handleSelectionChange(val) { this.ids = val.map(item => item.id) },如何获取id和name
时间: 2024-09-25 18:04:51 浏览: 6
在你提供的代码片段中,`handleSelectionChange(val)` 函数的作用是在`val`(假设是一个对象数组,其中包含`id`和`name`字段)变化时,将`val`数组中的`id`值提取出来,并存储到`this.ids`数组中。这里的`item`代表`val`数组中的每一个元素。
如果你想要同时获取`id`和`name`,你可以稍微修改一下这个函数,使其返回一个新数组,数组的每个元素是包含`id`和`name`的键值对对象。例如:
```javascript
handleSelectionChange(val) {
this.selectedItems = val.map(item => ({ id: item.id, name: item.name }));
}
```
现在,当你调用 `this.selectedItems`,它将返回一个对象数组,每个对象都有`id`和`name`属性。
如果你只需要`id`和`name`的原始值,你可以直接遍历`val`,像下面这样操作:
```javascript
handleSelectionChange(val) {
this.selectedIdsAndNames = [];
for (const item of val) {
this.selectedIdsAndNames.push({ id: item.id, name: item.name });
}
}
```
这两个例子都假设`val`数组中的每个对象都有`id`和`name`属性。
相关问题
import os import json import torch from PIL import Image from torchvision import transforms from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image # 指向需要遍历预测的图像文件夹 imgs_root = "../dataset/val" assert os.path.exists(imgs_root), f"file: '{imgs_root}' dose not exist." # 读取指定文件夹下所有jpg图像路径 img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")] # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), f"file: '{json_path}' dose not exist." json_file = open(json_path, "r") class_indict = json.load(json_file) # create model model = resnet34(num_classes=16).to(device) # load model weights weights_path = "./newresNet34.pth" assert os.path.exists(weights_path), f"file: '{weights_path}' dose not exist." model.load_state_dict(torch.load(weights_path, map_location=device)) # prediction model.eval() batch_size = 8 # 每次预测时将多少张图片打包成一个batch with torch.no_grad(): for ids in range(0, len(img_path_list) // batch_size): img_list = [] for img_path in img_path_list[ids * batch_size: (ids + 1) * batch_size]: assert os.path.exists(img_path), f"file: '{img_path}' dose not exist." img = Image.open(img_path) img = data_transform(img) img_list.append(img) # batch img # 将img_list列表中的所有图像打包成一个batch batch_img = torch.stack(img_list, dim=0) # predict class output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1) for idx, (pro, cla) in enumerate(zip(probs, classes)): print("image: {} class: {} prob: {:.3}".format(img_path_list[ids * batch_size + idx], class_indict[str(cla.numpy())], pro.numpy())) if __name__ == '__main__': main()
这段代码实现了导入必要的包和模块,包括操作系统、JSON、PyTorch、PIL及其转换模块、还有定义的resnet34模型。在主函数中,首先根据可用GPU情况使用cuda或cpu作为设备,然后定义数据的处理流程,包括缩放、剪裁、转换为Tensor并进行标准化。
给下面这段代码每行注释import os import json import torch from PIL import Image from torchvision import transforms from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image # 指向需要遍历预测的图像文件夹 imgs_root = "../dataset/val" assert os.path.exists(imgs_root), f"file: '{imgs_root}' dose not exist." # 读取指定文件夹下所有jpg图像路径 img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")] # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), f"file: '{json_path}' dose not exist." json_file = open(json_path, "r") class_indict = json.load(json_file) # create model model = resnet34(num_classes=16).to(device) # load model weights weights_path = "./newresNet34.pth" assert os.path.exists(weights_path), f"file: '{weights_path}' dose not exist." model.load_state_dict(torch.load(weights_path, map_location=device)) # prediction model.eval() batch_size = 8 # 每次预测时将多少张图片打包成一个batch with torch.no_grad(): for ids in range(0, len(img_path_list) // batch_size): img_list = [] for img_path in img_path_list[ids * batch_size: (ids + 1) * batch_size]: assert os.path.exists(img_path), f"file: '{img_path}' dose not exist." img = Image.open(img_path) img = data_transform(img) img_list.append(img) # batch img # 将img_list列表中的所有图像打包成一个batch batch_img = torch.stack(img_list, dim=0) # predict class output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1) for idx, (pro, cla) in enumerate(zip(probs, classes)): print("image: {} class: {} prob: {:.3}".format(img_path_list[ids * batch_size + idx], class_indict[str(cla.numpy())], pro.numpy())) if __name__ == '__main__': main()
1. import os:导入操作系统模块
2. import json:导入处理json格式的模块
3. import torch:导入深度学习框架PyTorch模块
4. from PIL import Image:从PIL模块中导入Image模块,用于图像处理
5. from torchvision import transforms:从torchvision模块中导入transforms模块,用于数据预处理
6. from model import resnet34:从自定义模块model中导入resnet34模型
7. def main(): 定义一个名为main的函数
8. device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"):使用GPU加速计算,如果GPU可用,就使用GPU,否则使用CPU
9. data_transform = transforms.Compose([...]):定义一个数据预处理的组合操作,对图像进行缩放、中心裁剪、转换为张量并标准化
10. transforms.Resize(256):将图像缩放至256*256大小
11. transforms.CenterCrop(224):对缩放后的图像从中心裁剪出224*224大小的图像
12. transforms.ToTensor():将图像转换为张量
13. transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]):将张量标准化,均值为0.485、0.456、0.406,标准差为0.229、0.224、0.225