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deeplab_resnet.py
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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
affine_par = True
def outS(i):
i = int(i)
i = (i + 1) / 2
i = int(np.ceil((i + 1) / 2.0))
i = (i + 1) / 2
return i
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes, affine=affine_par)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes, affine=affine_par)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation_=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
self.bn1 = nn.BatchNorm2d(planes, affine=affine_par)
for i in self.bn1.parameters():
i.requires_grad = False
padding = 1
if dilation_ == 2:
padding = 2
elif dilation_ == 4:
padding = 4
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=1, # change
padding=padding,
bias=False,
dilation=dilation_)
self.bn2 = nn.BatchNorm2d(planes, affine=affine_par)
for i in self.bn2.parameters():
i.requires_grad = False
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4, affine=affine_par)
for i in self.bn3.parameters():
i.requires_grad = False
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Classifier_Module(nn.Module):
def __init__(self, dilation_series, padding_series, NoLabels):
super(Classifier_Module, self).__init__()
self.conv2d_list = nn.ModuleList()
for dilation, padding in zip(dilation_series, padding_series):
self.conv2d_list.append(nn.Conv2d(2048, NoLabels, kernel_size=3, stride=1, padding=padding, dilation=dilation, bias=True))
for m in self.conv2d_list:
m.weight.data.normal_(0, 0.01)
def forward(self, x):
out = self.conv2d_list[0](x)
for i in range(len(self.conv2d_list) - 1):
out += self.conv2d_list[i + 1](x)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, NoLabels):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64, affine=affine_par)
for i in self.bn1.parameters():
i.requires_grad = False
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation__=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__=4)
self.layer5 = self._make_pred_layer(Classifier_Module, [6, 12, 18, 24], [6, 12, 18, 24], NoLabels)
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, 0.01)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
# for i in m.parameters():
# i.requires_grad = False
def _make_layer(self, block, planes, blocks, stride=1, dilation__=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion, affine=affine_par), )
for i in downsample._modules['1'].parameters():
i.requires_grad = False
layers = []
layers.append(block(self.inplanes, planes, stride, dilation_=dilation__, downsample=downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation_=dilation__))
return nn.Sequential(*layers)
def _make_pred_layer(self, block, dilation_series, padding_series, NoLabels):
return block(dilation_series, padding_series, NoLabels)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
return x
class MS_Deeplab(nn.Module):
def __init__(self, block, NoLabels):
super(MS_Deeplab, self).__init__()
self.Scale = ResNet(block, [3, 4, 23, 3], NoLabels) #changed to fix #4
def forward(self, x):
input_size = x.size()[2]
self.interp1 = nn.Upsample(size=(int(input_size * 0.75) + 1, int(input_size * 0.75) + 1), mode='bilinear')
self.interp2 = nn.Upsample(size=(int(input_size * 0.5) + 1, int(input_size * 0.5) + 1), mode='bilinear')
self.interp3 = nn.Upsample(size=(input_size, input_size), mode='bilinear')
out = []
x2 = self.interp1(x)
x3 = self.interp2(x)
out.append(self.interp3(self.Scale(x))) # for original scale
out.append(self.interp3(self.Scale(x2))) # for 0.75x scale
out.append(self.interp3(self.Scale(x3))) # for 0.5x scale
x2Out_interp = out[1]
x3Out_interp = out[2]
temp1 = torch.max(out[0], x2Out_interp)
out.append(torch.max(temp1, x3Out_interp))
return out
def Res_Deeplab(NoLabels=21):
model = MS_Deeplab(Bottleneck, NoLabels)
return model