@tensorflow-models/coco-ssd

/** * @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 { Conv2DInfo, Conv3DInfo } from '../ops/conv_util'; import { Activation } from '../ops/fused_util'; import { Backend, DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D } from '../tensor'; import { DataType, DataValues, Rank, ShapeMap } from '../types'; export interface BackendTimingInfo { kernelMs: number; getExtraProfileInfo?(): string; } export interface TensorStorage { read(dataId: DataId): Promise<DataValues>; readSync(dataId: DataId): DataValues; disposeData(dataId: DataId): void; write(dataId: DataId, values: DataValues): void; fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D; register(dataId: DataId, shape: number[], dtype: DataType): void; memory(): { unreliable: boolean; }; } /** Convenient class for storing tensor-related data. */ export declare class DataStorage<T> { private dataMover; private data; constructor(dataMover: DataMover); get(dataId: DataId): T; set(dataId: DataId, value: T): void; has(dataId: DataId): boolean; delete(dataId: DataId): boolean; } export interface DataMover { /** * To be called by backends whenever they see a dataId that they don't own. * Upon calling this method, the mover will fetch the tensor from another * backend and register it with the current active backend. */ moveData(dataId: DataId): void; } export interface BackendTimer { time(f: () => void): Promise<BackendTimingInfo>; } /** * The interface that defines the kernels that should be implemented when * adding a new backend. New backends don't need to implement every one of the * methods, this can be done gradually (throw an error for unimplemented * methods). */ export declare class KernelBackend implements TensorStorage, Backend, BackendTimer { time(f: () => void): Promise<BackendTimingInfo>; read(dataId: object): Promise<DataValues>; readSync(dataId: object): DataValues; disposeData(dataId: object): void; write(dataId: object, values: DataValues): void; fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor<Rank.R3>; register(dataId: object, shape: number[], dtype: DataType): void; memory(): { unreliable: boolean; reasons?: string[]; }; /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ floatPrecision(): number; batchMatMul(a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean): Tensor3D; fusedBatchMatMul(a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean, bias?: Tensor, activation?: Activation): Tensor3D; slice<T extends Tensor>(x: T, begin: number[], size: number[]): T; stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): T; unstack(x: Tensor, axis: number): Tensor[]; reverse<T extends Tensor>(a: T, axis: number[]): T; concat(tensors: Tensor[], axis: number): Tensor; neg<T extends Tensor>(a: T): T; add(a: Tensor, b: Tensor): Tensor; addN<T extends Tensor>(tensors: T[]): T; subtract(a: Tensor, b: Tensor): Tensor; multiply(a: Tensor, b: Tensor): Tensor; realDivide(a: Tensor, b: Tensor): Tensor; floorDiv(a: Tensor, b: Tensor): Tensor; sum(x: Tensor, axes: number[]): Tensor; prod(x: Tensor, axes: number[]): Tensor; unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D, numSegments: number): Tensor; argMin(x: Tensor, axis: number): Tensor; argMax(x: Tensor, axis: number): Tensor; equal(a: Tensor, b: Tensor): Tensor; notEqual(a: Tensor, b: Tensor): Tensor; less(a: Tensor, b: Tensor): Tensor; lessEqual(a: Tensor, b: Tensor): Tensor; greater(a: Tensor, b: Tensor): Tensor; greaterEqual(a: Tensor, b: Tensor): Tensor; logicalNot<T extends Tensor>(a: T): T; logicalAnd(a: Tensor, b: Tensor): Tensor; logicalOr(a: Tensor, b: Tensor): Tensor; where(condition: Tensor): Tensor2D; select(condition: Tensor, a: Tensor, b: Tensor): Tensor; topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T]; min(x: Tensor, axes: number[]): Tensor; minimum(a: Tensor, b: Tensor): Tensor; mod(a: Tensor, b: Tensor): Tensor; max(x: Tensor, axes: number[]): Tensor; maximum(a: Tensor, b: Tensor): Tensor; all(x: Tensor, axes: number[]): Tensor; any(x: Tensor, axes: number[]): Tensor; squaredDifference(a: Tensor, b: Tensor): Tensor; ceil<T extends Tensor>(x: T): T; floor<T extends Tensor>(x: T): T; round<T extends Tensor>(x: T): T; sign<T extends Tensor>(x: T): T; pow<T extends Tensor>(a: T, b: Tensor): T; exp<T extends Tensor>(x: T): T; expm1<T extends Tensor>(x: T): T; log<T extends Tensor>(x: T): T; log1p<T extends Tensor>(x: T): T; sqrt<T extends Tensor>(x: T): T; rsqrt<T extends Tensor>(x: T): T; square<T extends Tensor>(x: T): T; reciprocal<T extends Tensor>(x: T): T; relu<T extends Tensor>(x: T): T; prelu<T extends Tensor>(x: T, a: T): T; elu<T extends Tensor>(x: T): T; eluDer<T extends Tensor>(dy: T, y: T): T; selu<T extends Tensor>(x: T): T; int<T extends Tensor>(x: T): T; clip<T extends Tensor>(x: T, min: number, max: number): T; abs<T extends Tensor>(x: T): T; complexAbs<T extends Tensor>(x: T): T; sigmoid<T extends Tensor>(x: T): T; softplus<T extends Tensor>(x: T): T; sin<T extends Tensor>(x: T): T; cos<T extends Tensor>(x: T): T; tan<T extends Tensor>(x: T): T; asin<T extends Tensor>(x: T): T; acos<T extends Tensor>(x: T): T; atan<T extends Tensor>(x: T): T; atan2<T extends Tensor>(a: T, b: T): T; sinh<T extends Tensor>(x: T): T; cosh<T extends Tensor>(x: T): T; tanh<T extends Tensor>(x: T): T; asinh<T extends Tensor>(x: T): T; acosh<T extends Tensor>(x: T): T; atanh<T extends Tensor>(x: T): T; erf<T extends Tensor>(x: T): T; step<T extends Tensor>(x: T, alpha: number): T; conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D; conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D; conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D; depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D; depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D; depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D; conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D; conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D; conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D; maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D; maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo): Tensor4D; avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D; avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D; reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]): Tensor<R>; cast<T extends Tensor>(x: T, dtype: DataType): T; tile<T extends Tensor>(x: T, reps: number[]): T; pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T; transpose<T extends Tensor>(x: T, perm: number[]): T; gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T; gatherND(x: Tensor, indices: Tensor): Tensor; scatterND<R extends Rank>(indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R>; batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T; spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: number[][]): T; resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D; resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D; resizeNearestNeighbor(x: Tensor4D, newHEight: number, newWidth: number, alignCorners: boolean): Tensor4D; resizeNearestNeighborBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D; batchNormalization(x: Tensor4D, mean: Tensor4D | Tensor1D, variance: Tensor4D | Tensor1D, varianceEpsilon: number, scale?: Tensor4D | Tensor1D, offset?: Tensor4D | Tensor1D): Tensor4D; localResponseNormalization4D(x: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D; LRNGrad(dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D; multinomial(logits: Tensor2D, normalized: boolean, numSamples: number, seed: number): Tensor2D; oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number): Tensor2D; cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): Tensor; nonMaxSuppression(boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number, iouThreshold: number, scoreThreshold?: number): Tensor1D; fft(x: Tensor2D): Tensor2D; ifft(x: Tensor2D): Tensor2D; complex<T extends Tensor>(real: T, imag: T): T; real<T extends Tensor>(input: T): T; imag<T extends Tensor>(input: T): T; cropAndResize(image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D, cropSize: [number, number], method: 'bilinear' | 'nearest', extrapolationValue: number): Tensor4D; depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D; split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[]; sparseToDense<R extends Rank>(sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R], defaultValue: Scalar): Tensor<R>; fill<R extends Rank>(shape: ShapeMap[R], value: number | string, dtype?: DataType): Tensor<R>; /** * Sets the data mover for this backend. Backends should use the mover to * move data from other backends to this backend. */ setDataMover(dataMover: DataMover): void; dispose(): void; }