Script started on 2023-08-25 15:14:31-04:00 [TERM="xterm-256color" TTY="/dev/pts/10" COLUMNS="170" LINES="24"] groups: cannot find name for group ID 1013 [?2004h(base) ]0;baetes01@cbiapplpdcdvm01: /usr/local/MATLAB/R2023a/bin/glnxa64baetes01@cbiapplpdcdvm01:/usr/local/MATLAB/R2023a/bin/glnxa64$ exitconda create -n pyapetnet python~=3.10 pip[1@d[1@e[1@b[1@u[1@g [?2004l Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done Solving environment: / - \ | done ==> WARNING: A newer version of conda exists. <== current version: 23.7.2 latest version: 23.7.3 Please update conda by running $ conda update -n base -c conda-forge conda Or to minimize the number of packages updated during conda update use conda install conda=23.7.3 ## Package Plan ## environment location: /home/baetes01/anaconda3/envs/pyapetnetdebug added / updated specs: - pip - python~=3.10 The following packages will be downloaded: package | build ---------------------------|----------------- libsqlite-3.43.0 | h2797004_0 821 KB conda-forge setuptools-68.1.2 | pyhd8ed1ab_0 451 KB conda-forge wheel-0.41.2 | pyhd8ed1ab_0 56 KB conda-forge ------------------------------------------------------------ Total: 1.3 MB The following NEW packages will be INSTALLED: _libgcc_mutex conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge _openmp_mutex conda-forge/linux-64::_openmp_mutex-4.5-2_gnu bzip2 conda-forge/linux-64::bzip2-1.0.8-h7f98852_4 ca-certificates conda-forge/linux-64::ca-certificates-2023.7.22-hbcca054_0 ld_impl_linux-64 conda-forge/linux-64::ld_impl_linux-64-2.40-h41732ed_0 libexpat conda-forge/linux-64::libexpat-2.5.0-hcb278e6_1 libffi conda-forge/linux-64::libffi-3.4.2-h7f98852_5 libgcc-ng conda-forge/linux-64::libgcc-ng-13.1.0-he5830b7_0 libgomp conda-forge/linux-64::libgomp-13.1.0-he5830b7_0 libnsl conda-forge/linux-64::libnsl-2.0.0-h7f98852_0 libsqlite conda-forge/linux-64::libsqlite-3.43.0-h2797004_0 libuuid conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 libzlib conda-forge/linux-64::libzlib-1.2.13-hd590300_5 ncurses conda-forge/linux-64::ncurses-6.4-hcb278e6_0 openssl conda-forge/linux-64::openssl-3.1.2-hd590300_0 pip conda-forge/noarch::pip-23.2.1-pyhd8ed1ab_0 python conda-forge/linux-64::python-3.11.4-hab00c5b_0_cpython readline conda-forge/linux-64::readline-8.2-h8228510_1 setuptools conda-forge/noarch::setuptools-68.1.2-pyhd8ed1ab_0 tk conda-forge/linux-64::tk-8.6.12-h27826a3_0 tzdata conda-forge/noarch::tzdata-2023c-h71feb2d_0 wheel conda-forge/noarch::wheel-0.41.2-pyhd8ed1ab_0 xz conda-forge/linux-64::xz-5.2.6-h166bdaf_0 Proceed ([y]/n)? y Downloading and Extracting Packages wheel-0.41.2 | 56 KB | | 0% setuptools-68.1.2 | 451 KB | | 0%  libsqlite-3.43.0 | 821 KB | | 0%  setuptools-68.1.2 | 451 KB | ########################### | 21%  libsqlite-3.43.0 | 821 KB | ##4 | 2%  wheel-0.41.2 | 56 KB | ####################################1 | 28% libsqlite-3.43.0 | 821 KB | ############################################################################################################################### | 100%  libsqlite-3.43.0 | 821 KB | ############################################################################################################################### | 100%  wheel-0.41.2 | 56 KB | ############################################################################################################################### | 100% wheel-0.41.2 | 56 KB | ############################################################################################################################### | 100% setuptools-68.1.2 | 451 KB | ############################################################################################################################### | 100%  setuptools-68.1.2 | 451 KB | ############################################################################################################################### | 100%    Preparing transaction: - \ | / - \ | / - \ | / - \ | / - done Verifying transaction: | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done Executing transaction: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done # # To activate this environment, use # # $ conda activate pyapetnetdebug # # To deactivate an active environment, use # # $ conda deactivate [?2004h(base) ]0;baetes01@cbiapplpdcdvm01: /usr/local/MATLAB/R2023a/bin/glnxa64baetes01@cbiapplpdcdvm01:/usr/local/MATLAB/R2023a/bin/glnxa64$ conda activate pyapetnetdebug [?2004l [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: /usr/local/MATLAB/R2023a/bin/glnxa64baetes01@cbiapplpdcdvm01:/usr/local/MATLAB/R2023a/bin/glnxa64$ cd ~ [?2004l [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~baetes01@cbiapplpdcdvm01:~$ ppip install pyapetnet [?2004l Collecting pyapetnet Downloading pyapetnet-1.5.1-py3-none-any.whl (11.6 MB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/11.6 MB ? eta -:--:--  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.3/11.6 MB 10.0 MB/s eta 0:00:02  ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/11.6 MB 34.6 MB/s eta 0:00:01  ━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.1/11.6 MB 37.1 MB/s eta 0:00:01  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━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 8.4/11.6 MB 16.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 8.4/11.6 MB 16.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 10.5/11.6 MB 16.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 11.6/11.6 MB 10.1 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 11.6/11.6 MB 10.1 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 11.6/11.6 MB 9.4 MB/s eta 0:00:00 [?25hCollecting pymirc>=0.22 Using cached pymirc-0.29-py3-none-any.whl (52 kB) Requirement already satisfied: pydicom>=2.0 in /usr/lib/python3/dist-packages (from pyapetnet) (2.2.2) Requirement already satisfied: matplotlib>=3.1 in /usr/lib/python3/dist-packages (from pyapetnet) (3.5.1) Collecting tensorflow>=2.2 Downloading tensorflow-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (524.1 MB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/524.1 MB ? eta -:--:--  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/524.1 MB 167.0 MB/s eta 0:00:04  ╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.6/524.1 MB 158.4 MB/s eta 0:00:04  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.7/524.1 MB 155.6 MB/s eta 0:00:04  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.9/524.1 MB 123.8 MB/s eta 0:00:05  ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 22.8/524.1 MB 110.9 MB/s eta 0:00:05  ━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 25.4/524.1 MB 100.6 MB/s eta 0:00:05  ━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 31.5/524.1 MB 128.8 MB/s eta 0:00:04  ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 38.0/524.1 MB 181.9 MB/s eta 0:00:03  ━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.4/524.1 MB 182.9 MB/s eta 0:00:03  ━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.4/524.1 MB 178.7 MB/s eta 0:00:03  ━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 55.9/524.1 MB 171.7 MB/s eta 0:00:03  ━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.9/524.1 MB 179.2 MB/s eta 0:00:03  ━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 69.3/524.1 MB 188.5 MB/s eta 0:00:03  ━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 75.8/524.1 MB 191.4 MB/s eta 0:00:03  ━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 82.8/524.1 MB 193.3 MB/s eta 0:00:03  ━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 89.4/524.1 MB 196.4 MB/s eta 0:00:03  ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 96.9/524.1 MB 210.9 MB/s eta 0:00:03  ━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 102.8/524.1 MB 216.4 MB/s eta 0:00:02  ━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 109.5/524.1 MB 175.8 MB/s eta 0:00:03  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 116.4/524.1 MB 200.6 MB/s eta 0:00:03  ━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 123.0/524.1 MB 187.0 MB/s eta 0:00:03  ━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 129.0/524.1 MB 185.9 MB/s eta 0:00:03  ━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 135.3/524.1 MB 171.2 MB/s eta 0:00:03  ━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 140.4/524.1 MB 164.9 MB/s eta 0:00:03  ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 146.8/524.1 MB 186.6 MB/s eta 0:00:03  ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 150.5/524.1 MB 151.3 MB/s eta 0:00:03  ━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 154.7/524.1 MB 123.6 MB/s eta 0:00:03  ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━ 161.6/524.1 MB 190.6 MB/s eta 0:00:02  ━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━ 167.4/524.1 MB 180.6 MB/s eta 0:00:02  ━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━ 173.4/524.1 MB 165.8 MB/s eta 0:00:03  ━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━ 177.0/524.1 MB 134.2 MB/s eta 0:00:03  ━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━ 182.0/524.1 MB 128.1 MB/s eta 0:00:03  ━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━ 189.2/524.1 MB 171.5 MB/s eta 0:00:02  ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━ 196.2/524.1 MB 204.4 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━ 199.2/524.1 MB 194.3 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━ 205.5/524.1 MB 135.9 MB/s eta 0:00:03  ━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━ 210.8/524.1 MB 165.7 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 217.9/524.1 MB 176.8 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━ 223.2/524.1 MB 173.0 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━ 230.1/524.1 MB 184.2 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 236.7/524.1 MB 191.0 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━ 242.6/524.1 MB 176.0 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━ 250.3/524.1 MB 202.2 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━ 252.7/524.1 MB 215.4 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━ 259.3/524.1 MB 135.0 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 264.2/524.1 MB 167.6 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━ 271.6/524.1 MB 169.9 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━ 279.8/524.1 MB 238.8 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 285.0/524.1 MB 182.7 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━ 292.3/524.1 MB 205.2 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 298.8/524.1 MB 226.9 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━ 304.5/524.1 MB 166.7 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━ 310.8/524.1 MB 185.5 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━ 316.4/524.1 MB 163.4 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ 321.6/524.1 MB 153.6 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 328.6/524.1 MB 193.1 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 334.1/524.1 MB 173.8 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 339.0/524.1 MB 151.6 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━ 345.0/524.1 MB 173.8 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 352.3/524.1 MB 174.6 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━ 357.5/524.1 MB 169.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━ 363.2/524.1 MB 173.6 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 369.0/524.1 MB 168.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 375.2/524.1 MB 172.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 381.7/524.1 MB 188.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━ 386.0/524.1 MB 152.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━ 390.6/524.1 MB 130.7 MB/s eta 0:00:02  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 397.4/524.1 MB 158.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 403.1/524.1 MB 172.8 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━ 409.0/524.1 MB 167.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 414.5/524.1 MB 162.6 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━ 417.3/524.1 MB 156.1 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━ 422.8/524.1 MB 122.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 428.7/524.1 MB 179.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━ 435.7/524.1 MB 193.7 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 441.7/524.1 MB 184.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━ 448.5/524.1 MB 180.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 454.7/524.1 MB 184.8 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 460.1/524.1 MB 163.9 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 466.8/524.1 MB 170.2 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 473.3/524.1 MB 187.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 479.6/524.1 MB 181.6 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 485.7/524.1 MB 175.9 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 491.9/524.1 MB 176.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 498.0/524.1 MB 179.6 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 504.6/524.1 MB 184.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 510.5/524.1 MB 179.9 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 513.1/524.1 MB 128.9 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 520.0/524.1 MB 137.0 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 524.1/524.1 MB 198.5 MB/s eta 0:00:01  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tensorflow_io_gcs_filesystem-0.33.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.4 MB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/2.4 MB ? eta -:--:--  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 2.4/2.4 MB 135.3 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 38.5 MB/s eta 0:00:00 [?25hCollecting grpcio<2.0,>=1.24.3 Downloading grpcio-1.57.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.3 MB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/5.3 MB ? eta -:--:--  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 5.3/5.3 MB 237.8 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 5.3/5.3 MB 237.8 MB/s eta 0:00:01  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.3/5.3 MB 70.3 MB/s eta 0:00:00 [?25hCollecting tensorflow-estimator<2.14,>=2.13.0 Using cached tensorflow_estimator-2.13.0-py2.py3-none-any.whl (440 kB) Collecting wrapt>=1.11.0 Downloading wrapt-1.15.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (78 kB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/78.4 KB ? eta -:--:--  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 78.4/78.4 KB 5.0 MB/s eta 0:00:00 [?25hCollecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 Downloading protobuf-4.24.1-cp37-abi3-manylinux2014_x86_64.whl (311 kB) [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/311.4 KB ? eta -:--:--  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 311.4/311.4 KB 17.0 MB/s eta 0:00:00 [?25hRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/lib/python3/dist-packages (from astunparse>=1.6.0->tensorflow>=2.2->pyapetnet) (0.37.1) Collecting llvmlite<0.41,>=0.40.0dev0 Using cached llvmlite-0.40.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (42.1 MB) Collecting imageio>=2.27 Using cached imageio-2.31.1-py3-none-any.whl (313 kB) Collecting networkx>=2.8 Using cached 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Using cached werkzeug-2.3.7-py3-none-any.whl (242 kB) Collecting markdown>=2.6.8 Using cached Markdown-3.4.4-py3-none-any.whl (94 kB) Collecting cachetools<6.0,>=2.0.0 Using cached cachetools-5.3.1-py3-none-any.whl (9.3 kB) Collecting rsa<5,>=3.1.4 Using cached rsa-4.9-py3-none-any.whl (34 kB) Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/lib/python3/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=2.2->pyapetnet) (0.2.1) Requirement already satisfied: urllib3<2.0 in /usr/lib/python3/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=2.2->pyapetnet) (1.26.5) Collecting requests-oauthlib>=0.7.0 Using cached requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB) Collecting MarkupSafe>=2.1.1 Downloading MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB) Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow>=2.2->pyapetnet) (3.2.0) Requirement already satisfied: pyasn1>=0.1.3 in /usr/lib/python3/dist-packages (from rsa<5,>=3.1.4->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=2.2->pyapetnet) (0.4.8) Installing collected packages: SimpleITK, libclang, flatbuffers, wrapt, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, rsa, requests-oauthlib, protobuf, numpy, networkx, MarkupSafe, markdown, llvmlite, lazy_loader, keras, grpcio, google-pasta, gast, cachetools, astunparse, absl-py, werkzeug, tifffile, PyWavelets, opt-einsum, numba, imageio, h5py, google-auth, scikit-image, google-auth-oauthlib, tensorboard, pymirc, tensorflow, pyapetnet Attempting uninstall: protobuf Found existing installation: protobuf 3.12.4 Not uninstalling protobuf at /usr/lib/python3/dist-packages, outside environment /usr Can't uninstall 'protobuf'. No files were found to uninstall. Attempting uninstall: numpy Found existing installation: numpy 1.21.5 Not uninstalling numpy at /usr/lib/python3/dist-packages, outside environment /usr Can't uninstall 'numpy'. No files were found to uninstall. Attempting uninstall: MarkupSafe Found existing installation: MarkupSafe 2.0.1 Not uninstalling markupsafe at /usr/lib/python3/dist-packages, outside environment /usr Can't uninstall 'MarkupSafe'. No files were found to uninstall. Attempting uninstall: gast Found existing installation: gast 0.5.2 Not uninstalling gast at /usr/lib/python3/dist-packages, outside environment /usr Can't uninstall 'gast'. No files were found to uninstall. Successfully installed MarkupSafe-2.1.3 PyWavelets-1.4.1 SimpleITK-2.2.1 absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.1 flatbuffers-23.5.26 gast-0.4.0 google-auth-2.22.0 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.57.0 h5py-3.9.0 imageio-2.31.1 keras-2.13.1 lazy_loader-0.3 libclang-16.0.6 llvmlite-0.40.1 markdown-3.4.4 networkx-3.1 numba-0.57.1 numpy-1.24.3 opt-einsum-3.3.0 protobuf-4.24.1 pyapetnet-1.5.1 pymirc-0.29 requests-oauthlib-1.3.1 rsa-4.9 scikit-image-0.21.0 tensorboard-2.13.0 tensorboard-data-server-0.7.1 tensorflow-2.13.0 tensorflow-estimator-2.13.0 tensorflow-io-gcs-filesystem-0.33.0 termcolor-2.3.0 tifffile-2023.8.12 typing-extensions-4.5.0 werkzeug-2.3.7 wrapt-1.15.0 [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~baetes01@cbiapplpdcdvm01:~$ python [?2004l Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import pyapetnet >>> print(pyatpetnet.__version_)__) Traceback (most recent call last): File "", line 1, in NameError: name 'pyatpetnet' is not defined. Did you mean: 'pyapetnet'? >>> print(pyatpetnet.__version__)ptpetnet.__version__) etpetnet.__version__) petnet.__version__) etnet.__version__) tnet.__version__)  1.5.1 >>> print(pyapetnet.__version__)__)__)__)__)__)__)__)f__)i__)l__)e__) /usr/local/lib/python3.10/dist-packages/pyapetnet/__init__.py >>> quit*(()t() [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~baetes01@cbiapplpdcdvm01:~$ git clone git@gituhub.com:gschramm/pyapetnet.git pyepetnapyapetnetdebug [?2004l Cloning into 'pyapetnetdebug'... remote: Enumerating objects: 1708, done. remote: Counting objects: 0% (1/240) remote: Counting objects: 1% (3/240) remote: Counting objects: 2% (5/240) remote: Counting objects: 3% (8/240) remote: Counting objects: 4% (10/240) remote: Counting objects: 5% (12/240) remote: Counting objects: 6% (15/240) remote: Counting objects: 7% (17/240) remote: Counting objects: 8% (20/240) remote: Counting objects: 9% (22/240) remote: Counting objects: 10% (24/240) remote: Counting objects: 11% 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objects: 87% (209/240) remote: Counting objects: 88% (212/240) remote: Counting objects: 89% (214/240) remote: Counting objects: 90% (216/240) remote: Counting objects: 91% (219/240) remote: Counting objects: 92% (221/240) remote: Counting objects: 93% (224/240) remote: Counting objects: 94% (226/240) remote: Counting objects: 95% (228/240) remote: Counting objects: 96% (231/240) remote: Counting objects: 97% (233/240) remote: Counting objects: 98% (236/240) remote: Counting objects: 99% (238/240) remote: Counting objects: 100% (240/240) remote: Counting objects: 100% (240/240), done. remote: Compressing objects: 1% (1/92) remote: Compressing objects: 2% (2/92) remote: Compressing objects: 3% (3/92) remote: Compressing objects: 4% (4/92) remote: Compressing objects: 5% (5/92) remote: Compressing objects: 6% (6/92) remote: Compressing objects: 7% (7/92) remote: Compressing objects: 8% (8/92) remote: Compressing objects: 9% (9/92) remote: Compressing objects: 10% (10/92) remote: Compressing objects: 11% (11/92) remote: Compressing objects: 13% (12/92) remote: Compressing objects: 14% (13/92) remote: Compressing objects: 15% (14/92) remote: Compressing objects: 16% (15/92) remote: Compressing objects: 17% (16/92) remote: Compressing objects: 18% (17/92) remote: Compressing objects: 19% (18/92) remote: Compressing objects: 20% (19/92) remote: Compressing objects: 21% (20/92) remote: Compressing objects: 22% (21/92) remote: Compressing objects: 23% (22/92) remote: Compressing objects: 25% (23/92) remote: Compressing objects: 26% (24/92) remote: Compressing objects: 27% (25/92) remote: Compressing objects: 28% (26/92) remote: Compressing objects: 29% (27/92) remote: Compressing objects: 30% (28/92) remote: Compressing objects: 31% (29/92) remote: Compressing objects: 32% (30/92) remote: Compressing objects: 33% (31/92) remote: Compressing objects: 34% (32/92) remote: Compressing objects: 35% (33/92) remote: Compressing objects: 36% (34/92) remote: Compressing objects: 38% (35/92) remote: Compressing objects: 39% (36/92) remote: Compressing objects: 40% (37/92) remote: Compressing objects: 41% (38/92) remote: Compressing objects: 42% (39/92) remote: Compressing objects: 43% (40/92) remote: Compressing objects: 44% (41/92) remote: Compressing objects: 45% (42/92) remote: Compressing objects: 46% (43/92) remote: Compressing objects: 47% (44/92) remote: Compressing objects: 48% (45/92) remote: Compressing objects: 50% (46/92) remote: Compressing objects: 51% (47/92) remote: Compressing objects: 52% (48/92) remote: Compressing objects: 53% (49/92) remote: Compressing objects: 54% (50/92) remote: Compressing objects: 55% (51/92) remote: Compressing objects: 56% (52/92) remote: Compressing objects: 57% (53/92) remote: Compressing objects: 58% (54/92) remote: Compressing objects: 59% (55/92) remote: Compressing objects: 60% (56/92) remote: Compressing objects: 61% (57/92) remote: Compressing objects: 63% (58/92) remote: Compressing objects: 64% (59/92) remote: Compressing objects: 65% (60/92) remote: Compressing objects: 66% (61/92) remote: Compressing objects: 67% (62/92) remote: Compressing objects: 68% (63/92) remote: Compressing objects: 69% (64/92) remote: Compressing objects: 70% (65/92) remote: Compressing objects: 71% (66/92) remote: Compressing objects: 72% (67/92) remote: Compressing objects: 73% (68/92) remote: Compressing objects: 75% (69/92) remote: Compressing objects: 76% (70/92) remote: Compressing objects: 77% (71/92) remote: Compressing objects: 78% (72/92) remote: Compressing objects: 79% (73/92) remote: Compressing objects: 80% (74/92) remote: Compressing objects: 81% (75/92) remote: Compressing objects: 82% (76/92) remote: Compressing objects: 83% (77/92) remote: Compressing objects: 84% (78/92) remote: Compressing objects: 85% (79/92) remote: Compressing objects: 86% (80/92) remote: Compressing objects: 88% (81/92) remote: Compressing objects: 89% (82/92) remote: Compressing objects: 90% (83/92) remote: Compressing objects: 91% (84/92) remote: Compressing objects: 92% (85/92) remote: Compressing objects: 93% (86/92) remote: Compressing objects: 94% (87/92) remote: Compressing objects: 95% (88/92) remote: Compressing objects: 96% (89/92) remote: Compressing objects: 97% (90/92) remote: Compressing objects: 98% (91/92) remote: Compressing objects: 100% (92/92) remote: Compressing objects: 100% (92/92), done. Receiving objects: 0% (1/1708) Receiving objects: 1% (18/1708) Receiving objects: 2% (35/1708) Receiving objects: 3% (52/1708) Receiving objects: 4% (69/1708) Receiving objects: 5% (86/1708) Receiving objects: 6% (103/1708) Receiving objects: 7% (120/1708) Receiving objects: 8% (137/1708) Receiving objects: 9% (154/1708) Receiving objects: 10% (171/1708) Receiving objects: 11% (188/1708) Receiving objects: 12% (205/1708) Receiving objects: 13% (223/1708) Receiving objects: 14% (240/1708) Receiving objects: 15% (257/1708) Receiving objects: 16% (274/1708) Receiving objects: 17% (291/1708) Receiving objects: 18% (308/1708) Receiving objects: 19% (325/1708) Receiving objects: 20% (342/1708) Receiving objects: 21% (359/1708) Receiving objects: 22% (376/1708) Receiving objects: 23% (393/1708) Receiving objects: 24% (410/1708) Receiving objects: 25% (427/1708) Receiving objects: 26% (445/1708) Receiving objects: 27% (462/1708) Receiving objects: 28% (479/1708) Receiving objects: 29% (496/1708) Receiving objects: 30% (513/1708) Receiving objects: 31% (530/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 32% (547/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 33% (564/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 34% (581/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 35% (598/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 36% (615/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 37% (632/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 38% (650/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 39% (667/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 40% (684/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 41% (701/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 42% (718/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 43% (735/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 44% (752/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 45% (769/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 46% (786/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 47% (803/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 48% (820/1708), 18.79 MiB | 37.56 MiB/s Receiving objects: 48% (827/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 49% (837/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 50% (854/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 51% (872/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 52% (889/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 53% (906/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 54% (923/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 55% (940/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 56% (957/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 57% (974/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 58% (991/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 59% (1008/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 60% (1025/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 61% (1042/1708), 43.21 MiB | 42.78 MiB/s Receiving objects: 61% (1049/1708), 95.39 MiB | 47.46 MiB/s Receiving objects: 62% (1059/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 63% (1077/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 64% (1094/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 65% (1111/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 66% (1128/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 67% (1145/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 68% (1162/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 69% (1179/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 70% (1196/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 71% (1213/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 72% (1230/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 73% (1247/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 74% (1264/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 75% (1281/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 76% (1299/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 77% (1316/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 78% (1333/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 79% (1350/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 80% (1367/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 81% (1384/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 82% (1401/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 83% (1418/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 84% (1435/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 85% (1452/1708), 112.61 MiB | 44.87 MiB/s remote: Total 1708 (delta 145), reused 236 (delta 142), pack-reused 1468 Receiving objects: 86% (1469/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 87% (1486/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 88% (1504/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 89% (1521/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 90% (1538/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 91% (1555/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 92% (1572/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 93% (1589/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 94% (1606/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 95% (1623/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 96% (1640/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 97% (1657/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 98% (1674/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 99% (1691/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 100% (1708/1708), 112.61 MiB | 44.87 MiB/s Receiving objects: 100% (1708/1708), 127.24 MiB | 43.23 MiB/s, done. Resolving deltas: 0% (0/867) Resolving deltas: 1% (9/867) Resolving deltas: 2% (18/867) Resolving deltas: 3% (27/867) Resolving deltas: 4% (35/867) Resolving deltas: 5% (44/867) Resolving deltas: 6% (53/867) Resolving deltas: 7% (61/867) Resolving deltas: 8% (70/867) Resolving deltas: 9% (80/867) Resolving deltas: 10% (87/867) Resolving deltas: 11% (96/867) Resolving deltas: 12% (105/867) Resolving deltas: 13% (113/867) Resolving deltas: 14% (122/867) Resolving deltas: 15% (131/867) Resolving deltas: 16% (139/867) Resolving deltas: 17% (148/867) Resolving deltas: 18% (158/867) Resolving deltas: 19% (165/867) Resolving deltas: 20% (174/867) Resolving deltas: 21% (183/867) Resolving deltas: 22% (191/867) Resolving deltas: 23% (200/867) Resolving deltas: 24% (209/867) Resolving deltas: 25% (217/867) Resolving deltas: 26% (226/867) Resolving deltas: 27% (235/867) Resolving deltas: 28% (243/867) Resolving deltas: 29% (254/867) Resolving deltas: 30% (261/867) Resolving deltas: 31% (269/867) Resolving deltas: 32% (278/867) Resolving deltas: 33% (287/867) Resolving deltas: 34% (297/867) Resolving deltas: 35% (304/867) Resolving deltas: 36% (313/867) Resolving deltas: 37% (321/867) Resolving deltas: 38% (330/867) Resolving deltas: 39% (339/867) Resolving deltas: 40% (347/867) Resolving deltas: 41% (356/867) Resolving deltas: 42% (365/867) Resolving deltas: 43% (373/867) Resolving deltas: 44% (382/867) Resolving deltas: 45% (391/867) Resolving deltas: 46% (399/867) Resolving deltas: 47% (408/867) Resolving deltas: 48% (417/867) Resolving deltas: 49% (425/867) Resolving deltas: 50% (434/867) Resolving deltas: 51% (443/867) Resolving deltas: 52% (451/867) Resolving deltas: 53% (460/867) Resolving deltas: 54% (469/867) Resolving deltas: 55% (477/867) Resolving deltas: 56% (486/867) Resolving deltas: 57% (495/867) Resolving deltas: 58% (503/867) Resolving deltas: 59% (512/867) Resolving deltas: 60% (521/867) Resolving deltas: 61% (529/867) Resolving deltas: 62% (538/867) Resolving deltas: 63% (547/867) Resolving deltas: 64% (555/867) Resolving deltas: 65% (564/867) Resolving deltas: 66% (574/867) Resolving deltas: 67% (581/867) Resolving deltas: 68% (590/867) Resolving deltas: 69% (600/867) Resolving deltas: 70% (607/867) Resolving deltas: 71% (616/867) Resolving deltas: 72% (626/867) Resolving deltas: 73% (635/867) Resolving deltas: 74% (642/867) Resolving deltas: 75% (651/867) Resolving deltas: 76% (661/867) Resolving deltas: 77% (669/867) Resolving deltas: 78% (677/867) Resolving deltas: 79% (685/867) Resolving deltas: 80% (696/867) Resolving deltas: 81% (703/867) Resolving deltas: 82% (711/867) Resolving deltas: 83% (721/867) Resolving deltas: 84% (729/867) Resolving deltas: 85% (737/867) Resolving deltas: 86% (746/867) Resolving deltas: 87% (755/867) Resolving deltas: 88% (763/867) Resolving deltas: 89% (772/867) Resolving deltas: 90% (782/867) Resolving deltas: 91% (789/867) Resolving deltas: 92% (799/867) Resolving deltas: 93% (809/867) Resolving deltas: 94% (815/867) Resolving deltas: 95% (824/867) Resolving deltas: 96% (833/867) Resolving deltas: 97% (841/867) Resolving deltas: 98% (850/867) Resolving deltas: 99% (859/867) Resolving deltas: 100% (867/867) Resolving deltas: 100% (867/867), done. Updating files: 4% (26/603) Updating files: 5% (31/603) Updating files: 6% (37/603) Updating files: 7% (43/603) Updating files: 8% (49/603) Updating files: 9% (55/603) Updating files: 10% (61/603) Updating files: 11% (67/603) Updating files: 12% (73/603) Updating files: 13% (79/603) Updating files: 14% (85/603) Updating files: 15% (91/603) Updating files: 16% (97/603) Updating files: 17% (103/603) Updating files: 18% (109/603) Updating files: 19% (115/603) Updating files: 20% (121/603) Updating files: 21% (127/603) Updating files: 22% (133/603) Updating files: 23% (139/603) Updating files: 24% (145/603) Updating files: 25% (151/603) Updating files: 26% (157/603) Updating files: 27% (163/603) Updating files: 28% (169/603) Updating files: 29% (175/603) Updating files: 30% (181/603) Updating files: 31% (187/603) Updating files: 32% (193/603) Updating files: 33% (199/603) Updating files: 34% (206/603) Updating files: 35% (212/603) Updating files: 36% (218/603) Updating files: 37% (224/603) Updating files: 38% (230/603) Updating files: 39% (236/603) Updating files: 40% (242/603) Updating files: 41% (248/603) Updating files: 41% (252/603) Updating files: 42% (254/603) Updating files: 43% (260/603) Updating files: 44% (266/603) Updating files: 45% (272/603) Updating files: 46% (278/603) Updating files: 47% (284/603) Updating files: 48% (290/603) Updating files: 49% (296/603) Updating files: 50% (302/603) Updating files: 51% (308/603) Updating files: 52% (314/603) Updating files: 52% (317/603) Updating files: 53% (320/603) Updating files: 54% (326/603) Updating files: 55% (332/603) Updating files: 56% (338/603) Updating files: 57% (344/603) Updating files: 58% (350/603) Updating files: 59% (356/603) Updating files: 60% (362/603) Updating files: 61% (368/603) Updating files: 62% (374/603) Updating files: 63% (380/603) Updating files: 64% (386/603) Updating files: 65% (392/603) Updating files: 66% (398/603) Updating files: 67% (405/603) Updating files: 68% (411/603) Updating files: 69% (417/603) Updating files: 70% (423/603) Updating files: 71% (429/603) Updating files: 71% (432/603) Updating files: 72% (435/603) Updating files: 73% (441/603) Updating files: 74% (447/603) Updating files: 75% (453/603) Updating files: 76% (459/603) Updating files: 77% (465/603) Updating files: 78% (471/603) Updating files: 79% (477/603) Updating files: 80% (483/603) Updating files: 81% (489/603) Updating files: 82% (495/603) Updating files: 83% (501/603) Updating files: 84% (507/603) Updating files: 85% (513/603) Updating files: 86% (519/603) Updating files: 87% (525/603) Updating files: 88% (531/603) Updating files: 89% (537/603) Updating files: 90% (543/603) Updating files: 91% (549/603) Updating files: 91% (552/603) Updating files: 92% (555/603) Updating files: 93% (561/603) Updating files: 94% (567/603) Updating files: 95% (573/603) Updating files: 96% (579/603) Updating files: 97% (585/603) Updating files: 98% (591/603) Updating files: 99% (597/603) Updating files: 100% (603/603) Updating files: 100% (603/603), done. [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~baetes01@cbiapplpdcdvm01:~$ cd pyapetnetdebug/demo_data/ [?2004l [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~/pyapetnetdebug/demo_databaetes01@cbiapplpdcdvm01:~/pyapetnetdebug/demo_data$ ls [?2004l brainweb_06_osem_dcm brainweb_06_osem.nii brainweb_06_t1_dcm brainweb_06_t1.nii misalign_pet.py [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~/pyapetnetdebug/demo_databaetes01@cbiapplpdcdvm01:~/pyapetnetdebug/demo_data$ pyapetnet_proedict_from_nifti brainweb_06_osem.nii brainweb_06_t1,.nii ni fdg_pe21 --show [?2004l 2023-08-25 15:24:20.751856: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-08-25 15:24:20.810739: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-08-25 15:24:21.870357: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT Traceback (most recent call last): File "/usr/local/bin/pyapetnet_predict_from_nifti", line 8, in sys.exit(main()) File "/usr/local/lib/python3.10/dist-packages/pyapetnet/predict_from_nifti.py", line 93, in main model = tf.keras.models.load_model(os.path.join(model_path, model_name), File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py", line 238, in load_model return legacy_sm_saving_lib.load_model( File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/save.py", line 234, in load_model raise IOError( OSError: No file or directory found at /usr/local/lib/python3.10/dist-packages/pyapetnet/trained_models/fdg_pe21 [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~/pyapetnetdebug/demo_databaetes01@cbiapplpdcdvm01:~/pyapetnetdebug/demo_data$ pip install tensorTensorRT [?2004l Collecting TensorRT Downloading tensorrt-8.6.1.post1.tar.gz (18 kB) Preparing metadata (setup.py) ... [?25l- done [?25hBuilding wheels for collected packages: TensorRT Building wheel for TensorRT (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | done [?25h Created wheel for TensorRT: filename=tensorrt-8.6.1.post1-py2.py3-none-any.whl size=17299 sha256=850b0352689ea1bdf90d8e2b51a3a6d7b5526f6e441fb5f3ff53397c7390548b Stored in directory: /home/baetes01/.cache/pip/wheels/f4/c8/0e/b79b08e45752491b9acfdbd69e8a609e8b2ed7640dda5a3e59 Successfully built TensorRT Installing collected packages: TensorRT Successfully installed TensorRT-8.6.1.post1 [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~/pyapetnetdebug/demo_databaetes01@cbiapplpdcdvm01:~/pyapetnetdebug/demo_data$ pip install TensorRTyapetnet_predict_from_nifti brainweb_06_osem.nii brainweb_06_t1.nii fdg_pe21 --show --show --show --show --show --show --show --show --showS2_osem_b10_fdg_pe2i --showS2_osem_b10_fdg_pe2i [?2004l 2023-08-25 15:27:34.645140: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-08-25 15:27:34.696036: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-08-25 15:27:35.637625: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. 2023-08-25 15:27:36.237695: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected /usr/local/lib/python3.10/dist-packages/keras/src/layers/core/lambda_layer.py:327: UserWarning: tensorflow.python.keras.utils.multi_gpu_utils is not loaded, but a Lambda layer uses it. It may cause errors. function = cls._parse_function_from_config( Traceback (most recent call last): File "/usr/local/bin/pyapetnet_predict_from_nifti", line 8, in sys.exit(main()) File "/usr/local/lib/python3.10/dist-packages/pyapetnet/predict_from_nifti.py", line 124, in main pred = model.predict(x).squeeze() File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error: Detected at node 'functional_3/functional_1/batchnorm_ind_0_0/FusedBatchNormV3' defined at (most recent call last): File "/usr/local/bin/pyapetnet_predict_from_nifti", line 8, in sys.exit(main()) File "/usr/local/lib/python3.10/dist-packages/pyapetnet/predict_from_nifti.py", line 124, in main pred = model.predict(x).squeeze() File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 2554, in predict tmp_batch_outputs = self.predict_function(iterator) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 2341, in predict_function return step_function(self, iterator) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 2327, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 2315, in run_step outputs = model.predict_step(data) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 2283, in predict_step return self(x, training=False) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 569, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py", line 1150, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py", line 512, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 569, in __call__ return super().__call__(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py", line 1150, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py", line 512, in call return self._run_internal_graph(inputs, training=training, mask=mask) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py", line 1150, in __call__ outputs = call_fn(inputs, *args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/normalization/batch_normalization.py", line 597, in call outputs = self._fused_batch_norm( File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/normalization/batch_normalization.py", line 990, in _fused_batch_norm output, mean, variance = control_flow_util.smart_cond( File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/control_flow_util.py", line 108, in smart_cond return tf.__internal__.smart_cond.smart_cond( File "/usr/local/lib/python3.10/dist-packages/keras/src/layers/normalization/batch_normalization.py", line 979, in _fused_batch_norm_inference return tf.compat.v1.nn.fused_batch_norm( Node: 'functional_3/functional_1/batchnorm_ind_0_0/FusedBatchNormV3' scale must have the same number of elements as the channels of x, got 15 and 1 [[{{node functional_3/functional_1/batchnorm_ind_0_0/FusedBatchNormV3}}]] [Op:__inference_predict_function_14236] [?2004h(pyapetnetdebug) ]0;baetes01@cbiapplpdcdvm01: ~/pyapetnetdebug/demo_databaetes01@cbiapplpdcdvm01:~/pyapetnetdebug/demo_data$ exit [?2004l exit Script done on 2023-08-25 15:28:43-04:00 [COMMAND_EXIT_CODE="127"]