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| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d | |
| class _ASPPModule(nn.Module): | |
| def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm): | |
| super(_ASPPModule, self).__init__() | |
| self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, | |
| stride=1, padding=padding, dilation=dilation, bias=False) | |
| self.bn = BatchNorm(planes) | |
| self.relu = nn.ReLU() | |
| self._init_weight() | |
| def forward(self, x): | |
| x = self.atrous_conv(x) | |
| x = self.bn(x) | |
| return self.relu(x) | |
| def _init_weight(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| torch.nn.init.kaiming_normal_(m.weight) | |
| elif isinstance(m, SynchronizedBatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| class ASPP(nn.Module): | |
| def __init__(self, backbone, output_stride, BatchNorm): | |
| super(ASPP, self).__init__() | |
| if backbone == 'drn': | |
| inplanes = 512 | |
| elif backbone == 'mobilenet': | |
| inplanes = 320 | |
| else: | |
| inplanes = 2048 | |
| if output_stride == 16: | |
| dilations = [1, 6, 12, 18] | |
| elif output_stride == 8: | |
| dilations = [1, 12, 24, 36] | |
| else: | |
| raise NotImplementedError | |
| self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm) | |
| self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm) | |
| self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm) | |
| self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm) | |
| self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), | |
| nn.Conv2d(inplanes, 256, 1, stride=1, bias=False), | |
| BatchNorm(256), | |
| nn.ReLU()) | |
| self.conv1 = nn.Conv2d(1280, 256, 1, bias=False) | |
| self.bn1 = BatchNorm(256) | |
| self.relu = nn.ReLU() | |
| self.dropout = nn.Dropout(0.5) | |
| self._init_weight() | |
| def forward(self, x): | |
| x1 = self.aspp1(x) | |
| x2 = self.aspp2(x) | |
| x3 = self.aspp3(x) | |
| x4 = self.aspp4(x) | |
| x5 = self.global_avg_pool(x) | |
| x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True) | |
| x = torch.cat((x1, x2, x3, x4, x5), dim=1) | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| return self.dropout(x) | |
| def _init_weight(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| # m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| torch.nn.init.kaiming_normal_(m.weight) | |
| elif isinstance(m, SynchronizedBatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def build_aspp(backbone, output_stride, BatchNorm): | |
| return ASPP(backbone, output_stride, BatchNorm) |