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Source code for mmedit.models.backbones.encoder_decoders.decoders.indexnet_decoder

# Copyright (c) OpenMMLab. All rights reserved.
import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init, normal_init

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


[docs]class IndexedUpsample(nn.Module): """Indexed upsample module. Args: in_channels (int): Input channels. out_channels (int): Output channels. kernel_size (int, optional): Kernel size of the convolution layer. Defaults to 5. norm_cfg (dict, optional): Config dict for normalization layer. Defaults to dict(type='BN'). conv_module (ConvModule | DepthwiseSeparableConvModule, optional): Conv module. Defaults to ConvModule. """ def __init__(self, in_channels, out_channels, kernel_size=5, norm_cfg=dict(type='BN'), conv_module=ConvModule): super().__init__() self.conv = conv_module( in_channels, out_channels, kernel_size, padding=(kernel_size - 1) // 2, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU6')) self.init_weights()
[docs] def init_weights(self): """Init weights for the module. """ for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m, mode='fan_in', nonlinearity='leaky_relu')
[docs] def forward(self, x, shortcut, dec_idx_feat=None): """Forward function. Args: x (Tensor): Input feature map with shape (N, C, H, W). shortcut (Tensor): The shortcut connection with shape (N, C, H', W'). dec_idx_feat (Tensor, optional): The decode index feature map with shape (N, C, H', W'). Defaults to None. Returns: Tensor: Output tensor with shape (N, C, H', W'). """ if dec_idx_feat is not None: assert shortcut.dim() == 4, ( 'shortcut must be tensor with 4 dimensions') x = dec_idx_feat * F.interpolate(x, size=shortcut.shape[2:]) out = torch.cat((x, shortcut), dim=1) return self.conv(out)
[docs]@COMPONENTS.register_module() class IndexNetDecoder(nn.Module): def __init__(self, in_channels, kernel_size=5, norm_cfg=dict(type='BN'), separable_conv=False): # TODO: remove in_channels argument super().__init__() if separable_conv: conv_module = DepthwiseSeparableConvModule else: conv_module = ConvModule blocks_in_channels = [ in_channels * 2, 96 * 2, 64 * 2, 32 * 2, 24 * 2, 16 * 2, 32 * 2 ] blocks_out_channels = [96, 64, 32, 24, 16, 32, 32] self.decoder_layers = nn.ModuleList() for in_channel, out_channel in zip(blocks_in_channels, blocks_out_channels): self.decoder_layers.append( IndexedUpsample(in_channel, out_channel, kernel_size, norm_cfg, conv_module)) self.pred = nn.Sequential( conv_module( 32, 1, kernel_size, padding=(kernel_size - 1) // 2, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU6')), nn.Conv2d( 1, 1, kernel_size, padding=(kernel_size - 1) // 2, bias=False))
[docs] def init_weights(self): """Init weights for the module. """ for m in self.modules(): if isinstance(m, nn.Conv2d): std = math.sqrt(2. / (m.out_channels * m.kernel_size[0]**2)) normal_init(m, mean=0, std=std)
[docs] def forward(self, inputs): """Forward function. Args: inputs (dict): Output dict of IndexNetEncoder. Returns: Tensor: Predicted alpha matte of the current batch. """ shortcuts = reversed(inputs['shortcuts']) dec_idx_feat_list = reversed(inputs['dec_idx_feat_list']) out = inputs['out'] group = (self.decoder_layers, shortcuts, dec_idx_feat_list) for decode_layer, shortcut, dec_idx_feat in zip(*group): out = decode_layer(out, shortcut, dec_idx_feat) out = self.pred(out) return out
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