Я пытаюсь преобразовать модель Pytorch в MLModel с помощью Onnx.
Мой код:
import torch
from onnx_coreml import convert
import coremltools
net = BiSeNet(19)
net.cuda()
net.load_state_dict(torch.load('model.pth'))
#net.eval()
dummy = torch.rand(1,3,512,512).cuda()
torch.onnx.export(net, dummy, "Model.onnx", input_names=["image"], output_names=["output"], opset_version=11)
finalModel = convert(model='Model.onnx', minimum_ios_deployment_target='12')
finalModel.save('ModelML.mlmodel')
После запуска кода сгенерирован Model.onnx, однако файл .mlmodel не генерируется. В консоли нет ошибок. Это вывод:
2020-04-15 21:49:32.367179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
WARNING:root:TensorFlow version 2.2.0-rc2 detected. Last version known to be fully compatible is 1.14.0 .
WARNING:root:Keras version 2.3.1 detected. Last version known to be fully compatible of Keras is 2.2.4 .
1.4.0
/content/drive/My Drive/Collab/fp/model.py:116: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
size_array = [int(s) for s in feat32.size()[2:]]
/content/drive/My Drive/Collab/fp/model.py:80: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
size_array = [int(s) for s in feat.size()[2:]]
/content/drive/My Drive/Collab/fp/model.py:211: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
size_array = [int(s) for s in feat.size()[2:]]
В чем может быть проблема?