Interface: Torch

torchlive/torch.Torch

Properties

channelsLast

• channelsLast: "channelsLast"

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contiguousFormat

• contiguousFormat: "contiguousFormat"

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double

• double: "double"

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float

• float: "float"

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float32

• float32: "float32"

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float64

• float64: "float64"

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int

• int: "int"

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int16

• int16: "int16"

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int32

• int32: "int32"

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int64

• int64: "int64"

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int8

• int8: "int8"

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jit

• jit: JIT

JIT module

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long

• long: "long"

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preserveFormat

• preserveFormat: "preserveFormat"

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short

• short: "short"

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uint8

• uint8: "uint8"

Methods

arange

▸ arange(end, options?): Tensor

Returns a 1-D tensor of size (end - 0) / 1 with values from the interval [0, end) taken with common difference step beginning from start.

https://pytorch.org/docs/1.12/generated/torch.arange.html

Parameters

Name	Type	Description
end	number	The ending value for the set of points.
options?	TensorOptions

Returns

Tensor

▸ arange(start, end, options?): Tensor

Returns a 1-D tensor of size (end - start) / 1 with values from the interval [start, end) taken with common difference 1 beginning from start.

https://pytorch.org/docs/1.12/generated/torch.arange.html

Parameters

Name	Type	Description
start	number	The starting value for the set of points.
end	number	The ending value for the set of points.
options?	TensorOptions

Returns

Tensor

▸ arange(start, end, step, options?): Tensor

Returns a 1-D tensor of size (end - start) / step with values from the interval [start, end) taken with common difference step beginning from start.

https://pytorch.org/docs/1.12/generated/torch.arange.html

Parameters

Name	Type	Description
start	number	The starting value for the set of points.
end	number	The ending value for the set of points.
step	number	The gap between each pair of adjacent points.
options?	TensorOptions

Returns

Tensor

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cat

▸ cat(tensors, options?): Tensor

Concatenate a list of tensors along the specified axis, which default to be axis 0

https://pytorch.org/docs/1.12/generated/torch.cat.html

Parameters

Name	Type	Description
tensors	Tensor[]	A sequence of Tensor to be concatenated.
options?	Object	used to specify the dimenstion to concate.
options.dim?	number	-

Returns

Tensor

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empty

▸ empty(size, options?): Tensor

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

https://pytorch.org/docs/1.12/generated/torch.empty.html

Parameters

Name	Type	Description
size	number[]	A sequence of integers defining the shape of the output tensor.
options?	TensorOptions	-

Returns

Tensor

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eye

▸ eye(n, m?, options?): Tensor

Returns a tensor filled with ones on the diagonal, and zeroes elsewhere. The shape of the tensor is defined by the arguments n and m.

https://pytorch.org/docs/1.12/generated/torch.eye.html

Parameters

Name	Type	Description
n	number	An integer defining the number of rows in the result.
m?	number	An integer defining the number of columns in the result. Optional, defaults to n.
options?	TensorOptions	-

Returns

Tensor

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fromBlob

▸ fromBlob(blob, sizes?, options?): Tensor

Exposes the given data as a Tensor without taking ownership of the original data.

note

The function exists in JavaScript and C++ (torch::from_blob).

Parameters

Name	Type	Description
blob	any	The blob holding the data.
sizes?	number[]	Should specify the shape of the tensor, strides the stride
options?	TensorOptions	Tensor options in each dimension.

Returns

Tensor

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full

▸ full(size, fillValue, options?): Tensor

Creates a tensor of size size filled with fillValue. The tensor’s dtype is default to be torch.float32, unless specified with options.

https://pytorch.org/docs/1.12/generated/torch.full.html

Parameters

Name	Type	Description
size	number[]	A list of integers defining the shape of the output tensor.
fillValue	number	The value to fill the output tensor with.
options?	TensorOptions	Object to customizing dtype, etc. default to be {dtype: torch.float32}

Returns

Tensor

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linspace

▸ linspace(start, end, steps, options?): Tensor

Creates a one-dimensional tensor of size steps whose values are evenly spaced from start to end, inclusive.

https://pytorch.org/docs/1.12/generated/torch.linspace.html

Parameters

Name	Type	Description
start	number	Starting value for the set of points
end	number	Ending value for the set of points
steps	number	Size of the constructed tensor
options?	TensorOptions	Object to customizing dtype. default to be {dtype: torch.float32}

Returns

Tensor

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logspace

▸ logspace(start, end, steps, options?): Tensor

Returns a one-dimensional tensor of size steps whose values are evenly spaced from base^start to base^end, inclusive, on a logarithmic scale with base.

https://pytorch.org/docs/1.12/generated/torch.logspace.html

Parameters

Name	Type	Description
start	number	Starting value for the set of points
end	number	Ending value for the set of points
steps	number	Size of the constructed tensor
options?	TensorOptions & { base: number }	Object to customizing base and dtype. default to be {base: 10, dtype: torch.float32}

Returns

Tensor

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ones

▸ ones(size, options?): Tensor

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

https://pytorch.org/docs/1.12/generated/torch.ones.html

Parameters

Name	Type	Description
size	number[]	A sequence of integers defining the shape of the output tensor.
options?	TensorOptions	Tensor options.

Returns

Tensor

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rand

▸ rand(size, options?): Tensor

Returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1).

Parameters

Name	Type	Description
size	number[]	A sequence of integers defining the shape of the output tensor.
options?	TensorOptions	Tensor options.

Returns

Tensor

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randint

▸ randint(high, size): Tensor

Returns a tensor filled with random integers generated uniformly between 0 (inclusive) and high (exclusive).

https://pytorch.org/docs/1.12/generated/torch.randint.html

Parameters

Name	Type	Description
high	number	One above the highest integer to be drawn from the distribution.
size	number[]	A tuple defining the shape of the output tensor.

Returns

Tensor

▸ randint(low, high, size): Tensor

Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive).

https://pytorch.org/docs/1.12/generated/torch.randint.html

Parameters

Name	Type	Description
low	number	Lowest integer to be drawn from the distribution.
high	number	One above the highest integer to be drawn from the distribution.
size	number[]	A tuple defining the shape of the output tensor.

Returns

Tensor

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randn

▸ randn(size, options?): Tensor

Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).

https://pytorch.org/docs/1.12/generated/torch.randn.html

Parameters

Name	Type	Description
size	number[]	A sequence of integers defining the shape of the output tensor.
options?	TensorOptions	Tensor options.

Returns

Tensor

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randperm

▸ randperm(n, options?): Tensor

Returns a random permutation of integers from 0 to n - 1

https://pytorch.org/docs/1.12/generated/torch.randperm.html

Parameters

Name	Type	Description
n	number	The upper bound (exclusive)
options?	TensorOptions	Object to customizing dtype, etc. default to be {dtype: torch.int64}.

Returns

Tensor

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tensor

▸ tensor(data, options?): Tensor

Constructs a tensor with no autograd history.

Parameters

Name	Type	Description
data	number | ItemArray	Tensor data as multi-dimensional array.
options?	TensorOptions	Tensor options.

Returns

Tensor

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zeros

▸ zeros(size, options?): Tensor

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

https://pytorch.org/docs/1.12/generated/torch.zeros.html

Parameters

Name	Type	Description
size	number[]	A sequence of integers defining the shape of the output tensor.
options?	TensorOptions	Tensor options.

Returns

Tensor