45 lines
1.3 KiB
JavaScript
45 lines
1.3 KiB
JavaScript
// 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']
|
|
});
|
|
|
|
self.context = await navigator.ml.createContext();
|
|
|
|
self.builder = new MLGraphBuilder(self.context);
|
|
self.input =
|
|
builder.input('input', {dataType: 'float32', dimensions: [1, 1, 5, 5]});
|
|
self.filter = builder.constant(
|
|
{dataType: '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"});
|
|
|
|
self.graph = await builder.build({output});
|
|
}
|
|
);
|