MobileNetV1
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
class Block(nn.Module):
'''
Depthwise conv + Pointwise conv
输入参数:
in_planes: 输入特征图的通道数
out_planes: 输出特征图的通道数
stride: 步长，默认值 1
功能:
首先构造深度卷积，设置卷积核个数和输入通道数都为in_planes
然后构造点卷积，输入通道数in_planes,卷积核个数out_planes
'''
def __init__(self, in_planes, out_planes, stride=1):
super(Block, self).__init__()
# Depthwise 卷积，3*3 的卷积核，分为 in_planes，即各层单独进行卷积
self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)
self.bn1 = nn.BatchNorm2d(in_planes)
# Pointwise 卷积，1*1 的卷积核
self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
return out
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
class MobileNetV1(nn.Module):
# (128,2) means conv planes=128, stride=2
cfg = [(64,1), (128,2), (128,1), (256,2), (256,1), (512,2), (512,1),
(1024,2), (1024,1)]
def __init__(self, num_classes=10):
super(MobileNetV1, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = self._make_layers(in_planes=32)
self.linear = nn.Linear(1024, num_classes)
def _make_layers(self, in_planes):
layers = []
for x in self.cfg:
out_planes = x[0]
stride = x[1]
layers.append(Block(in_planes, out_planes, stride))
in_planes = out_planes
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layers(out)
out = F.avg_pool2d(out, 2)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
net = MobileNetV1().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
net.train()
for epoch in range(10): # 重复多轮训练
for i, (inputs, labels) in enumerate(trainloader):
inputs = inputs.to(device)
labels = labels.to(device)
# 优化器梯度归零
optimizer.zero_grad()
# 正向传播 + 反向传播 + 优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 输出统计信息
if i % 100 == 0:
print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item()))
print('Finished Training')
net.eval()
count = 0
for inputs,labels in testloader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
_,preds = torch.max(outputs,1)
count += (preds == labels).sum().item()
print("Test ACC:{}".format(count/len(testset)))
运行如果：
这段代码需要注意的地方是卷积核有个参数groups，作用是将通道进行分组。传统卷积层 groups参数为默认值 1。分组个数必须能被输入通道数和输出通道数整除。
举个例子：
in_channels = 6,feature_map = 3 x 3,kernel_size = 1 x 1,假设out_channels = 6；
和 groups = 1的联系：
groups=1，6个channel属于同一组，卷积核大小是 1 x 6 x 1 x 1,out_channels/groups = 6,需要6个 1 x 6 x 1 x 1，传统卷积层参数的个数是6 x 6 x 1 x 1。
in_channels = 6,feature_map = 3 x 3,kernel_size = 1 x 1,假设out_channels = 6；
和 groups = 3的联系：
groups=3，2个channel划分为一组，每组使用1个2 x 1 x 1的卷积核，3组就是3 x 2 x 1 x 1，out_channels/groups = 2，需要2个 3 x 2 x 1 x 1，那么该层的参数个数是6 x 2 x 1 x 1。理解为划分为 N 组，每组 in_channels/N 个channel，每组用out_channels/N 个卷积核进行卷积，得到 out_channels/N 个feature_map,那么N组就共有 N * out_channels/N = out_channels 个feature_map。
深度卷积中设置groups=in_channels,假设 in_channels = 6,groups = 6，feature_map = 3 x 3,kernel_size = 1 x 1,out_channels = 6；按照上面的逻辑，1个channel划分为一组，每组使用1个1 x 1 x 1的卷积核，6组就是6 x 1 x 1 x 1,out_channels/groups = 1,需要1个6 x 1 x 1 x 1，该层的参数个数是6 x 1 x 1 x 1。符合我们的理解。
MobileNetV2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
class Block(nn.Module):
'''expand + depthwise + pointwise'''
def __init__(self, in_planes, out_planes, expansion, stride):
super(Block, self).__init__()
self.stride = stride
# 通过 expansion 增大 feature map 的数量
planes = expansion * in_planes
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
self.bn3 = nn.BatchNorm2d(out_planes)
# 步长为 1 时，如果 in 和 out 的 feature map 通道不同，用一个卷积改变通道数
if stride == 1 and in_planes != out_planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_planes))
# 步长为 1 时，如果 in 和 out 的 feature map 通道相同，直接返回输入
if stride == 1 and in_planes == out_planes:
self.shortcut = nn.Sequential()
def forward(self, x):
#print('x:',x.shape)
out = F.relu(self.bn1(self.conv1(x)))
#print('out1:',out.shape)
out = F.relu(self.bn2(self.conv2(out)))
#print('out2:',out.shape)
out = self.bn3(self.conv3(out))
#print('out3'out.shape)
# 步长为1，加 shortcut 操作
if self.stride == 1:
return out + self.shortcut(x)
# 步长为2，直接输出
else:
return out
class MobileNetV2(nn.Module):
# (expansion, out_planes, num_blocks, stride)
cfg = [(1, 16, 1, 1),
(6, 24, 2, 1),
(6, 32, 3, 2),
(6, 64, 4, 2),
(6, 96, 3, 1),
(6, 160, 3, 2),
(6, 320, 1, 1)]
def __init__(self, num_classes=10):
super(MobileNetV2, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = self._make_layers(in_planes=32)
self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(1280)
self.linear = nn.Linear(1280, num_classes)
def _make_layers(self, in_planes):
layers = []
for expansion, out_planes, num_blocks, stride in self.cfg:
strides = [stride] + [1]*(num_blocks-1)
for stride in strides:
layers.append(Block(in_planes, out_planes, expansion, stride))
in_planes = out_planes
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layers(out)
out = F.relu(self.bn2(self.conv2(out)))
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
net = MobileNetV2().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
net.train()
for epoch in range(10): # 重复多轮训练
for i, (inputs, labels) in enumerate(trainloader):
inputs = inputs.to(device)
labels = labels.to(device)
# 优化器梯度归零
optimizer.zero_grad()
# 正向传播 + 反向传播 + 优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 输出统计信息
if i % 100 == 0:
print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item()))
print('Finished Training')
net.eval()
count = 0
for inputs,labels in testloader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
_,preds = torch.max(outputs,1)
count += (preds == labels).sum().item()
print("Test ACC:{}".format(count/len(testset)))
MobileNetV2的结构图如下所示：
t 是输入通道的倍增系数 结构中先1 x 1 卷积升维,升维就是从in_channels 升到 t * in_channels。
n 是该模块重复次数
c 是输出通道数
s 是该模块第一个Block的 stride(第一个Block的 深度卷积层的stride)，后面的Block都是 stride = 1
另外，代码中给的结构和实际模型的参数不相同，原因是MobileNet V2 的结构的参数是按照 224 x 224 x 3的输入设计的，CIFAR-10数据集的大小是32 x 32，假设我们不改变MobileNetV2 的参数，那么在32 x32 x 3的输入在最后一个卷积层得到的特征图大小是1 x 1，那么运行后面avg_pool的时候 参数如果大于1就会出错。可以验证将代码中 out = F.avg_pool2d(out, 4)改为 out = F.avg_pool2d(out, 1)是可以运行的。
HybridSN
! wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat
! wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat
! pip install spectral
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
import spectral
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class_num = 16
class HybridSN(nn.Module):
def __init__(self):
super(HybridSN,self).__init__()
self.conv3d1 = nn.Conv3d(1,8,kernel_size=(7,3,3),stride=1,padding=0)
self.bn1 = nn.BatchNorm3d(8)
self.conv3d2 = nn.Conv3d(8,16,kernel_size=(5,3,3),stride=1,padding=0)
self.bn2 = nn.BatchNorm3d(16)
self.conv3d3 = nn.Conv3d(16,32,kernel_size=(3,3,3),stride=1,padding=0)
self.bn3 = nn.BatchNorm3d(32)
self.conv2d4 = nn.Conv2d(576,64,kernel_size=(3,3),stride=1,padding=0)
self.bn4 = nn.BatchNorm2d(64)
self.fc1 = nn.Linear(18496,256)
self.fc2 = nn.Linear(256,128)
self.fc3 = nn.Linear(128,16)
self.dropout = nn.Dropout(0.4)
def forward(self,x):
out = F.relu(self.bn1(self.conv3d1(x)))
out = F.relu(self.bn2(self.conv3d2(out)))
out = F.relu(self.bn3(self.conv3d3(out)))
out = F.relu(self.bn4(self.conv2d4(out.reshape(out.shape[0],-1,19,19))))
out = out.reshape(out.shape[0],-1)
out = F.relu(self.dropout(self.fc1(out)))
out = F.relu(self.dropout(self.fc2(out)))
out = self.fc3(out)
return out
def applyPCA(X, numComponents):
newX = np.reshape(X, (-1, X.shape[2]))
pca = PCA(n_components=numComponents, whiten=True)
newX = pca.fit_transform(newX)
newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents))
return newX
# 对单个像素周围提取 patch 时，边缘像素就无法取了，因此，给这部分像素进行 padding 操作
def padWithZeros(X, margin=2):
newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))
x_offset = margin
y_offset = margin
newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X
return newX
# 在每个像素周围提取 patch ，然后创建成符合 keras 处理的格式
def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):
# 给 X 做 padding
margin = int((windowSize - 1) / 2)
zeroPaddedX = padWithZeros(X, margin=margin)
# split patches
patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))
patchesLabels = np.zeros((X.shape[0] * X.shape[1]))
patchIndex = 0
for r in range(margin, zeroPaddedX.shape[0] - margin):
for c in range(margin, zeroPaddedX.shape[1] - margin):
patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]
patchesData[patchIndex, :, :, :] = patch
patchesLabels[patchIndex] = y[r-margin, c-margin]
patchIndex = patchIndex + 1
if removeZeroLabels:
patchesData = patchesData[patchesLabels>0,:,:,:]
patchesLabels = patchesLabels[patchesLabels>0]
patchesLabels -= 1
return patchesData, patchesLabels
def splitTrainTestSet(X, y, testRatio, randomState=345):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)
return X_train, X_test, y_train, y_test
# 地物类别
class_num = 16
X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']
y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']
# 用于测试样本的比例
test_ratio = 0.90
# 每个像素周围提取 patch 的尺寸
patch_size = 25
# 使用 PCA 降维，得到主成分的数量
pca_components = 30
print('Hyperspectral data shape: ', X.shape)
print('Label shape: ', y.shape)
print('
... ... PCA tranformation ... ...')
X_pca = applyPCA(X, numComponents=pca_components)
print('Data shape after PCA: ', X_pca.shape)
print('
... ... create data cubes ... ...')
X_pca, y = createImageCubes(X_pca, y, windowSize=patch_size)
print('Data cube X shape: ', X_pca.shape)
print('Data cube y shape: ', y.shape)
print('
... ... create train & test data ... ...')
Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X_pca, y, test_ratio)
print('Xtrain shape: ', Xtrain.shape)
print('Xtest shape: ', Xtest.shape)
# 改变 Xtrain, Ytrain 的形状，以符合 keras 的要求
Xtrain = Xtrain.reshape(-1, patch_size, patch_size, pca_components, 1)
Xtest = Xtest.reshape(-1, patch_size, patch_size, pca_components, 1)
print('before transpose: Xtrain shape: ', Xtrain.shape)
print('before transpose: Xtest shape: ', Xtest.shape)
# 为了适应 pytorch 结构，数据要做 transpose
Xtrain = Xtrain.transpose(0, 4, 3, 1, 2)
Xtest = Xtest.transpose(0, 4, 3, 1, 2)
print('after transpose: Xtrain shape: ', Xtrain.shape)
print('after transpose: Xtest shape: ', Xtest.shape)
""" Training dataset"""
class TrainDS(torch.utils.data.Dataset):
def __init__(self):
self.len = Xtrain.shape[0]
self.x_data = torch.FloatTensor(Xtrain)
self.y_data = torch.LongTensor(ytrain)
def __getitem__(self, index):
# 根据索引返回数据和对应的标签
return self.x_data[index], self.y_data[index]
def __len__(self):
# 返回文件数据的数目
return self.len
""" Testing dataset"""
class TestDS(torch.utils.data.Dataset):
def __init__(self):
self.len = Xtest.shape[0]
self.x_data = torch.FloatTensor(Xtest)
self.y_data = torch.LongTensor(ytest)
def __getitem__(self, index):
# 根据索引返回数据和对应的标签
return self.x_data[index], self.y_data[index]
def __len__(self):
# 返回文件数据的数目
return self.len
# 创建 trainloader 和 testloader
trainset = TrainDS()
testset = TestDS()
train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(dataset=testset, batch_size=128, shuffle=False, num_workers=2)
# 使用GPU训练，可以在菜单 "代码执行工具" -> "更改运行时类型" 里进行设置
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 网络放到GPU上
net = HybridSN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
# 开始训练
total_loss = 0
net.train()
for epoch in range(100):
for i, (inputs, labels) in enumerate(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
# 优化器梯度归零
optimizer.zero_grad()
# 正向传播 + 反向传播 + 优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
nn.ReLU()
print('[Epoch: %d] [loss avg: %.4f] [current loss: %.4f]' %(epoch + 1, total_loss/(epoch+1), loss.item()))
print('Finished Training')
net.eval()
count = 0
for inputs,labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
_,preds = torch.max(outputs,1)
count += (preds == labels).sum().item()
print("Test ACC:{}".format(count/len(testset)))
添加了BN层，准确度略有提高。
添加学习率衰减 scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min',verbose=True,factor=0.9,min_lr=1e-6)
，准确度略有提高。
