Open jmcdonal opened 1 month ago
Hi,
Thanks for reporting. I have been using tensorflow2.5 to test everything. If you can roll back to a older version, that would be great.
We are working on reproducing your issue and update IsoNet so that it can matches latest versions of dependencies.
Hi, I'm trying to help some users use the isonet.py program. We are running on a Rocky Linux 9 platform (RHEL linux 9 clone). The user is trying to run a refine job and we're seeing errors like the ones below. In a naive google search looking at the issue it seems that some of the loss functions need to be serialized unless I should be using a different package version (below is the full list of packages in the conda environment). Please advise, Jeff
$ isonet.py refine ./subtomo.star --gpuID 0 --preprocessing_ncpus 16 --iterations 4 --noise_start_iter 10,15,20,25 --noise_level 0.05,0.1,0.15,0.2 07-10 15:44:31, INFO
Isonet starts refining
07-10 15:44:34, INFO Start Iteration1! 07-10 15:44:34, WARNING The results folder already exists
The old results folder will be renamed (to results~) /panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument
alpha
is deprecated. Usenegative_slope
instead. warnings.warn( 07-10 15:44:36, WARNING You are saving your model as an HDF5 file viamodel.save()
orkeras.saving.save_model(model)
. This file format is considered legacy. We recommend using instead the native Keras format, e.g.model.save('my_model.keras')
orkeras.saving.save_model(model, 'my_model.keras')
. 07-10 15:44:39, INFO Noise Level:0.0 07-10 15:45:22, INFO Done preparing subtomograms! 07-10 15:45:22, INFO Start training! 07-10 15:45:23, ERROR Traceback (most recent call last): File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/IsoNet/bin/refine.py", line 128, in run history = train_data(args) #train based on init model and save new one as model_iter{num_iter}.h5 ^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/IsoNet/models/unet/train.py", line 93, in train_data history = train3D_continue('{}/model_iter{:0>2d}.h5'.format(settings.result_dir,settings.iter_count), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/IsoNet/models/unet/train.py", line 38, in train3D_continue model = load_model( model_file) ^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/saving/saving_api.py", line 189, in load_model return legacy_h5_format.load_model_from_hdf5( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/legacy/saving/legacy_h5_format.py", line 155, in load_model_from_hdf5 **saving_utils.compile_args_from_training_config( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/legacy/saving/saving_utils.py", line 143, in compile_args_from_training_config loss = _deserialize_nested_config(losses.deserialize, loss_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/legacy/saving/saving_utils.py", line 202, in _deserialize_nested_config return deserialize_fn(config) ^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/losses/init.py", line 149, in deserialize return serialization_lib.deserialize_keras_object( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py", line 575, in deserialize_keras_object return deserialize_keras_object( ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py", line 678, in deserialize_keras_object return _retrieve_class_or_fn( ^^^^^^^^^^^^^^^^^^^^^^ File "/panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo/lib/python3.12/site-packages/keras/src/saving/serialization_lib.py", line 812, in _retrieve_class_or_fn raise TypeError( TypeError: Could not locate function 'mae'. Make sure custom classes are decorated with@keras.saving.register_keras_serializable()
. Full object config: {'module': 'keras.metrics', 'class_name': 'function', 'config': 'mae', 'registered_name': 'mae'}$ conda list
packages in environment at /panfs/hisoftware/rocky9/miniconda3/py39_23.1.0/envs/tomo:
#
Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge absl-py 2.1.0 pypi_0 pypi astunparse 1.6.3 pypi_0 pypi bzip2 1.0.8 hd590300_5 conda-forge ca-certificates 2024.6.2 hbcca054_0 conda-forge certifi 2024.6.2 pypi_0 pypi charset-normalizer 3.3.2 pypi_0 pypi fire 0.6.0 pypi_0 pypi flatbuffers 24.3.25 pypi_0 pypi gast 0.6.0 pypi_0 pypi google-pasta 0.2.0 pypi_0 pypi grpcio 1.64.1 pypi_0 pypi h5py 3.11.0 pypi_0 pypi idna 3.7 pypi_0 pypi imageio 2.34.2 pypi_0 pypi keras 3.4.1 pypi_0 pypi lazy-loader 0.4 pypi_0 pypi ld_impl_linux-64 2.40 hf3520f5_7 conda-forge libclang 18.1.1 pypi_0 pypi libexpat 2.6.2 h59595ed_0 conda-forge libffi 3.4.2 h7f98852_5 conda-forge libgcc-ng 14.1.0 h77fa898_0 conda-forge libgomp 14.1.0 h77fa898_0 conda-forge libnsl 2.0.1 hd590300_0 conda-forge libsqlite 3.46.0 hde9e2c9_0 conda-forge libuuid 2.38.1 h0b41bf4_0 conda-forge libxcrypt 4.4.36 hd590300_1 conda-forge libzlib 1.3.1 h4ab18f5_1 conda-forge markdown 3.6 pypi_0 pypi markdown-it-py 3.0.0 pypi_0 pypi markupsafe 2.1.5 pypi_0 pypi mdurl 0.1.2 pypi_0 pypi ml-dtypes 0.3.2 pypi_0 pypi mrcfile 1.5.0 pypi_0 pypi namex 0.0.8 pypi_0 pypi ncurses 6.5 h59595ed_0 conda-forge networkx 3.3 pypi_0 pypi numpy 1.26.4 pypi_0 pypi openssl 3.3.1 h4ab18f5_1 conda-forge opt-einsum 3.3.0 pypi_0 pypi optree 0.11.0 pypi_0 pypi packaging 24.1 pypi_0 pypi pillow 10.4.0 pypi_0 pypi pip 24.0 pyhd8ed1ab_0 conda-forge protobuf 4.25.3 pypi_0 pypi pygments 2.18.0 pypi_0 pypi pyqt5 5.15.10 pypi_0 pypi pyqt5-qt5 5.15.14 pypi_0 pypi pyqt5-sip 12.13.0 pypi_0 pypi python 3.12.4 h194c7f8_0_cpython conda-forge readline 8.2 h8228510_1 conda-forge requests 2.32.3 pypi_0 pypi rich 13.7.1 pypi_0 pypi scikit-image 0.24.0 pypi_0 pypi scipy 1.14.0 pypi_0 pypi setuptools 70.1.1 pyhd8ed1ab_0 conda-forge six 1.16.0 pypi_0 pypi tensorboard 2.16.2 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tensorflow 2.16.1 pypi_0 pypi termcolor 2.4.0 pypi_0 pypi tifffile 2024.6.18 pypi_0 pypi tk 8.6.13 noxft_h4845f30_101 conda-forge tqdm 4.66.4 pypi_0 pypi typing-extensions 4.12.2 pypi_0 pypi tzdata 2024a h0c530f3_0 conda-forge urllib3 2.2.2 pypi_0 pypi werkzeug 3.0.3 pypi_0 pypi wheel 0.43.0 pyhd8ed1ab_1 conda-forge wrapt 1.16.0 pypi_0 pypi xz 5.2.6 h166bdaf_0 conda-forge