def _load_model(self, model_network_path, model_weight_path):
"""Load a paddle model from disk
Parameters
----------
model_network_path: str
Path where the model network path is (json file)
model_weight_path: str
Path where the model network weights are (hd5 file)
Returns
-------
model: A paddle model
"""
from paddle.proto import ModelConfig_pb2
from mmdnn.conversion.common.IR.IR_graph import load_protobuf_from_file
loaded_model = ModelConfig_pb2.ModelConfig()
load_protobuf_from_file(loaded_model, model_network_path)
if model_weight_path:
if os.path.isfile(model_weight_path):
parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_weight_path, 'r'))
self.weight_loaded = True
print("Network file [{}] and [{}] is loaded successfully.".format(model_network_path, model_weight_path))
else:
print("Warning: Weights File [%s] is not found." % (model_weight_path))
return loaded_model, parameters
def _load_model(model_file):
"""Load a ONNX model file from disk
Parameters
----------
model_file: str
Path where the model file path is (protobuf file)
Returns
-------
model: A ONNX protobuf model
"""
from onnx import onnx_pb2
from mmdnn.conversion.common.IR.IR_graph import load_protobuf_from_file
model = onnx_pb2.ModelProto()
load_protobuf_from_file(model, model_file)
print("ONNX model file [%s] loaded successfully." % model_file)
return model
def _load_meta(model_network_path):
"""Load a tensorflow meta file from disk
Parameters
----------
model_network_path: str
Path where the model network path is (protobuf meta file)
Returns
-------
model: A tensorflow protobuf file
"""
from tensorflow.core.protobuf import meta_graph_pb2
from mmdnn.conversion.common.IR.IR_graph import load_protobuf_from_file
meta_graph = meta_graph_pb2.MetaGraphDef()
load_protobuf_from_file(meta_graph, model_network_path)
graph = meta_graph.graph_def
print ("Tensorflow model file [%s] loaded successfully." % model_network_path)
return graph
def _load_meta(model_network_path):
"""Load a tensorflow meta file from disk
Parameters
----------
model_network_path: str
Path where the model network path is (protobuf meta file)
Returns
-------
model: A tensorflow protobuf file
"""
from tensorflow.core.protobuf import meta_graph_pb2
from mmdnn.conversion.common.IR.IR_graph import load_protobuf_from_file
meta_graph = meta_graph_pb2.MetaGraphDef()
load_protobuf_from_file(meta_graph, model_network_path)
graph = meta_graph.graph_def
print ("Tensorflow model file [%s] loaded successfully." % model_network_path)
return graph
