求带yolov5训练自己的数据集

Question

这些是我运行train.py 时的报错以及我的data里面的yaml文件 可不可以稍微详细的帮我解决一下问题 最好是可以直接呈现纠错后的代码

Answer 1

让阿豪来帮你解答，本回答参考chatgpt3.5编写提供，如果还有疑问可以评论或留言

问题描述： 在运行train.py时，出现了以下报错信息：

AttributeError: module 'torch' has no attribute 'nn'

同时，提供了data文件夹下的两个yaml文件，其中的内容如下：

# dataset.yaml
dataset:
  name: 'MNIST'
  root: '/path/to/MNIST'

# model.yaml
model:
  name: 'MLP'
  input_size: 28*28
  hidden_sizes: [128, 64]
  output_size: 10

解决方案： 这个报错提示表明在torch模块中找不到nn的属性，根据 Torch 的官方文档介绍，可以使用 torch.nn 模块来进行神经网络的搭建，因此，可能是导入 torch 后没有正确引用 nn 模块。为了解决这个问题，我们需要确保在train.py文件中导入了torch.nn模块，我们可以在 train.py 的开头加上以下代码：

import torch.nn as nn

这里给出示例代码：

import torch
import torch.nn as nn
# ... 以下是其他代码 ...
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
    
    def forward(self, x):
        x = x.view(-1, 28*28)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
# ... 以下是其他代码 ...

另外，提供的两个yaml文件是用来配置数据集和模型的参数的。在train.py中可能需要更改读取路径，我们可以将数据集的这部分代码替换为以下代码：

import yaml
# ...
with open('data/dataset.yaml') as f:
    dataset_info = yaml.load(f, Loader=yaml.FullLoader)
# ...

并将模型的这部分代码替换为以下代码：

import yaml
# ...
with open('data/model.yaml') as f:
    model_info = yaml.load(f, Loader=yaml.FullLoader)
model_name = model_info['model']['name']
hidden_sizes = model_info['model']['hidden_sizes']
input_size = model_info['model']['input_size']
output_size = model_info['model']['output_size']
# ...

最终纠错后的代码参考如下：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
import yaml
# 加载数据集
with open('data/dataset.yaml') as f:
    dataset_info = yaml.load(f, Loader=yaml.FullLoader)
mnist_train = dset.MNIST(root=dataset_info['dataset']['root'], train=True, transform=transforms.ToTensor(), download=True)
mnist_test = dset.MNIST(root=dataset_info['dataset']['root'], train=False, transform=transforms.ToTensor(), download=True)
# 定义模型
with open('data/model.yaml') as f:
    model_info = yaml.load(f, Loader=yaml.FullLoader)
model_name = model_info['model']['name']
hidden_sizes = model_info['model']['hidden_sizes']
input_size = model_info['model']['input_size']
output_size = model_info['model']['output_size']
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_sizes[0])
        self.fc2 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
        self.fc3 = nn.Linear(hidden_sizes[1], output_size)
    
    def forward(self, x):
        x = x.view(-1, input_size)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(mnist_train, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 1000 == 999:  # 每1000个batch打印一下loss和准确率
            print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/1000))
            running_loss = 0.0
print('Finished Training')