@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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/**
* @license
* Copyright 2018 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import {ENGINE} from '../engine';
import {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';
import {convertToTensor, convertToTensorArray} from '../tensor_util_env';
import {TensorLike} from '../types';
import {assert, sizeFromShape} from '../util';
import {parseAxisParam} from '../util';
import {assertParamsConsistent, computeOutShape} from './concat_util';
import {op} from './operation';
import {tensor} from './tensor_ops';
/**
* Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.
*
* For example, if:
* A: shape(3) = |r1, g1, b1|
* B: shape(2) = |r2, g2|
* C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|
*
* @param tensors A list of`tf.Tensor`s to concatenate.
* @return The concatenated array.
*/
function concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {
return concat(tensors, 0 /* axis */);
}
/**
* Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.
*
* For example, if:
* A: shape(2, 3) = | r1, g1, b1 |
* | r2, g2, b2 |
*
* B: shape(2, 3) = | r3, g3, b3 |
* | r4, g4, b4 |
*
* C = tf.concat2d([A, B], axis)
*
* if axis = 0:
* C: shape(4, 3) = | r1, g1, b1 |
* | r2, g2, b2 |
* | r3, g3, b3 |
* | r4, g4, b4 |
*
* if axis = 1:
* C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |
* | r2, g2, b2, r4, g4, b4 |
*
*
* @param tensors A list of `tf.Tensor`s to concatenate.
* @param axis The axis to concatenate along.
* @return The concatenated array.
*/
function concat2d_(
tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {
return concat(tensors, axis);
}
/**
* Concatenates a list of `tf.Tensor3D`s along an axis.
* See `concat` for details.
*
* For example, if:
* A: shape(2, 1, 3) = | r1, g1, b1 |
* | r2, g2, b2 |
*
* B: shape(2, 1, 3) = | r3, g3, b3 |
* | r4, g4, b4 |
*
* C = tf.concat3d([A, B], axis)
*
* if axis = 0:
* C: shape(4, 1, 3) = | r1, g1, b1 |
* | r2, g2, b2 |
* | r3, g3, b3 |
* | r4, g4, b4 |
*
* if axis = 1:
* C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |
* | r2, g2, b2, r4, g4, b4 |
*
* if axis = 2:
* C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |
* | r2, g2, b2, r4, g4, b4 |
*
* @param tensors A list of`tf.Tensor`s to concatenate.
* @param axis The axis to concate along.
* @return The concatenated array.
*/
function concat3d_(
tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {
return concat(tensors, axis);
}
/**
* Concatenates a list of `tf.Tensor4D`s along an axis.
* See `concat` for details.
*
* @param tensors A list of `tf.Tensor`s to concatenate.
* @param axis The axis to concate along.
* @return The concatenated array.
*/
function concat4d_(
tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {
return concat(tensors, axis);
}
/**
* Concatenates a list of `tf.Tensor`s along a given axis.
*
* The tensors ranks and types must match, and their sizes must match in all
* dimensions except `axis`.
*
* Also available are stricter rank-specific methods that assert that
* `tensors` are of the given rank:
* - `tf.concat1d`
* - `tf.concat2d`
* - `tf.concat3d`
* - `tf.concat4d`
*
* Except `tf.concat1d` (which does not have axis param), all methods have
* same signature as this method.
*
* ```js
* const a = tf.tensor1d([1, 2]);
* const b = tf.tensor1d([3, 4]);
* a.concat(b).print(); // or a.concat(b)
* ```
*
* ```js
* const a = tf.tensor1d([1, 2]);
* const b = tf.tensor1d([3, 4]);
* const c = tf.tensor1d([5, 6]);
* tf.concat([a, b, c]).print();
* ```
*
* ```js
* const a = tf.tensor2d([[1, 2], [10, 20]]);
* const b = tf.tensor2d([[3, 4], [30, 40]]);
* const axis = 1;
* tf.concat([a, b], axis).print();
* ```
* @param tensors A list of tensors to concatenate.
* @param axis The axis to concate along. Defaults to 0 (the first dim).
*/
/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
function concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {
assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');
let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');
if ($tensors[0].dtype === 'complex64') {
$tensors.forEach(tensor => {
if (tensor.dtype !== 'complex64') {
throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${tensor.dtype}. `);
}
});
}
axis = parseAxisParam(axis, $tensors[0].shape)[0];
const outShape = computeOutShape($tensors.map(t => t.shape), axis);
if (sizeFromShape(outShape) === 0) {
return tensor([], outShape) as T;
}
// Keep only non-empty tensors (ignore tensors with 0 in their shape).
$tensors = $tensors.filter(t => t.size > 0);
if ($tensors.length === 1) {
return $tensors[0];
}
const shapes = $tensors.map(t => t.shape);
assertParamsConsistent(shapes, axis);
const der = (dy: T) => {
const sizeSplits = shapes.map(s => s[axis]);
const derTensors = split(dy, sizeSplits, axis);
return derTensors.map(t => () => t) as {};
};
const inputs = $tensors as {};
const attr = {axis};
return ENGINE.runKernelFunc(
backend => backend.concat($tensors, axis) as T, inputs, der, 'Concat',
attr);
}
/**
* Splits a `tf.Tensor` into sub tensors.
*
* If `numOrSizeSplits` is a number, splits `x` along dimension `axis`
* into `numOrSizeSplits` smaller tensors.
* Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.
*
* If `numOrSizeSplits` is a number array, splits `x` into
* `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the
* same size as `x` except along dimension `axis` where the size is
* `numOrSizeSplits[i]`.
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
* const [a, b] = tf.split(x, 2, 1);
* a.print();
* b.print();
*
* const [c, d, e] = tf.split(x, [1, 2, 1], 1);
* c.print();
* d.print();
* e.print();
* ```
*
* @param x The input tensor to split.
* @param numOrSizeSplits Either an integer indicating the number of
* splits along the axis or an array of integers containing the sizes of
* each output tensor along the axis. If a number then it must evenly divide
* `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.
* @param axis The dimension along which to split. Defaults to 0 (the first
* dim).
*/
/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
function split_<T extends Tensor>(
x: T|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {
const $x = convertToTensor(x, 'x', 'split');
axis = parseAxisParam(axis, $x.shape)[0];
let splitSizes: number[];
if (typeof (numOrSizeSplits) === 'number') {
assert(
$x.shape[axis] % numOrSizeSplits === 0,
() => 'Number of splits must evenly divide the axis.');
splitSizes =
new Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);
} else {
assert(
$x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),
() => 'The sum of sizes must match the size of the axis dimension.');
splitSizes = numOrSizeSplits;
}
const der = (dy: T[]) => ({$x: () => concat(dy, axis)});
return ENGINE.runKernelFunc(
backend => backend.split($x, splitSizes, axis), {$x}, der);
}
export const concat = op({concat_});
export const concat1d = op({concat1d_});
export const concat2d = op({concat2d_});
export const concat3d = op({concat3d_});
export const concat4d = op({concat4d_});
export const split = op({split_});