def run(model, config_file):
global nn, pre_process, post_process
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if file_extension == '.so': # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
run_impl(config)
def run(model, config_file):
global nn, pre_process, post_process
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if file_extension == '.so': # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
if config.TASK == "IMAGE.CLASSIFICATION":
run_classification(config)
if config.TASK == "IMAGE.OBJECT_DETECTION":
run_object_detection(config)
if config.TASK == "IMAGE.SEMANTIC_SEGMENTATION":
run_sementic_segmentation(config)
def run(model, config_file, port=80):
global nn, pre_process, post_process, config, stream, pool
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
if file_extension == '.so': # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
nn = NNLib()
nn.load(model)
stream = VideoStream(CAMERA_SOURCE, CAMERA_WIDTH, CAMERA_HEIGHT,
CAMERA_FPS)
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
pool = Pool(processes=1, initializer=_init_worker)
try:
server = ThreadedHTTPServer(('', port), MotionJpegHandler)
print("server starting")
server.serve_forever()
except KeyboardInterrupt as e:
print("KeyboardInterrpt in server - ending server")
stream.release()
pool.terminate()
pool.join()
server.socket.close()
server.shutdown()
return
def run(library, config_file):
global nn, pre_process, post_process
nn = NNLib()
nn.load(library)
nn.init()
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if config.TASK == "IMAGE.CLASSIFICATION":
run_classification(config)
if config.TASK == "IMAGE.OBJECT_DETECTION":
run_object_detection(config)
def _post_process(output, post_processor):
post_process = build_post_process(post_processor)
output = post_process(outputs=output)['outputs']
return output