import { Activation } from '../../library'; import Tensor, { DType, PadMode } from '../../types'; import { TensorF32 as WTF32, TensorF64 as WTF64, TensorI32 as WTI32, TensorI16 as WTI16, TensorI8 as WTI8, TensorU32 as WTU32, TensorU16 as WTU16, TensorU8 as WTU8 } from '../../wasm/rust_wasm_tensor'; import { CPUTensor } from '../cpu/tensor'; export declare let tensorConstructor: { [name: string]: any; }; export declare const wasmLoaded: Promise; export declare type WT = { float64: WTF64; float32: WTF32; int32: WTI32; int16: WTI16; int8: WTI8; uint32: WTU32; uint16: WTU16; uint8: WTU8; }; export declare type DTypeWasm = 'float64' | 'float32' | 'int32' | 'int16' | 'int8' | 'uint32' | 'uint16' | 'uint8'; export declare class WASMTensor extends Tensor { static range(start: number, limit: number, delta: number): WASMTensor<"float32">; wasmTensor: WT[DTpe]; constructor(values: number[] | WT[DTpe], shape?: Uint32Array, dtype?: DTpe); cast(dtype: DTpe2): Tensor; getValues(): any; getShape(): readonly number[]; constantLike(value: number): Tensor; singleConstant(value: number): Tensor; delete(): void; copy(): Tensor; exp(): Tensor; log(): Tensor; sqrt(): Tensor; abs(): Tensor; sin(): Tensor; cos(): Tensor; tan(): Tensor; asin(): Tensor; acos(): Tensor; atan(): Tensor; sinh(): Tensor; cosh(): Tensor; tanh(): Tensor; asinh(): Tensor; acosh(): Tensor; atanh(): Tensor; sigmoid(): Tensor; hardSigmoid(alpha: number, beta: number): Tensor; negate(): Tensor; powerScalar(power: number, factor: number): Tensor; addMultiplyScalar(factor: number, add: number): Tensor; sign(): Tensor; setValues(values: Tensor, starts: number[]): Tensor; add_impl(th: Tensor, tensor: Tensor, _resultShape: readonly number[], alpha: number, beta: number): Tensor; subtract_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number, beta: number): Tensor; multiply_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number): Tensor; divide_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[], alpha: number): Tensor; power_impl(th: Tensor, tensor: Tensor, resultShape: readonly number[]): Tensor; matMul(tensor: Tensor): Tensor; gemm_impl(b: Tensor, aTranspose: boolean, bTranspose: boolean, alpha: number, beta: number, c?: Tensor): Tensor; sum_impl(axes: number[], keepDims: boolean): Tensor; sumSquare_impl(axes: number[], keepDims: boolean): Tensor; product_impl(axes: number[], keepDims: boolean): Tensor; max_impl(axes: number[], keepDims: boolean): Tensor; min_impl(axes: number[], keepDims: boolean): Tensor; reduceMean_impl(axes: number[], keepDims: boolean): Tensor; reduceMeanSquare_impl(axes: number[], keepDims: boolean): Tensor; protected reduceLogSum_impl(axes: number[], keepDims: boolean): Tensor; protected reduceLogSumExp_impl(axes: number[], keepDims: boolean): Tensor; getActivationFlag(activation: Activation): 0 | 1 | 2; conv_impl(kernel: Tensor, dilations: number[], group: number, pads: number[], strides: number[], activation: Activation, bias?: Tensor): Tensor; protected convTranspose_impl(kernel: Tensor, dilations: number[], group: number, pads: number[], strides: number[]): Tensor; averagePool_impl(kernelShape: number[], pads: number[], strides: number[], includePad: boolean): Tensor; reshape_impl(shape: number[]): Tensor; concat(tensor: Tensor, axis: number): Tensor; transpose_impl(permutation: number[]): Tensor; clip(min?: number, max?: number): Tensor; clipBackward(grad: Tensor, min?: number, max?: number): Tensor; repeat(repeats: number[]): Tensor; expand(shape: readonly number[]): Tensor; static padModeToInt: { constant: number; reflect: number; edge: number; }; pad_impl(pads: number[], mode: PadMode, value: number): Tensor; gather(axis: number, indices: CPUTensor<'uint32'>): Tensor; floor(): Tensor; ceil(): Tensor; round(): Tensor; slice_impl(starts: number[], ends: number[], axes: number[], steps: number[]): Tensor; upsample(scales: number[]): Tensor; normalize(mean: Tensor, variance: Tensor, epsilon: number, scale: Tensor, bias: Tensor): Tensor; }