jetson Orin NX 上,python3 train.py --model-dir=models/fruit data/fruit_A, 为了训练fruit_A, train.py 应该如何写/
时间: 2024-10-04 20:02:05 浏览: 28
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要在Jetson Orin NX上使用Python 3编写`train.py`脚本来针对fruit_A数据集进行训练,你需要遵循机器学习项目的标准流程,通常包括以下步骤:
1. **导入所需库**:
```python
import os
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torchvision.models as models
from torch.optim import Adam
from torch.nn import CrossEntropyLoss
```
2. **设置数据加载器**:
- 定义数据集路径和变换(例如,归一化和大小调整)
```python
data_dir = 'data/fruit_A'
model_dir = 'models/fruit'
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=transform_train)
test_dataset = datasets.ImageFolder(os.path.join(data_dir, 'test'), transform=transform_test)
dataloader_train = DataLoader(train_dataset, batch_size=32, shuffle=True)
dataloader_test = DataLoader(test_dataset, batch_size=32, shuffle=False)
```
3. **选择模型和损失函数**:
```python
num_classes = len(sorted(os.listdir(os.path.join(data_dir, 'train')))) # 计算水果种类数量
model = models.resnet18(pretrained=True) # 或者根据需求选择其他模型
if num_classes != 1000: # 如果不是预设的ImageNet分类数目,则替换fc层
model.fc = nn.Linear(model.fc.in_features, num_classes)
criterion = CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001)
```
4. **定义训练循环**:
```python
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 判断是否使用GPU
for epoch in range(num_epochs): # 设置训练轮数
for images, labels in dataloader_train:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}")
```
5. **保存模型**:
当训练完成后,保存模型以便于后续使用:
```python
model_path = os.path.join(model_dir, f'model_epoch_{epoch}.pth')
torch.save(model.state_dict(), model_path)
```
完整的`train.py`脚本可能会更长,包含了验证、日志记录等功能,但上述部分涵盖了基本的训练过程。记得根据实际情况调整数据集结构和参数。
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