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#author("2022-04-12T08:49:50+00:00","default:sysosa","sysosa") #author("2023-01-27T12:15:57+00:00","default:sysosa","sysosa") 本家様 [[http://sphire.mpg.de/wiki/doku.php?id=auto_2d_class_selection>+http://sphire.mpg.de/wiki/doku.php?id=auto_2d_class_selection]] Automatic 2D class selection ***インストール [#h6b44929] まずはpyenv環境を構築する[[crYOLO#w789c07e]] その次に Cinderella 向けの仮想実行環境を作る #code(nonumber){{ [root@c ~]# conda create -n cinderella-gpu -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy==1.14.5 h5py=2.7 (h5pyのバージョンを固定しないと動かないみたい) [root@c ~]# source activate cinderella-gpu (cinderella-gpu) [root@c ~]# pip install cinderella[gpu] (cinderella-gpu) [root@c ~]# which sp_cinderella_predict.py /apps/pyenv/versions/anaconda3-2021.11/envs/cinderella-0.7.0-cpu/bin/sp_cinderella_predict.py (cinderella-gpu) [root@c ~]# (cinderella-gpu) [root@c ~]# conda deactivate [root@c ~]# }} ***EnvironmentModules [#a77b2f7e] [[EnvironmentModules]]で実行環境を整備するならmodiilefileを下記のようにする &color(red){*};&size(10){普通は「/etc/modulefiles/cinderella」かな}; #code(nonumber){{ [root@c ~]# vi /home/Common/modules/cinderella #%Module -*- tcl -*- proc ModulesHelp { } { puts stderr "\tAdds anaconda to your environment variables," } module-whatis "Adds anaconda to your environment variables" set root /Appl/pyenv/versions/anaconda3-5.3.1/envs/cinderella prepend-path PATH $root/bin [root@c ~]# }} ロード方法は「module load cinderella」 ***使い方 [#j8202fe4] 参照[[http://sphire.mpg.de/wiki/doku.php?id=auto2d_tutorial>+http://sphire.mpg.de/wiki/doku.php?id=auto2d_tutorial]] Pretrained modelが提供されているので、それを取得します。 「relion30_tutorial_precalculated_results/Class2D/job008」を使ってみた 「relion40_tutorial_precalculated_results/Class2D/job008」を使ってみた #code(nonumber){{ (cinderella) [saber@c cinderella]$ sp_cinderella_predict.py -i run_it025_classes.mrcs -w /Appl/cinderella/model_cinderella_20190708.h5 -o out/ -t 0.7 --gpu 1 (cinderella) [saber@c cinderella]$ cp ../relion40_tutorial_precalculated_results/Class2D/job008/run_it025_classes.mrcs . (cinderella) [saber@c cinderella]$ sp_cinderella_predict.py -i run_it025_classes.mrcs -w /apps/cinderella/gmodel_cinderella07_202008_N10163.h5 -o out/ -t 0.7 Using TensorFlow backend. 2019-09-13 00:40:33.505544: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-09-13 00:40:33.518586: E tensorflow/stream_executor/cuda/cuda_driver.cc:397] failed call to cuInit: CUDA_ERROR_NO_DEVICE 2019-09-13 00:40:33.519113: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:163] retrieving CUDA diagnostic information for host: c.sybyl.local 2019-09-13 00:40:33.519131: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:170] hostname: c.sybyl.local 2019-09-13 00:40:33.519178: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:194] libcuda reported version is: 418.39.0 2019-09-13 00:40:33.519230: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:198] kernel reported version is: 418.39.0 2019-09-13 00:40:33.519244: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:305] kernel version seems to match DSO: 418.39.0 Model: "model_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 75, 75, 1) 0 _________________________________________________________________ model_1 (Model) (None, 4, 4, 1024) 50646432 _________________________________________________________________ global_average_pooling2d_1 ( (None, 1024) 0 _________________________________________________________________ denseL1 (Dense) (None, 64) 65600 _________________________________________________________________ denseL2 (Dense) (None, 10) 650 _________________________________________________________________ denseL3 (Dense) (None, 1) 11 ================================================================= Total params: 50,712,693 Trainable params: 50,691,637 Non-trainable params: 21,056 _________________________________________________________________ 2019-09-13 00:40:37.687003: W tensorflow/core/framework/allocator.cc:108] Allocation of 37748736 exceeds 10% of system memory. 2019-09-13 00:40:37.687099: W tensorflow/core/framework/allocator.cc:108] Allocation of 37748736 exceeds 10% of system memory. 2019-09-13 00:40:37.728333: W tensorflow/core/framework/allocator.cc:108] Allocation of 47185920 exceeds 10% of system memory. 2019-09-13 00:40:37.808678: W tensorflow/core/framework/allocator.cc:108] Allocation of 37748736 exceeds 10% of system memory. 2019-09-13 00:40:37.826402: W tensorflow/core/framework/allocator.cc:108] Allocation of 37748736 exceeds 10% of system memory. Try to list images on run_it025_classes.mrcs 100%|????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????| 2/2 [00:01<00:00, 1.94it/s] Good classes: 2 / 50 ( 4 % ) Bad classes: 48 / 50 ( 96 % ) (cinderella) [saber@c cinderella]$ }} 「relion_display --gui」でoutputのファイルを開いてみる。 ***メモ [#d7017857] #code(nonumber){{ line 1224, in load_weights_from_hdf5_group_by_name original_keras_version = f.attrs['keras_version'].decode('utf8') AttributeError: 'str' object has no attribute 'decode' }} とエラーが発生した場合は、pythonモジュールの「h5py」が新しいのが原因。古いのにダウングレードすれば回避可能 #code(nonumber){{ pip uninstall h5py pip install h5py==2.10.0 }} あとRTX 30x0 でcinderellaを動かすには crYOLO と同じように cuda11対応tensorflow 1.xを用意する必要がある.
