layer loading...提示(转)

本文介绍如何在网页中引入Layer UI库,并在AJAX请求过程中使用它来显示加载提示,提升用户体验。通过设置自定义的图标、遮罩和超时时间,确保即使在数据加载耗时较长的情况下,也能保持良好的交互效果。

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1、页面引用
<link rel="stylesheet" href="../Js/layer/skin/layer.css"  />
<script type="text/javascript" src="../Js/layer/layer.js"></script>

2、ajax请求中应用
<script type="text/javascript">
var loadingFlag;
$.ajax({
    url: "../FlightHandler/JDFlightHandler.ashx",
    type: "post",
    data: info,
    dataType: "text",
    beforeSend: function (XMLHttpRequest) {
        //注意,layer.msg默认3秒自动关闭,如果数据加载耗时比较长,需要设置time
        loadingFlag= layer.msg('正在读取数据,请稍候……', { icon: 16, shade: 0.01,shadeClose:false,time:60000 });
    },
    success: function (data) {
    
            layer.close(loadingFlag);
    },
    complete: function (XMLHttpRequest, textStatus) {

    },
    error: function (XMLHttpRequest, textStatus, errorThrown) {

    }
});
</script>
File "/home/orin/lcz/dockerfile/qwen/Qwen2.5-VL-main/signal_api.py", line 25, in <module> model.load_state_dict(torch.load('resnet18.pth', map_location='cpu')) File "/home/orin/anaconda3/envs/minicpmo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2593, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for ResNetForModulation: Missing key(s) in state_dict: "resnet.conv1.weight", "resnet.bn1.weight", "resnet.bn1.bias", "resnet.bn1.running_mean", "resnet.bn1.running_var", "resnet.layer1.0.conv1.weight", "resnet.layer1.0.bn1.weight", "resnet.layer1.0.bn1.bias", "resnet.layer1.0.bn1.running_mean", "resnet.layer1.0.bn1.running_var", "resnet.layer1.0.conv2.weight", "resnet.layer1.0.bn2.weight", "resnet.layer1.0.bn2.bias", "resnet.layer1.0.bn2.running_mean", "resnet.layer1.0.bn2.running_var", "resnet.layer1.1.conv1.weight", "resnet.layer1.1.bn1.weight", "resnet.layer1.1.bn1.bias", "resnet.layer1.1.bn1.running_mean", "resnet.layer1.1.bn1.running_var", "resnet.layer1.1.conv2.weight", "resnet.layer1.1.bn2.weight", "resnet.layer1.1.bn2.bias", "resnet.layer1.1.bn2.running_mean", "resnet.layer1.1.bn2.running_var", "resnet.layer2.0.conv1.weight", "resnet.layer2.0.bn1.weight", "resnet.layer2.0.bn1.bias", "resnet.layer2.0.bn1.running_mean", "resnet.layer2.0.bn1.running_var", "resnet.layer2.0.conv2.weight", "resnet.layer2.0.bn2.weight", "resnet.layer2.0.bn2.bias", "resnet.layer2.0.bn2.running_mean", "resnet.layer2.0.bn2.running_var", "resnet.layer2.0.downsample.0.weight", "resnet.layer2.0.downsample.1.weight", "resnet.layer2.0.downsample.1.bias", "resnet.layer2.0.downsample.1.running_mean", "resnet.layer2.0.downsample.1.running_var", "resnet.layer2.1.conv1.weight", "resnet.layer2.1.bn1.weight", "resnet.layer2.1.bn1.bias", "resnet.layer2.1.bn1.running_mean", "resnet.layer2.1.bn1.running_var", "resnet.layer2.1.conv2.weight", "resnet.layer2.1.bn2.weight", "resnet.layer2.1.bn2.bias", "resnet.layer2.1.bn2.running_mean", "resnet.layer2.1.bn2.running_var", "resnet.layer3.0.conv1.weight", "resnet.layer3.0.bn1.weight", "resnet.layer3.0.bn1.bias", "resnet.layer3.0.bn1.running_mean", "resnet.layer3.0.bn1.running_var", "resnet.layer3.0.conv2.weight", "resnet.layer3.0.bn2.weight", "resnet.layer3.0.bn2.bias", "resnet.layer3.0.bn2.running_mean", "resnet.layer3.0.bn2.running_var", "resnet.layer3.0.downsample.0.weight", "resnet.layer3.0.downsample.1.weight", "resnet.layer3.0.downsample.1.bias", "resnet.layer3.0.downsample.1.running_mean", "resnet.layer3.0.downsample.1.running_var", "resnet.layer3.1.conv1.weight", "resnet.layer3.1.bn1.weight", "resnet.layer3.1.bn1.bias", "resnet.layer3.1.bn1.running_mean", "resnet.layer3.1.bn1.running_var", "resnet.layer3.1.conv2.weight", "resnet.layer3.1.bn2.weight", "resnet.layer3.1.bn2.bias", "resnet.layer3.1.bn2.running_mean", "resnet.layer3.1.bn2.running_var", "resnet.layer4.0.conv1.weight", "resnet.layer4.0.bn1.weight", "resnet.layer4.0.bn1.bias", "resnet.layer4.0.bn1.running_mean", "resnet.layer4.0.bn1.running_var", "resnet.layer4.0.conv2.weight", "resnet.layer4.0.bn2.weight", "resnet.layer4.0.bn2.bias", "resnet.layer4.0.bn2.running_mean", "resnet.layer4.0.bn2.running_var", "resnet.layer4.0.downsample.0.weight", "resnet.layer4.0.downsample.1.weight", "resnet.layer4.0.downsample.1.bias", "resnet.layer4.0.downsample.1.running_mean", "resnet.layer4.0.downsample.1.running_var", "resnet.layer4.1.conv1.weight", "resnet.layer4.1.bn1.weight", "resnet.layer4.1.bn1.bias", "resnet.layer4.1.bn1.running_mean", "resnet.layer4.1.bn1.running_var", "resnet.layer4.1.conv2.weight", "resnet.layer4.1.bn2.weight", "resnet.layer4.1.bn2.bias", "resnet.layer4.1.bn2.running_mean", "resnet.layer4.1.bn2.running_var", "resnet.fc.weight", "resnet.fc.bias". Unexpected key(s) in state_dict: "conv1.weight", "bn1.running_mean", "bn1.running_var", "bn1.weight", "bn1.bias", "layer1.0.conv1.weight", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.conv2.weight", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.1.conv1.weight", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.conv2.weight", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer2.0.conv1.weight", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.conv2.weight", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.downsample.0.weight", "layer2.0.downsample.1.running_mean", "layer2.0.downsample.1.running_var", "layer2.0.downsample.1.weight", "layer2.0.downsample.1.bias", "layer2.1.conv1.weight", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.conv2.weight", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer3.0.conv1.weight", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.conv2.weight", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.downsample.0.weight", "layer3.0.downsample.1.running_mean", "layer3.0.downsample.1.running_var", "layer3.0.downsample.1.weight", "layer3.0.downsample.1.bias", "layer3.1.conv1.weight", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.conv2.weight", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer4.0.conv1.weight", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.conv2.weight", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.downsample.0.weight", "layer4.0.downsample.1.running_mean", "layer4.0.downsample.1.running_var", "layer4.0.downsample.1.weight", "layer4.0.downsample.1.bias", "layer4.1.conv1.weight", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.conv2.weight", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "fc.weight", "fc.bias".出现这种问题
最新发布
07-10
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