Google Colab: Unmasking the Real-ESRGAN Error and Finding a Solution
Have you ever been excited to upscale your low-resolution images using Real-ESRGAN on Google Colab, only to be met with a frustrating error message? This common issue, while frustrating, has a simple explanation and an equally straightforward fix. Let's dive into the issue, understand why it arises, and discover the solution together.
The Problem
You've diligently set up your Google Colab notebook, imported the necessary libraries, and eagerly executed the code to upscale your images. But instead of beautiful, high-resolution images, you're greeted with an error message:
File "/content/Real-ESRGAN/realesrgan/archs/network_arch.py", line 275, in forward
x = self.upconv1(x)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 725, in _call_impl
return forward_call(*input, **kwargs)
File "/content/Real-ESRGAN/realesrgan/archs/network_arch.py", line 199, in forward
return self.sub_mean(x)
File "/content/Real-ESRGAN/realesrgan/archs/network_arch.py", line 163, in sub_mean
return x - self.mean
TypeError: unsupported operand type(s) for -: 'Tensor' and 'list'
The Cause
This error stems from a mismatch in data types within the Real-ESRGAN code. The mean attribute in the sub_mean function is defined as a list, while the input x is a PyTorch Tensor. PyTorch's Tensor subtraction operation expects both operands to be tensors, hence the error.
The Solution
The fix is simple: Convert the mean attribute from a list to a PyTorch Tensor. Here's how to modify the relevant code:
1. Locate the sub_mean function in your network_arch.py file:
class ResidualDenseBlock(nn.Module):
def __init__(self, channels, growth_rate, num_layers, kernel_size=3, use_bias=False):
super(ResidualDenseBlock, self).__init__()
self.layers = nn.ModuleList()
for i in range(num_layers):
self.layers.append(
nn.Conv2d(channels + i * growth_rate, growth_rate, kernel_size, padding=(kernel_size - 1) // 2, bias=use_bias)
)
self.relu = nn.ReLU(inplace=True)
self.conv1x1 = nn.Conv2d(channels + num_layers * growth_rate, channels, 1, bias=use_bias)
self.mean = [0.4488, 0.4371, 0.4040] # This is the line to change
def sub_mean(self, x):
return x - self.mean # Error occurs here
# ... rest of the code ...
2. Modify the mean attribute to a PyTorch Tensor:
self.mean = torch.Tensor([0.4488, 0.4371, 0.4040])
3. Save the changes to network_arch.py and rerun your Colab notebook.
Now, your Real-ESRGAN model should run smoothly, upscaling your images with no issues.
Additional Insights
- This error might occur with other image restoration models that utilize similar structures.
- Understanding the error message and tracing it back to the source code is crucial for effective debugging.
- The Real-ESRGAN model, developed by xinntao, is a powerful tool for image super-resolution. Check out its GitHub repository for more details and examples.
Conclusion
By understanding the source of the error and making a simple code modification, you can overcome this obstacle and successfully utilize Real-ESRGAN on Google Colab. Happy upscaling!
