implement MaxPooling3D/AveragePooling3D, with tests

2026-09-12 12:33:40 +08:00 · 2016-09-05 15:35:24 -04:00
parent 948b9973a4
commit 2c4f27f6c3
12 changed files with 2006 additions and 3 deletions
@@ -0,0 +1,91 @@
+/* eslint-env browser, mocha */
+
+describe('pooling layer: AveragePooling3D', 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, 4, 2],
+      attrs: { poolSize: [2, 2, 2], strides: null, borderMode: 'valid', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [2, 2, 2], strides: [1, 1, 1], borderMode: 'valid', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 5, 2, 3],
+      attrs: { poolSize: [2, 2, 2], strides: [2, 1, 1], borderMode: 'valid', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [3, 3, 3], strides: null, borderMode: 'valid', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [3, 3, 3], strides: [3, 3, 3], borderMode: 'valid', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [2, 2, 2], strides: null, borderMode: 'same', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [2, 2, 2], strides: [1, 1, 1], borderMode: 'same', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 5, 4, 2],
+      attrs: { poolSize: [2, 2, 2], strides: [1, 2, 1], borderMode: 'same', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [3, 3, 3], strides: null, borderMode: 'same', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [4, 4, 4, 2],
+      attrs: { poolSize: [3, 3, 3], strides: [3, 3, 3], borderMode: 'same', dimOrdering: 'tf' }
+    },
+    {
+      inputShape: [2, 3, 3, 4],
+      attrs: { poolSize: [3, 3, 3], strides: [2, 2, 2], borderMode: 'valid', dimOrdering: 'th' }
+    },
+    {
+      inputShape: [2, 3, 3, 4],
+      attrs: { poolSize: [3, 3, 3], strides: [1, 1, 1], borderMode: 'same', dimOrdering: 'th' }
+    },
+    {
+      inputShape: [3, 4, 4, 3],
+      attrs: { poolSize: [2, 2, 2], strides: null, borderMode: 'valid', dimOrdering: 'th' }
+    }
+  ]
+
+  before(function () {
+    console.log('\n%cpooling layer: AveragePooling3D', styles.h1)
+  })
+
+  testParams.forEach(({ inputShape, attrs }, i) => {
+    const key = `pooling.AveragePooling3D.${i}`
+    const [inputDim1, inputDim2, inputDim3, inputChannels] = inputShape
+    const title = `[${key}] test: ${inputDim1}x${inputDim2}x${inputDim3}x${inputChannels} input, poolSize='${attrs.poolSize}', strides=${attrs.strides}, borderMode=${attrs.borderMode}, dimOrdering=${attrs.dimOrdering}`
+
+    it(title, function () {
+      console.log(`\n%c${title}`, styles.h3)
+      let testLayer = new layers.AveragePooling3D(attrs)
+      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))
+    })
+  })
+})