@hoff97/tensor-js/dist/lib/ops/gpu/conv/convTranspose.d.ts

PyTorch like deep learning inferrence library

import { DTypeGpu, GPUTensorConstructor, GPUTensorI } from '../../../tensor/gpu/interface';
import { GPUMemoryAllocator } from '../../../tensor/gpu/memory';
import { Input, Operation } from '../operation';
export interface ConvTransposeInfo {
    shapeX?: readonly number[];
    widthX?: number;
    heightX?: number;
    shapeW?: readonly number[];
    widthW?: number;
    heightW?: number;
    shapeOutput?: readonly number[];
    widthOutput?: number;
    heightOutput?: number;
    pads?: readonly number[];
    dilations?: readonly number[];
    strides?: readonly number[];
    CG?: number;
    kernelSize?: number;
    dataRank?: number;
    C?: number;
}
export interface ConvTransposeInput {
    X: GPUTensorI;
    W: GPUTensorI;
    pads: readonly number[];
    dilations: readonly number[];
    strides: readonly number[];
}
export declare class ConvTransposeOperation<GPUTensor extends GPUTensorI> extends Operation<GPUTensor, ConvTransposeInfo, ConvTransposeInput> {
    protected maxIterations: number;
    constructor(tensorConstructor: GPUTensorConstructor<GPUTensor>, dtype: DTypeGpu, allocator?: GPUMemoryAllocator);
    updateInputIx(): string;
    getMainBody(): string;
    getVariables(): string;
    getFragmentShader(info: ConvTransposeInfo): string;
    getTextureNames(): string[];
    getUniformAttrs(): Input[];
    calc(input: ConvTransposeInput): GPUTensor;
    getOutputShape(input: ConvTransposeInput): readonly number[];
    compile(info: ConvTransposeInfo): void;
    getCompilationInfo(input: ConvTransposeInput): ConvTransposeInfo;
    getInputInfoString(input: ConvTransposeInput): string;
}