def to_torch(self, device=None):
import torch # pylint: disable=C0415
arr = torch.zeros(size=self.shape,
dtype=to_pytorch_type(self.dtype),
device=device)
taichi.lang.meta.tensor_to_ext_arr(self, arr)
ti.sync()
return arr
def to_torch(self, device=None):
import torch
from taichi.lang.meta import tensor_to_ext_arr
arr = torch.zeros(size=self.shape,
dtype=to_pytorch_type(self.dtype),
device=device)
tensor_to_ext_arr(self, arr)
ti.sync()
return arr
def to_torch(self, device=None):
import torch # pylint: disable=C0415
# pylint: disable=E1101
arr = torch.zeros(size=self.shape,
dtype=to_pytorch_type(self.dtype),
device=device)
from taichi._kernels import tensor_to_ext_arr # pylint: disable=C0415
tensor_to_ext_arr(self, arr)
taichi.lang.runtime_ops.sync()
return arr
def to_torch(self, device=None, keep_dims=False):
import torch
as_vector = self.m == 1 and not keep_dims
shape_ext = (self.n, ) if as_vector else (self.n, self.m)
ret = torch.empty(self.shape + shape_ext,
dtype=to_pytorch_type(self.dtype),
device=device)
from taichi.lang.meta import matrix_to_ext_arr
matrix_to_ext_arr(self, ret, as_vector)
ti.sync()
return ret
def __init__(self, dtype, shape):
self.host_accessor = None
if impl.current_cfg().ndarray_use_torch:
assert has_pytorch(
), "PyTorch must be available if you want to create a Taichi ndarray with PyTorch as its underlying storage."
self.arr = torch.zeros(shape,
dtype=to_pytorch_type(cook_dtype(dtype)))
if impl.current_cfg().arch == _ti_core.Arch.cuda:
self.arr = self.arr.cuda()
else:
self.arr = _ti_core.Ndarray(impl.get_runtime().prog,
cook_dtype(dtype), shape)
def __init__(self, dtype, shape):
if isinstance(shape, numbers.Number):
shape = (shape, )
assert has_pytorch(
), "PyTorch must be available if you want to create a Taichi ndarray."
import torch
if impl.current_cfg().arch == _ti_core.Arch.cuda:
device = 'cuda:0'
else:
device = 'cpu'
self.arr = torch.empty(shape,
dtype=to_pytorch_type(dtype),
device=device)
def to_torch(self, device=None):
"""Create a torch array containing the same elements when the class itself represents GlobalVariableExpression (field) or ExternalTensorExpression internally.
This is an unified interface to match :func:`taichi.lang.Matrix.to_torch`.
Args:
device (DeviceType): The device type as a parameter passed into torch.zeros().
Returns:
The torch array containing the same elements when the class itself represents GlobalVariableExpression (field) or ExternalTensorExpression internally.
"""
import torch
from taichi.lang.meta import tensor_to_ext_arr
arr = torch.zeros(size=self.shape,
dtype=to_pytorch_type(self.dtype),
device=device)
tensor_to_ext_arr(self, arr)
ti.sync()
return arr
def to_torch(self, device=None, keep_dims=False):
"""Converts `self` to a torch tensor.
Args:
device (torch.device, optional): The desired device of returned tensor.
keep_dims (bool, optional): Whether to keep the dimension after conversion.
See :meth:`~taichi.lang.field.MatrixField.to_numpy` for more detailed explanation.
Returns:
torch.tensor: The result torch tensor.
"""
import torch
as_vector = self.m == 1 and not keep_dims
shape_ext = (self.n, ) if as_vector else (self.n, self.m)
arr = torch.empty(self.shape + shape_ext,
dtype=to_pytorch_type(self.dtype),
device=device)
from taichi.lang.meta import matrix_to_ext_arr
matrix_to_ext_arr(self, arr, as_vector)
ti.sync()
return arr
