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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/batch_normalization_constant.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/batch_normalization_constant.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/batch_normalization_constant.https.any.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API batchNormalization operation with constant inputs
// META: global=window
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Normalize the values of the input tensor using Batch-Normalization.
//
// dictionary MLBatchNormalizationOptions {
// MLOperand scale;
// MLOperand bias;
// [EnforceRange] unsigned long axis = 1;
// double epsilon = 1e-5;
// };
//
// MLOperand batchNormalization(
// MLOperand input, MLOperand mean, MLOperand, variance,
// optional MLBatchNormalizationOptions options = {});
const batchNormTests = [
{
'name': 'batchNormalization float32 2D constant tensors default options',
'graph': {
'inputs': {
'bnInput': {
'data': [
-41.30733108520508, 64.08863830566406, -63.376670837402344,
-46.790367126464844, 83.02227020263672, -80.08049011230469,
-62.144378662109375, -0.10012771934270859, -40.90216064453125,
56.96306228637695, 37.37249755859375, 57.046478271484375,
82.05680084228516, -86.1164321899414, 76.8831787109375,
97.03362274169922, -21.35103988647461, -96.93824005126953,
-9.359310150146484, 80.20824432373047, -85.36802673339844,
62.35185241699219, -68.4724349975586, -12.10716724395752
],
'descriptor': {shape: [4, 6], dataType: 'float32'},
'constant': true
},
'bnMean': {
'data': [
-7.814267635345459, -95.64129638671875, 38.15440368652344,
-55.95203399658203, -87.86500549316406, -41.63645553588867
],
'descriptor': {shape: [6], dataType: 'float32'},
'constant': true
},
'bnVariance': {
'data': [
60.31186294555664, 26.43260383605957, 53.275634765625,
40.146121978759766, 59.41098403930664, 35.99981689453125
],
'descriptor': {shape: [6], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'batchNormalization',
'arguments': [
{'input': 'bnInput'}, {'mean': 'bnMean'}, {'variance': 'bnVariance'}
],
'outputs': 'bnOutput'
}],
'expectedOutputs': {
'bnOutput': {
'data': [
-4.312741756439209, 31.068212509155273, -13.910240173339844,
1.4459478855133057, 22.170541763305664, -6.407354354858398,
-6.995829105377197, 18.583200454711914, -10.831125259399414,
17.820920944213867, 16.2480411529541, 16.447195053100586,
11.57226848602295, 1.8526301383972168, 5.306026458740234,
24.145092010498047, 8.629376411437988, -9.216986656188965,
-0.1989477425813675, 34.203548431396484, -16.923160552978516,
18.671411514282227, 2.5159497261047363, 4.921559810638428
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'batchNormalization float16 2D constant tensors default options',
'graph': {
'inputs': {
'bnInput': {
'data': [
-41.3125, 64.0625, -63.375, -46.78125, 83,
-80.0625, -62.15625, -0.10009765625, -40.90625, 56.96875,
37.375, 57.03125, 82.0625, -86.125, 76.875,
97.0625, -21.34375, -96.9375, -9.359375, 80.1875,
-85.375, 62.34375, -68.5, -12.109375
],
'descriptor': {shape: [4, 6], dataType: 'float16'},
'constant': true
},
'bnMean': {
'data': [-7.8125, -95.625, 38.15625, -55.9375, -87.875, -41.625],
'descriptor': {shape: [6], dataType: 'float16'},
'constant': true
},
'bnVariance': {
'data': [60.3125, 26.4375, 53.28125, 40.15625, 59.40625, 36],
'descriptor': {shape: [6], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'batchNormalization',
'arguments': [
{'input': 'bnInput'}, {'mean': 'bnMean'}, {'variance': 'bnVariance'}
],
'outputs': 'bnOutput'
}],
'expectedOutputs': {
'bnOutput': {
'data': [
-4.3125, 31.0625, -13.90625, 1.4453125, 22.171875,
-6.40625, -6.99609375, 18.578125, -10.828125, 17.8125,
16.25, 16.4375, 11.5703125, 1.84765625, 5.3046875,
24.140625, 8.6328125, -9.21875, -0.19921875, 34.1875,
-16.921875, 18.671875, 2.513671875, 4.91796875
],
'descriptor': {shape: [4, 6], dataType: 'float16'}
}
}
}
}
];
webnn_conformance_test(
batchNormTests, buildAndExecuteGraph, getPrecisionTolerance);