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Source code for mmedit.models.components.refiners.deepfill_refiner

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F

from mmedit.models.builder import build_component
from mmedit.models.registry import COMPONENTS


[docs]@COMPONENTS.register_module() class DeepFillRefiner(nn.Module): """Refiner used in DeepFill model. This implementation follows: Generative Image Inpainting with Contextual Attention. Args: encoder_attention (dict): Config dict for encoder used in branch with contextual attention module. encoder_conv (dict): Config dict for encoder used in branch with just convolutional operation. dilation_neck (dict): Config dict for dilation neck in branch with just convolutional operation. contextual_attention (dict): Config dict for contextual attention neck. decoder (dict): Config dict for decoder used to fuse and decode features. """ def __init__(self, encoder_attention=dict( type='DeepFillEncoder', encoder_type='stage2_attention'), encoder_conv=dict( type='DeepFillEncoder', encoder_type='stage2_conv'), dilation_neck=dict( type='GLDilationNeck', in_channels=128, act_cfg=dict(type='ELU')), contextual_attention=dict( type='ContextualAttentionNeck', in_channels=128), decoder=dict(type='DeepFillDecoder', in_channels=256)): super().__init__() self.encoder_attention = build_component(encoder_attention) self.encoder_conv = build_component(encoder_conv) self.contextual_attention_neck = build_component(contextual_attention) self.dilation_neck = build_component(dilation_neck) self.decoder = build_component(decoder)
[docs] def forward(self, x, mask): """Forward Function. Args: x (torch.Tensor): Input tensor with shape of (n, c, h, w). mask (torch.Tensor): Input tensor with shape of (n, 1, h, w). Returns: torch.Tensor: Output tensor with shape of (n, c, h', w'). """ # conv branch encoder_dict = self.encoder_conv(x) conv_x = self.dilation_neck(encoder_dict['out']) # contextual attention branch attention_x = self.encoder_attention(x)['out'] h_x, w_x = attention_x.shape[-2:] # resale mask to a smaller size resized_mask = F.interpolate(mask, size=(h_x, w_x)) attention_x, offset = self.contextual_attention_neck( attention_x, resized_mask) # concat two branches x = torch.cat([conv_x, attention_x], dim=1) x = self.decoder(dict(out=x)) return x, offset
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