@hoff97/tensor-js/dist/lib/ops/gpu/conv/upsample.js

PyTorch like deep learning inferrence library

import { defaultAllocator } from '../../../tensor/gpu/gl';
import { getSize } from '../../../util/shape';
import { Operation } from '../operation';
export class UpsampleOperation extends Operation {
    constructor(tensorConstructor, dtype, allocator) {
        super(tensorConstructor, dtype, allocator);
    }
    // eslint-disable-next-line @typescript-eslint/no-unused-vars
    getFragmentShader(info) {
        return `
    float process(int index[${this.maxRank}]) {
      int inIx[${this.maxRank}];
      ${this.initIndex('inIx')}

      for (int i = 0; i < ${this.maxRank}; i++) {
        if (index[i] == -1) {
          break;
        }

        inIx[i] = int(floor(float(index[i]) / scales[i]));
      }

      return _X(inIx);
    }

    ${this.getDefaultMain()}
    `;
    }
    getTextureNames() {
        return ['X'];
    }
    getVariables() {
        return `
    ${this.getVarModifier('scales')} float scales[${this.maxRank}];
    `;
    }
    getUniformAttrs() {
        return [{ name: 'scales', length: this.maxRank, type: 'float' }];
    }
    calc(input) {
        if (this.fullyStatic && this.outputShape !== undefined) {
            return this.compute(this.outputShape, { X: input.X });
        }
        const resultShape = this.getOutputShape(input);
        return this.compute(resultShape, { X: input.X }, {
            scales: this.copyPad(input.scales),
        });
    }
    getOutputShape(input) {
        const rank = input.X.shape.length;
        const resultShape = [...input.X.shape];
        for (let i = 0; i < rank; i++) {
            resultShape[i] = Math.floor(resultShape[i] * input.scales[i]);
        }
        return resultShape;
    }
    compile(info) {
        if (info.shapeX !== undefined) {
            this.maxRank = info.shapeX.length;
        }
        super.compile(info);
    }
    getCompilationInfo(input) {
        const outputShape = this.getOutputShape(input);
        const outputSize = defaultAllocator.getAllocationDimensions(getSize(outputShape), this.dtype);
        return {
            shapeX: input.X.shape,
            widthX: input.X.memory.width,
            heightX: input.X.memory.height,
            shapeOutput: outputShape,
            widthOutput: outputSize.width,
            heightOutput: outputSize.height,
            scales: input.scales,
        };
    }
    getInputInfoString(input) {
        return `${input.X.shape}-${input.scales}`;
    }
}
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