PdfText.__init__() missing 6 required positional arguments: 'pageNum', 'blockNum', 'lineNum', 'lineText', 'fontSize', and 'titleLevel
时间: 2023-12-24 15:02:10 浏览: 29
这个错误提示意味着你在创建一个 PdfText 对象时,没有正确地提供初始化所需的 6 个参数:'pageNum', 'blockNum', 'lineNum', 'lineText', 'fontSize' 和 'titleLevel'。请检查你的代码并确保在创建 PdfText 对象时提供了这些参数。你可以使用类似以下的代码来创建 PdfText 对象:
```
my_pdf_text = PdfText(pageNum, blockNum, lineNum, lineText, fontSize, titleLevel)
```
请将上述代码中的每个参数替换为你实际使用的值,以确保正确创建 PdfText 对象。
相关问题
CubicSpline.__init__() missing 1 required positional argument: 'y'
根据提供的引用内容,报错信息是`CubicSpline.__init__() missing 1 required positional argument: 'y'`。这个错误通常发生在使用`CubicSpline`类的构造函数时,缺少了一个必需的参数`y`。
为了解决这个问题,你需要确保在创建`CubicSpline`对象时提供了正确的参数。根据错误信息,你需要提供一个名为`y`的参数。
下面是一个示例代码,演示了如何使用`CubicSpline`类的构造函数并提供正确的参数:
```python
from scipy.interpolate import CubicSpline
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
cs = CubicSpline(x, y)
```
在上面的示例中,我们创建了一个`CubicSpline`对象`cs`,并提供了两个参数`x`和`y`。这样就可以避免`CubicSpline.__init__() missing 1 required positional argument: 'y'`错误。
Inception.__init__() missing 2 required positional arguments: 'ch5x5' and 'pool_proj'
非常抱歉,我刚才的代码有误,已经修正。以下是正确的代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
super(Inception, self).__init__()
# 1x1 conv branch
self.branch1 = nn.Sequential(
nn.Conv2d(in_channels, ch1x1, kernel_size=1),
nn.BatchNorm2d(ch1x1),
nn.ReLU(inplace=True)
)
# 1x1 conv -> 3x3 conv branch
self.branch2 = nn.Sequential(
nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
nn.BatchNorm2d(ch3x3red),
nn.ReLU(inplace=True),
nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
nn.BatchNorm2d(ch3x3),
nn.ReLU(inplace=True)
)
# 1x1 conv -> 5x5 conv branch
self.branch3 = nn.Sequential(
nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
nn.BatchNorm2d(ch5x5red),
nn.ReLU(inplace=True),
nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
nn.BatchNorm2d(ch5x5),
nn.ReLU(inplace=True)
)
# 3x3 pool -> 1x1 conv branch
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, pool_proj, kernel_size=1),
nn.BatchNorm2d(pool_proj),
nn.ReLU(inplace=True)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return torch.cat(outputs, 1)
class GoogLeNet(nn.Module):
def __init__(self):
super(GoogLeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.inception1 = Inception(64, 64, 96, 128, 16, 32, 32)
self.inception2 = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception3 = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4 = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception5 = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception6 = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception7 = Inception(528, 256, 160, 320, 32, 128, 128)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=0.4)
self.fc = nn.Linear(832, 10)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x = self.inception1(x)
x = self.inception2(x)
x = self.maxpool(x)
x = self.inception3(x)
x = self.inception4(x)
x = self.inception5(x)
x = self.inception6(x)
x = self.inception7(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
```
这个GoogLeNet模型可以通过在CIFAR-10数据集上进行训练来实现图像分类任务。
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