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| import os
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
from torchsummary import summary
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
# 定义设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义模型
class AnimalCNN(nn.Module):
def __init__(self, num_classes):
super(AnimalCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.fc1 = nn.Linear(256 * 9 * 9, 512)
self.fc2 = nn.Linear(512, 64)
self.fc3 = nn.Linear(64, num_classes)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.bn1(self.pool(self.conv1(x))))
x = self.relu(self.bn2(self.pool(self.conv2(x))))
x = self.relu(self.bn3(self.pool(self.conv3(x))))
x = self.relu(self.bn4(self.pool(self.conv4(x))))
x = x.view(x.size(0), -1)
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
# 训练集和测试集路径
train_path = './dataset/train'
test_path = './dataset/test'
# 获取训练集和测试集集的标签
train_labels = sorted(os.listdir(train_path))
test_labels = sorted(os.listdir(test_path))
if train_labels == test_labels:
animal_labels = train_labels = test_labels
print("animal_labels: ", animal_labels)
def plot_training_results(filename, save_path=None):
with open(filename, 'r') as f:
lines = f.readlines()[1:]
data = [line.strip().split(',') for line in lines]
epochs = np.array([int(row[0]) for row in data])
train_losses = np.array([float(row[1]) for row in data])
train_accuracies = np.array([float(row[2]) for row in data])
test_losses = np.array([float(row[3]) for row in data])
test_accuracies = np.array([float(row[4]) for row in data])
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.plot(epochs, train_losses, label='Training Loss', color='blue')
plt.plot(epochs, test_losses, label='Testing Loss', color='orange')
plt.title('Training and Testing Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(epochs, train_accuracies * 100, label='Training Accuracy', color='blue')
plt.plot(epochs, test_accuracies * 100, label='Testing Accuracy', color='orange')
plt.title('Training and Testing Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy (%)')
plt.legend()
if save_path:
plt.savefig(save_path, bbox_inches='tight')
plt.tight_layout()
plt.show()
# 绘制训练过程中的损失和准确率曲线图
training_results = './training_results/training_results.txt'
save_path = './Image/LossAndAccuracy.png'
plot_training_results(training_results, save_path)
# 评估模型
def evaluate(loader):
model.eval()
correct = 0
total = 0
loss = 0.0
all_labels = []
all_preds = []
with torch.no_grad():
with tqdm(total=len(loader), desc='Evaluating', unit='batch') as pbar:
for images, labels in loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss += criterion(outputs, labels).item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
all_labels.extend(labels.cpu().numpy())
all_preds.extend(predicted.cpu().numpy())
pbar.update(1)
accuracy = correct / total
return loss / len(loader), accuracy, all_labels, all_preds
# 加载模型
model_path = './weights/best_model.pth'
model = AnimalCNN(num_classes=len(animal_labels)).to(device)
summary(model, (3, 148, 148))
# model.load_state_dict(torch.load(model_path))
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'), weights_only=False))
criterion = nn.CrossEntropyLoss()
# 数据增强
train_transform = transforms.Compose([
transforms.RandomRotation(10),
transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(148, scale=(0.8, 1.0)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4, 0.4, 0.4], std=[0.2, 0.2, 0.2])
])
test_transform = transforms.Compose([
transforms.Resize((148, 148)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4, 0.4, 0.4], std=[0.2, 0.2, 0.2])
])
train_dataset = ImageFolder(train_path, transform=train_transform)
test_dataset = ImageFolder(test_path, transform=test_transform)
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 评估模型
train_loss, train_acc, train_labels, train_preds = evaluate(train_loader)
test_loss, test_acc, test_labels, test_preds = evaluate(test_loader)
print('Train loss: ', train_loss)
print('Train accuracy: ', train_acc)
print('Test loss: ', test_loss)
print('Test accuracy: ', test_acc)
# 输出分类报告表
train_report = classification_report(train_labels, train_preds, target_names=animal_labels)
test_report = classification_report(test_labels, test_preds, target_names=animal_labels)
print('Train Classification Report:\n', train_report)
print('Test Classification Report:\n', test_report)
# 保存分类报告表
with open('./training_results/classification_report.txt', 'w') as f:
f.write('Train Classification Report:\n')
f.write(train_report)
f.write('\nTest Classification Report:\n')
f.write(test_report)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
def display_predictions(model, data_loader, animal_labels, num_images=10, images_per_row=5):
model.eval()
images, labels, predictions = [], [], []
with torch.no_grad():
for i, (img_batch, lbl_batch) in enumerate(data_loader):
if len(images) >= num_images:
break
img_batch, lbl_batch = img_batch.to(device), lbl_batch.to(device)
outputs = model(img_batch)
_, predicted = torch.max(outputs, 1)
images.extend(img_batch.cpu())
labels.extend(lbl_batch.cpu())
predictions.extend(predicted.cpu())
fig, axes = plt.subplots(nrows=num_images // images_per_row, ncols=images_per_row, figsize=(10, 4))
axes = axes.flatten()
for i in range(num_images):
img = images[i].permute(1, 2, 0).numpy() # 转置图像数据
true_label = labels[i].item()
pred_label = predictions[i].item()
true_animal = animal_labels[true_label]
pred_animal = animal_labels[pred_label]
axes[i].imshow(img)
axes[i].axis('off')
color = 'green' if true_label == pred_label else 'red'
axes[i].set_title(f'True: {true_animal}\nPred: {pred_animal}', color=color, fontsize=8)
plt.tight_layout()
plt.subplots_adjust(hspace=0.5, wspace=0.1)
plt.show()
# 显示训练集的预测结果(随机抽取10张图片)
display_predictions(model, train_loader, animal_labels)
# 显示测试集的预测结果(随机抽取10张图片)
display_predictions(model, test_loader, animal_labels)
def plot_confusion_matrix(true_labels, pred_labels, animal_labels, save_path=None):
cm = confusion_matrix(true_labels, pred_labels)
plt.figure(figsize=(10, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Greens', xticklabels=animal_labels, yticklabels=animal_labels)
plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.title('Confusion Matrix')
if save_path:
plt.savefig(save_path, bbox_inches='tight')
plt.show()
# 计算并绘制训练集的混淆矩阵
plot_confusion_matrix(train_labels, train_preds, animal_labels, save_path='./Image/train_confusion_matrix.png')
# 计算并绘制测试集的混淆矩阵
plot_confusion_matrix(test_labels, test_preds, animal_labels, save_path='./Image/test_confusion_matrix.png')
|
