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mmagic.models.editors.aotgan.aot_decoder 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule

from mmagic.registry import MODELS


@MODELS.register_module()
[文档]class AOTDecoder(BaseModule): """Decoder used in AOT-GAN model. This implementation follows: Aggregated Contextual Transformations for High-Resolution Image Inpainting Args: in_channels (int, optional): Channel number of input feature. Default: 256. mid_channels (int, optional): Channel number of middle feature. Default: 128. out_channels (int, optional): Channel number of output feature. Default 3. act_cfg (dict, optional): Config dict for activation layer, "relu" by default. """ def __init__(self, in_channels=256, mid_channels=128, out_channels=3, act_cfg=dict(type='ReLU')): super().__init__() self.decoder = nn.ModuleList([ ConvModule( in_channels, mid_channels, kernel_size=3, stride=1, padding=1, act_cfg=act_cfg), ConvModule( mid_channels, mid_channels // 2, kernel_size=3, stride=1, padding=1, act_cfg=act_cfg), ConvModule( mid_channels // 2, out_channels, kernel_size=3, stride=1, padding=1, act_cfg=None) ]) self.output_act = nn.Tanh()
[文档] def forward(self, x): """Forward Function. Args: x (Tensor): Input tensor with shape of (n, c, h, w). Returns: Tensor: Output tensor with shape of (n, c, h', w'). """ for i in range(0, len(self.decoder)): if i <= 1: x = F.interpolate( x, scale_factor=2, mode='bilinear', align_corners=True) x = self.decoder[i](x) return self.output_act(x)
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