19.1 图像水平翻转与垂直翻转
import torch
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
figsize = (num_cols * scale, num_rows * scale)
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
axes = axes.flatten()
for ax, y in zip(axes, Y):
ax.imshow(y)
ax.axis('off')
plt.tight_layout()
plt.show()
apply(image,torchvision.transforms.RandomHorizontalFlip())
apply(image,torchvision.transforms.RandomVerticalFlip())
19.1 图像随机裁剪与色彩调整
import torch
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
figsize = (num_cols * scale, num_rows * scale)
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
axes = axes.flatten()
for ax, y in zip(axes, Y):
ax.imshow(y)
ax.axis('off')
plt.tight_layout()
plt.show()
apply(image,torchvision.transforms.RandomResizedCrop(size=(200,200),scale=(0.2,1),ratio=(1,2)))
apply(image,torchvision.transforms.ColorJitter(brightness=0.5,contrast=0.5,saturation=0.5,hue=0.5))
19.3 图像整体增强变换
import torch
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
figsize = (num_cols * scale, num_rows * scale)
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
axes = axes.flatten()
for ax, y in zip(axes, Y):
ax.imshow(y)
ax.axis('off')
plt.tight_layout()
plt.show()
loc_aug=torchvision.transforms.RandomHorizontalFlip()
shape_aug = torchvision.transforms.RandomResizedCrop((200, 200), scale=(0.1, 1), ratio=(0.5, 2))
color_aug = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
augs = torchvision.transforms.Compose([loc_aug,color_aug, shape_aug])
apply(image, augs)
19.4 基于CiFar-10数据集的图像增强效果对比
import torch
import torchvision
from torch import nn
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from tqdm import tqdm
import torchvision.models as models
from sklearn.metrics import accuracy_score
from torch.nn import functional as F
plt.rcParams['font.family']=['Times New Roman']
class Reshape(torch.nn.Module):
def forward(self,x):
return x.view(-1,1,28,28)
def plot_metrics(train_loss_list, train_acc_list, test_acc_list, title='Training Curve'):
epochs = range(1, len(train_loss_list) + 1)
plt.figure(figsize=(4, 3))
plt.plot(epochs, train_loss_list, label='Train Loss')
plt.plot(epochs, train_acc_list, label='Train Acc',linestyle='--')
plt.plot(epochs, test_acc_list, label='Test Acc', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('Value')
plt.title(title)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
def train_model(model,train_data,test_data,num_epochs):
train_loss_list = []
train_acc_list = []
test_acc_list = []
for epoch in range(num_epochs):
total_loss=0
total_acc_sample=0
total_samples=0
loop1=tqdm(train_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
for X,y in loop1:
X=X.to(device)
y=y.to(device)
y_hat=model(X)
loss=CEloss(y_hat,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss+=loss.item()*X.shape[0]
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
total_acc_sample+=accuracy_score(y_pred,y_true)*X.shape[0]
total_samples+=X.shape[0]
test_acc_samples=0
test_samples=0
loop2=tqdm(test_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
for X,y in loop2:
X=X.to(device)
y=y.to(device)
y_hat=model(X)
y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
y_true=y.detach().cpu().numpy()
test_acc_samples+=accuracy_score(y_pred,y_true)*X.shape[0]
test_samples+=X.shape[0]
avg_train_loss=total_loss/total_samples
avg_train_acc=total_acc_sample/total_samples
avg_test_acc=test_acc_samples/test_samples
train_loss_list.append(avg_train_loss)
train_acc_list.append(avg_train_acc)
test_acc_list.append(avg_test_acc)
print(f"Epoch {epoch+1}: Loss: {avg_train_loss:.4f},Trian Accuracy: {avg_train_acc:.4f},test Accuracy: {avg_test_acc:.4f}")
plot_metrics(train_loss_list, train_acc_list, test_acc_list)
return model
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
transforms_train=transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.5),
transforms.ToTensor(),
transforms.Normalize((0.5,),(0.5,))])
transforms_test=transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.5),
transforms.ToTensor(),
transforms.Normalize((0.5,),(0.5,))])
train_img=torchvision.datasets.CIFAR10(root="./data",train=True,transform=transforms_train,download=True)
test_img=torchvision.datasets.CIFAR10(root="./data",train=False,transform=transforms_test,download=True)
train_data=DataLoader(train_img,batch_size=128,num_workers=4,shuffle=True)
test_data=DataLoader(test_img,batch_size=128,num_workers=4,shuffle=False)
device=torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
model=models.resnet50(pretrained=True)
model.fc=nn.Linear(model.fc.in_features,10)
model.to(device)
model.apply(init_weights)
optimizer=torch.optim.SGD(model.parameters(),lr=0.05,momentum=0.9)
CEloss=nn.CrossEntropyLoss()
model=train_model(model,train_data,test_data,num_epochs=20)
