def convertModel():
coreML_name = infile_name.get()
onnx_name = outfile_name.get()
if coreML_name == "":
status_variable.set("\t (!) Input file doesn't exist.")
return
if onnx_name == "":
status_variable.set("\t (!) Output file doesn't exist.")
return
## check coreML_name
if len(coreML_name) <= len(
".mlmodel") or coreML_name[-len(".mlmodel"):] != ".mlmodel":
status_variable.set(
"\t (!) File name error: \"" + coreML_name +
"\" is not a coreMLmodel. It's not ended with \".mlmodel\"")
return
## check file exist
if not os.path.isfile(coreML_name):
status_variable.set("\t (!) File doesn't exist: \"" + coreML_name +
"\" does not exist, please provide another one.")
return
status_variable.set(">> load model from:" + coreML_name + "...")
model_coreml = load_spec(coreML_name)
status_variable.set(">> covert model...")
model_onnx = convert_coreml(model_coreml)
status_variable.set(">> save model to:" + onnx_name + "...")
save_model(model_onnx, onnx_name)
status_variable.set(">> Convert Finished! ")
def coreml_converter(args):
# When imported, CoreML tools checks for the current version of Keras and TF and prints warnings if they are
# outside its expected range. We don't want it to import these packages (since they are big and take seconds to
# load) and we don't want to clutter the console with unrelated Keras warnings when converting from CoreML.
import sys
sys.modules['keras'] = None
import coremltools
source_model = coremltools.utils.load_spec(args.source)
onnx_model = winmltools.convert_coreml(source_model, get_opset(args.ONNXVersion), args.name)
return onnx_model
from coremltools.models.utils import load_spec
# Load model file
model_coreml = load_spec('example.mlmodel')
from winmltools import convert_coreml
# Convert it!
# The automatic code generator (mlgen) uses the name parameter to generate class names.
model_onnx = convert_coreml(model_coreml, name='ExampleModel')
from winmltools.utils import save_model
# Save the produced ONNX model in binary format
save_model(model_onnx, 'example.onnx')
from winmltools.utils import save_model
from winmltools import convert_coreml
from coremltools.models.utils import load_spec
input_coreml_model = '../webservice/pretrain_models/water_meter_yolov2_tiny/yolo-obj-416-water_yolov2_tiny_15000.mlmodel'
output_onnx_model = '../webservice/pretrain_models/water_meter_yolov2_tiny/yolo-obj-416-water_yolov2_tiny_15000_winmltools.onnx'
model_coreml = load_spec(input_coreml_model)
model_onnx = convert_coreml(
model_coreml, 8, name='yolo-obj-416-water_yolov2_tiny_15000_winmltools')
save_model(model_onnx, output_onnx_model)
from coremltools.models.utils import load_spec
from winmltools import convert_coreml
from winmltools.utils import save_model, save_text
# ml model load
model_coreml = load_spec("MobileNet.mlmodel")
# convert coreml models to onnx model
model_onnx = convert_coreml(model_coreml, name="mobilenet")
# save onnx format
save_model(model_onnx, "mobilenet.onnx")
save_text(model_onnx, "mobilenet.txt")
from coremltools.models.utils import load_spec
from winmltools import convert_coreml
from winmltools.utils import save_model
# Load model file
model_coreml = load_spec('Fruit.mlmodel')
# Convert it!
# The automatic code generator (mlgen) uses the name parameter to generate class names.
model_onnx = convert_coreml(model_coreml, name='Fruit')
# Save the produced ONNX model in binary format
save_model(model_onnx, 'Fruit.onnx')
folder_path = ml_path[0]
format = ml_path[-1].split('.')[-1]
if format == 'onnx':
model_onnx = load_model(ml_file)
else:
# Load model file
model_coreml = load_spec(ml_file)
print('ONNX model name:')
ml_name = str(input())
# Convert it!
# The automatic code generator (mlgen) uses the name parameter to generate
# class names.
model_onnx = convert_coreml(model_coreml, name=ml_name)
onnx_file_name = os.path.join(folder_path, ml_path[1].split('.')[0])
print('Convert to floating point 16? (y/n)')
need_float_16 = input()
if (need_float_16 == 'y'):
model_onnx = convert_float_to_float16(model_onnx)
# Save the produced ONNX model in binary format
onnx_path = onnx_file_name + '16bit.onnx'
save_model(model_onnx, onnx_path)
print(onnx_path)
#save_text(model_onnx, 'example.txt')
# Save as text
def main():
model_coreml = load_spec(
'D:/Project/ShuffleNet/ncnn/save1/mobilenetv1.mlmodel')
model_onnx = convert_coreml(model_coreml, 7, name='ExampleModel')
save_model(model_onnx, 'D:/Project/ShuffleNet/ncnn/save1/mobilenetv1.onnx')
from coremltools.models.utils import load_spec
from winmltools import convert_coreml
from winmltools.utils import save_model
# Load model file
model_coreml = load_spec('ContosoIT.mlmodel')
# Convert it!
# The automatic code generator (mlgen) uses the name parameter to generate class names.
model_onnx = convert_coreml(model_coreml, name='ContosoIT')
# Save the produced ONNX model in binary format
save_model(model_onnx, 'ContosoIT.onnx')
from winmltools.utils import save_model from winmltools import convert_coreml from coremltools.models.utils import load_spec input_coreml_model = '../../pretrain_models/yolov4/yolov4.mlmodel' output_onnx_model = '../../pretrain_models/yolov4/yolov4_winmltools.onnx' model_coreml = load_spec(input_coreml_model) model_onnx = convert_coreml(model_coreml, 8, name='yolov4_winmltools') save_model(model_onnx, output_onnx_model)
"\t (!) Usage: python convertCoreML.py [coreML model name] [onnx model name]"
)
print(
"\t\t * coreML_model : The name of the model you want to convert, ended with \".mlmodel\"."
)
print("\t\t * onnx_model \t: The output name of the onnx model.")
exit()
else:
coreML_name = sys.argv[1]
onnx_name = sys.argv[2]
if len(coreML_name) <= len(
".mlmodel") or coreML_name[-len(".mlmodel"):] != ".mlmodel":
print("\t (!) File name error: ", coreML_name,
"is not a coreMLmodel. It's not ended with \".mlmodel\"")
exit()
print(">> load model from:", coreML_name, "...")
from coremltools.models.utils import load_spec
model_coreml = load_spec(coreML_name)
print(">> covert model...")
from winmltools import convert_coreml
model_onnx = convert_coreml(model_coreml)
print(">> save model to:", onnx_name, "...")
from winmltools.utils import save_model
save_model(model_onnx, onnx_name)
print("Finished!")
from coremltools.models.utils import load_spec
from winmltools import convert_coreml
from winmltools.utils import save_model #, save_text
from pathlib import Path
mlmodel_list = []
for mlmodel in Path("./").glob("*.mlmodel"):
# ml model load
model_coreml = load_spec(mlmodel)
# convert coreml models to onnx model
tgt_opset = 8
model_onnx = convert_coreml(model_coreml,
tgt_opset,
name=mlmodel.stem.lower())
# save onnx format
save_model(model_onnx, f"{mlmodel.stem.lower()}.onnx")
# save_text(model_onnx, f"{mlmodel.stem.lower()}.txt")