Convert ONNX models into Apple Core ML format
- 安裝onnx-coreml
/// 如果報錯 使用python3 因爲你的電腦可能有兩個版本的python mac自帶的python的是2
pip install torchvision onnx-coreml
/// 或者
pip3 install torchvision onnx-coreml
- 創建 onnx_to_coreml2.py
import sys
from onnx import onnx_pb
from onnx_coreml import convert
model_in = sys.argv[1]
model_out = sys.argv[2]
"""
IMAGE_NET_MEAN = [0.485, 0.456, 0.406]
IMAGE_NET_STD = [0.229, 0.224, 0.225]
"""
scale = 1.0 / (1.0 * 255.0)
args = dict(is_bgr=False, red_bias = -(0 * 255.0) * scale , green_bias = -(0 * 255.0) * scale , blue_bia = -(0 * 255.0) * scale, image_scale = scale)
model_file = open(model_in, 'rb')
model_proto = onnx_pb.ModelProto()
model_proto.ParseFromString(model_file.read())
coreml_model = convert(model_proto,image_input_names = ['input'],preprocessing_args = args, minimum_ios_deployment_target='13')
coreml_model.save(model_out)
需要注意的是在做模型轉換的時候 需要設置 image_input_names = [‘input’] 這樣 mlmodel 纔會識別是圖片而不是數組 不然會報錯 還需要注意歸一化處理和 通道顏色值偏差
- 執行腳本
/// python onnx_to_coreml2.py inputfileName.onnx outputfileName.mlmodel
/// 報錯的話 直接使用python3
python3 onnx_to_coreml2.py inputfileName.onnx outputfileName.mlmodel
- 結果
demon@Demon Coreml % python3 /Users/demon/Desktop/Coreml/onnx_to_coreml2.py nima_mobilenetv2-simple.onnx dezhi_nima.mlmodel
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Translation to CoreML spec completed. Now compiling the CoreML model.
Model Compilation done.
需要注意的是 在輸入輸出的時候 你可能需要查看 .onnx .mlmodel等輸入輸出的變量等可以使用
netron 這個軟件 如圖
