用VGG16分析JAFEE数据时出现RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[32, 1, 224, 224] to have 3 channels, but got 1 channels instead错误怎样改代码

这个错误是由于输入的数据通道数不匹配导致的。解决方法是在输入数据时将通道数从1改为3，可以使用torch的torch.squeeze()函数来实现。具体代码如下：

import torch
import torch.nn as nn
from torchvision import models, transforms, datasets

vgg16 = models.vgg16(pretrained=True)
vgg16.classifier = nn.Sequential(
    nn.Linear(in_features=25088, out_features=4096),
    nn.ReLU(inplace=True),
    nn.Dropout(p=0.5),
    nn.Linear(in_features=4096, out_features=4096),
    nn.ReLU(inplace=True),
    nn.Dropout(p=0.5),
    nn.Linear(in_features=4096, out_features=7),
    nn.LogSoftmax(dim=1)
)

data_transforms = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 修改输入数据，从1到3通道
train_data = datasets.ImageFolder('path/to/train', transform=data_transforms)
train_data.samples = [(p, p.replace('train', 'train_labels')) for p, _ in train_data.samples]
train_data.targets = [int(open(label).read()) for _, label in train_data.samples]
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)

for images, labels in train_loader:
    outputs = vgg16(images)
    break

print(outputs.shape)