From 3467e898cd1c280cc0c87f4b18218792e495b1b3 Mon Sep 17 00:00:00 2001 From: bmandracchia Date: Thu, 17 Oct 2024 19:02:26 +0200 Subject: [PATCH] updated env & tutorial1 --- bioMONAI/callbacks.py | 2 + bioMONAI/core.py | 2 + bioMONAI/data.py | 2 + bioMONAI/datasets.py | 2 + bioMONAI/io.py | 2 + bioMONAI/losses.py | 2 + bioMONAI/metrics.py | 2 + bioMONAI/nets.py | 2 + bioMONAI/transforms.py | 2 + bioMONAI/visualize.py | 2 + biomonai.yml | 12 +- biomonai2.yml | 251 --------------------------- nbs/902_tutorial_1.ipynb | 359 +++++++++++++++++++++++++++++++-------- 13 files changed, 320 insertions(+), 322 deletions(-) delete mode 100644 biomonai2.yml diff --git a/bioMONAI/callbacks.py b/bioMONAI/callbacks.py index 4d388a6..804a777 100644 --- a/bioMONAI/callbacks.py +++ b/bioMONAI/callbacks.py @@ -1,3 +1,5 @@ +"""Event handlers""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/07_callbacks.ipynb. # %% auto 0 diff --git a/bioMONAI/core.py b/bioMONAI/core.py index 9d486f7..26345d4 100644 --- a/bioMONAI/core.py +++ b/bioMONAI/core.py @@ -1,3 +1,5 @@ +"""bioMONAI core functions""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/00_core.ipynb. # %% auto 0 diff --git a/bioMONAI/data.py b/bioMONAI/data.py index b479518..0010d44 100644 --- a/bioMONAI/data.py +++ b/bioMONAI/data.py @@ -1,3 +1,5 @@ +"""Data classes and functions""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/01_data.ipynb. # %% auto 0 diff --git a/bioMONAI/datasets.py b/bioMONAI/datasets.py index 1cbd6d5..d247788 100644 --- a/bioMONAI/datasets.py +++ b/bioMONAI/datasets.py @@ -1,3 +1,5 @@ +"""Download and store datasets""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/08_datasets.ipynb. # %% auto 0 diff --git a/bioMONAI/io.py b/bioMONAI/io.py index 8a70c63..156b6c6 100644 --- a/bioMONAI/io.py +++ b/bioMONAI/io.py @@ -1,3 +1,5 @@ +"""input/output management""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_io.ipynb. # %% auto 0 diff --git a/bioMONAI/losses.py b/bioMONAI/losses.py index 1c57bae..cfc38bc 100644 --- a/bioMONAI/losses.py +++ b/bioMONAI/losses.py @@ -1,3 +1,5 @@ +"""A set of custom loss functions""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_losses.ipynb. # %% auto 0 diff --git a/bioMONAI/metrics.py b/bioMONAI/metrics.py index 100634d..ac27f53 100644 --- a/bioMONAI/metrics.py +++ b/bioMONAI/metrics.py @@ -1,3 +1,5 @@ +"""Metric tracking and analysis tools""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/06_metrics.ipynb. # %% auto 0 diff --git a/bioMONAI/nets.py b/bioMONAI/nets.py index f701611..bd6a8da 100644 --- a/bioMONAI/nets.py +++ b/bioMONAI/nets.py @@ -1,3 +1,5 @@ +"""Neural networks""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/04_nets.ipynb. # %% auto 0 diff --git a/bioMONAI/transforms.py b/bioMONAI/transforms.py index 6f89cc3..241a05f 100644 --- a/bioMONAI/transforms.py +++ b/bioMONAI/transforms.py @@ -1,3 +1,5 @@ +"""Data transformations and augmentations for 2D and 3D BioImages""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/05_transforms.ipynb. # %% auto 0 diff --git a/bioMONAI/visualize.py b/bioMONAI/visualize.py index 0f266d3..506f9b5 100644 --- a/bioMONAI/visualize.py +++ b/bioMONAI/visualize.py @@ -1,3 +1,5 @@ +"""Data visualization""" + # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/09_visualize.ipynb. # %% auto 0 diff --git a/biomonai.yml b/biomonai.yml index c1a05e3..79181e6 100644 --- a/biomonai.yml +++ b/biomonai.yml @@ -78,7 +78,6 @@ dependencies: - execnb=0.1.5=py_0 - executing=0.8.3=pyhd3eb1b0_0 - expat=2.5.0=h6a678d5_0 - - fastcore=1.5.29=py_0 - fastdownload=0.0.7=py_0 - fastprogress=1.0.3=py_0 - ffmpeg=4.3=hf484d3e_0 @@ -205,7 +204,6 @@ dependencies: - munkres=1.1.4=py_0 - murmurhash=1.0.7=py311h6a678d5_0 - mysql=5.7.24=h721c034_2 - - nbdev=2.3.13=py_0 - ncurses=6.4=h6a678d5_0 - nest-asyncio=1.5.6=py311h06a4308_0 - nettle=3.7.3=hbbd107a_1 @@ -324,14 +322,17 @@ dependencies: - distro==1.8.0 - einops==0.8.0 - elementpath==4.4.0 - - fastai==2.7.12 + - fastai==2.7.17 - fastapi==0.105.0 + - fastcore==1.7.17 - fasteners==0.19 - fastjsonschema==2.20.0 - ffmpy==0.3.1 + - fire==0.6.0 - frozenlist==1.4.1 - fsspec==2023.6.0 - gdown==4.7.1 + - ghp-import==2.1.0 - gradio==4.9.0 - gradio-client==0.7.2 - h11==0.14.0 @@ -356,10 +357,12 @@ dependencies: - locket==1.0.0 - lxml==4.9.4 - mamba-ssm==2.2.2 + - medmnist==3.0.2 - monai==1.3.2 - msgpack==1.0.8 - multidict==6.0.5 - multipagetiff==3.0.9 + - nbdev==2.3.31 - nbformat==5.10.4 - nibabel==5.2.0 - ninja==1.11.1.1 @@ -414,6 +417,7 @@ dependencies: - starlette==0.27.0 - tblib==3.0.0 - tenacity==8.2.3 + - termcolor==2.4.0 - thinc==8.2.1 - tifffile==2023.2.28 - tokenizers==0.19.1 @@ -435,4 +439,4 @@ dependencies: - zarr==2.15.0 - zict==3.0.0 - zipp==3.17.0 -prefix: /home/biagio/miniconda3/envs/bioMONAI-env \ No newline at end of file +prefix: /home/biagio/miniconda3/envs/bioMONAI-env diff --git a/biomonai2.yml b/biomonai2.yml deleted file mode 100644 index 82397d9..0000000 --- a/biomonai2.yml +++ /dev/null @@ -1,251 +0,0 @@ -name: biomonai -channels: - - conda-forge - - defaults -dependencies: - - cuda-runtime==11.8.0=0 - - cffi==1.16.0=py311h5eee18b_0 - - gstreamer==1.14.1=h5eee18b_1 - - nss==3.89.1=h6a678d5_0 - - tzdata==2023c=h04d1e81_0 - - torchvision==0.15.2=py311_cu118 - - filelock==3.13.1=py311h06a4308_0 - - font-ttf-dejavu-sans-mono==2.37=hd3eb1b0_0 - - libffi==3.4.4=h6a678d5_0 - - libunistring==0.9.10=h27cfd23_0 - - six==1.16.0=pyhd3eb1b0_1 - - pcre==8.45=h295c915_0 - - icu==73.1=h6a678d5_0 - - fastcore==1.5.29=py_0 - - pickleshare==0.7.5=pyhd3eb1b0_1003 - - libgfortran-ng==11.2.0=h00389a5_1 - - cymem==2.0.6=py311h6a678d5_0 - - executing==0.8.3=pyhd3eb1b0_0 - - charset-normalizer==2.0.4=pyhd3eb1b0_0 - - cuda-cupti==11.8.87=0 - - gst-plugins-base==1.14.1=h6a678d5_1 - - jedi==0.18.1=py311h06a4308_1 - - markupsafe==2.1.1=py311h5eee18b_0 - - debugpy==1.6.7=py311h6a678d5_0 - - pygments==2.15.1=py311h06a4308_1 - - traitlets==5.7.1=py311h06a4308_0 - - bottleneck==1.3.5=py311hbed6279_0 - - libidn2==2.3.4=h5eee18b_0 - - stack_data==0.2.0=pyhd3eb1b0_0 - - pexpect==4.8.0=pyhd3eb1b0_3 - - libgomp==11.2.0=h1234567_1 - - libcurl==8.4.0=h251f7ec_1 - - libbrotlienc==1.0.9=h5eee18b_7 - - _libgcc_mutex==0.1=main - - torchtriton==2.0.0=py311 - - libstdcxx-ng==11.2.0=h1234567_1 - - mpmath==1.3.0=py311h06a4308_0 - - urllib3==1.26.18=py311h06a4308_0 - - pure_eval==0.2.2=pyhd3eb1b0_0 - - pytz==2023.3.post1=py311h06a4308_0 - - openh264==2.1.1=h4ff587b_0 - - libxkbcommon==1.0.1=h5eee18b_1 - - networkx==3.1=py311h06a4308_0 - - libxcb==1.15=h7f8727e_0 - - libiconv==1.16=h7f8727e_2 - - font-ttf-inconsolata==2.001=hcb22688_0 - - pathy==0.10.1=py311h06a4308_0 - - python-dateutil==2.8.2=pyhd3eb1b0_0 - - boost-cpp==1.82.0=hdb19cb5_2 - - gnutls==3.6.15=he1e5248_0 - - lcms2==2.12=h3be6417_0 - - ncurses==6.4=h6a678d5_0 - - cryptography==41.0.7=py311hdda0065_0 - - ninja-base==1.10.2=hd09550d_5 - - libev==4.33=h7f8727e_1 - - pillow==10.0.1=py311ha6cbd5a_0 - - preshed==3.0.6=py311h6a678d5_0 - - libssh2==1.10.0=hdbd6064_2 - - brotli==1.0.9=h5eee18b_7 - - certifi==2024.7.4 - - libclang13==14.0.6=default_he11475f_1 - - libxml2==2.10.4=hf1b16e4_1 - - smart_open==5.2.1=py311h06a4308_0 - - packaging==23.1=py311h06a4308_0 - - gobject-introspection==1.72.0=py311hbb6d50b_2 - - ca-certificates==2024.8.30 - - matplotlib-inline==0.1.6=py311h06a4308_0 - - gts==0.7.6=hb67d8dd_3 - - libwebp-base==1.3.2=h5eee18b_0 - - pycparser==2.21=pyhd3eb1b0_0 - - typer==0.9.0=py311h06a4308_0 - - ghapi==1.0.3=py_0 - - fastprogress==1.0.3=py_0 - - asttokens==2.0.5=pyhd3eb1b0_0 - - poppler==22.12.0=h9614445_3 - - pip==23.3.1=py311h06a4308_0 - - matplotlib-base==3.8.0=py311ha02d727_0 - - pixman==0.40.0=h7f8727e_1 - - fonts-conda-ecosystem==1=hd3eb1b0_0 - - mpfr==4.0.2=hb69a4c5_1 - - ipython==8.15.0=py311h06a4308_0 - - pysocks==1.7.1=py311h06a4308_0 - - lame==3.100=h7b6447c_0 - - sqlite==3.41.2=h5eee18b_0 - - ffmpeg==4.3=hf484d3e_0 - - mpc==1.1.0=h10f8cd9_1 - - libclang==14.0.6=default_hc6dbbc7_1 - - kiwisolver==1.4.4=py311h6a678d5_0 - - _openmp_mutex==5.1=1_gnu - - pyqt5-sip==12.13.0=py311h5eee18b_0 - - expat==2.5.0=h6a678d5_0 - - glib==2.69.1=he621ea3_2 - - mkl_random==1.2.4=py311hdb19cb5_0 - - ipywidgets==8.0.4=py311h06a4308_0 - - libgfortran5==11.2.0=h1234567_1 - - zeromq==4.3.4=h2531618_0 - - jupyter_client==8.6.0=py311h06a4308_0 - - font-ttf-source-code-pro==2.030=hd3eb1b0_0 - - libgcc-ng==11.2.0=h1234567_1 - - libcusolver==11.4.1.48=0 - - graphviz==2.50.0=h1b29801_1 - - xz==5.4.5=h5eee18b_0 - - libwebp==1.3.2=h11a3e52_0 - - libcufft==10.9.0.58=0 - - python-tzdata==2023.3=pyhd3eb1b0_0 - - libjpeg-turbo==2.0.0=h9bf148f_0 - - libgd==2.3.3=h695aa2c_1 - - libllvm14==14.0.6=hdb19cb5_3 - - atk-1.0==2.36.0=ha1a6a79_0 - - libbrotlicommon==1.0.9=h5eee18b_7 - - nettle==3.7.3=hbbd107a_1 - - colorama==0.4.6=py311h06a4308_0 - - pango==1.50.7=h05da053_0 - - astunparse==1.6.3=py_0 - - libpng==1.6.39=h5eee18b_0 - - srsly==2.4.8=py311h6a678d5_0 - - contourpy==1.2.0=py311hdb19cb5_0 - - pyyaml==6.0.1=py311h5eee18b_0 - - pytorch-cuda==11.8=h7e8668a_5 - - numexpr==2.8.7=py311h65dcdc2_0 - - bzip2==1.0.8=h7b6447c_0 - - harfbuzz==4.3.0=hf52aaf7_2 - - libnpp==11.8.0.86=0 - - libtiff==4.5.1=h6a678d5_0 - - mysql==5.7.24=h721c034_2 - - lerc==3.0=h295c915_0 - - matplotlib==3.8.0=py311h06a4308_0 - - platformdirs==3.10.0=py311h06a4308_0 - - cairo==1.16.0=hb05425b_5 - - fonts-anaconda==1=h8fa9717_0 - - graphite2==1.3.14=h295c915_1 - - python-graphviz==0.20.1=py311h06a4308_0 - - libcurand==10.3.4.101=0 - - fontconfig==2.14.1=h4c34cd2_2 - - gdk-pixbuf==2.42.10=h5eee18b_0 - - idna==3.4=py311h06a4308_0 - - libbrotlidec==1.0.9=h5eee18b_7 - - munkres==1.1.4=py_0 - - llvm-openmp==14.0.6=h9e868ea_0 - - readline==8.2=h5eee18b_0 - - nbdev==2.3.13=py_0 - - watchdog==2.1.6=py311h06a4308_0 - - ld_impl_linux-64==2.38=h1181459_1 - - cyrus-sasl==2.1.28=h52b45da_1 - - catalogue==2.0.7=py311h06a4308_0 - - openssl==3.0.12=h7f8727e_0 - - gmp==6.2.1=h295c915_3 - - pyzmq==25.1.0=py311h6a678d5_0 - - fonttools==4.25.0=pyhd3eb1b0_0 - - cuda-libraries==11.8.0=0 - - scipy==1.11.4=py311h08b1b3b_0 - - tk==8.6.12=h1ccaba5_0 - - cuda-cudart==11.8.89=0 - - intel-openmp==2023.1.0=hdb19cb5_46306 - - wcwidth==0.2.5=pyhd3eb1b0_0 - - decorator==5.1.1=pyhd3eb1b0_0 - - poppler-data==0.4.11=h06a4308_1 - - scikit-learn==1.2.2=py311h6a678d5_1 - - parso==0.8.3=pyhd3eb1b0_0 - - torchview==0.2.6=pyhd8ed1ab_0 - - shellingham==1.5.0=py311h06a4308_0 - - psutil==5.9.0=py311h5eee18b_0 - - ipykernel==6.25.0=py311h92b7b1e_0 - - pyqt==5.15.10=py311h6a678d5_0 - - openjpeg==2.4.0=h3ad879b_0 - - libtasn1==4.19.0=h5eee18b_0 - - brotli-python==1.0.9=py311h6a678d5_7 - - jpeg==9e=h5eee18b_1 - - jupyterlab_widgets==3.0.9=py311h06a4308_0 - - libtool==2.4.6=h6a678d5_1009 - - setuptools==68.2.2=py311h06a4308_0 - - fastdownload==0.0.7=py_0 - - gmpy2==2.1.2=py311hc9b5ff0_0 - - fribidi==1.0.10=h7b6447c_0 - - font-ttf-ubuntu==0.83=h8b1ccd4_0 - - krb5==1.20.1=h143b758_1 - - tornado==6.3.3=py311h5eee18b_0 - - spacy-loggers==1.0.4=py311h06a4308_0 - - zlib==1.2.13=h5eee18b_0 - - libcups==2.4.2=h2d74bed_1 - - cuda-nvtx==11.8.86=0 - - mkl-service==2.4.0=py311h5eee18b_1 - - libcusparse==11.7.5.86=0 - - gtk2==2.24.33=h73c1081_2 - - execnb==0.1.5=py_0 - - pyparsing==3.0.9=py311h06a4308_0 - - click==8.1.7=py311h06a4308_0 - - libpq==12.15=hdbd6064_1 - - mkl_fft==1.3.8=py311h5eee18b_0 - - python==3.11.5=h955ad1f_0 - - pytorch==2.0.1=py3.11_cuda11.8_cudnn8.7.0_0 - - c-ares==1.19.1=h5eee18b_0 - - libdeflate==1.17=h5eee18b_1 - - lz4-c==1.9.4=h6a678d5_0 - - threadpoolctl==2.2.0=pyh0d69192_0 - - mkl==2023.1.0=h213fc3f_46344 - - libsodium==1.0.18=h7b6447c_0 - - freetype==2.12.1=h4a9f257_0 - - sip==6.7.12=py311h6a678d5_0 - - nspr==4.35=h6a678d5_0 - - brotli-bin==1.0.9=h5eee18b_7 - - comm==0.1.2=py311h06a4308_0 - - murmurhash==1.0.7=py311h6a678d5_0 - - numpy==1.26.2=py311h08b1b3b_0 - - widgetsnbextension==4.0.5=py311h06a4308_0 - - prompt-toolkit==3.0.36=py311h06a4308_0 - - backcall==0.2.0=pyhd3eb1b0_0 - - joblib==1.2.0=py311h06a4308_0 - - torchaudio==2.0.2=py311_cu118 - - numpy-base==1.26.2=py311hf175353_0 - - requests==2.31.0=py311h06a4308_0 - - librsvg==2.54.4=h36cc946_3 - - nest-asyncio==1.5.6=py311h06a4308_0 - - wheel==0.41.2=py311h06a4308_0 - - qt-main==5.15.2=h53bd1ea_10 - - giflib==5.2.1=h5eee18b_3 - - sympy==1.12=py311h06a4308_0 - - cycler==0.11.0=pyhd3eb1b0_0 - - ninja==1.10.2=h06a4308_5 - - ply==3.11=py311h06a4308_0 - - yaml==0.2.5=h7b6447c_0 - - langcodes==3.3.0=pyhd3eb1b0_0 - - cuda-nvrtc==11.8.89=0 - - libboost==1.82.0=h109eef0_2 - - pyopenssl==23.2.0=py311h06a4308_0 - - spacy-legacy==3.0.12=py311h06a4308_0 - - blas==1.0=mkl - - libedit==3.1.20221030=h5eee18b_0 - - dbus==1.13.18=hb2f20db_0 - - tbb==2021.8.0=hdb19cb5_0 - - libnghttp2==1.57.0=h2d74bed_0 - - wasabi==0.9.1=py311h06a4308_0 - - libcublas==11.11.3.6=0 - - libcufile==1.8.1.2=0 - - jupyter_core==5.5.0=py311h06a4308_0 - - libnvjpeg==11.9.0.86=0 - - pandas==2.1.4=py311ha02d727_0 - - ptyprocess==0.7.0=pyhd3eb1b0_2 - - pytorch-mutex==1.0=cuda - - tqdm==4.65.0=py311h92b7b1e_0 - - zstd==1.5.5=hc292b87_0 - - cython-blis==0.7.9=py311hf4808d0_0 - - jinja2==3.1.2=py311h06a4308_0 - - libuuid==1.41.5=h5eee18b_0 - - cuda -prefix: /home/biagio/miniconda3/envs/bioMONAI-env diff --git a/nbs/902_tutorial_1.ipynb b/nbs/902_tutorial_1.ipynb index 6c8ae3a..218c492 100644 --- a/nbs/902_tutorial_1.ipynb +++ b/nbs/902_tutorial_1.ipynb @@ -22,20 +22,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'ResNetFeatures' from 'monai.networks.nets' (/home/biagio/miniforge3/envs/biomonai/lib/python3.11/site-packages/monai/networks/nets/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbioMONAI\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbioMONAI\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_image_files, get_target, RandomSplitter\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbioMONAI\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BasicUNet, DynUNet\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbioMONAI\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlosses\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbioMONAI\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", - "File \u001b[0;32m/media/biagio/Nuevo vol/Code/bioMONAI/bioMONAI/nets.py:23\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmonai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnetworks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfactories\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Act, Norm, Pool\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmonai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m set_determinism\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmonai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnetworks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BasicUNet, AttentionUnet, DynUNet, UNet, BasicUNet, ResNet, ResNetFeatures\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_device\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# %% ../nbs/04_nets.ipynb 8\u001b[39;00m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'ResNetFeatures' from 'monai.networks.nets' (/home/biagio/miniforge3/envs/biomonai/lib/python3.11/site-packages/monai/networks/nets/__init__.py)" - ] - } - ], + "outputs": [], "source": [ "from bioMONAI.data import *\n", "from bioMONAI.transforms import *\n", @@ -64,6 +51,13 @@ "text": [ "cpu\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343995622/work/c10/cuda/CUDAFunctions.cpp:109.)\n" + ] } ], "source": [ @@ -84,15 +78,109 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'download_medmnist' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m image_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../_data/medmnist_data/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m info \u001b[38;5;241m=\u001b[39m download_medmnist(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbloodmnist\u001b[39m\u001b[38;5;124m'\u001b[39m, image_path, download_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m info\n", - "\u001b[0;31mNameError\u001b[0m: name 'download_medmnist' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://zenodo.org/records/10519652/files/bloodmnist.npz?download=1 to ../_data/medmnist_data/bloodmnist.npz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 35461855/35461855 [00:11<00:00, 3024955.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using downloaded and verified file: ../_data/medmnist_data/bloodmnist.npz\n", + "Using downloaded and verified file: ../_data/medmnist_data/bloodmnist.npz\n", + "Saving training images to ../_data/medmnist_data/...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11959/11959 [00:02<00:00, 5592.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving validation images to ../_data/medmnist_data/...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1712/1712 [00:00<00:00, 5460.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving test images to ../_data/medmnist_data/...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3421/3421 [00:00<00:00, 5433.55it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed bloodmnist.npz\n", + "Datasets downloaded to ../_data/medmnist_data/\n", + "Dataset info for 'bloodmnist': {'python_class': 'BloodMNIST', 'description': 'The BloodMNIST is based on a dataset of individual normal cells, captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. It contains a total of 17,092 images and is organized into 8 classes. We split the source dataset with a ratio of 7:1:2 into training, validation and test set. The source images with resolution 3×360×363 pixels are center-cropped into 3×200×200, and then resized into 3×28×28.', 'url': 'https://zenodo.org/records/10519652/files/bloodmnist.npz?download=1', 'MD5': '7053d0359d879ad8a5505303e11de1dc', 'url_64': 'https://zenodo.org/records/10519652/files/bloodmnist_64.npz?download=1', 'MD5_64': '2b94928a2ae4916078ca51e05b6b800b', 'url_128': 'https://zenodo.org/records/10519652/files/bloodmnist_128.npz?download=1', 'MD5_128': 'adace1e0ed228fccda1f39692059dd4c', 'url_224': 'https://zenodo.org/records/10519652/files/bloodmnist_224.npz?download=1', 'MD5_224': 'b718ff6835fcbdb22ba9eacccd7b2601', 'task': 'multi-class', 'label': {'0': 'basophil', '1': 'eosinophil', '2': 'erythroblast', '3': 'immature granulocytes(myelocytes, metamyelocytes and promyelocytes)', '4': 'lymphocyte', '5': 'monocyte', '6': 'neutrophil', '7': 'platelet'}, 'n_channels': 3, 'n_samples': {'train': 11959, 'val': 1712, 'test': 3421}, 'license': 'CC BY 4.0'}\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'python_class': 'BloodMNIST',\n", + " 'description': 'The BloodMNIST is based on a dataset of individual normal cells, captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. It contains a total of 17,092 images and is organized into 8 classes. We split the source dataset with a ratio of 7:1:2 into training, validation and test set. The source images with resolution 3×360×363 pixels are center-cropped into 3×200×200, and then resized into 3×28×28.',\n", + " 'url': 'https://zenodo.org/records/10519652/files/bloodmnist.npz?download=1',\n", + " 'MD5': '7053d0359d879ad8a5505303e11de1dc',\n", + " 'url_64': 'https://zenodo.org/records/10519652/files/bloodmnist_64.npz?download=1',\n", + " 'MD5_64': '2b94928a2ae4916078ca51e05b6b800b',\n", + " 'url_128': 'https://zenodo.org/records/10519652/files/bloodmnist_128.npz?download=1',\n", + " 'MD5_128': 'adace1e0ed228fccda1f39692059dd4c',\n", + " 'url_224': 'https://zenodo.org/records/10519652/files/bloodmnist_224.npz?download=1',\n", + " 'MD5_224': 'b718ff6835fcbdb22ba9eacccd7b2601',\n", + " 'task': 'multi-class',\n", + " 'label': {'0': 'basophil',\n", + " '1': 'eosinophil',\n", + " '2': 'erythroblast',\n", + " '3': 'immature granulocytes(myelocytes, metamyelocytes and promyelocytes)',\n", + " '4': 'lymphocyte',\n", + " '5': 'monocyte',\n", + " '6': 'neutrophil',\n", + " '7': 'platelet'},\n", + " 'n_channels': 3,\n", + " 'n_samples': {'train': 11959, 'val': 1712, 'test': 3421},\n", + " 'license': 'CC BY 4.0'}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -136,42 +224,42 @@ "Building one sample\n", " Pipeline: BioImageMulti.create -> Tensor2BioImage -- {}\n", " starting from\n", - " ../_data/medmnist_data/train/0/train_4525.png\n", + " ../_data/medmnist_data/train/5/train_2282.png\n", " applying BioImageMulti.create gives\n", " BioImageMulti of size 3x28x28\n", " applying Tensor2BioImage -- {} gives\n", " BioImageMulti of size 3x28x28\n", " Pipeline: parent_label -> Categorize -- {'vocab': ['0', '1', '2', '3', '4', '5', '6', '7'], 'sort': True, 'add_na': False}\n", " starting from\n", - " ../_data/medmnist_data/train/0/train_4525.png\n", + " ../_data/medmnist_data/train/5/train_2282.png\n", " applying parent_label gives\n", - " 0\n", + " 5\n", " applying Categorize -- {'vocab': ['0', '1', '2', '3', '4', '5', '6', '7'], 'sort': True, 'add_na': False} gives\n", - " TensorCategory(0)\n", + " TensorCategory(5)\n", "\n", - "Final sample: (BioImageMulti([[[255., 255., 253., ..., 215., 225., 216.],\n", - " [253., 255., 254., ..., 198., 206., 203.],\n", - " [239., 253., 255., ..., 187., 194., 198.],\n", + "Final sample: (BioImageMulti([[[209., 222., 224., ..., 252., 253., 253.],\n", + " [219., 211., 210., ..., 252., 253., 253.],\n", + " [212., 211., 219., ..., 252., 253., 253.],\n", " ...,\n", - " [230., 206., 194., ..., 255., 255., 255.],\n", - " [244., 217., 197., ..., 255., 255., 255.],\n", - " [254., 226., 200., ..., 255., 255., 255.]],\n", + " [255., 255., 255., ..., 199., 215., 231.],\n", + " [251., 255., 255., ..., 212., 232., 242.],\n", + " [250., 255., 255., ..., 223., 242., 250.]],\n", "\n", - " [[232., 230., 225., ..., 178., 186., 175.],\n", - " [226., 229., 226., ..., 162., 168., 163.],\n", - " [213., 227., 229., ..., 153., 158., 163.],\n", + " [[171., 187., 189., ..., 232., 231., 231.],\n", + " [184., 177., 176., ..., 232., 231., 231.],\n", + " [179., 178., 186., ..., 232., 231., 231.],\n", " ...,\n", - " [192., 167., 155., ..., 229., 229., 231.],\n", - " [206., 179., 158., ..., 229., 229., 231.],\n", - " [217., 189., 162., ..., 229., 230., 231.]],\n", + " [230., 233., 232., ..., 176., 194., 210.],\n", + " [228., 233., 233., ..., 190., 210., 221.],\n", + " [228., 233., 235., ..., 200., 221., 228.]],\n", "\n", - " [[225., 221., 213., ..., 196., 204., 189.],\n", - " [217., 218., 212., ..., 174., 179., 171.],\n", - " [200., 212., 210., ..., 152., 158., 161.],\n", + " [[182., 194., 193., ..., 207., 208., 207.],\n", + " [188., 178., 175., ..., 207., 207., 207.],\n", + " [172., 171., 179., ..., 205., 207., 208.],\n", " ...,\n", - " [191., 168., 156., ..., 206., 202., 201.],\n", - " [203., 178., 161., ..., 206., 202., 201.],\n", - " [208., 181., 161., ..., 204., 200., 201.]]]), TensorCategory(0))\n", + " [190., 196., 197., ..., 184., 189., 193.],\n", + " [187., 195., 195., ..., 192., 199., 200.],\n", + " [187., 194., 196., ..., 194., 204., 204.]]]), TensorCategory(5))\n", "\n", "\n", "Collecting items from ../_data/medmnist_data\n", @@ -187,17 +275,17 @@ "Applying item_tfms to the first sample:\n", " Pipeline: ScaleIntensity -> RandCrop2D -- {'size': (128, 128), 'lazy': False, 'p': 1.0} -> RandRot90 -- {'prob': 0.5, 'max_k': 3, 'spatial_axes': (0, 1), 'ndim': 2, 'lazy': False, 'p': 1.0} -> RandFlip -- {'prob': 0.75, 'spatial_axis': None, 'ndim': 2, 'lazy': False, 'p': 1.0} -> ToTensor\n", " starting from\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", " applying ScaleIntensity gives\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", " applying RandCrop2D -- {'size': (128, 128), 'lazy': False, 'p': 1.0} gives\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", " applying RandRot90 -- {'prob': 0.5, 'max_k': 3, 'spatial_axes': (0, 1), 'ndim': 2, 'lazy': False, 'p': 1.0} gives\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", " applying RandFlip -- {'prob': 0.75, 'spatial_axis': None, 'ndim': 2, 'lazy': False, 'p': 1.0} gives\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", " applying ToTensor gives\n", - " (BioImageMulti of size 3x28x28, TensorCategory(0))\n", + " (BioImageMulti of size 3x28x28, TensorCategory(5))\n", "\n", "Adding the next 3 samples\n", "\n", @@ -245,7 +333,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -271,7 +359,8 @@ "metadata": {}, "outputs": [], "source": [ - "from monai.networks.nets import densenet121" + "# from monai.networks.nets import densenet121\n", + "# model = densenet121(spatial_dims=2, in_channels=3, out_channels=8)" ] }, { @@ -280,24 +369,150 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - "Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n" + "Sequential (Input shape: 4 x 3 x 28 x 28)\n", + "============================================================================\n", + "Layer (type) Output Shape Param # Trainable \n", + "============================================================================\n", + " 4 x 64 x 14 x 14 \n", + "Conv2d 9408 True \n", + "BatchNorm2d 128 True \n", + "ReLU \n", + "____________________________________________________________________________\n", + " 4 x 64 x 7 x 7 \n", + "MaxPool2d \n", + "Conv2d 36864 True \n", + "BatchNorm2d 128 True \n", + "ReLU \n", + "Conv2d 36864 True \n", + "BatchNorm2d 128 True \n", + "Conv2d 36864 True \n", + "BatchNorm2d 128 True \n", + "ReLU \n", + "Conv2d 36864 True \n", + "BatchNorm2d 128 True \n", + "____________________________________________________________________________\n", + " 4 x 128 x 4 x 4 \n", + "Conv2d 73728 True \n", + "BatchNorm2d 256 True \n", + "ReLU \n", + "Conv2d 147456 True \n", + "BatchNorm2d 256 True \n", + "Conv2d 8192 True \n", + "BatchNorm2d 256 True \n", + "Conv2d 147456 True \n", + "BatchNorm2d 256 True \n", + "ReLU \n", + "Conv2d 147456 True \n", + "BatchNorm2d 256 True \n", + "____________________________________________________________________________\n", + " 4 x 256 x 2 x 2 \n", + "Conv2d 294912 True \n", + "BatchNorm2d 512 True \n", + "ReLU \n", + "Conv2d 589824 True \n", + "BatchNorm2d 512 True \n", + "Conv2d 32768 True \n", + "BatchNorm2d 512 True \n", + "Conv2d 589824 True \n", + "BatchNorm2d 512 True \n", + "ReLU \n", + "Conv2d 589824 True \n", + "BatchNorm2d 512 True \n", + "____________________________________________________________________________\n", + " 4 x 512 x 1 x 1 \n", + "Conv2d 1179648 True \n", + "BatchNorm2d 1024 True \n", + "ReLU \n", + "Conv2d 2359296 True \n", + "BatchNorm2d 1024 True \n", + "Conv2d 131072 True \n", + "BatchNorm2d 1024 True \n", + "Conv2d 2359296 True \n", + "BatchNorm2d 1024 True \n", + "ReLU \n", + "Conv2d 2359296 True \n", + "BatchNorm2d 1024 True \n", + "AdaptiveAvgPool2d \n", + "AdaptiveMaxPool2d \n", + "____________________________________________________________________________\n", + " 4 x 1024 \n", + "Flatten \n", + "BatchNorm1d 2048 True \n", + "Dropout \n", + "____________________________________________________________________________\n", + " 4 x 512 \n", + "Linear 524288 True \n", + "ReLU \n", + "BatchNorm1d 1024 True \n", + "Dropout \n", + "____________________________________________________________________________\n", + " 4 x 8 \n", + "Linear 4096 True \n", + "____________________________________________________________________________\n", + "\n", + "Total params: 11,707,968\n", + "Total trainable params: 11,707,968\n", + "Total non-trainable params: 0\n", + "\n", + "Optimizer used: \n", + "Loss function: FlattenedLoss of CrossEntropyLoss()\n", + "\n", + "Callbacks:\n", + " - TrainEvalCallback\n", + " - CastToTensor\n", + " - Recorder\n", + " - ProgressCallback\n", + " - ShowGraphCallback\n" ] } ], "source": [ - "# model = densenet121(spatial_dims=2, in_channels=3, out_channels=8)\n", "model = resnet18\n", "\n", "loss = CrossEntropyLossFlat()\n", "metrics = accuracy\n", "\n", - "# trainer = fastTrainer(data, model, loss_fn=loss, metrics=metrics, show_summary=False)\n", - "\n", - "trainer = vision_learner(data, model, loss_func=loss, metrics=accuracy)" + "trainer = visionTrainer(data, model, loss_fn=loss, metrics=metrics, show_summary=True)" ] }, { @@ -348,10 +563,10 @@ " \n", " \n", " 0\n", - " 1.251936\n", - " 1.008712\n", - " 0.655061\n", - " 00:31\n", + " 1.941148\n", + " 2.192013\n", + " 0.252487\n", + " 05:51\n", " \n", " \n", "" @@ -362,6 +577,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", 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", 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AiunXhlut2AlNLPbvQ30ft1qxDo/mRpNQLLRuPByMQkcdHR2FcjO92D3ZatXflK127EZeXlkM5Q5369dnSymlaeqvs92O3VsrwZ9tc/NyKLexUb9SPDdXv1BcSilHR8ehXKsVW4nuD/r1mX595t+Sm4YnFABSKBQAUigUAFIoFABSKBQAUigUAFIoFABSKBQAUigUAFIoFABSKBQAUigUAFIoFABSTL823D4NHdBqAqupo9hqbenGlj5bvdhqaisw2tmMYyumi0ux12TYPwzlHn13/ZLvG2/8beis0umFYouLV0O55c5adabfj90j994ahnLr3djPVk7q76+NpZXQUTcfvhDKXbk6G8otXTipzmzemAmdtbi0EcoNhrHvye2t+vvk9h92Q2fdPwh+v07BEwoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKaZfG+7EVjtLN7Ak269fFS2llP5pYP63lDI7mf5l+CMz9Qu0s7OxpdUyOQjFWq3YAvMjj9yozgyHc6Gzbt/eC+UOD2L3yUlgkXc8akJnNU39anMppRwPtkK5S5tL1ZlrNzZDZ918PJZbXYstMLfbu9WZ+fnY5+3KldiScm829l0yF/guOTkKHVWG/djnbRqeUABIoVAASKFQAEihUABIoVAASKFQAEihUABIoVAASKFQAEihUABIoVAASKFQAEgx9ZLZcL9+UK+UUmaWFupDkUHJUkpnNAnlSjc4Dtmrv852O9bhg0Fs+PL3f7gbypWyWp24eGkldNJwGHvfWq3YYOZgMK7OjMaxccjhMDaE+P7//Fgo15urfy3nF0ehs5bXYu/bzGzsNen369/vnd3YOGp3pv4eKaWUa9euhXKjwPjocBB7/Qf92L08DU8oAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKSYema3mcRWO8u4VZ9pxXqu3Z4J5UpwpLgM6xdJZ3qxZeMLF+vXf0spZWd3EMpNxovVmdWVudBZV65cCeUmk9h9MgysUp+cnITOiubWl2OLsK124J4Mrv+uXQh8tkspc3Ox9+3eTv3PdnS0Fzpr0pyGcns7h6Hc9t36JeX792JnnR7F3u9peEIBIIVCASCFQgEghUIBIIVCASCFQgEghUIBIIVCASCFQgEghUIBIIVCASCFQgEghUIBIMXU07e91UuxE8aBtdtRP3RUM4mtn44H9SumpZRShvVLsgtzs6Gj3v3ux0K5t+7sh3LbW/Urrfv790Jn9Wbql41LKaUzMx/KNaV+bbU7G7tH1hZi9+TGWuwzcGlzrT5zeSV01kPX10O5/YO7odzhya3qzNHJbuisg9jHpjSTnVCuf1L/u/1oFFuk7nR6odw0PKEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkGLqteH+zkHogMlkVJ1pz4SOKrMLc7FgO7YIOxnW/2xNbCC0dGc7odx7Hn8klJvp3gmk7ofOGk8Ci9SllHZ7EsrNzNW/32trq6GzNjcvhnI3bsTe76Xl+s9Arxu7KVursdXauaWlUO7o5Gp15v52bP13+27s++70OPZaNk39PdltxZbLJ53gl9AUPKEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQYupxyDd//XehAzq9+pG7+YXY6NzKykIot7yyGMr1ZuvH2ZoyDp11crQXyl28tBLK7R8eVWcODmPXeHLcD+UWl2PjeJuXN6ozV69eDp21sbEeys3Ox4Y2S7t+sPToIHZWbzc2YDkzFxtxffSxd9VnHrkZOmvr7di9vHUnNiq5d7/+M7B9N3aNTXMayk3DEwoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKaZeG3777r3YCa1JdWQ4jK1hdrpNKHft+mYod/Oxx6ozy2sXQ2d1gtXflNgi7Pxc/YEznfql21JK6XeHodzGRmyV+tFH69/v9Y2l0Fnj8XEod3i4HcotXaxfUl5cnQ+dNR4MQrnTw/1QrjRTf139H3OLF0JHra3Wv46llLJ59a9Cua1bW9WZZvIPobMmw7uh3DQ8oQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQQqEAkEKhAJBCoQCQYur5zoODo9ABCwtzoVxEq9UK5d6+E1vfjGwbP/XUYuis7sxCKNfqxhZ5l5bqF2gvXloLnXWhqV+kLqWUK1cvhXKrK5HXpB86azg+DOVmZ2dDueak/jqbSWwlejiI5Zom9jmdnQmsIsdurdKbjX3eyij2mmxu1q+Qt2Pj6qXXjb3+0/CEAkAKhQJACoUCQAqFAkAKhQJACoUCQAqFAkAKhQJACoUCQAqFAkAKhQJACoUCQAqFAkCKqdeGL1y4EDpgcal+NXUwOAmdddqPLbseHu6Hcru7u9WZ/f3YWUuLse7vzcdWa1eW61eR3/XIw6GzOp3Y+unSSmB9tpTSnp/6tv+/mtPQWd1RbBK204qtRLda9T9bqxt4PUopc62ZUC6u/rxJP7b+2z+N5Qb92Cr7/MxSdWZlpT5TSilPPPmeUG4anlAASKFQAEihUABIoVAASKFQAEihUABIoVAASKFQAEihUABIoVAASKFQAEihUABIMfUq3EMPXQsdsLpWPzI4HA5CZ93f2QrlhqPYGOVwUH+dx8exkcFOux/K/c/f/CqUGw7rRw37/dg1Ns04lOvNxkYlV9fqRyXXN1ZCZy0uxsY5J5PY8GIn8jtipxM6K/r7aDOZhHL9k/r7a+f+Qeis3Z3Yd8L2nfuh3OzsQnVmYT42DrkwH7u3lv7jf/izf8YTCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgApFAoAKRQKACkUCgAppl4bXlmvXw0upZSNCxvVmVYZhc6anYutzx6f7IdykXXd8TD2sx0dHIZyd26/Hcr1+/XXeXx8HDorui4904u935uXV6szc7OxhdaV9bVQrhlM/dH8I5NJYLk5uP7bbsVe/9Ewti69v1f/GbhzJ7ZAvn1nJ5RrJrH3bX+vft241QouG/di13hzij/jCQWAFAoFgBQKBYAUCgWAFAoFgBQKBYAUCgWAFAoFgBQKBYAUCgWAFAoFgBQKBYAUCgWAFK2maZqzvggA/v3zhAJACoUCQAqFAkAKhQJACoUCQAqFAkAKhQJACoUCQAqFAkCK/wVxDSNY7HQCzQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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