Source code for mmedit.models.backbones.encoder_decoders.necks.gl_dilation

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

from mmedit.models.common import SimpleGatedConvModule
from mmedit.models.registry import COMPONENTS

[docs]@COMPONENTS.register_module() class GLDilationNeck(nn.Module): """Dilation Backbone used in Global&Local model. This implementation follows: Globally and locally Consistent Image Completion Args: in_channels (int): Channel number of input feature. conv_type (str): The type of conv module. In DeepFillv1 model, the `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type` should be 'gated_conv'. norm_cfg (dict): Config dict to build norm layer. act_cfg (dict): Config dict for activation layer, "relu" by default. kwargs (keyword arguments). """ _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule) def __init__(self, in_channels=256, conv_type='conv', norm_cfg=None, act_cfg=dict(type='ReLU'), **kwargs): super().__init__() conv_module = self._conv_type[conv_type] dilation_convs_ = [] for i in range(4): dilation_ = int(2**(i + 1)) dilation_convs_.append( conv_module( in_channels, in_channels, kernel_size=3, padding=dilation_, dilation=dilation_, stride=1, norm_cfg=norm_cfg, act_cfg=act_cfg, **kwargs)) self.dilation_convs = nn.Sequential(*dilation_convs_)
[docs] def forward(self, x): """Forward Function. Args: x (torch.Tensor): Input tensor with shape of (n, c, h, w). Returns: torch.Tensor: Output tensor with shape of (n, c, h', w'). """ x = self.dilation_convs(x) return x
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