@tensorflow/tfjs-layers
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TensorFlow layers API in JavaScript
106 lines • 15.6 kB
JavaScript
/**
* @license
* Copyright 2023 CodeSmith LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
import { image, serialization, tidy } from '@tensorflow/tfjs-core';
import { getExactlyOneTensor, getExactlyOneShape } from '../../utils/types_utils';
import { ValueError } from '../../errors';
import { BaseRandomLayer } from '../../engine/base_random_layer';
import { randomUniform } from '@tensorflow/tfjs-core';
const INTERPOLATION_KEYS = ['bilinear', 'nearest'];
export const INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);
/**
* Preprocessing Layer with randomly varies image during training
*
* This layer randomly adjusts the width of a batch of images of a
* batch of images by a random factor.
*
* The input should be a 3D (unbatched) or
* 4D (batched) tensor in the `"channels_last"` image data format. Input pixel
* values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of integer
* or floating point dtype. By default, the layer will output floats.
*
* tf methods implemented in tfjs: 'bilinear', 'nearest',
* tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',
* 'gaussian', 'mitchellcubic'
*
*/
class RandomWidth extends BaseRandomLayer {
constructor(args) {
super(args);
const { factor, interpolation = 'bilinear' } = args;
this.factor = factor;
if (Array.isArray(this.factor) && this.factor.length === 2) {
this.widthLower = this.factor[0];
this.widthUpper = this.factor[1];
}
else if (!Array.isArray(this.factor) && this.factor > 0) {
this.widthLower = -this.factor;
this.widthUpper = this.factor;
}
else {
throw new ValueError(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);
}
if (this.widthLower < -1.0 || this.widthUpper < -1.0) {
throw new ValueError(`factor must have values larger than -1. Got: ${this.factor}`);
}
if (this.widthUpper < this.widthLower) {
throw new ValueError(`factor cannot have upper bound less than lower bound.
Got upper bound: ${this.widthUpper}.
Got lower bound: ${this.widthLower}
`);
}
if (interpolation) {
if (INTERPOLATION_METHODS.has(interpolation)) {
this.interpolation = interpolation;
}
else {
throw new ValueError(`Invalid interpolation parameter: ${interpolation} is not implemented`);
}
}
}
getConfig() {
const config = {
'factor': this.factor,
'interpolation': this.interpolation,
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const numChannels = inputShape[2];
return [this.imgHeight, -1, numChannels];
}
call(inputs, kwargs) {
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 = [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`);
}
});
}
}
/** @nocollapse */
RandomWidth.className = 'RandomWidth';
export { RandomWidth };
serialization.registerClass(RandomWidth);
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Input pixel\n * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of integer\n * or floating point dtype. By default, the layer will output floats.\n *\n * tf methods implemented in tfjs: 'bilinear', 'nearest',\n * tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',\n *                                   'gaussian', 'mitchellcubic'\n *\n */\n\nexport class RandomWidth extends BaseRandomLayer {\n  /** @nocollapse */\n  static override className = 'RandomWidth';\n  private readonly factor: number | [number, number];\n  private readonly interpolation?: InterpolationType;  // default = 'bilinear\n  private widthLower: number;\n  private widthUpper: number;\n  private imgHeight: number;\n  private widthFactor: Tensor<Rank.R1>;\n\n  constructor(args: RandomWidthArgs) {\n    super(args);\n    const {factor, interpolation = 'bilinear'} = args;\n\n    this.factor = factor;\n\n    if (Array.isArray(this.factor) && this.factor.length === 2) {\n      this.widthLower = this.factor[0];\n      this.widthUpper = this.factor[1];\n    } else if (!Array.isArray(this.factor) && this.factor > 0){\n      this.widthLower = -this.factor;\n      this.widthUpper = this.factor;\n    } else {\n      throw new ValueError(\n        `Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`\n      );\n    }\n    if (this.widthLower < -1.0 || this.widthUpper < -1.0) {\n      throw new ValueError(\n        `factor must have values larger than -1. Got: ${this.factor}`\n      );\n    }\n\n    if (this.widthUpper < this.widthLower) {\n      throw new ValueError(\n        `factor cannot have upper bound less than lower bound.\n        Got upper bound: ${this.widthUpper}.\n        Got lower bound: ${this.widthLower}\n      `);\n    }\n\n    if (interpolation) {\n      if (INTERPOLATION_METHODS.has(interpolation)) {\n        this.interpolation = interpolation;\n      } else {\n        throw new ValueError(`Invalid interpolation parameter: ${\n            interpolation} is not implemented`);\n      }\n    } \n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const config: serialization.ConfigDict = {\n      'factor': this.factor,\n      'interpolation': this.interpolation,\n    };\n\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n\n  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {\n    inputShape = getExactlyOneShape(inputShape);\n    const numChannels = inputShape[2];\n    return [this.imgHeight, -1, numChannels];\n  }\n\n  override call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>,\n    kwargs: Kwargs): Tensor[]|Tensor {\n\n    return tidy(() => {\n      const input = getExactlyOneTensor(inputs);\n      this.imgHeight = input.shape[input.shape.length - 3];\n      const imgWidth = input.shape[input.shape.length - 2];\n\n      this.widthFactor = randomUniform([1],\n        (1.0 + this.widthLower), (1.0 + this.widthUpper),\n        'float32', this.randomGenerator.next()\n      );\n\n      let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth;\n      adjustedWidth = Math.round(adjustedWidth);\n\n      const size:[number, number] = [this.imgHeight, adjustedWidth];\n\n      switch (this.interpolation) {\n        case 'bilinear':\n          return image.resizeBilinear(inputs, size);\n        case 'nearest':\n          return image.resizeNearestNeighbor(inputs, size);\n        default:\n          throw new Error(`Interpolation is ${this.interpolation}\n          but only ${[...INTERPOLATION_METHODS]} are supported`);\n      }\n    });\n  }\n}\n\nserialization.registerClass(RandomWidth);\n"]}