@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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text/typescript
/**
* @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 {Tensor} from '../tensor';
import {nearestDivisor} from '../util';
import {PARALLELIZE_THRESHOLD} from './reduce_util';
export interface SegOpInfo {
windowSize: number;
batchSize: number;
inSize: number;
numSegments: number;
}
export function segOpComputeOptimalWindowSize(
inSize: number, numSegments: number): number {
let done = false;
let res;
if (inSize <= PARALLELIZE_THRESHOLD) {
res = inSize;
done = true;
} else {
res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
}
while (!done) {
if (res > numSegments || res === inSize) {
done = true;
} else {
res = nearestDivisor(inSize, res + 1);
}
}
return res;
}
export function computeOutShape(
aShape: number[], axis: number, numSegments: number): number[] {
const outShape = [];
const rank = aShape.length;
for (let dim = 0; dim < rank; dim++) {
if (dim !== axis) {
outShape.push(aShape[dim]);
} else {
outShape.push(numSegments);
}
}
return outShape;
}
export interface GatherOpShapeInfo {
batchSize: number;
sliceSize: number;
dimSize: number;
outputShape: number[];
}
export function collectGatherOpShapeInfo(
x: Tensor, indices: Tensor, axis: number): GatherOpShapeInfo {
const dimSize = x.shape[axis];
const outputShape: number[] = [];
let batchSize = 1;
let sliceSize = 1;
for (let i = 0; i < axis; i++) {
outputShape.push(x.shape[i]);
batchSize *= x.shape[i];
}
for (let i = 0; i < indices.rank; i++) {
outputShape.push(indices.shape[i]);
}
for (let i = axis + 1; i < x.rank; i++) {
outputShape.push(x.shape[i]);
sliceSize *= x.shape[i];
}
return {batchSize, sliceSize, dimSize, outputShape};
}