Resizing Layer

tfjs-layers/src/exports_layers.ts

@@ -24,8 +24,10 @@ import {ZeroPadding2D, ZeroPadding2DLayerArgs} from './layers/padding';
 import {AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, GlobalPooling2DLayerArgs, MaxPooling1D, MaxPooling2D, MaxPooling3D, Pooling1DLayerArgs, Pooling2DLayerArgs, Pooling3DLayerArgs} from './layers/pooling';
 import {GRU, GRUCell, GRUCellLayerArgs, GRULayerArgs, LSTM, LSTMCell, LSTMCellLayerArgs, LSTMLayerArgs, RNN, RNNCell, RNNLayerArgs, SimpleRNN, SimpleRNNCell, SimpleRNNCellLayerArgs, SimpleRNNLayerArgs, StackedRNNCells, StackedRNNCellsArgs} from './layers/recurrent';
 import {Bidirectional, BidirectionalLayerArgs, TimeDistributed, WrapperLayerArgs} from './layers/wrappers';
-import { Rescaling, RescalingArgs } from './layers/preprocessing/image_preprocessing';
-import { CategoryEncoding, CategoryEncodingArgs } from './layers/preprocessing/category_encoding';
+import {Rescaling, RescalingArgs} from './layers/preprocessing/image_preprocessing';
+import {Resizing, ResizingArgs} from './layers/preprocessing/image_resizing';
+import {CategoryEncoding, CategoryEncodingArgs} from './layers/preprocessing/category_encoding';
+
 // TODO(cais): Add doc string to all the public static functions in this
 //   class; include exectuable JavaScript code snippets where applicable
 //   (b/74074458).
@@ -1730,6 +1732,32 @@ export function rescaling(args?: RescalingArgs) {
   return new Rescaling(args);
 }
 
+/**
+ * A preprocessing layer which resizes images.
+ * This layer resizes an image input to a target height and width. The input
+ * should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"`
+ * format.  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0,
+ * 255]`) and of interger or floating point dtype. By default, the layer will
+ * output floats.
+ *
+ * Arguments:
+ *   - `height`: number, the height for the output tensor.
+ *   - `width`: number, the width for the output tensor.
+ *   - `interpolation`: string, the method for image resizing interpolation.
+ *   - `cropToAspectRatio`: boolean, whether to keep image aspect ratio.
+ *
+ * Input shape:
+ *   Arbitrary.
+ *
+ * Output shape:
+ *   height, width, num channels.
+ *
+ * @doc {heading: 'Layers', subheading: 'Resizing', namespace: 'layers'}
+ */
+export function resizing(args?: ResizingArgs) {
+  return new Resizing(args);
+}
+
 /**
  * A preprocessing layer which encodes integer features.
  *

tfjs-layers/src/layers/preprocessing/image_preprocessing_test.ts

@@ -1,3 +1,13 @@
+/**
+ * @license
+ * Copyright 2022 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 { Tensor, randomNormal, mul, add} from '@tensorflow/tfjs-core';
 import { Rescaling } from './image_preprocessing';
 import { describeMathCPUAndGPU, expectTensorsClose } from '../../utils/test_utils';

tfjs-layers/src/layers/preprocessing/image_resizing.ts

@@ -0,0 +1,102 @@
+/**
+ * @license
+ * Copyright 2022 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, Rank, serialization, Tensor, tidy} from '@tensorflow/tfjs-core';  // mul, add
+
+import {Layer, LayerArgs} from '../../engine/topology';
+import {ValueError} from '../../errors';
+import {Shape} from '../../keras_format/common';
+import {Kwargs} from '../../types';
+import {getExactlyOneShape} from '../../utils/types_utils';  //, getExactlyOneTensor
+
+// tf methods unimplemented in tfjs: 'bicubic', 'area', 'lanczos3', 'lanczos5',
+//                                   'gaussian', 'mitchellcubic'
+const INTERPOLATION_KEYS = ['bilinear', 'nearest'] as const;
+const INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);
+type InterpolationType = typeof INTERPOLATION_KEYS[number];
+
+export declare interface ResizingArgs extends LayerArgs {
+  height: number;
+  width: number;
+  interpolation?: InterpolationType; // default = 'bilinear';
+  cropToAspectRatio?: boolean;       // default = false;
+}
+
+/**
+ * Preprocessing Resizing Layer
+ *
+ * This resizes images by a scaling and offset factor
+ */
+
+export class Resizing extends Layer {
+  /** @nocollapse */
+  static className = 'Resizing';
+  private readonly height: number;
+  private readonly width: number;
+  // method of interpolation to be used; default = "bilinear";
+  private readonly interpolation: InterpolationType;
+  // toggle whether the aspect ratio should be preserved; default = false;
+  private readonly cropToAspectRatio: boolean;
+
+  constructor(args: ResizingArgs) {
+    super(args);
+
+    this.height = args.height;
+    this.width = args.width;
+
+    if (args.interpolation) {
+      if (INTERPOLATION_METHODS.has(args.interpolation)) {
+        this.interpolation = args.interpolation;
+      } else {
+        throw new ValueError(`Invalid interpolation parameter: ${
+            args.interpolation} is not implemented`);
+      }
+    } else {
+      this.interpolation = 'bilinear';
+    }
+    this.cropToAspectRatio = Boolean(args.cropToAspectRatio);
+  }
+
+  computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {
+    inputShape = getExactlyOneShape(inputShape);
+    const numChannels = inputShape[2];
+    return [this.height, this.width, numChannels];
+  }
+
+  getConfig(): serialization.ConfigDict {
+    const config: serialization.ConfigDict = {
+      'height': this.height,
+      'width': this.width,
+      'interpolation': this.interpolation,
+      'cropToAspectRatio': this.cropToAspectRatio
+    };
+
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+
+  call(inputs: Tensor<Rank.R3>|Tensor<Rank.R4>, kwargs: Kwargs):
+      Tensor[]|Tensor {
+    return tidy(() => {
+      const size: [number, number] = [this.height, this.width];
+      if (this.interpolation === 'bilinear') {
+        return image.resizeBilinear(inputs, size, !this.cropToAspectRatio);
+      } else if (this.interpolation === 'nearest') {
+        return image.resizeNearestNeighbor(
+            inputs, size, !this.cropToAspectRatio);
+      } else {
+        throw new Error(`Interpolation is ${this.interpolation} but only ${[...INTERPOLATION_METHODS]} are supported`);
+      }
+    });
+  }
+}
+
+serialization.registerClass(Resizing);

tfjs-layers/src/layers/preprocessing/image_resizing_test.ts

@@ -0,0 +1,125 @@
+/**
+ * @license
+ * Copyright 2022 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.
+ * =============================================================================
+ */
+
+/**
+ * Unit Tests for image resizing layer.
+ */
+
+import {image, Rank, Tensor, tensor, zeros, range, reshape} from '@tensorflow/tfjs-core';
+
+// import {Shape} from '../../keras_format/common';
+import {describeMathCPUAndGPU, expectTensorsClose} from '../../utils/test_utils';
+
+import {Resizing, ResizingArgs} from './image_resizing';
+
+describeMathCPUAndGPU('Resizing Layer', () => {
+  it('Check if output shape matches specifications', () => {
+    // resize and check output shape
+    const maxHeight = 40;
+    const height = Math.floor(Math.random() * maxHeight);
+    const maxWidth = 60;
+    const width = Math.floor(Math.random() * maxWidth);
+    const numChannels = 3;
+    const inputTensor = zeros([height * 2, width * 2, numChannels]);
+    const expectedOutputShape = [height, width, numChannels];
+    const resizingLayer = new Resizing({height, width});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    expect(layerOutputTensor.shape).toEqual(expectedOutputShape);
+  });
+
+  it('Returns correctly downscaled tensor', () => {
+    // resize and check output content (not batched)
+    const rangeTensor = range(0, 16);
+    const inputTensor = reshape(rangeTensor, [4,4,1]);
+    const height = 2;
+    const width = 2;
+    const interpolation = 'nearest';
+    const resizingLayer = new Resizing({height, width, interpolation});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    const expectedArr = [[0, 3], [12, 15]];
+    const expectedOutput = tensor(expectedArr, [2,2,1]);
+    expectTensorsClose(layerOutputTensor, expectedOutput);
+  });
+
+  it('Returns correctly downscaled tensor', () => {
+    // resize and check output content (batched)
+    const rangeTensor = range(0, 36);
+    const inputTensor = reshape(rangeTensor, [1,6,6,1]);
+    const height = 3;
+    const width = 3;
+    const interpolation = 'nearest';
+    const resizingLayer = new Resizing({height, width, interpolation});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    const expectedArr = [[0,3,5], [18,21,23], [30,33,35]];
+    const expectedOutput = tensor([expectedArr], [1,3,3,1]);
+    expectTensorsClose(layerOutputTensor, expectedOutput);
+  });
+
+  it('Returns correctly upscaled tensor', () => {
+    const rangeTensor = range(0, 4);
+    const inputTensor = reshape(rangeTensor, [1, 2, 2, 1]);
+    const height = 4;
+    const width = 4;
+    const interpolation = 'nearest';
+    const resizingLayer = new Resizing({height, width, interpolation});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    const expectedArr = [[0,0,1,1], [0,0,1,1], [2,2,3,3], [2,2,3,3]];
+    const expectedOutput = tensor([expectedArr], [1,4,4,1]);
+    expectTensorsClose(layerOutputTensor, expectedOutput);
+  });
+
+  it('Returns the same tensor when given same shape as input', () => {
+    // create a resizing layer with same shape as input
+    const height = 64;
+    const width = 32;
+    const numChannels = 1;
+    const rangeTensor = range(0, height * width);
+    const inputTensor = reshape(rangeTensor, [height, width, numChannels]);
+    const resizingLayer = new Resizing({height, width});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    expectTensorsClose(layerOutputTensor, inputTensor);
+  });
+
+  it('Returns a tensor of the correct dtype', () => {
+    // do a same resizing operation, cheeck tensors dtypes and content
+    const height = 40;
+    const width = 60;
+    const numChannels = 3;
+    const inputTensor: Tensor<Rank.R3> =
+        zeros([height, width, numChannels]);
+    const size: [number, number] = [height, width];
+    const expectedOutputTensor = image.resizeBilinear(inputTensor, size);
+    const resizingLayer = new Resizing({height, width});
+    const layerOutputTensor = resizingLayer.apply(inputTensor) as Tensor;
+    expect(layerOutputTensor.dtype).toBe(inputTensor.dtype);
+    expectTensorsClose(layerOutputTensor, expectedOutputTensor);
+  });
+
+  it('Throws an error given incorrect parameters', () => {
+    // pass incorrect interpolation method string to layer init
+    const height = 16;
+    const width = 16;
+    const interpolation = 'unimplemented';
+    const incorrectArgs = {height, width, interpolation};
+    const expectedError =
+        `Invalid interpolation parameter: ${interpolation} is not implemented`;
+    expect(() => new Resizing(incorrectArgs as ResizingArgs))
+        .toThrowError(expectedError);
+  });
+
+  it('Config holds correct name', () => {
+    // layer name property set properly
+    const height = 40;
+    const width = 60;
+    const resizingLayer = new Resizing({height, width, name:'Resizing'});
+    const config = resizingLayer.getConfig();
+    expect(config.name).toEqual('Resizing');
+  });
+});