深度学习基础--CNN经典网络之InceptionV3详解与复现(pytorch)
- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
前言
- InceptionV3是InceptionV1的升级版，虽然加大了计算量，但是当时效果是比VGG效果要好的。
- 本次任务是探究InceptionV3结构并进行复现实验；
- 欢迎收藏 + 关注，本人将会持续更新
文章目录
- 1、模型简介
- 1、模型特点
- 2、模型结构简介
- 2、模型复现
- 准备工作
- 1、InceptionA
- 2、InceptionB
- 3、InceptionC
- 4、ReductionA
- 5、ReductionB
- 6、辅助分支
- 7、模型搭建
- 8、查看模型详情
- 3、参考资料
1、模型简介
1、模型特点
InceptionV3是谷歌在2015年提出，是InceptionV1的进阶版，对于Inception系列网络来说，他是当时第一个在100层卷积网络却依然可以取得好效果的网络(ResNet还没有提出来)，相比于InceptionV1来说，他主要特点是：
- 更深入得网络结构，在
InceptionV3中，包含了48层卷积网络，这可以提取出更多特征，从而获得更好成果； - 使用分解卷积，将较大的卷积核分解为多个较小的卷积核，在保持良好性能的情况下，依然降低了网络参数量，减少计算复杂度；
- 使用BN层，
InceptionV3中每个卷积层后都添加了BN层，使数据符合高斯分布，这样有助于缓解网络梯度消失和梯度爆炸的效果，同时也有助网络于收敛和提高泛化能力； - 辅助分类器，在
InceptionV3中提出来辅助分类器结构模块，主要用于缓解深层网络训练中梯度消失问题，加快模型收敛，辅助分类器结构：平均池化层 + 全连接层 + Softmax激活函数组成； - 基于
RMSPeop优化器代替SGD方法，可以使用模型更快收敛。
📚 分解卷积
在
InceptionV3网络结构中，采用空间可分离卷积结构，这里简介该结构。
👁 在介绍该结构前，先学习一下深度卷积(DW)：
- 与常规卷积相比，深度卷积一个卷积核负责一个通道，一个通道只被一个卷积核卷，在常规卷积中是同时操作图片的每个通道。
- 举例：对于一张3通道一条图片，在深度卷积首先经过第一次卷积运算，和常规卷积相比，深度卷积完全是在二维平面内进行。卷积核个数和通道数一一对应，如图所示：
- 而对于常规卷积中，以三通道数为例(参考某一个大神的图片)：
-
从上图可以看出，他是在多维平面内进行卷积操作，且一个卷积核同时进行多个通道卷积，然后再生产特征。
👀 现在介绍深度可分离卷积，分为两步：深度卷积 + 逐点卷积。
以输入 12 * 12 * 3图像，5 * 5 卷积核为例：
1️⃣ 第一步：深度卷积
通过上面学习可以发现，这里就是使用3个 5 * 5 * 1的卷积核分别提取3个特征，每个卷积核计算完都会得到一个 8 * 8 * 1 的输出特征，然后将3个堆积在一起，就得出了 8 * 8 * 3 大小的最终输出特征图。
❔ 发现：DW卷积缺少特征之间的融合，解决这个问题就是用到了下面介绍的逐点卷积;
2️⃣ 第二步：逐点卷积
逐点卷积就是用1 * 1的卷积核去遍历每一个点；在第一步中，我们得到了 8 * 8 * 3 尺寸的特征图，这里我们采用一个3通道的1 * 1卷积(1 * 1 * 3)对该特征图进行计算，去融合3个通道间的特征功能,如图：
最后就得到了 8 * 8 * 1的输出图，如果这个时候使用256个 1 * 1 * 3的卷积对该特征图进行卷积，得到结果如图：
2、模型结构简介
先回忆一下InceptionV1的核心网络结构：
一：
将 5 * 5 的卷积结构分解成两个3 * 3的卷积运算以提高速度(通过计算发现两个3 * 3卷积结构的计算量远小于一个5 * 5的计算量)：
二：
作者将n * n 的卷积核分解为 1 * n 和 n * 1的两个卷积核，如：一个3 * 3的卷积核=先执行一个1 * 3的卷积核在执行一个3 * 1的卷积，作者发现这种方法比单使用3 * 3卷积降低 33% 成本，如图：
三：
作者还在InceptionV1核心结构中进行了横向扩展，解决性能瓶颈问题(训练神经网络很多时候会遇到精度上不去的现象)，这一模块主要进行宽度扩展，如图：
最后通过模块搭建，InceptionV1结构如下：
2、模型复现
准备工作
import torch
import torch.nn as nn
import torch.nn.functional as F # 封装 Conv2d + BN + ReLU
class BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x):x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)
1、InceptionA
# 这一部分对于InceptionV1核心部分来说，没什么变化，不同的是 3 * 3变成了两个
class InceptionA(nn.Module):def __init__(self, in_channels, pool_features):super(InceptionA, self).__init__()# 1 * 1卷积, BasicConv2d是封装好的卷积Conv2d + BN + ReLUself.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)# 1 * 1 + 5 * 5self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)# 1 * 1 + 3 * 3 + 3 * 3self.branch3x3_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)# 池化self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1)def forward(self, x):branch1x1 = self.branch1x1(x)branch5x5 = self.branch5x5_1(x)branch5x5 = self.branch5x5_2(branch5x5)branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch3x3 = self.branch3x3_3(branch3x3)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)out = [branch1x1, branch5x5, branch3x3, branch_pool]# 拼接, 通道拼接return torch.cat(out, dim=1)2、InceptionB
# 这个模块将 3 * 3,，5 * 5变成（1 * n，n * 1）/ （n * 1 + 1 * n）结构
class InceptionB(nn.Module):def __init__(self, in_channels, channels_7x7):super(InceptionB, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)c7 = channels_7x7# 1 * 1 + 7 * 1 + 1 * 7self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0))# 1 * 1 + (7 * 1 + 1 * 7) * 2self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3))self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)def forward(self, x):branch1x1 = self.branch1x1(x)branch7x7 = self.branch7x7_1(x)branch7x7 = self.branch7x7_2(branch7x7)branch7x7 = self.branch7x7_3(branch7x7)branch7x7dbl = self.branch7x7dbl_1(x)branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]return torch.cat(outputs, 1)
3、InceptionC
# 这个部分采用横向扩展，主要用与处理性能瓶颈
class InceptionC(nn.Module):def __init__(self, in_channels):super(InceptionC, self).__init__()# 1 * 1self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)# 1 * 1 + 1 * 3 + 3 * 1self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))# 1 * 1 + 3 * 3 + 3 * 1 + 3 * 1self.branch3x3b1_1 = BasicConv2d(in_channels, 448, kernel_size=1)self.branch3x3b1_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)self.branch3x3b1_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3b1_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)def forward(self, x):branch1x1 = self.branch1x1(x)branch3x3 = self.branch3x3_1(x)branch3x3 = [self.branch3x3_2a(branch3x3),self.branch3x3_2b(branch3x3),]branch3x3 = torch.cat(branch3x3, dim=1) # 拼接branch3x3b1 = self.branch3x3b1_1(x)branch3x3b1 = self.branch3x3b1_2(branch3x3b1)branch3x3b1 = [self.branch3x3b1_3a(branch3x3b1),self.branch3x3b1_3b(branch3x3b1)]branch3x3b1 = torch.cat(branch3x3b1, dim=1)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)out = [branch1x1, branch3x3, branch3x3b1, branch_pool]return torch.cat(out, dim=1)
4、ReductionA
从总体模块可以看出，这个位置在于连接InceptionA/B/C模块后，主要用于特征提取后降维操作，总体模型结构如下：
class ReductionA(nn.Module):def __init__(self, in_channels):super(ReductionA, self).__init__()# 3 * 3self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)# 1 * 1 + 3 * 3 + 3 * 3self.branch3x3db1_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3db1_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3db1_3 = BasicConv2d(96, 96, kernel_size=3, stride=2)def forward(self, x):branch3x3 = self.branch3x3(x)branch3x3db1 = self.branch3x3db1_1(x)branch3x3db1 = self.branch3x3db1_2(branch3x3db1)branch3x3db1 = self.branch3x3db1_3(branch3x3db1)# 这里采用最大池化branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)out = [branch3x3, branch3x3db1, branch_pool]return torch.cat(out, dim=1)
5、ReductionB
class ReductionB(nn.Module):def __init__(self, in_channels):super(ReductionB, self).__init__()self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2)self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3))self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2)def forward(self, x):branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch7x7x3 = self.branch7x7x3_1(x)branch7x7x3 = self.branch7x7x3_2(branch7x7x3)branch7x7x3 = self.branch7x7x3_3(branch7x7x3)branch7x7x3 = self.branch7x7x3_4(branch7x7x3)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)out = [branch3x3, branch7x7x3, branch_pool]return torch.cat(out, dim=1)
6、辅助分支
# 辅助分类器
class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes):super(InceptionAux, self).__init__()self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)self.conv1 = BasicConv2d(128, 768, kernel_size=5)self.conv1.stddev = 0.01 # 设置卷积层权重的标准差self.fc = nn.Linear(768, num_classes)self.fc.saddev = 0.001 # 设置全连接层权重的标准差def forward(self, x):# 17 x 17 x 768x = F.avg_pool2d(x, kernel_size=5, stride=5)# 5 x 5 x 768x = self.conv0(x)# 5 x 5 x 128x = self.coinv1(x)# 1 x 1 x 768x = x.view(x.size(0), -1) # 展开# 768x = self.fc(x)return x
7、模型搭建
class InceptionV3(nn.Module):def __init__(self, num_classes=1000, aux_logits=False, transform_input=False):super(InceptionV3, self).__init__()''' aux_logits 是否使用辅助分类器transform_input 是否对数据进行转换'''self.aux_logits = aux_logitsself.transform_input = transform_input# 头，输出初处理self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)# InceptionAself.Mixed_5b = InceptionA(192, pool_features=32)self.Mixed_5c = InceptionA(256, pool_features=64)self.Mixed_5d = InceptionA(288, pool_features=64)# 降维，通道增加self.Mixed_6a = ReductionA(288)# InceptionBself.Mixed_6b = InceptionB(768, channels_7x7=128)self.Mixed_6c = InceptionB(768, channels_7x7=160)self.Mixed_6d = InceptionB(768, channels_7x7=160)self.Mixed_6e = InceptionB(768, channels_7x7=192)# 辅助if aux_logits:self.AuxLogits = InceptionAux(768, num_classes)# 降维，通道增加self.Mixed_7a = ReductionB(768)# InceptionCself.Mixed_7b = InceptionC(1280)self.Mixed_7c = InceptionC(2048)self.fc = nn.Linear(2048, num_classes)def forward(self, x):# 数据转换if self.transform_input: # 对数据进行标准化x = x.clone()x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5# 299 x 299 x 3x = self.Conv2d_1a_3x3(x)# 129 x 149 x 32x = self.Conv2d_2a_3x3(x)# 147 x 147 x 32x = self.Conv2d_2b_3x3(x)# 147 x 147 x 64x = F.max_pool2d(x, kernel_size=3, stride=2)# 73 x 73 x 64x = self.Conv2d_3b_1x1(x)# 73 x 73 x 80x = self.Conv2d_4a_3x3(x)# 71 x 71 x 192 x = F.max_pool2d(x, kernel_size=3, stride=2) # 35 x 35 x 192 x = self.Mixed_5b(x) # 35 x 35 x 256 x = self.Mixed_5c(x) # 35 x 35 x 288 x = self.Mixed_5d(x) # 35 x 35 x 288 x = self.Mixed_6a(x) # 17 x 17 x 768 x = self.Mixed_6b(x) # 17 x 17 x 768 x = self.Mixed_6c(x) # 17 x 17 x 768 x = self.Mixed_6d(x) # 17 x 17 x 768 x = self.Mixed_6e(x) # 17 x 17 x 768 if self.training and self.aux_logits: # 在训练模型中使用 aux = self.AuxLogits(x) # 17 x 17 x 768 x = self.Mixed_7a(x) # 8 x 8 x 1280 x = self.Mixed_7b(x) # 8 x 8 x 2048 x = self.Mixed_7c(x) # 8 x 8 x 2048 x = F.avg_pool2d(x, kernel_size=8) # 1 x 1 x 2048 x = F.dropout(x, training=self.training) # 1 x 1 x 2048 x = x.view(x.size(0), -1) # 2048 x = self.fc(x) # num_classes if self.training and self.aux_logits: # 在训练模型中使用return x, auxreturn x
# 测试
device = "cuda" if torch.cuda.is_available() else "cpu"model = InceptionV3().to(device)
model
InceptionV3((Conv2d_1a_3x3): BasicConv2d((conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(Conv2d_2a_3x3): BasicConv2d((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(Conv2d_2b_3x3): BasicConv2d((conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(Conv2d_3b_1x1): BasicConv2d((conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(Conv2d_4a_3x3): BasicConv2d((conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(Mixed_5b): InceptionA((branch1x1): BasicConv2d((conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_1): BasicConv2d((conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_2): BasicConv2d((conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_1): BasicConv2d((conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2): BasicConv2d((conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_3): BasicConv2d((conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_5c): InceptionA((branch1x1): BasicConv2d((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_1): BasicConv2d((conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_2): BasicConv2d((conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_1): BasicConv2d((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2): BasicConv2d((conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_3): BasicConv2d((conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_5d): InceptionA((branch1x1): BasicConv2d((conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_1): BasicConv2d((conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch5x5_2): BasicConv2d((conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_1): BasicConv2d((conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2): BasicConv2d((conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_3): BasicConv2d((conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_6a): ReductionA((branch3x3): BasicConv2d((conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3db1_1): BasicConv2d((conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3db1_2): BasicConv2d((conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3db1_3): BasicConv2d((conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)(bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_6b): InceptionB((branch1x1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_1): BasicConv2d((conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_2): BasicConv2d((conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_3): BasicConv2d((conv): Conv2d(128, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_1): BasicConv2d((conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_2): BasicConv2d((conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_3): BasicConv2d((conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_4): BasicConv2d((conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_5): BasicConv2d((conv): Conv2d(128, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_6c): InceptionB((branch1x1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_1): BasicConv2d((conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_2): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_3): BasicConv2d((conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_1): BasicConv2d((conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_2): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_3): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_4): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_5): BasicConv2d((conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_6d): InceptionB((branch1x1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_1): BasicConv2d((conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_2): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_3): BasicConv2d((conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_1): BasicConv2d((conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_2): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_3): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_4): BasicConv2d((conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_5): BasicConv2d((conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_6e): InceptionB((branch1x1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_2): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7_3): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_2): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_3): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_4): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7dbl_5): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_7a): ReductionB((branch3x3_1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2): BasicConv2d((conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7x3_1): BasicConv2d((conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7x3_2): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7x3_3): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch7x7x3_4): BasicConv2d((conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_7b): InceptionC((branch1x1): BasicConv2d((conv): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_1): BasicConv2d((conv): Conv2d(1280, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2a): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2b): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_1): BasicConv2d((conv): Conv2d(1280, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_2): BasicConv2d((conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_3a): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_3b): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(1280, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(Mixed_7c): InceptionC((branch1x1): BasicConv2d((conv): Conv2d(2048, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_1): BasicConv2d((conv): Conv2d(2048, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2a): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3_2b): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_1): BasicConv2d((conv): Conv2d(2048, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_2): BasicConv2d((conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_3a): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch3x3b1_3b): BasicConv2d((conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True))(branch_pool): BasicConv2d((conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)))(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
model(torch.randn(32, 3, 299, 299).to(device)).shape
torch.Size([32, 1000])
8、查看模型详情
import torchsummary as summary summary.summary(model, (3, 299, 299))
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv2d-1 [-1, 32, 149, 149] 864BatchNorm2d-2 [-1, 32, 149, 149] 64BasicConv2d-3 [-1, 32, 149, 149] 0Conv2d-4 [-1, 32, 147, 147] 9,216BatchNorm2d-5 [-1, 32, 147, 147] 64BasicConv2d-6 [-1, 32, 147, 147] 0Conv2d-7 [-1, 64, 147, 147] 18,432BatchNorm2d-8 [-1, 64, 147, 147] 128BasicConv2d-9 [-1, 64, 147, 147] 0Conv2d-10 [-1, 80, 73, 73] 5,120BatchNorm2d-11 [-1, 80, 73, 73] 160BasicConv2d-12 [-1, 80, 73, 73] 0Conv2d-13 [-1, 192, 71, 71] 138,240BatchNorm2d-14 [-1, 192, 71, 71] 384BasicConv2d-15 [-1, 192, 71, 71] 0Conv2d-16 [-1, 64, 35, 35] 12,288BatchNorm2d-17 [-1, 64, 35, 35] 128BasicConv2d-18 [-1, 64, 35, 35] 0Conv2d-19 [-1, 48, 35, 35] 9,216BatchNorm2d-20 [-1, 48, 35, 35] 96BasicConv2d-21 [-1, 48, 35, 35] 0Conv2d-22 [-1, 64, 35, 35] 76,800BatchNorm2d-23 [-1, 64, 35, 35] 128BasicConv2d-24 [-1, 64, 35, 35] 0Conv2d-25 [-1, 64, 35, 35] 12,288BatchNorm2d-26 [-1, 64, 35, 35] 128BasicConv2d-27 [-1, 64, 35, 35] 0Conv2d-28 [-1, 96, 35, 35] 55,296BatchNorm2d-29 [-1, 96, 35, 35] 192BasicConv2d-30 [-1, 96, 35, 35] 0Conv2d-31 [-1, 96, 35, 35] 82,944BatchNorm2d-32 [-1, 96, 35, 35] 192BasicConv2d-33 [-1, 96, 35, 35] 0Conv2d-34 [-1, 32, 35, 35] 6,144BatchNorm2d-35 [-1, 32, 35, 35] 64BasicConv2d-36 [-1, 32, 35, 35] 0InceptionA-37 [-1, 256, 35, 35] 0Conv2d-38 [-1, 64, 35, 35] 16,384BatchNorm2d-39 [-1, 64, 35, 35] 128BasicConv2d-40 [-1, 64, 35, 35] 0Conv2d-41 [-1, 48, 35, 35] 12,288BatchNorm2d-42 [-1, 48, 35, 35] 96BasicConv2d-43 [-1, 48, 35, 35] 0Conv2d-44 [-1, 64, 35, 35] 76,800BatchNorm2d-45 [-1, 64, 35, 35] 128BasicConv2d-46 [-1, 64, 35, 35] 0Conv2d-47 [-1, 64, 35, 35] 16,384BatchNorm2d-48 [-1, 64, 35, 35] 128BasicConv2d-49 [-1, 64, 35, 35] 0Conv2d-50 [-1, 96, 35, 35] 55,296BatchNorm2d-51 [-1, 96, 35, 35] 192BasicConv2d-52 [-1, 96, 35, 35] 0Conv2d-53 [-1, 96, 35, 35] 82,944BatchNorm2d-54 [-1, 96, 35, 35] 192BasicConv2d-55 [-1, 96, 35, 35] 0Conv2d-56 [-1, 64, 35, 35] 16,384BatchNorm2d-57 [-1, 64, 35, 35] 128BasicConv2d-58 [-1, 64, 35, 35] 0InceptionA-59 [-1, 288, 35, 35] 0Conv2d-60 [-1, 64, 35, 35] 18,432BatchNorm2d-61 [-1, 64, 35, 35] 128BasicConv2d-62 [-1, 64, 35, 35] 0Conv2d-63 [-1, 48, 35, 35] 13,824BatchNorm2d-64 [-1, 48, 35, 35] 96BasicConv2d-65 [-1, 48, 35, 35] 0Conv2d-66 [-1, 64, 35, 35] 76,800BatchNorm2d-67 [-1, 64, 35, 35] 128BasicConv2d-68 [-1, 64, 35, 35] 0Conv2d-69 [-1, 64, 35, 35] 18,432BatchNorm2d-70 [-1, 64, 35, 35] 128BasicConv2d-71 [-1, 64, 35, 35] 0Conv2d-72 [-1, 96, 35, 35] 55,296BatchNorm2d-73 [-1, 96, 35, 35] 192BasicConv2d-74 [-1, 96, 35, 35] 0Conv2d-75 [-1, 96, 35, 35] 82,944BatchNorm2d-76 [-1, 96, 35, 35] 192BasicConv2d-77 [-1, 96, 35, 35] 0Conv2d-78 [-1, 64, 35, 35] 18,432BatchNorm2d-79 [-1, 64, 35, 35] 128BasicConv2d-80 [-1, 64, 35, 35] 0InceptionA-81 [-1, 288, 35, 35] 0Conv2d-82 [-1, 384, 17, 17] 995,328BatchNorm2d-83 [-1, 384, 17, 17] 768BasicConv2d-84 [-1, 384, 17, 17] 0Conv2d-85 [-1, 64, 35, 35] 18,432BatchNorm2d-86 [-1, 64, 35, 35] 128BasicConv2d-87 [-1, 64, 35, 35] 0Conv2d-88 [-1, 96, 35, 35] 55,296BatchNorm2d-89 [-1, 96, 35, 35] 192BasicConv2d-90 [-1, 96, 35, 35] 0Conv2d-91 [-1, 96, 17, 17] 82,944BatchNorm2d-92 [-1, 96, 17, 17] 192BasicConv2d-93 [-1, 96, 17, 17] 0ReductionA-94 [-1, 768, 17, 17] 0Conv2d-95 [-1, 192, 17, 17] 147,456BatchNorm2d-96 [-1, 192, 17, 17] 384BasicConv2d-97 [-1, 192, 17, 17] 0Conv2d-98 [-1, 128, 17, 17] 98,304BatchNorm2d-99 [-1, 128, 17, 17] 256BasicConv2d-100 [-1, 128, 17, 17] 0Conv2d-101 [-1, 128, 17, 17] 114,688BatchNorm2d-102 [-1, 128, 17, 17] 256BasicConv2d-103 [-1, 128, 17, 17] 0Conv2d-104 [-1, 192, 17, 17] 172,032BatchNorm2d-105 [-1, 192, 17, 17] 384BasicConv2d-106 [-1, 192, 17, 17] 0Conv2d-107 [-1, 128, 17, 17] 98,304BatchNorm2d-108 [-1, 128, 17, 17] 256BasicConv2d-109 [-1, 128, 17, 17] 0Conv2d-110 [-1, 128, 17, 17] 114,688BatchNorm2d-111 [-1, 128, 17, 17] 256BasicConv2d-112 [-1, 128, 17, 17] 0Conv2d-113 [-1, 128, 17, 17] 114,688BatchNorm2d-114 [-1, 128, 17, 17] 256BasicConv2d-115 [-1, 128, 17, 17] 0Conv2d-116 [-1, 128, 17, 17] 114,688BatchNorm2d-117 [-1, 128, 17, 17] 256BasicConv2d-118 [-1, 128, 17, 17] 0Conv2d-119 [-1, 192, 17, 17] 172,032BatchNorm2d-120 [-1, 192, 17, 17] 384BasicConv2d-121 [-1, 192, 17, 17] 0Conv2d-122 [-1, 192, 17, 17] 147,456BatchNorm2d-123 [-1, 192, 17, 17] 384BasicConv2d-124 [-1, 192, 17, 17] 0InceptionB-125 [-1, 768, 17, 17] 0Conv2d-126 [-1, 192, 17, 17] 147,456BatchNorm2d-127 [-1, 192, 17, 17] 384BasicConv2d-128 [-1, 192, 17, 17] 0Conv2d-129 [-1, 160, 17, 17] 122,880BatchNorm2d-130 [-1, 160, 17, 17] 320BasicConv2d-131 [-1, 160, 17, 17] 0Conv2d-132 [-1, 160, 17, 17] 179,200BatchNorm2d-133 [-1, 160, 17, 17] 320BasicConv2d-134 [-1, 160, 17, 17] 0Conv2d-135 [-1, 192, 17, 17] 215,040BatchNorm2d-136 [-1, 192, 17, 17] 384BasicConv2d-137 [-1, 192, 17, 17] 0Conv2d-138 [-1, 160, 17, 17] 122,880BatchNorm2d-139 [-1, 160, 17, 17] 320BasicConv2d-140 [-1, 160, 17, 17] 0Conv2d-141 [-1, 160, 17, 17] 179,200BatchNorm2d-142 [-1, 160, 17, 17] 320BasicConv2d-143 [-1, 160, 17, 17] 0Conv2d-144 [-1, 160, 17, 17] 179,200BatchNorm2d-145 [-1, 160, 17, 17] 320BasicConv2d-146 [-1, 160, 17, 17] 0Conv2d-147 [-1, 160, 17, 17] 179,200BatchNorm2d-148 [-1, 160, 17, 17] 320BasicConv2d-149 [-1, 160, 17, 17] 0Conv2d-150 [-1, 192, 17, 17] 215,040BatchNorm2d-151 [-1, 192, 17, 17] 384BasicConv2d-152 [-1, 192, 17, 17] 0Conv2d-153 [-1, 192, 17, 17] 147,456BatchNorm2d-154 [-1, 192, 17, 17] 384BasicConv2d-155 [-1, 192, 17, 17] 0InceptionB-156 [-1, 768, 17, 17] 0Conv2d-157 [-1, 192, 17, 17] 147,456BatchNorm2d-158 [-1, 192, 17, 17] 384BasicConv2d-159 [-1, 192, 17, 17] 0Conv2d-160 [-1, 160, 17, 17] 122,880BatchNorm2d-161 [-1, 160, 17, 17] 320BasicConv2d-162 [-1, 160, 17, 17] 0Conv2d-163 [-1, 160, 17, 17] 179,200BatchNorm2d-164 [-1, 160, 17, 17] 320BasicConv2d-165 [-1, 160, 17, 17] 0Conv2d-166 [-1, 192, 17, 17] 215,040BatchNorm2d-167 [-1, 192, 17, 17] 384BasicConv2d-168 [-1, 192, 17, 17] 0Conv2d-169 [-1, 160, 17, 17] 122,880BatchNorm2d-170 [-1, 160, 17, 17] 320BasicConv2d-171 [-1, 160, 17, 17] 0Conv2d-172 [-1, 160, 17, 17] 179,200BatchNorm2d-173 [-1, 160, 17, 17] 320BasicConv2d-174 [-1, 160, 17, 17] 0Conv2d-175 [-1, 160, 17, 17] 179,200BatchNorm2d-176 [-1, 160, 17, 17] 320BasicConv2d-177 [-1, 160, 17, 17] 0Conv2d-178 [-1, 160, 17, 17] 179,200BatchNorm2d-179 [-1, 160, 17, 17] 320BasicConv2d-180 [-1, 160, 17, 17] 0Conv2d-181 [-1, 192, 17, 17] 215,040BatchNorm2d-182 [-1, 192, 17, 17] 384BasicConv2d-183 [-1, 192, 17, 17] 0Conv2d-184 [-1, 192, 17, 17] 147,456BatchNorm2d-185 [-1, 192, 17, 17] 384BasicConv2d-186 [-1, 192, 17, 17] 0InceptionB-187 [-1, 768, 17, 17] 0Conv2d-188 [-1, 192, 17, 17] 147,456BatchNorm2d-189 [-1, 192, 17, 17] 384BasicConv2d-190 [-1, 192, 17, 17] 0Conv2d-191 [-1, 192, 17, 17] 147,456BatchNorm2d-192 [-1, 192, 17, 17] 384BasicConv2d-193 [-1, 192, 17, 17] 0Conv2d-194 [-1, 192, 17, 17] 258,048BatchNorm2d-195 [-1, 192, 17, 17] 384BasicConv2d-196 [-1, 192, 17, 17] 0Conv2d-197 [-1, 192, 17, 17] 258,048BatchNorm2d-198 [-1, 192, 17, 17] 384BasicConv2d-199 [-1, 192, 17, 17] 0Conv2d-200 [-1, 192, 17, 17] 147,456BatchNorm2d-201 [-1, 192, 17, 17] 384BasicConv2d-202 [-1, 192, 17, 17] 0Conv2d-203 [-1, 192, 17, 17] 258,048BatchNorm2d-204 [-1, 192, 17, 17] 384BasicConv2d-205 [-1, 192, 17, 17] 0Conv2d-206 [-1, 192, 17, 17] 258,048BatchNorm2d-207 [-1, 192, 17, 17] 384BasicConv2d-208 [-1, 192, 17, 17] 0Conv2d-209 [-1, 192, 17, 17] 258,048BatchNorm2d-210 [-1, 192, 17, 17] 384BasicConv2d-211 [-1, 192, 17, 17] 0Conv2d-212 [-1, 192, 17, 17] 258,048BatchNorm2d-213 [-1, 192, 17, 17] 384BasicConv2d-214 [-1, 192, 17, 17] 0Conv2d-215 [-1, 192, 17, 17] 147,456BatchNorm2d-216 [-1, 192, 17, 17] 384BasicConv2d-217 [-1, 192, 17, 17] 0InceptionB-218 [-1, 768, 17, 17] 0Conv2d-219 [-1, 192, 17, 17] 147,456BatchNorm2d-220 [-1, 192, 17, 17] 384BasicConv2d-221 [-1, 192, 17, 17] 0Conv2d-222 [-1, 320, 8, 8] 552,960BatchNorm2d-223 [-1, 320, 8, 8] 640BasicConv2d-224 [-1, 320, 8, 8] 0Conv2d-225 [-1, 192, 17, 17] 147,456BatchNorm2d-226 [-1, 192, 17, 17] 384BasicConv2d-227 [-1, 192, 17, 17] 0Conv2d-228 [-1, 192, 17, 17] 258,048BatchNorm2d-229 [-1, 192, 17, 17] 384BasicConv2d-230 [-1, 192, 17, 17] 0Conv2d-231 [-1, 192, 17, 17] 258,048BatchNorm2d-232 [-1, 192, 17, 17] 384BasicConv2d-233 [-1, 192, 17, 17] 0Conv2d-234 [-1, 192, 8, 8] 331,776BatchNorm2d-235 [-1, 192, 8, 8] 384BasicConv2d-236 [-1, 192, 8, 8] 0ReductionB-237 [-1, 1280, 8, 8] 0Conv2d-238 [-1, 320, 8, 8] 409,600BatchNorm2d-239 [-1, 320, 8, 8] 640BasicConv2d-240 [-1, 320, 8, 8] 0Conv2d-241 [-1, 384, 8, 8] 491,520BatchNorm2d-242 [-1, 384, 8, 8] 768BasicConv2d-243 [-1, 384, 8, 8] 0Conv2d-244 [-1, 384, 8, 8] 442,368BatchNorm2d-245 [-1, 384, 8, 8] 768BasicConv2d-246 [-1, 384, 8, 8] 0Conv2d-247 [-1, 384, 8, 8] 442,368BatchNorm2d-248 [-1, 384, 8, 8] 768BasicConv2d-249 [-1, 384, 8, 8] 0Conv2d-250 [-1, 448, 8, 8] 573,440BatchNorm2d-251 [-1, 448, 8, 8] 896BasicConv2d-252 [-1, 448, 8, 8] 0Conv2d-253 [-1, 384, 8, 8] 1,548,288BatchNorm2d-254 [-1, 384, 8, 8] 768BasicConv2d-255 [-1, 384, 8, 8] 0Conv2d-256 [-1, 384, 8, 8] 442,368BatchNorm2d-257 [-1, 384, 8, 8] 768BasicConv2d-258 [-1, 384, 8, 8] 0Conv2d-259 [-1, 384, 8, 8] 442,368BatchNorm2d-260 [-1, 384, 8, 8] 768BasicConv2d-261 [-1, 384, 8, 8] 0Conv2d-262 [-1, 192, 8, 8] 245,760BatchNorm2d-263 [-1, 192, 8, 8] 384BasicConv2d-264 [-1, 192, 8, 8] 0InceptionC-265 [-1, 2048, 8, 8] 0Conv2d-266 [-1, 320, 8, 8] 655,360BatchNorm2d-267 [-1, 320, 8, 8] 640BasicConv2d-268 [-1, 320, 8, 8] 0Conv2d-269 [-1, 384, 8, 8] 786,432BatchNorm2d-270 [-1, 384, 8, 8] 768BasicConv2d-271 [-1, 384, 8, 8] 0Conv2d-272 [-1, 384, 8, 8] 442,368BatchNorm2d-273 [-1, 384, 8, 8] 768BasicConv2d-274 [-1, 384, 8, 8] 0Conv2d-275 [-1, 384, 8, 8] 442,368BatchNorm2d-276 [-1, 384, 8, 8] 768BasicConv2d-277 [-1, 384, 8, 8] 0Conv2d-278 [-1, 448, 8, 8] 917,504BatchNorm2d-279 [-1, 448, 8, 8] 896BasicConv2d-280 [-1, 448, 8, 8] 0Conv2d-281 [-1, 384, 8, 8] 1,548,288BatchNorm2d-282 [-1, 384, 8, 8] 768BasicConv2d-283 [-1, 384, 8, 8] 0Conv2d-284 [-1, 384, 8, 8] 442,368BatchNorm2d-285 [-1, 384, 8, 8] 768BasicConv2d-286 [-1, 384, 8, 8] 0Conv2d-287 [-1, 384, 8, 8] 442,368BatchNorm2d-288 [-1, 384, 8, 8] 768BasicConv2d-289 [-1, 384, 8, 8] 0Conv2d-290 [-1, 192, 8, 8] 393,216BatchNorm2d-291 [-1, 192, 8, 8] 384BasicConv2d-292 [-1, 192, 8, 8] 0InceptionC-293 [-1, 2048, 8, 8] 0Linear-294 [-1, 1000] 2,049,000
================================================================
Total params: 23,834,568
Trainable params: 23,834,568
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.02
Forward/backward pass size (MB): 224.12
Params size (MB): 90.92
Estimated Total Size (MB): 316.07
----------------------------------------------------------------
3、参考资料
如何理解卷积神经网络中的通道（channel）_神经网络通道数-CSDN博客