nip.utils.torch.Conv2dSimulateBatchDims

class nip.utils.torch.Conv2dSimulateBatchDims(in_channels: int, out_channels: int, kernel_size: int | Tuple[int, int], stride: int | Tuple[int, int] = 1, padding: str | int | Tuple[int, int] = 0, dilation: int | Tuple[int, int] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', device=None, dtype=None)

2D convolutional layer with arbitrary batch dimensions.

See torch.nn.Conv2d for documentation.

Assumes an input of shape (… channels height width).

Methods Summary

forward(x)

Apply the module to the input tensor, simulating multiple batch dimensions.

Attributes

T_destination
call_super_init
dump_patches
feature_dims
bias
in_channels
out_channels
kernel_size
stride
padding
dilation
transposed
output_padding
groups
padding_mode
weight
training

Methods

forward(x: Tensor) → Tensor

Apply the module to the input tensor, simulating multiple batch dimensions.

Parameters:

x (Tensor) – The input tensor.

Returns:

out (Tensor) – The output tensor after applying the module.