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| import torch
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| from torch import nn
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| from torch.nn import functional as F
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| from lib.models.tools.module_helper import ModuleHelper
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| class Edge_Module(nn.Module):
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| def __init__(self, mid_fea, out_fea, bn_type=None, factor=1):
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| super(Edge_Module, self).__init__()
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| self.conv1 = nn.Sequential(
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| nn.Conv2d(factor*256, mid_fea, kernel_size=1, padding=0, dilation=1, bias=False),
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| ModuleHelper.BNReLU(mid_fea, bn_type=bn_type),
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| )
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| self.conv2 = nn.Sequential(
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| nn.Conv2d(factor*512, mid_fea, kernel_size=1, padding=0, dilation=1, bias=False),
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| ModuleHelper.BNReLU(mid_fea, bn_type=bn_type),
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| )
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| self.conv3 = nn.Sequential(
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| nn.Conv2d(factor*1024, mid_fea, kernel_size=1, padding=0, dilation=1, bias=False),
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| ModuleHelper.BNReLU(mid_fea, bn_type=bn_type),
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| )
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| self.conv4 = nn.Conv2d(mid_fea, out_fea, kernel_size=3, padding=1, dilation=1, bias=True)
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| self.conv5 = nn.Conv2d(out_fea*3, out_fea, kernel_size=1, padding=0, dilation=1, bias=True)
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| def forward(self, x1, x2, x3):
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| _, _, h, w = x1.size()
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| edge1_fea = self.conv1(x1)
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| edge1 = self.conv4(edge1_fea)
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| edge2_fea = self.conv2(x2)
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| edge2 = self.conv4(edge2_fea)
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| edge3_fea = self.conv3(x3)
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| edge3 = self.conv4(edge3_fea)
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| edge2_fea = F.interpolate(edge2_fea, size=(h, w), mode='bilinear', align_corners=True)
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| edge3_fea = F.interpolate(edge3_fea, size=(h, w), mode='bilinear', align_corners=True)
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| edge2 = F.interpolate(edge2, size=(h, w), mode='bilinear', align_corners=True)
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| edge3 = F.interpolate(edge3, size=(h, w), mode='bilinear', align_corners=True)
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| edge_fea = torch.cat([edge1_fea, edge2_fea, edge3_fea], dim=1)
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| edge = torch.cat([edge1, edge2, edge3], dim=1)
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| edge = self.conv5(edge)
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| return edge, edge_fea |
