98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
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 models.invertibility.sync_batchnorm import SynchronizedBatchNorm2d
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class _ASPPModule(nn.Module):
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def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):
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super(_ASPPModule, self).__init__()
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self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
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stride=1, padding=padding, dilation=dilation, bias=False)
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self.bn = BatchNorm(planes)
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self.relu = nn.ReLU()
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self._init_weight()
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def forward(self, x):
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x = self.atrous_conv(x)
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x = self.bn(x)
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return self.relu(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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class ASPP(nn.Module):
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def __init__(self, backbone, output_stride, BatchNorm):
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super(ASPP, self).__init__()
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if backbone == 'drn':
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inplanes = 512
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elif backbone == 'mobilenet':
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inplanes = 320
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else:
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inplanes = 2048
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if output_stride == 16:
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dilations = [1, 6, 12, 18]
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elif output_stride == 8:
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dilations = [1, 12, 24, 36]
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else:
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raise NotImplementedError
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self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
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self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm)
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self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm)
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self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm)
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self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
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nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
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BatchNorm(256),
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nn.ReLU())
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self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
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self.bn1 = BatchNorm(256)
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(0.5)
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self._init_weight()
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def forward(self, x):
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x1 = self.aspp1(x)
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x2 = self.aspp2(x)
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x3 = self.aspp3(x)
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x4 = self.aspp4(x)
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x5 = self.global_avg_pool(x)
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x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)
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x = torch.cat((x1, x2, x3, x4, x5), dim=1)
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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return self.dropout(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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# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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# m.weight.data.normal_(0, math.sqrt(2. / n))
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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_aspp(backbone, output_stride, BatchNorm):
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return ASPP(backbone, output_stride, BatchNorm)
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