def start_service(model_path, config_file_path):
"""
:type model_path: str valid path of the TF model where a .pb model file is present
:type config_file_path: str valid path of the config file in JSON format
:return: None
"""
with open(config_file_path) as f:
config = json.load(f)
pre_process_pipeline = create_pre_process_pipeline(config)
post_process_pipeline = create_post_process_pipeline(config)
tensorflow_prediction = Prediction(
model_path=model_path,
input_tensors_names=["images:0"],
input_fields=["images"],
output_tensors_names=["logits:0"],
output_fields=["images"] # replace images with results because it's more convenient for the transforms
)
application = TomaatApp(
preprocess_fun=pre_process_pipeline,
inference_fun=tensorflow_prediction,
postprocess_fun=post_process_pipeline
)
service = TomaatService(
config=config,
app=application,
input_interface=input_interface,
output_interface=output_interface
)
if config['announce']:
service.start_service_announcement()
service.run()
"port": 9000,
"announce": False,
"api_key": "",
}
iface_in = [{'type':'fiducials','destination':'fids'}]
iface_out = [{'type':'Fiducials','field':'fids'}]
def preprocess(data_in):
return {'fids':[data_in['fids'][0]] }
def inference(data):
fids_np = data['fids'][0]
fids_neg = fids_np * -1.0
return {
'fids_neg': fids_neg,
}
def postprocess(output):
fids_neg = output['fids_neg'].astype(np.float)
return {
'fids':[fids_neg]
}
my_app = TomaatApp(preprocess,inference,postprocess)
my_service = TomaatService(config, my_app, iface_in, iface_out)
my_service.run()
def pre_processing_mock_function(data):
data['input_dict_field'] = \
(np.asarray(data['input_dict_field']) + np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])).astype(np.float32)
return data
def post_processing_mock_function(data):
data['output_dict_field'] /= 2
return data
mock_app = TomaatApp(
preprocess_fun=pre_processing_mock_function,
inference_fun=pred_object,
postprocess_fun=post_processing_mock_function
)
def test_tomaatapp_pytorch_functionality():
data = {'input_dict_field': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
result = mock_app(data)
return result
def test_tomaatapp_pytorch_functionality_answer():
result = test_tomaatapp_pytorch_functionality()