# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value# ``tensor.T`` returns the transpose of a tensor#矩阵乘法y1=tensor@tensor.Ty2=tensor.matmul(tensor.T)y3=torch.rand_like(y1)torch.matmul(tensor,tensor.T,out=y3)# This computes the element...
# Tracing with Tensor("a:0", shape=(), dtype=int32) # tf.Tensor(2, shape=(), dtype=int32) # Tracing with Tensor("a:0", shape=(), dtype=float32) # tf.Tensor(2.2, shape=(), dtype=float32) # Tracing with Tensor("a:0", shape=(2,), dtype=int32) # tf.Tensor([2 4],...
🐛 Describe the bug torch.compile returns wrong value for conditional mask tensor operation import torch torch.manual_seed(420) x = torch.randn(1, 3, 2, 2) class Model(torch.nn.Module): def forward(self, x): out = x mask1 = out > 0 out[ma...
When data is a tensor x,torch.tensor()reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Thereforetorch.tensor(x)is equivalent tox.clone().detach()andtorch.tensor(x, requires_grad=True)is equivalent tox.clone().detach().requires_grad_(True). The equivalen...
tensor_1.expand_as(tensor_2) :把tensor_1扩展成和tensor_2一样的形状 TORCH.ONES_LIKE torch.ones_like(input,*,dtype=None,layout=None,device=None,requires_grad=False,memory_format=torch.preserve_format) →Tensor Returns a tensor filled with the scalar value1, with the same size asinput.torch...
🐛 Describe the bug I am trying to optimize a code that calls the radius function from pytorch_cluster: import torch from torch_cluster import radius import torch._dynamo as dynamo def myradius(x: torch.Tensor, y: torch.Tensor, r: float, ...
解决报错Input type (torch.cuda.DoubleTensor) and weight type (torch.cuda.FloatTensor) should be the same 今天用pytorch编写代码时,遇到了一个小错误(如下图所示)。 根据报错信息的意思可以推断,这个错误是由输入和权重的数据类型不一致引起的。 因此解决方法很简单,就是将输入的数据和模型参数的数据类型统一...
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.HalfTensor) should be the same 8 个月前 日懿关注 在改写yolox网络架构过程中出现的问题,错误内容大概就是指输入类型是GPU,而权重类型是CPU。当时一直在找weight的加载方式,根据Input type (torch.cuda.FloatTensor) and weight type ...
整体模型量化方式:activation为per-tensor,weight为per-channel int8对称量化 -128-127 量化的模型是Centernet-resnet50,包含卷积、反卷积、add、concat,bn 设置好FX的量化config: 代码语言:javascript 复制 qconfig=ao.quantization.qconfig.QConfig(activation=ao.quantization.observer.HistogramObserver.with_args(qscheme...
(0x7f6f441d0d87 in /usr/local/lib/python3.10/site-packages/torch/lib/libc10.so) frame #1: c10::TensorImpl::throw_cannot_call_with_symbolic(char const*) const + 0x9c (0x7f6f4417fe40 in /usr/local/lib/python3.10/site-packages/torch/lib/libc10.so) frame #2: <unknown function> +...