ASPP（Atrous Spatial Pyramid Pooling）是一种用于图像分割任务的模块，旨在解决语义分割中空间上下文信息不足的问题。该模块在多个深度学习框架中得到了广泛的应用，如在DeepLab系列中发挥了关键作用。下面将从多个方面对ASPP模块进行详细的阐述。
一、ASPP模块原理
ASPP模块是基于空洞卷积（或称孔卷积，dilated convolution）的思想。空洞卷积是一种可以在不增加网络参数的情况下，增大感受野的技术，可以帮助模型获取更大范围的图像信息。ASPP模块采用多个空洞卷积，不同采样率的空洞卷积可捕获不同尺度的局部信息，最终得到具有不同感受野的特征图。下面是ASPP模块的代码实现：
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels, rates):
super(ASPP, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[0], dilation=rates[0])
self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[1], dilation=rates[1])
self.conv4 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[2], dilation=rates[2])
self.conv5 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.bn = nn.BatchNorm2d(out_channels)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
feat1 = self.conv1(x)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
out = torch.cat((feat1, feat2, feat3, feat4), dim=1)
out = self.bn(self.conv5(out))
out = F.relu(out)
out = self.dropout(out)
return out
ASPP模块实现了上述原理，使用四个不同采样率（rates）的空洞卷积，之后对输出进行合并，再通过一次卷积和BatchNorm层得到最终的输出。该模块中还加入了Dropout层防止过拟合。
二、多尺度ASPP模块
为进一步提高模型的准确性，可以在ASPP模块中引入多尺度的特征图。具体方法是在不同大小的特征图上分别使用ASPP模块，之后将它们合并得到最终的输出。多尺度ASPP模块的代码实现如下：
import torch
def ASPP_module(x, in_channels, out_channels, rates):
feat1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1)(x)
feat2 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[0], dilation=rates[0])(x)
feat3 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[1], dilation=rates[1])(x)
feat4 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[2], dilation=rates[2])(x)
out = torch.cat((feat1, feat2, feat3, feat4), dim=1)
out = torch.nn.BatchNorm2d(out_channels)(out)
out = torch.nn.ReLU()(out)
out = torch.nn.Dropout2d()(out)
return out
class MultiScaleASPP(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
rates = [1, 6, 12]
self.aspp1 = ASPP_module(in_channels, out_channels, [1, 1, 1])
self.aspp2 = ASPP_module(in_channels, out_channels, [6, 12, 18])
self.aspp3 = ASPP_module(in_channels, out_channels, rates)
self.global_avg_pool = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_channels, out_channels, 1, stride=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
self.conv = nn.Conv2d(out_channels*4, out_channels, kernel_size=1, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.dropout = nn.Dropout2d(p=0.1)
def forward(self, x):
feat1 = self.aspp1(x)
feat2 = self.aspp2(x)
feat3 = self.aspp3(x)
global_avg_pool = self.global_avg_pool(x).expand(x.size()[0], -1, x.size()[2], x.size()[3])
out = torch.cat([feat1, feat2, feat3, global_avg_pool], dim=1)
out = self.conv(out)
out = self.bn(out)
out = torch.nn.ReLU()(out)
out = self.dropout(out)
return out
利用多尺度ASPP模块，可以容易地在已有的ASPP模块中实现定制化的模型结构。
三、ASPP模块在DeepLab系列网络中的应用
DeepLab是语义分割任务中的一类经典网络，使用ASPP模块在网络中成功地解决了空间上下文信息不足问题，取得了较好的效果。下面以DeepLab-v3+网络为例，说明ASPP模块在其中的应用。该网络在ImageNet数据集上预训练，在PASCAL VOC、Cityscapes等数据集上微调。
import torch.nn as nn
class DeepLabv3(nn.Module):
def __init__(self, backbone, classifier, aspp_dilate=[6,12,18]):
super(DeepLabv3, self).__init__()
self.backbone = backbone
self.classifier = classifier
self.aspp = MultiScaleASPP(in_channels=2048, out_channels=256)
self.final_conv = nn.Conv2d(256, 256, kernel_size=1)
self._init_weight()
def forward(self, x):
input_shape = x.shape[-2:]
feature_map = self.backbone(x)
feature_map = self.aspp(feature_map)
feature_map = self.final_conv(feature_map)
output = self.classifier(feature_map)
output = F.interpolate(output, size=input_shape,
mode='bilinear', align_corners=False)
return output
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
在DeepLabv3网络中，ASPP模块的输出经过一次卷积和上采样操作之后用于分类器进行预测。该网络在PASCAL VOC数据集上取得了当时最优秀的性能。
四、ASPP模块的优化
由于ASPP模块经常被用于深度学习网络的预测部分，而该部分常常需要对每个像素进行操作，因此ASPP模块的计算量很大。为此，研究者尝试减少ASPP模块的计算量，提出了多种方法，如使用深度可分离卷积（depthwise separable convolution）等。下面是一种改进ASPP模块的方法：
import torch.nn as nn
class GDASPP(nn.Module):
def __init__(self, in_channels, out_channels, rates):
super(GDASPP, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[0], dilation=rates[0], groups=out_channels)
self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[1], dilation=rates[1], groups=out_channels)
self.conv4 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[2], dilation=rates[2], groups=out_channels)
self.conv5 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.bn = nn.BatchNorm2d(out_channels)
self.dropout = nn.Dropout2d(0.5)
def forward(self, x):
feat1 = self.conv1(x)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
out = torch.cat((feat1, feat2, feat3, feat4), dim=1)
out = self.bn(self.conv5(out))
out = F.relu(out)
out = self.dropout(out)
return out
所述改进的ASPP模块将普通卷积替换为深度可分离卷积，可以大大降低计算量，同时保持模型准确性。该模块应用于DeepLabv3+中可以取得比原版ASPP模块更好的结果。 至此，我们详细地介绍了ASPP模块及其应用。ASPP模块在图像分割任务中具有重要作用，值得广大研究者深入研究。
