def KerasEmit(original_framework, architecture_name, architecture_path, weight_path, image_path): from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter original_framework = checkfrozen(original_framework) # IR to code converted_file = original_framework + '_keras_' + architecture_name + "_converted" converted_file = converted_file.replace('.', '_') emitter = Keras2Emitter((architecture_path, weight_path)) emitter.run(converted_file + '.py', None, 'test') del emitter del Keras2Emitter # import converted model model_converted = __import__(converted_file).KitModel(weight_path) func = TestKit.preprocess_func[original_framework][architecture_name] img = func(image_path) input_data = np.expand_dims(img, 0) predict = model_converted.predict(input_data) converted_predict = np.squeeze(predict) del model_converted del sys.modules[converted_file] import keras.backend as K K.clear_session() os.remove(converted_file + '.py') return converted_predict
def KerasEmit(original_framework, architecture_name, architecture_path, weight_path, image_path): print("Testing {} from {} to Keras.".format(architecture_name, original_framework)) # IR to code emitter = Keras2Emitter((architecture_path, weight_path)) emitter.run("converted_model.py", None, 'test') del emitter # import converted model import converted_model reload_module(converted_model) model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy") func = TestKit.preprocess_func[original_framework][architecture_name] img = func(image_path) input_data = np.expand_dims(img, 0) predict = model_converted.predict(input_data) converted_predict = np.squeeze(predict) del model_converted del converted_model import keras.backend as K K.clear_session() os.remove("converted_model.py") return converted_predict
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 _convert(args): if args.dstModelFormat == 'caffe': raise NotImplementedError( "Destination [Caffe] is not implemented yet.") elif args.dstModelFormat == 'keras': from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter emitter = Keras2Emitter(args.IRModelPath) elif args.dstModelFormat == 'tensorflow': from mmdnn.conversion.tensorflow.tensorflow_emitter import TensorflowEmitter if args.IRWeightPath == None: emitter = TensorflowEmitter(args.IRModelPath) else: emitter = TensorflowEmitter((args.IRModelPath, args.IRWeightPath)) elif args.dstModelFormat == 'cntk': from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter if args.IRWeightPath == None: emitter = CntkEmitter(args.IRModelPath) else: emitter = CntkEmitter((args.IRModelPath, args.IRWeightPath)) elif args.dstModelFormat == 'coreml': raise NotImplementedError("CoreML emitter is not finished yet.") assert args.IRWeightPath != None from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter emitter = CoreMLEmitter((args.IRModelPath, args.IRWeightPath)) model = emitter.gen_model() print("Saving the CoreML model [{}].".format(args.dstModelPath + '.mlmodel')) model.save(args.dstModelPath + '.mlmodel') print("The converted CoreML model saved as [{}].".format( args.dstModelPath + '.mlmodel')) return 0 elif args.dstModelFormat == '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.dstModelFormat == 'mxnet': from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter if args.IRWeightPath == None: emitter = MXNetEmitter(args.IRModelPath) else: emitter = MXNetEmitter((args.IRModelPath, args.IRWeightPath, args.inputShape, args.dstWeightPath)) else: assert False emitter.run(args.dstModelPath, args.dstWeightPath, args.phase) return 0
def keras_emit(original_framework, architecture_name, architecture_path, weight_path, test_input_path): from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter # IR to code converted_file = TestModels.tmpdir + original_framework + '_keras_' + architecture_name + "_converted" converted_file = converted_file.replace('.', '_') emitter = Keras2Emitter((architecture_path, weight_path)) emitter.run(converted_file + '.py', None, 'test') del emitter del Keras2Emitter # import converted model model_converted = imp.load_source('KerasModel', converted_file + '.py').KitModel(weight_path) original_framework = checkfrozen(original_framework) if 'rnn' not in architecture_name: func = TestKit.preprocess_func[original_framework][ architecture_name] img = func(test_input_path(architecture_name)) input_data = np.expand_dims(img, 0) else: input_data = np.load(test_input_path(architecture_name)) predict = model_converted.predict(input_data) if original_framework == "darknet": converted_predict = None else: converted_predict = np.squeeze(predict) del model_converted del sys.modules['KerasModel'] import keras.backend as K K.clear_session() os.remove(converted_file + '.py') return converted_predict