Semantic Segmentation Sample: no attribute 'convert_objects' Error

I am trying to run the Semantic Segmentation with Deep Learning in Knime sample. I can execute the script within the DL Python Network Creator node, but when I try to execute the node itself, I get the following error:

ERROR DL Python Network Creator 5:16 Execute failed: ‘DataFrame’ object has no attribute ‘convert_objects’
Traceback (most recent call last):
File “”, line 5, in
File “C:\Program Files\KNIME\plugins\org.knime.dl.python_4.1.0.v201909231406\py\DLPythonNetworkSpecToDataFrameConverter.py”, line 97, in get_layer_data_specs_as_data_frames
input_specs = extractor.input_specs_to_data_frame()
File “C:\Program Files\KNIME\plugins\org.knime.dl.python_4.1.0.v201909231406\py\DLPythonNetworkSpecToDataFrameConverter.py”, line 57, in input_specs_to_data_frame
return self.__layer_data_specs_to_data_frame(self._network_spec.input_specs)
File “C:\Program Files\KNIME\plugins\org.knime.dl.python_4.1.0.v201909231406\py\DLPythonNetworkSpecToDataFrameConverter.py”, line 88, in __layer_data_specs_to_data_frame
specs_with_numeric_types = specs.convert_objects(convert_numeric=True)
File “C:\Users\pvkoh.conda\envs\knime\lib\site-packages\pandas\core\generic.py”, line 5274, in getattr
return object.getattribute(self, name)
AttributeError: ‘DataFrame’ object has no attribute ‘convert_objects’

I presume this has something todo with some incompatible versions, but could not find the KNIME documentation on which versions of what packages are expected.

I am running conda on Windows 10 with python 3.6.1 packages with the versions as below. Any help how to resolve this error would be greatly appreciated.

_tflow_select 2.1.0 gpu
absl-py 0.9.0 py36_0
arrow-cpp 0.11.1 py36h8e05e8c_1
asammdf 5.14.5 py_0 conda-forge
astor 0.8.0 py36_0
attrs 19.3.0 py_0
backcall 0.1.0 py36_0
blas 1.0 mkl
boost 1.68.0 py36hf75dd32_1001 conda-forge
boost-cpp 1.68.0 h6a4c333_1000 conda-forge
ca-certificates 2019.11.28 hecc5488_0 conda-forge
cairo 1.14.12 hf171d8a_3
canmatrix 0.8 py_0 conda-forge
cchardet 2.1.5 py36h6538335_0 conda-forge
certifi 2019.11.28 py36_0 conda-forge
colorama 0.4.3 py_0
cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
cycler 0.10.0 py36h009560c_0
decorator 4.4.1 py_0
freetype 2.9.1 ha9979f8_1
future 0.18.2 py36_0
gast 0.3.3 py_0
gflags 2.2.2 ha925a31_0
glog 0.3.5 h6538335_1
grpcio 1.14.1 py36h5c4b210_0
h5py 2.10.0 py36h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 vc14hc45fdbb_0 [vc14] anaconda
importlib_metadata 1.5.0 py36_0
intel-openmp 2020.0 166
ipython 7.1.1 py36h39e3cac_0
ipython_genutils 0.2.0 py36_0
jedi 0.13.3 py36_0
joblib 0.14.1 pypi_0 pypi
jpeg 9b vc14h4d7706e_1 [vc14] anaconda
jpype1 0.6.3 py36h79cbd7a_1001 conda-forge
jsonschema 3.2.0 py36_0
jupyter_core 4.6.1 py36_0
keras-applications 1.0.8 py_0
keras-base 2.3.1 py36_0
keras-gpu 2.3.1 0
keras-preprocessing 1.1.0 py_1
kiwisolver 1.1.0 py36ha925a31_0
libboost 1.67.0 hd9e427e_4
libiconv 1.15 h1df5818_7
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.11.4 h7bd577a_0
libtiff 4.1.0 h56a325e_0
lxml 3.8.0 py36_0 anaconda
lz4-c 1.8.1.2 h2fa13f4_0
m2w64-gcc-libgfortran 5.3.0 6
m2w64-gcc-libs 5.3.0 7
m2w64-gcc-libs-core 5.3.0 7
m2w64-gmp 6.1.0 2
m2w64-libwinpthread-git 5.0.0.4634.697f757 2
markdown 3.1.1 py36_0
matplotlib 3.0.3 py36hc8f65d3_0
mkl 2020.0 166
mkl-service 2.3.0 py36hb782905_0
mkl_fft 1.0.15 py36h14836fe_0
mkl_random 1.1.0 py36h675688f_0
msys2-conda-epoch 20160418 1
natsort 7.0.1 py_0
nbformat 4.4.0 py36_0
numexpr 2.7.1 py36h25d0782_0
numpy 1.16.1 py36h19fb1c0_1
numpy-base 1.16.1 py36hc3f5095_1
olefile 0.46 py36_0
openssl 1.0.2u hfa6e2cd_0 conda-forge
pandas 1.0.1 py36h47e9c7a_0
parso 0.6.1 py_0
pickleshare 0.7.5 py36_0
pillow 5.3.0 py36hdc69c19_0
pip 20.0.2 py36_1
pixman 0.34.0 vc14h00fde18_1 [vc14] anaconda
prompt_toolkit 2.0.10 py_0
protobuf 3.11.4 py36h33f27b4_0
pyarrow 0.11.1 py36h33f27b4_0
pycairo 1.18.0 py36hee13c1a_1000 conda-forge
pygments 2.5.2 py_0
pyparsing 2.4.6 py_0
pyqt 5.9.2 py36h6538335_2
pyreadline 2.1 py36_1
pyrsistent 0.15.7 py36he774522_0
python 3.6.10 h9f7ef89_0
python-dateutil 2.7.5 py36_0
pytz 2019.3 py_0
pywin32 227 py36he774522_1
pyyaml 5.3 py36he774522_0
qt 5.9.6 vc14h1e9a669_2 [vc14] anaconda
rdkit 2018.09.1 py36h059c30f_1000 conda-forge
scikit-learn 0.22.2.post1 pypi_0 pypi
scipy 1.1.0 py36h29ff71c_2
setuptools 45.2.0 py36_0
sip 4.19.8 py36h6538335_0
six 1.14.0 py36_0
sklearn 0.0 pypi_0 pypi
snappy 1.1.7 vc14h2dea872_1 [vc14] anaconda
sqlite 3.31.1 he774522_0
tensorboard 1.14.0 py36he3c9ec2_0
tensorflow 1.14.0 gpu_py36h305fd99_0
tensorflow-base 1.14.0 gpu_py36h55fc52a_0
tensorflow-estimator 1.14.0 py_0
tensorflow-gpu 1.14.0 h0d30ee6_0
termcolor 1.1.0 py36_1
thrift-cpp 0.11.0 h3d941d7_3
tk 8.6.8 hfa6e2cd_0
tornado 6.0.3 py36he774522_3
traitlets 4.3.3 py36_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
wcwidth 0.1.8 py_0
werkzeug 1.0.0 py_0
wheel 0.34.2 py36_0
wincertstore 0.2 py36h7fe50ca_0
wrapt 1.11.2 py36he774522_0
xlrd 1.2.0 py36_0
xlsxwriter 1.2.7 py_0
xlwt 1.3.0 py36h1a4751e_0
xz 5.2.4 h2fa13f4_4
yaml 0.1.7 vc14h4cb57cf_1 [vc14] anaconda
zipp 2.2.0 py_0
zlib 1.2.11 vc14h1cdd9ab_1 [vc14] anaconda
zstd 1.3.7 h508b16e_0

Hi there @pk4thir,

There is a page with KNIME Documentation that can be pretty useful:
https://docs.knime.com/

Ther you’ll find Python Integration guide:
https://docs.knime.com/2019-12/python_installation_guide/index.html

And Deep Learning Integration guide:
https://docs.knime.com/2019-12/deep_learning_installation_guide/index.html

From quick look seems pandas version is too recent…

Br,
Ivan

3 Likes

I think so. Replacing pandas by version 0.23 should fix the error.

3 Likes

That was it!

Downgrading to 0.23 indeed fixed the error.

Thanks a lot!

3 Likes

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