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skip tests when model opset > released opset (#5096)

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* skip tests when model opset > released opset

* remove multiple model load

* nit fixes

* plus some comments
This commit is contained in:
Ashwini Khade 2020-09-10 17:25:32 -07:00 committed by GitHub
parent 782ccff207
commit a8557b3f0f
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 51 additions and 31 deletions
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48
nodejs/test/test-runner.ts
View file

@ -52,11 +52,26 @@ export function run(testDataFolder: string): void {
// add cases
describe(`${model}`, () => {
let session: InferenceSession;
let session: InferenceSession|null = null;
const skipModel = shouldSkipModel(model, ['cpu']);
if (!skipModel) {
before(async () => {
session = await InferenceSession.create(modelPath);
before(async function () {
try {
session = await InferenceSession.create(modelPath);
} catch (e) {
// By default ort allows models with opsets from an official onnx release only. If it encounters
// a model with opset > than released opset, ValidateOpsetForDomain throws an error and model load fails.
// Since this is by design such a failure is acceptable in the context of this test. Therefore we simply
// skip this test. Setting env variable ALLOW_RELEASED_ONNX_OPSET_ONLY=0 allows loading a model with
// opset > released onnx opset.
if (process.env.ALLOW_RELEASED_ONNX_OPSET_ONLY !== '0' && e.message.includes("ValidateOpsetForDomain")) {
session = null;
console.log(`Skipping ${model}. To run this test set env variable ALLOW_RELEASED_ONNX_OPSET_ONLY=0`);
this.skip();
} else {
throw e;
}
}
});
} else {
console.log(`[test-runner] skipped: ${model}`);
@ -68,19 +83,24 @@ export function run(testDataFolder: string): void {
const expectedOutputs = testCase[1];
if (!skipModel && !inputs.some(t => t === undefined) && !expectedOutputs.some(t => t === undefined)) {
it(`case${i}`, async () => {
const feeds = {};
if (inputs.length !== session.inputNames.length) {
throw new RangeError('input length does not match name list');
}
for (let i = 0; i < inputs.length; i++) {
feeds[session.inputNames[i]] = inputs[i];
}
const outputs = await session.run(feeds);
if (session !== null) {
const feeds = {};
if (inputs.length !== session.inputNames.length) {
throw new RangeError('input length does not match name list');
}
for (let i = 0; i < inputs.length; i++) {
feeds[session.inputNames[i]] = inputs[i];
}
const outputs = await session.run(feeds);
let j = 0;
for (const name of session.outputNames) {
assertTensorEqual(outputs[name], expectedOutputs[j++]!);
let j = 0;
for (const name of session.outputNames) {
assertTensorEqual(outputs[name], expectedOutputs[j++]!);
}
} else {
throw new TypeError('session is null');
}
});
}
}

34
nodejs/test/test-utils.ts
View file

@ -20,7 +20,7 @@ export const SQUEEZENET_INPUT0_DATA: number[] = require(path.join(TEST_DATA_ROOT
export const SQUEEZENET_OUTPUT0_DATA: number[] = require(path.join(TEST_DATA_ROOT, 'squeezenet.output0.json'));
export const BACKEND_TEST_SERIES_FILTERS: {[name: string]: string[]} =
jsonc.readSync(path.join(ORT_ROOT, 'onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc'));
jsonc.readSync(path.join(ORT_ROOT, 'onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc'));
export const NUMERIC_TYPE_MAP = new Map<Tensor.Type, new (len: number) => Tensor.DataType>([
@ -105,7 +105,7 @@ export function assertDataEqual(type: Tensor.Type, actual: Tensor.DataType, expe
case 'float32':
case 'float64':
assertFloatEqual(
actual as number[] | Float32Array | Float64Array, expected as number[] | Float32Array | Float64Array);
actual as number[] | Float32Array | Float64Array, expected as number[] | Float32Array | Float64Array);
break;
case 'uint8':
@ -127,7 +127,7 @@ export function assertDataEqual(type: Tensor.Type, actual: Tensor.DataType, expe
}
export function assertFloatEqual(
actual: number[]|Float32Array|Float64Array, expected: number[]|Float32Array|Float64Array): void {
actual: number[]|Float32Array|Float64Array, expected: number[]|Float32Array|Float64Array): void {
const THRESHOLD_ABSOLUTE_ERROR = 1.0e-4;
const THRESHOLD_RELATIVE_ERROR = 1.000001;
@ -181,19 +181,19 @@ export function loadTensorFromFile(pbFile: string): Tensor {
return new Tensor('string', tensorProto.stringData.map(i => i.toString()), dims);
} else {
switch (tensorProto.dataType) {
// FLOAT = 1,
// UINT8 = 2,
// INT8 = 3,
// UINT16 = 4,
// INT16 = 5,
// INT32 = 6,
// INT64 = 7,
// STRING = 8,
// BOOL = 9,
// FLOAT16 = 10,
// DOUBLE = 11,
// UINT32 = 12,
// UINT64 = 13,
// FLOAT = 1,
// UINT8 = 2,
// INT8 = 3,
// UINT16 = 4,
// INT16 = 5,
// INT32 = 6,
// INT64 = 7,
// STRING = 8,
// BOOL = 9,
// FLOAT16 = 10,
// DOUBLE = 11,
// UINT32 = 12,
// UINT64 = 13,
case onnx_proto.onnx.TensorProto.DataType.FLOAT:
transferredTypedArray = new Float32Array(tensorProto.rawData.byteLength / 4);
type = 'float32';
@ -242,7 +242,7 @@ export function loadTensorFromFile(pbFile: string): Tensor {
throw new Error(`not supported tensor type: ${tensorProto.dataType}`);
}
const transferredTypedArrayRawDataView =
new Uint8Array(transferredTypedArray.buffer, transferredTypedArray.byteOffset, tensorProto.rawData.byteLength);
new Uint8Array(transferredTypedArray.buffer, transferredTypedArray.byteOffset, tensorProto.rawData.byteLength);
transferredTypedArrayRawDataView.set(tensorProto.rawData);
return new Tensor(type, transferredTypedArray, dims);