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