def post(self, bot_id):
"""
Provides a REST Interface to retrieve the classification of an image for a pretrained model belonging to a bot.
The bot_id is passed as URL parameter /bot_id.
The image is mandatory and needs to be part of the http Request Body as file, retrievable with the key 'image'.
Optionally the number of return_labels can be passed to the Request Body Form with the key 'return_labels'
:param bot_id: Id of the Bot to identify the correct Model
:return: HTTP Response with the classification result or an Error
"""
response = request.get_json(force=True)
base64_image = response['base64Image']
return handler.handle_post(bot_id, base64_image)
def test_train(self):
msg, status = tl_handler.handle_put(TEST_BOT_ID,
test=True,
max_train_time=100)
self.assertEqual(200, status, 'http status code is %s' % status)
bot_model_dir = dirs.get_model_data_dir(TEST_BOT_ID)
# Check if the bot model dir contains a model now
self.assertTrue(
os.listdir(bot_model_dir),
'bot_model_dir %s is empty after transfer learning' %
bot_model_dir)
self.assertTrue(
os.path.isfile(os.path.join(bot_model_dir, 'checkpoint')),
'not checkpoints file in bot_model_dir %s after transfer learning'
% bot_model_dir)
# Mock a file for classification
temp_file = tempfile.NamedTemporaryFile()
temp_file.write(
base64.b64encode(
open(os.path.join(FILES_DIR, 'tulip.jpg'), "rb").read()))
temp_file.seek(0)
json_result, status = handler.handle_post(TEST_BOT_ID,
temp_file.read(),
return_labels=5)
print(json_result)
temp_file.close()
self.assertTrue(json_result, 'Classification result is empty')
json_result = json.loads(json_result)
self.assertTrue(json_result['labels'],
'No labels in json result %s' % json_result)
self.assertTrue(json_result['probabilities'],
'No predictions in json result %s' % json_result)
print(json_result)
# Clean the bot_model directory for next test run
for file in os.listdir(bot_model_dir):
file_path = os.path.join(bot_model_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
print(e)
def test_handle_post(self):
if not os.listdir(dirs.get_protobuf_dir(TEST_BOT_ID)):
shutil.copytree(os.path.join(FILES_DIR, 'flower_protobuf'), dirs.get_protobuf_dir(TEST_BOT_ID))
if os.path.exists(dirs.get_model_data_dir(TEST_BOT_ID)):
shutil.rmtree(dirs.get_model_data_dir(TEST_BOT_ID))
shutil.copytree(os.path.join(FILES_DIR, 'protobuf/bot_test'), dirs.get_model_data_dir(TEST_BOT_ID))
expected_return_labels = 3
temp_file = tempfile.NamedTemporaryFile()
temp_file.write(
base64.b64encode(
open(
os.path.join(FILES_DIR, 'tulip.jpg'), "rb"
).read()
)
)
temp_file.seek(0)
json_result, status = handler.handle_post(TEST_BOT_ID, temp_file.read(), return_labels=expected_return_labels)
temp_file.close()
json_result = json.loads(json_result)
labels = json_result['labels']
probs = json_result['probabilities']
self.assertTrue(labels)
self.assertTrue(probs)
self.assertEqual(expected_return_labels, len(labels))
self.assertEqual(expected_return_labels, len(probs))
# Clean the bot_model directory for next test run
for file in os.listdir(dirs.get_model_data_dir(TEST_BOT_ID)):
file_path = os.path.join(dirs.get_model_data_dir(TEST_BOT_ID), file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
print(e)
def test_transfer_learning(self):
# Root model to initialize from
root_model_dir = dirs.get_test_root_model_dir()
if not os.listdir(root_model_dir):
print('root_model_dir %s empty. Cannot start test' %
root_model_dir)
return None
if not os.path.isfile(os.path.join(root_model_dir, 'checkpoint')):
print('No Checkpoint File in %s. Cannot start test.' %
root_model_dir)
return None
# Folder to load the additional training data from
bot_protobuf_dir = dirs.get_protobuf_dir(TEST_BOT_ID)
if not os.path.isdir(bot_protobuf_dir):
print('bot_protobuf_dir %s does not exist. Cannot start test' %
bot_protobuf_dir)
return None
if not os.listdir(bot_protobuf_dir):
print("bot_protobuf_dir %s is empty. Cannot start test." %
bot_protobuf_dir)
# Bot model folder to write the transfer learned model back to
bot_model_dir = dirs.get_model_data_dir(TEST_BOT_ID)
if not os.path.isdir(bot_model_dir):
print('bot_model_dir %s does not exist. Cannot start test' %
bot_model_dir)
return None
if os.listdir(bot_model_dir):
print('bot_model_dir %s is not emtpy. Cannot start test.' %
bot_model_dir)
return None
# Just run one step to make sure checkpoint files are written appropriately
transfer_learning.transfer_learning(root_model_dir=root_model_dir,
bot_model_dir=bot_model_dir,
protobuf_dir=bot_protobuf_dir,
max_train_time_sec=100,
log_every_n_steps=2)
# Check if the root model dir is still intact
self.assertTrue(
os.listdir(root_model_dir),
'root_model_dir %s is empty after transfer learning.' %
root_model_dir)
self.assertTrue(
os.path.isfile(os.path.join(root_model_dir, 'checkpoint')),
'checkpoints file in root_model_dir %s is gone after transfer learning.'
% root_model_dir)
# Check if the bot model dir contains a model now
self.assertTrue(
os.listdir(bot_model_dir),
'bot_model_dir %s is empty after transfer learning' %
bot_model_dir)
self.assertTrue(
os.path.isfile(os.path.join(bot_model_dir, 'checkpoint')),
'not checkpoints file in bot_model_dir %s after transfer learning'
% bot_model_dir)
# Mock a file for classification
temp_file = tempfile.NamedTemporaryFile()
temp_file.write(
base64.b64encode(
open(os.path.join(FILES_DIR, 'tulip.jpg'), "rb").read()))
temp_file.seek(0)
json_result, status = handler.handle_post(TEST_BOT_ID,
temp_file.read(),
return_labels=5)
print(json_result)
temp_file.close()
self.assertTrue(json_result, 'Classification result is empty')
json_result = json.loads(json_result)
self.assertTrue(json_result['labels'],
'No labels in json result %s' % json_result)
self.assertTrue(json_result['probabilities'],
'No predictions in json result %s' % json_result)
print(json_result)
# Clean the bot_model directory for next test run
for file in os.listdir(bot_model_dir):
file_path = os.path.join(bot_model_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
print(e)