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| from tqdm import tqdm
import torch
import torchvision
from torch import nn
from torchvision import transforms,datasets
import torchvision.transforms as trans
import os
import sys
import matplotlib.pyplot as plt
def get_net(num_classes1):
class Alexnet(nn.Module):
def __init__(self,num_classes):
super(Alexnet,self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(96, 256, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(256, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def forward(self,x):
x = self.features(x)
x = torch.flatten(x,start_dim=1)
x = self.classifier(x)
return x
net = Alexnet(num_classes1)
return net
def data_loader(data_path,batch_size):
transforms1 = {
"train":transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.RandomHorizontalFlip(224),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
]),
"test":transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),
])
}
train_dataset = datasets.ImageFolder(root=os.path.join(data_path,"train"),transform=transforms1["train"])
test_dataset = datasets.ImageFolder(root=os.path.join(data_path,"val"),transform=transforms1["test"])
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size,shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size,shuffle=False)
return train_loader,test_loader
def plot_acc(epochs,train_acc_li,test_acc_li):
plt.plot(range(1,epochs+1), train_acc_li, label="train_acc",color="red")
plt.plot(range(1,epochs+1), test_acc_li, label="test_acc",color="blue")
plt.xlabel("epochs")
plt.ylabel("acc")
plt.legend()
plt.title("epoch-acc")
plt.show()
def test(net,test_loader,device):
net.eval()
acc_num = torch.zeros(1).to(device)
sample_num = 0
test_bar = tqdm(test_loader,file=sys.stdout,ncols=100)
with torch.no_grad():
for data in test_bar:
images,label = data
sample_num += images.shape[0]
images = images.to(device)
label = label.to(device)
output = net(images)
pred_class = torch.max(output,dim=1)[1]
acc_num += torch.eq(pred_class,label).sum()
test_acc = acc_num.item()/sample_num
return test_acc
def train(net,train_loader,loss_func,optimzer,lr,device):
net.train()
acc_num = torch.zeros(1).to(device)
sample_num = 0
train_bar = tqdm(train_loader,file=sys.stdout,ncols=100)
for data in train_bar:
images,label = data
sample_num += images.shape[0]
images = images.to(device)
label = label.to(device)
optimzer.zero_grad()
output = net(images)
pred_class = torch.max(output,dim=1)[1]
acc_num += torch.eq(pred_class,label).sum()
loss = loss_func(output,label)
loss.backward()
optimzer.step()
train_acc = acc_num.item()/sample_num
return train_acc
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_path = "./data"
batch_size = 64
train_loader,test_loader = data_loader(data_path,64)
num_classes = 5
net = get_net(num_classes)
net.to(device)
lr = 0.001
epochs = 50
loss_func = nn.CrossEntropyLoss()
optimzer = torch.optim.Adam(net.parameters(),lr=lr)
print(f"using {device}---")
save_path = os.path.abspath(os.path.join(os.getcwd(),"result/alexnet"))
if not os.path.exists(save_path):
os.makedirs(save_path)
train_acc_li,test_acc_li = [],[]
for epoch in range(epochs):
train_acc_li.append(train(net,train_loader,loss_func,optimzer,lr,device))
test_acc_li.append(test(net,test_loader,device))
plot_acc(epochs,train_acc_li,test_acc_li)
torch.save(net.state_dict(), os.path.join(save_path, "AlexNet.pth") )
print(train_acc_li[-1],test_acc_li[-1])
print("train is finished---")
main()
|
