torch.Storage A torch.Storage is a contiguous, one-dimensional array of a single data type. Every torch.Tensor has a corresponding storage of the same data type.  
class torch.FloatStorage(*args, **kwargs) [source]
 
 
bfloat16()  
Casts this storage to bfloat16 type 
  
bool()  
Casts this storage to bool type 
  
byte()  
Casts this storage to byte type 
  
char()  
Casts this storage to char type 
  
clone()  
Returns a copy of this storage 
  
complex_double()  
Casts this storage to complex double type 
  
complex_float()  
Casts this storage to complex float type 
  
copy_() 
  
cpu()  
Returns a CPU copy of this storage if it’s not already on the CPU 
  
cuda(device=None, non_blocking=False, **kwargs)  
Returns a copy of this object in CUDA memory. If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.  Parameters 
 
device (int) – The destination GPU id. Defaults to the current device. 
non_blocking (bool) – If True and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. 
**kwargs – For compatibility, may contain the key async in place of the non_blocking argument.    
  
data_ptr() 
  
device 
  
double()  
Casts this storage to double type 
  
dtype 
  
element_size() 
  
fill_() 
  
float()  
Casts this storage to float type 
  
static from_buffer() 
  
static from_file(filename, shared=False, size=0) → Storage  
If shared is True, then memory is shared between all processes. All changes are written to the file. If shared is False, then the changes on the storage do not affect the file. size is the number of elements in the storage. If shared is False, then the file must contain at least size * sizeof(Type) bytes (Type is the type of storage). If shared is True the file will be created if needed.  Parameters 
 
filename (str) – file name to map 
shared (bool) – whether to share memory 
size (int) – number of elements in the storage    
  
get_device() 
  
half()  
Casts this storage to half type 
  
int()  
Casts this storage to int type 
  
is_cuda: bool = False 
  
is_pinned() 
  
is_shared() 
  
is_sparse: bool = False 
  
long()  
Casts this storage to long type 
  
new() 
  
pin_memory()  
Copies the storage to pinned memory, if it’s not already pinned. 
  
resize_() 
  
share_memory_()  
Moves the storage to shared memory. This is a no-op for storages already in shared memory and for CUDA storages, which do not need to be moved for sharing across processes. Storages in shared memory cannot be resized. Returns: self 
  
short()  
Casts this storage to short type 
  
size() 
  
tolist()  
Returns a list containing the elements of this storage 
  
type(dtype=None, non_blocking=False, **kwargs)  
Returns the type if dtype is not provided, else casts this object to the specified type. If this is already of the correct type, no copy is performed and the original object is returned.  Parameters 
 
dtype (type or string) – The desired type 
non_blocking (bool) – If True, and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect. 
**kwargs – For compatibility, may contain the key async in place of the non_blocking argument. The async arg is deprecated.