文章目錄

• • 一、文章出發點
  • 二、方法
  • 三、效果

一、文章出發點

每個像素點的類別（label）應該是它所屬目標（object）的類別。

所以這篇文章對像素的上下文信息建模

建模方法：求每個像素點和每個類別的相關性

二、方法

# prev_output: 19d的粗糙預測結果 # feats：將hrnet輸出的720d的變成512d # context：每個類別圖和每個特征圖的關系(19x512) # object_context：self-attention

1、prev_output：

2、feats獲得

3、context獲得

4、self-attention

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import ConvModulefrom mmseg.ops import resize from ..builder import HEADS from ..utils import SelfAttentionBlock as _SelfAttentionBlock from .cascade_decode_head import BaseCascadeDecodeHead # from .Attention_layer import HardClassAttention as HCAclass SpatialGatherModule(nn.Module):"""Aggregate the context features according to the initial predictedprobability distribution.Employ the soft-weighted method to aggregate the context."""def __init__(self, scale):super(SpatialGatherModule, self).__init__()self.scale = scaledef forward(self, feats, probs):"""Forward function."""batch_size, num_classes, height, width = probs.size()channels = feats.size(1)probs = probs.view(batch_size, num_classes, -1)feats = feats.view(batch_size, channels, -1)# [batch_size, height*width, num_classes]feats = feats.permute(0, 2, 1)# [batch_size, channels, height*width]probs = F.softmax(self.scale * probs, dim=2)# [batch_size, channels, num_classes]ocr_context = torch.matmul(probs, feats)ocr_context = ocr_context.permute(0, 2, 1).contiguous().unsqueeze(3)return ocr_context class ClassRelationGatherModule(nn.Module):"""Aggregate the context features according to the initial predictedprobability distribution.Employ the soft-weighted method to aggregate the context."""def __init__(self, scale):super(ClassRelationGatherModule, self).__init__()self.scale = scaledef forward(self, feats, probs):"""Forward function."""batch_size, num_classes, height, width = probs.size()channels = feats.size(1)probs_1 = probs.view(batch_size, num_classes, -1)probs_2 = probs.view(batch_size, num_classes, -1)# [batch_size, height*width, num_classes]probs_2 = probs_2.permute(0, 2, 1)# [batch_size, channels, height*width]probs_1 = F.softmax(self.scale * probs_1, dim=2)# [batch_size, channels, num_classes]class_gather = torch.matmul(probs_1, probs_2)class_gather = class_gather.permute(0, 2, 1).contiguous().unsqueeze(3)return class_gatherclass ObjectAttentionBlock(_SelfAttentionBlock):"""Make a OCR used SelfAttentionBlock."""def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg,act_cfg):if scale > 1:query_downsample = nn.MaxPool2d(kernel_size=scale)else:query_downsample = Nonesuper(ObjectAttentionBlock, self).__init__(key_in_channels=in_channels,query_in_channels=in_channels,channels=channels,out_channels=in_channels,share_key_query=False,query_downsample=query_downsample,key_downsample=None,key_query_num_convs=2,key_query_norm=True,value_out_num_convs=1,value_out_norm=True,matmul_norm=True,with_out=True,conv_cfg=conv_cfg,norm_cfg=norm_cfg,act_cfg=act_cfg)self.bottleneck = ConvModule(in_channels * 2,in_channels,1,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg)def forward(self, query_feats, key_feats):"""Forward function."""context = super(ObjectAttentionBlock, self).forward(query_feats, key_feats)output = self.bottleneck(torch.cat([context, query_feats], dim=1))if self.query_downsample is not None:output = resize(query_feats)return output@HEADS.register_module() class OCRHead(BaseCascadeDecodeHead):"""Object-Contextual Representations for Semantic Segmentation.This head is the implementation of `OCRNet<https://arxiv.org/abs/1909.11065>`_.Args:ocr_channels (int): The intermediate channels of OCR block.scale (int): The scale of probability map in SpatialGatherModule inDefault: 1."""def __init__(self, ocr_channels, scale=1, **kwargs):super(OCRHead, self).__init__(**kwargs)self.ocr_channels = ocr_channelsself.scale = scaleself.object_context_block = ObjectAttentionBlock(self.channels,self.ocr_channels,self.scale,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg)self.spatial_gather_module = SpatialGatherModule(self.scale)self.class_relation_gather_module = ClassRelationGatherModule(self.scale)self.bottleneck = ConvModule(self.in_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, prev_output):"""Forward function."""# concat multi-level img featurex = self._transform_inputs(inputs) # (1,720,128,256) hr18, 512x1024feats = self.bottleneck(x) # (1,512,128,256)context = self.spatial_gather_module(feats, prev_output) # (1,512,19,1)object_context = self.object_context_block(feats, context) #(1,512,128,256)output = self.cls_seg(object_context) # (1,19,128,256)return output

三、效果

經過OCR頭后的效果對比如下圖，每個類別的響應比較全面且穩定。

cityscape類別和通道的對應：

總結

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