onnxruntime/js/node/test/test-runner.ts

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import * as fs from 'fs-extra';
import * as path from 'path';

import {InferenceSession, Tensor} from '../lib';

import {assertTensorEqual, loadTensorFromFile, shouldSkipModel} from './test-utils';

export function run(testDataFolder: string): void {
  const models = fs.readdirSync(testDataFolder);

  for (const model of models) {
    // read each model folders
    const modelFolder = path.join(testDataFolder, model);
    let modelPath: string;
    const modelTestCases: Array<[Array<Tensor|undefined>, Array<Tensor|undefined>]> = [];
    for (const currentFile of fs.readdirSync(modelFolder)) {
      const currentPath = path.join(modelFolder, currentFile);
      const stat = fs.lstatSync(currentPath);
      if (stat.isFile()) {
        const ext = path.extname(currentPath);
        if (ext.toLowerCase() === '.onnx') {
          modelPath = currentPath;
        }
      } else if (stat.isDirectory()) {
        const inputs: Array<Tensor|undefined> = [];
        const outputs: Array<Tensor|undefined> = [];
        for (const dataFile of fs.readdirSync(currentPath)) {
          const dataFileFullPath = path.join(currentPath, dataFile);
          const ext = path.extname(dataFile);

          if (ext.toLowerCase() === '.pb') {
            let tensor: Tensor|undefined;
            try {
              tensor = loadTensorFromFile(dataFileFullPath);
            } catch (e) {
              console.warn(`[${model}] Failed to load test data: ${e.message}`);
            }

            if (dataFile.indexOf('input') !== -1) {
              inputs.push(tensor);
            } else if (dataFile.indexOf('output') !== -1) {
              outputs.push(tensor);
            }
          }
        }
        modelTestCases.push([inputs, outputs]);
      }
    }

    // add cases
    describe(`${model}`, () => {
      let session: InferenceSession|null = null;
      let skipModel = shouldSkipModel(model, ['cpu']);
      if (!skipModel) {
        before(async () => {
          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`);
              skipModel = true;
            } else {
              throw e;
            }
          }
        });
      } else {
        console.log(`[test-runner] skipped: ${model}`);
      }

      for (let i = 0; i < modelTestCases.length; i++) {
        const testCase = modelTestCases[i];
        const inputs = testCase[0];
        const expectedOutputs = testCase[1];
        if (!skipModel && !inputs.some(t => t === undefined) && !expectedOutputs.some(t => t === undefined)) {
          it(`case${i}`, async () => {
            if (skipModel) {
              return;
            }

            if (session !== null) {
              const feeds: Record<string, Tensor> = {};
              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++]!);
              }
            } else {
              throw new TypeError('session is null');
            }
          });
        }
      }
    });
  }
}