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# Copyright 2021 Tencent
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, \
stride=1, NL='relu', same_padding=False, bn=False, dilation=1):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) // 2) if same_padding else 0
self.conv = []
if dilation==1:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=dilation, dilation=dilation)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0, affine=True) if bn else nn.Identity()
if NL == 'relu' :
self.relu = nn.ReLU(inplace=True)
elif NL == 'prelu':
self.relu = nn.PReLU()
else:
self.relu = None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
# the main implementation of the SASNet
class SASNet(nn.Module):
def __init__(self, pretrained=False, args=None):
super(SASNet, self).__init__()
# define the backbone network
vgg = models.vgg16_bn(pretrained=pretrained)
features = list(vgg.features.children())
# get each stage of the backbone
self.features1 = nn.Sequential(*features[0:6])
self.features2 = nn.Sequential(*features[6:13])
self.features3 = nn.Sequential(*features[13:23])
self.features4 = nn.Sequential(*features[23:33])
self.features5 = nn.Sequential(*features[33:43])
# docoder definition
self.de_pred5 = nn.Sequential(
Conv2d(512, 1024, 3, same_padding=True, NL='relu'),
Conv2d(1024, 512, 3, same_padding=True, NL='relu'),
)
self.de_pred4 = nn.Sequential(
Conv2d(512 + 512, 512, 3, same_padding=True, NL='relu'),
Conv2d(512, 256, 3, same_padding=True, NL='relu'),
)
self.de_pred3 = nn.Sequential(
Conv2d(256 + 256, 256, 3, same_padding=True, NL='relu'),
Conv2d(256, 128, 3, same_padding=True, NL='relu'),
)
self.de_pred2 = nn.Sequential(
Conv2d(128 + 128, 128, 3, same_padding=True, NL='relu'),
Conv2d(128, 64, 3, same_padding=True, NL='relu'),
)
self.de_pred1 = nn.Sequential(
Conv2d(64 + 64, 64, 3, same_padding=True, NL='relu'),
Conv2d(64, 64, 3, same_padding=True, NL='relu'),
)
# density head definition
self.density_head5 = nn.Sequential(
MultiBranchModule(512),
Conv2d(2048, 1, 1, same_padding=True)
)
self.density_head4 = nn.Sequential(
MultiBranchModule(256),
Conv2d(1024, 1, 1, same_padding=True)
)
self.density_head3 = nn.Sequential(
MultiBranchModule(128),
Conv2d(512, 1, 1, same_padding=True)
)
self.density_head2 = nn.Sequential(
MultiBranchModule(64),
Conv2d(256, 1, 1, same_padding=True)
)
self.density_head1 = nn.Sequential(
MultiBranchModule(64),
Conv2d(256, 1, 1, same_padding=True)
)
# confidence head definition
self.confidence_head5 = nn.Sequential(
Conv2d(512, 256, 1, same_padding=True, NL='relu'),
Conv2d(256, 1, 1, same_padding=True, NL=None)
)
self.confidence_head4 = nn.Sequential(
Conv2d(256, 128, 1, same_padding=True, NL='relu'),
Conv2d(128, 1, 1, same_padding=True, NL=None)
)
self.confidence_head3 = nn.Sequential(
Conv2d(128, 64, 1, same_padding=True, NL='relu'),
Conv2d(64, 1, 1, same_padding=True, NL=None)
)
self.confidence_head2 = nn.Sequential(
Conv2d(64, 32, 1, same_padding=True, NL='relu'),
Conv2d(32, 1, 1, same_padding=True, NL=None)
)
self.confidence_head1 = nn.Sequential(
Conv2d(64, 32, 1, same_padding=True, NL='relu'),
Conv2d(32, 1, 1, same_padding=True, NL=None)
)
self.block_size = 32
# the forward process
def forward(self, x):
size = x.size()
x1 = self.features1(x)
x2 = self.features2(x1)
x3 = self.features3(x2)
x4 = self.features4(x3)
x5 = self.features5(x4)
# begining of decoding
x = self.de_pred5(x5)
x5_out = x
x = F.upsample_bilinear(x, size=x4.size()[2:])
x = torch.cat([x4, x], 1)
x = self.de_pred4(x)
x4_out = x
x = F.upsample_bilinear(x, size=x3.size()[2:])
x = torch.cat([x3, x], 1)
x = self.de_pred3(x)
x3_out = x
x = F.upsample_bilinear(x, size=x2.size()[2:])
x = torch.cat([x2, x], 1)
x = self.de_pred2(x)
x2_out = x
x = F.upsample_bilinear(x, size=x1.size()[2:])
x = torch.cat([x1, x], 1)
x = self.de_pred1(x)
x1_out = x
# density prediction
x5_density = self.density_head5(x5_out)
x4_density = self.density_head4(x4_out)
x3_density = self.density_head3(x3_out)
x2_density = self.density_head2(x2_out)
x1_density = self.density_head1(x1_out)
# get patch features for confidence prediction
x5_confi = F.adaptive_avg_pool2d(x5_out, output_size=(size[-2] // self.block_size, size[-1] // self.block_size))
x4_confi = F.adaptive_avg_pool2d(x4_out, output_size=(size[-2] // self.block_size, size[-1] // self.block_size))
x3_confi = F.adaptive_avg_pool2d(x3_out, output_size=(size[-2] // self.block_size, size[-1] // self.block_size))
x2_confi = F.adaptive_avg_pool2d(x2_out, output_size=(size[-2] // self.block_size, size[-1] // self.block_size))
x1_confi = F.adaptive_avg_pool2d(x1_out, output_size=(size[-2] // self.block_size, size[-1] // self.block_size))
# confidence prediction
x5_confi = self.confidence_head5(x5_confi)
x4_confi = self.confidence_head4(x4_confi)
x3_confi = self.confidence_head3(x3_confi)
x2_confi = self.confidence_head2(x2_confi)
x1_confi = self.confidence_head1(x1_confi)
# upsample the density prediction to be the same with the input size
x5_density = F.upsample_nearest(x5_density, size=x1.size()[2:])
x4_density = F.upsample_nearest(x4_density, size=x1.size()[2:])
x3_density = F.upsample_nearest(x3_density, size=x1.size()[2:])
x2_density = F.upsample_nearest(x2_density, size=x1.size()[2:])
x1_density = F.upsample_nearest(x1_density, size=x1.size()[2:])
# upsample the confidence prediction to be the same with the input size
x5_confi_upsample = F.upsample_nearest(x5_confi, size=x1.size()[2:])
x4_confi_upsample = F.upsample_nearest(x4_confi, size=x1.size()[2:])
x3_confi_upsample = F.upsample_nearest(x3_confi, size=x1.size()[2:])
x2_confi_upsample = F.upsample_nearest(x2_confi, size=x1.size()[2:])
x1_confi_upsample = F.upsample_nearest(x1_confi, size=x1.size()[2:])
# =============================================================================================================
# soft √
confidence_map = torch.cat([x5_confi_upsample, x4_confi_upsample,
x3_confi_upsample, x2_confi_upsample, x1_confi_upsample], 1)
confidence_map = torch.nn.functional.sigmoid(confidence_map)
# use softmax to normalize
confidence_map = torch.nn.functional.softmax(confidence_map, 1)
density_map = torch.cat([x5_density, x4_density, x3_density, x2_density, x1_density], 1)
# soft selection
density_map *= confidence_map
density = torch.sum(density_map, 1, keepdim=True)
return density
# the module definition for the multi-branch in the density head
class MultiBranchModule(nn.Module):
def __init__(self, in_channels, sync=False):
super(MultiBranchModule, self).__init__()
self.branch1x1 = BasicConv2d(in_channels, in_channels//2, kernel_size=1, sync=sync)
self.branch1x1_1 = BasicConv2d(in_channels//2, in_channels, kernel_size=1, sync=sync)
self.branch3x3_1 = BasicConv2d(in_channels, in_channels//2, kernel_size=1, sync=sync)
self.branch3x3_2 = BasicConv2d(in_channels // 2, in_channels, kernel_size=(3, 3), padding=(1, 1), sync=sync)
self.branch3x3dbl_1 = BasicConv2d(in_channels, in_channels//2, kernel_size=1, sync=sync)
self.branch3x3dbl_2 = BasicConv2d(in_channels // 2, in_channels, kernel_size=5, padding=2, sync=sync)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch1x1 = self.branch1x1_1(branch1x1)
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
outputs = [branch1x1, branch3x3, branch3x3dbl, x]
return torch.cat(outputs, 1)
# the module definition for the basic conv module
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, sync=False, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
if sync:
# for sync bn
print('use sync inception')
self.bn = nn.SyncBatchNorm(out_channels, eps=0.001)
else:
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
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