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...
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_name(0)...
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...
pytorch从0.4开始提出了Tensor Attributes,主要包含了torch.dtype,torch.device,torch.layout。pytorch可以使用他们管理数据类型属性。以下内容为pytorch0.4文档内容,具体可以查看Tensor Attributes Tensor Attributes torch.dtype torch.device torch.layout 每个torch.Tensor都有torch.dtype,torch.device,和torch.layout。
Tensor(t).to(device) for t in batch ] batch = torch.nn.utils.rnn.pad_sequence(batch) ## compute mask mask = (batch != 0).to(device) return batch, lengths, mask (2)单机单卡-内存不可容纳 Dataset 处理内存不可容纳数据的方式就是按需读文件(虽然dataset内部做一些魔改,比如修改__iter__,...
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 ...
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 Code: torch.Tensor((1,2,3,4)) #output: #tensor([1., 2., 3., 4.]) # PaddlePaddle Code: paddle.to_tensor((1,2,3,4)) # 全部为整数 #output: #Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, # [1, 2, 3, 4]) paddle.to_tensor((1,2,3,...
torch.get_rng_state()[source] Returns the random number generator state as a torch.ByteTensor. torch.set_rng_state(new_state)[source] Sets the random number generator state. Parameters new_state(torch.ByteTensor) – The desired state