Question

对于新的Android应用更新，我必须将compileSdkversion设置为26。

执行此操作后，我发现自动售货许可证库出现问题，请使用以下功能（ServerManagedPolicy.java）：

private Map<String, String> decodeExtras(String extras) {
    Map<String, String> results = new HashMap<String, String>();
    try {
        URI rawExtras = new URI("?" + extras);
        List<**NameValuePair**> extraList = 
        **URLEncodedUtils**.parse(rawExtras, "UTF-8");
        for (**NameValuePair** item : extraList) {
            results.put(item.getName(), item.**getValue**());
        }
    } catch (URISyntaxException e) {
        Log.w(TAG, "Invalid syntax error while decoding extras data 
        from server.");
    }
    return results;
}

我知道这些功能已过时，但是没有Android Vending Licensing库的更新版本，而且我无法找到如何使它适用于Oreo或通常适用于高于Android 19（即{{1 }}我现在使用。

有谁可以帮助您？

PS。 compileSdkversion不起作用。该应用程序将直接崩溃。

Answer 1

再试一次：

ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
  (fc): Linear(in_features=512, out_features=1000, bias=True)
)
------------------------------------------------------------
conv1.weight : (64, 3, 7, 7)
bn1.weight : (64,)
bn1.bias : (64,)
layer1.0.conv1.weight : (64, 64, 3, 3)
layer1.0.bn1.weight : (64,)
layer1.0.bn1.bias : (64,)
layer1.0.conv2.weight : (64, 64, 3, 3)
layer1.0.bn2.weight : (64,)
layer1.0.bn2.bias : (64,)
layer1.1.conv1.weight : (64, 64, 3, 3)
layer1.1.bn1.weight : (64,)
layer1.1.bn1.bias : (64,)
layer1.1.conv2.weight : (64, 64, 3, 3)
layer1.1.bn2.weight : (64,)
layer1.1.bn2.bias : (64,)
layer2.0.conv1.weight : (128, 64, 3, 3)
layer2.0.bn1.weight : (128,)
layer2.0.bn1.bias : (128,)
layer2.0.conv2.weight : (128, 128, 3, 3)
layer2.0.bn2.weight : (128,)
layer2.0.bn2.bias : (128,)
layer2.0.downsample.0.weight : (128, 64, 1, 1)
layer2.0.downsample.1.weight : (128,)
layer2.0.downsample.1.bias : (128,)
layer2.1.conv1.weight : (128, 128, 3, 3)
layer2.1.bn1.weight : (128,)
layer2.1.bn1.bias : (128,)
layer2.1.conv2.weight : (128, 128, 3, 3)
layer2.1.bn2.weight : (128,)
layer2.1.bn2.bias : (128,)
layer3.0.conv1.weight : (256, 128, 3, 3)
layer3.0.bn1.weight : (256,)
layer3.0.bn1.bias : (256,)
layer3.0.conv2.weight : (256, 256, 3, 3)
layer3.0.bn2.weight : (256,)
layer3.0.bn2.bias : (256,)
layer3.0.downsample.0.weight : (256, 128, 1, 1)
layer3.0.downsample.1.weight : (256,)
layer3.0.downsample.1.bias : (256,)
layer3.1.conv1.weight : (256, 256, 3, 3)
layer3.1.bn1.weight : (256,)
layer3.1.bn1.bias : (256,)
layer3.1.conv2.weight : (256, 256, 3, 3)
layer3.1.bn2.weight : (256,)
layer3.1.bn2.bias : (256,)
layer4.0.conv1.weight : (512, 256, 3, 3)
layer4.0.bn1.weight : (512,)
layer4.0.bn1.bias : (512,)
layer4.0.conv2.weight : (512, 512, 3, 3)
layer4.0.bn2.weight : (512,)
layer4.0.bn2.bias : (512,)
layer4.0.downsample.0.weight : (512, 256, 1, 1)
layer4.0.downsample.1.weight : (512,)
layer4.0.downsample.1.bias : (512,)
layer4.1.conv1.weight : (512, 512, 3, 3)
layer4.1.bn1.weight : (512,)
layer4.1.bn1.bias : (512,)
layer4.1.conv2.weight : (512, 512, 3, 3)
layer4.1.bn2.weight : (512,)
layer4.1.bn2.bias : (512,)
fc.weight : (1000, 512)
fc.bias : (1000,)

oreo的许可证检查

1 个答案: