一、pytorch finetuning 自己的图片进行训练
这种读取图片的方式用的是torch自带的 ImageFolder，读取的文件夹必须在一个大的子文件下，按类别归好类。
就像我现在要区分三个类别。
#perpare data set
#train data
train_data=torchvision.datasets.ImageFolder('F:/eyeDataSet/trainData',transform=transforms.Compose(
[
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor()
]))
print(len(train_data))
train_loader=DataLoader(train_data,batch_size=20,shuffle=True)
然后就是fine tuning自己的网络，在torch中可以对整个网络修改后，训练全部的参数也可以只训练其中的一部分，我这里就只训练最后一个全连接层。
torchvision中提供了很多常用的模型，比如resnet ，Vgg，Alexnet等等
# prepare model
mode1_ft_res18=torchvision.models.resnet18(pretrained=True)
for param in mode1_ft_res18.parameters():
param.requires_grad=False
num_fc=mode1_ft_res18.fc.in_features
mode1_ft_res18.fc=torch.nn.Linear(num_fc,3)
定义自己的优化器，注意这里的参数只传入最后一层的
#loss function and optimizer criterion=torch.nn.CrossEntropyLoss() #parameters only train the last fc layer optimizer=torch.optim.Adam(mode1_ft_res18.fc.parameters(),lr=0.001)
然后就可以开始训练了，定义好各种参数。
#start train
#label not one-hot encoder
EPOCH=1
for epoch in range(EPOCH):
train_loss=0.
train_acc=0.
for step,data in enumerate(train_loader):
batch_x,batch_y=data
batch_x,batch_y=Variable(batch_x),Variable(batch_y)
#batch_y not one hot
#out is the probability of eatch class
# such as one sample[-1.1009 0.1411 0.0320],need to calculate the max index
# out shape is batch_size * class
out=mode1_ft_res18(batch_x)
loss=criterion(out,batch_y)
train_loss+=loss.data[0]
# pred is the expect class
#batch_y is the true label
pred=torch.max(out,1)[1]
train_correct=(pred==batch_y).sum()
train_acc+=train_correct.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step%14==0:
print('Epoch: ',epoch,'Step',step,
'Train_loss: ',train_loss/((step+1)*20),'Train acc: ',train_acc/((step+1)*20))
测试部分和训练部分类似这里就不一一说明。
这样就完整了对自己网络的训练测试，完整代码如下：
import torch
import numpy as np
import torchvision
from torchvision import transforms,utils
from torch.utils.data import DataLoader
from torch.autograd import Variable
#perpare data set
#train data
train_data=torchvision.datasets.ImageFolder('F:/eyeDataSet/trainData',transform=transforms.Compose(
[
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor()
]))
print(len(train_data))
train_loader=DataLoader(train_data,batch_size=20,shuffle=True)
#test data
test_data=torchvision.datasets.ImageFolder('F:/eyeDataSet/testData',transform=transforms.Compose(
[
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor()
]))
test_loader=DataLoader(test_data,batch_size=20,shuffle=True)
# prepare model
mode1_ft_res18=torchvision.models.resnet18(pretrained=True)
for param in mode1_ft_res18.parameters():
param.requires_grad=False
num_fc=mode1_ft_res18.fc.in_features
mode1_ft_res18.fc=torch.nn.Linear(num_fc,3)
#loss function and optimizer
criterion=torch.nn.CrossEntropyLoss()
#parameters only train the last fc layer
optimizer=torch.optim.Adam(mode1_ft_res18.fc.parameters(),lr=0.001)
#start train
#label not one-hot encoder
EPOCH=1
for epoch in range(EPOCH):
train_loss=0.
train_acc=0.
for step,data in enumerate(train_loader):
batch_x,batch_y=data
batch_x,batch_y=Variable(batch_x),Variable(batch_y)
#batch_y not one hot
#out is the probability of eatch class
# such as one sample[-1.1009 0.1411 0.0320],need to calculate the max index
# out shape is batch_size * class
out=mode1_ft_res18(batch_x)
loss=criterion(out,batch_y)
train_loss+=loss.data[0]
# pred is the expect class
#batch_y is the true label
pred=torch.max(out,1)[1]
train_correct=(pred==batch_y).sum()
train_acc+=train_correct.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step%14==0:
print('Epoch: ',epoch,'Step',step,
'Train_loss: ',train_loss/((step+1)*20),'Train acc: ',train_acc/((step+1)*20))
#print('Epoch: ', epoch, 'Train_loss: ', train_loss / len(train_data), 'Train acc: ', train_acc / len(train_data))
# test model
mode1_ft_res18.eval()
eval_loss=0
eval_acc=0
for step ,data in enumerate(test_loader):
batch_x,batch_y=data
batch_x,batch_y=Variable(batch_x),Variable(batch_y)
out=mode1_ft_res18(batch_x)
loss = criterion(out, batch_y)
eval_loss += loss.data[0]
# pred is the expect class
# batch_y is the true label
pred = torch.max(out, 1)[1]
test_correct = (pred == batch_y).sum()
eval_acc += test_correct.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
print( 'Test_loss: ', eval_loss / len(test_data), 'Test acc: ', eval_acc / len(test_data))
二、PyTorch 利用预训练模型进行Fine-tuning
在Deep Learning领域，很多子领域的应用，比如一些动物识别，食物的识别等，公开的可用的数据库相对于ImageNet等数据库而言，其规模太小了，无法利用深度网络模型直接train from scratch，容易引起过拟合，这时就需要把一些在大规模数据库上已经训练完成的模型拿过来，在目标数据库上直接进行Fine-tuning（微调），这个已经经过训练的模型对于目标数据集而言，只是一种相对较好的参数初始化方法而已，尤其是大数据集与目标数据集结构比较相似的话，经过在目标数据集上微调能够得到不错的效果。
Fine-tune预训练网络的步骤：
1. 首先更改预训练模型分类层全连接层的数目，因为一般目标数据集的类别数与大规模数据库的类别数不一致，更改为目标数据集上训练集的类别数目即可，一致的话则无需更改；
2. 把分类器前的网络的所有层的参数固定，即不让它们参与学习，不进行反向传播，只训练分类层的网络，这时学习率可以设置的大一点，如是原来初始学习率的10倍或几倍或0.01等，这时候网络训练的比较快，因为除了分类层，其它层不需要进行反向传播，可以多尝试不同的学习率设置。
3.接下来是设置相对较小的学习率，对整个网络进行训练，这时网络训练变慢啦。
下面对利用PyTorch深度学习框架Fine-tune预训练网络的过程中涉及到的固定可学习参数，对不同的层设置不同的学习率等进行详细讲解。
1. PyTorch对某些层固定网络的可学习参数的方法：
class Net(nn.Module):
def __init__(self, num_classes=546):
super(Net, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.Conv1_1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
)
for p in self.parameters():
p.requires_grad=False
self.Conv1_2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
)
如上述代码，则模型Net网络中self.features与self.Conv1_1层中的参数便是固定，不可学习的。这主要看代码：
for p in self.parameters():
p.requires_grad=False
插入的位置，这段代码前的所有层的参数是不可学习的，也就没有反向传播过程。也可以指定某一层的参数不可学习，如下：
for p in self.features.parameters():
p.requires_grad=False
则 self.features层所有参数均是不可学习的。
注意，上述代码设置若要真正生效，在训练网络时需要在设置优化器如下：
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
2. PyTorch之为不同的层设置不同的学习率
model = Net()
conv1_2_params = list(map(id, model.Conv1_2.parameters()))
base_params = filter(lambda p: id(p) not in conv1_2_params,
model.parameters())
optimizer = torch.optim.SGD([
{'params': base_params},
{'params': model.Conv1_2.parameters(), 'lr': 10 * args.lr}], args.lr,
momentum=args.momentum, weight_decay=args.weight_decay)
上述代码表示将模型Net网络的 self.Conv1_2层的学习率设置为传入学习率的10倍，base_params的学习没有明确设置，则默认为传入的学习率args.lr。
注意：
[{'params': base_params}, {'params': model.Conv1_2.parameters(), 'lr': 10 * args.lr}]
表示为列表中的字典结构。
这种方法设置不同的学习率显得不够灵活，可以为不同的层设置灵活的学习率，可以采用如下方法在adjust_learning_rate函数中设置：
def adjust_learning_rate(optimizer, epoch, args):
lre = []
lre.extend([0.01] * 10)
lre.extend([0.005] * 10)
lre.extend([0.0025] * 10)
lr = lre[epoch]
optimizer.param_groups[0]['lr'] = 0.9 * lr
optimizer.param_groups[1]['lr'] = 10 * lr
print(param_group[0]['lr'])
print(param_group[1]['lr'])
上述代码中的optimizer.param_groups[0]就代表[{'params': base_params}, {'params': model.Conv1_2.parameters(), 'lr': 10 * args.lr}]中的'params': base_params}，optimizer.param_groups[1]代表{'params': model.Conv1_2.parameters(), 'lr': 10 * args.lr}，这里设置的学习率会把args.lr给覆盖掉，个人认为上述代码在设置学习率方面更灵活一些。上述代码也可如下变成实现（注意学习率随便设置的，未与上述代码保持一致）：
def adjust_learning_rate(optimizer, epoch, args):
lre = np.logspace(-2, -4, 40)
lr = lre[epoch]
for i in range(len(optimizer.param_groups)):
param_group = optimizer.param_groups[i]
if i == 0:
param_group['lr'] = 0.9 * lr
else:
param_group['lr'] = 10 * lr
print(param_group['lr'])
下面贴出SGD优化器的PyTorch实现，及其每个参数的设置和表示意义，具体如下：
import torch
from .optimizer import Optimizer, required
class SGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): learning rate
momentum (float, optional): momentum factor (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
dampening (float, optional): dampening for momentum (default: 0)
nesterov (bool, optional): enables Nesterov momentum (default: False)
Example:
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
.. note::
The implementation of SGD with Momentum/Nesterov subtly differs from
Sutskever et. al. and implementations in some other frameworks.
Considering the specific case of Momentum, the update can be written as
.. math::
v = \rho * v + g \\
p = p - lr * v
where p, g, v and :math:`\rho` denote the parameters, gradient,
velocity, and momentum respectively.
This is in contrast to Sutskever et. al. and
other frameworks which employ an update of the form
.. math::
v = \rho * v + lr * g \\
p = p - v
The Nesterov version is analogously modified.
"""
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(SGD, self).__init__(params, defaults)
def __setstate__(self, state):
super(SGD, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
buf.mul_(momentum).add_(d_p)
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
p.data.add_(-group['lr'], d_p)
return loss
经验总结：
在Fine-tuning时最好不要隔层设置层的参数的可学习与否，这样做一般效果饼不理想，一般准则即可，即先Fine-tuning分类层，学习率设置的大一些，然后在将整个网络设置一个较小的学习率，所有层一起训练。
至于不先经过Fine-tune分类层，而是将整个网络所有层一起训练，只是分类层的学习率相对设置大一些，这样做也可以，至于哪个效果更好，没评估过。当用三元组损失（triplet loss）微调用softmax loss训练的网络时，可以设置阶梯型的较小学习率，整个网络所有层一起训练，效果比较好，而不用先Fine-tune分类层前一层的输出。
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。