def test_from_pascal_voc(self):
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
model_spec = MockDetectorModelSpec('efficientdet-lite0')
data = object_detector_dataloader.DataLoader.from_pascal_voc(
images_dir, annotations_dir, label_map)
self.assertIsInstance(data, object_detector_dataloader.DataLoader)
self.assertLen(data, 1)
self.assertEqual(data.label_map, label_map)
self.assertTrue(os.path.isfile(data.annotations_json_file))
self.assertGreater(os.path.getsize(data.annotations_json_file), 0)
expected_json_file = test_util.get_test_data_path('annotations.json')
self.assertTrue(
filecmp.cmp(data.annotations_json_file, expected_json_file))
ds = data.gen_dataset(model_spec, batch_size=1, is_training=False)
for i, (images, labels) in enumerate(ds):
self.assertEqual(i, 0)
images_shape = tf.shape(images).numpy()
expected_shape = np.array([1, *model_spec.config.image_size, 3])
self.assertTrue((images_shape == expected_shape).all())
self.assertLen(labels, 15)
ds1 = data.gen_dataset(model_spec, batch_size=1, is_training=True)
# Comments out this assert since it fails externally.
# self.assertEqual(ds1.cardinality(), tf.data.INFINITE_CARDINALITY)
for images, labels in ds1.take(10):
images_shape = tf.shape(images).numpy()
expected_shape = np.array([1, *model_spec.config.image_size, 3])
self.assertTrue((images_shape == expected_shape).all())
self.assertLen(labels, 15)
def testEfficientDetLite0(self):
# Gets model specification.
spec = model_spec.get('efficientdet_lite0')
# Prepare data.
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
data = object_detector_dataloader.DataLoader.from_pascal_voc(
images_dir, annotations_dir, label_map)
# Train the model.
task = object_detector.create(data, spec, batch_size=1, epochs=1)
self.assertEqual(spec.config.num_classes, 2)
# Evaluate trained model
metrics = task.evaluate(data)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
# Export the model to saved model.
output_path = os.path.join(self.get_temp_dir(), 'saved_model')
task.export(self.get_temp_dir(),
export_format=ExportFormat.SAVED_MODEL)
self.assertTrue(os.path.isdir(output_path))
self.assertNotEqual(len(os.listdir(output_path)), 0)
# Export the model to the float TFLite model.
output_path = os.path.join(self.get_temp_dir(), 'float.tflite')
task.export(self.get_temp_dir(),
tflite_filename='float.tflite',
quantization_config=None,
export_format=ExportFormat.TFLITE,
with_metadata=True,
export_metadata_json_file=True)
# Checks the sizes of the float32 TFLite model files in bytes.
model_size = 13476379
self.assertNear(os.path.getsize(output_path), model_size, 50000)
json_output_file = os.path.join(self.get_temp_dir(), 'float.json')
self.assertTrue(os.path.isfile(json_output_file))
self.assertGreater(os.path.getsize(json_output_file), 0)
expected_json_file = test_util.get_test_data_path(
'efficientdet_lite0_metadata.json')
self.assertTrue(filecmp.cmp(json_output_file, expected_json_file))
# Evaluate the TFLite model.
task.evaluate_tflite(output_path, data)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
# Tests the default quantized model.
filename = 'model_quant.tflite'
output_path = os.path.join(self.get_temp_dir(), filename)
task.export(self.get_temp_dir(),
tflite_filename=filename,
export_format=ExportFormat.TFLITE)
model_size = 4312187
err = model_size * 0.05
self.assertTrue(os.path.isfile(output_path))
self.assertNear(os.path.getsize(output_path), model_size, err)
def test_pascal_voc_cache_writer(self):
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
cache_writer = dataloader_util.PascalVocCacheFilesWriter(label_map,
images_dir,
num_shards=1)
cache_files = dataloader_util.get_cache_files(
cache_dir=self.get_temp_dir(), cache_prefix_filename='pascal')
cache_writer.write_files(cache_files, annotations_dir)
# Checks the TFRecord file.
tfrecord_files = cache_files.tfrecord_files
self.assertTrue(os.path.isfile(tfrecord_files[0]))
self.assertGreater(os.path.getsize(tfrecord_files[0]), 0)
# Checks the annotation json file.
annotations_json_file = cache_files.annotations_json_file
self.assertTrue(os.path.isfile(annotations_json_file))
self.assertGreater(os.path.getsize(annotations_json_file), 0)
expected_json_file = test_util.get_test_data_path('annotations.json')
self.assertTrue(filecmp.cmp(annotations_json_file, expected_json_file))
# Checks the meta_data file.
meta_data_file = cache_files.meta_data_file
self.assertTrue(os.path.isfile(meta_data_file))
self.assertGreater(os.path.getsize(meta_data_file), 0)
with tf.io.gfile.GFile(meta_data_file, 'r') as f:
meta_data_dict = yaml.load(f, Loader=yaml.FullLoader)
self.assertEqual(meta_data_dict['size'], 1)
self.assertEqual(meta_data_dict['label_map'], label_map)
# Checks xml_dict from `_get_xml_dict` function.
xml_dict = next(cache_writer._get_xml_dict(annotations_dir))
expected_xml_dict = {
'filename':
'2012_12.jpg',
'folder':
'',
'object': [{
'difficult': '1',
'bndbox': {
'xmin': '64',
'ymin': '64',
'xmax': '192',
'ymax': '192',
},
'name': 'person',
'truncated': '0',
'pose': '',
}],
'size': {
'width': '256',
'height': '256',
}
}
self.assertEqual(xml_dict, expected_xml_dict)
def testEfficientDetLite0(self):
# Gets model specification.
spec = model_spec.get('efficientdet_lite0')
# Prepare data.
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
data = object_detector_dataloader.DataLoader.from_pascal_voc(
images_dir, annotations_dir, label_map)
# Train the model.
task = object_detector.create(data, spec, batch_size=1, epochs=1)
self.assertEqual(spec.config.num_classes, 2)
# Evaluate trained model
metrics = task.evaluate(data, batch_size=1)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
# Export the model to saved model.
output_path = os.path.join(self.get_temp_dir(), 'saved_model')
task.export(self.get_temp_dir(),
export_format=ExportFormat.SAVED_MODEL)
self.assertTrue(os.path.isdir(output_path))
self.assertNotEqual(len(os.listdir(output_path)), 0)
# Export the model to TFLite model.
output_path = os.path.join(self.get_temp_dir(), 'float.tflite')
task.export(self.get_temp_dir(),
tflite_filename='float.tflite',
export_format=ExportFormat.TFLITE,
with_metadata=True,
export_metadata_json_file=True)
self.assertTrue(tf.io.gfile.exists(output_path))
self.assertGreater(os.path.getsize(output_path), 0)
json_output_file = os.path.join(self.get_temp_dir(), 'float.json')
self.assertTrue(os.path.isfile(json_output_file))
self.assertGreater(os.path.getsize(json_output_file), 0)
expected_json_file = test_util.get_test_data_path(
'efficientdet_lite0_metadata.json')
self.assertTrue(filecmp.cmp(json_output_file, expected_json_file))
# Export the model to quantized TFLite model.
# TODO(b/175173304): Skips the test for stable tensorflow 2.4 for now since
# it fails. Will revert this change after TF upgrade.
if tf.__version__.startswith('2.4'):
return
output_path = os.path.join(self.get_temp_dir(),
'model_quantized.tflite')
config = configs.QuantizationConfig.create_full_integer_quantization(
data, is_integer_only=True)
task.export(self.get_temp_dir(),
tflite_filename='model_quantized.tflite',
quantization_config=config,
export_format=ExportFormat.TFLITE)
self.assertTrue(os.path.isfile(output_path))
self.assertGreater(os.path.getsize(output_path), 0)
def testEfficientDetLite0(self):
# Gets model specification.
hub_path = test_util.get_test_data_path('fake_effdet_lite0_hub')
spec = object_detector_spec.EfficientDetModelSpec(
model_name='efficientdet-lite0', uri=hub_path)
# Prepare data.
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
data = object_detector_dataloader.DataLoader.from_pascal_voc(
images_dir, annotations_dir, label_map)
# Train the model.
task = object_detector.create(data, spec, batch_size=1, epochs=1)
# Evaluate trained model
metrics = task.evaluate(data, batch_size=1)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
def testEfficientDetLite0(self):
# Gets model specification.
spec = model_spec.get('efficientdet_lite0')
# Prepare data.
images_dir, annotations_dir, label_map = test_util.create_pascal_voc(
self.get_temp_dir())
data = object_detector_dataloader.DataLoader.from_pascal_voc(
images_dir, annotations_dir, label_map)
# Train the model.
task = object_detector.create(data, spec, batch_size=1, epochs=1)
self.assertEqual(spec.config.num_classes, 2)
# Evaluate trained model
metrics = task.evaluate(data)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
# Export the model to saved model.
output_path = os.path.join(self.get_temp_dir(), 'saved_model')
task.export(self.get_temp_dir(),
export_format=ExportFormat.SAVED_MODEL)
self.assertTrue(os.path.isdir(output_path))
self.assertNotEqual(len(os.listdir(output_path)), 0)
# Export the model to TFLite model.
output_path = os.path.join(self.get_temp_dir(), 'float.tflite')
task.export(self.get_temp_dir(),
tflite_filename='float.tflite',
quantization_type=QuantizationType.FP32,
export_format=ExportFormat.TFLITE,
with_metadata=True,
export_metadata_json_file=True)
# Checks the sizes of the float32 TFLite model files in bytes.
model_size = 13476379
self.assertNear(os.path.getsize(output_path), model_size, 50000)
json_output_file = os.path.join(self.get_temp_dir(), 'float.json')
self.assertTrue(os.path.isfile(json_output_file))
self.assertGreater(os.path.getsize(json_output_file), 0)
expected_json_file = test_util.get_test_data_path(
'efficientdet_lite0_metadata.json')
self.assertTrue(filecmp.cmp(json_output_file, expected_json_file))
# Evaluate the TFLite model.
task.evaluate_tflite(output_path, data)
self.assertIsInstance(metrics, dict)
self.assertGreaterEqual(metrics['AP'], 0)
# Export the model to quantized TFLite model.
# TODO(b/175173304): Skips the test for stable tensorflow 2.4 for now since
# it fails. Will revert this change after TF upgrade.
if tf.__version__.startswith('2.4'):
return
# Not include QuantizationType.FP32 here since we have already tested it
# above together with metadata file test.
types = (QuantizationType.INT8, QuantizationType.FP16,
QuantizationType.DYNAMIC)
# The sizes of the TFLite model files in bytes.
model_sizes = (4439987, 6840331, 4289875)
for quantization_type, model_size in zip(types, model_sizes):
filename = quantization_type.name.lower() + '.tflite'
output_path = os.path.join(self.get_temp_dir(), filename)
task.export(self.get_temp_dir(),
quantization_type=quantization_type,
tflite_filename=filename,
export_format=ExportFormat.TFLITE)
self.assertNear(os.path.getsize(output_path), model_size, 50000)
