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Source code for mmedit.models.backbones.encoder_decoders.encoders.deepfill_encoder

# 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 DeepFillEncoder(nn.Module): """Encoder used in DeepFill model. This implementation follows: Generative Image Inpainting with Contextual Attention Args: in_channels (int): The number of input channels. Default: 5. 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. Default: None. act_cfg (dict): Config dict for activation layer, "elu" by default. encoder_type (str): Type of the encoder. Should be one of ['stage1', 'stage2_conv', 'stage2_attention']. Default: 'stage1'. channel_factor (float): The scale factor for channel size. Default: 1. kwargs (keyword arguments). """ _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule) def __init__(self, in_channels=5, conv_type='conv', norm_cfg=None, act_cfg=dict(type='ELU'), encoder_type='stage1', channel_factor=1., **kwargs): super().__init__() conv_module = self._conv_type[conv_type] channel_list_dict = dict( stage1=[32, 64, 64, 128, 128, 128], stage2_conv=[32, 32, 64, 64, 128, 128], stage2_attention=[32, 32, 64, 128, 128, 128]) channel_list = channel_list_dict[encoder_type] channel_list = [int(x * channel_factor) for x in channel_list] kernel_size_list = [5, 3, 3, 3, 3, 3] stride_list = [1, 2, 1, 2, 1, 1] for i in range(6): ks = kernel_size_list[i] padding = (ks - 1) // 2 self.add_module( f'enc{i + 1}', conv_module( in_channels, channel_list[i], kernel_size=ks, stride=stride_list[i], padding=padding, norm_cfg=norm_cfg, act_cfg=act_cfg, **kwargs)) in_channels = channel_list[i]
[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'). """ for i in range(6): x = getattr(self, f'enc{i + 1}')(x) outputs = dict(out=x) return outputs
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