问题描述
我有如下的 CNNLstm 模型。
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=3,out_channels=16,kernel_size=5,stride=1,padding=2,),nn.ReLU(),nn.MaxPool2d(kernel_size=2),)
self.conv2 = nn.Sequential(
nn.Conv2d(16,32,5,1,2),)
#print(num_classes)
self.out = nn.Linear(32 * 75 * 75,num_classes)#32 * 75 * 75/64 * 37 * 37/128 * 18 * 18
def forward(self,x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0),-1)
output = self.out(x)
return output,x
import torch
from torchvision import datasets,transforms
import torch.nn.functional as f
from torch_lr_finder import LRFinder
class CnnLstm(nn.Module):
def __init__(self):
super(CnnLstm,self).__init__()
self.cnn = CNN()
self.rnn = nn.LSTM(input_size=180000,hidden_size=256,num_layers=3,batch_first=True)#stacked LSTM with 2 layers
self.linear = nn.Linear(256,num_classes)
def forward(self,x):
batch_size,time_steps,channels,height,width = x.size()
c_in = x.view(batch_size * time_steps,width)
_,c_out = self.cnn(c_in)
r_in = c_out.view(batch_size,-1)
r_out,(_,_) = self.rnn(r_in)
r_out2 = self.linear(r_out[:,-1,:])
return f.log_softmax(r_out2,dim=1)
class TrainCNNLSTM:
def __init__(self):
self.seed = 1
self.batch_size = 8
self.validate_batch_size = 8
self.test_batch_size = 1
self.epoch = 50
self.learning_rate = 0.005
self.step = 100
self.train_loader = None
self.validate_loader = None
self.test_loader = None
self.modelloaded = False
self.model = CnnLstm().to(device)
self.criterion = nn.CrossEntropyLoss()
#self.optimizer = torch.optim.SGD(self.model.parameters(),lr=self.learning_rate)#self.learning_rate = 0.001
self.optimizer = torch.optim.AdamW(self.model.parameters())
#self.scheduler = optim.lr_scheduler.OneCycleLR(self.optimizer,2e-3,epochs=self.epoch,steps_per_epoch=len(train_loader))
def load_data(self):
data_loader = DataLoader()
self.train_loader = data_loader.get_train_data(self.batch_size)
self.validate_loader = data_loader.get_validate_data(self.validate_batch_size)
self.test_loader = data_loader.get_test_data(self.test_batch_size)
def do_lrfinder(self):
lr_finder = LRFinder(self.model,self.optimizer,self.criterion,device)
lr_finder.range_test(self.train_loader,end_lr=1,num_iter=1000)
lr_finder.plot()
plt.savefig("LRvsLoss.png")
plt.close()
def train(self):
for epoch in range(0,self.epoch):
t_losses=[]
for iteration,(data,target) in enumerate(self.train_loader):
print(data.shape)
data = np.expand_dims(data,axis=1)
print(data.shape)
data = torch.FloatTensor(data)
data,target = data.cuda(),target.cuda()
data,target = Variable(data),Variable(target)
self.optimizer.zero_grad()
由于是CNNLstm模型,模型的数据输入形状为batch_size、time_steps、channels、height、width。
(8,3,300,300)
要使用 torch_lr_finder
,我们需要运行以下代码。
lr_finder = LRFinder(self.model,device)
lr_finder.range_test(self.train_loader,num_iter=1000)
self.train_loader
输出形状为 (8,300)
。所以在求学习率时,不能使用self.model
。
如何将 torch_lr_finder
用于此类模型?
解决方法
一种可能性是,您可以将张量传递到模型的前向函数中,然后在那里使用 .unsqueeze(1),而不是在 for 循环中扩展 dims。像这样
print(data.shape)
print(data.shape)
data = torch.FloatTensor(data)
只需省略 expand dims 然后在您的转发功能中执行此操作
x = x.unsqueeze(1)