Helper module for Tensor
processing.
These functions and classes are only used internally, meaning an end-user shouldn’t need to access anything here.
new Tensor(...args)
.Symbol.iterator()
⇒ Iterator
._getitem(index)
⇒ Tensor
.indexOf(item)
⇒ number
._subarray(index, iterSize, iterDims)
⇒ Tensor
.item()
⇒ number
.tolist()
⇒ Array
.sigmoid()
⇒ Tensor
.sigmoid_()
⇒ Tensor
.transpose(...dims)
⇒ Tensor
.sum([dim], keepdim)
⇒.norm([p], [dim], [keepdim])
⇒ Tensor
.normalize_([p], [dim])
⇒ Tensor
.normalize([p], [dim])
⇒ Tensor
.stride()
⇒ Array.<number>
.squeeze([dim])
⇒.squeeze_()
.unsqueeze(dim)
⇒.unsqueeze_()
.flatten_()
.flatten(start_dim, end_dim)
⇒.view(...dims)
⇒ Tensor
.transpose(tensor, axes)
⇒ Tensor
.interpolate(input, size, mode, align_corners)
⇒ Tensor
.mean_pooling(last_hidden_state, attention_mask)
⇒ Tensor
.cat(tensors, dim)
⇒ Tensor
.stack(tensors, dim)
⇒ Tensor
.std_mean(input, dim, correction, keepdim)
⇒ Array.<Tensor>
.mean(input, dim, keepdim)
⇒.dynamicTimeWarping(matrix)
⇒ Array.<Array<number>>
~ONNXTensor
: Object
~reshape(data, dimensions)
⇒ *
~reshapedArray
: any
~AnyTypedArray
: *
~NestArray
: *
Kind: static class of utils/tensor
new Tensor(...args)
.Symbol.iterator()
⇒ Iterator
._getitem(index)
⇒ Tensor
.indexOf(item)
⇒ number
._subarray(index, iterSize, iterDims)
⇒ Tensor
.item()
⇒ number
.tolist()
⇒ Array
.sigmoid()
⇒ Tensor
.sigmoid_()
⇒ Tensor
.transpose(...dims)
⇒ Tensor
.sum([dim], keepdim)
⇒.norm([p], [dim], [keepdim])
⇒ Tensor
.normalize_([p], [dim])
⇒ Tensor
.normalize([p], [dim])
⇒ Tensor
.stride()
⇒ Array.<number>
.squeeze([dim])
⇒.squeeze_()
.unsqueeze(dim)
⇒.unsqueeze_()
.flatten_()
.flatten(start_dim, end_dim)
⇒.view(...dims)
⇒ Tensor
new Tensor(...args)
Create a new Tensor or copy an existing Tensor.
Param | Type |
---|---|
...args | * |
tensor.Symbol.iterator()
⇒ Iterator
Returns an iterator object for iterating over the tensor data in row-major order. If the tensor has more than one dimension, the iterator will yield subarrays.
Kind: instance method of Tensor
Returns: Iterator
- An iterator object for iterating over the tensor data in row-major order.
tensor._getitem(index)
⇒ Tensor
Index into a Tensor object.
Kind: instance method of Tensor
Returns: Tensor
- The data at the specified index.
Param | Type | Description |
---|---|---|
index | number | The index to access. |
tensor.indexOf(item)
⇒ number
Kind: instance method of Tensor
Returns: number
- The index of the first occurrence of item in the tensor data.
Param | Type | Description |
---|---|---|
item | number | bigint | The item to search for in the tensor |
tensor._subarray(index, iterSize, iterDims)
⇒ Tensor
Kind: instance method of Tensor
Param | Type |
---|---|
index | number |
iterSize | number |
iterDims | any |
tensor.item()
⇒ number
Returns the value of this tensor as a standard JavaScript Number. This only works
for tensors with one element. For other cases, see Tensor.tolist()
.
Kind: instance method of Tensor
Returns: number
- The value of this tensor as a standard JavaScript Number.
Throws:
Error
If the tensor has more than one element.tensor.tolist()
⇒ Array
Convert tensor data to a n-dimensional JS list
Kind: instance method of Tensor
tensor.sigmoid()
⇒ Tensor
Return a new Tensor with the sigmoid function applied to each element.
Kind: instance method of Tensor
Returns: Tensor
- The tensor with the sigmoid function applied.
tensor.sigmoid_()
⇒ Tensor
Applies the sigmoid function to the tensor in place.
Kind: instance method of Tensor
Returns: Tensor
- Returns this
.
tensor.transpose(...dims)
⇒ Tensor
Return a transposed version of this Tensor, according to the provided dimensions.
Kind: instance method of Tensor
Returns: Tensor
- The transposed tensor.
Param | Type | Description |
---|---|---|
...dims | number | Dimensions to transpose. |
tensor.sum([dim], keepdim)
⇒
Returns the sum of each row of the input tensor in the given dimension dim.
Kind: instance method of Tensor
Returns: The summed tensor
Param | Type | Default | Description |
---|---|---|---|
[dim] | number |
| The dimension or dimensions to reduce. If |
keepdim | boolean | false | Whether the output tensor has |
tensor.norm([p], [dim], [keepdim])
⇒ Tensor
Returns the matrix norm or vector norm of a given tensor.
Kind: instance method of Tensor
Returns: Tensor
- The norm of the tensor.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | string | 'fro' | The order of norm |
[dim] | number |
| Specifies which dimension of the tensor to calculate the norm across. If dim is None, the norm will be calculated across all dimensions of input. |
[keepdim] | boolean | false | Whether the output tensors have dim retained or not. |
tensor.normalize_([p], [dim])
⇒ Tensor
Performs L_p
normalization of inputs over specified dimension. Operates in place.
Kind: instance method of Tensor
Returns: Tensor
- this
for operation chaining.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | 2 | The exponent value in the norm formulation |
[dim] | number | 1 | The dimension to reduce |
tensor.normalize([p], [dim])
⇒ Tensor
Performs L_p
normalization of inputs over specified dimension.
Kind: instance method of Tensor
Returns: Tensor
- The normalized tensor.
Param | Type | Default | Description |
---|---|---|---|
[p] | number | 2 | The exponent value in the norm formulation |
[dim] | number | 1 | The dimension to reduce |
tensor.stride()
⇒ Array.<number>
Compute and return the stride of this tensor. Stride is the jump necessary to go from one element to the next one in the specified dimension dim.
Kind: instance method of Tensor
Returns: Array.<number>
- The stride of this tensor.
tensor.squeeze([dim])
⇒
Returns a tensor with all specified dimensions of input of size 1 removed.
NOTE: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.
If you would like a copy, use tensor.clone()
before squeezing.
Kind: instance method of Tensor
Returns: The squeezed tensor
Param | Type | Default | Description |
---|---|---|---|
[dim] | number |
| If given, the input will be squeezed only in the specified dimensions. |
tensor.squeeze_()
In-place version of @see Tensor.squeeze
Kind: instance method of Tensor
tensor.unsqueeze(dim)
⇒
Returns a new tensor with a dimension of size one inserted at the specified position.
NOTE: The returned tensor shares the same underlying data with this tensor.
Kind: instance method of Tensor
Returns: The unsqueezed tensor
Param | Type | Default | Description |
---|---|---|---|
dim | number |
| The index at which to insert the singleton dimension |
tensor.unsqueeze_()
In-place version of @see Tensor.unsqueeze
Kind: instance method of Tensor
tensor.flatten_()
In-place version of @see Tensor.flatten
Kind: instance method of Tensor
tensor.flatten(start_dim, end_dim)
⇒
Flattens input by reshaping it into a one-dimensional tensor.
If start_dim
or end_dim
are passed, only dimensions starting with start_dim
and ending with end_dim
are flattened. The order of elements in input is unchanged.
Kind: instance method of Tensor
Returns: The flattened tensor.
Param | Type | Default | Description |
---|---|---|---|
start_dim | number | 0 | the first dim to flatten |
end_dim | number | the last dim to flatten |
tensor.view(...dims)
⇒ Tensor
Returns a new tensor with the same data as the self
tensor but of a different shape
.
Kind: instance method of Tensor
Returns: Tensor
- The tensor with the same data but different shape
Param | Type | Description |
---|---|---|
...dims | number | the desired size |
utils/tensor.transpose(tensor, axes)
⇒ Tensor
Transposes a tensor according to the provided axes.
Kind: static method of utils/tensor
Returns: Tensor
- The transposed tensor.
Param | Type | Description |
---|---|---|
tensor | any | The input tensor to transpose. |
axes | Array | The axes to transpose the tensor along. |
utils/tensor.interpolate(input, size, mode, align_corners)
⇒ Tensor
Interpolates an Tensor to the given size.
Kind: static method of utils/tensor
Returns: Tensor
- The interpolated tensor.
Param | Type | Description |
---|---|---|
input | Tensor | The input tensor to interpolate. Data must be channel-first (i.e., [c, h, w]) |
size | Array.<number> | The output size of the image |
mode | string | The interpolation mode |
align_corners | boolean | Whether to align corners. |
utils/tensor.mean_pooling(last_hidden_state, attention_mask)
⇒ Tensor
Perform mean pooling of the last hidden state followed by a normalization step.
Kind: static method of utils/tensor
Returns: Tensor
- Returns a new Tensor of shape [batchSize, embedDim].
Param | Type | Description |
---|---|---|
last_hidden_state | Tensor | Tensor of shape [batchSize, seqLength, embedDim] |
attention_mask | Tensor | Tensor of shape [batchSize, seqLength] |
utils/tensor.cat(tensors, dim)
⇒ Tensor
Concatenates an array of tensors along a specified dimension.
Kind: static method of utils/tensor
Returns: Tensor
- The concatenated tensor.
Param | Type | Description |
---|---|---|
tensors | Array.<Tensor> | The array of tensors to concatenate. |
dim | number | The dimension to concatenate along. |
utils/tensor.stack(tensors, dim)
⇒ Tensor
Stack an array of tensors along a specified dimension.
Kind: static method of utils/tensor
Returns: Tensor
- The stacked tensor.
Param | Type | Description |
---|---|---|
tensors | Array.<Tensor> | The array of tensors to stack. |
dim | number | The dimension to stack along. |
utils/tensor.std_mean(input, dim, correction, keepdim)
⇒ Array.<Tensor>
Calculates the standard deviation and mean over the dimensions specified by dim. dim can be a single dimension or null
to reduce over all dimensions.
Kind: static method of utils/tensor
Returns: Array.<Tensor>
- A tuple of (std, mean) tensors.
Param | Type | Description |
---|---|---|
input | Tensor | the input tenso |
dim | number | null | the dimension to reduce. If None, all dimensions are reduced. |
correction | number | difference between the sample size and sample degrees of freedom. Defaults to Bessel's correction, correction=1. |
keepdim | boolean | whether the output tensor has dim retained or not. |
utils/tensor.mean(input, dim, keepdim)
⇒
Returns the mean value of each row of the input tensor in the given dimension dim.
Kind: static method of utils/tensor
Returns: A new tensor with means taken along the specified dimension.
Param | Type | Description |
---|---|---|
input | Tensor | the input tensor. |
dim | number | null | the dimension to reduce. |
keepdim | boolean | whether the output tensor has dim retained or not. |
utils/tensor.dynamicTimeWarping(matrix)
⇒ Array.<Array<number>>
Measures similarity between two temporal sequences (e.g., input audio and output tokens to generate token-level timestamps).
Kind: static method of utils/tensor
Param | Type |
---|---|
matrix | Tensor |
utils/tensor~ONNXTensor
: Object
Kind: inner constant of utils/tensor
utils/tensor~reshape(data, dimensions)
⇒ *
Reshapes a 1-dimensional array into an n-dimensional array, according to the provided dimensions.
Kind: inner method of utils/tensor
Returns: *
- The reshaped array.
Param | Type | Description |
---|---|---|
data | Array.<T> | The input array to reshape. |
dimensions | DIM | The target shape/dimensions. |
Example
reshape([10 ], [1 ]); // Type: number[] Value: [10]
reshape([1, 2, 3, 4 ], [2, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4]]
reshape([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]); // Type: number[][][] Value: [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
reshape([1, 2, 3, 4, 5, 6, 7, 8], [4, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4], [5, 6], [7, 8]]
reshape~reshapedArray
: any
Kind: inner property of reshape
utils/tensor~AnyTypedArray
: *
Kind: inner typedef of utils/tensor
utils/tensor~NestArray
: *
This creates a nested array of a given type and depth (see examples).
Kind: inner typedef of utils/tensor
Example
NestArray<string, 1>; // string[]
Example
NestArray<number, 2>; // number[][]
Example
NestArray<string, 3>; // string[][][] etc.