mmsegmentation加SKNet具体步骤

mmsegmentation是一个基于PyTorch的图像分割框架，而SKNet是一种卷积神经网络结构，可以用于图像分类和分割等任务。在mmsegmentation中使用SKNet，需要按照以下步骤进行：

1. 安装mmsegmentation和SKNet

在使用mmsegmentation之前，需要先安装mmcv-full和mmsegmentation。而SKNet则可以通过在PyTorch中导入相应的模块来使用。可以通过以下命令进行安装：

pip install mmcv-full mmsegmentation

2. 导入SKNet模块

在使用SKNet之前，需要先导入相应的模块。可以通过以下代码进行导入：

from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
import torch.nn as nn
import torch.nn.functional as F

3. 构建SKNet模块

在mmsegmentation中使用SKNet，需要先构建SKNet模块。可以通过以下代码进行构建：

class SKConv(BaseModule):
    def __init__(self, in_channels, out_channels, stride=1, M=2, r=16, L=32):
        super(SKConv, self).__init__()

        d = max(int(out_channels / r), L)
        self.M = M
        self.out_channels = out_channels
        self.conv1 = build_conv_layer(
            dict(type='Conv2d'),
            in_channels,
            out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False)
        self.pool = nn.ModuleList()
        for i in range(M):
            self.pool.append(nn.Sequential(
                nn.AdaptiveAvgPool2d(1),
                build_conv_layer(
                    dict(type='Conv2d'),
                    out_channels,
                    d,
                    kernel_size=1,
                    stride=1,
                    bias=False),
                build_norm_layer(dict(type='BN'), d)[1],
                nn.ReLU(inplace=True)
            ))
        self.attention = nn.Sequential(
            build_conv_layer(
                dict(type='Conv2d'),
                d * M,
                out_channels,
                kernel_size=1,
                stride=1,
                bias=False),
            build_norm_layer(dict(type='BN'), out_channels)[1],
            nn.Sigmoid()
        )

    def forward(self, x):
        batch_size = x.shape[0]
        x = self.conv1(x)
        feats = [pool(x).view(batch_size, self.out_channels, 1, 1) for pool in self.pool]
        feats = torch.cat(feats, dim=2)
        feats = feats.view(batch_size, self.out_channels * self.M, 1, 1)
        attention = self.attention(feats)
        feats = feats * attention
        feats = feats.view(batch_size, self.out_channels, self.M)
        feats = torch.sum(feats, dim=2)
        return feats

4. 使用SKNet进行分割

在mmsegmentation中使用SKNet进行分割，需要将SKNet模块嵌入到分割网络中。可以通过以下代码实现：

from mmcv.cnn import ConvModule

class SKNetBlock(ConvModule):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=None,
                 dilation=1, groups=1, bias=True, conv_cfg=None, norm_cfg=None,
                 activation=None, inplace=True, M=2, r=16, L=32):
        super(SKNetBlock, self).__init__(in_channels, out_channels, kernel_size, stride,
                                         padding, dilation, groups, bias, conv_cfg, norm_cfg,
                                         activation, inplace)
        self.sk_conv = SKConv(out_channels, out_channels, stride, M, r, L)

    def forward(self, x):
        x = super(SKNetBlock, self).forward(x)
        x = self.sk_conv(x)
        return x

然后在分割网络中使用SKNetBlock，例如：

model = dict(
    type='EncoderDecoder',
    encoder=dict(
        type='SKNet',
        in_channels=3,
        base_channels=64,
        out_indices=(0, 1, 2, 3),
        depth=5,
        num_stages=4,
        strides=(1, 2, 2, 2),
        dilations=(1, 1, 1, 1),
        out_channels=(64, 128, 256, 512),
        conv_cfg=dict(type='Conv2d'),
        norm_cfg=dict(type='BN'),
        act_cfg=dict(type='ReLU'),
        dcn=dict(
            type='DCNv2',
            deform_groups=1,
            fallback_on_stride=False,
            use_dcn_in_tower=True),
        stage_with_dcn=(False, False, False, False),
        with_cp=(False, False, False, False),
        with_se=(False, False, False, False),
        norm_eval=True),
    decoder=dict(
        type='CascadeDecoder',
        in_channels=(64, 128, 256, 512),
        out_channels=256,
        num_stages=3,
        cascades=[
            dict(
                type='FCNMaskHead',
                in_channels=256,
                in_index=0,
                channels=256,
                concat_input=False,
                num_classes=80,
                norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
                loss_mask=dict(
                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
            dict(
                type='CascadeRCNNMaskHead',
                in_channels=256,
                in_index=1,
                inst_aware=False,
                num_classes=80,
                share_cls_mask=True,
                mask_feat_channels=256,
                rcnn_loss_mask=dict(
                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
                rcnn_loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
            dict(
                type='CascadeRCNNMaskHead',
                in_channels=256,
                in_index=2,
                inst_aware=False,
                num_classes=80,
                share_cls_mask=True,
                mask_feat_channels=256,
                rcnn_loss_mask=dict(
                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
                rcnn_loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ],
        upsample_cfg=dict(type='deconv', scale_factor=2),
        norm_cfg=dict(type='BN'),
        act_cfg=dict(type='ReLU'),
        num_classes=80,
        in_channels_skip=(64, 128, 256, 512),
        conv_cfg=dict(type='Conv2d'),
        norm_cfg_skip=dict(type='BN'),
        act_cfg_skip=dict(type='ReLU'),
        align_corners=False),
    train_cfg=dict(),
    test_cfg=dict())

这就是在mmsegmentation中使用SKNet的大致步骤。需要注意的是，在使用SKNet时，还需要进行相应的超参数调整，以达到最优的分割效果。