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pytorch搭建ResNet50实现鸟类识别

news 文章来源：https://blog.csdn.net/suic009/article/details/129757087 2025/2/12 23:53:43

🍨 本文为🔗365天深度学习训练营中的学习记录博客

🍦 参考文章地址： 365天深度学习训练营-第J1周：ResNet-50算法实战与解析

🍖 作者：K同学啊

理论知识储备

深度残差网络ResNet（deep residual network）在2015年由何凯明等提出，因为它简单与实用并存，随后很多研究都是建立在ResNet-50或者ResNet-101基础上完成的。

ResNet主要解决深度卷积网络在深度加深时候的“退化”问题。在一般的卷积神经网络中，增大网络深度后带来的第一个问题就是梯度消失、爆炸，这个问题在Szegedy提出BN后被顺利解决。BN层能对各层的输出做归一化，这样梯度在反向层层传递后仍能保持大小稳定，不会出现过小或过大的情况。但是作者发现加了BN后，再加大深度仍然不容易收敛，其提到了第二个问题——准确率下降问题：层级大到一定程度时，准确率就会饱和，然后迅速下降。这种下降既不是梯度消失引起的，也不是过拟合造成的，而是由于网络过于复杂，以至于光靠不加约束的放养式的训练很难达到理想的错误率。准确率下降问题不是网络结构本身的问题，而是现有的训练方式不够理想造成的。当前广泛使用的训练方法，无论是SGD，还是RMSProp，或是Adam，都无法在网络深度变大后达到理论上最优的收敛结果。还可以证明只要有理想的训练方式，更深的网络肯定会比较浅的网络效果要好。证明过程也很简单：假设在一种网络A的后面添加几层形成新的网络B，如果增加的层级只是对A的输出做了个恒等映射（identity mapping），即A的输出经过新增的层级变成B的输出后没有发生变化，这样网络A和网络B的错误率就是相等的，也就证明了加深后的网络不会比加深前的网络效果差。

一、前期准备

1.设置GPU

import torch
from torch import nn
import torchvision
from torchvision import transforms,datasets,models
import matplotlib.pyplot as plt
import os,PIL,pathlib

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

device(type='cuda')

2.导入数据

data_dir = './J1/'
data_dir = pathlib.Path(data_dir)image_count = len(list(data_dir.glob('*/*/*.jpg')))
print("图片总数为：",image_count)

图片总数为： 565

classNames = [str(path).split('\\')[2] for path in data_dir.glob('bird_photos/*/')]
classNames

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

train_transforms = transforms.Compose([transforms.Resize([224, 224]),transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选
#       transforms.CenterCrop(224),#从中心开始裁剪transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率
#         transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转
#         transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相
#         transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=Btransforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差])# test_transforms = transforms.Compose([
#         transforms.Resize([224, 224]),
#         transforms.ToTensor(),
#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
#     ])

total_data = datasets.ImageFolder('./J1/bird_photos/',transform=train_transforms)
total_data

Dataset ImageFolderNumber of datapoints: 565Root location: ./J1/bird_photos/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=PIL.Image.BILINEAR)RandomRotation(degrees=[-45.0, 45.0], resample=False, expand=False)RandomHorizontalFlip(p=0.5)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

classNames = total_data.classes
classNames

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

total_data.class_to_idx

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

3.数据集划分

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])
train_dataset,test_dataset

(<torch.utils.data.dataset.Subset at 0x1a6883fe310>,<torch.utils.data.dataset.Subset at 0x1a6883fe370>)

train_size,test_size

(452, 113)

batch_size = 32
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)

imgs, labels = next(iter(train_dl))
imgs.shape

torch.Size([32, 3, 224, 224])

4. 数据可视化

import numpy as np# 指定图片大小，图像大小为20宽、5高的绘图(单位为英寸inch)
plt.figure(figsize=(20, 5)) 
for i, imgs in enumerate(imgs[:20]):npimg = imgs.numpy().transpose((1,2,0))npimg = npimg * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))npimg = npimg.clip(0, 1)# 将整个figure分成2行10列，绘制第i+1个子图。plt.subplot(2, 10, i+1)plt.imshow(npimg)plt.axis('off')

for X,y in test_dl:print('Shape of X [N, C, H, W]:', X.shape)print('Shape of y:', y.shape)break

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

二、构建ResNet50网络

n_class = 4
''' Same Padding '''
def autopad(k, p=None):  # kernel, padding# Pad to 'same'if p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-padreturn p''' Identity Block '''
class IdentityBlock(nn.Module):def __init__(self, in_channel, kernel_size, filters):super(IdentityBlock, self).__init__()filters1, filters2, filters3 = filtersself.conv1 = nn.Sequential(nn.Conv2d(in_channel, filters1, 1, stride=1, padding=0, bias=False),nn.BatchNorm2d(filters1),nn.ReLU(True))self.conv2 = nn.Sequential(nn.Conv2d(filters1, filters2, kernel_size, stride=1, padding=autopad(kernel_size), bias=False),nn.BatchNorm2d(filters2),nn.ReLU(True))self.conv3 = nn.Sequential(nn.Conv2d(filters2, filters3, 1, stride=1, padding=0, bias=False),nn.BatchNorm2d(filters3))self.relu = nn.ReLU(True)def forward(self, x):x1 = self.conv1(x)x1 = self.conv2(x1)x1 = self.conv3(x1)x = x1 + xself.relu(x)return x''' Conv Block '''
class ConvBlock(nn.Module):def __init__(self, in_channel, kernel_size, filters, stride=2):super(ConvBlock, self).__init__()filters1, filters2, filters3 = filtersself.conv1 = nn.Sequential(nn.Conv2d(in_channel, filters1, 1, stride=stride, padding=0, bias=False),nn.BatchNorm2d(filters1),nn.ReLU(True))self.conv2 = nn.Sequential(nn.Conv2d(filters1, filters2, kernel_size, stride=1, padding=autopad(kernel_size), bias=False),nn.BatchNorm2d(filters2),nn.ReLU(True))self.conv3 = nn.Sequential(nn.Conv2d(filters2, filters3, 1, stride=1, padding=0, bias=False),nn.BatchNorm2d(filters3))self.conv4 = nn.Sequential(nn.Conv2d(in_channel, filters3, 1, stride=stride, padding=0, bias=False),nn.BatchNorm2d(filters3))self.relu = nn.ReLU(True)def forward(self, x):x1 = self.conv1(x)x1 = self.conv2(x1)x1 = self.conv3(x1)x2 = self.conv4(x)x = x1 + x2self.relu(x)return x''' 构建ResNet-50 '''
class ResNet50(nn.Module):def __init__(self, classes=1000):super(ResNet50, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False, padding_mode='zeros'),nn.BatchNorm2d(64),nn.ReLU(),nn.MaxPool2d(kernel_size=3, stride=2, padding=0))self.conv2 = nn.Sequential(ConvBlock(64, 3, [64, 64, 256], stride=1),IdentityBlock(256, 3, [64, 64, 256]),IdentityBlock(256, 3, [64, 64, 256]))self.conv3 = nn.Sequential(ConvBlock(256, 3, [128, 128, 512]),IdentityBlock(512, 3, [128, 128, 512]),IdentityBlock(512, 3, [128, 128, 512]),IdentityBlock(512, 3, [128, 128, 512]))self.conv4 = nn.Sequential(ConvBlock(512, 3, [256, 256, 1024]),IdentityBlock(1024, 3, [256, 256, 1024]),IdentityBlock(1024, 3, [256, 256, 1024]),IdentityBlock(1024, 3, [256, 256, 1024]),IdentityBlock(1024, 3, [256, 256, 1024]),IdentityBlock(1024, 3, [256, 256, 1024]))self.conv5 = nn.Sequential(ConvBlock(1024, 3, [512, 512, 2048]),IdentityBlock(2048, 3, [512, 512, 2048]),IdentityBlock(2048, 3, [512, 512, 2048]))self.pool = nn.AvgPool2d(kernel_size=7, stride=7, padding=0)self.fc = nn.Linear(2048, n_class)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 xmodel = ResNet50().to(device)

# 查看网络结构
import torchsummary
torchsummary.summary(model, (3, 224, 224))
print(model)

三、训练模型

1.优化器设置

# 优化器设置
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)#要训练什么参数/
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.92)#学习率每5个epoch衰减成原来的1/10
loss_fn = nn.CrossEntropyLoss()

2.编写训练函数

# 训练循环
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，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

3.编写测试函数

def test (dataloader, model, loss_fn):size        = len(dataloader.dataset)  # 测试集的大小，一共10000张图片num_batches = len(dataloader)          # 批次数目，8（255/32=8，向上取整）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

4、正式训练

epochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []
best_acc = 0for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)scheduler.step()#学习率衰减model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最优模型if epoch_test_acc > best_acc:best_acc = epoch_train_accstate = {'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重'best_acc': best_acc,'optimizer' : optimizer.state_dict(),}train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')
print('best_acc：',best_acc)

Epoch:19, Train_acc:88.9%, Train_loss:0.264, Test_acc:87.6%，Test_loss:0.347
Epoch:20, Train_acc:86.1%, Train_loss:0.481, Test_acc:87.6%，Test_loss:0.319
Done
best_acc： 0.911504424778761

四、结果可视化

1.Loss与Accuracy图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.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()

2.指定图片进行预测

from PIL import Imageclasses = list(total_data.class_to_idx)def predict_one_img(image_path,model,transform,classes):test_img = Image.open(image_path).convert('RGB')plt.imshow(test_img)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}')

predict_one_img('./J1/bird_photos/Bananaquit/047.jpg', model, train_transforms, classNames)

预测结果是：Bananaquit