class TestCNNAutoencoderPrediction(unittest.TestCase):
@classmethod
def setUpClass(self):
self.is_initial_training_from_topic = True
self.initial_training_data = None
self.inference_data_topic = 'inference'
self.inference_data_acquisition = InferenceDataAcquisition(
topic=self.inference_data_topic)
# # Send training data
self.training_data_topic = 'training'
adoc_dataset_location = ADOC_DATASET_LOCATION
video_files = os.listdir(adoc_dataset_location)
train_video_files = [x for x in video_files if x[0:5] == 'train']
train_video_files.sort()
train_video_files = train_video_files[1:2] # not all videos for test
for video in train_video_files:
video_producer = VideoProducer("localhost:29092",
self.training_data_topic,
os.path.join(
adoc_dataset_location, video),
debug=True,
resize_to_dimension=(256, 256))
video_producer.send_video(extra_fields={"sequence_name": video})
self.training_data_acquirer = TrainingDataAcquisition(
topic=self.training_data_topic, group_id_suffix="training")
self.training_data_acquirer.load()
self.training_data_acquirer.data
self.training_data_acquirer.train_name
def test_cnn(self):
cnn = CNNAutoEncoder(model_name='cnn_1', epochs=4, batch_size=1)
cnn.fit(self.training_data_acquirer.data)
#cnn.load_last_model()
sequence_size = 12
sequence_counter = 0
sequence = []
for msg in self.inference_data_acquisition.consumer.consumer:
data = frame_from_bytes_str(msg.value['data'])
sequence.append(data)
sequence_counter += 1
if sequence_counter == sequence_size:
# TODO send to prediction topic?
prediciton = cnn.predict(sequence)
print(prediciton)
sequence_counter = 0
sequence = []
@classmethod
def tearDownClass(cls):
pass
def setUpClass(self):
self.is_initial_training_from_topic = True
self.initial_training_data = None
self.inference_data_topic = 'inference'
self.inference_data_acquisition = InferenceDataAcquisition(
topic=self.inference_data_topic)
# # Send training data
self.training_data_topic = 'training'
adoc_dataset_location = ADOC_DATASET_LOCATION
video_files = os.listdir(adoc_dataset_location)
train_video_files = [x for x in video_files if x[0:5] == 'train']
train_video_files.sort()
train_video_files = train_video_files[1:2] # not all videos for test
for video in train_video_files:
video_producer = VideoProducer("localhost:29092",
self.training_data_topic,
os.path.join(
adoc_dataset_location, video),
debug=True,
resize_to_dimension=(256, 256))
video_producer.send_video(extra_fields={"sequence_name": video})
self.training_data_acquirer = TrainingDataAcquisition(
topic=self.training_data_topic, group_id_suffix="training")
self.training_data_acquirer.load()
self.training_data_acquirer.data
self.training_data_acquirer.train_name
def test_dataacquisition_kafka(self):
adoc_dataset_location = ADOC_DATASET_LOCATION
video_files = os.listdir(adoc_dataset_location)
train_video_files = [x for x in video_files if x[0:5] == 'train']
train_video_files.sort()
train_video_files = train_video_files[1:2] #not all videos for test
for video in train_video_files:
video_producer = VideoProducer(self.broker,
"training",
os.path.join(
adoc_dataset_location, video),
debug=True,
resize_to_dimension=(256, 256))
video_producer.send_video(extra_fields={"sequence_name": video})
time.sleep(5)
data_acquirer = TrainingDataAcquisition(topic='training')
data_acquirer.load()
self.assertEqual(len(data_acquirer.data), 1)
self.assertEqual(len(data_acquirer.data['train_2.avi']), 200)
print(data_acquirer.train_name)