单机多GPU训练神经网络模型_适合单机训练的 模型有哪些

在拥有多卡的GPU服务器上面跑程序的时候，当迭代次数或者epoch足够大的时候，我们可以使用nn.DataParallel函数来用多个GPU来加速训练。

比如我们现在搭了一个目标检测的模型，以YOLOv4为例，下面代码参考Github上面的开源代码，换成其它网络也一样。

YOLOv4网络模型

import math
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F
#---------------------------------------------------#
#   CSPdarknet的结构块的组成部分
#   内部堆叠的残差块
#---------------------------------------------------#
class Resblock(nn.Module):

    def __init__(self, channels, hidden_channels=None):
        super(Resblock, self).__init__()

        if hidden_channels is None:
            hidden_channels = channels

        self.block = nn.Sequential(
            BasicConv(channels, hidden_channels, 1),
            BasicConv(hidden_channels, channels, 3)
        )

    def forward(self, x):
        return x + self.block(x)
#--------------------------------------------------------------------#
#   CSPdarknet的结构块
#   首先利用ZeroPadding2D和一个步长为2x2的卷积块进行高和宽的压缩
#   然后建立一个大的残差边shortconv、这个大残差边绕过了很多的残差结构
#   主干部分会对num_blocks进行循环，循环内部是残差结构。
#   对于整个CSPdarknet的结构块，就是一个大残差块+内部多个小残差块
#--------------------------------------------------------------------#
class Resblock_body(nn.Module):

    def __init__(self, in_channels, out_channels, num_blocks, first):
        super(Resblock_body, self).__init__()
        #----------------------------------------------------------------#
        #   利用一个步长为2x2的卷积块进行高和宽的压缩
        #----------------------------------------------------------------#
        self.downsample_conv = BasicConv(
            in_channels, out_channels, 3, stride=2)

        if first:
            #--------------------------------------------------------------------------#
            #   然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构
            #--------------------------------------------------------------------------#
            self.split_conv0 = BasicConv(out_channels, out_channels, 1)

            #----------------------------------------------------------------#
            #   主干部分会对num_blocks进行循环，循环内部是残差结构。
            #----------------------------------------------------------------#
            self.split_conv1 = BasicConv(out_channels, out_channels, 1)
            self.blocks_conv = nn.Sequential(
                Resblock(channels=out_channels,
                         hidden_channels=out_channels // 2),
                BasicConv(out_channels, out_channels, 1)
            )

            self.concat_conv = BasicConv(out_channels * 2, out_channels, 1)
        else:
            #--------------------------------------------------------------------------#
            #   然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构
            #--------------------------------------------------------------------------#
            self.split_conv0 = BasicConv(out_channels, out_channels // 2, 1)

            #----------------------------------------------------------------#
            #   主干部分会对num_blocks进行循环，循环内部是残差结构。
            #----------------------------------------------------------------#
            self.split_conv1 = BasicConv(out_channels, out_channels // 2, 1)
            self.blocks_conv = nn.Sequential(
                *[Resblock(out_channels // 2) for _ in range(num_blocks)],
                BasicConv(out_channels // 2, out_channels // 2, 1)
            )

            self.concat_conv = BasicConv(out_channels, out_channels, 1)

    def forward(self, x):
        x = self.downsample_conv(x)

        x0 = self.split_conv0(x)

        x1 = self.split_conv1(x)
        x1 = self.blocks_conv(x1)

        #------------------------------------#
        #   将大残差边再堆叠回来
        #------------------------------------#
        x = torch.cat([x1, x0], dim=1)
        #------------------------------------#
        #   最后对通道数进行整合
        #------------------------------------#
        x = self.concat_conv(x)

        return x
#---------------------------------------------------#
#   CSPdarknet53 的主体部分
#   输入为一张416x416x3的图片
#   输出为三个有效特征层
#---------------------------------------------------#
class CSPDarkNet(nn.Module):

    def __init__(self, layers):
        super(CSPDarkNet, self).__init__()
        self.inplanes = 32
        # 416,416,3 -> 416,416,32
        self.conv1 = BasicConv(3, self.inplanes, kernel_size=3, stride=1)
        # self.conv1 = BasicConv_AconC(self.inplanes)
        # self.conv1 = BasicConv_AconC(3, self.inplanes, kernel_size=3, stride=1)
        # self.conv1 = dwcConv_Mish(3, self.inplanes)
        self.feature_channels = [64, 128, 256, 512, 1024]

        self.stages = nn.ModuleList([
            # 416,416,32 -> 208,208,64
            Resblock_body(self.inplanes, self.feature_channels[
                          0], layers[0], first=True),
            # 208,208,64 -> 104,104,128
            Resblock_body(self.feature_channels[0], self.feature_channels[
                          1], layers[1], first=False),
            # 104,104,128 -> 52,52,256
            Resblock_body(self.feature_channels[1], self.feature_channels[
                          2], layers[2], first=False),
            # 52,52,256 -> 26,26,512
            Resblock_body(self.feature_channels[2], self.feature_channels[
                          3], layers[3], first=False),
            # 26,26,512 -> 13,13,1024
            Resblock_body(self.feature_channels[3], self.feature_channels[
                          4], layers[4], first=False)
        ])

        self.num_features = 1
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, x):
        x = self.conv1(x)
        print("conv1:",x.shape)
        x = self.stages[0](x)
        print("resblock_1:",x.shape)
        x = self.stages[1](x)
        print("resblock_2:",x.shape)
        out3 = self.stages[2](x)
        print("resblock_3:",out3.shape)
        out4 = self.stages[3](out3)
        print("resblock_4:",out4.shape)
        out5 = self.stages[4](out4)
        print("resblock_5:",out5.shape)

        return out3, out4, out5
        
def darknet53(pretrained, **kwargs):
    model = CSPDarkNet([1, 2, 8, 8, 4])
    # model = CSPDarkNet([1, 1, 1, 1, 1])
    if pretrained:
        if isinstance(pretrained, str):
            model.load_state_dict(torch.load(pretrained))
        else:
            raise Exception(
                "darknet request a pretrained path. got [{}]".format(pretrained))
    return model

def conv2d(filter_in, filter_out, kernel_size, stride=1):
    pad = (kernel_size - 1) // 2 if kernel_size else 0
    return nn.Sequential(OrderedDict([
        ("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=stride, padding=pad, bias=False)),
        ("bn", nn.BatchNorm2d(filter_out)),
        ("relu", nn.LeakyReLU(0.1)),
    ]))

class SpatialPyramidPooling(nn.Module):
    def __init__(self, pool_sizes=[5, 9, 13]):
        super(SpatialPyramidPooling, self).__init__()

        self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size//2) for pool_size in pool_sizes])

    def forward(self, x):
        features = [maxpool(x) for maxpool in self.maxpools[::-1]]
        features = torch.cat(features + [x], dim=1)

        return features


class Upsample(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Upsample, self).__init__()

        self.upsample = nn.Sequential(
            conv2d(in_channels, out_channels, 1),
            nn.Upsample(scale_factor=2, mode='nearest')
        )

    def forward(self, x,):
        x = self.upsample(x)
        return x

def make_three_conv(filters_list, in_filters):
    m = nn.Sequential(
        conv2d(in_filters, filters_list[0], 1),
        conv2d(filters_list[0], filters_list[1], 3),
        conv2d(filters_list[1], filters_list[0], 1),
    )
    return m

def make_five_conv(filters_list, in_filters):
    m = nn.Sequential(
        conv2d(in_filters, filters_list[0], 1),
        conv2d(filters_list[0], filters_list[1], 3),
        conv2d(filters_list[1], filters_list[0], 1),
        conv2d(filters_list[0], filters_list[1], 3),
        conv2d(filters_list[1], filters_list[0], 1),
    )
    return m

def yolo_head(filters_list, in_filters):
    m = nn.Sequential(
        conv2d(in_filters, filters_list[0], 3),
        nn.Conv2d(filters_list[0], filters_list[1], 1),
    )
    return m

class Yolo4(nn.Module):
    def __init__(self, num_anchors, num_classes):
        super(YoloBody, self).__init__()
        #---------------------------------------------------#   
        #   52,52,256
        #   26,26,512
        #   13,13,1024
        #---------------------------------------------------#
        self.backbone = darknet53(None)

        self.conv1 = make_three_conv([512,1024],1024)
        self.SPP = SpatialPyramidPooling()
        self.conv2 = make_three_conv([512,1024],2048)

        self.upsample1 = Upsample(512,256)
        self.conv_for_P4 = conv2d(512,256,1)
        self.make_five_conv1 = make_five_conv([256, 512],512)

        self.upsample2 = Upsample(256,128)
        self.conv_for_P3 = conv2d(256,128,1)
        self.make_five_conv2 = make_five_conv([128, 256],256)

        # 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75
        final_out_filter2 = num_anchors * (5 + num_classes)
        self.yolo_head3 = yolo_head([256, final_out_filter2],128)

        self.down_sample1 = conv2d(128,256,3,stride=2)
        self.make_five_conv3 = make_five_conv([256, 512],512)
        
        # 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75
        final_out_filter1 =  num_anchors * (5 + num_classes)
        self.yolo_head2 = yolo_head([512, final_out_filter1],256)

        self.down_sample2 = conv2d(256,512,3,stride=2)
        self.make_five_conv4 = make_five_conv([512, 1024],1024)

        # 3*(5+num_classes)=3*(5+20)=3*(4+1+20)=75
        final_out_filter0 =  num_anchors * (5 + num_classes)
        self.yolo_head1 = yolo_head([1024, final_out_filter0],512)

	def forward(self, x):
        #  backbone
        x2, x1, x0 = self.backbone(x)

        # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,2048 
        P5 = self.conv1(x0)
        P5 = self.SPP(P5)
        # 13,13,2048 -> 13,13,512 -> 13,13,1024 -> 13,13,512
        P5 = self.conv2(P5)

        # 13,13,512 -> 13,13,256 -> 26,26,256
        P5_upsample = self.upsample1(P5)
        # 26,26,512 -> 26,26,256
        P4 = self.conv_for_P4(x1)
        # 26,26,256 + 26,26,256 -> 26,26,512
        P4 = torch.cat([P4,P5_upsample],axis=1)
        # 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
        P4 = self.make_five_conv1(P4)

        # 26,26,256 -> 26,26,128 -> 52,52,128
        P4_upsample = self.upsample2(P4)
        # 52,52,256 -> 52,52,128
        P3 = self.conv_for_P3(x2)
        # 52,52,128 + 52,52,128 -> 52,52,256
        P3 = torch.cat([P3,P4_upsample],axis=1)
        # 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
        P3 = self.make_five_conv2(P3)
        P3 = self.involution1(P3)

        # 52,52,128 -> 26,26,256
        P3_downsample = self.down_sample1(P3)
        # 26,26,256 + 26,26,256 -> 26,26,512
        P4 = torch.cat([P3_downsample,P4],axis=1)
        # 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
        P4 = self.make_five_conv3(P4)
        P4 = self.involution2(P4)

        # 26,26,256 -> 13,13,512
        P4_downsample = self.down_sample2(P4)
        # 13,13,512 + 13,13,512 -> 13,13,1024
        P5 = torch.cat([P4_downsample,P5],axis=1)
        # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
        P5 = self.make_five_conv4(P5)
        P5 = self.involution3(P5)

        #---------------------------------------------------#
        #   y3=(batch_size,75,52,52)
        #---------------------------------------------------#
        out2 = self.yolo_head3(P3)
        #---------------------------------------------------#
        #   y2=(batch_size,75,26,26)
        #---------------------------------------------------#
        out1 = self.yolo_head2(P4)
        #---------------------------------------------------#
        #   y1=(batch_size,75,13,13)
        #---------------------------------------------------#
        out0 = self.yolo_head1(P5)

        return out0, out1, out2

x=torch.rand([1,3,608,608])
Yolo4=Yolo4(9,3)
out=YoloBody.forward(x)
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单机多卡训练

现在如果我么的服务器有4张显卡，那么我们就可以用多GPU的训练方式加快训练速度。
具体做法也很简单，只需要用nn.DataParallel函数将定义的模型使用多GPU来训练。只要把数据放在GPU设备上，训练时就可以多卡训练了。

需要添加的代码如下：

import torch.backends.cudnn as cudnn
# os.environ['CUDA_VISIBLE_DEVICES'] = '2,3'	# 需要用两张卡跑则加上，2、3为GPU编号，可指定
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = Yolo4(num_anchors=9, num_classes=3)
net = torch.nn.DataParallel(model)
cudnn.benchmark = True	# 听说设置benchmark=True可以提升训练效率，去掉也不影响
net = net.cuda()	# 或者用net=net.to(device)也可以，这时device得定义
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