test_nightly_pip_package.py

# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test the Python API and shell binary of the tensorflowjs pip package."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import glob
import os
import shutil
import subprocess
import tempfile

import tensorflow.compat.v2 as tf
import tf_keras
from tensorflow.python.saved_model.save import save

class APIAndShellTest(tf.test.TestCase):
  """Nightly tests for the Python API of the pip package."""

  def setUp(self):
    # Make sure this file is not being run from the source directory, to
    # avoid picking up source files.
    if os.path.isdir(
        os.path.join(os.path.dirname(__file__), 'tensorflowjs')):
      self.fail('Do not run this test from the Python source directory. '
                'This file is intended to be run on pip install.')

    self._tmp_dir = tempfile.mkdtemp()
    super(APIAndShellTest, self).setUp()

  def tearDown(self):
    if os.path.isdir(self._tmp_dir):
      shutil.rmtree(self._tmp_dir)
    super(APIAndShellTest, self).tearDown()

  def testConvertTfHubMobileNetV2ToTfjsGraphModel(self):
    # 1. Convert tfhub mobilenet v2 module.
    tfhub_url = (
        'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224'
        '/classification/3'
    )
    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
    process = subprocess.Popen([
        'tensorflowjs_converter', '--input_format', 'tf_hub',
        tfhub_url, graph_model_output_dir
    ])
    process.communicate()
    self.assertEqual(0, process.returncode)

    # 2. Check the files that belong to the conversion result.
    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
    weight_files = glob.glob(
        os.path.join(graph_model_output_dir, 'group*.bin'))
    self.assertEqual(len(weight_files), 4)

  def testConvertMobileNetV2ModelToTfjsGraphModel(self):
    """create the keras mobilenet v2 model."""
    # 1. Create a saved model from keras mobilenet v2.
    model = tf_keras.applications.MobileNetV2()

    save_dir = os.path.join(self._tmp_dir, 'mobilenetv2')
    save(model, save_dir)

    # 2. Convert to graph model.
    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
    process = subprocess.Popen([
        'tensorflowjs_converter', '--input_format', 'tf_saved_model',
        save_dir, graph_model_output_dir
    ])
    process.communicate()
    self.assertEqual(0, process.returncode)

    # 3. Check the files that belong to the conversion result.
    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
    weight_files = glob.glob(
        os.path.join(graph_model_output_dir, 'group*.bin'))
    self.assertEqual(len(weight_files), 4)

  def testConvertMobileNetV2Hdf5ToTfjsGraphModel(self):
    # 1. Create a model for testing.
    model = tf_keras.applications.MobileNetV2()

    h5_path = os.path.join(self._tmp_dir, 'model.h5')
    model.save(h5_path)

    # 2. Convert the keras hdf5 model to tfjs_layers_model format.
    graph_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_graph')
    # Implicit value of --output_format: tfjs_layers_model
    process = subprocess.Popen([
        'tensorflowjs_converter', '--input_format', 'keras',
        '--output_format', 'tfjs_graph_model',
        h5_path, graph_model_output_dir
    ])
    process.communicate()
    self.assertEqual(0, process.returncode)

    # 3. Check the files that belong to the conversion result.
    files = glob.glob(os.path.join(graph_model_output_dir, '*'))
    self.assertIn(os.path.join(graph_model_output_dir, 'model.json'), files)
    weight_files = glob.glob(
        os.path.join(graph_model_output_dir, 'group*.bin'))
    self.assertEqual(len(weight_files), 4)

if __name__ == '__main__':
  tf.test.main()