AvgPool3D.ts

/**
 * @license
 * Copyright 2020 Google LLC. 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 {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';

import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';
import {pool3d} from '../utils/pool_utils';

export function avgPool3D(args: {
  inputs: AvgPool3DInputs,
  backend: MathBackendCPU,
  attrs: AvgPool3DAttrs
}): TensorInfo {
  const {inputs, backend, attrs} = args;
  const {x} = inputs;
  const {filterSize, strides, pad, dimRoundingMode, dataFormat} = attrs;

  assertNotComplex(x, 'avgPool3d');

  const convInfo = backend_util.computePool3DInfo(
      x.shape as [number, number, number, number, number], filterSize, strides,
      1 /* dilations */, pad, dimRoundingMode, dataFormat);

  const xValues = backend.data.get(x.dataId).values as TypedArray;
  const outBuf = pool3d(
      xValues, x.shape, x.dtype, util.computeStrides(x.shape), convInfo, 'avg');

  return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values);
}

export const avgPool3DConfig: KernelConfig = {
  kernelName: AvgPool3D,
  backendName: 'cpu',
  kernelFunc: avgPool3D as unknown as KernelFunc
};