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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/pad.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/pad.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/pad.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/pad.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/pad.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/pad.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API pad operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Inflate the tensor with constant or mirrored values on the edges.
//
// enum MLPaddingMode {
// "constant",
// "edge",
// "reflection",
// "symmetric"
// };
//
// dictionary MLPadOptions {
// MLPaddingMode mode = "constant";
// MLNumber value = 0;
// };
//
// MLOperand pad(
// MLOperand input, sequence<[EnforceRange] unsigned long>beginningPadding,
// sequence<[EnforceRange] unsigned long>endingPadding,
// optional MLPadOptions options = {});
const getPadPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 0, float16: 0};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const padTests = [
{
'name': 'pad float32 1D constant tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [9], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1]},
{'endingPadding': [1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0, 22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547, 0
],
'descriptor': {shape: [11], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 1D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [9], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1]},
{'endingPadding': [1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0, 22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547, 0
],
'descriptor': {shape: [11], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 2D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [3, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1]},
{'endingPadding': [1, 1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0,
0,
0,
0,
0,
0,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
0,
0,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
0,
0,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
0,
0,
0,
0,
0,
0
],
'descriptor': {shape: [5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 3D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1, 1]},
{'endingPadding': [1, 1, 1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
0,
0,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
0,
0,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
'descriptor': {shape: [3, 5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 4D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 1, 1, 1]},
{'endingPadding': [0, 1, 1, 1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
22.76361846923828,
0,
0,
-21.168529510498047,
0,
0,
-91.66168975830078,
0,
0,
0,
0,
0,
0,
0,
0,
16.863798141479492,
0,
0,
60.51472091674805,
0,
0,
-70.56755065917969,
0,
0,
0,
0,
0,
0,
0,
0,
-60.643272399902344,
0,
0,
-47.8821907043457,
0,
0,
68.72557830810547,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
'descriptor': {shape: [1, 5, 5, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 5D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3, 1, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 1, 1, 0, 1]},
{'endingPadding': [0, 1, 1, 0, 1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
22.76361846923828,
0,
0,
-21.168529510498047,
0,
0,
-91.66168975830078,
0,
0,
0,
0,
0,
0,
0,
0,
16.863798141479492,
0,
0,
60.51472091674805,
0,
0,
-70.56755065917969,
0,
0,
0,
0,
0,
0,
0,
0,
-60.643272399902344,
0,
0,
-47.8821907043457,
0,
0,
68.72557830810547,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
'descriptor': {shape: [1, 5, 5, 1, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 2D tensor explicit options.mode=\'constant\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [3, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1]},
{'endingPadding': [1, 1]}, {'options': {'mode': 'constant'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0,
0,
0,
0,
0,
0,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
0,
0,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
0,
0,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
0,
0,
0,
0,
0,
0
],
'descriptor': {shape: [5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 2D tensor options.value default constant mode',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [3, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1]},
{'endingPadding': [1, 1]}, {'options': {'value': 1}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
1,
1,
1,
1,
1,
1,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
1,
1,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
1,
1,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
1,
1,
1,
1,
1,
1
],
'descriptor': {shape: [5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 4D tensor options.mode=\'edge\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 2, 2, 0]},
{'endingPadding': [0, 2, 2, 0]}, {'options': {'mode': 'edge'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
22.76361846923828, 22.76361846923828, 22.76361846923828,
-21.168529510498047, -91.66168975830078, -91.66168975830078,
-91.66168975830078, 22.76361846923828, 22.76361846923828,
22.76361846923828, -21.168529510498047, -91.66168975830078,
-91.66168975830078, -91.66168975830078, 22.76361846923828,
22.76361846923828, 22.76361846923828, -21.168529510498047,
-91.66168975830078, -91.66168975830078, -91.66168975830078,
16.863798141479492, 16.863798141479492, 16.863798141479492,
60.51472091674805, -70.56755065917969, -70.56755065917969,
-70.56755065917969, -60.643272399902344, -60.643272399902344,
-60.643272399902344, -47.8821907043457, 68.72557830810547,
68.72557830810547, 68.72557830810547, -60.643272399902344,
-60.643272399902344, -60.643272399902344, -47.8821907043457,
68.72557830810547, 68.72557830810547, 68.72557830810547,
-60.643272399902344, -60.643272399902344, -60.643272399902344,
-47.8821907043457, 68.72557830810547, 68.72557830810547,
68.72557830810547
],
'descriptor': {shape: [1, 7, 7, 1], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 4D tensor options.mode=\'reflection\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 2, 2, 0]},
{'endingPadding': [0, 2, 2, 0]}, {'options': {'mode': 'reflection'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
68.72557830810547, -47.8821907043457, -60.643272399902344,
-47.8821907043457, 68.72557830810547, -47.8821907043457,
-60.643272399902344, -70.56755065917969, 60.51472091674805,
16.863798141479492, 60.51472091674805, -70.56755065917969,
60.51472091674805, 16.863798141479492, -91.66168975830078,
-21.168529510498047, 22.76361846923828, -21.168529510498047,
-91.66168975830078, -21.168529510498047, 22.76361846923828,
-70.56755065917969, 60.51472091674805, 16.863798141479492,
60.51472091674805, -70.56755065917969, 60.51472091674805,
16.863798141479492, 68.72557830810547, -47.8821907043457,
-60.643272399902344, -47.8821907043457, 68.72557830810547,
-47.8821907043457, -60.643272399902344, -70.56755065917969,
60.51472091674805, 16.863798141479492, 60.51472091674805,
-70.56755065917969, 60.51472091674805, 16.863798141479492,
-91.66168975830078, -21.168529510498047, 22.76361846923828,
-21.168529510498047, -91.66168975830078, -21.168529510498047,
22.76361846923828
],
'descriptor': {shape: [1, 7, 7, 1], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 4D tensor options.mode=\'symmetric\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.76361846923828, -21.168529510498047, -91.66168975830078,
16.863798141479492, 60.51472091674805, -70.56755065917969,
-60.643272399902344, -47.8821907043457, 68.72557830810547
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 2, 2, 0]},
{'endingPadding': [0, 2, 2, 0]}, {'options': {'mode': 'symmetric'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
60.51472091674805, 16.863798141479492, 16.863798141479492,
60.51472091674805, -70.56755065917969, -70.56755065917969,
60.51472091674805, -21.168529510498047, 22.76361846923828,
22.76361846923828, -21.168529510498047, -91.66168975830078,
-91.66168975830078, -21.168529510498047, -21.168529510498047,
22.76361846923828, 22.76361846923828, -21.168529510498047,
-91.66168975830078, -91.66168975830078, -21.168529510498047,
60.51472091674805, 16.863798141479492, 16.863798141479492,
60.51472091674805, -70.56755065917969, -70.56755065917969,
60.51472091674805, -47.8821907043457, -60.643272399902344,
-60.643272399902344, -47.8821907043457, 68.72557830810547,
68.72557830810547, -47.8821907043457, -47.8821907043457,
-60.643272399902344, -60.643272399902344, -47.8821907043457,
68.72557830810547, 68.72557830810547, -47.8821907043457,
60.51472091674805, 16.863798141479492, 16.863798141479492,
60.51472091674805, -70.56755065917969, -70.56755065917969,
60.51472091674805
],
'descriptor': {shape: [1, 7, 7, 1], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
padTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getPadPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}