深度学习每周学习总结J9（Inception V3 算法实战与解析

🍨 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 | 接辅导、项目定制

- - 0. 总结
  - Inception V1 简介
  - Inception V3 简介
  - 1. 设置GPU
  - 2. 导入数据及处理部分
  - 3. 划分数据集
  - 4. 模型构建部分
  - 5. 设置超参数：定义损失函数，学习率，以及根据学习率定义优化器等
  - 6. 训练函数
  - 7. 测试函数
  - 8. 正式训练
  - 9. 结果可视化
  - 10. 模型的保存
  - 11.使用训练好的模型进行预测

0. 总结

数据导入及处理部分：本次数据导入没有使用torchvision自带的数据集，需要将原始数据进行处理包括数据导入，查看数据分类情况，定义transforms，进行数据类型转换等操作。

划分数据集：划定训练集测试集后，再使用torch.utils.data中的DataLoader()分别加载上一步处理好的训练及测试数据，查看批处理维度.

模型构建部分：Inception V3

设置超参数：在这之前需要定义损失函数，学习率（动态学习率），以及根据学习率定义优化器（例如SGD随机梯度下降），用来在训练中更新参数，最小化损失函数。

定义训练函数：函数的传入的参数有四个，分别是设置好的DataLoader(),定义好的模型，损失函数，优化器。函数内部初始化损失准确率为0，接着开始循环，使用DataLoader()获取一个批次的数据，对这个批次的数据带入模型得到预测值，然后使用损失函数计算得到损失值。接下来就是进行反向传播以及使用优化器优化参数，梯度清零放在反向传播之前或者是使用优化器优化之后都是可以的，一般是默认放在反向传播之前。

定义测试函数：函数传入的参数相比训练函数少了优化器，只需传入设置好的DataLoader(),定义好的模型，损失函数。此外除了处理批次数据时无需再设置梯度清零、返向传播以及优化器优化参数，其余部分均和训练函数保持一致。

训练过程：定义训练次数，有几次就使用整个数据集进行几次训练，初始化四个空list分别存储每次训练及测试的准确率及损失。使用model.train()开启训练模式，调用训练函数得到准确率及损失。使用model.eval()将模型设置为评估模式，调用测试函数得到准确率及损失。接着就是将得到的训练及测试的准确率及损失存储到相应list中并合并打印出来，得到每一次整体训练后的准确率及损失。

结果可视化

模型的保存，调取及使用。在PyTorch中，通常使用 torch.save(model.state_dict(), ‘model.pth’) 保存模型的参数，使用 model.load_state_dict(torch.load(‘model.pth’)) 加载参数。

需要改进优化的地方：确保模型和数据的一致性，都存到GPU或者CPU;注意numclasses不要直接用默认的1000，需要根据实际数据集改进；实例化模型也要注意numclasses这个参数；此外注意测试模型需要用（3,224,224）3表示通道数，这和tensorflow定义的顺序是不用的（224,224,3），做代码转换时需要注意。

关于调优（十分重要）：本次将测试集准确率提升到了96.03%（随机种子设置为42）
1：使用多卡不一定比单卡效果好，需要继续调优
2：本次微调参数主要调整了两点一是初始学习率从1e-4 增大为了3e-4;其次是原来图片预处理只加入了随机水平翻转，本次加入了小角度的随机翻转，随机缩放剪裁，光照变化等，发现有更好的效果。测试集准确率有了很大的提升。从训练后的准确率图像也可以看到，训练准确率和测试准确率很接近甚至能够超过。之前没有做这个改进之前，都是训练准确率远大于测试准确率。

有个疑问是为啥必须要把图片尺寸设置为（299，299）？（244，244）会报错

关键代码示例：

import torchvision.transforms as transforms# 定义猴痘识别的 transforms
train_transforms = transforms.Compose([transforms.Resize([299, 299]),            # 统一图片尺寸transforms.RandomHorizontalFlip(p=0.5),  # 随机水平翻转transforms.RandomRotation(degrees=15),   # 小角度随机旋转transforms.RandomResizedCrop(size=299, scale=(0.8, 1.2)),  # 随机缩放裁剪transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1),  # 光照变化transforms.ToTensor(),                   # 转换为 Tensor 格式transforms.Normalize(                    # 标准化mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
])

Inception V1 简介

什么是Inception V1？

Inception V1，也被称为GoogLeNet，是Google在2014年ILSVRC比赛中提出的一种卷积神经网络（CNN）架构，并且在比赛中获得了冠军。与当时流行的VGGNet相比，Inception V1在保持相似性能的同时，显著减少了参数数量，从而提高了计算效率。

Inception Module的核心思想

Inception V1的核心是Inception Module，它通过并行的卷积操作在同一层提取不同尺度的特征。这种设计不仅增加了网络的深度，还有效地捕捉了多种特征信息。

具体来说，一个Inception Module通常包含以下几个分支：

1x1卷积分支：用于降低输入特征图的通道数，减少计算量。
1x1卷积后接3x3卷积分支：先用1x1卷积降维，再进行3x3卷积提取特征。
1x1卷积后接5x5卷积分支：类似于3x3分支，但使用更大的卷积核以捕捉更大范围的特征。
3x3最大池化后接1x1卷积分支：先进行池化操作，再用1x1卷积进行特征整合。

通过将这些分支的输出在通道维度上拼接，Inception Module能够在同一层次上整合多种尺度的信息，提升模型的表达能力。

1x1卷积的作用

1x1卷积主要用于降维，即减少特征图的通道数。这不仅降低了网络的参数量和计算量，还间接增加了网络的深度，有助于提升模型性能。例如：

原始输入：100x100x128
经过1x1卷积降维到32通道，再进行5x5卷积，输出仍为100x100x256
参数量由原来的约8.192×10⁹降低到2.048×10⁹

辅助分类器

Inception V1还引入了辅助分类器，主要有两个作用：

缓解梯度消失：通过在中间层添加分类器，帮助梯度更好地传播。
模型融合：将中间层的输出用于分类，增强模型的泛化能力。

不过，在实际应用中，这些辅助分类器通常在训练过程中使用，推理时会被去掉。

Inception V3 简介

Inception v3由谷歌研究员Christian Szegedy等人在2015年的论文《Rethinking the Inception Architecture for Computer Vision》中提出。Inception v3是Inception网络系列的第三个版本，它在ImageNet图像识别竞赛中取得了优异成绩，尤其是在大规模图像识别任务中表现出色。

Inception v3的主要特点如下：

1：更深的网络结构：Inception v3比之前的Inception网络结构更深，包含了48层卷积层。这使得网络可以提取更多层次的特征，从而在图像识别任务上取得更好的效果。

2：使用Factorized Convolutions：Inception v3采用了Factorized Convolutions（分解卷积），将较大的卷积核分解为多个较小的卷积核。这种方法可以降低网络的参数数量，减少计算复杂度，同时保持良好的性能。

3：使用Batch Normalization：Inceptionv3在每个卷积层之后都添加了Batch Normalization（BN），这有助于网络的收敛和泛化能力。BN可以减少Internal Covariate Shift（内部协变量偏移）现象，加快训练速度，同时提高模型的鲁棒性。

4：辅助分类器：Inception v3引入了辅助分类器，可以在网络训练过程中提供额外的梯度信息，帮助网络更好地学习特征。辅助分类器位于网络的某个中间层，其输出会与主分类器的输出进行加权融合，从而得到最终的预测结果。

5：基于RMSProp的优化器：Inception v3使用了RMSProp优化器进行训练。相比于传统的随机梯度下降（SGD）方法，RMSProp可以自适应地调整学习率，使得训练过程更加稳定，收敛速度更快。

Inception v3在图像分类、物体检测和图像分割等计算机视觉任务中均取得了显著的效果。然而，由于其较大的网络结构和计算复杂度，Inception v3在实际应用中可能需要较高的硬件要求。

import torch
import torch.nn as nn
import torchvision
from torchvision import datasets,transforms
from torch.utils.data import DataLoader
import torchvision.models as models
import torch.nn.functional as F
from collections import OrderedDict import os,PIL,pathlib
import matplotlib.pyplot as plt
import warningswarnings.filterwarnings('ignore') # 忽略警告信息plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False   # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 # 分辨率

1. 设置GPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

device(type='cuda')

2. 导入数据及处理部分

# 获取数据分布情况
path_dir = './data/weather_recognize/weather_photos/'
path_dir = pathlib.Path(path_dir)paths = list(path_dir.glob('*'))
# classNames = [str(path).split("\\")[-1] for path in paths] # ['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']
classNames = [path.parts[-1] for path in paths]
classNames

['cloudy', 'rain', 'shine', 'sunrise']

# 定义transforms 并处理数据
# train_transforms = transforms.Compose([
#     transforms.Resize([224,224]),      # 将输入图片resize成统一尺寸
#     transforms.RandomHorizontalFlip(), # 随机水平翻转
#     transforms.ToTensor(),             # 将PIL Image 或 numpy.ndarray 装换为tensor,并归一化到[0,1]之间
#     transforms.Normalize(              # 标准化处理 --> 转换为标准正太分布（高斯分布），使模型更容易收敛
#         mean = [0.485,0.456,0.406],    # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
#         std = [0.229,0.224,0.225]
#     )
# ])# 定义猴痘识别的 transforms 并处理数据
train_transforms = transforms.Compose([transforms.Resize([299, 299]),            # 统一图片尺寸transforms.RandomHorizontalFlip(p=0.5),  # 随机水平翻转transforms.RandomRotation(degrees=15),   # 小角度随机旋转transforms.RandomResizedCrop(size=299, scale=(0.8, 1.2)),  # 随机缩放裁剪transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1),  # 光照变化transforms.ToTensor(),                   # 转换为 Tensor 格式transforms.Normalize(                    # 标准化mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
])test_transforms = transforms.Compose([transforms.Resize([299,299]),transforms.ToTensor(),transforms.Normalize(mean = [0.485,0.456,0.406],std = [0.229,0.224,0.225])
])
total_data = datasets.ImageFolder('./data/weather_recognize/weather_photos/',transform = train_transforms)
total_data

Dataset ImageFolderNumber of datapoints: 1125Root location: ./data/weather_recognize/weather_photos/StandardTransform
Transform: Compose(Resize(size=[299, 299], interpolation=bilinear, max_size=None, antialias=True)RandomHorizontalFlip(p=0.5)RandomRotation(degrees=[-15.0, 15.0], interpolation=nearest, expand=False, fill=0)RandomResizedCrop(size=(299, 299), scale=(0.8, 1.2), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=True)ColorJitter(brightness=(0.8, 1.2), contrast=(0.8, 1.2), saturation=(0.9, 1.1), hue=None)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

3. 划分数据集

# 设置随机种子
torch.manual_seed(42)# 划分数据集
train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_sizetrain_dataset,test_dataset = torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_dataset

(<torch.utils.data.dataset.Subset at 0x29e06793df0>,<torch.utils.data.dataset.Subset at 0x29e06793dc0>)

# 定义DataLoader用于数据集的加载batch_size = 32 # 如使用多显卡，请确保 batch_size 是显卡数量的倍数。train_dl = torch.utils.data.DataLoader(train_dataset,batch_size = batch_size,shuffle = True,num_workers = 1
)
test_dl = torch.utils.data.DataLoader(test_dataset,batch_size = batch_size,shuffle = True,num_workers = 1
)

# 观察数据维度
for X,y in test_dl:print("Shape of X [N,C,H,W]: ",X.shape)print("Shape of y: ", y.shape,y.dtype)break

Shape of X [N,C,H,W]:  torch.Size([32, 3, 299, 299])
Shape of y:  torch.Size([32]) torch.int64

4. 模型构建部分

import torch
import torch.nn as nn
import torch.nn.functional as Fclass InceptionA(nn.Module):def __init__(self, in_channels, pool_features):super(InceptionA, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1) # 1self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)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)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)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_7x7self.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))self.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)class InceptionC(nn.Module):def __init__(self, in_channels):super(InceptionC, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)self.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))self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3dbl_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, 1)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = [self.branch3x3dbl_3a(branch3x3dbl),self.branch3x3dbl_3b(branch3x3dbl),]branch3x3dbl = torch.cat(branch3x3dbl, 1)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)class ReductionA(nn.Module):def __init__(self, in_channels):super(ReductionA, self).__init__()self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2)def forward(self, x):branch3x3 = self.branch3x3(x)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)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)outputs = [branch3x3, branch7x7x3, branch_pool]return torch.cat(outputs, 1)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 = 0.01self.fc = nn.Linear(768, num_classes)self.fc.stddev = 0.001def forward(self, x):# 17 x 17 x 768x = F.avg_pool2d(x, kernel_size=5, stride=3)# 5 x 5 x 768x = self.conv0(x)# 5 x 5 x 128x = self.conv1(x)# 1 x 1 x 768x = x.view(x.size(0), -1)# 768x = self.fc(x)# 1000return xclass 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)class InceptionV3(nn.Module):def __init__(self, num_classes=1000, aux_logits=False, transform_input=False):super(InceptionV3, self).__init__()self.aux_logits = aux_logitsself.transform_input = transform_inputself.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)self.Mixed_5b = InceptionA(192, pool_features=32)self.Mixed_5c = InceptionA(256, pool_features=64)self.Mixed_5d = InceptionA(288, pool_features=64)self.Mixed_6a = ReductionA(288)self.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)if aux_logits:self.AuxLogits = InceptionAux(768, num_classes)self.Mixed_7a = ReductionB(768)self.Mixed_7b = InceptionC(1280)self.Mixed_7c = InceptionC(2048)self.fc = nn.Linear(2048, num_classes)def forward(self, x):if self.transform_input: # 1x = 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)# 149 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 192x = F.max_pool2d(x, kernel_size=3, stride=2)# 35 x 35 x 192x = self.Mixed_5b(x)# 35 x 35 x 256x = self.Mixed_5c(x)# 35 x 35 x 288x = self.Mixed_5d(x)# 35 x 35 x 288x = self.Mixed_6a(x)# 17 x 17 x 768x = self.Mixed_6b(x)# 17 x 17 x 768x = self.Mixed_6c(x)# 17 x 17 x 768x = self.Mixed_6d(x)# 17 x 17 x 768x = self.Mixed_6e(x)# 17 x 17 x 768if self.training and self.aux_logits:aux = self.AuxLogits(x)# 17 x 17 x 768x = self.Mixed_7a(x)# 8 x 8 x 1280x = self.Mixed_7b(x)# 8 x 8 x 2048x = self.Mixed_7c(x)# 8 x 8 x 2048x = F.avg_pool2d(x, kernel_size=8)# 1 x 1 x 2048x = F.dropout(x, training=self.training)# 1 x 1 x 2048x = x.view(x.size(0), -1)# 2048x = self.fc(x)# 1000 (num_classes)if self.training and self.aux_logits:return x, auxreturn x

model = InceptionV3(num_classes=len(classNames)).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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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))(branch3x3dbl_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=4, bias=True)
)

# 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, 4]           8,196
================================================================
Total params: 21,793,764
Trainable params: 21,793,764
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.02
Forward/backward pass size (MB): 224.12
Params size (MB): 83.14
Estimated Total Size (MB): 308.28
----------------------------------------------------------------

5. 设置超参数：定义损失函数，学习率，以及根据学习率定义优化器等

# loss_fn = nn.CrossEntropyLoss() # 创建损失函数# learn_rate = 1e-3 # 初始学习率
# def adjust_learning_rate(optimizer,epoch,start_lr):
#     # 每两个epoch 衰减到原来的0.98
#     lr = start_lr * (0.92 ** (epoch//2))
#     for param_group in optimizer.param_groups:
#         param_group['lr'] = lr# optimizer = torch.optim.Adam(model.parameters(),lr=learn_rate)

# 调用官方接口示例
loss_fn = nn.CrossEntropyLoss()# learn_rate = 1e-4  
learn_rate = 3e-4
lambda1 = lambda epoch:(0.92**(epoch//2))optimizer = torch.optim.Adam(model.parameters(),lr = learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda=lambda1) # 选定调整方法

6. 训练函数

# 训练函数
def train(dataloader,model,loss_fn,optimizer):size = len(dataloader.dataset) # 训练集大小num_batches = len(dataloader) # 批次数目train_loss,train_acc = 0,0for X,y in dataloader:X,y = X.to(device),y.to(device)# 计算预测误差pred = model(X)loss = loss_fn(pred,y)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()# 记录acc与losstrain_acc += (pred.argmax(1)==y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc,train_loss

7. 测试函数

# 测试函数
def test(dataloader,model,loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)test_acc,test_loss = 0,0with torch.no_grad():for X,y in dataloader:X,y = X.to(device),y.to(device)# 计算losspred = model(X)loss = loss_fn(pred,y)test_acc += (pred.argmax(1)==y).type(torch.float).sum().item()test_loss += loss.item()test_acc /= sizetest_loss /= num_batchesreturn test_acc,test_loss

8. 正式训练

import copyepochs = 60train_acc = []
train_loss = []
test_acc = []
test_loss = []best_acc = 0.0# 检查 GPU 可用性并打印设备信息
if torch.cuda.is_available():for i in range(torch.cuda.device_count()):print(f"GPU {i}: {torch.cuda.get_device_name(i)}")print(f"Initial Memory Allocated: {torch.cuda.memory_allocated(i)/1024**2:.2f} MB")print(f"Initial Memory Cached: {torch.cuda.memory_reserved(i)/1024**2:.2f} MB")
else:print("No GPU available. Using CPU.")# 多显卡设置 当前使用的是使用 PyTorch 自带的 DataParallel，后续如有需要可以设置为DistributedDataParallel，这是更加高效的方式
# 且多卡不一定比单卡效果就好，需要调整优化
# if torch.cuda.device_count() > 1:
#     print(f"Using {torch.cuda.device_count()} GPUs")
#     model = nn.DataParallel(model)
# model = model.to('cuda')for epoch in range(epochs):# 更新学习率——使用自定义学习率时使用# adjust_learning_rate(optimizer,epoch,learn_rate)model.train()epoch_train_acc,epoch_train_loss = train(train_dl,model,loss_fn,optimizer)scheduler.step() # 更新学习率——调用官方动态学习率时使用model.eval()epoch_test_acc,epoch_test_loss = test(test_dl,model,loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d},Train_acc:{:.1f}%,Train_loss:{:.3f},Test_acc:{:.1f}%,Test_loss:{:.3f},Lr:{:.2E}')print(template.format(epoch+1,epoch_train_acc*100,epoch_train_loss,epoch_test_acc*100,epoch_test_loss,lr))# 实时监控 GPU 状态if torch.cuda.is_available():for i in range(torch.cuda.device_count()):print(f"GPU {i} Usage:")print(f"  Memory Allocated: {torch.cuda.memory_allocated(i)/1024**2:.2f} MB")print(f"  Memory Cached: {torch.cuda.memory_reserved(i)/1024**2:.2f} MB")print(f"  Max Memory Allocated: {torch.cuda.max_memory_allocated(i)/1024**2:.2f} MB")print(f"  Max Memory Cached: {torch.cuda.max_memory_reserved(i)/1024**2:.2f} MB")print('Done','best_acc: ',best_acc)

GPU 0: NVIDIA GeForce RTX 4070 Laptop GPU
Initial Memory Allocated: 92.00 MB
Initial Memory Cached: 524.00 MB
Epoch: 1,Train_acc:75.0%,Train_loss:0.664,Test_acc:35.6%,Test_loss:3.178,Lr:3.00E-04
GPU 0 Usage:Memory Allocated: 442.28 MBMemory Cached: 4330.00 MBMax Memory Allocated: 3518.43 MBMax Memory Cached: 4330.00 MB
Epoch: 2,Train_acc:85.4%,Train_loss:0.473,Test_acc:75.6%,Test_loss:1.198,Lr:2.76E-04
GPU 0 Usage:Memory Allocated: 441.65 MBMemory Cached: 4366.00 MBMax Memory Allocated: 3601.60 MBMax Memory Cached: 4366.00 MB
Epoch: 3,Train_acc:84.9%,Train_loss:0.458,Test_acc:87.6%,Test_loss:0.368,Lr:2.76E-04
GPU 0 Usage:Memory Allocated: 442.17 MBMemory Cached: 4366.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4366.00 MB
Epoch: 4,Train_acc:86.0%,Train_loss:0.446,Test_acc:87.1%,Test_loss:0.414,Lr:2.54E-04
GPU 0 Usage:Memory Allocated: 443.47 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch: 5,Train_acc:89.0%,Train_loss:0.383,Test_acc:89.3%,Test_loss:0.277,Lr:2.54E-04
GPU 0 Usage:Memory Allocated: 440.66 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch: 6,Train_acc:87.6%,Train_loss:0.382,Test_acc:88.4%,Test_loss:0.378,Lr:2.34E-04
GPU 0 Usage:Memory Allocated: 438.65 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch: 7,Train_acc:87.6%,Train_loss:0.389,Test_acc:87.6%,Test_loss:0.297,Lr:2.34E-04
GPU 0 Usage:Memory Allocated: 440.96 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch: 8,Train_acc:89.2%,Train_loss:0.325,Test_acc:88.4%,Test_loss:0.267,Lr:2.15E-04
GPU 0 Usage:Memory Allocated: 441.04 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch: 9,Train_acc:89.1%,Train_loss:0.341,Test_acc:87.6%,Test_loss:0.309,Lr:2.15E-04
GPU 0 Usage:Memory Allocated: 441.02 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:10,Train_acc:88.7%,Train_loss:0.340,Test_acc:92.4%,Test_loss:0.233,Lr:1.98E-04
GPU 0 Usage:Memory Allocated: 439.24 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:11,Train_acc:89.9%,Train_loss:0.307,Test_acc:90.2%,Test_loss:0.254,Lr:1.98E-04
GPU 0 Usage:Memory Allocated: 440.40 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:12,Train_acc:89.0%,Train_loss:0.322,Test_acc:91.6%,Test_loss:0.250,Lr:1.82E-04
GPU 0 Usage:Memory Allocated: 442.53 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:13,Train_acc:92.7%,Train_loss:0.242,Test_acc:92.0%,Test_loss:0.223,Lr:1.82E-04
GPU 0 Usage:Memory Allocated: 441.67 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:14,Train_acc:91.1%,Train_loss:0.311,Test_acc:91.1%,Test_loss:0.224,Lr:1.67E-04
GPU 0 Usage:Memory Allocated: 440.82 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:15,Train_acc:90.1%,Train_loss:0.300,Test_acc:93.3%,Test_loss:0.196,Lr:1.67E-04
GPU 0 Usage:Memory Allocated: 442.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:16,Train_acc:89.1%,Train_loss:0.334,Test_acc:89.3%,Test_loss:0.333,Lr:1.54E-04
GPU 0 Usage:Memory Allocated: 440.50 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:17,Train_acc:89.7%,Train_loss:0.335,Test_acc:89.8%,Test_loss:0.232,Lr:1.54E-04
GPU 0 Usage:Memory Allocated: 441.43 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:18,Train_acc:93.1%,Train_loss:0.260,Test_acc:90.7%,Test_loss:0.260,Lr:1.42E-04
GPU 0 Usage:Memory Allocated: 441.24 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:19,Train_acc:91.3%,Train_loss:0.231,Test_acc:92.0%,Test_loss:0.179,Lr:1.42E-04
GPU 0 Usage:Memory Allocated: 441.50 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:20,Train_acc:90.4%,Train_loss:0.238,Test_acc:92.4%,Test_loss:0.656,Lr:1.30E-04
GPU 0 Usage:Memory Allocated: 440.64 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:21,Train_acc:93.3%,Train_loss:0.195,Test_acc:92.9%,Test_loss:0.189,Lr:1.30E-04
GPU 0 Usage:Memory Allocated: 441.06 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:22,Train_acc:93.7%,Train_loss:0.261,Test_acc:91.1%,Test_loss:0.206,Lr:1.20E-04
GPU 0 Usage:Memory Allocated: 441.77 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:23,Train_acc:94.2%,Train_loss:0.195,Test_acc:92.4%,Test_loss:0.205,Lr:1.20E-04
GPU 0 Usage:Memory Allocated: 441.27 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:24,Train_acc:91.9%,Train_loss:0.236,Test_acc:93.8%,Test_loss:0.186,Lr:1.10E-04
GPU 0 Usage:Memory Allocated: 443.12 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:25,Train_acc:93.4%,Train_loss:0.309,Test_acc:94.2%,Test_loss:0.149,Lr:1.10E-04
GPU 0 Usage:Memory Allocated: 443.39 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3602.83 MBMax Memory Cached: 4368.00 MB
Epoch:26,Train_acc:93.2%,Train_loss:0.177,Test_acc:92.4%,Test_loss:0.192,Lr:1.01E-04
GPU 0 Usage:Memory Allocated: 441.71 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.01 MBMax Memory Cached: 4368.00 MB
Epoch:27,Train_acc:94.9%,Train_loss:0.222,Test_acc:92.0%,Test_loss:0.227,Lr:1.01E-04
GPU 0 Usage:Memory Allocated: 443.70 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.28 MBMax Memory Cached: 4368.00 MB
Epoch:28,Train_acc:93.8%,Train_loss:0.200,Test_acc:94.2%,Test_loss:0.144,Lr:9.34E-05
GPU 0 Usage:Memory Allocated: 441.37 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.28 MBMax Memory Cached: 4368.00 MB
Epoch:29,Train_acc:94.9%,Train_loss:0.158,Test_acc:92.4%,Test_loss:0.168,Lr:9.34E-05
GPU 0 Usage:Memory Allocated: 441.79 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.28 MBMax Memory Cached: 4368.00 MB
Epoch:30,Train_acc:95.7%,Train_loss:0.151,Test_acc:93.3%,Test_loss:0.161,Lr:8.59E-05
GPU 0 Usage:Memory Allocated: 442.08 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:31,Train_acc:94.7%,Train_loss:0.137,Test_acc:94.2%,Test_loss:0.136,Lr:8.59E-05
GPU 0 Usage:Memory Allocated: 441.25 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:32,Train_acc:95.9%,Train_loss:0.133,Test_acc:94.2%,Test_loss:0.197,Lr:7.90E-05
GPU 0 Usage:Memory Allocated: 442.79 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:33,Train_acc:94.7%,Train_loss:0.144,Test_acc:94.2%,Test_loss:0.366,Lr:7.90E-05
GPU 0 Usage:Memory Allocated: 442.73 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:34,Train_acc:96.2%,Train_loss:0.109,Test_acc:92.0%,Test_loss:0.248,Lr:7.27E-05
GPU 0 Usage:Memory Allocated: 442.30 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:35,Train_acc:96.0%,Train_loss:0.138,Test_acc:92.9%,Test_loss:0.177,Lr:7.27E-05
GPU 0 Usage:Memory Allocated: 440.91 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:36,Train_acc:96.7%,Train_loss:0.179,Test_acc:92.4%,Test_loss:0.206,Lr:6.69E-05
GPU 0 Usage:Memory Allocated: 441.46 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:37,Train_acc:95.2%,Train_loss:0.148,Test_acc:91.1%,Test_loss:0.219,Lr:6.69E-05
GPU 0 Usage:Memory Allocated: 440.19 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:38,Train_acc:94.0%,Train_loss:0.162,Test_acc:93.3%,Test_loss:0.157,Lr:6.15E-05
GPU 0 Usage:Memory Allocated: 443.07 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:39,Train_acc:97.4%,Train_loss:0.117,Test_acc:92.9%,Test_loss:0.279,Lr:6.15E-05
GPU 0 Usage:Memory Allocated: 442.75 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:40,Train_acc:95.0%,Train_loss:0.203,Test_acc:92.0%,Test_loss:0.590,Lr:5.66E-05
GPU 0 Usage:Memory Allocated: 442.67 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:41,Train_acc:96.2%,Train_loss:0.127,Test_acc:93.3%,Test_loss:0.199,Lr:5.66E-05
GPU 0 Usage:Memory Allocated: 442.07 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:42,Train_acc:96.2%,Train_loss:0.110,Test_acc:96.0%,Test_loss:0.132,Lr:5.21E-05
GPU 0 Usage:Memory Allocated: 441.30 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:43,Train_acc:97.3%,Train_loss:0.132,Test_acc:94.2%,Test_loss:0.175,Lr:5.21E-05
GPU 0 Usage:Memory Allocated: 442.24 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:44,Train_acc:96.0%,Train_loss:0.111,Test_acc:93.3%,Test_loss:0.179,Lr:4.79E-05
GPU 0 Usage:Memory Allocated: 440.67 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:45,Train_acc:97.8%,Train_loss:0.065,Test_acc:93.8%,Test_loss:0.175,Lr:4.79E-05
GPU 0 Usage:Memory Allocated: 441.61 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:46,Train_acc:97.7%,Train_loss:0.078,Test_acc:92.9%,Test_loss:0.188,Lr:4.41E-05
GPU 0 Usage:Memory Allocated: 441.65 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:47,Train_acc:97.3%,Train_loss:0.090,Test_acc:92.4%,Test_loss:0.183,Lr:4.41E-05
GPU 0 Usage:Memory Allocated: 441.28 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:48,Train_acc:98.1%,Train_loss:0.065,Test_acc:95.1%,Test_loss:0.110,Lr:4.06E-05
GPU 0 Usage:Memory Allocated: 441.88 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:49,Train_acc:97.8%,Train_loss:0.068,Test_acc:92.9%,Test_loss:0.181,Lr:4.06E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:50,Train_acc:98.7%,Train_loss:0.061,Test_acc:94.2%,Test_loss:0.153,Lr:3.73E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:51,Train_acc:98.3%,Train_loss:0.149,Test_acc:92.0%,Test_loss:0.185,Lr:3.73E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:52,Train_acc:97.8%,Train_loss:0.067,Test_acc:90.7%,Test_loss:0.237,Lr:3.43E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:53,Train_acc:98.3%,Train_loss:0.056,Test_acc:93.3%,Test_loss:0.158,Lr:3.43E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:54,Train_acc:99.0%,Train_loss:0.101,Test_acc:95.6%,Test_loss:0.151,Lr:3.16E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:55,Train_acc:98.4%,Train_loss:0.111,Test_acc:94.2%,Test_loss:0.155,Lr:3.16E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:56,Train_acc:98.1%,Train_loss:0.065,Test_acc:94.7%,Test_loss:0.133,Lr:2.91E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:57,Train_acc:97.0%,Train_loss:0.137,Test_acc:94.7%,Test_loss:0.141,Lr:2.91E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:58,Train_acc:98.6%,Train_loss:0.054,Test_acc:92.4%,Test_loss:0.171,Lr:2.67E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:59,Train_acc:98.7%,Train_loss:0.043,Test_acc:95.1%,Test_loss:0.176,Lr:2.67E-05
GPU 0 Usage:Memory Allocated: 442.35 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Epoch:60,Train_acc:98.7%,Train_loss:0.084,Test_acc:92.4%,Test_loss:0.211,Lr:2.46E-05
GPU 0 Usage:Memory Allocated: 441.16 MBMemory Cached: 4368.00 MBMax Memory Allocated: 3604.74 MBMax Memory Cached: 4368.00 MB
Done best_acc:  0.96

9. 结果可视化

epochs_range = range(epochs)plt.figure(figsize = (12,3))plt.subplot(1,2,1)
plt.plot(epochs_range,train_acc,label = 'Training Accuracy')
plt.plot(epochs_range,test_acc,label = 'Test Accuracy')
plt.legend(loc = 'lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1,2,2)
plt.plot(epochs_range,train_loss,label = 'Test Accuracy')
plt.plot(epochs_range,test_loss,label = 'Test Loss')
plt.legend(loc = 'lower right')
plt.title('Training and validation Loss')
plt.show()

在这里插入图片描述

10. 模型的保存

# 自定义模型保存
# 状态字典保存
torch.save(model.state_dict(),'./模型参数/J9_InceptionV3_model_state_dict.pth') # 仅保存状态字典# 定义模型用来加载参数
best_model = InceptionV3(num_classes=len(classNames)).to(device)best_model.load_state_dict(torch.load('./模型参数/J9_InceptionV3_model_state_dict.pth')) # 加载状态字典到模型

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11.使用训练好的模型进行预测

# 指定路径图片预测
from PIL import Image
import torchvision.transforms as transformsclasses = list(total_data.class_to_idx) # classes = list(total_data.class_to_idx)def predict_one_image(image_path,model,transform,classes):test_img = Image.open(image_path).convert('RGB')# plt.imshow(test_img) # 展示待预测的图片test_img = transform(test_img)img = test_img.to(device).unsqueeze(0)model.eval()output = model(img)print(output) # 观察模型预测结果的输出数据_,pred = torch.max(output,1)pred_class = classes[pred]print(f'预测结果是:{pred_class}')

# 预测训练集中的某张照片
predict_one_image(image_path='./data/weather_recognize/weather_photos/sunrise/sunrise10.jpg',model = model,transform = test_transforms,classes = classes)

tensor([[-3.4108, -5.2150, -6.3457,  8.8984]], device='cuda:0',grad_fn=<AddmmBackward0>)
预测结果是:sunrise

classes

['cloudy', 'rain', 'shine', 'sunrise']