文章目錄

• • 一、主要思想
  • 二、方法

一、主要思想

提出了pyramid pooling module (PPM) 模塊，聚合不同區域的上下文信息，從而提高獲取全局信息的能力。

現有的深度網絡方法中，某一個操作的感受野直接決定了這個操作可以獲得多少上下文信息，所以提升感受野可以為網絡引入更多的上下文信息。

二、方法

Step1：使用global averag pooling得到不同尺度的特征，PPM模塊融合了4個不同尺度的特征：

• 紅色是最粗糙尺度，使用一個global average pooling 實現
• 其他的都是將特征圖切分為不同數量的塊，在每個塊內使用global average pooling （文中四個尺度分別是 1x1, 2x2, 3x3, 6x6）

Step2：global average pooling 之后，每層都接一個1x1的卷積來降低通道維度。

Step3：上采樣到和原圖相同的尺寸，然后和進入PPM頭之前的feature map 進行concat 來預測結果。

import torch import torch.nn as nn from mmcv.cnn import ConvModulefrom mmseg.ops import resize from ..builder import HEADS from .decode_head import BaseDecodeHead from .Attention_layer import HardClassAttention as HCAclass PPM(nn.ModuleList):"""Pooling Pyramid Module used in PSPNet.Args:pool_scales (tuple[int]): Pooling scales used in Pooling PyramidModule.in_channels (int): Input channels.channels (int): Channels after modules, before conv_seg.conv_cfg (dict|None): Config of conv layers.norm_cfg (dict|None): Config of norm layers.act_cfg (dict): Config of activation layers.align_corners (bool): align_corners argument of F.interpolate."""def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,act_cfg, align_corners):super(PPM, self).__init__()self.pool_scales = pool_scalesself.align_corners = align_cornersself.in_channels = in_channelsself.channels = channelsself.conv_cfg = conv_cfgself.norm_cfg = norm_cfgself.act_cfg = act_cfgfor pool_scale in pool_scales:self.append(nn.Sequential(nn.AdaptiveAvgPool2d(pool_scale),ConvModule(self.in_channels,self.channels,1,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg)))def forward(self, x):"""Forward function."""ppm_outs = []for ppm in self:ppm_out = ppm(x)upsampled_ppm_out = resize(ppm_out,size=x.size()[2:],mode='bilinear',align_corners=self.align_corners)ppm_outs.append(upsampled_ppm_out)return ppm_outs@HEADS.register_module() class PSPHead(BaseDecodeHead):"""Pyramid Scene Parsing Network.This head is the implementation of`PSPNet <https://arxiv.org/abs/1612.01105>`_.Args:pool_scales (tuple[int]): Pooling scales used in Pooling PyramidModule. Default: (1, 2, 3, 6)."""def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):super(PSPHead, self).__init__(**kwargs)assert isinstance(pool_scales, (list, tuple))self.pool_scales = pool_scalesself.psp_modules = PPM(self.pool_scales,self.in_channels,self.channels,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg,align_corners=self.align_corners)self.bottleneck = ConvModule(self.in_channels + len(pool_scales) * self.channels,self.channels,3,padding=1,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg)def forward(self, inputs):"""Forward function."""# inputs [4, 512, 64, 128]x = self._transform_inputs(inputs) #[4, 2048, 64, 128]psp_outs = [x] # list, len=1, psp_outs[0].shape = [4, 2048, 64, 128]# self.psp_models(x), list, len=4psp_outs.extend(self.psp_modules(x)) # len(psp_outs) = 5, psp_out[1-4].shape = [4, 512, 64, 128]psp_outs = torch.cat(psp_outs, dim=1) # [4, 4096, 64, 128]output = self.bottleneck(psp_outs) # [4, 512, 64, 128]output = self.cls_seg(output) # [4, 19, 64, 128]# import pdb; pdb.set_trace()return output

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