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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
from models.sync_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(inplace=True)
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_no4level(nn.Module):
def __init__(self, backbone, output_stride, BatchNorm):
super(ASPP_no4level, self).__init__()
if backbone == 'drn':
inplanes = 512
elif backbone == 'mobilenet':
inplanes = 320
else:
inplanes = 2048
low_level_inplanes = 256 #
if output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError
self.aspp1_128 = _ASPPModule(64, 64, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
self.aspp1_256 = _ASPPModule(256, 64, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
self.aspp1_1024 = _ASPPModule(1024, 128, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
self.bn1_128 = BatchNorm(64)
self.bn1_256 = BatchNorm(64)
self.bn1_1024 = BatchNorm(128)
# self.bn1_2048 = BatchNorm(256)
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(0.5)
self.last_conv = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
BatchNorm(256),
nn.ReLU(inplace=True),
nn.Dropout(0.5))
self._init_weight()
print("ASPP_4level")
def forward(self, x_1, x_2, x_3):
x_1 = self.aspp1_128(x_1)
x_1 = self.bn1_128(x_1)
x_1 = self.relu(x_1)
x_1 = self.dropout(x_1)
x_2 = self.aspp1_256(x_2)
x_2 = self.bn1_256(x_2)
x_2 = self.relu(x_2)
x_2 = self.dropout(x_2)
x_3 = self.aspp1_1024(x_3)
x_3 = self.bn1_1024(x_3)
x_3 = self.relu(x_3)
x_3 = self.dropout(x_3)
x_2 = F.interpolate(x_2, size=x_1.size()[2:], mode='bilinear', align_corners=True)
x_3 = F.interpolate(x_3, size=x_1.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x_1, x_2, x_3), dim=1)
x = self.last_conv(x)
return 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_()
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