def CoremlParse(architecture_name, image_path):
from mmdnn.conversion.examples.coreml.extractor import coreml_extractor
from mmdnn.conversion.coreml.coreml_parser import CoremlParser
# download model
architecture_file = coreml_extractor.download(architecture_name, TestModels.cachedir)
# get original model prediction result
original_predict = coreml_extractor.inference(architecture_name, architecture_file, image_path)
del coreml_extractor
# original to IR
IR_file = TestModels.tmpdir + 'coreml_' + architecture_name + "_converted"
parser = CoremlParser(architecture_file)
parser.run(IR_file)
del parser
del CoremlParser
return original_predict
def _convert(args):
if args.inputShape != None:
inputshape = []
for x in args.inputShape:
shape = x.split(',')
inputshape.append([int(x) for x in shape])
else:
inputshape = [None]
if args.srcFramework == 'caffe':
from mmdnn.conversion.caffe.transformer import CaffeTransformer
transformer = CaffeTransformer(args.network,
args.weights,
"tensorflow",
inputshape[0],
phase=args.caffePhase)
graph = transformer.transform_graph()
data = transformer.transform_data()
from mmdnn.conversion.caffe.writer import JsonFormatter, ModelSaver, PyWriter
JsonFormatter(graph).dump(args.dstPath + ".json")
print("IR network structure is saved as [{}.json].".format(
args.dstPath))
prototxt = graph.as_graph_def().SerializeToString()
with open(args.dstPath + ".pb", 'wb') as of:
of.write(prototxt)
print("IR network structure is saved as [{}.pb].".format(args.dstPath))
import numpy as np
with open(args.dstPath + ".npy", 'wb') as of:
np.save(of, data)
print("IR weights are saved as [{}.npy].".format(args.dstPath))
return 0
elif args.srcFramework == 'caffe2':
raise NotImplementedError("Caffe2 is not supported yet.")
elif args.srcFramework == 'keras':
if args.network != None:
model = (args.network, args.weights)
else:
model = args.weights
from mmdnn.conversion.keras.keras2_parser import Keras2Parser
parser = Keras2Parser(model)
elif args.srcFramework == 'tensorflow' or args.srcFramework == 'tf':
if args.dstNodeName is None:
raise ValueError(
"Need to provide the output node of Tensorflow model.")
assert args.network or args.weights
if not args.network:
if args.inNodeName is None:
raise ValueError(
"Need to provide the input node of Tensorflow model.")
if inputshape is None:
raise ValueError(
"Need to provide the input node shape of Tensorflow model."
)
assert len(args.inNodeName) == len(inputshape)
from mmdnn.conversion.tensorflow.tensorflow_frozenparser import TensorflowParser2
parser = TensorflowParser2(args.weights, inputshape,
args.inNodeName, args.dstNodeName)
else:
from mmdnn.conversion.tensorflow.tensorflow_parser import TensorflowParser
if args.inNodeName and inputshape[0]:
parser = TensorflowParser(args.network, args.weights,
args.dstNodeName, inputshape[0],
args.inNodeName)
else:
parser = TensorflowParser(args.network, args.weights,
args.dstNodeName)
elif args.srcFramework == 'mxnet':
assert inputshape != None
if args.weights == None:
model = (args.network, inputshape[0])
else:
import re
if re.search('.', args.weights):
args.weights = args.weights[:-7]
prefix, epoch = args.weights.rsplit('-', 1)
model = (args.network, prefix, epoch, inputshape[0])
from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
parser = MXNetParser(model)
elif args.srcFramework == 'cntk':
from mmdnn.conversion.cntk.cntk_parser import CntkParser
model = args.network or args.weights
parser = CntkParser(model)
elif args.srcFramework == 'pytorch':
assert inputshape != None
from mmdnn.conversion.pytorch.pytorch_parser import PytorchParser
model = args.network or args.weights
assert model != None
parser = PytorchParser(model, inputshape[0])
elif args.srcFramework == 'torch' or args.srcFramework == 'torch7':
from mmdnn.conversion.torch.torch_parser import TorchParser
model = args.network or args.weights
assert model != None
parser = TorchParser(model, inputshape[0])
elif args.srcFramework == 'onnx':
from mmdnn.conversion.onnx.onnx_parser import ONNXParser
parser = ONNXParser(args.network)
elif args.srcFramework == 'darknet':
from mmdnn.conversion.darknet.darknet_parser import DarknetParser
parser = DarknetParser(args.network, args.weights, args.darknetStart)
elif args.srcFramework == 'coreml':
from mmdnn.conversion.coreml.coreml_parser import CoremlParser
parser = CoremlParser(args.network)
else:
raise ValueError("Unknown framework [{}].".format(args.srcFramework))
parser.run(args.dstPath)
return 0
def main():
loaded_model_ml = _MLModel('result/posenet257.mlmodel')
coremlParser = CoremlParser(loaded_model_ml)
coremlParser.run('result/posenet257_ir')