faster r-cnn pycharm代码
时间: 2023-07-30 12:06:26 浏览: 152
代码 FasterRcnn
以下是使用PyCharm实现Faster R-CNN的Python代码示例:
```python
# 导入必要的库
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.datasets import CocoDetection
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader
# 加载COCO数据集
train_dataset = CocoDetection(root="./data/train2017", annFile="./data/annotations/instances_train2017.json", transform=ToTensor())
test_dataset = CocoDetection(root="./data/val2017", annFile="./data/annotations/instances_val2017.json", transform=ToTensor())
# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False, num_workers=4)
# 加载预训练的Faster R-CNN模型
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
# 替换分类器
num_classes = 91 # 用于COCO数据集的类别数
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
# 训练和测试模型
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
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)
num_epochs = 10
for epoch in range(num_epochs):
model.train()
for images, targets in train_loader:
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
optimizer.zero_grad()
losses.backward()
optimizer.step()
lr_scheduler.step()
model.eval()
test_loss = 0.0
for images, targets in test_loader:
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
with torch.no_grad():
loss_dict = model(images, targets)
test_loss += sum(loss for loss in loss_dict.values()).item()
print(f"Epoch {epoch}: train_loss = {losses.item()}, test_loss = {test_loss / len(test_loader)}")
```
需要注意的是,此处的代码仅提供了一个基本框架,具体实现需要根据实际需求进行相应的修改和调整。
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