torch.nn.functional.fold

torch.nn.functional.fold applies a sliding window operation on a tensor and returns a new tensor by aggregating the values of the elements in the window. It is commonly used in image processing tasks such as down-sampling or pooling.

The function takes a tensor of shape (batch_size, channels, height, width), a window size (kernel_size), and a stride value. The window slides across the height and width dimensions of the input tensor with the given stride value, and the elements in the window are aggregated using a specified function (e.g. max, mean, sum). The resulting tensor has a shape of (batch_size, channels, output_height, output_width).

Here's an example usage of torch.nn.functional.fold:

import torch
import torch.nn.functional as F

# Define input tensor
input_tensor = torch.randn(1, 3, 5, 5)

# Apply 2x2 max pooling using fold
kernel_size = (2, 2)
stride = (2, 2)
output_tensor = F.fold(input_tensor, kernel_size, stride, (0, 0), max)

print(output_tensor.shape) # Output: torch.Size([1, 3, 2, 2])

In this example, the input tensor has shape (1, 3, 5, 5) which means there is one image in the batch, with 3 channels, and a height and width of 5. We apply 2x2 max pooling using fold by setting kernel_size to (2, 2), stride to (2, 2), and using the max function to aggregate the elements in the window. The resulting tensor has shape (1, 3, 2, 2) which means there is one image in the batch, with 3 channels, and a height and width of 2.