知识蒸馏属于模型的压缩一种方法，但其实这种方法又属于一种伪压缩，是将一个性能较好的teacher网络“压缩”进一个性能较差的student网络中，或者是可类似于在teacher的指导下让student进行学习进而提高性能。
知识蒸馏是一种思想，并不像其他压缩方法有现成的库，因此对于实际需求与场景需要自己去实现。蒸馏也分为“离线”蒸馏与“在线”蒸馏。前者是建立T-S进行KD训练，而后者可以说是一种自学习，让student自己做自己的teacher。
同时蒸馏还分为逻辑蒸馏和特征蒸馏，前者是在两个网络最终输出部分建立loss关系，而后者是在网络中间的某些特征部分建立loss进行蒸馏。
本文是以手写数字为例，teacher选用的resnet18，student选用的resnet50【大家可能会想resnet50比resnet18强啊，为啥resnet50是student，这是因为我在实际测试的时候发现在手写数字这个数据上resnet18的准确率比resnet50高，猜测是因为在低分辨率下resnet50虽然loss在下降，但由于网络较深，特征丢失也明显，网络退化较明显】。当然这里你也可以尝试resnet做teacher，mobilnet做student【我这样训练了一下发现对mobilnet提升变化不大】
注：这里不做模型和蒸馏改进，仅仅是给大家展示一下效果，至于更细化的蒸馏有兴趣的可以自己去研究。【有关目标检测方面的KD 训练，我将会在明年以后推出】
目录
teacher train代码
参数说明：
teacher_model:选用的teacher网络
train_loader:训练集
test_loader:测试集
loss_func:损失函数
epochs:训练迭代数
def teacher_train(teacher_model, train_loader, test_loader, loss_func, epochs):
teacher_model.train()
teacher_model.cuda()
# train
for i in range(epochs):
for data, label in train_loader:
data = data.to(device)
label = label.to(device)
output = teacher_model(data)
loss = loss_func(output, label)
optimizer_teacher.zero_grad()
loss.backward()
optimizer_teacher.step()
print("loss: ", loss)
# eval
correct = 0
teacher_model.eval()
teacher_model.cuda()
for test_data, test_label in test_loader:
test_data = test_data.to(device)
test_label = test_label.to(device)
with torch.no_grad():
output = teacher_model(test_data)
# acc = torch.mean((torch.argmax(F.softmax(output, dim=-1), dim=-1) == test_label).type(torch.FloatTensor))
# print("teacher acc: ", acc)
_, pred = torch.max(output, dim=1)
correct += float(torch.sum(pred == test_label))
print('test_acc:{}'.format(correct / len(test_dataset)))
return teacher_model训练结果(我只训练了5轮)：
teacher model train
loss: tensor(0.0891, device='cuda:0', grad_fn=<NllLossBackward>)
test_acc:0.9845
loss: tensor(0.0132, device='cuda:0', grad_fn=<NllLossBackward>)
test_acc:0.9865
loss: tensor(0.0019, device='cuda:0', grad_fn=<NllLossBackward>)
test_acc:0.9909
loss: tensor(0.0042, device='cuda:0', grad_fn=<NllLossBackward>)
test_acc:0.9909
loss: tensor(0.0034, device='cuda:0', grad_fn=<NllLossBackward>)
test_acc:0.9917
teacher model trained finished!student未KD 训练
参数说明：
student_model:选用的student网络
train_loader:训练集
test_loader:测试集
loss_func:损失函数
epochs:训练迭代数
def student_train(student_model, train_loader, test_loader, loss_func, epochs):
student_model.train()
student_model.cuda()
# train
for i in range(epochs):
for data, label in train_loader:
data = data.to(device)
label = label.to(device)
output = student_model(data)
loss = loss_func(output, label)
optimizer_student.zero_grad()
loss.backward()
optimizer_student.step()
print("student loss: ", loss)
# eval
correct = 0
student_model.eval()
student_model.cuda()
for test_data, test_label in test_loader:
test_data = test_data.to(device)
test_label = test_label.to(device)
with torch.no_grad():
output = student_model(test_data)
# acc = torch.mean((torch.argmax(F.softmax(output, dim=-1), dim=-1) == test_label).type(torch.FloatTensor))
# print("teacher acc: ", acc)
_, pred = torch.max(output, dim=1)
correct += float(torch.sum(pred == test_label))
print('student test_acc:{}'.format(correct / len(test_dataset)))没有KD train的效果如下：
student model ready train
student loss: tensor(0.1876, device='cuda:0', grad_fn=<NllLossBackward>)
student test_acc:0.9588
student loss: tensor(0.0219, device='cuda:0', grad_fn=<NllLossBackward>)
student test_acc:0.9737
student loss: tensor(0.0588, device='cuda:0', grad_fn=<NllLossBackward>)
student test_acc:0.9812
student loss: tensor(0.0024, device='cuda:0', grad_fn=<NllLossBackward>)
student test_acc:0.9853
student loss: tensor(0.0022, device='cuda:0', grad_fn=<NllLossBackward>)
student test_acc:0.9814
student model trained finished!KD train代码
参数说明：
teacher_model:为已经训练好的teacher
student_model:待KD的student网络
train_loader:训练集
test_loader:测试集
def KD_train(teacher_model, student_model, train_loader, test_loader,loss_func, epochs):
teacher_model.eval()
student_model.train()
student_model.cuda()
HL = nn.CrossEntropyLoss()
for i in range(epochs):
for data, labels in train_loader:
data = data.to(device)
labels = labels.to(device)
teacher_output = teacher_model(data)
student_output = student_model(data)
soft_loss = KD_loss(teacher_output, student_output)
hard_loss = HL(student_output, labels)
loss = hard_loss + alpha*soft_loss
optimizer_student.zero_grad()
loss.backward()
optimizer_student.step()
print("KD loss: ", loss)
student_model.eval()
ACC = 0
for data, labels in test_loader:
with torch.no_grad():
data = data.to(device)
labels = labels.to(device)
output = student_model(data)
_, pred = torch.max(output, dim=1)
ACC += float(torch.sum(pred == labels))
print('KD test_acc:{}'.format(ACC / len(test_dataset)))代码中的teacher_output是teacher网络的输出，student_output是student的输出，两者之间设计的KD_loss代码如下：
KD_loss代码：
Temp为温度系数，默认为2【可以根据自己的数据集去尝试】
alpha是hard与soft的平衡系数【默认0.5，也是根据自己的实际情况调整】
损失函数采用的KL，你也可以改为交叉熵。
Temp = 2. # 温度常数
alpha = 0.5
def KD_loss(p, q): # p指的老师老师的预测(经过softmax)，q是学生的预测
pt = F.softmax(p / Temp, dim=1)
ps = F.log_softmax(q / Temp, dim=1)
return nn.KLDivLoss(reduction='mean')(ps, pt) * (Temp**2)KD tran后student结果：
KD loss: tensor(0.2580, device='cuda:0', grad_fn=<AddBackward0>)
KD test_acc:0.9753
KD loss: tensor(0.1686, device='cuda:0', grad_fn=<AddBackward0>)
KD test_acc:0.9748
KD loss: tensor(0.0827, device='cuda:0', grad_fn=<AddBackward0>)
KD test_acc:0.9849
KD loss: tensor(0.0098, device='cuda:0', grad_fn=<AddBackward0>)
KD test_acc:0.9865
KD loss: tensor(0.0114, device='cuda:0', grad_fn=<AddBackward0>)
KD test_acc:0.988
可以看出经过KD训练后student略有提升【主要手写数字这个太容易训练，稍微一训练就可以有较高的准确率】，如果换成别的数据集【比如猫狗数据集可能会明显点，可以自己试试】。
如果要换teacher和student网络，只需要在代码中将teacher_model和student_model网络进行替换即可。
完整代码
目标检测方面的KD比较麻烦，这个以后再讲。
import torch
from torch.optim import Adam, SGD
import torch.nn.functional as F
import torch.nn as nn
from torchvision.models import resnet50, resnet34, resnet18, MobileNetV2
import torchvision
import torchvision.transforms as transforms
Temp = 2. # 温度常数
alpha = 0.5
def KD_loss(p, q): # p指的老师老师的预测(经过softmax)，q是学生的预测
pt = F.softmax(p / Temp, dim=1)
ps = F.log_softmax(q / Temp, dim=1)
return nn.KLDivLoss(reduction='mean')(ps, pt) * (Temp**2)
def teacher_train(teacher_model, train_loader, test_loader, loss_func, epochs):
teacher_model.train()
teacher_model.cuda()
# train
for i in range(epochs):
for data, label in train_loader:
data = data.to(device)
label = label.to(device)
output = teacher_model(data)
loss = loss_func(output, label)
optimizer_teacher.zero_grad()
loss.backward()
optimizer_teacher.step()
print("loss: ", loss)
# eval
correct = 0
teacher_model.eval()
teacher_model.cuda()
for test_data, test_label in test_loader:
test_data = test_data.to(device)
test_label = test_label.to(device)
with torch.no_grad():
output = teacher_model(test_data)
# acc = torch.mean((torch.argmax(F.softmax(output, dim=-1), dim=-1) == test_label).type(torch.FloatTensor))
# print("teacher acc: ", acc)
_, pred = torch.max(output, dim=1)
correct += float(torch.sum(pred == test_label))
print('test_acc:{}'.format(correct / len(test_dataset)))
return teacher_model
def student_train(student_model, train_loader, test_loader, loss_func, epochs):
student_model.train()
student_model.cuda()
# train
for i in range(epochs):
for data, label in train_loader:
data = data.to(device)
label = label.to(device)
output = student_model(data)
loss = loss_func(output, label)
optimizer_student.zero_grad()
loss.backward()
optimizer_student.step()
print("student loss: ", loss)
# eval
correct = 0
student_model.eval()
student_model.cuda()
for test_data, test_label in test_loader:
test_data = test_data.to(device)
test_label = test_label.to(device)
with torch.no_grad():
output = student_model(test_data)
# acc = torch.mean((torch.argmax(F.softmax(output, dim=-1), dim=-1) == test_label).type(torch.FloatTensor))
# print("teacher acc: ", acc)
_, pred = torch.max(output, dim=1)
correct += float(torch.sum(pred == test_label))
print('student test_acc:{}'.format(correct / len(test_dataset)))
def KD_train(teacher_model, student_model, train_loader, test_loader,loss_func, epochs):
teacher_model.eval()
student_model.train()
student_model.cuda()
HL = nn.CrossEntropyLoss()
for i in range(epochs):
for data, labels in train_loader:
data = data.to(device)
labels = labels.to(device)
teacher_output = teacher_model(data)
student_output = student_model(data)
soft_loss = KD_loss(teacher_output, student_output)
hard_loss = HL(student_output, labels)
loss = hard_loss + alpha*soft_loss
optimizer_student.zero_grad()
loss.backward()
optimizer_student.step()
print("KD loss: ", loss)
student_model.eval()
ACC = 0
for data, labels in test_loader:
with torch.no_grad():
data = data.to(device)
labels = labels.to(device)
output = student_model(data)
_, pred = torch.max(output, dim=1)
ACC += float(torch.sum(pred == labels))
print('KD test_acc:{}'.format(ACC / len(test_dataset)))
def do_train(teacher_model, student_model, train_loader, test_loader, loss_func, epochs):
#教师训练
teacher_model.train()
teacher_model.to(device)
print("teacher model train")
Teacher = teacher_train(teacher_model, train_loader, test_loader, loss_func, epochs)
print("teacher model trained finished!")
# print("\n student model ready train")
# student_train(student_model, train_loader, test_loader, loss_func, epochs)
# print("\n student model trained finished!")
print("\n KD model ready train")
KD_train(Teacher, student_model, train_loader, test_loader, loss_func, epochs)
if __name__=="__main__":
# 准备数据集
batch_size = 64
train_dataset = torchvision.datasets.MNIST('./data/', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(28),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
transforms.Grayscale(num_output_channels=3)
])
)
test_dataset = torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(28), # resnet默认图片输入大小224*224
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
transforms.Grayscale(num_output_channels=3)
])
)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
sample, label = next(iter(train_loader))
print(sample.shape)
print("当前类： ", label)
num_classes = 10
lr = 0.01
epochs = 5
device = torch.device('cuda:0')
teacher_model = resnet18(num_classes=num_classes)
student_model = resnet50(num_classes=num_classes)
optimizer_teacher = SGD(teacher_model.parameters(), lr=lr, momentum=0.9)
optimizer_student = SGD(student_model.parameters(), lr=lr, momentum=0.9)
loss_function = nn.CrossEntropyLoss()
do_train(teacher_model, student_model, train_loader, test_loader, loss_function, epochs)