mipcandy.common.module.conv

Module Contents

Classes

AbstractConvBlock
WSConv2d
WSConv3d

Functions

_conv_block

Data

ConvBlock2d
ConvBlock3d

API

class mipcandy.common.module.conv.AbstractConvBlock(in_ch: int, out_ch: int, kernel_size: int, *, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', conv: mipcandy.layer.LayerT = ..., norm: mipcandy.layer.LayerT = ..., act: mipcandy.layer.LayerT = ...)

Bases: torch.nn.Module

forward(x: torch.Tensor) → torch.Tensor

mipcandy.common.module.conv._conv_block(default_conv: mipcandy.layer.LayerT, default_norm: mipcandy.layer.LayerT, default_act: mipcandy.layer.LayerT) → type[mipcandy.common.module.conv.AbstractConvBlock]

mipcandy.common.module.conv.ConvBlock2d: type[mipcandy.common.module.conv.AbstractConvBlock] = '_conv_block(...)'

mipcandy.common.module.conv.ConvBlock3d: type[mipcandy.common.module.conv.AbstractConvBlock] = '_conv_block(...)'

class mipcandy.common.module.conv.WSConv2d(in_channels: int, out_channels: int, kernel_size: torch.nn.common_types._size_2_t, stride: torch.nn.common_types._size_2_t = 1, padding: str | torch.nn.common_types._size_2_t = 0, dilation: torch.nn.common_types._size_2_t = 1, groups: int = 1, bias: bool = True, padding_mode: Literal[zeros, reflect, replicate, circular] = 'zeros', device=None, dtype=None)

Bases: torch.nn.Conv2d

forward(x: torch.Tensor) → torch.Tensor

class mipcandy.common.module.conv.WSConv3d(in_channels: int, out_channels: int, kernel_size: torch.nn.common_types._size_3_t, stride: torch.nn.common_types._size_3_t = 1, padding: str | torch.nn.common_types._size_3_t = 0, dilation: torch.nn.common_types._size_3_t = 1, groups: int = 1, bias: bool = True, padding_mode: Literal[zeros, reflect, replicate, circular] = 'zeros', device=None, dtype=None)

Bases: torch.nn.Conv3d

forward(x: torch.Tensor) → torch.Tensor