x],1)        # 图 c的 concatelse:x = out+x                       # 图 b的 addreturn self.relu(x)# shuffleNet V1
class ShuffleNet_V1(nn.Module):def __init__(self, classes=1000,group=3):super(ShuffleNet_V1, self).__init__()setting = {1:[3,24,144,288,576],    # 不同分组个数对应的channel2:[3,24,200,400,800],3:[3,24,240,480,960],4:[3,24,272,544,1088],8:[3,24,384,768,1536]}repeat = [3,7,3]        # stage 里面 bottleneck 重复的次数channels = setting[group]self.conv1 = nn.Sequential(     # Conv1 没有组卷积，channel太少了，输出只有24nn.Conv2d(channels[0],channels[1],kernel_size=3,stride=2,padding=1),    # 输出图像大小 112*112nn.BatchNorm2d(channels[1]),nn.ReLU(inplace=True))self.pool1 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)  # 输出图像size 56*56self.block = BLOCKself.stages = nn.ModuleList([])for i,j in enumerate(repeat):   # i =0,1,2  j=3,7,3self.stages.append(self.block(channels[1+i],channels[2+i],stride=2, group=group))   # stage 中第一个block，对应图 cfor _ in range(j):self.stages.append(self.block(channels[2+i], channels[2+i], stride=1, group=group))   # stage 中第二个block，对应图 bself.pool2 = nn.AdaptiveAvgPool2d(1)        # global poolingself.fc = nn.Sequential(nn.Dropout(0.2),nn.Linear(channels[-1],classes))# 初始化权重for m in self.modules():if isinstance(m,nn.Conv2d):nn.init.kaiming_normal_(m.weight,mode='fan_out')if m.bias is not None:nn.init.zeros_(m.bias)elif isinstance(m,nn.BatchNorm2d):nn.init.ones_(m.weight)nn.init.zeros_(m.bias)elif isinstance(m,nn.Linear):nn.init.normal_(m.weight,0,0.01)nn.init.zeros_(m.bias)def forward(self,x):x = self.conv1(x)x = self.pool1(x)for stage in self.stages:x = stage(x)x = self.pool2(x)x = x.view(x.size(0),-1)x = self.fc(x)return x

3. train 训练

代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from model import ShuffleNet_V1
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
from tqdm import tqdm
import json# 定义超参数
DEVICE = 'cuda' if torch.cuda.is_available() else "cpu"
LEARNING_RATE = 0.001
EPOCHS = 10
BATCH_SIZE = 16TRAINSET_PATH = './flower_data/train'       # 训练集
TESTSET_PATH = './flower_data/test'         # 测试集# 预处理
data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),"test": transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}# 加载训练集
trainSet = ImageFolder(root=TRAINSET_PATH,transform=data_transform['train'])
trainLoader = DataLoader(trainSet,batch_size=BATCH_SIZE,shuffle=True)# 加载测试集
testSet = ImageFolder(root=TESTSET_PATH,transform=data_transform['test'])
testLoader = DataLoader(testSet,batch_size=BATCH_SIZE,shuffle=False)# 数据的个数
num_train = len(trainSet)       # 3306
num_test = len(testSet)         # 364# 保存数据的label文件
dataSetClasses = trainSet.class_to_idx
class_dict = dict((val, key) for key, val in dataSetClasses.items())
json_str = json.dumps(class_dict, indent=4)
with open('class_indices.json', 'w') as json_file:json_file.write(json_str)# 实例化网络
net = ShuffleNet_V1(classes=5)
net.to(DEVICE)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(),lr=LEARNING_RATE,weight_decay=4e-5)# train
best_acc = 0.0
for epoch in range(EPOCHS):net.train()         # train 模式running_loss = 0.0for images, labels in tqdm(trainLoader):images, labels = images.to(DEVICE), labels.to(DEVICE)optimizer.zero_grad()  # 梯度下降outputs = net(images)  # 前向传播loss = loss_fn(outputs, labels)  # 计算损失loss.backward()  # 反向传播optimizer.step()  # 梯度更新running_loss += loss.item()net.eval()      # 测试模式acc = 0.0with torch.no_grad():for x, y in tqdm(testLoader):x, y = x.to(DEVICE), y.to(DEVICE)outputs = net(x)predicted = torch.max(outputs, dim=1)[1]acc += (predicted == y).sum().item()accurate = acc / num_test  # 计算正确率train_loss = running_loss / num_train  # 计算损失print('[epoch %d] train_loss: %.3f   accuracy: %.3f' %(epoch + 1, train_loss, accurate))if accurate > best_acc:best_acc = accuratetorch.save(net.state_dict(), './ShuffleNet_V1.pth')print('Finished Training....')

这里训练的数据是花数据集，共有五个类别，这里只训练了10个epoch。

100%|██████████| 207/207 [01:34<00:00,  2.19it/s]
100%|██████████| 23/23 [00:05<00:00,  4.09it/s]
[epoch 1] train_loss: 0.089   accuracy: 0.527
100%|██████████| 207/207 [01:45<00:00,  1.97it/s]
100%|██████████| 23/23 [00:05<00:00,  3.84it/s]
[epoch 2] train_loss: 0.076   accuracy: 0.610
100%|██████████| 207/207 [02:03<00:00,  1.68it/s]
100%|██████████| 23/23 [00:05<00:00,  3.89it/s]
[epoch 3] train_loss: 0.067   accuracy: 0.665
100%|██████████| 207/207 [02:42<00:00,  1.28it/s]
100%|██████████| 23/23 [00:07<00:00,  3.26it/s]
  0%|          | 0/207 [00:00<?, ?it/s][epoch 4] train_loss: 0.061   accuracy: 0.651
100%|██████████| 207/207 [02:47<00:00,  1.23it/s]
100%|██████████| 23/23 [00:07<00:00,  3.27it/s]
[epoch 5] train_loss: 0.058   accuracy: 0.731
100%|██████████| 207/207 [01:54<00:00,  1.81it/s]
100%|██████████| 23/23 [00:06<00:00,  3.60it/s]
[epoch 6] train_loss: 0.055   accuracy: 0.777
100%|██████████| 207/207 [01:53<00:00,  1.83it/s]
100%|██████████| 23/23 [00:06<00:00,  3.46it/s]
[epoch 7] train_loss: 0.053   accuracy: 0.739
100%|██████████| 207/207 [01:52<00:00,  1.84it/s]
100%|██████████| 23/23 [00:06<00:00,  3.57it/s]
[epoch 8] train_loss: 0.051   accuracy: 0.734
100%|██████████| 207/207 [01:53<00:00,  1.83it/s]
100%|██████████| 23/23 [00:06<00:00,  3.52it/s]
[epoch 9] train_loss: 0.048   accuracy: 0.758
100%|██████████| 207/207 [01:53<00:00,  1.82it/s]
100%|██████████| 23/23 [00:06<00:00,  3.56it/s]
[epoch 10] train_loss: 0.045   accuracy: 0.761
Finished Training....

4. Net performance on flower datasets

import osos.environ['KMP_DUPLICATE_LIB_OK'] = 'True'import json
import torch
import numpy as np
import matplotlib.pyplot as plt
from model import ShuffleNet_V1
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder# 获取 label
try:json_file = open('./class_indices.json', 'r')classes = json.load(json_file)
except Exception as e:print(e)# 预处理
transformer = transforms.Compose([transforms.Resize(256),  # 保证比例不变，短边变为256transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.255])])# 加载模型
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model = ShuffleNet_V1(classes=5)
model.load_state_dict(torch.load('./ShuffleNet_V1.pth'))
model.to(DEVICE)# 加载数据
testSet = ImageFolder(root='./flower_data/test',transform=transformer)
testLoader = DataLoader(testSet, batch_size=12, shuffle=True)# 获取一批数据
imgs, labels = next(iter(testLoader))
imgs = imgs.to(DEVICE)# show
with torch.no_grad():model.eval()prediction = model(imgs)  # 预测prediction = torch.max(prediction, dim=1)[1]prediction = prediction.data.cpu().numpy()plt.figure(figsize=(12, 8))for i, (img, label) in enumerate(zip(imgs, labels)):x = np.transpose(img.data.cpu().numpy(), (1, 2, 0))  # 图像x[:, :, 0] = x[:, :, 0] * 0.229 + 0.485  # 去 normalizationx[:, :, 1] = x[:, :, 1] * 0.224 + 0.456  # 去 normalizationx[:, :, 2] = x[:, :, 2] * 0.255 + 0.406  # 去 normalizationy = label.numpy().item()  # labelplt.subplot(3, 4, i + 1)plt.axis(False)plt.imshow(x)plt.title('R:{},P:{}'.format(classes[str(y)], classes[str(prediction[i])]))plt.show()

结果如下：