Pytorch实现鲜花分类(102 Category Flower Dataset)

• 花匠小妙招
• 2024-09-05 17:11

本文主要讲解该算法的实现过程

实验环境

python3.6+pytorch1.2+cuda10.1

数据集

102 Category Flower Dataset数据集由102类产自英国的花卉组成，每类由40-258张图片组成

下边使用的数据集看好多人私信要，我就上传到CSDN了：鲜花分类集（已划分）-深度学习文档类资源-CSDN下载

下边是代码实现过程及讲解

数据加载

device = torch.device("cuda:0")

data_transforms = {

'train': transforms.Compose([

transforms.RandomRotation(30),

transforms.RandomResizedCrop(224),

transforms.RandomHorizontalFlip(),

transforms.ToTensor(),

transforms.Normalize([0.485, 0.456, 0.406],

[0.229, 0.224, 0.225])

]),

'valid': transforms.Compose([

transforms.Resize(256),

transforms.CenterCrop(224),

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])

])

}

data_dir = "./data"

image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),

data_transforms[x]) for x in ['train', 'valid','test']}

traindataset = image_datasets['train']

validdataset = image_datasets['valid']

testdataset = image_datasets['test']

batch_size = 8

dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size,

shuffle=True, num_workers=4) for x in ['train', 'valid','test']}

print(dataloaders)

traindataloader = dataloaders['train']

validdataloader = dataloaders['valid']

testdataloader = dataloaders['test']

dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid','test']}

定义网络结构  使用ResNet152

class Net(nn.Module):

def __init__(self,model):

super(Net,self).__init__()

self.resnet = nn.Sequential(*list(model.children())[:-1])

self.fc = nn.Linear(in_features=2048,out_features=102)

def forward(self,x):

x = self.resnet(x)

x = x.view(x.shape[0], -1)

x = self.fc(x)

return x

resnet152 = models.resnet152(pretrained=True)

net = Net(resnet152)

 使用VGG19

class Net(nn.Module):

def __init__(self,model):

super(Net,self).__init__()

self.features = model.features

self.classifier = nn.Sequential(

nn.Linear(25088, 4096,bias=True),

nn.ReLU(inplace=True),

nn.Dropout(p=0.5,inplace=False),

nn.Linear(4096, 4096,bias=True),

nn.ReLU(inplace=True),

nn.Dropout(p=0.5,inplace=False),

nn.Linear(4096, 102,bias=True)

)

def forward(self,x):

x = self.features(x)

x = x.view(x.shape[0], -1)

x = self.classifier(x)

return x

vgg = models.vgg19(pretrained=True)

net = Net(vgg)

参数设定

criterion = nn.CrossEntropyLoss()

optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, net.parameters()),lr=0.0001,momentum=0.9)

测试集检验

def valid_model(model, criterion):

best_acc = 0.0

print('-' * 10)

running_loss = 0.0

running_corrects = 0

model = model.to(device)

for inputs, labels in validdataloader:

inputs = inputs.to(device)

labels = labels.to(device)

model.eval()

with torch.no_grad():

outputs = model(inputs)

loss = criterion(outputs, labels)

_, preds = torch.max(outputs, 1)

running_loss += loss.item()

running_corrects += torch.sum(preds == labels)

epoch_loss = running_loss / dataset_sizes['valid']

print(running_corrects.double())

epoch_acc = running_corrects.double() / dataset_sizes['valid']

print('{} Loss: {:.4f} Acc: {:.4f}'.format(

'valid', epoch_loss, epoch_acc))

print('-' * 10)

print()

验证集检验

def test_model(model, criterion):

best_acc = 0.0

print('-' * 10)

running_loss = 0.0

running_corrects = 0

model = model.to(device)

for inputs, labels in testdataloader:

inputs = inputs.to(device)

labels = labels.to(device)

model.eval()

with torch.no_grad():

outputs = model(inputs)

loss = criterion(outputs, labels)

_, preds = torch.max(outputs, 1)

running_loss += loss.item()

running_corrects += torch.sum(preds == labels)

epoch_loss = running_loss / dataset_sizes['test']

print(running_corrects.double())

epoch_acc = running_corrects.double() / dataset_sizes['test']

print('{} Loss: {:.4f} Acc: {:.4f}'.format(

'test', epoch_loss, epoch_acc))

print('-' * 10)

print()

训练模型

def train_model(model, criterion, optimizer, num_epochs=5):

since = time.time()

best_acc = 0.0

for epoch in range(num_epochs):

if (epoch+1)%5==0:

test_model(model, criterion)

print('-' * 10)

print('Epoch {}/{}'.format(epoch+1, num_epochs))

running_loss = 0.0

running_corrects = 0

model = model.to(device)

for inputs, labels in traindataloader:

inputs = inputs.to(device)

labels = labels.to(device)

model.train()

optimizer.zero_grad()

outputs = model(inputs)

loss = criterion(outputs, labels)

loss.backward()

optimizer.step()

_, preds = torch.max(outputs, 1)

running_loss += loss.item()

running_corrects += torch.sum(preds == labels)

epoch_loss = running_loss / dataset_sizes['train']

print(dataset_sizes['train'])

print(running_corrects.double())

epoch_acc = running_corrects.double() / dataset_sizes['train']

best_acc = max(best_acc,epoch_acc)

print('{} Loss: {:.4f} Acc: {:.4f}'.format(

'train', epoch_loss, epoch_acc))

print()

time_elapsed = time.time() - since

print('Training complete in {:.0f}m {:.0f}s'.format(

time_elapsed // 60, time_elapsed % 60))

print('Best val Acc: {:4f}'.format(best_acc))

return model

开始训练

epochs = 48

model = train_model(net, criterion, optimizer, epochs)

valid_model(model,criterion)

torch.save(model, 'model.pkl')

输出结果

在最后，训练集的精度达到了97.01%，测试集上达到了97.45%

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