ASPP模塊詳解

ASPP（Atrous Spatial Pyramid Pooling）是一種用於圖像分割任務的模塊，旨在解決語義分割中空間上下文信息不足的問題。該模塊在多個深度學習框架中得到了廣泛的應用，如在DeepLab系列中發揮了關鍵作用。下面將從多個方面對ASPP模塊進行詳細的闡述。

一、ASPP模塊原理

ASPP模塊是基於空洞卷積（或稱孔卷積，dilated convolution）的思想。空洞卷積是一種可以在不增加網絡參數的情況下，增大感受野的技術，可以幫助模型獲取更大範圍的圖像信息。ASPP模塊採用多個空洞卷積，不同採樣率的空洞卷積可捕獲不同尺度的局部信息，最終得到具有不同感受野的特徵圖。下面是ASPP模塊的代碼實現：

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模塊實現了上述原理，使用四個不同採樣率（rates）的空洞卷積，之後對輸出進行合併，再通過一次卷積和BatchNorm層得到最終的輸出。該模塊中還加入了Dropout層防止過擬合。

二、多尺度ASPP模塊

為進一步提高模型的準確性，可以在ASPP模塊中引入多尺度的特徵圖。具體方法是在不同大小的特徵圖上分別使用ASPP模塊，之後將它們合併得到最終的輸出。多尺度ASPP模塊的代碼實現如下：

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模塊，可以容易地在已有的ASPP模塊中實現定製化的模型結構。

三、ASPP模塊在DeepLab系列網絡中的應用

DeepLab是語義分割任務中的一類經典網絡，使用ASPP模塊在網絡中成功地解決了空間上下文信息不足問題，取得了較好的效果。下面以DeepLab-v3+網絡為例，說明ASPP模塊在其中的應用。該網絡在ImageNet數據集上預訓練，在PASCAL VOC、Cityscapes等數據集上微調。

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網絡中，ASPP模塊的輸出經過一次卷積和上採樣操作之後用於分類器進行預測。該網絡在PASCAL VOC數據集上取得了當時最優秀的性能。

四、ASPP模塊的優化

由於ASPP模塊經常被用於深度學習網絡的預測部分，而該部分常常需要對每個像素進行操作，因此ASPP模塊的計算量很大。為此，研究者嘗試減少ASPP模塊的計算量，提出了多種方法，如使用深度可分離卷積（depthwise separable convolution）等。下面是一種改進ASPP模塊的方法：

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

所述改進的ASPP模塊將普通卷積替換為深度可分離卷積，可以大大降低計算量，同時保持模型準確性。該模塊應用於DeepLabv3+中可以取得比原版ASPP模塊更好的結果。

至此，我們詳細地介紹了ASPP模塊及其應用。ASPP模塊在圖像分割任務中具有重要作用，值得廣大研究者深入研究。