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83 lines
4.1 KiB
JavaScript
83 lines
4.1 KiB
JavaScript
/* eslint-env browser, mocha */
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describe('convolutional layer: Cropping2D', function () {
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const assert = chai.assert
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const styles = testGlobals.styles
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const logTime = testGlobals.logTime
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const stringifyCondensed = testGlobals.stringifyCondensed
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const approxEquals = KerasJS.testUtils.approxEquals
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const layers = KerasJS.layers
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before(function () {
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console.log('\n%cconvolutional layer: Cropping2D', styles.h1)
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})
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it(`[convolutional.Cropping2D.0] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=tf`, function () {
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const key = `convolutional.Cropping2D.0`
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console.log(`\n%c[${key}] cropping (1,1),(1, 1) on 3x5x4 input, dimOrdering=tf`, styles.h3)
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let testLayer = new layers.Cropping2D({ cropping: [[1, 1], [1, 1]], dimOrdering: 'tf' })
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it(`[convolutional.Cropping2D.0] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=th`, function () {
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const key = `convolutional.Cropping2D.1`
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console.log(`\n%c[${key}] cropping (1,1),(1, 1) on 3x5x4 input, dimOrdering=th`, styles.h3)
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let testLayer = new layers.Cropping2D({ cropping: [[1, 1], [1, 1]], dimOrdering: 'th' })
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it(`[convolutional.Cropping2D.2] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=tf`, function () {
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const key = `convolutional.Cropping2D.2`
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console.log(`\n%c[${key}] cropping (4,2),(3,1) on 8x7x6 input, dimOrdering=tf`, styles.h3)
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let testLayer = new layers.Cropping2D({ cropping: [[4,2],[3,1]], dimOrdering: 'tf' })
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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it(`[convolutional.Cropping2D.2] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=th`, function () {
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const key = `convolutional.Cropping2D.3`
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console.log(`\n%c[${key}] cropping (4,2),(3,1) on 8x7x6 input, dimOrdering=th`, styles.h3)
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let testLayer = new layers.Cropping2D({ cropping: [[4,2],[3,1]], dimOrdering: 'th' })
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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