def to_numpy(self):
import numpy as np
from taichi.lang.meta import tensor_to_ext_arr
arr = np.zeros(shape=self.shape, dtype=to_numpy_type(self.dtype))
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_numpy(self, dtype=None):
if dtype is None:
dtype = to_numpy_type(self.dtype)
import numpy as np
arr = np.zeros(shape=self.shape, dtype=dtype)
from taichi.lang.meta import tensor_to_ext_arr
tensor_to_ext_arr(self, arr)
ti.sync()
return arr
def to_numpy(self):
"""Create a numpy 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_numpy`.
Returns:
The numpy array containing the same elements when the class itself represents GlobalVariableExpression (field) or ExternalTensorExpression internally.
"""
import numpy as np
from taichi.lang.meta import tensor_to_ext_arr
arr = np.zeros(shape=self.shape, dtype=to_numpy_type(self.dtype))
tensor_to_ext_arr(self, arr)
ti.sync()
return arr
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