Method call

tfjs-layers/src/layers/preprocessing/random_width.ts:115–143

(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>,
    kwargs: Kwargs)

  }

  override call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>,
    kwargs: Kwargs): Tensor[]|Tensor {

    return tidy(() => {
      const input = getExactlyOneTensor(inputs);
      this.imgHeight = input.shape[input.shape.length - 3];
      const imgWidth = input.shape[input.shape.length - 2];

      this.widthFactor = randomUniform([1],
        (1.0 + this.widthLower), (1.0 + this.widthUpper),
        'float32', this.randomGenerator.next()
      );

      let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth;
      adjustedWidth = Math.round(adjustedWidth);

      const size:[number, number] = [this.imgHeight, adjustedWidth];

      switch (this.interpolation) {
        case 'bilinear':
          return image.resizeBilinear(inputs, size);
        case 'nearest':
          return image.resizeNearestNeighbor(inputs, size);
        default:
          throw new Error(`Interpolation is ${this.interpolation}
          but only ${[...INTERPOLATION_METHODS]} are supported`);
      }
    });
  }
}

serialization.registerClass(RandomWidth);