// META: title=validation tests for WebNN API conv2d operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// 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]
};
const label = `conv_2d_*`;
multi_builder_test(async (t, builder, otherBuilder) => {
const inputFromOtherBuilder =
otherBuilder.input('input', kExampleInputDescriptor);
const filter = builder.input('filter', kExampleFilterDescriptor);
assert_throws_js(
TypeError, () => builder.conv2d(inputFromOtherBuilder, filter));
}, '[conv2d] 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.conv2d(input, filterFromOtherBuilder));
}, '[conv2d] 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.conv2d(input, filter, options));
}, '[conv2d] throw if bias option is from another builder');
const tests = [
{
name: '[conv2d] Test with default options.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with padding.',
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: '[conv2d] Test with strides and padding.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 3, 3]},
options: {
padding: [1, 1, 1, 1],
strides: [2, 2],
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with strides and asymmetric padding.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 4, 2]},
options: {
padding: [1, 2, 0, 1],
strides: [2, 2],
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test depthwise conv2d by setting groups to input channels.',
input: {dataType: 'float32', dimensions: [1, 4, 2, 2]},
filter: {dataType: 'float32', dimensions: [4, 1, 2, 2]},
options: {
groups: 4,
},
output: {dataType: 'float32', dimensions: [1, 4, 1, 1]}
},
{
name:
'[conv2d] Test depthwise conv2d with groups, inputLayout="nhwc" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 2, 2, 4]},
filter: {dataType: 'float32', dimensions: [1, 2, 2, 4]},
options: {
groups: 4,
inputLayout: 'nhwc',
filterLayout: 'ihwo',
},
output: {dataType: 'float32', dimensions: [1, 1, 1, 4]}
},
{
name:
'[conv2d] Test with dilations, inputLayout="nhwc" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 65, 65, 1]},
filter: {dataType: 'float32', dimensions: [1, 3, 3, 1]},
options: {
inputLayout: 'nhwc',
filterLayout: 'ihwo',
dilations: [4, 4],
},
output: {dataType: 'float32', dimensions: [1, 57, 57, 1]}
},
{
name: '[conv2d] Test with inputLayout="nchw" and filterLayout="oihw".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
inputLayout: 'nchw',
filterLayout: 'oihw',
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with inputLayout="nchw" and filterLayout="hwio".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
inputLayout: 'nchw',
filterLayout: 'hwio',
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with inputLayout="nchw" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
options: {
inputLayout: 'nchw',
filterLayout: 'ohwi',
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with inputLayout="nchw" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
inputLayout: 'nchw',
filterLayout: 'ihwo',
},
output: {dataType: 'float32', dimensions: [1, 1, 3, 3]}
},
{
name: '[conv2d] Test with inputLayout="nhwc" and filterLayout="oihw".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
inputLayout: 'nhwc',
filterLayout: 'oihw',
},
output: {dataType: 'float32', dimensions: [1, 3, 3, 1]}
},
{
name: '[conv2d] Test with inputLayout="nhwc" and filterLayout="hwio".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
inputLayout: 'nhwc',
filterLayout: 'hwio',
},
output: {dataType: 'float32', dimensions: [1, 3, 3, 1]}
},
{
name: '[conv2d] Test with inputLayout="nhwc" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
options: {
inputLayout: 'nhwc',
filterLayout: 'ohwi',
},
output: {dataType: 'float32', dimensions: [1, 3, 3, 1]}
},
{
name: '[conv2d] Test with inputLayout="nhwc" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
inputLayout: 'nhwc',
filterLayout: 'ihwo',
},
output: {dataType: 'float32', dimensions: [1, 3, 3, 1]}
},
{
name: '[conv2d] Throw if the input is not a 4-D tensor.',
input: {dataType: 'float32', dimensions: [1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 2, 2, 1]},
options: {label},
},
{
name: '[conv2d] 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]},
options: {label},
},
{
name: '[conv2d] Throw if the filter is not a 4-D tensor.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 2]},
options: {label},
},
{
name:
'[conv2d] 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]},
options: {
label: label,
},
},
{
name: '[conv2d] 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],
label: label,
},
},
{
name: '[conv2d] 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],
label: label,
},
},
{
name: '[conv2d] Throw if strideHeight is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
strides: [0, 1],
label: label,
},
},
{
name: '[conv2d] Throw if strideWidth is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
strides: [1, 0],
label: label,
},
},
{
name: '[conv2d] 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],
label: label,
},
},
{
name: '[conv2d] Throw if dilationHeight is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
dilations: [0, 1],
label: label,
},
},
{
name: '[conv2d] Throw if dilationWidth is smaller than 1.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
dilations: [1, 0],
label: label,
},
},
{
name: '[conv2d] Throw if inputChannels % groups is not 0.',
input: {dataType: 'float32', dimensions: [1, 4, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
groups: 3,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels.',
input: {dataType: 'float32', dimensions: [1, 4, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
groups: 2,
label: label,
},
},
{
name: '[conv2d] 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,
label: label,
},
},
{
name:
'[conv2d] 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: [328442, 1],
label: label,
},
},
{
name:
'[conv2d] 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],
label: label,
},
},
{
name: '[conv2d] 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, 3]},
options: {
dilations: [kMaxUnsignedLong, 1],
label: label,
},
},
{
name: '[conv2d] 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, 3]},
options: {
dilations: [1, kMaxUnsignedLong],
label: label,
},
},
{
name: '[conv2d] Throw due to underflow when calculating the output height.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 4, 2]},
options: {
dilations: [4, 1],
padding: [1, 1, 1, 1],
strides: [2, 2],
label: label,
},
},
{
name: '[conv2d] Throw due to underflow when calculating the output width.',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 8]},
options: {
dilations: [1, 4],
padding: [1, 1, 1, 1],
strides: [2, 2],
label: label,
},
},
{
name: '[conv2d] 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]},
label: label,
},
},
{
name:
'[conv2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="oihw".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
options: {
bias: {dataType: 'float32', dimensions: [2]},
label: label,
},
},
{
name:
'[conv2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="hwio".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 2, 1, 1]},
options: {
bias: {dataType: 'float32', dimensions: [2]},
label: label,
},
},
{
name:
'[conv2d] 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: {
bias: {dataType: 'float32', dimensions: [2]},
label: label,
},
},
{
name:
'[conv2d] Throw if the bias shape is not equal to [output_channels] with filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 2, 2, 1]},
options: {
bias: {dataType: 'float32', dimensions: [2]},
label: label,
},
},
{
name:
'[conv2d] 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]},
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nchw" and filterLayout="oihw".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
inputLayout: 'nchw',
filterLayout: 'oihw',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nchw" and filterLayout="hwio".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
inputLayout: 'nchw',
filterLayout: 'hwio',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nchw" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
options: {
inputLayout: 'nchw',
filterLayout: 'ohwi',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nchw" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
inputLayout: 'nchw',
filterLayout: 'ihwo',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nhwc" and filterLayout="oihw".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [1, 2, 3, 3]},
options: {
inputLayout: 'nhwc',
filterLayout: 'oihw',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nhwc" and filterLayout="hwio".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [3, 3, 2, 1]},
options: {
inputLayout: 'nhwc',
filterLayout: 'hwio',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nhwc" and filterLayout="ohwi".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [1, 3, 3, 2]},
options: {
inputLayout: 'nhwc',
filterLayout: 'ohwi',
groups: 2,
label: label,
},
},
{
name:
'[conv2d] Throw if inputChannels / groups is not equal to filterInputChannels with inputLayout="nhwc" and filterLayout="ihwo".',
input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
filter: {dataType: 'float32', dimensions: [2, 3, 3, 1]},
options: {
inputLayout: 'nhwc',
filterLayout: 'ihwo',
groups: 2,
label: label,
},
},
];
tests.forEach(
test => promise_test(async t => {
const builder = new MLGraphBuilder(context);
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.conv2d(input, filter, test.options);
assert_equals(output.dataType(), test.output.dataType);
assert_array_equals(output.shape(), test.output.dimensions);
} else {
const regrexp = /\[conv_2d_\*\]/;
assert_throws_with_label(
() => builder.conv2d(input, filter, test.options), regrexp);
}
}, test.name));
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