PyTorch框架实现Faster RCNN模型

Faster RCNN是一个流行的目标检测模型，其结构包括两个部分：Region Proposal Network（RPN）和Fast R-CNN。PyTorch框架提供了实现Faster RCNN模型的工具包torchvision.models.detection。

以下是实现Faster RCNN模型的步骤：

1. 导入所需的库和模块：

import torch
import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

2. 定义一个自定义的数据集类来加载训练和测试数据。

class MyDataset(torch.utils.data.Dataset):
    def __init__(self, images, targets):
        self.images = images
        self.targets = targets

    def __getitem__(self, index):
        image = self.images[index]
        target = self.targets[index]
        return image, target

    def __len__(self):
        return len(self.images)

3. 加载数据集和对应的标签，并将它们转换为模型所需的格式。

train_dataset = MyDataset(train_images, train_labels)
test_dataset = MyDataset(test_images, test_labels)

def collate_fn(batch):
    return tuple(zip(*batch))

train_data_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=2, shuffle=True, num_workers=4,
    collate_fn=collate_fn)

test_data_loader = torch.utils.data.DataLoader(
    test_dataset, batch_size=1, shuffle=False, num_workers=4,
    collate_fn=collate_fn)

4. 定义Faster RCNN模型。

backbone = torchvision.models.mobilenet_v2(pretrained=True).features
backbone.out_channels = 1280

anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
                                   aspect_ratios=((0.5, 1.0, 2.0),))

roi_pooler = torchvision.ops.MultiScaleRoIAlign(
    featmap_names=['0'], output_size=7, sampling_ratio=2)

model = FasterRCNN(
    backbone, num_classes=2,
    rpn_anchor_generator=anchor_generator,
    box_roi_pool=roi_pooler)

5. 定义损失函数和优化器。

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)

def collate_fn(batch):
    return tuple(zip(*batch))

train_data_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=2, shuffle=True, num_workers=4,
    collate_fn=collate_fn)

test_data_loader = torch.utils.data.DataLoader(
    test_dataset, batch_size=1, shuffle=False, num_workers=4,
    collate_fn=collate_fn)

6. 训练模型。

num_epochs = 10

for epoch in range(num_epochs):
    model.train()
    i = 0
    for images, targets in train_data_loader:
        images = list(image for image in images)
        targets = [{k: v 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()

        if i % 50 == 0:
            print(f"Epoch {epoch+1}, iteration {i}: {losses}")
        i += 1

    lr_scheduler.step()

    model.eval()
    i = 0
    for images, targets in test_data_loader:
        images = list(image for image in images)
        targets = [{k: v for k, v in t.items()} for t in targets]

        with torch.no_grad():
            loss_dict = model(images, targets)

        if i % 50 == 0:
            print(f"Epoch {epoch+1}, iteration {i}: {loss_dict}")
        i += 1

7. 测试模型。

model.eval()

for images, targets in test_data_loader:
    images = list(image for image in images)
    targets = [{k: v for k, v in t.items()} for t in targets]

    with torch.no_grad():
        output = model(images)

    print(output)