Welcome to pytorch-types’s documentation!

Pytorch typing for pydantic.

class pytorch_types.Tensor

classmethod validate(data) → torch.Tensor

classmethod ndim(ndim) → pytorch_types.tensor.Tensor

Alias for dim()

classmethod dims(dims) → pytorch_types.tensor.Tensor

classmethod shape(*sizes) → pytorch_types.tensor.Tensor

classmethod between(ge, le) → pytorch_types.tensor.Tensor

classmethod ge(other) → Tensor

See torch.ge()

classmethod le(other) → Tensor

See torch.le()

classmethod gt(other) → Tensor

See torch.gt()

classmethod lt(other) → Tensor

See torch.lt()

classmethod ne(other) → Tensor

See torch.ne()

classmethod device(device) → pytorch_types.tensor.Tensor

Is the torch.device where this Tensor is.

classmethod cpu(memory_format=torch.preserve_format) → Tensor

Returns a copy of this object in CPU memory.

If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod cuda(device=None, non_blocking=False, memory_format=torch.preserve_format) → Tensor

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.

Args:

device (torch.device): The destination GPU device.

Defaults to the current CUDA 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. Default: False.

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod dtype(dtype) → pytorch_types.tensor.Tensor

classmethod float(memory_format=torch.preserve_format) → Tensor

self.float() is equivalent to self.to(torch.float32). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod float32() → pytorch_types.tensor.Tensor

classmethod half(memory_format=torch.preserve_format) → Tensor

self.half() is equivalent to self.to(torch.float16). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod float16()

classmethod double(memory_format=torch.preserve_format) → Tensor

self.double() is equivalent to self.to(torch.float64). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod float64() → pytorch_types.tensor.Tensor

classmethod int(memory_format=torch.preserve_format) → Tensor

self.int() is equivalent to self.to(torch.int32). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod int32() → pytorch_types.tensor.Tensor

classmethod long(memory_format=torch.preserve_format) → Tensor

self.long() is equivalent to self.to(torch.int64). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod int64() → pytorch_types.tensor.Tensor

classmethod short(memory_format=torch.preserve_format) → Tensor

self.short() is equivalent to self.to(torch.int16). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod int16() → pytorch_types.tensor.Tensor

classmethod byte(memory_format=torch.preserve_format) → Tensor

self.byte() is equivalent to self.to(torch.uint8). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

classmethod uint8() → pytorch_types.tensor.Tensor

classmethod bool(memory_format=torch.preserve_format) → Tensor

self.bool() is equivalent to self.to(torch.bool). See to().

Args:

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.

class pytorch_types.Numpy

classmethod validate(data) → numpy.ndarray

classmethod ndim(ndim) → pytorch_types.numpy.Numpy

Number of array dimensions.

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3

classmethod dims(dims) → pytorch_types.numpy.Numpy

classmethod shape(*sizes) → pytorch_types.numpy.Numpy

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.

numpy.reshape : similar function ndarray.reshape : similar method

classmethod between(ge, le) → pytorch_types.numpy.Numpy

classmethod ge(ge) → pytorch_types.numpy.Numpy

classmethod le(le) → pytorch_types.numpy.Numpy

classmethod gt(gt) → pytorch_types.numpy.Numpy

classmethod lt(lt) → pytorch_types.numpy.Numpy

classmethod ne(ne) → pytorch_types.numpy.Numpy

classmethod dtype(dtype) → pytorch_types.numpy.Numpy

Data-type of the array’s elements.

None

d : numpy dtype object

numpy.dtype

>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>

classmethod float() → pytorch_types.numpy.Numpy

classmethod float32() → pytorch_types.numpy.Numpy

classmethod half() → pytorch_types.numpy.Numpy

classmethod float16()

classmethod double() → pytorch_types.numpy.Numpy

classmethod float64() → pytorch_types.numpy.Numpy

classmethod int() → pytorch_types.numpy.Numpy

classmethod int32() → pytorch_types.numpy.Numpy

classmethod long() → pytorch_types.numpy.Numpy

classmethod int64() → pytorch_types.numpy.Numpy

classmethod short() → pytorch_types.numpy.Numpy

classmethod int16() → pytorch_types.numpy.Numpy

classmethod byte() → pytorch_types.numpy.Numpy

classmethod uint8() → pytorch_types.numpy.Numpy

classmethod bool() → pytorch_types.numpy.Numpy