Merge branch 'master' into ios-text-cocoapods-force-load

mediapipe/model_maker/python/vision/object_detector/BUILD

@@ -175,11 +175,7 @@ py_test(
     data = [":testdata"],
     tags = ["requires-net:external"],
     deps = [
-        ":dataset",
+        ":object_detector_import",
-        ":hyperparameters",
-        ":model_spec",
-        ":object_detector",
-        ":object_detector_options",
         "//mediapipe/tasks/python/test:test_utils",
     ],
 )

mediapipe/model_maker/python/vision/object_detector/object_detector_test.py

@@ -19,11 +19,7 @@ from unittest import mock as unittest_mock
 from absl.testing import parameterized
 import tensorflow as tf
 
-from mediapipe.model_maker.python.vision.object_detector import dataset
+from mediapipe.model_maker.python.vision import object_detector
-from mediapipe.model_maker.python.vision.object_detector import hyperparameters
-from mediapipe.model_maker.python.vision.object_detector import model_spec as ms
-from mediapipe.model_maker.python.vision.object_detector import object_detector
-from mediapipe.model_maker.python.vision.object_detector import object_detector_options
 from mediapipe.tasks.python.test import test_utils as task_test_utils
 
 
@@ -33,7 +29,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     super().setUp()
     dataset_folder = task_test_utils.get_test_data_path('coco_data')
     cache_dir = self.create_tempdir()
-    self.data = dataset.Dataset.from_coco_folder(
+    self.data = object_detector.Dataset.from_coco_folder(
         dataset_folder, cache_dir=cache_dir
     )
     # Mock tempfile.gettempdir() to be unique for each test to avoid race
@@ -48,15 +44,16 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     self.addCleanup(mock_gettempdir.stop)
 
   def test_object_detector(self):
-    hparams = hyperparameters.HParams(
+    hparams = object_detector.HParams(
         epochs=1,
         batch_size=2,
         learning_rate=0.9,
         shuffle=False,
         export_dir=self.create_tempdir(),
     )
-    options = object_detector_options.ObjectDetectorOptions(
+    options = object_detector.ObjectDetectorOptions(
-        supported_model=ms.SupportedModels.MOBILENET_V2, hparams=hparams
+        supported_model=object_detector.SupportedModels.MOBILENET_V2,
+        hparams=hparams,
     )
     # Test `create``
     model = object_detector.ObjectDetector.create(
@@ -79,7 +76,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
     self.assertGreater(os.path.getsize(output_metadata_file), 0)
 
     # Test `quantization_aware_training`
-    qat_hparams = hyperparameters.QATHParams(
+    qat_hparams = object_detector.QATHParams(
         learning_rate=0.9,
         batch_size=2,
         epochs=1,

mediapipe/tasks/python/test/text/text_embedder_test.py

@@ -32,6 +32,7 @@ _TextEmbedderOptions = text_embedder.TextEmbedderOptions
 
 _BERT_MODEL_FILE = 'mobilebert_embedding_with_metadata.tflite'
 _REGEX_MODEL_FILE = 'regex_one_embedding_with_metadata.tflite'
+_USE_MODEL_FILE = 'universal_sentence_encoder_qa_with_metadata.tflite'
 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
 # Tolerance for embedding vector coordinate values.
 _EPSILON = 1e-4
@@ -138,6 +139,24 @@ class TextEmbedderTest(parameterized.TestCase):
           16,
           (0.549632, 0.552879),
       ),
+      (
+          False,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_NAME,
+          0.851961,
+          100,
+          (1.422951, 1.404664),
+      ),
+      (
+          True,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_CONTENT,
+          0.851961,
+          100,
+          (0.127049, 0.125416),
+      ),
   )
   def test_embed(self, l2_normalize, quantize, model_name, model_file_type,
                  expected_similarity, expected_size, expected_first_values):
@@ -213,6 +232,24 @@ class TextEmbedderTest(parameterized.TestCase):
           16,
           (0.549632, 0.552879),
       ),
+      (
+          False,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_NAME,
+          0.851961,
+          100,
+          (1.422951, 1.404664),
+      ),
+      (
+          True,
+          False,
+          _USE_MODEL_FILE,
+          ModelFileType.FILE_CONTENT,
+          0.851961,
+          100,
+          (0.127049, 0.125416),
+      ),
   )
   def test_embed_in_context(self, l2_normalize, quantize, model_name,
                             model_file_type, expected_similarity, expected_size,
@@ -251,6 +288,7 @@ class TextEmbedderTest(parameterized.TestCase):
   @parameterized.parameters(
       # TODO: The similarity should likely be lower
       (_BERT_MODEL_FILE, 0.980880),
+      (_USE_MODEL_FILE, 0.780334),
   )
   def test_embed_with_different_themes(self, model_file, expected_similarity):
     # Creates embedder.