Attributeerror: ‘upsample’ object has no attribute ‘recompute_scale_factor’

AttributeError: ‘Upsample’ object has no attribute ‘recompute_scale_factor’

This error typically occurs when you try to access the recompute_scale_factor attribute of an object of the ‘Upsample’ class, but the attribute does not exist.

To understand this error better, let’s look at an example:

    
# Importing necessary libraries
import torch
import torch.nn as nn

# Defining a simple model
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.upsample = nn.Upsample(scale_factor=2)

    def forward(self, x):
        x = self.upsample(x)
        return x

# Creating an instance of the model
model = MyModel()

# Passing an input tensor through the model
input_tensor = torch.randn((1, 3, 16, 16))
output = model(input_tensor)
    
  

In the above example, we define a simple model called “MyModel” that uses the ‘Upsample’ class from the PyTorch library. We create an instance of the model and pass an input tensor through it. However, when we execute the code, we encounter the ‘AttributeError’ mentioned in the query.

This error occurs because the ‘Upsample’ class does not have a ‘recompute_scale_factor’ attribute in recent versions of PyTorch. In earlier versions, this attribute was used to determine the scale factor for upsampling. However, in newer versions, the ‘recompute_scale_factor’ attribute has been removed.

To fix this error, you have a few options:

1. Update your code to use the appropriate attribute or method for achieving the desired functionality.
2. Check the PyTorch documentation and examples for the version you are using to find the correct attribute or method to use.
3. Downgrade your PyTorch version to one that supports the ‘recompute_scale_factor’ attribute if it is crucial for your code.

It’s important to note that the specific solution will depend on your use case and the version of PyTorch you are working with. You may need to adapt the code accordingly.