torch、(一) Tensor

目录

torch.is_tensor(obj)[source]

torch.is_storage(obj)[source]

torch.set_default_dtype(d)[source]

torch.get_default_dtype() → torch.dtype

torch.set_default_tensor_type(t)[source]

torch.numel(input) → int

torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None, sci_mode=None)[source]

torch.set_flush_denormal(mode) → bool

Creation Ops

torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

torch.sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) → Tensor

torch.as_tensor(data, dtype=None, device=None) → Tensor

torch.as_strided(input, size, stride, storage_offset=0) → Tensor

torch.from_numpy(ndarray) → Tensor

torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

torch.ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.ones_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) → Tensor

torch.empty_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

torch.empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) → Tensor

torch.full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Indexing, Slicing, Joining, Mutating Ops

torch.chunk(input, chunks, dim=0) → List of Tensors

torch.gather(input, dim, index, out=None, sparse_grad=False) → Tensor

torch.index_select(input, dim, index, out=None) → Tensor

torch.narrow(input, dim, start, length) → Tensor

torch.nonzero(input, *, out=None, as_tuple=False) → LongTensor or tuple of LongTensors

torch.reshape(input, shape) → Tensor

torch.split(tensor, split_size_or_sections, dim=0)[source]

torch.squeeze(input, dim=None, out=None) → Tensor

torch.stack(tensors, dim=0, out=None) → Tensor

torch.take(input, index) → Tensor

torch.transpose(input, dim0, dim1) → Tensor

torch.unbind(input, dim=0) → seq

torch.unsqueeze(input, dim, out=None) → Tensor

torch.where(condition, x, y) → Tensor

---

The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities.It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0.

torch.is_tensor(obj)[source]

Returns True if obj is a PyTorch tensor.

Parameters

obj (Object) – Object to test

torch.is_storage(obj)[source]

Returns True if obj is a PyTorch storage object.

Parameters

obj (Object) – Object to test

torch.is_floating_point(input) -> (bool)

Returns True if the data type of input is a floating point data type i.e., one of torch.float64, torch.float32 and torch.float16.

Parameters

input (Tensor) – the PyTorch tensor to test

torch.set_default_dtype(d)[source]

Sets the default floating point dtype to d. This type will be used as default floating point type for type inference in torch.tensor().

The default floating point dtype is initially torch.float32.

Parameters

d (torch.dtype) – the floating point dtype to make the default

Example:

>>> torch.tensor([1.2, 3]).dtype           # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_dtype(torch.float64)
>>> torch.tensor([1.2, 3]).dtype           # a new floating point tensor
torch.float64

torch.get_default_dtype() → torch.dtype

Get the current default floating point torch.dtype.

Example:

>>> torch.get_default_dtype()  # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_dtype(torch.float64)
>>> torch.get_default_dtype()  # default is now changed to torch.float64
torch.float64
>>> torch.set_default_tensor_type(torch.FloatTensor)  # setting tensor type also affects this
>>> torch.get_default_dtype()  # changed to torch.float32, the dtype for torch.FloatTensor
torch.float32

torch.set_default_tensor_type(t)[source]

Sets the default torch.Tensor type to floating point tensor type t. This type will also be used as default floating point type for type inference in torch.tensor().

The default floating point tensor type is initially torch.FloatTensor.

Parameters

t (type or string) – the floating point tensor type or its name

Example:

>>> torch.tensor([1.2, 3]).dtype    # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_tensor_type(torch.DoubleTensor)
>>> torch.tensor([1.2, 3]).dtype    # a new floating point tensor
torch.float64

torch.numel(input) → int

Returns the total number of elements in the input tensor.

Parameters

input (Tensor) – the input tensor.

Example:

>>> a = torch.randn(1, 2, 3, 4, 5)
>>> torch.numel(a)
120
>>> a = torch.zeros(4,4)
>>> torch.numel(a)
16

torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None, sci_mode=None)[source]

Set options for printing. Items shamelessly taken from NumPy

Parameters

• precision – Number of digits of precision for floating point output (default = 4).

• threshold – Total number of array elements which trigger summarization rather than full repr (default = 1000).

• edgeitems – Number of array items in summary at beginning and end of each dimension (default = 3).

• linewidth – The number of characters per line for the purpose of inserting line breaks (default = 80). Thresholded matrices will ignore this parameter.

• profile – Sane defaults for pretty printing. Can override with any of the above options. (any one of default, short, full)

• sci_mode – Enable (True) or disable (False) scientific notation. If None (default) is specified, the value is defined by _Formatter

torch.set_flush_denormal(mode) → bool

Disables denormal floating numbers on CPU.

Returns True if your system supports flushing denormal numbers and it successfully configures flush denormal mode. set_flush_denormal() is only supported on x86 architectures supporting SSE3.

Parameters

mode (bool) – Controls whether to enable flush denormal mode or not

Example:

>>> torch.set_flush_denormal(True)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor([ 0.], dtype=torch.float64)
>>> torch.set_flush_denormal(False)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor(9.88131e-324 *
       [ 1.0000], dtype=torch.float64)

Creation Ops

Note

Random sampling creation ops are listed under Random sampling and include: torch.rand() torch.rand_like() torch.randn() torch.randn_like() torch.randint() torch.randint_like() torch.randperm() You may also use torch.empty() with the In-place random sampling methods to create torch.Tensor s with values sampled from a broader range of distributions.

torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

Constructs a tensor with data.

Warning

torch.tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a NumPy ndarray and want to avoid a copy, use torch.as_tensor().

Warning

When data is a tensor x, torch.tensor() reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore torch.tensor(x) is equivalent to x.clone().detach() and torch.tensor(x, requires_grad=True) is equivalent to x.clone().detach().requires_grad_(True). The equivalents using clone() and detach() are recommended.

Parameters

• data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from data.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[ 0.1000,  1.2000],
        [ 2.2000,  3.1000],
        [ 4.9000,  5.2000]])

>>> torch.tensor([0, 1])  # Type inference on data
tensor([ 0,  1])

>>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
                 dtype=torch.float64,
                 device=torch.device('cuda:0'))  # creates a torch.cuda.DoubleTensor
tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')

>>> torch.tensor(3.14159)  # Create a scalar (zero-dimensional tensor)
tensor(3.1416)

>>> torch.tensor([])  # Create an empty tensor (of size (0,))
tensor([])

torch.sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) → Tensor

Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given indices with the given values. A sparse tensor can be uncoalesced, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch.sparse.

Parameters

• indices (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types. Will be cast to a torch.LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values.

• values (array_like) – Initial values for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

• size (list, tuple, or torch.Size, optional) – Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from values.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> i = torch.tensor([[0, 1, 1],
                      [2, 0, 2]])
>>> v = torch.tensor([3, 4, 5], dtype=torch.float32)
>>> torch.sparse_coo_tensor(i, v, [2, 4])
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       size=(2, 4), nnz=3, layout=torch.sparse_coo)

>>> torch.sparse_coo_tensor(i, v)  # Shape inference
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       size=(2, 3), nnz=3, layout=torch.sparse_coo)

>>> torch.sparse_coo_tensor(i, v, [2, 4],
                            dtype=torch.float64,
                            device=torch.device('cuda:0'))
tensor(indices=tensor([[0, 1, 1],
                       [2, 0, 2]]),
       values=tensor([3., 4., 5.]),
       device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64,
       layout=torch.sparse_coo)

# Create an empty sparse tensor with the following invariants:
#   1. sparse_dim + dense_dim = len(SparseTensor.shape)
#   2. SparseTensor._indices().shape = (sparse_dim, nnz)
#   3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])
#
# For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and
# sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0))
>>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
tensor(indices=tensor([], size=(1, 0)),
       values=tensor([], size=(0,)),
       size=(1,), nnz=0, layout=torch.sparse_coo)

# and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and
# sparse_dim = 1
>>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])
tensor(indices=tensor([], size=(1, 0)),
       values=tensor([], size=(0, 2)),
       size=(1, 2), nnz=0, layout=torch.sparse_coo)

torch.as_tensor(data, dtype=None, device=None) → Tensor

Convert the data into a torch.Tensor. If the data is already a Tensor with the same dtype and device, no copy will be performed, otherwise a new Tensor will be returned with computational graph retained if data Tensor has requires_grad=True. Similarly, if the data is an ndarray of the corresponding dtype and the device is the cpu, no copy will be performed.

Parameters

• data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from data.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

Example:

>>> a = numpy.array([1, 2, 3])
>>> t = torch.as_tensor(a)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

>>> a = numpy.array([1, 2, 3])
>>> t = torch.as_tensor(a, device=torch.device('cuda'))
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([1,  2,  3])

torch.as_strided(input, size, stride, storage_offset=0) → Tensor

Create a view of an existing torch.Tensor input with specified size, stride and storage_offset.

Warning

More than one element of a created tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.

Many PyTorch functions, which return a view of a tensor, are internally implemented with this function. Those functions, like torch.Tensor.expand(), are easier to read and are therefore more advisable to use.

Parameters

• input (Tensor) – the input tensor.

• size (tuple or ints) – the shape of the output tensor

• stride (tuple or ints) – the stride of the output tensor

• storage_offset (int, optional) – the offset in the underlying storage of the output tensor

Example:

>>> x = torch.randn(3, 3)
>>> x
tensor([[ 0.9039,  0.6291,  1.0795],
        [ 0.1586,  2.1939, -0.4900],
        [-0.1909, -0.7503,  1.9355]])
>>> t = torch.as_strided(x, (2, 2), (1, 2))
>>> t
tensor([[0.9039, 1.0795],
        [0.6291, 0.1586]])
>>> t = torch.as_strided(x, (2, 2), (1, 2), 1)
tensor([[0.6291, 0.1586],
        [1.0795, 2.1939]])

torch.from_numpy(ndarray) → Tensor

Creates a Tensor from a numpy.ndarray.

The returned tensor and ndarray share the same memory. Modifications to the tensor will be reflected in the ndarray and vice versa. The returned tensor is not resizable.

It currently accepts ndarray with dtypes of numpy.float64, numpy.float32, numpy.float16, numpy.int64, numpy.int32, numpy.int16, numpy.int8, numpy.uint8, and numpy.bool.

Example:

>>> a = numpy.array([1, 2, 3])
>>> t = torch.from_numpy(a)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size.

Parameters

• size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.zeros(2, 3)
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]])

>>> torch.zeros(5)
tensor([ 0.,  0.,  0.,  0.,  0.])

torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 0, with the same size as input. torch.zeros_like(input) is equivalent to torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Warning

As of 0.4, this function does not support an out keyword. As an alternative, the old torch.zeros_like(input, out=output) is equivalent to torch.zeros(input.size(), out=output).

Parameters

• input (Tensor) – the size of input will determine size of the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (torch.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> input = torch.empty(2, 3)
>>> torch.zeros_like(input)
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]])

torch.ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size.

Parameters

• size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.ones(2, 3)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])

>>> torch.ones(5)
tensor([ 1.,  1.,  1.,  1.,  1.])

torch.ones_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 1, with the same size as input. torch.ones_like(input) is equivalent to torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Warning

As of 0.4, this function does not support an out keyword. As an alternative, the old torch.ones_like(input, out=output) is equivalent to torch.ones(input.size(), out=output).

Parameters

• input (Tensor) – the size of input will determine size of the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (torch.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> input = torch.empty(2, 3)
>>> torch.ones_like(input)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])

torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil⌈stepend−start​⌉ with values from the interval [start, end) taken with common difference step beginning from start.

Note that non-integer step is subject to floating point rounding errors when comparing against end; to avoid inconsistency, we advise adding a small epsilon to end in such cases.

outi+1=outi+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step} outi+1​=outi​+step

Parameters

• start (Number) – the starting value for the set of points. Default: 0.

• end (Number) – the ending value for the set of points

• step (Number) – the gap between each pair of adjacent points. Default: 1.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see get_default_dtype(). Otherwise, the dtype is inferred to be torch.int64.

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.arange(5)
tensor([ 0,  1,  2,  3,  4])
>>> torch.arange(1, 4)
tensor([ 1,  2,  3])
>>> torch.arange(1, 2.5, 0.5)
tensor([ 1.0000,  1.5000,  2.0000])

torch.range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 1-D tensor of size ⌊end−startstep⌋+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1⌊stepend−start​⌋+1 with values from start to end with step step. Step is the gap between two values in the tensor.

outi+1=outi+step.\text{out}_{i+1} = \text{out}_i + \text{step}. outi+1​=outi​+step.

Warning

This function is deprecated in favor of torch.arange().

Parameters

• start (float) – the starting value for the set of points. Default: 0.

• end (float) – the ending value for the set of points

• step (float) – the gap between each pair of adjacent points. Default: 1.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see get_default_dtype(). Otherwise, the dtype is inferred to be torch.int64.

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.range(1, 4)
tensor([ 1.,  2.,  3.,  4.])
>>> torch.range(1, 4, 0.5)
tensor([ 1.0000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  4.0000])

torch.linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a one-dimensional tensor of steps equally spaced points between start and end.

The output tensor is 1-D of size steps.

Parameters

• start (float) – the starting value for the set of points

• end (float) – the ending value for the set of points

• steps (int) – number of points to sample between start and end. Default: 100.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.linspace(3, 10, steps=5)
tensor([  3.0000,   4.7500,   6.5000,   8.2500,  10.0000])
>>> torch.linspace(-10, 10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])
>>> torch.linspace(start=-10, end=10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])
>>> torch.linspace(start=-10, end=10, steps=1)
tensor([-10.])

torch.logspace(start, end, steps=100, base=10.0, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a one-dimensional tensor of steps points logarithmically spaced with base base between basestart{\text{base}}^{\text{start}}basestart and baseend{\text{base}}^{\text{end}}baseend .

The output tensor is 1-D of size steps.

Parameters

• start (float) – the starting value for the set of points

• end (float) – the ending value for the set of points

• steps (int) – number of points to sample between start and end. Default: 100.

• base (float) – base of the logarithm function. Default: 10.0.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.logspace(start=-10, end=10, steps=5)
tensor([ 1.0000e-10,  1.0000e-05,  1.0000e+00,  1.0000e+05,  1.0000e+10])
>>> torch.logspace(start=0.1, end=1.0, steps=5)
tensor([  1.2589,   2.1135,   3.5481,   5.9566,  10.0000])
>>> torch.logspace(start=0.1, end=1.0, steps=1)
tensor([1.2589])
>>> torch.logspace(start=2, end=2, steps=1, base=2)
tensor([4.0])

torch.eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.

Parameters

• n (int) – the number of rows

• m (int, optional) – the number of columns with default being n

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Returns

A 2-D tensor with ones on the diagonal and zeros elsewhere

Return type

Tensor

Example:

>>> torch.eye(3)
tensor([[ 1.,  0.,  0.],
        [ 0.,  1.,  0.],
        [ 0.,  0.,  1.]])

torch.empty(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) → Tensor

Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument size.

Parameters

• size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> torch.empty(2, 3)
tensor(1.00000e-08 *
       [[ 6.3984,  0.0000,  0.0000],
        [ 0.0000,  0.0000,  0.0000]])

torch.empty_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns an uninitialized tensor with the same size as input. torch.empty_like(input) is equivalent to torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Parameters

• input (Tensor) – the size of input will determine size of the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (torch.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.empty((2,3), dtype=torch.int64)
tensor([[ 9.4064e+13,  2.8000e+01,  9.3493e+13],
        [ 7.5751e+18,  7.1428e+18,  7.5955e+18]])

torch.empty_strided(size, stride, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) → Tensor

Returns a tensor filled with uninitialized data. The shape and strides of the tensor is defined by the variable argument size and stride respectively. torch.empty_strided(size, stride) is equivalent to torch.empty(size).as_strided(size, stride).

Warning

More than one element of the created tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.

Parameters

• size (tuple of python:ints) – the shape of the output tensor

• stride (tuple of python:ints) – the strides of the output tensor

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> a = torch.empty_strided((2, 3), (1, 2))
>>> a
tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07],
        [0.0000e+00, 0.0000e+00, 3.0705e-41]])
>>> a.stride()
(1, 2)
>>> a.size()
torch.Size([2, 3])

torch.full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor of size size filled with fill_value.

Parameters

• size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor.

• fill_value – the number to fill the output tensor with.

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.full((2, 3), 3.141592)
tensor([[ 3.1416,  3.1416,  3.1416],
        [ 3.1416,  3.1416,  3.1416]])

torch.full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor with the same size as input filled with fill_value. torch.full_like(input, fill_value) is equivalent to torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).

Parameters

• input (Tensor) – the size of input will determine size of the output tensor.

• fill_value – the number to fill the output tensor with.

• dtype (torch.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (torch.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Indexing, Slicing, Joining, Mutating Ops

torch.cat(tensors, dim=0, out=None) → Tensor

Concatenates the given sequence of seq tensors in the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be empty.

torch.cat() can be seen as an inverse operation for torch.split() and torch.chunk().

torch.cat() can be best understood via examples.

Parameters

• tensors (sequence of Tensors) – any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.

• dim (int, optional) – the dimension over which the tensors are concatenated

• out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,
         -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,
         -0.5790,  0.1497]])

torch.chunk(input, chunks, dim=0) → List of Tensors

Splits a tensor into a specific number of chunks.

Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by chunks.

Parameters

• input (Tensor) – the tensor to split

• chunks (int) – number of chunks to return

• dim (int) – dimension along which to split the tensor

torch.gather(input, dim, index, out=None, sparse_grad=False) → Tensor

Gathers values along an axis specified by dim.

For a 3-D tensor the output is specified by:

out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2

If input is an n-dimensional tensor with size (x0,x1...,xi−1,xi,xi+1,...,xn−1)(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})(x0​,x1​...,xi−1​,xi​,xi+1​,...,xn−1​) and dim = i, then index must be an nnn -dimensional tensor with size (x0,x1,...,xi−1,y,xi+1,...,xn−1)(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})(x0​,x1​,...,xi−1​,y,xi+1​,...,xn−1​) where y≥1y \geq 1y≥1 and out will have the same size as index.

Parameters

• input (Tensor) – the source tensor

• dim (int) – the axis along which to index

• index (LongTensor) – the indices of elements to gather

• out (Tensor, optional) – the destination tensor

• sparse_grad (bool,optional) – If True, gradient w.r.t. input will be a sparse tensor.

Example:

>>> t = torch.tensor([[1,2],[3,4]])
>>> torch.gather(t, 1, torch.tensor([[0,0],[1,0]]))
tensor([[ 1,  1],
        [ 4,  3]])

torch.index_select(input, dim, index, out=None) → Tensor

Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.

The returned tensor has the same number of dimensions as the original tensor (input). The dimth dimension has the same size as the length of index; other dimensions have the same size as in the original tensor.

Note

The returned tensor does not use the same storage as the original tensor. If out has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary.

Parameters

• input (Tensor) – the input tensor.

• dim (int) – the dimension in which we index

• index (LongTensor) – the 1-D tensor containing the indices to index

• out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-0.4664,  0.2647, -0.1228, -1.1068],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> indices = torch.tensor([0, 2])
>>> torch.index_select(x, 0, indices)
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> torch.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
        [-0.4664, -0.1228],
        [-1.1734,  0.7230]])

torch.masked_select(input, mask, out=None) → Tensor

Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor.

The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable.

Note

The returned tensor does not use the same storage as the original tensor

Parameters

• input (Tensor) – the input tensor.

• mask (ByteTensor) – the tensor containing the binary mask to index with

• out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],
        [-1.2035,  1.2252,  0.5002,  0.6248],
        [ 0.1307, -2.0608,  0.1244,  2.0139]])
>>> mask = x.ge(0.5)
>>> mask
tensor([[False, False, False, False],
        [False, True, True, True],
        [False, False, False, True]])
>>> torch.masked_select(x, mask)
tensor([ 1.2252,  0.5002,  0.6248,  2.0139])

torch.narrow(input, dim, start, length) → Tensor

Returns a new tensor that is a narrowed version of input tensor. The dimension dim is input from start to start + length. The returned tensor and input tensor share the same underlying storage.

Parameters

• input (Tensor) – the tensor to narrow

• dim (int) – the dimension along which to narrow

• start (int) – the starting dimension

• length (int) – the distance to the ending dimension

Example:

>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> torch.narrow(x, 0, 0, 2)
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])
>>> torch.narrow(x, 1, 1, 2)
tensor([[ 2,  3],
        [ 5,  6],
        [ 8,  9]])

torch.nonzero(input, *, out=None, as_tuple=False) → LongTensor or tuple of LongTensors

When as_tuple is false or unspecified:

Returns a tensor containing the indices of all non-zero elements of input. Each row in the result contains the indices of a non-zero element in input. The result is sorted lexicographically, with the last index changing the fastest (C-style).

If input has n dimensions, then the resulting indices tensor out is of size (z×n)(z \times n)(z×n) , where zzz is the total number of non-zero elements in the input tensor.

When as_tuple is true:

Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input .

If input has n dimensions, then the resulting tuple contains n tensors of size z, where z is the total number of non-zero elements in the input tensor.

As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element.

Parameters

• input (Tensor) – the input tensor.

• out (LongTensor, optional) – the output tensor containing indices

Returns

If as_tuple is false, the output tensor containing indices. If as_tuple is true, one 1-D tensor for each dimension, containing the indices of each nonzero element along that dimension.

Return type

LongTensor or tuple of LongTensor

Example:

>>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]))
tensor([[ 0],
        [ 1],
        [ 2],
        [ 4]])
>>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
                                [0.0, 0.4, 0.0, 0.0],
                                [0.0, 0.0, 1.2, 0.0],
                                [0.0, 0.0, 0.0,-0.4]]))
tensor([[ 0,  0],
        [ 1,  1],
        [ 2,  2],
        [ 3,  3]])
>>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]), as_tuple=True)
(tensor([0, 1, 2, 4]),)
>>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
                                [0.0, 0.4, 0.0, 0.0],
                                [0.0, 0.0, 1.2, 0.0],
                                [0.0, 0.0, 0.0,-0.4]]), as_tuple=True)
(tensor([0, 1, 2, 3]), tensor([0, 1, 2, 3]))
>>> torch.nonzero(torch.tensor(5), as_tuple=True)
(tensor([0]),)

torch.reshape(input, shape) → Tensor

Returns a tensor with the same data and number of elements as input, but with the specified shape. When possible, the returned tensor will be a view of input. Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

See torch.Tensor.view() on when it is possible to return a view.

A single dimension may be -1, in which case it’s inferred from the remaining dimensions and the number of elements in input.

Parameters

• input (Tensor) – the tensor to be reshaped

• shape (tuple of python:ints) – the new shape

Example:

>>> a = torch.arange(4.)
>>> torch.reshape(a, (2, 2))
tensor([[ 0.,  1.],
        [ 2.,  3.]])
>>> b = torch.tensor([[0, 1], [2, 3]])
>>> torch.reshape(b, (-1,))
tensor([ 0,  1,  2,  3])

torch.split(tensor, split_size_or_sections, dim=0)[source]

Splits the tensor into chunks.

If split_size_or_sections is an integer type, then tensor will be split into equally sized chunks (if possible). Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by split_size.

If split_size_or_sections is a list, then tensor will be split into len(split_size_or_sections) chunks with sizes in dim according to split_size_or_sections.

Parameters

• tensor (Tensor) – tensor to split.

• split_size_or_sections (int) or (list(int)) – size of a single chunk or list of sizes for each chunk

• dim (int) – dimension along which to split the tensor.

torch.squeeze(input, dim=None, out=None) → Tensor

Returns a tensor with all the dimensions of input of size 1 removed.

For example, if input is of shape: (A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D)(A×1×B×C×1×D) then the out tensor will be of shape: (A×B×C×D)(A \times B \times C \times D)(A×B×C×D) .

When dim is given, a squeeze operation is done only in the given dimension. If input is of shape: (A×1×B)(A \times 1 \times B)(A×1×B) , squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1) will squeeze the tensor to the shape (A×B)(A \times B)(A×B) .

Note

The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.

Parameters

• input (Tensor) – the input tensor.

• dim (int, optional) – if given, the input will be squeezed only in this dimension

• out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])

torch.stack(tensors, dim=0, out=None) → Tensor

Concatenates sequence of tensors along a new dimension.

All tensors need to be of the same size.

Parameters

• tensors (sequence of Tensors) – sequence of tensors to concatenate

• dim (int) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive)

• out (Tensor, optional) – the output tensor.

torch.t(input) → Tensor

Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.

0-D and 1-D tensors are returned as it is and 2-D tensor can be seen as a short-hand function for transpose(input, 0, 1).

Parameters

input (Tensor) – the input tensor.

Example:

>>> x = torch.randn(())
>>> x
tensor(0.1995)
>>> torch.t(x)
tensor(0.1995)
>>> x = torch.randn(3)
>>> x
tensor([ 2.4320, -0.4608,  0.7702])
>>> torch.t(x)
tensor([.2.4320,.-0.4608,..0.7702])
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.4875,  0.9158, -0.5872],
        [ 0.3938, -0.6929,  0.6932]])
>>> torch.t(x)
tensor([[ 0.4875,  0.3938],
        [ 0.9158, -0.6929],
        [-0.5872,  0.6932]])

torch.take(input, index) → Tensor

Returns a new tensor with the elements of input at the given indices. The input tensor is treated as if it were viewed as a 1-D tensor. The result takes the same shape as the indices.

Parameters

• input (Tensor) – the input tensor.

• indices (LongTensor) – the indices into tensor

Example:

>>> src = torch.tensor([[4, 3, 5],
                        [6, 7, 8]])
>>> torch.take(src, torch.tensor([0, 2, 5]))
tensor([ 4,  5,  8])

torch.transpose(input, dim0, dim1) → Tensor

Returns a tensor that is a transposed version of input. The given dimensions dim0 and dim1 are swapped.

The resulting out tensor shares it’s underlying storage with the input tensor, so changing the content of one would change the content of the other.

Parameters

• input (Tensor) – the input tensor.

• dim0 (int) – the first dimension to be transposed

• dim1 (int) – the second dimension to be transposed

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 1.0028, -0.9893,  0.5809],
        [-0.1669,  0.7299,  0.4942]])
>>> torch.transpose(x, 0, 1)
tensor([[ 1.0028, -0.1669],
        [-0.9893,  0.7299],
        [ 0.5809,  0.4942]])

torch.unbind(input, dim=0) → seq

Removes a tensor dimension.

Returns a tuple of all slices along a given dimension, already without it.

Parameters

• input (Tensor) – the tensor to unbind

• dim (int) – dimension to remove

Example:

>>> torch.unbind(torch.tensor([[1, 2, 3],
>>>                            [4, 5, 6],
>>>                            [7, 8, 9]]))
(tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9]))

torch.unsqueeze(input, dim, out=None) → Tensor

Returns a new tensor with a dimension of size one inserted at the specified position.

The returned tensor shares the same underlying data with this tensor.

A dim value within the range [-input.dim() - 1, input.dim() + 1) can be used. Negative dim will correspond to unsqueeze() applied at dim = dim + input.dim() + 1.

Parameters

• input (Tensor) – the input tensor.

• dim (int) – the index at which to insert the singleton dimension

• out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1,  2,  3,  4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4]])

torch.where()

torch.where(condition, x, y) → Tensor

Return a tensor of elements selected from either x or y, depending on condition.

The operation is defined as:

outi={xiif conditioniyiotherwise\text{out}_i = \begin{cases} \text{x}_i & \text{if } \text{condition}_i \\ \text{y}_i & \text{otherwise} \\ \end{cases} outi​={xi​yi​​if conditioni​otherwise​

Note

The tensors condition, x, y must be broadcastable.

Parameters

• condition (BoolTensor) – When True (nonzero), yield x, otherwise yield y

• x (Tensor) – values selected at indices where condition is True

• y (Tensor) – values selected at indices where condition is False

Returns

A tensor of shape equal to the broadcasted shape of condition, x, y

Return type

Tensor

Example:

>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620,  0.3139],
        [ 0.3898, -0.7197],
        [ 0.0478, -0.1657]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000,  0.3139],
        [ 0.3898,  1.0000],
        [ 0.0478,  1.0000]])

torch.where(condition) → tuple of LongTensor

torch.where(condition) is identical to torch.nonzero(condition, as_tuple=True).

Note

See also torch.nonzero().