def test_result_shape(self, image_height, image_width, num_instances,
regenerate_source_id):
decoder = tf_example_decoder.TfExampleDecoder(
include_mask=True, regenerate_source_id=regenerate_source_id)
serialized_example = tfexample_utils.create_detection_test_example(
image_height=image_height,
image_width=image_width,
image_channel=3,
num_instances=num_instances).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(value=serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertAllEqual((image_height, image_width, 3),
results['image'].shape)
if not regenerate_source_id:
self.assertEqual(tfexample_utils.DUMP_SOURCE_ID,
results['source_id'])
self.assertEqual(image_height, results['height'])
self.assertEqual(image_width, results['width'])
self.assertAllEqual((num_instances, ),
results['groundtruth_classes'].shape)
self.assertAllEqual((num_instances, ),
results['groundtruth_is_crowd'].shape)
self.assertAllEqual((num_instances, ),
results['groundtruth_area'].shape)
self.assertAllEqual((num_instances, 4),
results['groundtruth_boxes'].shape)
self.assertAllEqual((num_instances, image_height, image_width),
results['groundtruth_instance_masks'].shape)
self.assertAllEqual((num_instances, ),
results['groundtruth_instance_masks_png'].shape)
def test_export_tflite_detection(self, experiment, quant_type,
input_image_size):
test_tfrecord_file = os.path.join(self.get_temp_dir(), 'det_test.tfrecord')
example = tfexample_utils.create_detection_test_example(
image_height=input_image_size[0],
image_width=input_image_size[1],
image_channel=3,
num_instances=10)
self._create_test_tfrecord(
tfrecord_file=test_tfrecord_file, example=example, num_samples=10)
params = exp_factory.get_exp_config(experiment)
params.task.validation_data.input_path = test_tfrecord_file
params.task.train_data.input_path = test_tfrecord_file
temp_dir = self.get_temp_dir()
module = detection_serving.DetectionModule(
params=params,
batch_size=1,
input_image_size=input_image_size,
input_type='tflite')
self._export_from_module(
module=module,
input_type='tflite',
saved_model_dir=os.path.join(temp_dir, 'saved_model'))
tflite_model = export_tflite_lib.convert_tflite_model(
saved_model_dir=os.path.join(temp_dir, 'saved_model'),
quant_type=quant_type,
params=params,
calibration_steps=5)
self.assertIsInstance(tflite_model, bytes)
def test_retinanet_task(self, test_config, is_training):
"""RetinaNet task test for training and val using toy configs."""
input_image_size = [384, 384]
test_tfrecord_file = os.path.join(self.get_temp_dir(),
'det_test.tfrecord')
example = tfexample_utils.create_detection_test_example(
image_height=input_image_size[0],
image_width=input_image_size[1],
image_channel=3,
num_instances=10)
self._create_test_tfrecord(tfrecord_file=test_tfrecord_file,
example=example,
num_samples=10)
config = exp_factory.get_exp_config(test_config)
# modify config to suit local testing
config.task.model.input_size = [128, 128, 3]
config.trainer.steps_per_loop = 1
config.task.train_data.global_batch_size = 1
config.task.validation_data.global_batch_size = 1
config.task.train_data.shuffle_buffer_size = 2
config.task.validation_data.shuffle_buffer_size = 2
config.task.validation_data.input_path = test_tfrecord_file
config.task.train_data.input_path = test_tfrecord_file
config.train_steps = 1
task = retinanet.RetinaNetTask(config.task)
model = task.build_model()
metrics = task.build_metrics(training=is_training)
strategy = tf.distribute.get_strategy()
data_config = config.task.train_data if is_training else config.task.validation_data
dataset = orbit.utils.make_distributed_dataset(strategy,
task.build_inputs,
data_config)
iterator = iter(dataset)
opt_factory = optimization.OptimizerFactory(
config.trainer.optimizer_config)
optimizer = opt_factory.build_optimizer(
opt_factory.build_learning_rate())
if is_training:
task.train_step(next(iterator), model, optimizer, metrics=metrics)
else:
task.validation_step(next(iterator), model, metrics=metrics)
def test_result_shape(self, image_height, image_width, num_instances):
label_map_dir = self.get_temp_dir()
label_map_name = 'label_map.csv'
label_map_path = os.path.join(label_map_dir, label_map_name)
with open(label_map_path, 'w') as f:
f.write(LABEL_MAP_CSV_CONTENT)
decoder = tf_example_label_map_decoder.TfExampleDecoderLabelMap(
label_map_path, include_mask=True)
serialized_example = tfexample_utils.create_detection_test_example(
image_height=image_height,
image_width=image_width,
image_channel=3,
num_instances=num_instances).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(value=serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertAllEqual(
(image_height, image_width, 3), results['image'].shape)
self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id'])
self.assertEqual(image_height, results['height'])
self.assertEqual(image_width, results['width'])
self.assertAllEqual(
(num_instances,), results['groundtruth_classes'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_is_crowd'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_area'].shape)
self.assertAllEqual(
(num_instances, 4), results['groundtruth_boxes'].shape)
self.assertAllEqual(
(num_instances, image_height, image_width),
results['groundtruth_instance_masks'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_instance_masks_png'].shape)
def test_scan_and_generator_annotation_file(self):
num_samples = 10
example = tfexample_utils.create_detection_test_example(
image_height=512,
image_width=512,
image_channel=3,
num_instances=10)
tf_examples = [example] * num_samples
data_file = os.path.join(self.create_tempdir(), 'test.tfrecord')
tfexample_utils.dump_to_tfrecord(record_file=data_file,
tf_examples=tf_examples)
annotation_file = os.path.join(self.create_tempdir(),
'annotation.json')
coco_utils.scan_and_generator_annotation_file(
file_pattern=data_file,
file_type='tfrecord',
num_samples=num_samples,
include_mask=True,
annotation_file=annotation_file)
self.assertTrue(
tf.io.gfile.exists(annotation_file),
msg='Annotation file {annotation_file} does not exists.')
