onnxruntime/js/web/lib/onnxjs/backends/webgl/ops/image-scaler.ts

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';
import {Graph} from '../../../graph';
import {OperatorImplementation, OperatorInitialization} from '../../../operators';
import {Tensor} from '../../../tensor';
import {WebGLInferenceHandler} from '../inference-handler';
import {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';

export interface ImageScalerAttributes extends AttributeWithCacheKey {
  scale: number;
  bias: number[];
}

export const imageScaler: OperatorImplementation<ImageScalerAttributes> =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ImageScalerAttributes): Tensor[] => {
      validateInputs(inputs);
      const output =
          inferenceHandler.run(createImageScalerProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);
      return [output];
    };

export const parseImageScalerAttributes: OperatorInitialization<ImageScalerAttributes> =
    (node: Graph.Node): ImageScalerAttributes => {
      const scale = node.attributes.getFloat('scale');
      const bias = node.attributes.getFloats('bias');
      return createAttributeWithCacheKey({scale, bias});
    };

const imageScalerProgramMetadata = {
  name: 'ImageScaler',
  inputNames: ['X'],
  inputTypes: [TextureType.unpacked],
};

const createImageScalerProgramInfo =
    (_handler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[], attributes: ImageScalerAttributes):
        ProgramInfo => {
          const outputShape = inputs[0].dims.slice();
          const rank = outputShape.length;
          const getBiasMethod = createGetBiasMethod(attributes.bias.length);
          const shaderSource = `
      ${getBiasMethod}
      float process(int indices[${rank}]) {
        return _X(indices) * scale + getBias(bias, indices[1]);
      }`;
          return {
            ...metadata,
            output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},
            variables: [
              {name: 'bias', type: 'float', arrayLength: attributes.bias.length, data: attributes.bias},
              {name: 'scale', type: 'float', data: attributes.scale}
            ],
            shaderSource
          };
        };

const createImageScalerProgramInfoLoader =
    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ImageScalerAttributes): ProgramInfoLoader => {
      const metadata = {...imageScalerProgramMetadata, cacheHint: attributes.cacheKey};
      return {...metadata, get: () => createImageScalerProgramInfo(handler, metadata, inputs, attributes)};
    };

const createGetBiasMethod = (numChannels: number): string => {
  const codeLines: string[] = [`float getBias(float bias[${numChannels}], int channel) {`];
  for (let i = 0; i < numChannels; ++i) {
    if (i === 0) {
      codeLines.push(
          '\t' +
          `if (channel == ${i}) { return bias[${i}]; }`);
    } else if (i === numChannels - 1) {
      codeLines.push(
          '\t' +
          `else { return bias[${i}]; }`);
    } else {
      codeLines.push(
          '\t' +
          `else if (channel == ${i}) { return bias[${i}]; }`);
    }
  }
  codeLines.push(
      '\t' +
      '}');
  return codeLines.join('\n');
};

const validateInputs = (inputs: Tensor[]): void => {
  if (!inputs || inputs.length !== 1) {
    throw new Error('ImageScaler requires 1 input.');
  }
  if (inputs[0].dims.length !== 4) {
    throw new Error('Invalid input shape.');
  }
  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {
    throw new Error('Invalid input type.');
  }
};