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=F
torch.device使用方法 torch.device代表将torch.Tensor分配到的设备的对象,有cpu和cuda两种,这里的cuda就是gpu,至于为什么不直接用gpu与cpu对应,是因为gpu的编程接口采用的是cuda print(torch.cuda.is_available()) #cuda是否可用; print(torch.cuda.device_count()) #返回gpu数量; print(torch.cuda.get_device_na...
这匹配Tensor.get_device(),它为cuda张量返回一个序数,并且不支持cpu张量。 >>torch.device(1) device(type='cuda', index=1) 注意指定设备的方法可以使用(properly formatted)字符串或(legacy)整数型设备序数,即以下示例均等效: >>torch.randn((2,3), device=torch.device('cuda:1'))>>torch.randn((2,...
import torch a = torch.rand(4,3) print(a.dtype, a.device) print(torch.get_default_dtype()) # torch.float32 cpu # torch.float32 torch.set_default_tensor_type(torch.cuda.FloatTensor) b = torch.rand(2,3) print(b.dtype, b.device) print(torch.get_default_dtype()) # torch.float32 ...
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. requires...
torch.cuda.device_count() 查看当前GPU索引号(从0开始): torch.cuda.current_device() 根据索引号查看GPU名字: torch.cuda.get_device_name(#输入索引号) 3、torch.tensor存储在GPU上 使用.cuda()可以将内存中的Tensor转换到GPU上。如果有多块GPU,可以使用.cuda(i)来表示第i块GPU所对应的显存(从0开始),...
warnings.warn( Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/HwHiAiUser/.local/lib/python3.10/site-packages/torch/utils/backend_registration.py", line 153, in wrap_tensor_to device_idx = _normalization_device(custom_backend_name, device) File "/...
torch.Tensor.new_zeros Supported 7 torch.Tensor.is_cuda Supported 8 torch.Tensor.is_quantized Supported 9 torch.Tensor.device Supported 10 torch.Tensor.ndim Supported 11 torch.Tensor.T Supported 12 torch.Tensor.abs Supported 13 torch.Tensor.abs_ ...
self.quant=torch.quantization.QuantStub()self.conv=torch.nn.Conv2d(1,1,1)self.relu=torch.nn.ReLU()# DeQuantStub converts tensors from quantized to floating point self.dequant=torch.quantization.DeQuantStub()defforward(self,x):# 自己指定开始量化的层 ...
print('We will use the GPU:', torch.cuda.get_device_name(0)) else: print('No GPU available, using the CPU instead.') device = torch.device("cpu") 然后将出现 .cuda()的地方改成 .to(device) 就可以在无gpu的环境中运行。