mozilla-central/testing/web-platform/tests/webnn/validation_tests/instanceNormalization.https.any.js (file symbol)

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// META: title=validation tests for WebNN API instanceNormalization operation
// META: global=window,dedicatedworker
// META: script=../resources/utils_validation.js
'use strict';
const kExampleInputDescriptor = {
dataType: 'float32',
dimensions: [2, 2, 2, 2]
};
// 1D tensor descriptor which may be used for `scale`, or `bias` inputs.
const kExample1DTensorDescriptor = {
dataType: 'float32',
dimensions: [2]
};
multi_builder_test(async (t, builder, otherBuilder) => {
const inputFromOtherBuilder =
otherBuilder.input('input', kExampleInputDescriptor);
assert_throws_js(
TypeError, () => builder.instanceNormalization(inputFromOtherBuilder));
}, '[instanceNormalization] throw if input is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const scaleFromOtherBuilder =
otherBuilder.input('scale', kExample1DTensorDescriptor);
const options = {scale: scaleFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
assert_throws_js(
TypeError, () => builder.instanceNormalization(input, options));
}, '[instanceNormalization] throw if scale option is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const biasFromOtherBuilder =
otherBuilder.input('bias', kExample1DTensorDescriptor);
const options = {bias: biasFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
assert_throws_js(
TypeError, () => builder.instanceNormalization(input, options));
}, '[instanceNormalization] throw if bias option is from another builder');
const tests = [
{
name: '[instanceNormalization] Test with default options for 4-D input.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]}
},
{
name:
'[instanceNormalization] Test with scale, bias and default epsilon value.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {dataType: 'float32', dimensions: [2]},
bias: {dataType: 'float32', dimensions: [2]},
epsilon: 1e-5,
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]}
},
{
name: '[instanceNormalization] Test with a non-default epsilon value.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
epsilon: 1e-4,
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]}
},
{
name: '[instanceNormalization] Test with layout=nhwc.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
layout: 'nhwc',
scale: {dataType: 'float32', dimensions: [4]},
bias: {dataType: 'float32', dimensions: [4]},
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]}
},
{
name: '[instanceNormalization] Test when the input data type is float16.',
input: {dataType: 'float16', dimensions: [1, 2, 3, 4]},
output: {dataType: 'float16', dimensions: [1, 2, 3, 4]}
},
{
name: '[instanceNormalization] Throw if the input is not a 4-D tensor.',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5, 2]},
},
{
name:
'[instanceNormalization] Throw if the input data type is not one of floating point types.',
input: {dataType: 'int32', dimensions: [1, 2, 5, 5]},
},
{
name:
'[instanceNormalization] Throw if the scale data type is not the same as the input data type.',
input: {dataType: 'float16', dimensions: [1, 2, 5, 5]},
options: {
scale: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[instanceNormalization] Throw if the scale operand is not a 1-D tensor.',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
options: {
scale: {dataType: 'float32', dimensions: [2, 1]},
},
},
{
name:
'[instanceNormalization] Throw if the size of scale operand is not equal to the size of the feature dimension of the input with layout=nhwc.',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
options: {
layout: 'nhwc',
scale: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[instanceNormalization] Throw if the size of scale operand is not equal to the size of the feature dimension of the input with layout=nchw.',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
options: {
layout: 'nchw',
scale: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[instanceNormalization] Throw if the bias data type is not the same as the input data type.',
input: {dataType: 'float16', dimensions: [1, 2, 5, 5]},
options: {
bias: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[instanceNormalization] Throw if the bias operand is not a 1-D tensor.',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
options: {
scale: {dataType: 'float32', dimensions: [2, 1]},
},
},
{
name:
'[instanceNormalization] Throw if the size of bias operand is not equal to the size of the feature dimension of the input with layout=nhwc.',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
options: {
layout: 'nhwc',
bias: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[instanceNormalization] Throw if the size of bias operand is not equal to the size of the feature dimension of the input with layout=nchw.',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
options: {
layout: 'nchw',
bias: {dataType: 'float32', dimensions: [2]},
},
},
];
tests.forEach(
test => promise_test(async t => {
const input = builder.input(
'input',
{dataType: test.input.dataType, dimensions: test.input.dimensions});
if (test.options && test.options.bias) {
test.options.bias = builder.input('bias', {
dataType: test.options.bias.dataType,
dimensions: test.options.bias.dimensions
});
}
if (test.options && test.options.scale) {
test.options.scale = builder.input('scale', {
dataType: test.options.scale.dataType,
dimensions: test.options.scale.dimensions
});
}
if (test.output) {
const output = builder.instanceNormalization(input, test.options);
assert_equals(output.dataType(), test.output.dataType);
assert_array_equals(output.shape(), test.output.dimensions);
} else {
assert_throws_js(
TypeError,
() => builder.instanceNormalization(input, test.options));
}
}, test.name));