在使用PyTorch的torch.where函数时遇到RuntimeError: expected scalar type float but found double错误,通常意味着torch.where期望的输入数据类型是float,但实际上提供的是double类型的数据。为了解决这个问题,我们需要确保传入torch.where的所有张量数据类型一致。以下是一些步骤和代码示例,帮助你解决这个问题: 分析torch....
toBool(7)); return wrap(dispatch_randint(high, size, generator, options)); } else { check_out_type_matches( r.tensor(3), r.scalartype(4), r.isNone(4), r.layout(5), r.device(6), r.isNone(6)); return wrap(dispatch_randint( r.toInt64(0), r.intlist(1), r.generator(2)...
scalar_type(); // long auto device = tensor_a.device(); // cpu std::cout << tensor_a << std::endl; std::cout << dim << std::endl; std::cout << sizes << std::endl; std::cout << size_0 << std::endl; std::cout << numel << std::endl; std::cout << dtype <<...
torch.result_type does not accept combinations of scalars and dtypes. (These are permitted by the standard; only one of the arguments has to be an array or dtype.) from array_api_compat import torch as xp types = ['scalar', 'array', 'dtype'] dtypes = [float, int, complex] for ...
// 指定测试数据集var test_data = datasets.FashionMNIST(root:"data",// 数据集在那个目录下train:false,// 加载该数据集,用于训练download:true,// 如果数据集不存在,是否下载target_transform: transforms.ConvertImageDtype(ScalarType.Float32)// 指定特征和标签转换,将标签转换为Float32); ...
import torch scalar = torch.tensor(5) # 创建一个标量(0维张量) value = scalar.item() # 获取标量的值 print("标量的值:", value) 提取非最大值的元素位置 如果您只想提取最大值的位置(索引),而不是值本身,可以使用 torch.nonzero() 函数来找到最大值的位置信息。以下是一个示例: import torch #...
where the -1 has type int, and in principle, JIT should find and execute the overload aten::view(Tensor(a) self, int[] size) -> Tensor(a) However, JIT does not have a separate type for ScalarType in jit_type.h, and all ScalarType are considered as int. As a result, JIT wil...
torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)报错,复现HOReID代码时遇到的问题,详细报错:ExpectedobjectofscalartypeLongbutgotscalartypeIn
result_type rnn_relu rnn_relu_cell rnn_tanh rnn_tanh_cell roll rot90 round round_ row_stack rrelu rrelu_ rsqrt rsqrt_ rsub saddmm save scalar_tensor scatter scatter_add searchsorted seed select selu selu_ serialization set_anomaly_enabled set_autocast_enabled set_default_dtype set_default_...
prepared=prepare_fx(fx_model,{"":qconfig,"object_type":[# 这里设置反卷积的量化规则,注意看维度的per-channel量化ch_axis=1(torch.nn.ConvTranspose2d,ao.quantization.qconfig.QConfig(activation=ao.quantization.observer.HistogramObserver.with_args(qscheme=torch.per_tensor_symmetric,dtype=torch.qint8,),...