🍨 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]

要求：

1. 探索ResNet与DenseNet结合的可能性
2. 根据模型特性构建新的模型框架
3. 验证改进后模型的效果

一、 基础配置

• 语言环境：Python3.8
• 编译器选择：Pycharm
• 深度学习环境：

• • torch==1.12.1+cu113
  • torchvision==0.13.1+cu113

二、 前期准备 

1.设置GPU

import pathlib
import torch
import torch.nn as nn
from torchvision import transforms, datasetsdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(device)

2. 导入数据

本项目所采用的数据集未收录于公开数据中，故需要自己在文件目录中导入相应数据集合，并设置对应文件目录，以供后续学习过程中使用。

运行下述代码：

data_dir = './data/bird_photos'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2] for path in data_paths]
print(classeNames)image_count = len(list(data_dir.glob('*/*')))
print("图片总数为：", image_count)

得到如下输出：

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']
图片总数为： 565

接下来，我们通过transforms.Compose对整个数据集进行预处理：

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转换为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],std=[0.229, 0.224, 0.225])  # 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。
])test_transform = transforms.Compose([transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸transforms.ToTensor(),  # 将PIL Image或numpy.ndarray转换为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],std=[0.229, 0.224, 0.225])  # 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。
])total_data = datasets.ImageFolder("./data/bird_photos/", transform=train_transforms)
print(total_data.class_to_idx)

得到如下输出：

{'Bananaquit': 0, 'Black Skimmer': 1, 'Black Throated Bushtiti': 2, 'Cockatoo': 3}

3. 划分数据集

 此处数据集需要做按比例划分的操作：

train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])

接下来，根据划分得到的训练集和验证集对数据集进行包装：

batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True,num_workers=0)
test_dl = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True,num_workers=0)

并通过：

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

输出测试数据集的数据分布情况：

Shape of X [N, C, H, W]:  torch.Size([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

4.搭建模型

DPN网络通过High Order RNN（HORNN）将ResNet和DenseNet进行了融合，实现了ResNet特征复用及DenseNet特征生成，在保持了二者复用特征+挖掘特征能力的同时，避免了像原始DenseNet那样臃肿的结构。

1.模型搭建

class Block(nn.Module):def __init__(self, in_channel, mid_channel, out_channel, dense_channel, stride, groups, is_shortcut=False):# in_channel,是输入通道数,mid_channel是中间经历的通道数，out_channels是经过一次板块之后的输出通道数。# dense_channels设置这个参数的原因就是一边进行着resnet方式的卷积运算，另一边也同时进行着dense的卷积计算，之后特征图融合形成新的特征图super().__init__()self.is_shortcut = is_shortcutself.out_channel = out_channelself.conv1 = nn.Sequential(nn.Conv2d(in_channel, mid_channel, kernel_size=1, bias=False),nn.BatchNorm2d(mid_channel),nn.ReLU())self.conv2 = nn.Sequential(nn.Conv2d(mid_channel, mid_channel, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False),nn.BatchNorm2d(mid_channel),nn.ReLU())self.conv3 = nn.Sequential(nn.Conv2d(mid_channel, out_channel + dense_channel, kernel_size=1, bias=False),nn.BatchNorm2d(out_channel + dense_channel))if self.is_shortcut:self.shortcut = nn.Sequential(nn.Conv2d(in_channel, out_channel + dense_channel, kernel_size=3, padding=1, stride=stride, bias=False),nn.BatchNorm2d(out_channel + dense_channel))self.relu = nn.ReLU(inplace=True)def forward(self, x):a = xx = self.conv1(x)x = self.conv2(x)x = self.conv3(x)if self.is_shortcut:a = self.shortcut(a)d = self.out_channelx = torch.cat([a[:, :d, :, :] + x[:, :d, :, :], a[:, d:, :, :], x[:, d:, :, :]], dim=1)x = self.relu(x)return xclass DPN(nn.Module):def __init__(self, cfg):super(DPN, self).__init__()self.group = cfg['group']self.in_channel = cfg['in_channel']mid_channels = cfg['mid_channels']out_channels = cfg['out_channels']dense_channels = cfg['dense_channels']num = cfg['num']self.conv1 = nn.Sequential(nn.Conv2d(3, self.in_channel, 7, stride=2, padding=3, bias=False, padding_mode='zeros'),nn.BatchNorm2d(self.in_channel),nn.ReLU(),nn.MaxPool2d(kernel_size=3, stride=2, padding=0))self.conv2 = self._make_layers(mid_channels[0], out_channels[0], dense_channels[0], num[0], stride=1)self.conv3 = self._make_layers(mid_channels[1], out_channels[1], dense_channels[1], num[1], stride=2)self.conv4 = self._make_layers(mid_channels[2], out_channels[2], dense_channels[2], num[2], stride=2)self.conv5 = self._make_layers(mid_channels[3], out_channels[3], dense_channels[3], num[3], stride=2)self.pool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(cfg['out_channels'][3] + (num[3] + 1) * cfg['dense_channels'][3], cfg['classes'])  # fc层需要计算def _make_layers(self, mid_channel, out_channel, dense_channel, num, stride=2):layers = []layers.append(Block(self.in_channel, mid_channel, out_channel, dense_channel, stride=stride, groups=self.group,is_shortcut=True))# block_1里面is_shortcut=True就是resnet中的shortcut连接，将浅层的特征进行一次卷积之后与进行三次卷积的特征图相加# 后面几次相同的板块is_shortcut=False简单的理解就是一个多次重复的板块，第一次利用就可以满足浅层特征的利用，后面重复的不在需要self.in_channel = out_channel + dense_channel * 2# 由于里面包含dense这种一直在叠加的特征图计算，# 所以第一次是2倍的dense_channel,后面每次一都会多出1倍，所以有(i+2)*dense_channelfor i in range(1, num):layers.append(Block(self.in_channel, mid_channel, out_channel, dense_channel, stride=1, groups=self.group))self.in_channel = self.in_channel + dense_channel# self.in_channel = out_channel + (i+2)*dense_channelreturn nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = self.conv3(x)x = self.conv4(x)x = self.conv5(x)x = self.pool(x)x = torch.flatten(x, start_dim=1)x = self.fc(x)return xdef DPN92(n_class=10):cfg = {'group': 32,'in_channel': 64,'mid_channels': (96, 192, 384, 768),'out_channels': (256, 512, 1024, 2048),'dense_channels': (16, 32, 24, 128),'num': (3, 4, 20, 3),'classes': (n_class)}return DPN(cfg)def DPN98(n_class=10):cfg = {'group': 40,'in_channel': 96,'mid_channels': (160, 320, 640, 1280),'out_channels': (256, 512, 1024, 2048),'dense_channels': (16, 32, 32, 128),'num': (3, 6, 20, 3),'classes': (n_class)}return DPN(cfg)

2.查看模型信息

x = torch.randn(2, 3, 224, 224)
model = DPN98(4)
model.to(device)
import torchsummary as summary
summary.summary(model, (3, 224, 224))

得到如下输出：

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 96, 112, 112]          14,112BatchNorm2d-2         [-1, 96, 112, 112]             192ReLU-3         [-1, 96, 112, 112]               0MaxPool2d-4           [-1, 96, 55, 55]               0Conv2d-5          [-1, 160, 55, 55]          15,360BatchNorm2d-6          [-1, 160, 55, 55]             320ReLU-7          [-1, 160, 55, 55]               0Conv2d-8          [-1, 160, 55, 55]           5,760BatchNorm2d-9          [-1, 160, 55, 55]             320ReLU-10          [-1, 160, 55, 55]               0Conv2d-11          [-1, 272, 55, 55]          43,520BatchNorm2d-12          [-1, 272, 55, 55]             544Conv2d-13          [-1, 272, 55, 55]         235,008BatchNorm2d-14          [-1, 272, 55, 55]             544ReLU-15          [-1, 288, 55, 55]               0Block-16          [-1, 288, 55, 55]               0Conv2d-17          [-1, 160, 55, 55]          46,080BatchNorm2d-18          [-1, 160, 55, 55]             320ReLU-19          [-1, 160, 55, 55]               0Conv2d-20          [-1, 160, 55, 55]           5,760BatchNorm2d-21          [-1, 160, 55, 55]             320ReLU-22          [-1, 160, 55, 55]               0Conv2d-23          [-1, 272, 55, 55]          43,520BatchNorm2d-24          [-1, 272, 55, 55]             544ReLU-25          [-1, 304, 55, 55]               0Block-26          [-1, 304, 55, 55]               0Conv2d-27          [-1, 160, 55, 55]          48,640BatchNorm2d-28          [-1, 160, 55, 55]             320ReLU-29          [-1, 160, 55, 55]               0Conv2d-30          [-1, 160, 55, 55]           5,760BatchNorm2d-31          [-1, 160, 55, 55]             320ReLU-32          [-1, 160, 55, 55]               0Conv2d-33          [-1, 272, 55, 55]          43,520BatchNorm2d-34          [-1, 272, 55, 55]             544ReLU-35          [-1, 320, 55, 55]               0Block-36          [-1, 320, 55, 55]               0Conv2d-37          [-1, 320, 55, 55]         102,400BatchNorm2d-38          [-1, 320, 55, 55]             640ReLU-39          [-1, 320, 55, 55]               0Conv2d-40          [-1, 320, 28, 28]          23,040BatchNorm2d-41          [-1, 320, 28, 28]             640ReLU-42          [-1, 320, 28, 28]               0Conv2d-43          [-1, 544, 28, 28]         174,080BatchNorm2d-44          [-1, 544, 28, 28]           1,088Conv2d-45          [-1, 544, 28, 28]       1,566,720BatchNorm2d-46          [-1, 544, 28, 28]           1,088ReLU-47          [-1, 576, 28, 28]               0Block-48          [-1, 576, 28, 28]               0Conv2d-49          [-1, 320, 28, 28]         184,320BatchNorm2d-50          [-1, 320, 28, 28]             640ReLU-51          [-1, 320, 28, 28]               0Conv2d-52          [-1, 320, 28, 28]          23,040BatchNorm2d-53          [-1, 320, 28, 28]             640ReLU-54          [-1, 320, 28, 28]               0Conv2d-55          [-1, 544, 28, 28]         174,080BatchNorm2d-56          [-1, 544, 28, 28]           1,088ReLU-57          [-1, 608, 28, 28]               0Block-58          [-1, 608, 28, 28]               0Conv2d-59          [-1, 320, 28, 28]         194,560BatchNorm2d-60          [-1, 320, 28, 28]             640ReLU-61          [-1, 320, 28, 28]               0Conv2d-62          [-1, 320, 28, 28]          23,040BatchNorm2d-63          [-1, 320, 28, 28]             640ReLU-64          [-1, 320, 28, 28]               0Conv2d-65          [-1, 544, 28, 28]         174,080BatchNorm2d-66          [-1, 544, 28, 28]           1,088ReLU-67          [-1, 640, 28, 28]               0Block-68          [-1, 640, 28, 28]               0Conv2d-69          [-1, 320, 28, 28]         204,800BatchNorm2d-70          [-1, 320, 28, 28]             640ReLU-71          [-1, 320, 28, 28]               0Conv2d-72          [-1, 320, 28, 28]          23,040BatchNorm2d-73          [-1, 320, 28, 28]             640ReLU-74          [-1, 320, 28, 28]               0Conv2d-75          [-1, 544, 28, 28]         174,080BatchNorm2d-76          [-1, 544, 28, 28]           1,088ReLU-77          [-1, 672, 28, 28]               0Block-78          [-1, 672, 28, 28]               0Conv2d-79          [-1, 320, 28, 28]         215,040BatchNorm2d-80          [-1, 320, 28, 28]             640ReLU-81          [-1, 320, 28, 28]               0Conv2d-82          [-1, 320, 28, 28]          23,040BatchNorm2d-83          [-1, 320, 28, 28]             640ReLU-84          [-1, 320, 28, 28]               0Conv2d-85          [-1, 544, 28, 28]         174,080BatchNorm2d-86          [-1, 544, 28, 28]           1,088ReLU-87          [-1, 704, 28, 28]               0Block-88          [-1, 704, 28, 28]               0Conv2d-89          [-1, 320, 28, 28]         225,280BatchNorm2d-90          [-1, 320, 28, 28]             640ReLU-91          [-1, 320, 28, 28]               0Conv2d-92          [-1, 320, 28, 28]          23,040BatchNorm2d-93          [-1, 320, 28, 28]             640ReLU-94          [-1, 320, 28, 28]               0Conv2d-95          [-1, 544, 28, 28]         174,080BatchNorm2d-96          [-1, 544, 28, 28]           1,088ReLU-97          [-1, 736, 28, 28]               0Block-98          [-1, 736, 28, 28]               0Conv2d-99          [-1, 640, 28, 28]         471,040BatchNorm2d-100          [-1, 640, 28, 28]           1,280ReLU-101          [-1, 640, 28, 28]               0Conv2d-102          [-1, 640, 14, 14]          92,160BatchNorm2d-103          [-1, 640, 14, 14]           1,280ReLU-104          [-1, 640, 14, 14]               0Conv2d-105         [-1, 1056, 14, 14]         675,840BatchNorm2d-106         [-1, 1056, 14, 14]           2,112Conv2d-107         [-1, 1056, 14, 14]       6,994,944BatchNorm2d-108         [-1, 1056, 14, 14]           2,112ReLU-109         [-1, 1088, 14, 14]               0Block-110         [-1, 1088, 14, 14]               0Conv2d-111          [-1, 640, 14, 14]         696,320BatchNorm2d-112          [-1, 640, 14, 14]           1,280ReLU-113          [-1, 640, 14, 14]               0Conv2d-114          [-1, 640, 14, 14]          92,160BatchNorm2d-115          [-1, 640, 14, 14]           1,280ReLU-116          [-1, 640, 14, 14]               0Conv2d-117         [-1, 1056, 14, 14]         675,840BatchNorm2d-118         [-1, 1056, 14, 14]           2,112ReLU-119         [-1, 1120, 14, 14]               0Block-120         [-1, 1120, 14, 14]               0Conv2d-121          [-1, 640, 14, 14]         716,800BatchNorm2d-122          [-1, 640, 14, 14]           1,280ReLU-123          [-1, 640, 14, 14]               0Conv2d-124          [-1, 640, 14, 14]          92,160BatchNorm2d-125          [-1, 640, 14, 14]           1,280ReLU-126          [-1, 640, 14, 14]               0Conv2d-127         [-1, 1056, 14, 14]         675,840BatchNorm2d-128         [-1, 1056, 14, 14]           2,112ReLU-129         [-1, 1152, 14, 14]               0Block-130         [-1, 1152, 14, 14]               0Conv2d-131          [-1, 640, 14, 14]         737,280BatchNorm2d-132          [-1, 640, 14, 14]           1,280ReLU-133          [-1, 640, 14, 14]               0Conv2d-134          [-1, 640, 14, 14]          92,160BatchNorm2d-135          [-1, 640, 14, 14]           1,280ReLU-136          [-1, 640, 14, 14]               0Conv2d-137         [-1, 1056, 14, 14]         675,840BatchNorm2d-138         [-1, 1056, 14, 14]           2,112ReLU-139         [-1, 1184, 14, 14]               0Block-140         [-1, 1184, 14, 14]               0Conv2d-141          [-1, 640, 14, 14]         757,760BatchNorm2d-142          [-1, 640, 14, 14]           1,280ReLU-143          [-1, 640, 14, 14]               0Conv2d-144          [-1, 640, 14, 14]          92,160BatchNorm2d-145          [-1, 640, 14, 14]           1,280ReLU-146          [-1, 640, 14, 14]               0Conv2d-147         [-1, 1056, 14, 14]         675,840BatchNorm2d-148         [-1, 1056, 14, 14]           2,112ReLU-149         [-1, 1216, 14, 14]               0Block-150         [-1, 1216, 14, 14]               0Conv2d-151          [-1, 640, 14, 14]         778,240BatchNorm2d-152          [-1, 640, 14, 14]           1,280ReLU-153          [-1, 640, 14, 14]               0Conv2d-154          [-1, 640, 14, 14]          92,160BatchNorm2d-155          [-1, 640, 14, 14]           1,280ReLU-156          [-1, 640, 14, 14]               0Conv2d-157         [-1, 1056, 14, 14]         675,840BatchNorm2d-158         [-1, 1056, 14, 14]           2,112ReLU-159         [-1, 1248, 14, 14]               0Block-160         [-1, 1248, 14, 14]               0Conv2d-161          [-1, 640, 14, 14]         798,720BatchNorm2d-162          [-1, 640, 14, 14]           1,280ReLU-163          [-1, 640, 14, 14]               0Conv2d-164          [-1, 640, 14, 14]          92,160BatchNorm2d-165          [-1, 640, 14, 14]           1,280ReLU-166          [-1, 640, 14, 14]               0Conv2d-167         [-1, 1056, 14, 14]         675,840BatchNorm2d-168         [-1, 1056, 14, 14]           2,112ReLU-169         [-1, 1280, 14, 14]               0Block-170         [-1, 1280, 14, 14]               0Conv2d-171          [-1, 640, 14, 14]         819,200BatchNorm2d-172          [-1, 640, 14, 14]           1,280ReLU-173          [-1, 640, 14, 14]               0Conv2d-174          [-1, 640, 14, 14]          92,160BatchNorm2d-175          [-1, 640, 14, 14]           1,280ReLU-176          [-1, 640, 14, 14]               0Conv2d-177         [-1, 1056, 14, 14]         675,840BatchNorm2d-178         [-1, 1056, 14, 14]           2,112ReLU-179         [-1, 1312, 14, 14]               0Block-180         [-1, 1312, 14, 14]               0Conv2d-181          [-1, 640, 14, 14]         839,680BatchNorm2d-182          [-1, 640, 14, 14]           1,280ReLU-183          [-1, 640, 14, 14]               0Conv2d-184          [-1, 640, 14, 14]          92,160BatchNorm2d-185          [-1, 640, 14, 14]           1,280ReLU-186          [-1, 640, 14, 14]               0Conv2d-187         [-1, 1056, 14, 14]         675,840BatchNorm2d-188         [-1, 1056, 14, 14]           2,112ReLU-189         [-1, 1344, 14, 14]               0Block-190         [-1, 1344, 14, 14]               0Conv2d-191          [-1, 640, 14, 14]         860,160BatchNorm2d-192          [-1, 640, 14, 14]           1,280ReLU-193          [-1, 640, 14, 14]               0Conv2d-194          [-1, 640, 14, 14]          92,160BatchNorm2d-195          [-1, 640, 14, 14]           1,280ReLU-196          [-1, 640, 14, 14]               0Conv2d-197         [-1, 1056, 14, 14]         675,840BatchNorm2d-198         [-1, 1056, 14, 14]           2,112ReLU-199         [-1, 1376, 14, 14]               0Block-200         [-1, 1376, 14, 14]               0Conv2d-201          [-1, 640, 14, 14]         880,640BatchNorm2d-202          [-1, 640, 14, 14]           1,280ReLU-203          [-1, 640, 14, 14]               0Conv2d-204          [-1, 640, 14, 14]          92,160BatchNorm2d-205          [-1, 640, 14, 14]           1,280ReLU-206          [-1, 640, 14, 14]               0Conv2d-207         [-1, 1056, 14, 14]         675,840BatchNorm2d-208         [-1, 1056, 14, 14]           2,112ReLU-209         [-1, 1408, 14, 14]               0Block-210         [-1, 1408, 14, 14]               0Conv2d-211          [-1, 640, 14, 14]         901,120BatchNorm2d-212          [-1, 640, 14, 14]           1,280ReLU-213          [-1, 640, 14, 14]               0Conv2d-214          [-1, 640, 14, 14]          92,160BatchNorm2d-215          [-1, 640, 14, 14]           1,280ReLU-216          [-1, 640, 14, 14]               0Conv2d-217         [-1, 1056, 14, 14]         675,840BatchNorm2d-218         [-1, 1056, 14, 14]           2,112ReLU-219         [-1, 1440, 14, 14]               0Block-220         [-1, 1440, 14, 14]               0Conv2d-221          [-1, 640, 14, 14]         921,600BatchNorm2d-222          [-1, 640, 14, 14]           1,280ReLU-223          [-1, 640, 14, 14]               0Conv2d-224          [-1, 640, 14, 14]          92,160BatchNorm2d-225          [-1, 640, 14, 14]           1,280ReLU-226          [-1, 640, 14, 14]               0Conv2d-227         [-1, 1056, 14, 14]         675,840BatchNorm2d-228         [-1, 1056, 14, 14]           2,112ReLU-229         [-1, 1472, 14, 14]               0Block-230         [-1, 1472, 14, 14]               0Conv2d-231          [-1, 640, 14, 14]         942,080BatchNorm2d-232          [-1, 640, 14, 14]           1,280ReLU-233          [-1, 640, 14, 14]               0Conv2d-234          [-1, 640, 14, 14]          92,160BatchNorm2d-235          [-1, 640, 14, 14]           1,280ReLU-236          [-1, 640, 14, 14]               0Conv2d-237         [-1, 1056, 14, 14]         675,840BatchNorm2d-238         [-1, 1056, 14, 14]           2,112ReLU-239         [-1, 1504, 14, 14]               0Block-240         [-1, 1504, 14, 14]               0Conv2d-241          [-1, 640, 14, 14]         962,560BatchNorm2d-242          [-1, 640, 14, 14]           1,280ReLU-243          [-1, 640, 14, 14]               0Conv2d-244          [-1, 640, 14, 14]          92,160BatchNorm2d-245          [-1, 640, 14, 14]           1,280ReLU-246          [-1, 640, 14, 14]               0Conv2d-247         [-1, 1056, 14, 14]         675,840BatchNorm2d-248         [-1, 1056, 14, 14]           2,112ReLU-249         [-1, 1536, 14, 14]               0Block-250         [-1, 1536, 14, 14]               0Conv2d-251          [-1, 640, 14, 14]         983,040BatchNorm2d-252          [-1, 640, 14, 14]           1,280ReLU-253          [-1, 640, 14, 14]               0Conv2d-254          [-1, 640, 14, 14]          92,160BatchNorm2d-255          [-1, 640, 14, 14]           1,280ReLU-256          [-1, 640, 14, 14]               0Conv2d-257         [-1, 1056, 14, 14]         675,840BatchNorm2d-258         [-1, 1056, 14, 14]           2,112ReLU-259         [-1, 1568, 14, 14]               0Block-260         [-1, 1568, 14, 14]               0Conv2d-261          [-1, 640, 14, 14]       1,003,520BatchNorm2d-262          [-1, 640, 14, 14]           1,280ReLU-263          [-1, 640, 14, 14]               0Conv2d-264          [-1, 640, 14, 14]          92,160BatchNorm2d-265          [-1, 640, 14, 14]           1,280ReLU-266          [-1, 640, 14, 14]               0Conv2d-267         [-1, 1056, 14, 14]         675,840BatchNorm2d-268         [-1, 1056, 14, 14]           2,112ReLU-269         [-1, 1600, 14, 14]               0Block-270         [-1, 1600, 14, 14]               0Conv2d-271          [-1, 640, 14, 14]       1,024,000BatchNorm2d-272          [-1, 640, 14, 14]           1,280ReLU-273          [-1, 640, 14, 14]               0Conv2d-274          [-1, 640, 14, 14]          92,160BatchNorm2d-275          [-1, 640, 14, 14]           1,280ReLU-276          [-1, 640, 14, 14]               0Conv2d-277         [-1, 1056, 14, 14]         675,840BatchNorm2d-278         [-1, 1056, 14, 14]           2,112ReLU-279         [-1, 1632, 14, 14]               0Block-280         [-1, 1632, 14, 14]               0Conv2d-281          [-1, 640, 14, 14]       1,044,480BatchNorm2d-282          [-1, 640, 14, 14]           1,280ReLU-283          [-1, 640, 14, 14]               0Conv2d-284          [-1, 640, 14, 14]          92,160BatchNorm2d-285          [-1, 640, 14, 14]           1,280ReLU-286          [-1, 640, 14, 14]               0Conv2d-287         [-1, 1056, 14, 14]         675,840BatchNorm2d-288         [-1, 1056, 14, 14]           2,112ReLU-289         [-1, 1664, 14, 14]               0Block-290         [-1, 1664, 14, 14]               0Conv2d-291          [-1, 640, 14, 14]       1,064,960BatchNorm2d-292          [-1, 640, 14, 14]           1,280ReLU-293          [-1, 640, 14, 14]               0Conv2d-294          [-1, 640, 14, 14]          92,160BatchNorm2d-295          [-1, 640, 14, 14]           1,280ReLU-296          [-1, 640, 14, 14]               0Conv2d-297         [-1, 1056, 14, 14]         675,840BatchNorm2d-298         [-1, 1056, 14, 14]           2,112ReLU-299         [-1, 1696, 14, 14]               0Block-300         [-1, 1696, 14, 14]               0Conv2d-301         [-1, 1280, 14, 14]       2,170,880BatchNorm2d-302         [-1, 1280, 14, 14]           2,560ReLU-303         [-1, 1280, 14, 14]               0Conv2d-304           [-1, 1280, 7, 7]         368,640BatchNorm2d-305           [-1, 1280, 7, 7]           2,560ReLU-306           [-1, 1280, 7, 7]               0Conv2d-307           [-1, 2176, 7, 7]       2,785,280BatchNorm2d-308           [-1, 2176, 7, 7]           4,352Conv2d-309           [-1, 2176, 7, 7]      33,214,464BatchNorm2d-310           [-1, 2176, 7, 7]           4,352ReLU-311           [-1, 2304, 7, 7]               0Block-312           [-1, 2304, 7, 7]               0Conv2d-313           [-1, 1280, 7, 7]       2,949,120BatchNorm2d-314           [-1, 1280, 7, 7]           2,560ReLU-315           [-1, 1280, 7, 7]               0Conv2d-316           [-1, 1280, 7, 7]         368,640BatchNorm2d-317           [-1, 1280, 7, 7]           2,560ReLU-318           [-1, 1280, 7, 7]               0Conv2d-319           [-1, 2176, 7, 7]       2,785,280BatchNorm2d-320           [-1, 2176, 7, 7]           4,352ReLU-321           [-1, 2432, 7, 7]               0Block-322           [-1, 2432, 7, 7]               0Conv2d-323           [-1, 1280, 7, 7]       3,112,960BatchNorm2d-324           [-1, 1280, 7, 7]           2,560ReLU-325           [-1, 1280, 7, 7]               0Conv2d-326           [-1, 1280, 7, 7]         368,640BatchNorm2d-327           [-1, 1280, 7, 7]           2,560ReLU-328           [-1, 1280, 7, 7]               0Conv2d-329           [-1, 2176, 7, 7]       2,785,280BatchNorm2d-330           [-1, 2176, 7, 7]           4,352ReLU-331           [-1, 2560, 7, 7]               0Block-332           [-1, 2560, 7, 7]               0
AdaptiveAvgPool2d-333           [-1, 2560, 1, 1]               0Linear-334                    [-1, 4]          10,244
================================================================
Total params: 95,008,356
Trainable params: 95,008,356
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 664.46
Params size (MB): 362.43
Estimated Total Size (MB): 1027.47
----------------------------------------------------------------

三、 训练模型 

1. 编写训练函数

def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 计算预测误差pred = model(X)  # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失# 反向传播optimizer.zero_grad()  # grad属性归零loss.backward()  # 反向传播optimizer.step()  # 每一步自动更新# 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

2. 编写测试函数

测试函数和训练函数大致相同，但是由于不进行梯度下降对网络权重进行更新，所以不需要传入优化器

def test(dataloader, model, loss_fn):size = len(dataloader.dataset)  # 测试集的大小num_batches = len(dataloader)  # 批次数目test_loss, test_acc = 0, 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 计算losstarget_pred = model(imgs)loss = loss_fn(target_pred, target)test_loss += loss.item()test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_acc /= sizetest_loss /= num_batchesreturn test_acc, test_loss

3.正式训练

import copyoptimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss()  # 创建损失函数epochs = 10train_loss = []
train_acc = []
test_loss = []
test_acc = []best_acc = 0  # 设置一个最佳准确率，作为最佳模型的判别指标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接口时使用）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)# 获取当前的学习率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))# 保存最佳模型到文件中
PATH = './best_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)print('Done')

得到如下输出：

Epoch: 1, Train_acc:35.0%, Train_loss:1.512, Test_acc:15.0%, Test_loss:2.101, Lr:1.00E-04
Epoch: 2, Train_acc:55.1%, Train_loss:1.088, Test_acc:15.9%, Test_loss:5.737, Lr:1.00E-04
Epoch: 3, Train_acc:71.0%, Train_loss:0.773, Test_acc:39.8%, Test_loss:2.180, Lr:1.00E-04
Epoch: 4, Train_acc:76.3%, Train_loss:0.616, Test_acc:62.8%, Test_loss:1.222, Lr:1.00E-04
Epoch: 5, Train_acc:79.4%, Train_loss:0.565, Test_acc:61.1%, Test_loss:2.034, Lr:1.00E-04
Epoch: 6, Train_acc:79.6%, Train_loss:0.492, Test_acc:61.9%, Test_loss:1.497, Lr:1.00E-04
Epoch: 7, Train_acc:83.2%, Train_loss:0.480, Test_acc:69.0%, Test_loss:1.305, Lr:1.00E-04
Epoch: 8, Train_acc:84.1%, Train_loss:0.403, Test_acc:56.6%, Test_loss:2.690, Lr:1.00E-04
Epoch: 9, Train_acc:90.0%, Train_loss:0.304, Test_acc:71.7%, Test_loss:1.104, Lr:1.00E-04
Epoch:10, Train_acc:93.8%, Train_loss:0.190, Test_acc:55.8%, Test_loss:2.481, Lr:1.00E-04
Done
预测结果是：CockatooProcess finished with exit code 0

四、 结果可视化

1. Loss&Accuracy

import matplotlib.pyplot as plt
# 隐藏警告
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='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

得到的可视化结果：

 2. 指定图片进行预测

首先，先定义出一个用于预测的函数：

 
from PIL import Imageclasses = list(total_data.class_to_idx)from PIL import Imageclasses = 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)  # 展示预测的图片test_img = transform(test_img)img = test_img.to(device).unsqueeze(0)model.eval()output = model(img)_, pred = torch.max(output, 1)pred_class = classes[pred]print(f'预测结果是：{pred_class}')

接着调用函数对指定图片进行预测：

# 预测训练集中的某张照片
predict_one_image(image_path='./data/bird_photos/Cockatoo/011.jpg',model=model,transform=train_transforms,classes=classes)

得到如下结果：

预测结果是：Cockatoo

五、网络架构及参数