Pointnet++改进：更换不同的激活函数，打造更优性能

简介：
1.该教程提供大量的首发改进的方式，降低上手难度，多种结构改进，助力寻找创新点！
2.本篇文章对Pointnet++进行激活函数的改进，助力解决RELU激活函数缺陷。
3.专栏持续更新，紧随最新的研究内容。

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代码地址

步骤一

新建activate.py文件，我存放在新建的block目录下，加入以下代码：

# Activation functions

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
    @staticmethod
    def forward(x):
        return x * torch.sigmoid(x)


class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
    @staticmethod
    def forward(x):
        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX


class MemoryEfficientSwish(nn.Module):
    class F(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x):
            ctx.save_for_backward(x)
            return x * torch.sigmoid(x)

        @staticmethod
        def backward(ctx, grad_output):
            x = ctx.saved_tensors[0]
            sx = torch.sigmoid(x)
            return grad_output * (sx * (1 + x * (1 - sx)))

    def forward(self, x):
        return self.F.apply(x)


# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
class Mish(nn.Module):
    @staticmethod
    def forward(x):
        return x * F.softplus(x).tanh()

class MemoryEfficientMish(nn.Module):
    class F(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x):
            ctx.save_for_backward(x)
            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + aconcxunlian(x)))

        @staticmethod
        def backward(ctx, grad_output):
            x = ctx.saved_tensors[0]
            sx = torch.sigmoid(x)
            fx = F.softplus(x).tanh()
            return grad_output * (fx + x * sx * (1 - fx * fx))

    def forward(self, x):
        return self.F.apply(x)


# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
class FReLU(nn.Module):
    def __init__(self, c1, k=3):  # ch_in, kernel
        super().__init__()
        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
        self.bn = nn.BatchNorm2d(c1)

    def forward(self, x):
        return torch.max(x, self.bn(self.conv(x)))

class GELU(nn.Module):
    def __init__(self):
        super(GELU, self).__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3))))

#
class MetaAconC(nn.Module):
    r""" ACON activation (activate or not).
    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
    according to "Activate or Not: Learning Customized Activation" .
    """

    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
        super().__init__()
        c2 = max(r, c1 // r)
        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
        # self.bn1 = nn.BatchNorm2d(c2)
        # self.bn2 = nn.BatchNorm2d(c1)

    def forward(self, x):
        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
        dpx = (self.p1 - self.p2) * x
        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
###
class AconC(nn.Module):
    """
    ACON https://arxiv.org/pdf/2009.04759.pdf
    ACON activation (activate or not).
    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
    according to "Activate or Not: Learning Customized Activation" .
    """

    def __init__(self, c1):
        super().__init__()
        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))

    def forward(self, x):
        dpx = (self.p1 - self.p2) * x
        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x

步骤二

在models/pointnet2_utils.py中加入以下代码，该代码将PointNetSetAbstraction中的mlp三层感知机重新封装成一个class Conv模块，便于直接在Conv模块中修改激活函数，修改后的代码和源码结构是一致的。修改不同的激活函数直接在Conv类中修改即可。
PointNetSetAbstraction结构图如下，PointNetSetAbstractionMSG比PointNetSetAbstraction多一个不同尺度的三层mlp,其他结构是一样的。

class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k)
        self.bn = nn.BatchNorm2d(c2)
        #self.act = nn.SiLU()
        #self.act = nn.LeakyReLU(0.1)
        self.act = nn.ReLU()
        #self.act = MetaAconC(c2)
        #self.act = AconC(c2)
        #self.act = Mish()
        #self.act = Hardswish()
        #self.act = FReLU(c2)
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))

class PointNetSetAbstractionAttention(nn.Module):
    def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
        super(PointNetSetAbstractionAttention, self).__init__()
        self.npoint = npoint
        self.radius = radius
        self.nsample = nsample
        #self.mlp_convs = nn.ModuleList()

        self.mlp_conv1 = Conv(in_channel,mlp[0],1)
        self.mlp_attention = CBAM(mlp[0])
        self.mlp_conv2 = Conv(mlp[0],mlp[1],1)
        self.mlp_conv3 = Conv(mlp[1],mlp[2],1)

        self.group_all = group_all

    def forward(self, xyz, points):
        """
        Input:
            xyz: input points position data, [B, C, N]
            points: input points data, [B, D, N]
        Return:
            new_xyz: sampled points position data, [B, C, S]
            new_points_concat: sample points feature data, [B, D', S]
        """
        xyz = xyz.permute(0, 2, 1)
        if points is not None:
            points = points.permute(0, 2, 1)

        if self.group_all:
            new_xyz, new_points = sample_and_group_all(xyz, points)
        else:
            new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points)
        # new_xyz: sampled points position data, [B, npoint, C]
        # new_points: sampled points data, [B, npoint, nsample, C+D]
        new_points = new_points.permute(0, 3, 2, 1)  # [B, C+D, nsample,npoint]

        new_points=self.mlp_conv1(new_points)
        new_points = self.mlp_attention(new_points)
        new_points = self.mlp_conv2(new_points)
        new_points = self.mlp_conv3(new_points)

        new_points = torch.max(new_points, 2)[0]
        new_xyz = new_xyz.permute(0, 2, 1)
        return new_xyz, new_points

class PointNetSetAbstractionMsgAttention(nn.Module):
    def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list):
        super(PointNetSetAbstractionMsgAttention, self).__init__()
        self.npoint = npoint
        self.radius_list = radius_list
        self.nsample_list = nsample_list

        self.mlp_conv00 = Conv(in_channel+3,mlp_list[0][0],1)
        self.mlp_conv01 = Conv(mlp_list[0][0],mlp_list[0][1],1)
        self.mlp_conv02 = Conv(mlp_list[0][1],mlp_list[0][2],1)

        self.mlp_conv10 = Conv(in_channel+3,mlp_list[1][0],1)
        self.mlp_conv11 = Conv(mlp_list[1][0],mlp_list[1][1],1)
        self.mlp_conv12 = Conv(mlp_list[1][1],mlp_list[1][2],1)


        # self.conv_blocks = nn.ModuleList()
        # self.bn_blocks = nn.ModuleList()
        # for i in range(len(mlp_list)):
        #     convs = nn.ModuleList()
        #     bns = nn.ModuleList()
        #     last_channel = in_channel + 3
        #     for out_channel in mlp_list[i]:
        #         convs.append(nn.Conv2d(last_channel, out_channel, 1))
        #         bns.append(nn.BatchNorm2d(out_channel))
        #         last_channel = out_channel
        #     self.conv_blocks.append(convs)
        #     self.bn_blocks.append(bns)

    def forward(self, xyz, points):
        """
        Input:
            xyz: input points position data, [B, C, N]
            points: input points data, [B, D, N]
        Return:
            new_xyz: sampled points position data, [B, C, S]
            new_points_concat: sample points feature data, [B, D', S]
        """
        xyz = xyz.permute(0, 2, 1)
        if points is not None:
            points = points.permute(0, 2, 1)

        B, N, C = xyz.shape
        S = self.npoint
        new_xyz = index_points(xyz, farthest_point_sample(xyz, S))
        new_points_list = []
        for i, radius in enumerate(self.radius_list):
            K = self.nsample_list[i]
            group_idx = query_ball_point(radius, K, xyz, new_xyz)
            grouped_xyz = index_points(xyz, group_idx)
            grouped_xyz -= new_xyz.view(B, S, 1, C)
            if points is not None:
                grouped_points = index_points(points, group_idx)
                grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)
            else:
                grouped_points = grouped_xyz

            grouped_points = grouped_points.permute(0, 3, 2, 1)  # [B, D, K, S]
            if i==0:
               grouped_points =self.mlp_conv00(grouped_points)
               grouped_points = self.mlp_conv01(grouped_points)
               grouped_points = self.mlp_conv02(grouped_points)
            else:
                grouped_points = self.mlp_conv10(grouped_points)
                grouped_points = self.mlp_conv11(grouped_points)
                grouped_points = self.mlp_conv12(grouped_points)
            # for j in range(len(self.conv_blocks[i])):
            #     conv = self.conv_blocks[i][j]
            #     bn = self.bn_blocks[i][j]
            #     grouped_points =  F.relu(bn(conv(grouped_points)))
            new_points = torch.max(grouped_points, 2)[0]  # [B, D', S]
            new_points_list.append(new_points)

        new_xyz = new_xyz.permute(0, 2, 1)
        new_points_concat = torch.cat(new_points_list, dim=1)
        return new_xyz, new_points_concat

步骤三

在不同的模型中修改调用即可，如在models/pointnet2_sem_seg.py文件中修改，训练即可

import torch.nn as nn
import torch.nn.functional as F
# from models.pointnet2_utils import PointNetSetAbstraction, PointNetFeaturePropagation, PointNetSetAbstractionKPconv, \
#     PointNetSetAbstractionAttention
from models.pointnet2_utils import *

class get_model(nn.Module):
    def __init__(self, num_classes):
        super(get_model, self).__init__()

        self.sa1 = PointNetSetAbstractionAttention(1024, 0.1, 32, 9 + 3, [32, 32, 64], False)
        self.sa2 = PointNetSetAbstraction(256, 0.2, 32, 64 + 3, [64, 64, 128], False)
        self.sa3 = PointNetSetAbstraction(64, 0.4, 32, 128 + 3, [128, 128, 256], False)
        self.sa4 = PointNetSetAbstraction(16, 0.8, 32, 256 + 3, [256, 256, 512], False)
        self.fp4 = PointNetFeaturePropagation(768, [256, 256])
        self.fp3 = PointNetFeaturePropagation(384, [256, 256])
        self.fp2 = PointNetFeaturePropagation(320, [256, 128])
        self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
        self.conv1 = nn.Conv1d(128, 128, 1)
        self.bn1 = nn.BatchNorm1d(128)
        self.drop1 = nn.Dropout(0.5)
        self.conv2 = nn.Conv1d(128, num_classes, 1)

    def forward(self, xyz):
        l0_points = xyz
        l0_xyz = xyz[:,:3,:]

        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)
        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
        l4_xyz, l4_points = self.sa4(l3_xyz, l3_points)

        l3_points = self.fp4(l3_xyz, l4_xyz, l3_points, l4_points)
        l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)
        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)
        l0_points = self.fp1(l0_xyz, l1_xyz, None, l1_points)

        x = self.drop1(F.relu(self.bn1(self.conv1(l0_points))))
        x = self.conv2(x)
        x = F.log_softmax(x, dim=1)
        x = x.permute(0, 2, 1)
        return x, l4_points


class get_loss(nn.Module):
    def __init__(self):
        super(get_loss, self).__init__()
        self.gamma=2
    def forward(self, pred, target, trans_feat, weight):#pred: 模型预测的输出   target: 真实的标签或数据，用于计算损失
        total_loss = F.nll_loss(pred, target, weight=weight)

        return total_loss
if __name__ == '__main__':
    import  torch
    model = get_model(13)
    xyz = torch.rand(6, 9, 2048)
    (model(xyz))