深度学习Day-18：ResNet50V2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]

要求：

根据本文Tensorflow代码，编写对应的Pytorch代码
了解ResNetV2与ResNetV的区别

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 torch
import torch.nn as nn
from torchvision import transforms, datasets
import pathlib, warningswarnings.filterwarnings("ignore")  # 忽略警告信息device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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)

得到如下输出：

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

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

1.模型搭建

class Block2(nn.Module):def __init__(self, in_channel, filters, kernel_size=3, stride=1, conv_shortcut=False):super(Block2, self).__init__()self.preact = nn.Sequential(nn.BatchNorm2d(in_channel),nn.ReLU(True))self.shortcut = conv_shortcutif self.shortcut:self.short = nn.Conv2d(in_channel, 4 * filters, 1, stride=stride, padding=0, bias=False)elif stride > 1:self.short = nn.MaxPool2d(kernel_size=1, stride=stride, padding=0)else:self.short = nn.Identity()self.conv1 = nn.Sequential(nn.Conv2d(in_channel, filters, 1, stride=1, bias=False),nn.BatchNorm2d(filters),nn.ReLU(True))self.conv2 = nn.Sequential(nn.Conv2d(filters, filters, kernel_size, stride=stride, padding=1, bias=False),nn.BatchNorm2d(filters),nn.ReLU(True))self.conv3 = nn.Conv2d(filters, 4 * filters, 1, stride=1, bias=False)def forward(self, x):x1 = self.preact(x)if self.shortcut:x2 = self.short(x1)else:x2 = self.short(x)x1 = self.conv1(x1)x1 = self.conv2(x1)x1 = self.conv3(x1)x = x1 + x2return xclass Stack2(nn.Module):def __init__(self, in_channel, filters, blocks, stride=2):super(Stack2, self).__init__()self.conv = nn.Sequential()self.conv.add_module(str(0), Block2(in_channel, filters, conv_shortcut=True))for i in range(1, blocks - 1):self.conv.add_module(str(i), Block2(4 * filters, filters))self.conv.add_module(str(blocks - 1), Block2(4 * filters, filters, stride=stride))def forward(self, x):x = self.conv(x)return xclass ResNet50V2(nn.Module):def __init__(self,include_top=True,  # 是否包含位于网络顶部的全链接层preact=True,  # 是否使用预激活use_bias=True,  # 是否对卷积层使用偏置input_shape=[224, 224, 3],classes=1000,pooling=None):  # 用于分类图像的可选类数super(ResNet50V2, self).__init__()self.conv1 = nn.Sequential()self.conv1.add_module('conv', nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=use_bias, padding_mode='zeros'))if not preact:self.conv1.add_module('bn', nn.BatchNorm2d(64))self.conv1.add_module('relu', nn.ReLU())self.conv1.add_module('max_pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))self.conv2 = Stack2(64, 64, 3)self.conv3 = Stack2(256, 128, 4)self.conv4 = Stack2(512, 256, 6)self.conv5 = Stack2(1024, 512, 3, stride=1)self.post = nn.Sequential()if preact:self.post.add_module('bn', nn.BatchNorm2d(2048))self.post.add_module('relu', nn.ReLU())if include_top:self.post.add_module('avg_pool', nn.AdaptiveAvgPool2d((1, 1)))self.post.add_module('flatten', nn.Flatten())self.post.add_module('fc', nn.Linear(2048, classes))else:if pooling == 'avg':self.post.add_module('avg_pool', nn.AdaptiveAvgPool2d((1, 1)))elif pooling == 'max':self.post.add_module('max_pool', nn.AdaptiveMaxPool2d((1, 1)))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.post(x)return xmodel = ResNet50V2().to(device)

2.查看模型信息

import torchsummary as summary
summary.summary(model, (3, 224, 224))

得到如下输出：

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,472MaxPool2d-2           [-1, 64, 56, 56]               0BatchNorm2d-3           [-1, 64, 56, 56]             128ReLU-4           [-1, 64, 56, 56]               0Conv2d-5          [-1, 256, 56, 56]          16,384Conv2d-6           [-1, 64, 56, 56]           4,096BatchNorm2d-7           [-1, 64, 56, 56]             128ReLU-8           [-1, 64, 56, 56]               0Conv2d-9           [-1, 64, 56, 56]          36,864BatchNorm2d-10           [-1, 64, 56, 56]             128ReLU-11           [-1, 64, 56, 56]               0Conv2d-12          [-1, 256, 56, 56]          16,384Block2-13          [-1, 256, 56, 56]               0BatchNorm2d-14          [-1, 256, 56, 56]             512ReLU-15          [-1, 256, 56, 56]               0Identity-16          [-1, 256, 56, 56]               0Conv2d-17           [-1, 64, 56, 56]          16,384BatchNorm2d-18           [-1, 64, 56, 56]             128ReLU-19           [-1, 64, 56, 56]               0Conv2d-20           [-1, 64, 56, 56]          36,864BatchNorm2d-21           [-1, 64, 56, 56]             128ReLU-22           [-1, 64, 56, 56]               0Conv2d-23          [-1, 256, 56, 56]          16,384Block2-24          [-1, 256, 56, 56]               0BatchNorm2d-25          [-1, 256, 56, 56]             512ReLU-26          [-1, 256, 56, 56]               0MaxPool2d-27          [-1, 256, 28, 28]               0Conv2d-28           [-1, 64, 56, 56]          16,384BatchNorm2d-29           [-1, 64, 56, 56]             128ReLU-30           [-1, 64, 56, 56]               0Conv2d-31           [-1, 64, 28, 28]          36,864BatchNorm2d-32           [-1, 64, 28, 28]             128ReLU-33           [-1, 64, 28, 28]               0Conv2d-34          [-1, 256, 28, 28]          16,384Block2-35          [-1, 256, 28, 28]               0Stack2-36          [-1, 256, 28, 28]               0BatchNorm2d-37          [-1, 256, 28, 28]             512ReLU-38          [-1, 256, 28, 28]               0Conv2d-39          [-1, 512, 28, 28]         131,072Conv2d-40          [-1, 128, 28, 28]          32,768BatchNorm2d-41          [-1, 128, 28, 28]             256ReLU-42          [-1, 128, 28, 28]               0Conv2d-43          [-1, 128, 28, 28]         147,456BatchNorm2d-44          [-1, 128, 28, 28]             256ReLU-45          [-1, 128, 28, 28]               0Conv2d-46          [-1, 512, 28, 28]          65,536Block2-47          [-1, 512, 28, 28]               0BatchNorm2d-48          [-1, 512, 28, 28]           1,024ReLU-49          [-1, 512, 28, 28]               0Identity-50          [-1, 512, 28, 28]               0Conv2d-51          [-1, 128, 28, 28]          65,536BatchNorm2d-52          [-1, 128, 28, 28]             256ReLU-53          [-1, 128, 28, 28]               0Conv2d-54          [-1, 128, 28, 28]         147,456BatchNorm2d-55          [-1, 128, 28, 28]             256ReLU-56          [-1, 128, 28, 28]               0Conv2d-57          [-1, 512, 28, 28]          65,536Block2-58          [-1, 512, 28, 28]               0BatchNorm2d-59          [-1, 512, 28, 28]           1,024ReLU-60          [-1, 512, 28, 28]               0Identity-61          [-1, 512, 28, 28]               0Conv2d-62          [-1, 128, 28, 28]          65,536BatchNorm2d-63          [-1, 128, 28, 28]             256ReLU-64          [-1, 128, 28, 28]               0Conv2d-65          [-1, 128, 28, 28]         147,456BatchNorm2d-66          [-1, 128, 28, 28]             256ReLU-67          [-1, 128, 28, 28]               0Conv2d-68          [-1, 512, 28, 28]          65,536Block2-69          [-1, 512, 28, 28]               0BatchNorm2d-70          [-1, 512, 28, 28]           1,024ReLU-71          [-1, 512, 28, 28]               0MaxPool2d-72          [-1, 512, 14, 14]               0Conv2d-73          [-1, 128, 28, 28]          65,536BatchNorm2d-74          [-1, 128, 28, 28]             256ReLU-75          [-1, 128, 28, 28]               0Conv2d-76          [-1, 128, 14, 14]         147,456BatchNorm2d-77          [-1, 128, 14, 14]             256ReLU-78          [-1, 128, 14, 14]               0Conv2d-79          [-1, 512, 14, 14]          65,536Block2-80          [-1, 512, 14, 14]               0Stack2-81          [-1, 512, 14, 14]               0BatchNorm2d-82          [-1, 512, 14, 14]           1,024ReLU-83          [-1, 512, 14, 14]               0Conv2d-84         [-1, 1024, 14, 14]         524,288Conv2d-85          [-1, 256, 14, 14]         131,072BatchNorm2d-86          [-1, 256, 14, 14]             512ReLU-87          [-1, 256, 14, 14]               0Conv2d-88          [-1, 256, 14, 14]         589,824BatchNorm2d-89          [-1, 256, 14, 14]             512ReLU-90          [-1, 256, 14, 14]               0Conv2d-91         [-1, 1024, 14, 14]         262,144Block2-92         [-1, 1024, 14, 14]               0BatchNorm2d-93         [-1, 1024, 14, 14]           2,048ReLU-94         [-1, 1024, 14, 14]               0Identity-95         [-1, 1024, 14, 14]               0Conv2d-96          [-1, 256, 14, 14]         262,144BatchNorm2d-97          [-1, 256, 14, 14]             512ReLU-98          [-1, 256, 14, 14]               0Conv2d-99          [-1, 256, 14, 14]         589,824BatchNorm2d-100          [-1, 256, 14, 14]             512ReLU-101          [-1, 256, 14, 14]               0Conv2d-102         [-1, 1024, 14, 14]         262,144Block2-103         [-1, 1024, 14, 14]               0BatchNorm2d-104         [-1, 1024, 14, 14]           2,048ReLU-105         [-1, 1024, 14, 14]               0Identity-106         [-1, 1024, 14, 14]               0Conv2d-107          [-1, 256, 14, 14]         262,144BatchNorm2d-108          [-1, 256, 14, 14]             512ReLU-109          [-1, 256, 14, 14]               0Conv2d-110          [-1, 256, 14, 14]         589,824BatchNorm2d-111          [-1, 256, 14, 14]             512ReLU-112          [-1, 256, 14, 14]               0Conv2d-113         [-1, 1024, 14, 14]         262,144Block2-114         [-1, 1024, 14, 14]               0BatchNorm2d-115         [-1, 1024, 14, 14]           2,048ReLU-116         [-1, 1024, 14, 14]               0Identity-117         [-1, 1024, 14, 14]               0Conv2d-118          [-1, 256, 14, 14]         262,144BatchNorm2d-119          [-1, 256, 14, 14]             512ReLU-120          [-1, 256, 14, 14]               0Conv2d-121          [-1, 256, 14, 14]         589,824BatchNorm2d-122          [-1, 256, 14, 14]             512ReLU-123          [-1, 256, 14, 14]               0Conv2d-124         [-1, 1024, 14, 14]         262,144Block2-125         [-1, 1024, 14, 14]               0BatchNorm2d-126         [-1, 1024, 14, 14]           2,048ReLU-127         [-1, 1024, 14, 14]               0Identity-128         [-1, 1024, 14, 14]               0Conv2d-129          [-1, 256, 14, 14]         262,144BatchNorm2d-130          [-1, 256, 14, 14]             512ReLU-131          [-1, 256, 14, 14]               0Conv2d-132          [-1, 256, 14, 14]         589,824BatchNorm2d-133          [-1, 256, 14, 14]             512ReLU-134          [-1, 256, 14, 14]               0Conv2d-135         [-1, 1024, 14, 14]         262,144Block2-136         [-1, 1024, 14, 14]               0BatchNorm2d-137         [-1, 1024, 14, 14]           2,048ReLU-138         [-1, 1024, 14, 14]               0MaxPool2d-139           [-1, 1024, 7, 7]               0Conv2d-140          [-1, 256, 14, 14]         262,144BatchNorm2d-141          [-1, 256, 14, 14]             512ReLU-142          [-1, 256, 14, 14]               0Conv2d-143            [-1, 256, 7, 7]         589,824BatchNorm2d-144            [-1, 256, 7, 7]             512ReLU-145            [-1, 256, 7, 7]               0Conv2d-146           [-1, 1024, 7, 7]         262,144Block2-147           [-1, 1024, 7, 7]               0Stack2-148           [-1, 1024, 7, 7]               0BatchNorm2d-149           [-1, 1024, 7, 7]           2,048ReLU-150           [-1, 1024, 7, 7]               0Conv2d-151           [-1, 2048, 7, 7]       2,097,152Conv2d-152            [-1, 512, 7, 7]         524,288BatchNorm2d-153            [-1, 512, 7, 7]           1,024ReLU-154            [-1, 512, 7, 7]               0Conv2d-155            [-1, 512, 7, 7]       2,359,296BatchNorm2d-156            [-1, 512, 7, 7]           1,024ReLU-157            [-1, 512, 7, 7]               0Conv2d-158           [-1, 2048, 7, 7]       1,048,576Block2-159           [-1, 2048, 7, 7]               0BatchNorm2d-160           [-1, 2048, 7, 7]           4,096ReLU-161           [-1, 2048, 7, 7]               0Identity-162           [-1, 2048, 7, 7]               0Conv2d-163            [-1, 512, 7, 7]       1,048,576BatchNorm2d-164            [-1, 512, 7, 7]           1,024ReLU-165            [-1, 512, 7, 7]               0Conv2d-166            [-1, 512, 7, 7]       2,359,296BatchNorm2d-167            [-1, 512, 7, 7]           1,024ReLU-168            [-1, 512, 7, 7]               0Conv2d-169           [-1, 2048, 7, 7]       1,048,576Block2-170           [-1, 2048, 7, 7]               0BatchNorm2d-171           [-1, 2048, 7, 7]           4,096ReLU-172           [-1, 2048, 7, 7]               0Identity-173           [-1, 2048, 7, 7]               0Conv2d-174            [-1, 512, 7, 7]       1,048,576BatchNorm2d-175            [-1, 512, 7, 7]           1,024ReLU-176            [-1, 512, 7, 7]               0Conv2d-177            [-1, 512, 7, 7]       2,359,296BatchNorm2d-178            [-1, 512, 7, 7]           1,024ReLU-179            [-1, 512, 7, 7]               0Conv2d-180           [-1, 2048, 7, 7]       1,048,576Block2-181           [-1, 2048, 7, 7]               0Stack2-182           [-1, 2048, 7, 7]               0BatchNorm2d-183           [-1, 2048, 7, 7]           4,096ReLU-184           [-1, 2048, 7, 7]               0
AdaptiveAvgPool2d-185           [-1, 2048, 1, 1]               0Flatten-186                 [-1, 2048]               0Linear-187                 [-1, 1000]       2,049,000
================================================================
Total params: 25,549,416
Trainable params: 25,549,416
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 241.69
Params size (MB): 97.46
Estimated Total Size (MB): 339.73
----------------------------------------------------------------

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:38.3%, Train_loss:5.263, Test_acc:31.9%, Test_loss:5.151, Lr:1.00E-04
Epoch: 2, Train_acc:71.9%, Train_loss:1.759, Test_acc:31.0%, Test_loss:3.492, Lr:1.00E-04
Epoch: 3, Train_acc:82.7%, Train_loss:0.822, Test_acc:85.8%, Test_loss:0.620, Lr:1.00E-04
Epoch: 4, Train_acc:89.2%, Train_loss:0.478, Test_acc:83.2%, Test_loss:0.762, Lr:1.00E-04
Epoch: 5, Train_acc:89.2%, Train_loss:0.444, Test_acc:86.7%, Test_loss:0.629, Lr:1.00E-04
Epoch: 6, Train_acc:91.2%, Train_loss:0.359, Test_acc:73.5%, Test_loss:0.802, Lr:1.00E-04
Epoch: 7, Train_acc:95.1%, Train_loss:0.173, Test_acc:79.6%, Test_loss:0.689, Lr:1.00E-04
Epoch: 8, Train_acc:96.5%, Train_loss:0.141, Test_acc:80.5%, Test_loss:0.704, Lr:1.00E-04
Epoch: 9, Train_acc:98.5%, Train_loss:0.089, Test_acc:78.8%, Test_loss:0.879, Lr:1.00E-04
Epoch:10, Train_acc:95.8%, Train_loss:0.196, Test_acc:81.4%, Test_loss:0.718, Lr:1.00E-04
Done
预测结果是：Bananaquit
0.8672566371681416 0.5955437496304512
0.8672566371681416Process 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)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：

predict_one_image(image_path='./data/bird_photos/Bananaquit/007.jpg',model=model,transform=train_transforms,classes=classes)

得到如下结果：

预测结果是：Bananaquit

3.模型评估

将模型调至评估模式：

best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
print(epoch_test_acc, epoch_test_loss)

得到如下输出：

0.8672566371681416 0.5955437496304512

观察得到和前文中一致。

五、个人理解

除完成Tensorflow与pytorch之间的代码转换，还需了解ResNetV2及ResNetV之间的关系及区别：

首先，对比两个残差结构：

可以看出(a)结构先卷积后进行 BN 和激活函数计算，最后执行 addition 后再进行ReLU 计算； (b)结构先进行 BN 和激活函数计算后卷积，把 addition 后的 ReLU 计算放到了残差结构内部。

ResNetV2的最终确定经过了两轮尝试：

5.1关于残差结构的尝试

作者用不同 shortcut 结构的 ResNet-110 在 CIFAR-10 数据集上做测试，发现最原始的(a)original 结构是最好的，也就是 identity mapping 恒等映射是最好的

5.2关于激活的尝试

经实验发现，最好的结果是(e)full pre-activation，其次到(a)original。