下面是一个示例代码,展示了如何在使用PyTorch时避免’RuntimeError: Trying to create tensor with negative dimension’错误。 import torch # 假设我们有一个张量A和一个标量b A = torch.randn(3, 4) # 创建一个3x4的张量 b = 2 # 定义一个标量 # 在进行数学运算时,确保结果始终为正数或零 result_dim ...
ERROR: [Torch-TensorRT TorchScript Conversion Context] - 9: [graphShapeAnalyzer.cpp::addVolumeCheck::739] Error Code 9: Internal Error ((Unnamed Layer* 183) [PluginV2DynamicExt]_output_0: dimension 0 never exceeds -2147483648) ERROR: [Torch-TensorRT TorchScript Conversion Context] - 9: [gra...
import torch a= torch.rand(4) a tensor([0.6699, 0.2215, 0.1245, 0.7439]) x=torch.rand(2,3) x tensor([[0.5936, 0.9785, 0.2229], [0.9487, 0.8609, 0.5941]]) y= torch.rand(10) y tensor([0.4178, 0.9666, 0.2885, 0.1685, 0.2009, 0.7904, 0.1492, 0.2379, 0.9642, 0.9226]) ...
transforms.ToTensor(), transforms.Normalize(mean=torch.tensor(mean),std=torch.tensor(std)) 图片转换为适合深度学习的tensor 并根据数据集均值和方差归一化 如果re_prob>0 则 使用RandomErasing RandomErasing(re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu') 使用...
我对torch中的gather函数的一点理解 根据得到的索引在输入中取值#[1,1],[4,3] c = torch.gather(a,0,torch.LongTensor([[0,0],[1,0]]))#1...根据得到的索引在输入中取值#[1,2],[3,2] 原理解释 假设输入与上同;index=B;输出为C B中每个元素分别为b(0,0)=0,b(0,1)=0 b(1,0)=...
1、确认修改config.json,只是修改了 { "modelName" : "qwen-14b", "modelWeightPath" : "/home/QWEN-14b" } 2、权重文件权限也修改为只读权限了 chmod 444 *.safetensors 3、然后nohup ./bin/mindieservice_daemon > output.log 2>&1 & ...
It's not for no reason, it's to convert "cuda" into torch.device("cuda:0") or None into the default device. We need to know the actual, potentially indexed, torch.device any of these things refer to, because that is how they will be stored in the device properties of the tensors...
torch.transpose(a, 0, 1) tensor([[ 1.00, 0.7300], [0.7921, -0.8219], [ -0.4120, 0.3819]]) Now, if our requirement is to use weight over the linear layer or embedded layer in the transpose, then the function changes the way and the code is also different. ...
Tensor] = None, aparam: Optional[torch.Tensor] = None, ): pass def output_def(self) -> FittingOutputDef: pass New models The PyTorch backend’s model architecture is meticulously structured with multiple layers of abstraction, ensuring a high degree of flexibility. Typically, the process ...
补充:上面的bug,当将tensor放到npu上时,是没有问题的。 #将tensor放到npu上就没有问题torch.rand(3,1).npu() @ torch.rand(1,1).npu()# 将tensor放到npu上就没有问题F.linear(torch.rand(6,3).npu(), torch.rand(1,3).npu()) 所以这个问题是torch的cpu版本matmul有问题?