mmagic.models.editors.pconv.mask_conv_module

Module Contents

Classes

MaskConvModule

Mask convolution module.

class mmagic.models.editors.pconv.mask_conv_module.MaskConvModule(*args, **kwargs)

Bases: mmcv.cnn.ConvModule

Mask convolution module.

This is a simple wrapper for mask convolution like: ‘partial conv’. Convolutions in this module always need a mask as extra input.

Parameters

• in_channels (int) – Same as nn.Conv2d.

• out_channels (int) – Same as nn.Conv2d.

• kernel_size (int or tuple[int]) – Same as nn.Conv2d.

• stride (int or tuple[int]) – Same as nn.Conv2d.

• padding (int or tuple[int]) – Same as nn.Conv2d.

• dilation (int or tuple[int]) – Same as nn.Conv2d.

• groups (int) – Same as nn.Conv2d.

• bias (bool or str) – If specified as auto, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False.

• conv_cfg (dict) – Config dict for convolution layer.

• norm_cfg (dict) – Config dict for normalization layer.

• act_cfg (dict) – Config dict for activation layer, “relu” by default.

• inplace (bool) – Whether to use inplace mode for activation.

• with_spectral_norm (bool) – Whether use spectral norm in conv module.

• padding_mode (str) – If the padding_mode has not been supported by current Conv2d in Pytorch, we will use our own padding layer instead. Currently, we support [‘zeros’, ‘circular’] with official implementation and [‘reflect’] with our own implementation. Default: ‘zeros’.

• order (tuple[str]) – The order of conv/norm/activation layers. It is a sequence of “conv”, “norm” and “act”. Examples are (“conv”, “norm”, “act”) and (“act”, “conv”, “norm”).

supported_conv_list = ['PConv']

forward(x, mask=None, activate=True, norm=True, return_mask=True)

Forward function for partial conv2d.

Parameters

• x (torch.Tensor) – Tensor with shape of (n, c, h, w).

• mask (torch.Tensor) – Tensor with shape of (n, c, h, w) or (n, 1, h, w). If mask is not given, the function will work as standard conv2d. Default: None.

• activate (bool) – Whether use activation layer.

• norm (bool) – Whether use norm layer.

• return_mask (bool) – If True and mask is not None, the updated mask will be returned. Default: True.

Returns

Result Tensor or 2-tuple of

Tensor: Results after partial conv.

Tensor: Updated mask will be returned if mask is given and return_mask is True.

Return type

Tensor or tuple