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model.py
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model.py
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import torch
import torch.nn as nn
from torch.nn import init
from resnet import resnet50, resnet18
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm)
return out
class Non_local(nn.Module):
def __init__(self, in_channels, reduc_ratio=2):
super(Non_local, self).__init__()
self.in_channels = in_channels
self.inter_channels = reduc_ratio//reduc_ratio
self.g = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1,
padding=0),
)
self.W = nn.Sequential(
nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(self.in_channels),
)
nn.init.constant_(self.W[1].weight, 0.0)
nn.init.constant_(self.W[1].bias, 0.0)
self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
def forward(self, x):
'''
:param x: (b, c, t, h, w)
:return:
'''
batch_size = x.size(0)
g_x = self.g(x).view(batch_size, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
f = torch.matmul(theta_x, phi_x)
N = f.size(-1)
# f_div_C = torch.nn.functional.softmax(f, dim=-1)
f_div_C = f / N
y = torch.matmul(f_div_C, g_x)
y = y.permute(0, 2, 1).contiguous()
y = y.view(batch_size, self.inter_channels, *x.size()[2:])
W_y = self.W(y)
z = W_y + x
return z
# #####################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_out')
init.zeros_(m.bias.data)
elif classname.find('BatchNorm1d') != -1:
init.normal_(m.weight.data, 1.0, 0.01)
init.zeros_(m.bias.data)
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal_(m.weight.data, 0, 0.001)
if m.bias:
init.zeros_(m.bias.data)
class visible_module(nn.Module):
def __init__(self, arch='resnet50'):
super(visible_module, self).__init__()
model_v = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.visible = model_v
def forward(self, x):
x = self.visible.conv1(x)
x = self.visible.bn1(x)
x = self.visible.relu(x)
x = self.visible.maxpool(x)
return x
class thermal_module(nn.Module):
def __init__(self, arch='resnet50'):
super(thermal_module, self).__init__()
model_t = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.thermal = model_t
def forward(self, x):
x = self.thermal.conv1(x)
x = self.thermal.bn1(x)
x = self.thermal.relu(x)
x = self.thermal.maxpool(x)
return x
class base_resnet(nn.Module):
def __init__(self, arch='resnet50'):
super(base_resnet, self).__init__()
model_base = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
model_base.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.base = model_base
def forward(self, x):
x = self.base.layer1(x)
x = self.base.layer2(x)
x = self.base.layer3(x)
x = self.base.layer4(x)
return x
class embed_net(nn.Module):
def __init__(self, class_num, no_local= 'on', gm_pool = 'on', arch='resnet50'):
super(embed_net, self).__init__()
self.thermal_module = thermal_module(arch=arch)
self.visible_module = visible_module(arch=arch)
self.base_resnet = base_resnet(arch=arch)
self.non_local = no_local
if self.non_local =='on':
layers=[3, 4, 6, 3]
non_layers=[0,2,3,0]
self.NL_1 = nn.ModuleList(
[Non_local(256) for i in range(non_layers[0])])
self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])
self.NL_2 = nn.ModuleList(
[Non_local(512) for i in range(non_layers[1])])
self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])
self.NL_3 = nn.ModuleList(
[Non_local(1024) for i in range(non_layers[2])])
self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])
self.NL_4 = nn.ModuleList(
[Non_local(2048) for i in range(non_layers[3])])
self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])
pool_dim = 2048
self.l2norm = Normalize(2)
self.bottleneck = nn.BatchNorm1d(pool_dim)
self.bottleneck.bias.requires_grad_(False) # no shift
self.bottleneck_2 = nn.BatchNorm1d(pool_dim)
self.bottleneck_2.bias.requires_grad_(False)
self.classifier = nn.Linear(pool_dim, class_num, bias=False)
self.bottleneck.apply(weights_init_kaiming)
self.bottleneck_2.apply(weights_init_kaiming)
self.classifier.apply(weights_init_classifier)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.gm_pool = gm_pool
def forward(self, x1, x2, modal=0):
if modal == 0:
x1 = self.visible_module(x1)
x2 = self.thermal_module(x2)
x = torch.cat((x1, x2), 0)
elif modal == 1:
x = self.visible_module(x1)
elif modal == 2:
x = self.thermal_module(x2)
# shared block
if self.non_local == 'on':
NL1_counter = 0
if len(self.NL_1_idx) == 0: self.NL_1_idx = [-1]
for i in range(len(self.base_resnet.base.layer1)):
x = self.base_resnet.base.layer1[i](x)
if i == self.NL_1_idx[NL1_counter]:
_, C, H, W = x.shape
x = self.NL_1[NL1_counter](x)
NL1_counter += 1
# Layer 2
NL2_counter = 0
if len(self.NL_2_idx) == 0: self.NL_2_idx = [-1]
for i in range(len(self.base_resnet.base.layer2)):
x = self.base_resnet.base.layer2[i](x)
if i == self.NL_2_idx[NL2_counter]:
_, C, H, W = x.shape
x = self.NL_2[NL2_counter](x)
NL2_counter += 1
# Layer 3
NL3_counter = 0
if len(self.NL_3_idx) == 0: self.NL_3_idx = [-1]
for i in range(len(self.base_resnet.base.layer3)):
x = self.base_resnet.base.layer3[i](x)
if i == self.NL_3_idx[NL3_counter]:
_, C, H, W = x.shape
x = self.NL_3[NL3_counter](x)
NL3_counter += 1
# Layer 4
NL4_counter = 0
if len(self.NL_4_idx) == 0: self.NL_4_idx = [-1]
for i in range(len(self.base_resnet.base.layer4)):
x = self.base_resnet.base.layer4[i](x)
if i == self.NL_4_idx[NL4_counter]:
_, C, H, W = x.shape
x = self.NL_4[NL4_counter](x)
NL4_counter += 1
else:
x = self.base_resnet(x)
if self.gm_pool == 'on':
b, c, h, w = x.shape
x = x.view(b, c, -1)
p = 3.0
x_pool = (torch.mean(x**p, dim=-1) + 1e-12)**(1/p)
else:
x_pool = self.avgpool(x)
x_pool = x_pool.view(x_pool.size(0), x_pool.size(1))
feat = self.bottleneck(x_pool)
# feat_2 = self.bottleneck_2(x_pool)
if self.training:
return x_pool, self.classifier(feat)
else:
return self.l2norm(x_pool), self.l2norm(feat)