给出class-agnostic模块作用于faster_RCNN目标检测的pytorch训练示例代码，数据集就定义为COCO数据集，最后给出推理测试代码

以下是class-agnostic模块作用于faster_RCNN目标检测的pytorch训练示例代码，数据集定义为COCO数据集：

import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.datasets import CocoDetection
from torchvision.transforms import transforms
from torch.utils.data import DataLoader

# define the model
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

# get the number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features

# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes=2)  # 2 classes: background and object

# define the dataset and data loader
dataset = CocoDetection(root='./coco_dataset', annFile='./coco_dataset/annotations/instances_train2017.json',
                        transforms=transforms.Compose([transforms.ToTensor()]))
data_loader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4, collate_fn=utils.collate_fn)

# define the optimizer and the learning rate scheduler
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)

# train the model for 10 epochs
num_epochs = 10
for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
    # update the learning rate
    lr_scheduler.step()
    # evaluate on the test dataset every epoch
    evaluate(model, data_loader_test, device=device)

# save the model
torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn_class_agnostic.pt')

以下是推理测试代码：

import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.transforms import transforms
import cv2

# define the model
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

# get the number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features

# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes=2)  # 2 classes: background and object

# load the trained weights
model.load_state_dict(torch.load('fasterrcnn_resnet50_fpn_class_agnostic.pt'))

# set the model to evaluation mode
model.eval()

# define the transforms
transform = transforms.Compose([transforms.ToTensor()])

# load the image
img = cv2.imread('test.jpg')

# apply the transforms
img = transform(img)

# add a batch dimension
img = img.unsqueeze(0)

# forward pass through the model
with torch.no_grad():
    predictions = model(img)

# display the results
print(predictions)