1、根据已有的数据,使用torch.tensor()创建tensor 2、torch.* 创建特殊大小的tensor,详见Creation Ops 3、torch.*_like 创建与其他tensor size相同或type相同的tensor,详见Creation Ops 4、tensor.new_*创建与其他tensor type相同,size不同的tensor 下面介绍t
new_full(size, fill_value, dtype=None, device=None, requires_grad=False) → Tensor new_empty(size, dtype=None, device=None, requires_grad=False) → Tensor new_ones(size, dtype=None, device=None, requires_grad=False) → Tensor new_zeros(size, dtype=None, device=None, requires_grad=Fals...
将输入Tensors喂进这个Graph,然后执行得到输出Tensors。 这种多态性是我们需要的,因为有时候我们希望输入不同shape或者dtype的Tensors,但是当"trace_cache_key"越来越多时,意味着你要cache了庞大的Graph,这点是要注意的。另外,tf.function提供了input_signature,这个参数采用tf.TensorSpec指定了输入到函数的Tensor的sha...
torch_npu.npu_fusion_attention(Tensor query, Tensor key, Tensor value, int head_num, str input_layout, Tensor? pse=None, Tensor? padding_mask=None, Tensor? atten_mask=None, float scale=1., float keep_prob=1., int pre_tockens=2147483647, int next_tockens=2147483647, int inner_precise=...
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 << std::endl; std::cout <...
torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda...
device (torch.device): Desired device of returned tensor. Returns: (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that contains coordinates for the regular grids. """ affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) grid = ...
return torch.tensor(keep_index), num_to_keep So for the difference between these two implementations, I guess maybe you visualized all lanes after nms. Notice thekeep indexandnum_to_keepinLane_nms()can be used to limit number of lanes, normally I just keep top 4 lanes in my task. ...
@register_node_handler("aten::sum")defaten_sum(inputs,attributes,scope):inp,dim,keepdim=inputsctx=current_context()net=ctx.networkifctx.is_tensorrtandhas_trt_tensor(inputs):ifnotisinstance(dim,list):dim=[dim]axis_trt=_axes_to_trt_axis(dim,len(inp.shape))layer=net.add_reduce(inp,trt...
对APE大模型进行3并发测试,报错。 (py39) root@gzxj-sys-rpm46kwprrx:~/APE# ./run_test.sh /root/miniconda3/envs/py39/lib/python3.9/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be *...