pytorch python加载本地标注yolo-txt数据集label进行模型训练生成pt文件的代码实现

在PyTorch中，YOLO (You Only Look Once) 数据集通常包含一个`*.txt`文件，该文件列出了每个bounding box的位置和类别信息。训练Yolo模型需要用到特定的数据预处理步骤，包括读取txt文件、生成目标图和数据加载器。这里是一个简单的例子，我们将使用Darknet YOLOv3作为模型框架：

import torch
from torchvision import transforms
from pycocotools.coco import COCO
from yolov3.models import create_yolov3_model
from yolov3.utils import get_yolo_input, preprocess_image

# 配置
txt_file = 'data.txt'  # YOLO txt文件路径
img_folder = 'images/'  # 图片根目录
weights_path = 'yolov3.weights'  # YOLO预训练权重路径
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 初始化模型并加载预训练权重
model = create_yolov3_model()
model.load_darknet_weights(weights_path)
model.to(device)

# 定义数据预处理函数
def parse_yolo_txt(txt_file):
    coco = COCO(txt_file)
    img_ids = coco.getImgIds()
    dataset = []
    for img_id in img_ids:
        ann_ids = coco.getAnnIds(imgIds=img_id)
        annotations = coco.loadAnns(ann_ids)
        img_info = coco.loadImgs(img_id)[0]
        boxes = [anno['bbox'] + [anno['category_id']] for anno in annotations]
        dataset.append((img_info['file_name'], boxes))
    return dataset

# 读取并预处理数据
dataset = parse_yolo_txt(txt_file)
transform = transforms.Compose([transforms.Resize(416), transforms.ToTensor()])
train_dataset = [preprocess_image(transform(Image.open(img_path)), img_shape=(416, 416)) for img_path, _ in dataset]

# 创建DataLoader
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True, collate_fn=get_yolo_input)

# 模拟训练步骤
model.train()
for inputs, targets in train_loader:
    inputs = inputs.to(device)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
    optimizer.zero_grad()  # 假设有优化器optimizer
    losses = model(inputs, targets)  # 填充模型的loss计算
    losses.backward()  # 反向传播
    optimizer.step()  # 更新权重

# 每一轮训练结束后可以保存模型
model.save_weights('yolov3_trained.pth')