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import torch
import torch.nn as nn
import torchvision
from .ResNet import ResNet50
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 TransBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, upsample=None, **kwargs):
super(TransBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, inplanes)
self.bn1 = nn.BatchNorm2d(inplanes)
self.relu = nn.ReLU(inplace=True)
if upsample is not None and stride != 1:
self.conv2 = nn.ConvTranspose2d(
inplanes,
planes,
kernel_size=3,
stride=stride,
padding=1,
output_padding=1,
bias=False,
)
else:
self.conv2 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.upsample = upsample
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.upsample is not None:
residual = self.upsample(x)
out += residual
out = self.relu(out)
return out
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
out = max_out
return self.sigmoid(out)
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), "kernel size must be 3 or 7"
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(1, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = max_out
x = self.conv1(x)
return self.sigmoid(x)
class BasicConv2d(nn.Module):
def __init__(
self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1
):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(
in_planes,
out_planes,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=False,
)
self.bn = nn.BatchNorm2d(out_planes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
# Global Contextual module
class GCM(nn.Module):
def __init__(self, in_channel, out_channel):
super(GCM, self).__init__()
self.relu = nn.ReLU(True)
self.branch0 = nn.Sequential(
BasicConv2d(in_channel, out_channel, 1),
)
self.branch1 = nn.Sequential(
BasicConv2d(in_channel, out_channel, 1),
BasicConv2d(out_channel, out_channel, kernel_size=(1, 3), padding=(0, 1)),
BasicConv2d(out_channel, out_channel, kernel_size=(3, 1), padding=(1, 0)),
BasicConv2d(out_channel, out_channel, 3, padding=3, dilation=3),
)
self.branch2 = nn.Sequential(
BasicConv2d(in_channel, out_channel, 1),
BasicConv2d(out_channel, out_channel, kernel_size=(1, 5), padding=(0, 2)),
BasicConv2d(out_channel, out_channel, kernel_size=(5, 1), padding=(2, 0)),
BasicConv2d(out_channel, out_channel, 3, padding=5, dilation=5),
)
self.branch3 = nn.Sequential(
BasicConv2d(in_channel, out_channel, 1),
BasicConv2d(out_channel, out_channel, kernel_size=(1, 7), padding=(0, 3)),
BasicConv2d(out_channel, out_channel, kernel_size=(7, 1), padding=(3, 0)),
BasicConv2d(out_channel, out_channel, 3, padding=7, dilation=7),
)
self.conv_cat = BasicConv2d(4 * out_channel, out_channel, 3, padding=1)
self.conv_res = BasicConv2d(in_channel, out_channel, 1)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
x_cat = self.conv_cat(torch.cat((x0, x1, x2, x3), 1))
x = self.relu(x_cat + self.conv_res(x))
return x
# aggregation of the high-level(teacher) features
class aggregation_init(nn.Module):
def __init__(self, channel):
super(aggregation_init, self).__init__()
self.relu = nn.ReLU(True)
self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample2 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample3 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample4 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample5 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)
self.conv_concat2 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)
self.conv_concat3 = BasicConv2d(3 * channel, 3 * channel, 3, padding=1)
self.conv4 = BasicConv2d(3 * channel, 3 * channel, 3, padding=1)
self.conv5 = nn.Conv2d(3 * channel, 1, 1)
def forward(self, x1, x2, x3):
x1_1 = x1
x2_1 = self.conv_upsample1(self.upsample(x1)) * x2
x3_1 = (
self.conv_upsample2(self.upsample(self.upsample(x1)))
* self.conv_upsample3(self.upsample(x2))
* x3
)
x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1)
x2_2 = self.conv_concat2(x2_2)
x3_2 = torch.cat((x3_1, self.conv_upsample5(self.upsample(x2_2))), 1)
x3_2 = self.conv_concat3(x3_2)
x = self.conv4(x3_2)
x = self.conv5(x)
return x
# aggregation of the low-level(student) features
class aggregation_final(nn.Module):
def __init__(self, channel):
super(aggregation_final, self).__init__()
self.relu = nn.ReLU(True)
self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample2 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample3 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample4 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample5 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)
self.conv_concat2 = BasicConv2d(2 * channel, 2 * channel, 3, padding=1)
self.conv_concat3 = BasicConv2d(3 * channel, 3 * channel, 3, padding=1)
def forward(self, x1, x2, x3):
x1_1 = x1
x2_1 = self.conv_upsample1(self.upsample(x1)) * x2
x3_1 = self.conv_upsample2(self.upsample(x1)) * self.conv_upsample3(x2) * x3
x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1)
x2_2 = self.conv_concat2(x2_2)
x3_2 = torch.cat((x3_1, self.conv_upsample5(x2_2)), 1)
x3_2 = self.conv_concat3(x3_2)
return x3_2
# Refinement flow
class Refine(nn.Module):
def __init__(self):
super(Refine, self).__init__()
self.upsample2 = nn.Upsample(
scale_factor=2, mode="bilinear", align_corners=True
)
def forward(self, attention, x1, x2, x3):
# Note that there is an error in the manuscript. In the paper, the refinement strategy is depicted as ""f'=f*S1"", it should be ""f'=f+f*S1"".
x1 = x1 + torch.mul(x1, self.upsample2(attention))
x2 = x2 + torch.mul(x2, self.upsample2(attention))
x3 = x3 + torch.mul(x3, attention)
return x1, x2, x3
# BBSNet
class BBSNet(nn.Module):
def __init__(self, channel=32):
super(BBSNet, self).__init__()
# Backbone model
self.resnet = ResNet50("rgb")
self.resnet_depth = ResNet50("rgbd")
# Decoder 1
self.rfb2_1 = GCM(512, channel)
self.rfb3_1 = GCM(1024, channel)
self.rfb4_1 = GCM(2048, channel)
self.agg1 = aggregation_init(channel)
# Decoder 2
self.rfb0_2 = GCM(64, channel)
self.rfb1_2 = GCM(256, channel)
self.rfb5_2 = GCM(512, channel)
self.agg2 = aggregation_final(channel)
# upsample function
self.upsample = nn.Upsample(scale_factor=8, mode="bilinear", align_corners=True)
self.upsample4 = nn.Upsample(
scale_factor=4, mode="bilinear", align_corners=True
)
self.upsample2 = nn.Upsample(
scale_factor=2, mode="bilinear", align_corners=True
)
# Refinement flow
self.HA = Refine()
# Components of DEM module
self.atten_depth_channel_0 = ChannelAttention(64)
self.atten_depth_channel_1 = ChannelAttention(256)
self.atten_depth_channel_2 = ChannelAttention(512)
self.atten_depth_channel_3_1 = ChannelAttention(1024)
self.atten_depth_channel_4_1 = ChannelAttention(2048)
self.atten_depth_spatial_0 = SpatialAttention()
self.atten_depth_spatial_1 = SpatialAttention()
self.atten_depth_spatial_2 = SpatialAttention()
self.atten_depth_spatial_3_1 = SpatialAttention()
self.atten_depth_spatial_4_1 = SpatialAttention()
# Components of PTM module
self.inplanes = 32 * 2
self.deconv1 = self._make_transpose(TransBasicBlock, 32 * 2, 3, stride=2)
self.inplanes = 32
self.deconv2 = self._make_transpose(TransBasicBlock, 32, 3, stride=2)
self.agant1 = self._make_agant_layer(32 * 3, 32 * 2)
self.agant2 = self._make_agant_layer(32 * 2, 32)
self.out0_conv = nn.Conv2d(32 * 3, 1, kernel_size=1, stride=1, bias=True)
self.out1_conv = nn.Conv2d(32 * 2, 1, kernel_size=1, stride=1, bias=True)
self.out2_conv = nn.Conv2d(32 * 1, 1, kernel_size=1, stride=1, bias=True)
#if self.training:
# self.initialize_weights()
def forward(self, x, x_depth):
x = self.resnet.conv1(x)
x = self.resnet.bn1(x)
x = self.resnet.relu(x)
x = self.resnet.maxpool(x)
x_depth = self.resnet_depth.conv1(x_depth)
x_depth = self.resnet_depth.bn1(x_depth)
x_depth = self.resnet_depth.relu(x_depth)
x_depth = self.resnet_depth.maxpool(x_depth)
# layer0 merge
temp = x_depth.mul(self.atten_depth_channel_0(x_depth))
temp = temp.mul(self.atten_depth_spatial_0(temp))
x = x + temp
# layer0 merge end
x1 = self.resnet.layer1(x) # 256 x 64 x 64
x1_depth = self.resnet_depth.layer1(x_depth)
# layer1 merge
temp = x1_depth.mul(self.atten_depth_channel_1(x1_depth))
temp = temp.mul(self.atten_depth_spatial_1(temp))
x1 = x1 + temp
# layer1 merge end
x2 = self.resnet.layer2(x1) # 512 x 32 x 32
x2_depth = self.resnet_depth.layer2(x1_depth)
# layer2 merge
temp = x2_depth.mul(self.atten_depth_channel_2(x2_depth))
temp = temp.mul(self.atten_depth_spatial_2(temp))
x2 = x2 + temp
# layer2 merge end
x2_1 = x2
x3_1 = self.resnet.layer3_1(x2_1) # 1024 x 16 x 16
x3_1_depth = self.resnet_depth.layer3_1(x2_depth)
# layer3_1 merge
temp = x3_1_depth.mul(self.atten_depth_channel_3_1(x3_1_depth))
temp = temp.mul(self.atten_depth_spatial_3_1(temp))
x3_1 = x3_1 + temp
# layer3_1 merge end
x4_1 = self.resnet.layer4_1(x3_1) # 2048 x 8 x 8
x4_1_depth = self.resnet_depth.layer4_1(x3_1_depth)
# layer4_1 merge
temp = x4_1_depth.mul(self.atten_depth_channel_4_1(x4_1_depth))
temp = temp.mul(self.atten_depth_spatial_4_1(temp))
x4_1 = x4_1 + temp
# layer4_1 merge end
# produce initial saliency map by decoder1
x2_1 = self.rfb2_1(x2_1)
x3_1 = self.rfb3_1(x3_1)
x4_1 = self.rfb4_1(x4_1)
attention_map = self.agg1(x4_1, x3_1, x2_1)
# Refine low-layer features by initial map
x, x1, x5 = self.HA(attention_map.sigmoid(), x, x1, x2)
# produce final saliency map by decoder2
x0_2 = self.rfb0_2(x)
x1_2 = self.rfb1_2(x1)
x5_2 = self.rfb5_2(x5)
y = self.agg2(x5_2, x1_2, x0_2) # *4
# PTM module
y = self.agant1(y)
y = self.deconv1(y)
y = self.agant2(y)
y = self.deconv2(y)
y = self.out2_conv(y)
return self.upsample(attention_map), y
def _make_agant_layer(self, inplanes, planes):
layers = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(planes),
nn.ReLU(inplace=True),
)
return layers
def _make_transpose(self, block, planes, blocks, stride=1):
upsample = None
if stride != 1:
upsample = nn.Sequential(
nn.ConvTranspose2d(
self.inplanes,
planes,
kernel_size=2,
stride=stride,
padding=0,
bias=False,
),
nn.BatchNorm2d(planes),
)
elif self.inplanes != planes:
upsample = nn.Sequential(
nn.Conv2d(
self.inplanes, planes, kernel_size=1, stride=stride, bias=False
),
nn.BatchNorm2d(planes),
)
layers = []
for i in range(1, blocks):
layers.append(block(self.inplanes, self.inplanes))
layers.append(block(self.inplanes, planes, stride, upsample))
self.inplanes = planes
return nn.Sequential(*layers)
# initialize the weights
def initialize_weights(self):
res50 = torchvision.models.resnet50(pretrained=True)
pretrained_dict = res50.state_dict()
all_params = {}
for k, v in self.resnet.state_dict().items():
if k in pretrained_dict.keys():
v = pretrained_dict[k]
all_params[k] = v
elif "_1" in k:
name = k.split("_1")[0] + k.split("_1")[1]
v = pretrained_dict[name]
all_params[k] = v
elif "_2" in k:
name = k.split("_2")[0] + k.split("_2")[1]
v = pretrained_dict[name]
all_params[k] = v
assert len(all_params.keys()) == len(self.resnet.state_dict().keys())
self.resnet.load_state_dict(all_params)
all_params = {}
for k, v in self.resnet_depth.state_dict().items():
if k == "conv1.weight":
all_params[k] = torch.nn.init.normal_(v, mean=0, std=1)
elif k in pretrained_dict.keys():
v = pretrained_dict[k]
all_params[k] = v
elif "_1" in k:
name = k.split("_1")[0] + k.split("_1")[1]
v = pretrained_dict[name]
all_params[k] = v
elif "_2" in k:
name = k.split("_2")[0] + k.split("_2")[1]
v = pretrained_dict[name]
all_params[k] = v
assert len(all_params.keys()) == len(self.resnet_depth.state_dict().keys())
self.resnet_depth.load_state_dict(all_params)
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