fasterrcnn进行指标评价代码

以下是一个使用Python和PyTorch库进行Faster R-CNN模型评价的示例代码：

import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from tqdm import tqdm

# 定义设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5), (0.5))
])
test_set = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=4, shuffle=False, num_workers=0)

# 加载模型
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = 10
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
model.load_state_dict(torch.load('faster_rcnn_model.pth'))
model.to(device)
model.eval()

# 定义指标
total_loss = 0
total_correct = 0
total_images = 0

# 在测试集上进行评价
with torch.no_grad():
    for images, targets in tqdm(test_loader):
        images, targets = images.to(device), targets.to(device)
        outputs = model(images)
        loss = sum(loss for loss in outputs.values())
        total_loss += loss.item()
        for i in range(len(targets)):
            total_images += 1
            pred_labels = outputs[i]['labels'].cpu().numpy()
            pred_boxes = outputs[i]['boxes'].cpu().numpy()
            true_labels = targets[i]['labels'].cpu().numpy()
            true_boxes = targets[i]['boxes'].cpu().numpy()
            for j in range(len(pred_labels)):
                if pred_labels[j] == true_labels[j]:
                    box_iou = torchvision.ops.box_iou(torch.Tensor([pred_boxes[j]]), torch.Tensor([true_boxes[j]]))
                    if box_iou > 0.5:
                        total_correct += 1

# 计算指标
accuracy = total_correct / total_images
average_loss = total_loss / len(test_loader)

# 输出结果
print('Accuracy: {:.2f}%'.format(accuracy*100))
print('Average Loss: {:.2f}'.format(average_loss))

以上代码演示了如何使用Faster R-CNN模型对MNIST数据集进行评价，其中包括计算准确率和平均损失两个指标。在实际应用中，可以根据具体的需求选择适当的数据集和评价指标。