keras-js/test/convolutional/Deconvolution2D.js

/* eslint-env browser, mocha */

describe('convolutional layer: Deconvolution2D', function () {
  const assert = chai.assert
  const styles = testGlobals.styles
  const logTime = testGlobals.logTime
  const stringifyCondensed = testGlobals.stringifyCondensed
  const approxEquals = KerasJS.testUtils.approxEquals
  const layers = KerasJS.layers

  const testParams = [
    {
      inputShape: [4, 4, 2],
      kernelShape: [4, 3, 3],
      outputShape: [6, 6, 4],
      attrs: { activation: 'linear', borderMode: 'valid', subsample: [1, 1], dimOrdering: 'tf', bias: false }
    },
    {
      inputShape: [4, 4, 2],
      kernelShape: [4, 3, 3],
      outputShape: [6, 6, 4],
      attrs: { activation: 'linear', borderMode: 'valid', subsample: [1, 1], dimOrdering: 'tf', bias: true }
    },
    {
      inputShape: [4, 4, 2],
      kernelShape: [4, 3, 3],
      outputShape: [9, 9, 4],
      attrs: { activation: 'relu', borderMode: 'valid', subsample: [2, 2], dimOrdering: 'tf', bias: true }
    },
    {
      inputShape: [4, 4, 2],
      kernelShape: [4, 3, 3],
      outputShape: [4, 4, 4],
      attrs: { activation: 'relu', borderMode: 'same', subsample: [1, 1], dimOrdering: 'tf', bias: true }
    },
    {
      inputShape: [4, 4, 2],
      kernelShape: [5, 3, 3],
      outputShape: [8, 8, 5],
      attrs: { activation: 'relu', borderMode: 'same', subsample: [2, 2], dimOrdering: 'tf', bias: true }
    },
    {
      inputShape: [4, 4, 2],
      kernelShape: [5, 3, 3],
      outputShape: [7, 7, 5],
      attrs: { activation: 'relu', borderMode: 'same', subsample: [2, 2], dimOrdering: 'tf', bias: true }
    }
  ]

  before(function () {
    console.log('\n%cconvolutional layer: Deconvolution2D', styles.h1)
  })

  /*********************************************************
  * CPU
  *********************************************************/

  describe('CPU', function () {
    before(function () {
      console.log('\n%cCPU', styles.h2)
    })

    testParams.forEach(({ inputShape, kernelShape, outputShape, attrs }, i) => {
      const key = `convolutional.Deconvolution2D.${i}`
      const [inputRows, inputCols, inputChannels] = inputShape
      const [nbFilter, nbRow, nbCol] = kernelShape
      const [outputRows, outputCols, outputChannels] = outputShape
      const title = `[${key}] [CPU] test: ${nbFilter} ${nbRow}x${nbCol} filters on ${inputRows}x${inputCols}x${inputChannels} input, ${outputRows}x${outputCols}x${outputChannels} output, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsample=${attrs.subsample}, dim_ordering='${attrs.dimOrdering}', bias=${attrs.bias}`

      it(title, function () {
        console.log(`\n%c${title}`, styles.h3)
        let testLayer = new layers.Deconvolution2D(Object.assign({ nbFilter, nbRow, nbCol, outputShape }, attrs))
        testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
        let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
        console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
        const startTime = performance.now()
        t = testLayer.call(t)
        const endTime = performance.now()
        console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
        logTime(startTime, endTime)
        const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
        const shapeExpected = TEST_DATA[key].expected.shape
        assert.deepEqual(t.tensor.shape, shapeExpected)
        assert.isTrue(approxEquals(t.tensor, dataExpected))
      })
    })
  })

  /*********************************************************
  * GPU
  *********************************************************/

  describe('GPU', function () {
    before(function () {
      console.log('\n%cGPU', styles.h2)
    })

    testParams.forEach(({ inputShape, kernelShape, outputShape, attrs }, i) => {
      const key = `convolutional.Deconvolution2D.${i}`
      const [inputRows, inputCols, inputChannels] = inputShape
      const [nbFilter, nbRow, nbCol] = kernelShape
      const [outputRows, outputCols, outputChannels] = outputShape
      const title = `[${key}] [GPU] test: ${nbFilter} ${nbRow}x${nbCol} filters on ${inputRows}x${inputCols}x${inputChannels} input, ${outputRows}x${outputCols}x${outputChannels} output, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsample=${attrs.subsample}, dim_ordering='${attrs.dimOrdering}', bias=${attrs.bias}`

      it(title, function () {
        console.log(`\n%c${title}`, styles.h3)
        let testLayer = new layers.Deconvolution2D(Object.assign({ nbFilter, nbRow, nbCol, outputShape }, attrs, { gpu: true }))
        testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
        let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
        console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
        const startTime = performance.now()
        t = testLayer.call(t)
        const endTime = performance.now()
        console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
        logTime(startTime, endTime)
        const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
        const shapeExpected = TEST_DATA[key].expected.shape
        assert.deepEqual(t.tensor.shape, shapeExpected)
        assert.isTrue(approxEquals(t.tensor, dataExpected))
      })
    })
  })
})