open-cd中的changerformer网络结构分析

open-cd

1.安装

在安装过程中遇到的问题：
1.pytorch版本问题，open-cd采用的mmcv版本比较低，建议安装2.3以下版本pytorch，太高了mmcv可能不太适配，先安装pytorch，在安装mmcv，我在安装时用的版本

pytorch                   2.1.2           py3.9_cuda12.1_cudnn8_0    pytorch
mmcv                      2.1.0                    pypi_0    pypi

mmcv安装方式
在这里插入图片描述
该方式同样适用于解决：

note: This error originates from a subprocess, and is likely not a problem with pip.ERROR: Failed building wheel for mmcvRunning setup.py clean for mmcv
Failed to build mmcv
ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (mmcv)

参考

之后参照博主的的安装步骤安装opencd的开原文件即可(建议安装源文件，直接 opencd第三方包形式后期不方便调试)：

# Install OpenMMLab Toolkits as Python packages
pip install -U openmim
mim install mmengine
mim install "mmpretrain>=1.0.0rc7"
pip install "mmsegmentation>=1.2.2"
pip install "mmdet>=3.0.0"# Install Opencd
git clone https://github.com/likyoo/open-cd.git
cd open-cd
pip install -v -e .

2.源码结构分析

在这里插入图片描述
该库主要的文件时1.config参数文件，2.opencd模型架构文件，3.训练推理分析工具，4.mmlab
这里主要介绍2.opencd模型框架文件,文件下包含：

其中，model文件夹下包含模型结构基础文件：

变化检测大致遵循语义分割的编码结构、neck结构、以及解码结构。
如果使用过mmsegmentation机会发现，backbone中存放着雨参数文件对应的主干网络，相应的是neck，decoder，这里changer_detector里面是主要的模型架构如Encoder-Decoder。
open-cd与mmseg这类参数化的文件非常适合进行模型复现或者进行工程化应用；但对一些科研小白，特别是非计算机专业的科研小白需要改进网络就不太友好；这里直观的作用下模型架构的组合使用，方便大家理解和魔改（~~别越看越迷糊就行）
这里以changerformer-mitb0为例：

前提是已完成open-cd的安装官方issue
下面内容摘自opencd/model/
删除每个类前面的注册表装饰器 @MODELS.register_module()，报错提示类已注册

主干网络

# Copyright (c) OpenMMLab. All rights reserved.
import math
import warningsimport torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import MultiheadAttention
from mmengine.model import BaseModule, ModuleList, Sequential
from mmengine.model.weight_init import (constant_init, normal_init,trunc_normal_init)from mmseg.registry import MODELS
## 下面两个依赖在opencd/model/utils中可以找到
from .embed import PatchEmbed
from .shape_convert import  nchw_to_nlc, nlc_to_nchwclass MixFFN(BaseModule):"""An implementation of MixFFN of Segformer.The differences between MixFFN & FFN:1. Use 1X1 Conv to replace Linear layer.2. Introduce 3X3 Conv to encode positional information.Args:embed_dims (int): The feature dimension. Same as`MultiheadAttention`. Defaults: 256.feedforward_channels (int): The hidden dimension of FFNs.Defaults: 1024.act_cfg (dict, optional): The activation config for FFNs.Default: dict(type='ReLU')ffn_drop (float, optional): Probability of an element to bezeroed in FFN. Default 0.0.dropout_layer (obj:`ConfigDict`): The dropout_layer usedwhen adding the shortcut.init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.Default: None."""def __init__(self,embed_dims,feedforward_channels,act_cfg=dict(type='GELU'),ffn_drop=0.,dropout_layer=None,init_cfg=None):super().__init__(init_cfg)self.embed_dims = embed_dimsself.feedforward_channels = feedforward_channelsself.act_cfg = act_cfgself.activate = build_activation_layer(act_cfg)in_channels = embed_dimsfc1 = Conv2d(in_channels=in_channels,out_channels=feedforward_channels,kernel_size=1,stride=1,bias=True)# 3x3 depth wise conv to provide positional encode informationpe_conv = Conv2d(in_channels=feedforward_channels,out_channels=feedforward_channels,kernel_size=3,stride=1,padding=(3 - 1) // 2,bias=True,groups=feedforward_channels)fc2 = Conv2d(in_channels=feedforward_channels,out_channels=in_channels,kernel_size=1,stride=1,bias=True)drop = nn.Dropout(ffn_drop)layers = [fc1, pe_conv, self.activate, drop, fc2, drop]self.layers = Sequential(*layers)self.dropout_layer = build_dropout(dropout_layer) if dropout_layer else torch.nn.Identity()def forward(self, x, hw_shape, identity=None):out = nlc_to_nchw(x, hw_shape)out = self.layers(out)out = nchw_to_nlc(out)if identity is None:identity = xreturn identity + self.dropout_layer(out)class EfficientMultiheadAttention(MultiheadAttention):"""An implementation of Efficient Multi-head Attention of Segformer.This module is modified from MultiheadAttention which is a module frommmcv.cnn.bricks.transformer.Args:embed_dims (int): The embedding dimension.num_heads (int): Parallel attention heads.attn_drop (float): A Dropout layer on attn_output_weights.Default: 0.0.proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.Default: 0.0.dropout_layer (obj:`ConfigDict`): The dropout_layer usedwhen adding the shortcut. Default: None.init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.Default: None.batch_first (bool): Key, Query and Value are shape of(batch, n, embed_dim)or (n, batch, embed_dim). Default: False.qkv_bias (bool): enable bias for qkv if True. Default True.norm_cfg (dict): Config dict for normalization layer.Default: dict(type='LN').sr_ratio (int): The ratio of spatial reduction of Efficient Multi-headAttention of Segformer. Default: 1."""def __init__(self,embed_dims,num_heads,attn_drop=0.,proj_drop=0.,dropout_layer=None,init_cfg=None,batch_first=True,qkv_bias=False,norm_cfg=dict(type='LN'),sr_ratio=1):super().__init__(embed_dims,num_heads,attn_drop,proj_drop,dropout_layer=dropout_layer,init_cfg=init_cfg,batch_first=batch_first,bias=qkv_bias)self.sr_ratio = sr_ratioif sr_ratio > 1:self.sr = Conv2d(in_channels=embed_dims,out_channels=embed_dims,kernel_size=sr_ratio,stride=sr_ratio)# The ret[0] of build_norm_layer is norm name.self.norm = build_norm_layer(norm_cfg, embed_dims)[1]# handle the BC-breaking from https://github.com/open-mmlab/mmcv/pull/1418 # noqafrom mmseg import digit_version, mmcv_versionif mmcv_version < digit_version('1.3.17'):warnings.warn('The legacy version of forward function in''EfficientMultiheadAttention is deprecated in''mmcv>=1.3.17 and will no longer support in the''future. Please upgrade your mmcv.')self.forward = self.legacy_forwarddef forward(self, x, hw_shape, identity=None):x_q = xif self.sr_ratio > 1:x_kv = nlc_to_nchw(x, hw_shape)x_kv = self.sr(x_kv)x_kv = nchw_to_nlc(x_kv)x_kv = self.norm(x_kv)else:x_kv = xif identity is None:identity = x_q# Because the dataflow('key', 'query', 'value') of# ``torch.nn.MultiheadAttention`` is (num_query, batch,# embed_dims), We should adjust the shape of dataflow from# batch_first (batch, num_query, embed_dims) to num_query_first# (num_query ,batch, embed_dims), and recover ``attn_output``# from num_query_first to batch_first.if self.batch_first:x_q = x_q.transpose(0, 1)x_kv = x_kv.transpose(0, 1)out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]if self.batch_first:out = out.transpose(0, 1)return identity + self.dropout_layer(self.proj_drop(out))def legacy_forward(self, x, hw_shape, identity=None):"""multi head attention forward in mmcv version < 1.3.17."""x_q = xif self.sr_ratio > 1:x_kv = nlc_to_nchw(x, hw_shape)x_kv = self.sr(x_kv)x_kv = nchw_to_nlc(x_kv)x_kv = self.norm(x_kv)else:x_kv = xif identity is None:identity = x_q# `need_weights=True` will let nn.MultiHeadAttention# `return attn_output, attn_output_weights.sum(dim=1) / num_heads`# The `attn_output_weights.sum(dim=1)` may cause cuda error. So, we set# `need_weights=False` to ignore `attn_output_weights.sum(dim=1)`.# This issue - `https://github.com/pytorch/pytorch/issues/37583` report# the error that large scale tensor sum operation may cause cuda error.out = self.attn(query=x_q, key=x_kv, value=x_kv, need_weights=False)[0]return identity + self.dropout_layer(self.proj_drop(out))class TransformerEncoderLayer(BaseModule):"""Implements one encoder layer in Segformer.Args:embed_dims (int): The feature dimension.num_heads (int): Parallel attention heads.feedforward_channels (int): The hidden dimension for FFNs.drop_rate (float): Probability of an element to be zeroed.after the feed forward layer. Default 0.0.attn_drop_rate (float): The drop out rate for attention layer.Default 0.0.drop_path_rate (float): stochastic depth rate. Default 0.0.qkv_bias (bool): enable bias for qkv if True.Default: True.act_cfg (dict): The activation config for FFNs.Default: dict(type='GELU').norm_cfg (dict): Config dict for normalization layer.Default: dict(type='LN').batch_first (bool): Key, Query and Value are shape of(batch, n, embed_dim)or (n, batch, embed_dim). Default: False.init_cfg (dict, optional): Initialization config dict.Default:None.sr_ratio (int): The ratio of spatial reduction of Efficient Multi-headAttention of Segformer. Default: 1.with_cp (bool): Use checkpoint or not. Using checkpoint will savesome memory while slowing down the training speed. Default: False."""def __init__(self,embed_dims,num_heads,feedforward_channels,drop_rate=0.,attn_drop_rate=0.,drop_path_rate=0.,qkv_bias=True,act_cfg=dict(type='GELU'),norm_cfg=dict(type='LN'),batch_first=True,sr_ratio=1,with_cp=False):super().__init__()# The ret[0] of build_norm_layer is norm name.self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]self.attn = EfficientMultiheadAttention(embed_dims=embed_dims,num_heads=num_heads,attn_drop=attn_drop_rate,proj_drop=drop_rate,dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),batch_first=batch_first,qkv_bias=qkv_bias,norm_cfg=norm_cfg,sr_ratio=sr_ratio)# The ret[0] of build_norm_layer is norm name.self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]self.ffn = MixFFN(embed_dims=embed_dims,feedforward_channels=feedforward_channels,ffn_drop=drop_rate,dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),act_cfg=act_cfg)self.with_cp = with_cpdef forward(self, x, hw_shape):def _inner_forward(x):x = self.attn(self.norm1(x), hw_shape, identity=x)x = self.ffn(self.norm2(x), hw_shape, identity=x)return xif self.with_cp and x.requires_grad:x = cp.checkpoint(_inner_forward, x)else:x = _inner_forward(x)return x# @MODELS.register_module()
class MixVisionTransformer(BaseModule):"""The backbone of Segformer.This backbone is the implementation of `SegFormer: Simple andEfficient Design for Semantic Segmentation withTransformers <https://arxiv.org/abs/2105.15203>`_.Args:in_channels (int): Number of input channels. Default: 3.embed_dims (int): Embedding dimension. Default: 768.num_stags (int): The num of stages. Default: 4.num_layers (Sequence[int]): The layer number of each transformer encodelayer. Default: [3, 4, 6, 3].num_heads (Sequence[int]): The attention heads of each transformerencode layer. Default: [1, 2, 4, 8].patch_sizes (Sequence[int]): The patch_size of each overlapped patchembedding. Default: [7, 3, 3, 3].strides (Sequence[int]): The stride of each overlapped patch embedding.Default: [4, 2, 2, 2].sr_ratios (Sequence[int]): The spatial reduction rate of eachtransformer encode layer. Default: [8, 4, 2, 1].out_indices (Sequence[int] | int): Output from which stages.Default: (0, 1, 2, 3).mlp_ratio (int): ratio of mlp hidden dim to embedding dim.Default: 4.qkv_bias (bool): Enable bias for qkv if True. Default: True.drop_rate (float): Probability of an element to be zeroed.Default 0.0attn_drop_rate (float): The drop out rate for attention layer.Default 0.0drop_path_rate (float): stochastic depth rate. Default 0.0norm_cfg (dict): Config dict for normalization layer.Default: dict(type='LN')act_cfg (dict): The activation config for FFNs.Default: dict(type='GELU').pretrained (str, optional): model pretrained path. Default: None.init_cfg (dict or list[dict], optional): Initialization config dict.Default: None.with_cp (bool): Use checkpoint or not. Using checkpoint will savesome memory while slowing down the training speed. Default: False."""def __init__(self,in_channels=3,embed_dims=64,num_stages=4,num_layers=[3, 4, 6, 3],num_heads=[1, 2, 4, 8],patch_sizes=[7, 3, 3, 3],strides=[4, 2, 2, 2],sr_ratios=[8, 4, 2, 1],out_indices=(0, 1, 2, 3),mlp_ratio=4,qkv_bias=True,drop_rate=0.,attn_drop_rate=0.,drop_path_rate=0.,act_cfg=dict(type='GELU'),norm_cfg=dict(type='LN', eps=1e-6),pretrained=None,init_cfg=None,with_cp=False):super().__init__(init_cfg=init_cfg)assert not (init_cfg and pretrained), \'init_cfg and pretrained cannot be set at the same time'if isinstance(pretrained, str):warnings.warn('DeprecationWarning: pretrained is deprecated, ''please use "init_cfg" instead')self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)elif pretrained is not None:raise TypeError('pretrained must be a str or None')self.embed_dims = embed_dimsself.num_stages = num_stagesself.num_layers = num_layersself.num_heads = num_headsself.patch_sizes = patch_sizesself.strides = stridesself.sr_ratios = sr_ratiosself.with_cp = with_cpassert num_stages == len(num_layers) == len(num_heads) \== len(patch_sizes) == len(strides) == len(sr_ratios)self.out_indices = out_indicesassert max(out_indices) < self.num_stages# transformer encoderdpr = [x.item()for x in torch.linspace(0, drop_path_rate, sum(num_layers))]  # stochastic num_layer decay rulecur = 0self.layers = ModuleList()for i, num_layer in enumerate(num_layers):embed_dims_i = embed_dims * num_heads[i]patch_embed = PatchEmbed(in_channels=in_channels,embed_dims=embed_dims_i,kernel_size=patch_sizes[i],stride=strides[i],padding=patch_sizes[i] // 2,norm_cfg=norm_cfg)layer = ModuleList([TransformerEncoderLayer(embed_dims=embed_dims_i,num_heads=num_heads[i],feedforward_channels=mlp_ratio * embed_dims_i,drop_rate=drop_rate,attn_drop_rate=attn_drop_rate,drop_path_rate=dpr[cur + idx],qkv_bias=qkv_bias,act_cfg=act_cfg,norm_cfg=norm_cfg,with_cp=with_cp,sr_ratio=sr_ratios[i]) for idx in range(num_layer)])in_channels = embed_dims_i# The ret[0] of build_norm_layer is norm name.norm = build_norm_layer(norm_cfg, embed_dims_i)[1]self.layers.append(ModuleList([patch_embed, layer, norm]))cur += num_layerdef init_weights(self):if self.init_cfg is None:for m in self.modules():if isinstance(m, nn.Linear):trunc_normal_init(m, std=.02, bias=0.)elif isinstance(m, nn.LayerNorm):constant_init(m, val=1.0, bias=0.)elif isinstance(m, nn.Conv2d):fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsfan_out //= m.groupsnormal_init(m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)else:super().init_weights()def forward(self, x):outs = []for i, layer in enumerate(self.layers):x, hw_shape = layer[0](x)for block in layer[1]:x = block(x, hw_shape)x = layer[2](x)x = nlc_to_nchw(x, hw_shape)if i in self.out_indices:outs.append(x)return outs

1.1 主干网络类

from torch import nn
from CDmodel.ly_utils.MixVisionTransformer import MixVisionTransformerclass backbone(nn.Module):def __init__(self):super(backbone,self).__init__()self.model = MixVisionTransformer(in_channels=3,embed_dims=32,num_stages=4,num_layers=[2, 2, 2, 2],num_heads=[1, 2, 5, 8],patch_sizes=[7, 3, 3, 3],sr_ratios=[8, 4, 2, 1],out_indices=(0, 1, 2, 3),mlp_ratio=4,qkv_bias=True,drop_rate=0.0,attn_drop_rate=0.0,drop_path_rate=0.1)def forward(self,x1):return self.model.forward(x1)

2.neck

# Copyright (c) Open-CD. All rights reserved.
import torch
from torch import nn
from opencd.registry import MODELSclass FeatureFusionNeck(nn.Module):"""Feature Fusion Neck.Args:policy (str): The operation to fuse features. candidatesare `concat`, `sum`, `diff` and `Lp_distance`.in_channels (Sequence(int)): Input channels.channels (int): Channels after modules, before conv_seg.out_indices (tuple[int]): Output from which layer."""def __init__(self,policy = 'concat',in_channels=None,channels=None,out_indices=(0, 1, 2, 3)):super(FeatureFusionNeck,self).__init__()self.policy = policyself.in_channels = in_channelsself.channels = channelsself.out_indices = out_indices@staticmethoddef fusion(x1, x2, policy):"""Specify the form of feature fusion"""_fusion_policies = ['concat', 'sum', 'diff', 'abs_diff']assert policy in _fusion_policies, 'The fusion policies {} are ' \'supported'.format(_fusion_policies)if policy == 'concat':x = torch.cat([x1, x2], dim=1)elif policy == 'sum':x = x1 + x2elif policy == 'diff':x = x2 - x1elif policy == 'abs_diff':x = torch.abs(x1 - x2)return xdef forward(self, x1, x2):"""Forward function."""assert len(x1) == len(x2), "The features x1 and x2 from the" \"backbone should be of equal length"outs = []for i in range(len(x1)):out = self.fusion(x1[i], x2[i], self.policy)outs.append(out)outs = [outs[i] for i in self.out_indices]return tuple(outs)

2.Decoder

changerformer解码头采用的是’mmseg.SegformerHead’
这个得在mmsegmentation/mmseg/model/head里面找
找到后，删除损失函数、预测推理保留下面部分内容

# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import List, Tuple
from mmcv.cnn import ConvModule
import torch
import torch.nn as nn
from mmengine.model import BaseModule
from torch import Tensorfrom mmseg.registry import MODELS
from mmseg.structures import build_pixel_sampler
from mmseg.utils import ConfigType, SampleList
# from ..losses import accuracy
from layer.resize import resizeclass BaseDecodeHead(nn.Module):"""Base class for BaseDecodeHead.1. The ``init_weights`` method is used to initialize decode_head'smodel parameters. After segmentor initialization, ``init_weights``is triggered when ``segmentor.init_weights()`` is called externally.2. The ``loss`` method is used to calculate the loss of decode_head,which includes two steps: (1) the decode_head model performs forwardpropagation to obtain the feature maps (2) The ``loss_by_feat`` methodis called based on the feature maps to calculate the loss... code:: textloss(): forward() -> loss_by_feat()3. The ``predict`` method is used to predict segmentation results,which includes two steps: (1) the decode_head model performs forwardpropagation to obtain the feature maps (2) The ``predict_by_feat`` methodis called based on the feature maps to predict segmentation resultsincluding post-processing... code:: textpredict(): forward() -> predict_by_feat()Args:in_channels (int|Sequence[int]): Input channels.channels (int): Channels after modules, before conv_seg.num_classes (int): Number of classes.out_channels (int): Output channels of conv_seg. Default: None.threshold (float): Threshold for binary segmentation in the case of`num_classes==1`. Default: None.dropout_ratio (float): Ratio of dropout layer. Default: 0.1.conv_cfg (dict|None): Config of conv layers. Default: None.norm_cfg (dict|None): Config of norm layers. Default: None.act_cfg (dict): Config of activation layers.Default: dict(type='ReLU')in_index (int|Sequence[int]): Input feature index. Default: -1ignore_index (int | None): The label index to be ignored. When usingmasked BCE loss, ignore_index should be set to None. Default: 255.align_corners (bool): align_corners argument of F.interpolate.Default: False.The all mlp Head of segformer.This head is the implementation of`Segformer <https://arxiv.org/abs/2105.15203>` _.Args:interpolate_mode: The interpolate mode of MLP head upsample operation.Default: 'bilinear'."""def __init__(self,in_channels = [v * 2 for v in [32, 64, 160, 256]],channels = 256,num_classes = 2,out_channels=None,threshold=None,dropout_ratio=0.1,conv_cfg=None,norm_cfg=dict(type='SyncBN', requires_grad=True),act_cfg=dict(type='ReLU'),in_index=[0, 1, 2, 3],input_transform='multiple_select',ignore_index=255,interpolate_mode='bilinear',align_corners=False,):super(BaseDecodeHead,self).__init__()# self._init_inputs(in_channels, in_index, input_transform)self.in_channels = in_channelsself.channels = channelsself.dropout_ratio = dropout_ratioself.conv_cfg = conv_cfgself.norm_cfg = norm_cfgself.act_cfg = act_cfgself.in_index = in_indexself.input_transform = input_transformself.ignore_index = ignore_indexself.align_corners = align_cornersself.interpolate_mode = interpolate_modenum_inputs = len(self.in_channels)assert num_inputs == len(self.in_index)self.convs = nn.ModuleList()self.convs = nn.ModuleList()for i in range(num_inputs):self.convs.append(ConvModule(in_channels=self.in_channels[i],out_channels=self.channels,kernel_size=1,stride=1,norm_cfg=self.norm_cfg,act_cfg=self.act_cfg))self.fusion_conv = ConvModule(in_channels=self.channels * num_inputs,out_channels=self.channels,kernel_size=1,norm_cfg=self.norm_cfg)if out_channels is None:if num_classes == 2:warnings.warn('For binary segmentation, we suggest using''`out_channels = 1` to define the output''channels of segmentor, and use `threshold`''to convert `seg_logits` into a prediction''applying a threshold')out_channels = num_classesif out_channels != num_classes and out_channels != 1:raise ValueError('out_channels should be equal to num_classes,''except binary segmentation set out_channels == 1 and'f'num_classes == 2, but got out_channels={out_channels}'f'and num_classes={num_classes}')if out_channels == 1 and threshold is None:threshold = 0.3warnings.warn('threshold is not defined for binary, and defaults''to 0.3')self.num_classes = num_classesself.out_channels = out_channelsself.threshold = thresholdself.conv_seg = nn.Conv2d(channels, self.out_channels, kernel_size=1)if dropout_ratio > 0:self.dropout = nn.Dropout2d(dropout_ratio)else:self.dropout = Nonedef _transform_inputs(self, inputs):"""Transform inputs for decoder.Args:inputs (list[Tensor]): List of multi-level img features.Returns:Tensor: The transformed inputs"""if self.input_transform == 'resize_concat':inputs = [inputs[i] for i in self.in_index]upsampled_inputs = [resize(input=x,size=inputs[0].shape[2:],mode='bilinear',align_corners=self.align_corners) for x in inputs]inputs = torch.cat(upsampled_inputs, dim=1)elif self.input_transform == 'multiple_select':inputs = [inputs[i] for i in self.in_index]else:inputs = inputs[self.in_index]return inputs@abstractmethoddef forward(self, inputs):"""Placeholder of forward function."""# Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32inputs = self._transform_inputs(inputs)outs = []for idx in range(len(inputs)):x = inputs[idx]conv = self.convs[idx]outs.append(resize(input=conv(x),size=inputs[0].shape[2:],mode=self.interpolate_mode,align_corners=self.align_corners))out = self.fusion_conv(torch.cat(outs, dim=1))out = self.cls_seg(out)return outdef cls_seg(self, feat):"""Classify each pixel."""if self.dropout is not None:feat = self.dropout(feat)output = self.conv_seg(feat)return output

3.测试模型


import torch
from torch import nn
# from torchsummary import summary
from torchinfo import summaryfrom backbone1 import backbone
from neck import FeatureFusionNeck
from head import BaseDecodeHeadclass siamencoderdecoder(nn.Module):def __init__(self):super(siamencoderdecoder, self).__init__()self.backbone = backbone()# self.backbone2 = backbone()self.neck = FeatureFusionNeck()self.head = BaseDecodeHead()def backboneforward(self,x1,x2):# 孪生主干网络特征提取x1,x2 = self.backbone(x1),self.backbone(x2)return x1,x2def forward(self,x1,x2):x1,x2 = self.backboneforward(x1,x2)x = self.neck(x1,x2)logit = self.head(x)return logitif __name__ == "__main__":inputs = torch.rand(3, 224, 224)model = siamencoderdecoder()device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model.to(device)summary(model,input_size=[(1,3,256,256),(1,3,256,256)])

summary

========================================================================================================================
Layer (type:depth-idx)                                                 Output Shape              Param #
========================================================================================================================
siamencoderdecoder                                                     [1, 2, 64, 64]            --
├─backbone: 1-1                                                        --                        (recursive)
│    └─MixVisionTransformer: 2-2                                       --                        (recursive)
│    │    └─ModuleList: 3-2                                            --                        (recursive)
├─backbone: 1-2                                                        [1, 32, 64, 64]           3,319,392
│    └─MixVisionTransformer: 2-2                                       --                        (recursive)
│    │    └─ModuleList: 3-2                                            --                        (recursive)
├─FeatureFusionNeck: 1-3                                               [1, 64, 64, 64]           --
├─BaseDecodeHead: 1-4                                                  [1, 2, 64, 64]            --
│    └─ModuleList: 2-3                                                 --                        --
│    │    └─ConvModule: 3-3                                            [1, 256, 64, 64]          16,896
│    │    └─ConvModule: 3-4                                            [1, 256, 32, 32]          33,280
│    │    └─ConvModule: 3-5                                            [1, 256, 16, 16]          82,432
│    │    └─ConvModule: 3-6                                            [1, 256, 8, 8]            131,584
│    └─ConvModule: 2-4                                                 [1, 256, 64, 64]          --
│    │    └─Conv2d: 3-7                                                [1, 256, 64, 64]          262,144
│    │    └─SyncBatchNorm: 3-8                                         [1, 256, 64, 64]          512
│    │    └─ReLU: 3-9                                                  [1, 256, 64, 64]          --
│    └─Dropout2d: 2-5                                                  [1, 256, 64, 64]          --
│    └─Conv2d: 2-6                                                     [1, 2, 64, 64]            514
========================================================================================================================
Total params: 7,166,146
Trainable params: 7,166,146
Non-trainable params: 0
Total mult-adds (G): 2.09
========================================================================================================================
Input size (MB): 1.57
Forward/backward pass size (MB): 141.75
Params size (MB): 12.29
Estimated Total Size (MB): 155.62
========================================================================================================================Process finished with exit code 0

6. changer主干网络

opencd作者提了一个主干网络特征交换的idea，通过继承ResNet实现的网络结构，并在主网络中给出了使用实例：

Example:
# >>> from opencd.models import IA_ResNet
# >>> import torch
# >>> self = IA_ResNet(depth=18)
# >>> self.eval()
# >>> inputs = torch.rand(1, 3, 32, 32)
# >>> level_outputs = self.forward(inputs, inputs)
# >>> for level_out in level_outputs:
# … print(tuple(level_out.shape))
(1, 128, 8, 8)
(1, 256, 4, 4)
(1, 512, 2, 2)
(1, 1024, 1, 1)
“”"

# Copyright (c) Open-CD. All rights reserved.
import torch
import torch.nn as nnfrom mmseg.models.backbones import ResNet
from opencd.registry import MODELS# @MODELS.register_module()
class IA_ResNet(ResNet):"""Interaction ResNet backbone.Args:interaction_cfg (Sequence[dict]): Interaction strategies for the stages.The length should be the same as `num_stages`. The details can befound in `opencd/models/ly_utils/interaction_layer.py`.Default: (None, None, None, None).depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.in_channels (int): Number of input image channels. Default: 3.stem_channels (int): Number of stem channels. Default: 64.base_channels (int): Number of base channels of res layer. Default: 64.num_stages (int): Resnet stages, normally 4. Default: 4.strides (Sequence[int]): Strides of the first block of each stage.Default: (1, 2, 2, 2).dilations (Sequence[int]): Dilation of each stage.Default: (1, 1, 1, 1).out_indices (Sequence[int]): Output from which stages.Default: (0, 1, 2, 3).style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-twolayer is the 3x3 conv layer, otherwise the stride-two layer isthe first 1x1 conv layer. Default: 'pytorch'.deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.Default: False.avg_down (bool): Use AvgPool instead of stride conv whendownsampling in the bottleneck. Default: False.frozen_stages (int): Stages to be frozen (stop grad and set eval mode).-1 means not freezing any parameters. Default: -1.conv_cfg (dict | None): Dictionary to construct and config conv layer.When conv_cfg is None, cfg will be set to dict(type='Conv2d').Default: None.norm_cfg (dict): Dictionary to construct and config norm layer.Default: dict(type='BN', requires_grad=True).norm_eval (bool): Whether to set norm layers to eval mode, namely,freeze running stats (mean and var). Note: Effect on Batch Normand its variants only. Default: False.dcn (dict | None): Dictionary to construct and config DCN conv layer.When dcn is not None, conv_cfg must be None. Default: None.stage_with_dcn (Sequence[bool]): Whether to set DCN conv for eachstage. The length of stage_with_dcn is equal to num_stages.Default: (False, False, False, False).plugins (list[dict]): List of plugins for stages, each dict contains:- cfg (dict, required): Cfg dict to build plugin.- position (str, required): Position inside block to insert plugin,options: 'after_conv1', 'after_conv2', 'after_conv3'.- stages (tuple[bool], optional): Stages to apply plugin, lengthshould be same as 'num_stages'.Default: None.multi_grid (Sequence[int]|None): Multi grid dilation rates of laststage. Default: None.contract_dilation (bool): Whether contract first dilation of each layerDefault: False.with_cp (bool): Use checkpoint or not. Using checkpoint will save somememory while slowing down the training speed. Default: False.zero_init_residual (bool): Whether to use zero init for last norm layerin resblocks to let them behave as identity. Default: True.pretrained (str, optional): model pretrained path. Default: None.init_cfg (dict or list[dict], optional): Initialization config dict.Default: None.Example:# >>> from opencd.models import IA_ResNet# >>> import torch# >>> self = IA_ResNet(depth=18)# >>> self.eval()# >>> inputs = torch.rand(1, 3, 32, 32)# >>> level_outputs = self.forward(inputs, inputs)# >>> for level_out in level_outputs:# ...     print(tuple(level_out.shape))(1, 128, 8, 8)(1, 256, 4, 4)(1, 512, 2, 2)(1, 1024, 1, 1)"""def __init__(self,interaction_cfg=(None, None, None, None),**kwargs):super().__init__(**kwargs)assert self.num_stages == len(interaction_cfg), \'The length of the `interaction_cfg` should be same as the `num_stages`.'# cross-correlationself.ccs = []for ia_cfg in interaction_cfg:if ia_cfg is None:ia_cfg = dict(type='TwoIdentity')self.ccs.append(MODELS.build(ia_cfg))self.ccs = nn.ModuleList(self.ccs)def forward(self, x1, x2):"""Forward function."""def _stem_forward(x):if self.deep_stem:x = self.stem(x)else:x = self.conv1(x)x = self.norm1(x)x = self.relu(x)x = self.maxpool(x)return xx1 = _stem_forward(x1)x2 = _stem_forward(x2)outs = []for i, layer_name in enumerate(self.res_layers):res_layer = getattr(self, layer_name)x1 = res_layer(x1)x2 = res_layer(x2)x1, x2 = self.ccs[i](x1, x2)if i in self.out_indices:outs.append(torch.cat([x1, x2], dim=1))return tuple(outs)# @MODELS.register_module()
class IA_ResNetV1c(IA_ResNet):"""ResNetV1c variant described in [1]_.Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv inthe input stem with three 3x3 convs. For more details please refer to `Bagof Tricks for Image Classification with Convolutional Neural Networks<https://arxiv.org/abs/1812.01187>`_."""def __init__(self, **kwargs):super(IA_ResNetV1c, self).__init__(deep_stem=True, avg_down=False, **kwargs)# @MODELS.register_module()
class IA_ResNetV1d(IA_ResNet):"""ResNetV1d variant described in [1]_.Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv inthe input stem with three 3x3 convs. And in the downsampling block, a 2x2avg_pool with stride 2 is added before conv, whose stride is changed to 1."""def __init__(self, **kwargs):super(IA_ResNetV1d, self).__init__(deep_stem=True, avg_down=True, **kwargs)

下面在上述环境下我们来调用一下

import torch
from IA_ResNet import IA_ResNetV1c
backbone = IA_ResNetV1c(depth=18)
backbone.eval()
inputs = torch.rand(1, 3, 256, 256)
level_outputs = backbone.forward(inputs, inputs)
for level_out in level_outputs:print(tuple(level_out.shape))
# output：
(1, 128, 64, 64)
(1, 256, 32, 32)
(1, 512, 16, 16)
(1, 1024, 8, 8)