【PyTorch】（基础七）---- 完整训练流程
起首要明确一点，我们在编写模型、训练和使用模型的时间通常都是分开的，所以应该把Module的编写以及train方法和test方法分开编写。调用gpu进行训练：在网络模型，数据，丧失函数对象背面都使用.cuda（）方法，如loss_fn = loss_fn.cuda()
【代码示例】完成完整CIFAR10模型的训练
按照官网给出的模型结构进行构建：
https://i-blog.csdnimg.cn/direct/b26173af74fd476096355112d71e0a2e.png
# model.py
class myModule(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, 1, 2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(64*4*4, 64),
nn.Linear(64, 10)
)
def forward(self, ingput):
output = self.model(ingput)
return output
导入自己创建的模型，实例化一个模型对象之后，导入CIFAR10数据集进行训练
# train.py
import torchvision
from torch.utils.tensorboard import SummaryWriter
from module import *
from torch import nn
from torch.utils.data import DataLoader
# 使用Dataset来下载数据集
train_data = torchvision.datasets.CIFAR10(root="dataset/CIFAR10", train=True, transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10(root="dataset/CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),
download=True)
# 数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))
# 利用 DataLoader 来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型，实例化自定义的模型
mymodule = myModule()
if torch.cuda.is_available():
mymodule = mymodule.cuda()
# 定义损失函数为交叉熵损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_fn = loss_fn.cuda()
# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(mymodule.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# tensorboard配置日志目录
writer = SummaryWriter("logs_train")
for i in range(epoch):
print("-------第 {} 轮训练开始-------".format(i+1))
# 训练步骤开始
mymodule.train()
for data in train_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = mymodule(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1 # 每读取一次图片+1
if total_train_step % 100 == 0:
print("训练次数：{}, Loss: {}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 测试步骤开始
mymodule.eval()
total_test_loss = 0 # 损失函数值
total_accuracy = 0# 准确率
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = mymodule(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的Loss: {}".format(total_test_loss))
print("整体测试集上的正确率: {}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
total_test_step = total_test_step + 1
# 每轮都保存模型
torch.save(mymodule, "mymodule{}.pth".format(i))
print("模型已保存")
writer.close()
# test.py
import torch
import torchvision
from PIL import Image
from torch import nn
image_path = "imgs/airplane.png"
image = Image.open(image_path)
print(image)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, 1, 2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(64*4*4, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
model = torch.load("mymodule9.pth", map_location=torch.device('cpu'))
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
model.eval()
with torch.no_grad():
output = model(image)
print(output)
print(output.argmax(1))
免责声明：如果侵犯了您的权益，请联系站长，我们会及时删除侵权内容，谢谢合作！更多信息从访问主页：qidao123.com:ToB企服之家，中国第一个企服评测及商务社交产业平台。
页:
[1]