102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
import torch
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import torch.nn as nn
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from BiRefNet.modules.aspp import ASPP, ASPPDeformable
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from BiRefNet.modules.attentions import PSA, SGE
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from ..config import Config
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config = Config()
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class BasicDecBlk(nn.Module):
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def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
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super(BasicDecBlk, self).__init__()
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inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
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self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
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self.relu_in = nn.ReLU(inplace=True)
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if config.dec_att == 'ASPP':
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self.dec_att = ASPP(in_channels=inter_channels)
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elif config.dec_att == 'ASPPDeformable':
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self.dec_att = ASPPDeformable(in_channels=inter_channels)
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self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
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self.bn_in = nn.BatchNorm2d(inter_channels)
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self.bn_out = nn.BatchNorm2d(out_channels)
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def forward(self, x):
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x = self.conv_in(x)
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x = self.bn_in(x)
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x = self.relu_in(x)
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if hasattr(self, 'dec_att'):
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x = self.dec_att(x)
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x = self.conv_out(x)
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x = self.bn_out(x)
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return x
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class ResBlk(nn.Module):
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def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
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super(ResBlk, self).__init__()
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if out_channels is None:
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out_channels = in_channels
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inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
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self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
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self.bn_in = nn.BatchNorm2d(inter_channels)
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self.relu_in = nn.ReLU(inplace=True)
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if config.dec_att == 'ASPP':
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self.dec_att = ASPP(in_channels=inter_channels)
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elif config.dec_att == 'ASPPDeformable':
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self.dec_att = ASPPDeformable(in_channels=inter_channels)
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self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
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self.bn_out = nn.BatchNorm2d(out_channels)
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self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
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def forward(self, x):
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_x = self.conv_resi(x)
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x = self.conv_in(x)
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x = self.bn_in(x)
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x = self.relu_in(x)
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if hasattr(self, 'dec_att'):
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x = self.dec_att(x)
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x = self.conv_out(x)
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x = self.bn_out(x)
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return x + _x
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class HierarAttDecBlk(nn.Module):
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def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
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super(HierarAttDecBlk, self).__init__()
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if out_channels is None:
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out_channels = in_channels
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inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
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self.split_y = 8 # must be divided by channels of all intermediate features
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self.split_x = 8
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self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
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self.psa = PSA(inter_channels*self.split_y*self.split_x, S=config.batch_size)
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self.sge = SGE(groups=config.batch_size)
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if config.dec_att == 'ASPP':
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self.dec_att = ASPP(in_channels=inter_channels)
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elif config.dec_att == 'ASPPDeformable':
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self.dec_att = ASPPDeformable(in_channels=inter_channels)
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self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
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def forward(self, x):
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x = self.conv_in(x)
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N, C, H, W = x.shape
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x_patchs = x.reshape(N, -1, H//self.split_y, W//self.split_x)
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# Hierarchical attention: group attention X patch spatial attention
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x_patchs = self.psa(x_patchs) # Group Channel Attention -- each group is a single image
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x_patchs = self.sge(x_patchs) # Patch Spatial Attention
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x = x.reshape(N, C, H, W)
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if hasattr(self, 'dec_att'):
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x = self.dec_att(x)
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x = self.conv_out(x)
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return x
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