59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
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class Decoder(nn.Module):
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def __init__(self, num_classes, backbone, BatchNorm):
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super(Decoder, self).__init__()
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if backbone == 'resnet' or backbone == 'drn':
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low_level_inplanes = 256
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elif backbone == 'xception':
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low_level_inplanes = 128
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elif backbone == 'mobilenet':
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low_level_inplanes = 24
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else:
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raise NotImplementedError
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self.conv1 = nn.Conv2d(low_level_inplanes, 48, 1, bias=False)
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self.bn1 = BatchNorm(48)
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self.relu = nn.ReLU()
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self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
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BatchNorm(256),
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nn.ReLU(),
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nn.Dropout(0.5),
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nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
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BatchNorm(256),
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nn.ReLU(),
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nn.Dropout(0.1),
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nn.Conv2d(256, num_classes, kernel_size=1, stride=1),
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nn.Sigmoid()
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)
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self._init_weight()
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def forward(self, x, low_level_feat):
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low_level_feat = self.conv1(low_level_feat)
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low_level_feat = self.bn1(low_level_feat)
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low_level_feat = self.relu(low_level_feat)
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x = F.interpolate(x, size=low_level_feat.size()[2:], mode='bilinear', align_corners=True)
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x = torch.cat((x, low_level_feat), dim=1)
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x = self.last_conv(x)
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return x
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def _init_weight(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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torch.nn.init.kaiming_normal_(m.weight)
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elif isinstance(m, SynchronizedBatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def build_decoder(num_classes, backbone, BatchNorm):
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return Decoder(num_classes, backbone, BatchNorm)
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