def to_numpy(self, keep_dims=False, as_vector=None, dtype=None):
"""Converts `self` to a numpy array.
Args:
keep_dims (bool, optional): Whether to keep the dimension after conversion.
When keep_dims=True, on an n-D matrix field, the numpy array always has n+2 dims, even for 1x1, 1xn, nx1 matrix fields.
When keep_dims=False, the resulting numpy array should skip the matrix dims with size 1.
For example, a 4x1 or 1x4 matrix field with 5x6x7 elements results in an array of shape 5x6x7x4.
as_vector (bool, deprecated): Whether to make the returned numpy array as a vector, i.e., with shape (n,) rather than (n, 1).
Note that this argument has been deprecated.
More discussion about `as_vector`: https://github.com/taichi-dev/taichi/pull/1046#issuecomment-633548858.
dtype (DataType, optional): The desired data type of returned numpy array.
Returns:
numpy.ndarray: The result numpy array.
"""
if as_vector is not None:
warning(
'v.to_numpy(as_vector=True) is deprecated, '
'please use v.to_numpy() directly instead',
DeprecationWarning,
stacklevel=3)
if dtype is None:
dtype = to_numpy_type(self.dtype)
as_vector = self.m == 1 and not keep_dims
shape_ext = (self.n, ) if as_vector else (self.n, self.m)
import numpy as np
arr = np.zeros(self.shape + shape_ext, dtype=dtype)
from taichi.lang.meta import matrix_to_ext_arr
matrix_to_ext_arr(self, arr, as_vector)
ti.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 to_numpy(self, keep_dims=False, as_vector=None, dtype=None):
# Discussion: https://github.com/taichi-dev/taichi/pull/1046#issuecomment-633548858
if as_vector is not None:
warning(
'v.to_numpy(as_vector=True) is deprecated, '
'please use v.to_numpy() directly instead',
DeprecationWarning,
stacklevel=3)
as_vector = self.m == 1 and not keep_dims
shape_ext = (self.n, ) if as_vector else (self.n, self.m)
if not self.is_global():
return np.array(self.entries).reshape(shape_ext)
if dtype is None:
dtype = to_numpy_type(self.dtype)
ret = np.zeros(self.shape + shape_ext, dtype=dtype)
from taichi.lang.meta import matrix_to_ext_arr
matrix_to_ext_arr(self, ret, as_vector)
return ret
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
def to_numpy(self, keep_dims=False, as_vector=None, dtype=None):
"""Convert the taichi matrix to a numpy.ndarray.
Args:
keep_dims (bool, optional): Whether keep the dimension after conversion.
When keep_dims=True, on an n-D matrix field, the numpy array always has n+2 dims, even for 1x1, 1xn, nx1 matrix fields.
When keep_dims=False, the resulting numpy array should skip the dimensionality with only 1 element, on the matrix shape dimensionalities.
For example, a 4x1 or 1x4 matrix field with 5x6x7 elements results in an array of shape 5x6x7x4.
as_vector (bool, deprecated): Make the returned numpy array as a vector i.e., has a shape (n,) rather than (n, 1)
Note that this argument has been deprecated.
More discussion about `as_vector`: https://github.com/taichi-dev/taichi/pull/1046#issuecomment-633548858.
dtype (DataType, optional): The desired data type of returned numpy array.
Returns:
numpy.ndarray: The numpy array that converted from the matrix field.
"""
# Discussion: https://github.com/taichi-dev/taichi/pull/1046#issuecomment-633548858
if as_vector is not None:
warning(
'v.to_numpy(as_vector=True) is deprecated, '
'please use v.to_numpy() directly instead',
DeprecationWarning,
stacklevel=3)
as_vector = self.m == 1 and not keep_dims
shape_ext = (self.n, ) if as_vector else (self.n, self.m)
if not self.is_global():
return np.array(self.entries).reshape(shape_ext)
if dtype is None:
dtype = to_numpy_type(self.dtype)
ret = np.zeros(self.shape + shape_ext, dtype=dtype)
from taichi.lang.meta import matrix_to_ext_arr
matrix_to_ext_arr(self, ret, as_vector)
return ret