def OnnxEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
original_framework = checkfrozen(original_framework)
# IR to code
converted_file = original_framework + '_onnx_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = OnnxEmitter(architecture_path, weight_path)
emitter.run(converted_file + '.py', None, 'test')
del emitter
del OnnxEmitter
# import converted model
from onnx_tf.backend import prepare
model_converted = __import__(converted_file).KitModel(weight_path)
tf_rep = prepare(model_converted)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
input_data = np.expand_dims(img, 0)
predict = tf_rep.run(input_data)[0]
del prepare
del model_converted
del tf_rep
os.remove(converted_file + '.py')
return predict
def OnnxEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
try:
from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
original_framework = checkfrozen(original_framework)
# IR to code
converted_file = original_framework + '_onnx_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = OnnxEmitter(architecture_path, weight_path)
emitter.run(converted_file + '.py', converted_file + '.npy',
'test')
del emitter
del OnnxEmitter
# import converted model
from onnx_tf.backend import prepare
model_converted = imp.load_source(
'OnnxModel',
converted_file + '.py').KitModel(converted_file + '.npy')
tf_rep = prepare(model_converted)
func = TestKit.preprocess_func[original_framework][
architecture_name]
img = func(image_path)
input_data = np.expand_dims(img, 0)
predict = tf_rep.run(input_data)[0]
return predict
except ImportError:
print(
'Please install Onnx! Or Onnx is not supported in your platform.',
file=sys.stderr)
except:
raise ValueError
finally:
del prepare
del model_converted
del tf_rep
del sys.modules['OnnxModel']
os.remove(converted_file + '.py')
os.remove(converted_file + '.npy')
def _convert(args):
if args.dstFramework == 'caffe':
from mmdnn.conversion.caffe.caffe_emitter import CaffeEmitter
if args.IRWeightPath is None:
emitter = CaffeEmitter(args.IRModelPath)
else:
assert args.dstWeightPath
emitter = CaffeEmitter((args.IRModelPath, args.IRWeightPath))
elif args.dstFramework == 'keras':
from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter
emitter = Keras2Emitter((args.IRModelPath, args.IRWeightPath))
elif args.dstFramework == 'tensorflow':
from mmdnn.conversion.tensorflow.tensorflow_emitter import TensorflowEmitter
if args.IRWeightPath is None:
# Convert network architecture only
emitter = TensorflowEmitter(args.IRModelPath)
else:
emitter = TensorflowEmitter((args.IRModelPath, args.IRWeightPath))
elif args.dstFramework == 'cntk':
from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
if args.IRWeightPath is None:
emitter = CntkEmitter(args.IRModelPath)
else:
emitter = CntkEmitter((args.IRModelPath, args.IRWeightPath))
elif args.dstFramework == 'coreml':
raise NotImplementedError("CoreML emitter is not finished yet.")
elif args.dstFramework == 'pytorch':
if not args.dstWeightPath or not args.IRWeightPath:
raise ValueError("Need to set a target weight filename.")
from mmdnn.conversion.pytorch.pytorch_emitter import PytorchEmitter
emitter = PytorchEmitter((args.IRModelPath, args.IRWeightPath))
elif args.dstFramework == 'mxnet':
from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
if args.IRWeightPath is None:
emitter = MXNetEmitter(args.IRModelPath)
else:
if args.dstWeightPath is None:
raise ValueError(
"MXNet emitter needs argument [dstWeightPath(dw)], like -dw mxnet_converted-0000.param"
)
emitter = MXNetEmitter(
(args.IRModelPath, args.IRWeightPath, args.dstWeightPath))
elif args.dstFramework == 'onnx':
from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
if args.IRWeightPath is None:
raise NotImplementedError("ONNX emitter needs IR weight file")
else:
emitter = OnnxEmitter(args.IRModelPath, args.IRWeightPath)
else:
assert False
emitter.run(args.dstModelPath, args.dstWeightPath, args.phase)
return 0
def onnx_emit(original_framework, architecture_name, architecture_path,
weight_path, test_input_path):
from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
# IR to code
converted_file = TestModels.tmpdir + original_framework + '_onnx_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
emitter = OnnxEmitter(architecture_path, weight_path)
emitter.run(converted_file + '.py', converted_file + '.npy', 'test')
del emitter
del OnnxEmitter
# import converted model
from onnx_tf.backend import prepare
model_converted = imp.load_source(
'OnnxModel',
converted_file + '.py').KitModel(converted_file + '.npy')
tf_rep = prepare(model_converted)
original_framework = checkfrozen(original_framework)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(test_input_path)
input_data = np.expand_dims(img, 0)
predict = tf_rep.run(input_data)[0]
del prepare
del model_converted
del tf_rep
del sys.modules['OnnxModel']
os.remove(converted_file + '.py')
os.remove(converted_file + '.npy')
return predict