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HRNet.py
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HRNet.py
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import torch
import torch.nn as nn
import torchvision
import numpy as np
IN_MOMENTUM = 0.1
class ReflectionConv(nn.Module):
'''
Reflection padding convolution
'''
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ReflectionConv, self).__init__()
reflection_padding = int(np.floor(kernel_size / 2))
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv(out)
return out
class ConvLayer(nn.Module):
'''
zero-padding convolution
'''
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
conv_padding = int(np.floor(kernel_size / 2))
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=conv_padding)
def forward(self, x):
return self.conv(x)
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(BasicBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.relu = nn.ReLU(inplace=True) # 1
self.identity_block = nn.Sequential(
ConvLayer(in_channels, out_channels // 4, kernel_size=1, stride=1),
nn.InstanceNorm2d(out_channels // 4, momentum=IN_MOMENTUM),
nn.ReLU(),
ConvLayer(out_channels // 4, out_channels // 4, kernel_size, stride=stride),
nn.InstanceNorm2d(out_channels // 4, momentum=IN_MOMENTUM),
nn.ReLU(),
ConvLayer(out_channels // 4, out_channels, kernel_size=1, stride=1),
nn.InstanceNorm2d(out_channels, momentum=IN_MOMENTUM),
nn.ReLU(),
)
self.shortcut = nn.Sequential(
ConvLayer(in_channels, out_channels, 1, stride),
nn.InstanceNorm2d(out_channels)
)
def forward(self, x):
out = self.identity_block(x)
if self.in_channels == self.out_channels:
residual = x
else:
residual = self.shortcut(x)
out += residual
out = self.relu(out)
return out
class Upsample(nn.Module):
'''
Since the number of channels of the feature map changes after upsampling in HRNet.
we have to write a new Upsample class.
'''
def __init__(self, in_channels, out_channels, scale_factor, mode):
super(Upsample, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.upsample = nn.Upsample(scale_factor=scale_factor, mode='nearest')
self.instance = nn.InstanceNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.conv(x)
out = self.upsample(out)
out = self.instance(out)
out = self.relu(out)
return out
class HRNet(nn.Module):
def __init__(self):
super(HRNet, self).__init__()
self.pass1_1 = BasicBlock(3, 16, kernel_size=3, stride=1)
self.pass1_2 = BasicBlock(16, 32, kernel_size=3, stride=1)
self.pass1_3 = BasicBlock(32, 32, kernel_size=3, stride=1)
self.pass1_4 = BasicBlock(64, 64, kernel_size=3, stride=1)
self.pass1_5 = BasicBlock(192, 64, kernel_size=3, stride=1)
self.pass1_6 = BasicBlock(64, 32, kernel_size=3, stride=1)
self.pass1_7 = BasicBlock(32, 16, kernel_size=3, stride=1)
self.pass1_8 = nn.Conv2d(16, 3, kernel_size=3, stride=1, padding=1)
self.pass2_1 = BasicBlock(32, 32, kernel_size=3, stride=1)
self.pass2_2 = BasicBlock(64, 64, kernel_size=3, stride=1)
self.downsample1_1 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
self.downsample1_2 = nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1)
self.downsample1_3 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)
self.downsample1_4 = nn.Conv2d(32, 32, kernel_size=3, stride=4, padding=1)
self.downsample1_5 = nn.Conv2d(64, 64, kernel_size=3, stride=4, padding=1)
self.downsample2_1 = nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1)
self.downsample2_2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)
self.upsample1_1 = nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True)
self.upsample1_2 = nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True)
self.upsample2_1 = nn.Upsample(scale_factor=4, mode='bilinear',align_corners=True)
self.upsample2_2 = nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True)
def forward(self, x):
map1 = self.pass1_1(x)
map2 = self.pass1_2(map1)
map3 = self.downsample1_1(map1)
map4 = torch.cat((self.pass1_3(map2), self.upsample1_1(map3)), 1)
map5 = torch.cat((self.downsample1_2(map2), self.pass2_1(map3)), 1)
map6 = torch.cat((self.downsample1_4(map2), self.downsample2_1(map3)), 1)
map7 = torch.cat((self.pass1_4(map4), self.upsample1_2(map5), self.upsample2_1(map6)), 1)
out = self.pass1_5(map7)
out = self.pass1_6(out)
out = self.pass1_7(out)
out = self.pass1_8(out)
return out