Source code
Revision control
Copy as Markdown
Other Tools
Test Info: Warnings
- This test has a WPT meta file that expects 1 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/hard_swish.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/hard_swish.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/hard_swish.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/hard_swish.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/hard_swish.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/hard_swish.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API tanh operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Computes the nonlinear function y = x * max(0, min(6, (x + 3))) / 6 that is
// introduced by MobileNetV3 on the input tensor element-wise.
//
// MLOperand hardSwish(MLOperand input);
const hardSwishTests = [
{
'name': 'hardSwish float32 0D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [0.7341583371162415],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [0.4569105803966522],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 1D constant tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 1D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 2D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 3D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 4D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'hardSwish float32 5D tensor',
'graph': {
'inputs': {
'hardSwishInput': {
'data': [
0.7341583371162415, 9.11885929107666, 3.545238494873047,
2.621943950653076, -6.445507526397705, -1.6835596561431885,
5.52318000793457, -5.958856105804443, -9.169190406799316,
6.420943737030029, -3.2930312156677246, 1.041016697883606,
-7.2463226318359375, -0.9472730755805969, -5.7783522605896,
3.1852290630340576, -7.261817932128906, 4.174602508544922,
3.7802627086639404, -6.071240425109863, -9.909919738769531,
-7.744259357452393, -8.286120414733887, 8.083491325378418
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'hardSwish',
'arguments': [{'input': 'hardSwishInput'}],
'outputs': 'hardSwishOutput'
}],
'expectedOutputs': {
'hardSwishOutput': {
'data': [
0.4569105803966522,
9.11885929107666,
3.545238494873047,
2.4567370414733887,
0,
-0.3693843185901642,
5.52318000793457,
0,
0,
6.420943737030029,
0,
0.7011276483535767,
0,
-0.3240821659564972,
0,
3.1852290630340576,
0,
4.174602508544922,
3.7802627086639404,
0,
0,
0,
0,
8.083491325378418
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
hardSwishTests.forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}