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// META: title=test WebNN API pad operation
// META: global=window
// 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"
// };
//
// 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 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 2D tensor with options.value as NaN',
'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': NaN}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
NaN,
NaN,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
NaN,
NaN,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN
],
'descriptor': {shape: [5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad float32 2D tensor with options.value as Infinity',
'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': Infinity}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
Infinity,
Infinity,
Infinity,
Infinity,
Infinity,
Infinity,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
Infinity,
Infinity,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
Infinity,
Infinity,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
Infinity,
Infinity,
Infinity,
Infinity,
Infinity,
Infinity
],
'descriptor': {shape: [5, 5], dataType: 'float32'}
}
}
}
},
{
'name': 'pad int64 2D tensor with options.value as bigint',
'graph': {
'inputs': {
'padInput': {
'data': [
22, -21, -91,
16, 60, -70,
-60, -47, 68
],
'descriptor': {shape: [3, 3], dataType: 'int64'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1]},
{'endingPadding': [1, 1]}, {'options': {'value': 9007199254740992n}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n,
22,
-21,
-91,
9007199254740992n,
9007199254740992n,
16,
60,
-70,
9007199254740992n,
9007199254740992n,
-60,
-47,
68,
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n,
9007199254740992n
],
'descriptor': {shape: [5, 5], dataType: 'int64'}
}
}
}
},
{
'name': 'pad float32 2D tensor with options.value as -Infinity',
'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': -Infinity}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
-Infinity,
-Infinity,
-Infinity,
-Infinity,
-Infinity,
-Infinity,
22.76361846923828,
-21.168529510498047,
-91.66168975830078,
-Infinity,
-Infinity,
16.863798141479492,
60.51472091674805,
-70.56755065917969,
-Infinity,
-Infinity,
-60.643272399902344,
-47.8821907043457,
68.72557830810547,
-Infinity,
-Infinity,
-Infinity,
-Infinity,
-Infinity,
-Infinity
],
'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'}
}
}
}
},
// float16 tests
{
'name': 'pad float16 1D constant tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [9], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1]},
{'endingPadding': [1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0, 22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75, 0
],
'descriptor': {shape: [11], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 1D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [9], dataType: 'float16'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1]},
{'endingPadding': [1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0, 22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75, 0
],
'descriptor': {shape: [11], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 2D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [3, 3], dataType: 'float16'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [1, 1]},
{'endingPadding': [1, 1]}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
0, 0, 0, 0, 0,
0, 22.765625, -21.171875, -91.6875, 0,
0, 16.859375, 60.5, -70.5625, 0,
0, -60.65625, -47.875, 68.75, 0,
0, 0, 0, 0, 0
],
'descriptor': {shape: [5, 5], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 3D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [1, 3, 3], dataType: 'float16'}
}
},
'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, 22.765625, -21.171875, -91.6875, 0, 0,
16.859375, 60.5, -70.5625, 0, 0, -60.65625,
-47.875, 68.75, 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: 'float16'}
}
}
}
},
{
'name': 'pad float16 4D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float16'}
}
},
'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, 22.765625, 0, 0, -21.171875, 0,
0, -91.6875, 0, 0, 0, 0, 0, 0, 0, 0, 16.859375, 0,
0, 60.5, 0, 0, -70.5625, 0, 0, 0, 0, 0, 0, 0,
0, -60.65625, 0, 0, -47.875, 0, 0, 68.75, 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: 'float16'}
}
}
}
},
{
'name': 'pad float16 5D tensor default options',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [1, 3, 3, 1, 1], dataType: 'float16'}
}
},
'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, 22.765625, 0, 0, -21.171875, 0,
0, -91.6875, 0, 0, 0, 0, 0, 0, 0, 0, 16.859375, 0,
0, 60.5, 0, 0, -70.5625, 0, 0, 0, 0, 0, 0, 0,
0, -60.65625, 0, 0, -47.875, 0, 0, 68.75, 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: 'float16'}
}
}
}
},
{
'name': 'pad float16 2D tensor explicit options.mode=\'constant\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [3, 3], dataType: 'float16'}
}
},
'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, 22.765625, -21.171875, -91.6875, 0,
0, 16.859375, 60.5, -70.5625, 0,
0, -60.65625, -47.875, 68.75, 0,
0, 0, 0, 0, 0
],
'descriptor': {shape: [5, 5], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 2D tensor options.value default constant mode',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [3, 3], dataType: 'float16'}
}
},
'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, 22.765625, -21.171875, -91.6875, 1,
1, 16.859375, 60.5, -70.5625, 1,
1, -60.65625, -47.875, 68.75, 1,
1, 1, 1, 1, 1
],
'descriptor': {shape: [5, 5], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 4D tensor options.mode=\'edge\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float16'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 2, 2, 0]},
{'endingPadding': [0, 2, 2, 0]}, {'options': {'mode': 'edge'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
22.765625, 22.765625, 22.765625, -21.171875, -91.6875, -91.6875,
-91.6875, 22.765625, 22.765625, 22.765625, -21.171875, -91.6875,
-91.6875, -91.6875, 22.765625, 22.765625, 22.765625, -21.171875,
-91.6875, -91.6875, -91.6875, 16.859375, 16.859375, 16.859375,
60.5, -70.5625, -70.5625, -70.5625, -60.65625, -60.65625,
-60.65625, -47.875, 68.75, 68.75, 68.75, -60.65625,
-60.65625, -60.65625, -47.875, 68.75, 68.75, 68.75,
-60.65625, -60.65625, -60.65625, -47.875, 68.75, 68.75,
68.75
],
'descriptor': {shape: [1, 7, 7, 1], dataType: 'float16'}
}
}
}
},
{
'name': 'pad float16 4D tensor options.mode=\'reflection\'',
'graph': {
'inputs': {
'padInput': {
'data': [
22.765625, -21.171875, -91.6875, 16.859375, 60.5, -70.5625,
-60.65625, -47.875, 68.75
],
'descriptor': {shape: [1, 3, 3, 1], dataType: 'float16'}
}
},
'operators': [{
'name': 'pad',
'arguments': [
{'input': 'padInput'}, {'beginningPadding': [0, 2, 2, 0]},
{'endingPadding': [0, 2, 2, 0]}, {'options': {'mode': 'reflection'}}
],
'outputs': 'padOutput'
}],
'expectedOutputs': {
'padOutput': {
'data': [
68.75, -47.875, -60.65625, -47.875, 68.75, -47.875,
-60.65625, -70.5625, 60.5, 16.859375, 60.5, -70.5625,
60.5, 16.859375, -91.6875, -21.171875, 22.765625, -21.171875,
-91.6875, -21.171875, 22.765625, -70.5625, 60.5, 16.859375,
60.5, -70.5625, 60.5, 16.859375, 68.75, -47.875,
-60.65625, -47.875, 68.75, -47.875, -60.65625, -70.5625,
60.5, 16.859375, 60.5, -70.5625, 60.5, 16.859375,
-91.6875, -21.171875, 22.765625, -21.171875, -91.6875, -21.171875,
22.765625
],
'descriptor': {shape: [1, 7, 7, 1], dataType: 'float16'}
}
}
}
}
];
if (navigator.ml) {
padTests.filter(isTargetTest).forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getZeroULPTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}