def CoreMLEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
from coremltools.models import MLModel
original_framework = checkfrozen(original_framework)
# scale, b, g, r, BGRTranspose
prep_for_coreml = {
'inception_v3': [0.00784313771874, -1.0, -1.0, -1.0, False],
'vgg16':
[1.0, -103.939002991, -116.778999329, -123.680000305, True],
'resnet50':
[1.0, -103.939002991, -116.778999329, -123.680000305, True],
'mobilenet': [
0.0170000009239, -1.76698005199, -1.98526000977,
-2.10256004333, True
],
'tinyyolo': [0.00392156885937, 0, 0, 0, False]
}
# IR to Model
# converted_file = original_framework + '_coreml_' + architecture_name + "_converted"
# converted_file = converted_file.replace('.', '_')
# image
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
prep_list = prep_for_coreml[architecture_name]
emitter = CoreMLEmitter(architecture_path, weight_path)
model, input_name, output_name = emitter.gen_model(
input_names=None,
output_names=None,
image_input_names=image_path,
is_bgr=prep_list[4],
red_bias=prep_list[3],
green_bias=prep_list[2],
blue_bias=prep_list[1],
gray_bias=0.0,
image_scale=prep_list[0],
class_labels=None,
predicted_feature_name=None,
predicted_probabilities_output='')
input_name = str(input_name[0][0])
output_name = str(output_name[0][0])
# load model
model = MLModel(model)
# inference
coreml_input = {input_name: img}
coreml_output = model.predict(coreml_input)
prob = coreml_output[output_name]
prob = np.array(prob).squeeze()
return prob
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 CoreMLEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
from coremltools.models import MLModel
import coremltools
from PIL import Image
original_framework = checkfrozen(original_framework)
def prep_for_coreml(prename, BGRTranspose):
# The list is in RGB oder
if prename == 'Standard':
return 0.00784313725490196,-1, -1, -1
elif prename == 'ZeroCenter' :
return 1, -123.68, -116.779, -103.939
elif prename == 'Identity':
return 1, 1, 1, 1
else:
raise ValueError()
# IR to Model
# converted_file = original_framework + '_coreml_' + architecture_name + "_converted"
# converted_file = converted_file.replace('.', '_')
func = TestKit.preprocess_func[original_framework][architecture_name]
import inspect
funcstr = inspect.getsource(func)
coreml_pre = funcstr.split('(')[0].split('.')[-1]
if len(funcstr.split(',')) == 3:
BGRTranspose = bool(0)
size = int(funcstr.split('path,')[1].split(')')[0])
prep_list = prep_for_coreml(coreml_pre, BGRTranspose)
elif len(funcstr.split(',')) == 4:
BGRTranspose = funcstr.split(',')[-2].split(')')[0].strip() == str(True)
size = int(funcstr.split('path,')[1].split(',')[0])
prep_list = prep_for_coreml(coreml_pre, BGRTranspose)
elif len(funcstr.split(',')) == 11:
BGRTranspose = funcstr.split(',')[-2].split(')')[0].strip() == str(True)
size = int(funcstr.split('path,')[1].split(',')[0])
prep_list = ( float(funcstr.split(',')[2]),
float(funcstr.split(',')[3].split('[')[-1]),
float(funcstr.split(',')[4]),
float(funcstr.split(',')[5].split(']')[0])
)
emitter = CoreMLEmitter(architecture_path, weight_path)
model, input_name, output_name = emitter.gen_model(
input_names=None,
output_names=None,
image_input_names=image_path,
is_bgr=BGRTranspose,
red_bias=prep_list[1],
green_bias=prep_list[2],
blue_bias=prep_list[3],
gray_bias=0.0,
image_scale=prep_list[0],
class_labels=None,
predicted_feature_name=None,
predicted_probabilities_output=''
)
input_name = str(input_name[0][0])
output_name = str(output_name[0][0])
# load model
model = MLModel(model)
# save model
# coremltools.utils.save_spec(model.get_spec(), converted_file)
from coremltools.models.utils import macos_version
if macos_version() < (10, 13):
return None
else:
from PIL import Image as pil_image
img = pil_image.open(image_path)
img = img.resize((size, size))
# inference
coreml_input = {input_name: img}
coreml_output = model.predict(coreml_input)
prob = coreml_output[output_name]
prob = np.array(prob).squeeze()
return prob
def CoreMLEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
from coremltools.models import MLModel
original_framework = checkfrozen(original_framework)
# scale, b, g, r, BGRTranspose
CoreML_preprocess = {
'inception_v3': [0.00784313771874, -1.0, -1.0, -1.0, False],
'vgg16':
[1.0, -103.939002991, -116.778999329, -123.680000305, True],
'resnet50':
[1.0, -103.939002991, -116.778999329, -123.680000305, True],
'mobilenet': [
0.0170000009239, -1.76698005199, -1.98526000977,
-2.10256004333, True
],
'tinyyolo': [0.00392156885937, 0, 0, 0, False]
}
# IR to Model
# image
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
if original_framework == 'CoreML':
prep_list = CoreML_preprocess[architecture_name]
else:
import inspect
funcstr = inspect.getsource(func)
coreml_pre = funcstr.split('(')[0].split('.')[-1]
if len(funcstr.split(',')) == 3:
BGRTranspose = bool(0)
img_size = int(funcstr.split('path,')[1].split(')')[0])
else:
BGRTranspose = bool(funcstr.split(',')[-2].split(')')[0])
img_size = int(funcstr.split('path,')[1].split(',')[0])
prep_list = TestModels.prep_for_coreml(coreml_pre, BGRTranspose)
emitter = CoreMLEmitter(architecture_path, weight_path)
model, input_name, output_name = emitter.gen_model(
input_names=None,
output_names=None,
image_input_names=image_path,
is_bgr=prep_list[4],
red_bias=prep_list[3],
green_bias=prep_list[2],
blue_bias=prep_list[1],
gray_bias=0.0,
image_scale=prep_list[0],
class_labels=None,
predicted_feature_name=None,
predicted_probabilities_output='')
input_name = str(input_name[0][0])
output_name = str(output_name[0][0])
# load model
model = MLModel(model)
# inference
coreml_input = {input_name: img}
coreml_output = model.predict(coreml_input)
prob = coreml_output[output_name]
prob = np.array(prob).squeeze()
return prob
def CoreMLEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
original_framework = checkfrozen(original_framework)
def prep_for_coreml(prepname, BGRTranspose):
if prepname == 'Standard':
return 0.00784313725490196, -1, -1, -1
elif prepname == 'ZeroCenter' and BGRTranspose == True:
return 1, -123.68, -116.779, -103.939
elif prepname == 'ZeroCenter' and BGRTranspose == False:
return 1, -103.939, -116.779, -123.68
elif prepname == 'Identity':
return 1, 1, 1, 1
else:
raise ValueError()
# IR to Model
converted_file = original_framework + '_coreml_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
print(converted_file)
func = TestKit.preprocess_func[original_framework][architecture_name]
import inspect
funcstr = inspect.getsource(func)
coreml_pre = funcstr.split('(')[0].split('.')[-1]
if len(funcstr.split(',')) == 3:
BGRTranspose = bool(0)
img_size = int(funcstr.split('path,')[1].split(')')[0])
else:
BGRTranspose = bool(funcstr.split(',')[-2].split(')')[0])
img_size = int(funcstr.split('path,')[1].split(',')[0])
prep_list = prep_for_coreml(coreml_pre, BGRTranspose)
emitter = CoreMLEmitter(architecture_path, weight_path)
model, input_name, output_name = emitter.gen_model(
input_names=None,
output_names=None,
image_input_names=image_path,
is_bgr=BGRTranspose,
red_bias=prep_list[1],
green_bias=prep_list[2],
blue_bias=prep_list[3],
gray_bias=0.0,
image_scale=prep_list[0],
class_labels=None,
predicted_feature_name=None,
predicted_probabilities_output=''
)
import coremltools
con_model = coremltools.models.MLModel(model)
print("Model loading success.")
from coremltools.models.utils import macos_version
if macos_version() < (10, 13):
return None
else:
from PIL import Image as pil_image
img = pil_image.open(image_path)
img = img.resize((img_size, img_size))
input_data = img
coreml_inputs = {str(input_name[0][0]): input_data}
coreml_output = con_model.predict(coreml_inputs, useCPUOnly=False)
converted_predict = coreml_output[str(output_name[0][0])]
converted_predict = np.squeeze(converted_predict)
return converted_predict
def _convert(args):
if args.framework == 'caffe':
raise NotImplementedError("Destination [Caffe] is not implemented yet.")
elif args.framework == 'keras':
raise NotImplementedError("Destination [Keras] is not implemented yet.")
elif args.framework == 'tensorflow':
raise NotImplementedError("Destination [Tensorflow] is not implemented yet.")
elif args.framework == 'cntk':
raise NotImplementedError("Destination [Tensorflow] is not implemented yet.")
elif args.framework == 'coreml':
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
assert args.inputNetwork is not None
assert args.inputWeight is not None
emitter = CoreMLEmitter(args.inputNetwork, args.inputWeight)
model = emitter.gen_model(
args.inputNames,
args.outputNames,
image_input_names = set(args.imageInputNames) if args.imageInputNames else None,
is_bgr = args.isBGR,
red_bias = args.redBias,
blue_bias = args.blueBias,
green_bias = args.greenBias,
gray_bias = args.grayBias,
image_scale = args.scale,
class_labels = args.classInputPath if args.classInputPath else None,
predicted_feature_name = args.predictedFeatureName)
"""
from google.protobuf import text_format
with open(args.output+'.txt', 'w') as f:
f.write(text_format.MessageToString(model))
"""
with open(args.output, 'wb') as f:
model = model.SerializeToString()
f.write(model)
return 0
elif args.framework == '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.framework == '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.output)
return 0
def _convert(args):
if args.framework == 'caffe':
raise NotImplementedError(
"Destination [Caffe] is not implemented yet.")
elif args.framework == 'keras':
raise NotImplementedError(
"Destination [Keras] is not implemented yet.")
elif args.framework == 'tensorflow':
raise NotImplementedError(
"Destination [Tensorflow] is not implemented yet.")
elif args.framework == 'cntk':
raise NotImplementedError(
"Destination [Tensorflow] is not implemented yet.")
elif args.framework == 'coreml':
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
assert args.inputNetwork is not None
assert args.inputWeight is not None
emitter = CoreMLEmitter(args.inputNetwork, args.inputWeight)
model = emitter.gen_model(
args.inputNames,
args.outputNames,
image_input_names=set(args.imageInputNames)
if args.imageInputNames else None,
is_bgr=args.isBGR,
red_bias=args.redBias,
blue_bias=args.blueBias,
green_bias=args.greenBias,
gray_bias=args.grayBias,
image_scale=args.scale,
class_labels=args.classInputPath if args.classInputPath else None,
predicted_feature_name=args.predictedFeatureName)
"""
from google.protobuf import text_format
with open(args.output+'.txt', 'w') as f:
f.write(text_format.MessageToString(model))
"""
with open(args.output, 'wb') as f:
model = model.SerializeToString()
f.write(model)
return 0
elif args.framework == '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.framework == '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.output)
return 0
def CoreMLEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
from mmdnn.conversion.coreml.coreml_emitter import CoreMLEmitter
def prep_for_coreml(prepname, BGRTranspose):
if prepname == 'Standard' and BGRTranspose == False:
return 0.00784313725490196, -1, -1, -1
elif prepname == 'ZeroCenter' and BGRTranspose == True:
return 1, -123.68, -116.779, -103.939
elif prepname == 'ZeroCenter' and BGRTranspose == False:
return 1,-103.939,-116.779,-123.68
elif prepname == 'Identity':
return 1, 1, 1, 1
# IR to Model
converted_file = original_framework + '_coreml_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
print(converted_file)
func = TestKit.preprocess_func[original_framework][architecture_name]
import inspect
funcstr = inspect.getsource(func)
coreml_pre = funcstr.split('(')[0].split('.')[-1]
if len(funcstr.split(',')) == 3:
BGRTranspose = bool(0)
img_size = int(funcstr.split('path,')[1].split(')')[0])
else:
BGRTranspose = bool(funcstr.split(',')[-2].split(')')[0])
img_size = int(funcstr.split('path,')[1].split(',')[0])
print(BGRTranspose)
print(coreml_pre)
print(img_size)
prep_list = prep_for_coreml(coreml_pre, BGRTranspose )
print(prep_list)
# assert False
# print(func.__name__)
emitter = CoreMLEmitter(architecture_path, weight_path)
model, input_name, output_name = emitter.gen_model(
input_names=None,
output_names=None,
image_input_names=image_path,
is_bgr=BGRTranspose,
red_bias=prep_list[1],
green_bias=prep_list[2],
blue_bias=prep_list[3],
gray_bias=0.0,
image_scale=prep_list[0],
class_labels=None,
predicted_feature_name=None,
predicted_probabilities_output=''
)
import coremltools
con_model = coremltools.models.MLModel(model)
print("Model loading success.")
from PIL import Image as pil_image
img = pil_image.open(image_path)
img = img.resize((img_size, img_size))
input_data = img
coreml_inputs = {str(input_name[0][0]): input_data}
coreml_output = con_model.predict(coreml_inputs, useCPUOnly=False)
converted_predict = coreml_output[str(output_name[0][0])]
return converted_predict