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Test Info: Warnings
- This test has a WPT meta file that expects 52 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/validation_tests/convTranspose2d.https.any.html - WPT Dashboard Interop Dashboard
- /webnn/validation_tests/convTranspose2d.https.any.worker.html - WPT Dashboard Interop Dashboard
// META: title=validation tests for WebNN API convTranspose2d operation
// META: global=window,dedicatedworker
// META: script=../resources/utils_validation.js
'use strict';
// Example input in NCHW layout.
const kExampleInputDescriptor = {
dataType: 'float32',
dimensions: [1, 1, 5, 5]
};
// Example filter in OIHW layout.
const kExampleFilterDescriptor = {
dataType: 'float32',
dimensions: [1, 1, 3, 3]
};
const kExampleBiasDescriptor = {
dataType: 'float32',
dimensions: [/* output channels */ 1]
};
multi_builder_test(async (t, builder, otherBuilder) => {
const inputFromOtherBuilder =
otherBuilder.input('input', kExampleInputDescriptor);
const filter = builder.input('filter', kExampleFilterDescriptor);
assert_throws_js(
TypeError, () => builder.convTranspose2d(inputFromOtherBuilder, filter));
}, '[convTranspose2d] throw if input is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const filterFromOtherBuilder =
otherBuilder.input('filter', kExampleFilterDescriptor);
const input = builder.input('input', kExampleInputDescriptor);
assert_throws_js(
TypeError, () => builder.convTranspose2d(input, filterFromOtherBuilder));
}, '[convTranspose2d] throw if filter is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const biasFromOtherBuilder =
otherBuilder.input('bias', kExampleBiasDescriptor);
const options = {inputLayout: 'nchw', bias: biasFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
const filter = builder.input('filter', kExampleFilterDescriptor);
assert_throws_js(
TypeError, () => builder.convTranspose2d(input, filter, options));
}, '[convTranspose2d] throw if bias option is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const activationFromOtherBuilder = otherBuilder.clamp();
const options = {activation: activationFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
const filter = builder.input('filter', kExampleFilterDescriptor);
assert_throws_js(
TypeError, () => builder.convTranspose2d(input, filter, options));
}, '[convTranspose2d] throw if activation option is from another builder');
const tests = [
{
name: '[convTranspose2d] Test with default options.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
output: {dataType: 'float32', dimensions: [1, 1, 5, 5]}
},
{
name:
'[convTranspose2d] Test with inputLayout="nchw" and filterLayout="hwoi".',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
filterLayout: 'hwoi',
inputLayout: 'nchw',
},
output: {dataType: 'float32', dimensions: [1, 2, 5, 5]}
},
{
name:
'[convTranspose2d] Test with inputLayout="nchw" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
filterLayout: 'ohwi',
inputLayout: 'nchw',
},
output: {dataType: 'float32', dimensions: [1, 2, 5, 5]}
},
{
name:
'[convTranspose2d] Test with inputLayout="nhwc" and filterLayout="iohw".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 1]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
filterLayout: 'iohw',
inputLayout: 'nhwc',
},
output: {dataType: 'float32', dimensions: [1, 5, 5, 2]}
},
{
name:
'[convTranspose2d] Test with inputLayout="nhwc" and filterLayout="hwoi".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 1]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
filterLayout: 'hwoi',
inputLayout: 'nhwc',
},
output: {dataType: 'float32', dimensions: [1, 5, 5, 2]}
},
{
name:
'[convTranspose2d] Test with inputLayout="nhwc" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 1]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
filterLayout: 'ohwi',
inputLayout: 'nhwc',
},
output: {dataType: 'float32', dimensions: [1, 5, 5, 2]}
},
{
name: '[convTranspose2d] Test with strides=[3, 2], outputSizes=[10, 8].',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
strides: [3, 2],
outputSizes: [10, 8],
},
output: {dataType: 'float32', dimensions: [1, 2, 10, 8]}
},
{
name: '[convTranspose2d] Test with strides=[3, 2], outputPadding=[1, 1].',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
strides: [3, 2],
outputPadding: [1, 1],
},
output: {dataType: 'float32', dimensions: [1, 2, 10, 8]}
},
{
name: '[convTranspose2d] Test with padding=1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
},
output: {dataType: 'float32', dimensions: [1, 1, 5, 5]}
},
{
name: '[convTranspose2d] Test with padding=1, groups=3.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
groups: 3,
},
output: {dataType: 'float32', dimensions: [1, 3, 5, 5]}
},
{
name: '[convTranspose2d] Test with strides=2.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
strides: [2, 2],
},
output: {dataType: 'float32', dimensions: [1, 2, 7, 7]}
},
{
name: '[convTranspose2d] Test with strides=2 and padding=1.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
strides: [2, 2],
},
output: {dataType: 'float32', dimensions: [1, 1, 5, 5]}
},
{
name:
'[convTranspose2d] Test when the output sizes are explicitly specified, the output padding values are ignored though padding value is not smaller than stride along the same axis.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
outputPadding: [3, 3],
strides: [3, 2],
outputSizes: [10, 8],
},
output: {dataType: 'float32', dimensions: [1, 2, 10, 8]}
},
{
name: '[convTranspose2d] Throw if the input is not a 4-D tensor.',
input: {dataType: 'float32', dimensions: [1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
},
{
name:
'[convTranspose2d] Throw if the input data type is not floating point.',
input: {dataType: 'int32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'int32', dimensions: [1, 1, 2, 2]},
},
{
name: '[convTranspose2d] Throw if the filter is not a 4-D tensor.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 2]},
},
{
name:
'[convTranspose2d] Throw if the filter data type doesn\'t match the input data type.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'int32', dimensions: [1, 1, 2, 2]},
},
{
name: '[convTranspose2d] Throw if the length of padding is not 4.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
padding: [2, 2],
},
},
{
name: '[convTranspose2d] Throw if the length of strides is not 2.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
strides: [2],
},
},
{
name: '[convTranspose2d] Throw if one stride value is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
strides: [1, 0],
},
},
{
name: '[convTranspose2d] Throw if the length of dilations is not 2.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
dilations: [1],
},
},
{
name:
'[convTranspose2d] Throw if the one dilation value is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
dilations: [1, 0],
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels with inputLayout="nchw" and filterLayout="iohw".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
filterLayout: 'iohw',
inputLayout: 'nchw',
groups: 1,
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels with inputLayout="nchw" and filterLayout="hwoi".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 3]},
filter: {dataType: 'float32', dimensions: [3, 1, 2, 1]},
options: {
filterLayout: 'hwoi',
inputLayout: 'nchw',
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels with inputLayout="nchw" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
filterLayout: 'ohwi',
inputLayout: 'nchw',
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels with inputLayout="nhwc" and filterLayout="iohw".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
filterLayout: 'iohw',
inputLayout: 'nhwc',
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels inputLayout="nhwc" and filterLayout="hwoi".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
filterLayout: 'hwoi',
inputLayout: 'nhwc',
},
},
{
name:
'[convTranspose2d] Throw if the input channels is not equal to the filter input channels with inputLayout="nhwc" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
filterLayout: 'ohwi',
inputLayout: 'nhwc',
},
},
{
name: '[convTranspose2d] Throw if output channels is too large.',
input: {dataType: 'float32', dimensions: [1, 4, 5, 5]},
filter: {dataType: 'float32', dimensions: [4, 2, 2, 2]},
options: {
groups: kMaxUnsignedLong,
},
},
{
name: '[convTranspose2d] Throw if the groups is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 4, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
groups: 0,
},
},
{
name:
'[convTranspose2d] Throw due to overflow when calculating the effective filter height.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 434983, 2]},
options: {
dilations: [328443, 1],
},
},
{
name:
'[convTranspose2d] Throw due to overflow when calculating the effective filter width.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 234545]},
options: {
dilations: [2, 843452],
},
},
{
name:
'[convTranspose2d] Throw due to overflow when dilation height is too large.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 2]},
options: {
dilations: [kMaxUnsignedLong, 1],
},
},
{
name:
'[convTranspose2d] Throw due to overflow when dilation width is too large.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 2]},
options: {
dilations: [1, kMaxUnsignedLong],
},
},
{
name: '[convTranspose2d] Throw if the bias is not a 1-D tensor.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
bias: {dataType: 'float32', dimensions: [1, 2]},
},
},
{
name:
'[convTranspose2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="iohw".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
filterLayout: 'iohw',
bias: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[convTranspose2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="hwoi".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 2, 1, 1]},
options: {
filterLayout: 'hwoi',
bias: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[convTranspose2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 2, 2, 1]},
options: {
filterLayout: 'ohwi',
bias: {dataType: 'float32', dimensions: [2]},
},
},
{
name:
'[convTranspose2d] Throw if the bias data type doesn\'t match input data type.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
bias: {dataType: 'int32', dimensions: [1]},
},
},
{
name:
'[convTranspose2d] Throw if the outputPadding is not a sequence of length 2.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
strides: [3, 2],
outputPadding: [1, 1, 1, 1],
},
},
{
name:
'[convTranspose2d] Throw if the outputPadding is not smaller than stride along the width dimension.',
input: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [0, 0, 3, 3],
strides: [2, 2],
outputPadding: [0, 2],
},
},
{
name:
'[convTranspose2d] Throw if the outputPadding is not smaller than stride along the height dimension.',
input: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [0, 0, 3, 3],
strides: [2, 2],
outputPadding: [2, 0],
},
},
{
name:
'[convTranspose2d] Throw if the outputSizes is not a sequence of length 2.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
strides: [3, 2],
outputSizes: [1, 2, 10, 8],
},
},
{
name: '[convTranspose2d] Throw if the padding height is too large.',
input: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [4, 4, 0, 0],
strides: [2, 2],
outputPadding: [1, 0],
},
},
{
name: '[convTranspose2d] Throw if the padding width is too large.',
input: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [0, 0, 4, 4],
strides: [2, 2],
outputPadding: [0, 1],
},
},
{
name:
'[convTranspose2d] Throw due to outputSizes values are smaller than the output sizes calculated by not using outputPadding.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
strides: [2, 2],
outputSizes: [4, 4],
outputPadding: [1, 1],
},
},
{
name:
'[convTranspose2d] Throw due to outputSizes values are greater than the output sizes calculated by not using outputPadding.',
input: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
strides: [2, 2],
outputSizes: [6, 8],
outputPadding: [1, 1],
},
},
];
tests.forEach(
test => promise_test(async t => {
const input = builder.input(
'input',
{dataType: test.input.dataType, dimensions: test.input.dimensions});
const filter = builder.input(
'filter',
{dataType: test.filter.dataType, dimensions: test.filter.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.output) {
const output = builder.convTranspose2d(input, filter, test.options);
assert_equals(output.dataType(), test.output.dataType);
assert_array_equals(output.shape(), test.output.dimensions);
} else {
assert_throws_js(
TypeError,
() => builder.convTranspose2d(input, filter, test.options));
}
}, test.name));