Source code for mmedit.models.common.mask_conv_module

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule

[docs]class MaskConvModule(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. Args: 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'] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) assert self.conv_cfg['type'] in self.supported_conv_list self.init_weights()
[docs] def forward(self, x, mask=None, activate=True, norm=True, return_mask=True): """Forward function for partial conv2d. Args: input (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: Tensor or tuple: 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. """ for layer in self.order: if layer == 'conv': if self.with_explicit_padding: x = self.padding_layer(x) mask = self.padding_layer(mask) if return_mask: x, updated_mask = self.conv( x, mask, return_mask=return_mask) else: x = self.conv(x, mask, return_mask=False) elif layer == 'norm' and norm and self.with_norm: x = self.norm(x) elif layer == 'act' and activate and self.with_activation: x = self.activate(x) if return_mask: return x, updated_mask return x
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