jetson 如何更新pytorch的代码
时间: 2023-03-26 20:03:21 浏览: 50
您可以通过以下步骤更新 Jetson 上的 PyTorch 代码:
1. 打开终端并进入 PyTorch 代码目录。
2. 使用 git pull 命令拉取最新的代码。
3. 运行 python setup.py install 命令重新安装 PyTorch。
希望这可以帮助您更新 Jetson 上的 PyTorch 代码。
相关问题
给出一个jetson使用pytorch训练图像模型的例子
以下是一个使用Jetson Nano运行PyTorch的图像分类器示例代码:
首先,安装必要的依赖项:
```
sudo apt-get install python3-pip libopenblas-dev libatlas-base-dev liblapack-dev libblas-dev liblapack-dev libjpeg-dev libpng-dev libtiff-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk-3-dev libhdf5-dev libprotobuf-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev libboost-all-dev libgphoto2-dev libeigen3-dev
```
然后,安装PyTorch:
```
sudo pip3 install torch torchvision
```
接下来,创建一个名为“image_classification.py”的文件,并将以下代码复制到其中:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import time
import os
import copy
# 定义数据转换
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
# 加载数据集
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# 判断是否使用GPU
use_gpu = torch.cuda.is_available()
# 定义模型
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
if use_gpu:
model_ft = model_ft.cuda()
# 定义损失函数
criterion = nn.CrossEntropyLoss()
# 定义优化器
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# 定义学习率调整策略
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
# 定义训练函数
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model = model
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 每个epoch都有训练和验证阶段
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train(True)
else:
model.train(False)
running_loss = 0.0
running_corrects = 0
# 迭代数据
for data in dataloaders[phase]:
# 获取输入
inputs, labels = data
if use_gpu:
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
# 梯度清零
optimizer.zero_grad()
# 前向传播
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
# 反向传播 + 优化
if phase == 'train':
loss.backward()
optimizer.step()
# 统计
running_loss += loss.item()
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# 保存最佳模型权重
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model = copy.deepcopy(model)
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
return best_model
# 训练模型
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
# 保存模型
torch.save(model_ft.state_dict(), 'model.pt')
```
此代码将加载一个预训练的ResNet-18模型,并使用Hymenoptera数据集对其进行微调。根据需要进行更改,例如更改数据集路径,更改迭代次数或更改模型架构。
运行代码:
```
python3 image_classification.py
```
代码将训练模型,并将最佳模型保存到“model.pt”文件中。
给出一个jetson使用pytorch训练图像识别模型的例子
以下是一个使用Jetson Nano进行图像分类训练的示例:
1. 安装PyTorch和Torchvision
首先,您需要在Jetson Nano上安装PyTorch和Torchvision。可以通过以下命令安装:
```
sudo apt-get install python3-pip libopenblas-dev libblas-dev m4 cmake cython python3-dev python3-yaml python3-setuptools
pip3 install numpy torch torchvision
```
2. 获取数据集
接下来,您需要获取一个图像分类数据集。您可以在Kaggle等网站上找到许多数据集。这里我们以CIFAR-10数据集为例,可以通过以下命令下载:
```
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar zxvf cifar-10-python.tar.gz
```
3. 准备数据
接下来,您需要将数据集转换为PyTorch所需的格式。您可以使用以下代码:
```python
import torchvision.datasets as datasets
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
```
4. 定义模型
接下来,您需要定义一个CNN模型。以下是一个简单的CNN模型:
```python
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
```
5. 训练模型
现在您已经准备好训练模型了。以下是训练过程的代码:
```python
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
6. 测试模型
最后,您可以测试模型的准确性。以下是测试代码:
```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
这就是一个使用Jetson Nano进行图像分类训练的示例!