@tensorflow/tfjs-core/src/ops/pad.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2020 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, ForwardFunc} from '../engine';
import {PadV2, PadV2Attrs, PadV2Inputs} from '../kernel_names';
import {NamedAttrMap} from '../kernel_registry';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';

import {op} from './operation';

/**
 * Pads a `tf.Tensor` with a given value and paddings.
 *
 * This operation currently only implements the `CONSTANT` mode.
 *
 * Also available are stricter rank-specific methods with the same signature
 * as this method that assert that `paddings` is of given length.
 *   - `tf.pad1d`
 *   - `tf.pad2d`
 *   - `tf.pad3d`
 *   - `tf.pad4d`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * x.pad([[1, 2]]).print();
 * ```
 * @param x The tensor to pad.
 * @param paddings An array of length `R` (the rank of the tensor), where
 * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
 * specifying how much to pad along each dimension of the tensor.
 * @param constantValue The pad value to use. Defaults to 0.
 */
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function pad_<T extends Tensor>(
    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {
  const $x = convertToTensor(x, 'x', 'pad');
  if ($x.rank === 0) {
    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
  }
  const forward: ForwardFunc<T> = (backend, save) => {
    save([$x]);
    return backend.pad($x, paddings, constantValue);
  };

  const attrs: PadV2Attrs = {paddings, constantValue};
  const inputs: PadV2Inputs = {x: $x};
  return ENGINE.runKernelFunc(
      forward, inputs as unknown as NamedTensorMap, null /* grad */, PadV2,
      attrs as unknown as NamedAttrMap);
}

export const pad = op({pad_});