Question

对于Pytorch中的图像分类问题，我尝试运行以下程序：

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.utils.data as data

# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Hyper parameters
num_epochs = 5
num_classes = 10
batch_size = 100
learning_rate = 0.001

TRAIN_DATA_PATH = "train/"
TEST_DATA_PATH = "test/"
TRANSFORM_IMG = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(256),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225] )
    ])

train_dataset = torchvision.datasets.ImageFolder(root=TRAIN_DATA_PATH, transform=TRANSFORM_IMG)
train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,  num_workers=4)
test_dataset = torchvision.datasets.ImageFolder(root=TEST_DATA_PATH, transform=TRANSFORM_IMG)
test_loader  = data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=4)

# Convolutional neural network (two convolutional layers)
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7 * 7 * 32, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out


model = ConvNet(num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

# Test the model
model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

# Save the model checkpoint
torch.save(model.state_dict(), 'model/model.ckpt')

但是我得到一个RuntimeError：

Traceback (most recent call last):

RuntimeError: Given groups=1, weight of size 16 1 5 5, expected input[100, 3, 256, 256] to have 1 channels, but got 3 channels instead

有人可以帮助修复该错误吗？非常感谢。

与参考相关：

https://discuss.pytorch.org/t/given-groups-1-weight-16-1-5-5-so-expected-input-100-3-64-64-to-have-1-channels-but-got-3-channels-instead/28831/17

RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[3, 1, 224, 224] to have 3 channels, but got 1 channels instead

Answer 1

您的输入层self.layer1以2d卷积nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)开始。该转换层期望输入具有两个空间尺寸和一个通道，并输出具有相同空间尺寸和16个通道的tesnor。
但是，您的输入只有三个通道而不是一个通道（RGB图像而不是灰度图像）。

确保您的网络和数据同步。

RuntimeError：给定组= 1，权重为16 1 5 5，预期输入[100，3，256，256]具有1个通道，但是却有3个通道

1 个答案: