pytorch finetuning 自己的圖片進行訓練操作
一、pytorch finetuning 自己的圖片進行訓練
這種讀取圖片的方式用的是torch自帶的 ImageFolder，讀取的文件夾必須在一個大的子文件下，按類別歸好類。
就像我現在要區分三個類別。
#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)
然後就是fine tuning自己的網絡，在torch中可以對整個網絡修改後，訓練全部的參數也可以隻訓練其中的一部分，我這裡就隻訓練最後一個全連接層。
torchvision中提供瞭很多常用的模型，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)
定義自己的優化器，註意這裡的參數隻傳入最後一層的
#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)
然後就可以開始訓練瞭，定義好各種參數。
#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))
測試部分和訓練部分類似這裡就不一一說明。
這樣就完整瞭對自己網絡的訓練測試，完整代碼如下：
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 利用預訓練模型進行Fine-tuning
在Deep Learning領域，很多子領域的應用，比如一些動物識別，食物的識別等，公開的可用的數據庫相對於ImageNet等數據庫而言，其規模太小瞭，無法利用深度網絡模型直接train from scratch，容易引起過擬合，這時就需要把一些在大規模數據庫上已經訓練完成的模型拿過來，在目標數據庫上直接進行Fine-tuning（微調），這個已經經過訓練的模型對於目標數據集而言，隻是一種相對較好的參數初始化方法而已，尤其是大數據集與目標數據集結構比較相似的話，經過在目標數據集上微調能夠得到不錯的效果。
Fine-tune預訓練網絡的步驟：
1. 首先更改預訓練模型分類層全連接層的數目，因為一般目標數據集的類別數與大規模數據庫的類別數不一致，更改為目標數據集上訓練集的類別數目即可，一致的話則無需更改；
2. 把分類器前的網絡的所有層的參數固定，即不讓它們參與學習，不進行反向傳播，隻訓練分類層的網絡，這時學習率可以設置的大一點，如是原來初始學習率的10倍或幾倍或0.01等，這時候網絡訓練的比較快，因為除瞭分類層，其它層不需要進行反向傳播，可以多嘗試不同的學習率設置。
3.接下來是設置相對較小的學習率，對整個網絡進行訓練，這時網絡訓練變慢啦。
下面對利用PyTorch深度學習框架Fine-tune預訓練網絡的過程中涉及到的固定可學習參數，對不同的層設置不同的學習率等進行詳細講解。
1. PyTorch對某些層固定網絡的可學習參數的方法：
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),
)
如上述代碼，則模型Net網絡中self.features與self.Conv1_1層中的參數便是固定，不可學習的。這主要看代碼：
for p in self.parameters():
p.requires_grad=False
插入的位置，這段代碼前的所有層的參數是不可學習的，也就沒有反向傳播過程。也可以指定某一層的參數不可學習，如下：
for p in self.features.parameters():
p.requires_grad=False
則 self.features層所有參數均是不可學習的。
註意，上述代碼設置若要真正生效，在訓練網絡時需要在設置優化器如下：
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
2. PyTorch之為不同的層設置不同的學習率
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)
上述代碼表示將模型Net網絡的 self.Conv1_2層的學習率設置為傳入學習率的10倍，base_params的學習沒有明確設置，則默認為傳入的學習率args.lr。
註意：
[{'params': base_params}, {'params': model.Conv1_2.parameters(), 'lr': 10 * args.lr}]
表示為列表中的字典結構。
這種方法設置不同的學習率顯得不夠靈活，可以為不同的層設置靈活的學習率，可以采用如下方法在adjust_learning_rate函數中設置：
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'])
上述代碼中的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}，這裡設置的學習率會把args.lr給覆蓋掉，個人認為上述代碼在設置學習率方面更靈活一些。上述代碼也可如下變成實現（註意學習率隨便設置的，未與上述代碼保持一致）：
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'])
下面貼出SGD優化器的PyTorch實現，及其每個參數的設置和表示意義，具體如下：
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
經驗總結：
在Fine-tuning時最好不要隔層設置層的參數的可學習與否，這樣做一般效果餅不理想，一般準則即可，即先Fine-tuning分類層，學習率設置的大一些，然後在將整個網絡設置一個較小的學習率，所有層一起訓練。
至於不先經過Fine-tune分類層，而是將整個網絡所有層一起訓練，隻是分類層的學習率相對設置大一些，這樣做也可以，至於哪個效果更好，沒評估過。當用三元組損失（triplet loss）微調用softmax loss訓練的網絡時，可以設置階梯型的較小學習率，整個網絡所有層一起訓練，效果比較好，而不用先Fine-tune分類層前一層的輸出。
以上為個人經驗，希望能給大傢一個參考，也希望大傢多多支持WalkonNet。
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