数据集
1.导包
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
import numpy as np
import matplotlib.pyplot as plt
import os
from tqdm.auto import tqdm # 引入tqdm库以显示进度条2.数据预处理
ResNet50模型适合的图片大小为224x244
# 定义数据转换
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
3.加载数据集和模型构建
# 加载数据集
data_dir = 'catdog_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'test']}
dataloaders = {x: DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}
class_names = image_datasets['train'].classes
# 加载ResNet-50模型
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
# 替换最后的全连接层以适配我们的分类问题
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(class_names))
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)4.训练
# 训练次数
num_epochs = 10
# 初始化训练次数计数器
train_count = 0
for epoch in range(num_epochs): # num_epochs 是你希望训练的轮数
for phase in ['train', 'test']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
# 使用tqdm显示进度条
with tqdm(total=len(dataloaders[phase]), desc=f'Epoch {epoch+1}/{num_epochs}', leave=False) as progress_bar:
for inputs, labels in dataloaders[phase]:
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
progress_bar.set_postfix(loss=epoch_loss, acc=epoch_acc)
progress_bar.update(1)
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
# 更新训练次数计数器
train_count += 1
print(f'Training Count: {train_count}')训练过程
5.预测
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
import matplotlib.pyplot as plt
# 定义模型的类别数量
num_classes = 2
# 加载模型
model = torchvision.models.resnet50(pretrained=False)
# 修改模型的fc层以匹配训练时的结构
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
# 加载保存的权重
model.load_state_dict(torch.load('mg_ResNet50model.pth'))
model.eval()
# 图像预处理
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# 测试图片
img_path = 'mao_1.jpg' # 替换为你的图片路径
img = Image.open(img_path)
img_t = preprocess(img)
# 扩展维度，因为模型需要4维输入(Batch, Channels, Height, Width)
batch_t = torch.unsqueeze(img_t, 0)
# 预测
with torch.no_grad():
out = model(batch_t)
# 获取最高分数的类别
_, index = torch.max(out, 1)
# 可视化结果
plt.imshow(img)
plt.title(f'Predicted: {index.item()}')
plt.show()预测效果
0就是猫咪，1就是小狗
全部代码
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
import numpy as np
import matplotlib.pyplot as plt
import os
from tqdm.auto import tqdm # 引入tqdm库以显示进度条
# 定义数据转换
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
# 加载数据集
data_dir = 'catdog_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'test']}
dataloaders = {x: DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}
class_names = image_datasets['train'].classes
# 加载ResNet-50模型
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
# 替换最后的全连接层以适配我们的分类问题
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(class_names))
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练次数
num_epochs = 10
# 初始化训练次数计数器
train_count = 0
for epoch in range(num_epochs): # num_epochs 是你希望训练的轮数
for phase in ['train', 'test']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
# 使用tqdm显示进度条
with tqdm(total=len(dataloaders[phase]), desc=f'Epoch {epoch+1}/{num_epochs}', leave=False) as progress_bar:
for inputs, labels in dataloaders[phase]:
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
progress_bar.set_postfix(loss=epoch_loss, acc=epoch_acc)
progress_bar.update(1)
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
# 更新训练次数计数器
train_count += 1
print(f'Training Count: {train_count}')
