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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/logical_not.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/logical_not.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/logical_not.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/logical_not.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/logical_not.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/logical_not.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API element-wise logicalNot operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Invert the values of the input tensor to values 0 or 1, element-wise.
//
// MLOperand logicalNot(MLOperand a);
const getLogicalNotPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {uint8: 0};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const logicalNotTests = [
{
'name': 'logicalNot uint8 0D scalar',
'graph': {
'inputs': {
'logicalNotInput':
{'data': [1], 'descriptor': {shape: [], dataType: 'uint8'}}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput':
{'data': [0], 'descriptor': {shape: [], dataType: 'uint8'}}
}
}
},
{
'name': 'logicalNot uint8 1D constant tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [24], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [24], dataType: 'uint8'}
}
}
}
},
{
'name': 'logicalNot uint8 1D tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [24], dataType: 'uint8'}
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [24], dataType: 'uint8'}
}
}
}
},
{
'name': 'logicalNot uint8 2D tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [4, 6], dataType: 'uint8'}
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [4, 6], dataType: 'uint8'}
}
}
}
},
{
'name': 'logicalNot uint8 3D tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [2, 3, 4], dataType: 'uint8'}
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [2, 3, 4], dataType: 'uint8'}
}
}
}
},
{
'name': 'logicalNot uint8 4D tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'uint8'}
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'uint8'}
}
}
}
},
{
'name': 'logicalNot uint8 5D tensor',
'graph': {
'inputs': {
'logicalNotInput': {
'data': [
204, 130, 90, 0, 147, 42, 10, 18, 13, 235, 0, 233,
53, 83, 9, 254, 69, 56, 219, 109, 171, 0, 228, 135
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'uint8'}
}
},
'operators': [{
'name': 'logicalNot',
'arguments': [{'input': 'logicalNotInput'}],
'outputs': 'logicalNotOutput'
}],
'expectedOutputs': {
'logicalNotOutput': {
'data': [
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'uint8'}
}
}
}
}
];
if (navigator.ml) {
logicalNotTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getLogicalNotPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}