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// META: global=window,dedicatedworker
// META: script=/resources/WebIDLParser.js
// META: script=/resources/idlharness.js
// META: script=./resources/utils.js
// META: timeout=long
// https://webmachinelearning.github.io/webnn/
'use strict';
idl_test(
['webnn'],
['html', 'webidl', 'webgpu'],
async (idl_array) => {
if (self.GLOBAL.isWindow()) {
idl_array.add_objects({ Navigator: ['navigator'] });
} else if (self.GLOBAL.isWorker()) {
idl_array.add_objects({ WorkerNavigator: ['navigator'] });
}
idl_array.add_objects({
NavigatorML: ['navigator'],
ML: ['navigator.ml'],
MLContext: ['context'],
MLOperand: ['input', 'filter', 'output'],
MLActivation: ['relu'],
MLGraphBuilder: ['builder'],
MLGraph: ['graph']
});
for (const executionType of ExecutionArray) {
const isSync = executionType === 'sync';
if (self.GLOBAL.isWindow() && isSync) {
continue;
}
if (isSync) {
self.context = navigator.ml.createContextSync();
} else {
self.context = await navigator.ml.createContext();
}
self.builder = new MLGraphBuilder(self.context);
self.input = builder.input('input', {type: 'float32', dimensions: [1, 1, 5, 5]});
self.filter = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array(9).fill(1));
self.relu = builder.relu();
self.output = builder.conv2d(input, filter, {activation: relu, inputLayout: "nchw"});
if (isSync) {
self.graph = builder.buildSync({output});
} else {
self.graph = await builder.build({output});
}
}
}
);
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