nip.utils.torch.Conv2dSimulateBatchDims

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)[source]#

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[source]#

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.