MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
I am using a version of the vgg16 tensorflow model that has been converted from ONNX using onnx-tf. Within this model there are split operators, at which the converter throws an error:
Traceback (most recent call last):
File "/home/mtc/anaconda3/envs/torch-onnx/bin/mmtoir", line 10, in <module>
sys.exit(_main())
File "/home/mtc/anaconda3/envs/torch-onnx/lib/python3.6/site-packages/mmdnn/conversion/_script/convertToIR.py", line 192, in _main
ret = _convert(args)
File "/home/mtc/anaconda3/envs/torch-onnx/lib/python3.6/site-packages/mmdnn/conversion/_script/convertToIR.py", line 115, in _convert
parser.run(args.dstPath)
File "/home/mtc/anaconda3/envs/torch-onnx/lib/python3.6/site-packages/mmdnn/conversion/common/DataStructure/parser.py", line 22, in run
self.gen_IR()
File "/home/mtc/anaconda3/envs/torch-onnx/lib/python3.6/site-packages/mmdnn/conversion/tensorflow/tensorflow_parser.py", line 424, in gen_IR
func(current_node)
File "/home/mtc/anaconda3/envs/torch-onnx/lib/python3.6/site-packages/mmdnn/conversion/tensorflow/tensorflow_parser.py", line 548, in rename_Conv2D
self.set_weight(source_node.name, 'weights', self.ckpt_data[W.name])
KeyError: 'split/split_dim'
Does the converter currently support converting split operators?
Any help would be appreciated, thanks!
Platform: Ubuntu 16.04
Python version: 3.6.9
Source framework with version: Tensorflow 1.4.0 with CPU
Destination framework with version: PyTorch 1.1.0 with CPU
Pre-trained model path: N/A
Running scripts:
mmtoir -f tensorflow -d vgg_output -n vgg.ckpt.meta -w vgg.ckpt --dstNodeName add_16
I am using a version of the vgg16 tensorflow model that has been converted from ONNX using onnx-tf. Within this model there are split operators, at which the converter throws an error: