Module/Function Name: FlexiConv

class FlexiConv(nn.Module)

FlexiConv is an experimental and flexible convolutional layer that adapts to the input data.

Args

Argument	Description	Data Type	Default Value
in_channels	Number of channels in the input image	int	-
out_channels	Number of channels produced by the convolution	int	-
kernel_size	Size of the convolving kernel	int/tuple	-
stride	Stride of the convolution	int/tuple	1
padding	Zero-padding added to the input	int/tuple	0
## Example

import torch

from zeta.nn import FlexiConv

flexi_conv = FlexiConv(
    in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1
)
input_tensor = torch.randn(1, 3, 224, 224)  # Example input batch
output = flexi_conv(input_tensor)
output.shape

Purpose

FlexiConv is aimed at providing a flexible convolutional layer that adapts to the input data using parameterized Gaussian functions to weigh the importance of each pixel in the receptive field and applies a depthwise separable convolution for efficiency.

Functionality

The FlexiConv class encapsulates a flexible convolutional layer that uses Gaussian functions to weigh the importance of each pixel in the receptive field. It applies a depthwise separable convolution to efficiently process input data. The user can specify the number of input and output channels, kernel size, and stride, among other parameters.

Usage

The FlexiConv layer can be instantiated by passing the required arguments and then used to process input tensors.

Example 1:

import torch

from zeta.nn import FlexiConv

flexi_conv = FlexiConv(
    in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1
)
input_tensor = torch.randn(1, 3, 224, 224)
output = flexi_conv(input_tensor)
output.shape

Example 2:

import torch

from zeta.nn import FlexiConv

flexi_conv = FlexiConv(
    in_channels=3, out_channels=64, kernel_size=3, stride=(2, 2), padding=1
)
input_tensor = torch.randn(1, 3, 224, 224)
output = flexi_conv(input_tensor)
output.shape

Example 3:

import torch

from zeta.nn import FlexiConv

flexi_conv = FlexiConv(
    in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(1, 2), padding=1
)
input_tensor = torch.randn(1, 3, 224, 224)
output = flexi_conv(input_tensor)
output.shape

References

Provide any references to further information or research papers related to the FlexiConv module or framework.

Additional Information

Provide any tips or additional details that may be useful for using the FlexiConv module effectively.

By documenting the FlexiConv example, the document provides an in-depth explanation of its purpose, usage, functionality, and examples to ensure the user understands how to effectively leverage the FlexiConv module.