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AvgPool.ts
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AvgPool.ts
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/**
* @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 {AvgPool, AvgPoolAttrs, AvgPoolInputs, backend_util, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';
import {pool} from '../utils/pool_utils';
import {identity} from './Identity';
export function avgPool(
args:
{inputs: AvgPoolInputs, backend: MathBackendCPU, attrs: AvgPoolAttrs}):
TensorInfo {
const {inputs, backend, attrs} = args;
const {x} = inputs;
assertNotComplex(x, 'avgPool');
const {filterSize, strides, pad, dimRoundingMode} = attrs;
const dilations = 1;
util.assert(
backend_util.eitherStridesOrDilationsAreOne(strides, dilations),
() => 'Error in avgPool: 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, 'avg');
res = backend.makeTensorInfo(
convInfo.outShape, x.dtype, buffer.values as TypedArray);
}
return res;
}
export const avgPoolConfig: KernelConfig = {
kernelName: AvgPool,
backendName: 'cpu',
kernelFunc: avgPool as unknown as KernelFunc
};