for epoch in range(num_epochs): for i, real_images in enumerate(dataloader): # 训练判别器 real_images = real_images.to(device) #print(real_images.shape) labels_real = torch.full((real_images.size(0),), 1.0, device=device) labels_fake = torch.full((real_images.size(0),), 0.0, device=device) 这段代码运行之后,报 'list' object has no attribute 'to'的错误,该如何修改
时间: 2024-04-14 10:25:31 浏览: 22
这个错误通常是因为你的 `dataloader` 返回的是一个列表而不是一个张量。你可以尝试将 `real_images` 转换为张量再进行操作。代码修改如下:
```python
for epoch in range(num_epochs):
for i, real_images_list in enumerate(dataloader):
real_images = torch.tensor(real_images_list).to(device)
#print(real_images.shape)
labels_real = torch.full((real_images.size(0),), 1.0, device=device)
labels_fake = torch.full((real_images.size(0),), 0.0, device=device)
```
这样,你应该能够解决 `'list' object has no attribute 'to'` 错误。
相关问题
我用于训练的代码报错:“TypeError: 'module' object is not callable”,你帮我看看是什么原因。代码如下:import os import sys import torch import torchvision from dataset import MyDataset from yolov5 import YOLOv5 # 修改批次大小,图像大小和训练轮数 batch_size = 16 img_size = 512 epochs = 50 # 指定数据集路径和标签文件路径 dataset_path = 'F:/moda/images' label_file = 'F:/moda/labels' # 加载数据集和标签 dataset = MyDataset(dataset_path, label_file, img_size=img_size) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True) # 初始化YOLOv5网络 model = YOLOv5(num_classes=1, num_anchors=3) # 定义损失函数和优化器 criterion = torch.nn.MSELoss() optimizer = torch.optim.Adam(model.parameters()) # 训练网络 for epoch in range(epochs): for i, (images, targets) in enumerate(dataloader): # 将数据加载到GPU上 images = images.cuda() targets = targets.cuda() # 前向传播 outputs = model(images) # 计算损失 loss = criterion(outputs, targets) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 打印训练状态 print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, i+1, len(dataloader), loss.item()))
这个错误通常发生在尝试将模块作为函数进行调用时。在你的代码中,这个错误可能是由于从 yolov5 模块中导入了 YOLOv5 类,但未将其实例化就尝试调用它。你可以尝试在实例化 YOLOv5 类之后将其传递给 optimizer 和 criterion。 代码如下:
```python
import os
import sys
import torch
import torchvision
from dataset import MyDataset
from yolov5 import YOLOv5
# 修改批次大小,图像大小和训练轮数
batch_size = 16
img_size = 512
epochs = 50
# 指定数据集路径和标签文件路径
dataset_path = 'F:/moda/images'
label_file = 'F:/moda/labels'
# 加载数据集和标签
dataset = MyDataset(dataset_path, label_file, img_size=img_size)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 初始化YOLOv5网络
model = YOLOv5(num_classes=1, num_anchors=3)
# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
# 训练网络
for epoch in range(epochs):
for i, (images, targets) in enumerate(dataloader):
# 将数据加载到GPU上
images = images.cuda()
targets = targets.cuda()
# 前向传播
outputs = model(images)
# 计算损失
loss = criterion(outputs, targets)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 打印训练状态
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, i+1, len(dataloader), loss.item()))
```
如果问题仍然存在,请提供完整的错误信息和堆栈跟踪。这将有助于更好地理解你的问题并提供更准确的解决方案。
dataset = CocoDetection(root=r'D:\file\study\data\COCO2017\train2017', annFile=r'D:\file\study\data\COCO2017\annotations\instances_train2017.json', transforms=transforms.Compose([transforms.ToTensor()])) # 定义训练集和测试集的比例 train_ratio = 0.8 test_ratio = 0.2 # 计算训练集和测试集的数据数量 num_data = len(dataset) num_train_data = int(num_data * train_ratio) num_test_data = num_data - num_train_data # 使用random_split函数将数据集划分为训练集和测试集 train_dataset, test_dataset = random_split(dataset, [num_train_data, num_test_data]) # 打印训练集和测试集的数据数量 print(f"Number of training data: {len(train_dataset)}") print(f"Number of test data: {len(test_dataset)}") train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=0) test_loader = DataLoader(test_dataset, batch_size=8, shuffle=True, num_workers=0) # define the optimizer and the learning rate scheduler params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1) # train the model for 10 epochs num_epochs = 10 for epoch in range(num_epochs): # 将模型设置为训练模式 model.train() # 初始化训练损失的累计值 train_loss = 0.0 # 构建一个迭代器,用于遍历数据集 for i, images, targets in train_loader: print(images) print(targets) # 将数据转移到设备上 images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets]上述代码报错:TypeError: call() takes 2 positional arguments but 3 were given
这个错误是因为在你的数据读取过程中,`train_loader`返回了三个值,包括索引、图像和目标,但是你在迭代器中只使用了后两个值。为了解决这个问题,你可以修改迭代器的定义,将索引也加入到迭代器中,如下所示:
```python
for i, (images, targets) in enumerate(train_loader):
# do something with images and targets
```
这样,你就可以使用`enumerate`函数获取每个批次数据的索引,同时获取图像和目标。同时,如果你希望在训练过程中输出批次数据的索引,你也可以在代码中加入打印语句,如下所示:
```python
for i, (images, targets) in enumerate(train_loader):
print(f"Batch {i}:")
# do something with images and targets
```
希望这可以帮助你解决问题。