Function maxPool

tfjs-backend-cpu/src/kernels/MaxPool.ts:24–55

(
    args:
        {inputs: MaxPoolInputs, backend: MathBackendCPU, attrs: MaxPoolAttrs})

import {identity} from './Identity';

export function maxPool(
    args:
        {inputs: MaxPoolInputs, backend: MathBackendCPU, attrs: MaxPoolAttrs}):
    TensorInfo {
  const {inputs, backend, attrs} = args;
  const {x} = inputs;
  assertNotComplex(x, 'maxPool');
  const {filterSize, strides, pad, dimRoundingMode} = attrs;
  const dilations = 1;

  util.assert(
      backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
      () => 'Error in maxPool: Either strides or dilations must be 1. ' +
          `Got strides ${strides} and dilations '${dilations}'`);

  const convInfo = backend_util.computePool2DInfo(
      x.shape as [number, number, number, number], filterSize, strides,
      dilations, pad, dimRoundingMode);
  let res: TensorInfo;

  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
      util.arraysEqual(convInfo.inShape, convInfo.outShape)) {
    res = identity({inputs: {x}, backend});
  } else {
    const xValues = backend.data.get(x.dataId).values as TypedArray;
    const strides = util.computeStrides(x.shape);
    const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');
    res = backend.makeTensorInfo(
        convInfo.outShape, x.dtype, buffer.values as TypedArray);
  }
  return res;
}

export const maxPoolConfig: KernelConfig = {
  kernelName: MaxPool,