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October 14, 2021 11:20
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A jupyter notebook to check the lightning-flash installation
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "989656fc", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import sys\n", | |
| "import torch\n", | |
| "import fastai\n", | |
| "import flash" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "e499bfa8", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "python: /mnt/stockage/Developp/EnvPLFlash/bin/python3.9\n", | |
| "numpy 1.21.2\n", | |
| "PyTorch 1.8.2\n", | |
| "Fastai: 2.3.1\n", | |
| "CUDA GPU True\n", | |
| "fastai: 2.3.1\n", | |
| "flash 0.5.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('python:',sys.executable)\n", | |
| "print('numpy', np.__version__)\n", | |
| "print(\"PyTorch\", torch.__version__)\n", | |
| "#print(\"Detectron2 \",detectron2.__version__)\n", | |
| "print(\"Fastai:\",fastai.__version__)\n", | |
| "print(\"CUDA GPU\",torch.cuda.is_available())\n", | |
| "print('fastai:', fastai.__version__)\n", | |
| "print('flash', flash.__version__)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "2185e320", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------------- --------------------------------------------------------------------------------\r\n", | |
| "sys.platform linux\r\n", | |
| "Python 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:23:11) [GCC 9.4.0]\r\n", | |
| "numpy 1.21.2\r\n", | |
| "detectron2 failed to import\r\n", | |
| "detectron2._C not built correctly: No module named 'detectron2'\r\n", | |
| "Compiler ($CXX) c++ (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\r\n", | |
| "CUDA compiler Build cuda_11.4.r11.4/compiler.30300941_0\r\n", | |
| "DETECTRON2_ENV_MODULE <not set>\r\n", | |
| "PyTorch 1.8.2 @/mnt/stockage/Developp/EnvPLFlash/lib/python3.9/site-packages/torch\r\n", | |
| "PyTorch debug build False\r\n", | |
| "GPU available True\r\n", | |
| "GPU 0 NVIDIA GeForce GTX 960 (arch=5.2)\r\n", | |
| "CUDA_HOME /usr/local/cuda-11.4\r\n", | |
| "Pillow 8.3.1\r\n", | |
| "torchvision 0.9.2 @/mnt/stockage/Developp/EnvPLFlash/lib/python3.9/site-packages/torchvision\r\n", | |
| "torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5\r\n", | |
| "fvcore 0.1.5.post20210924\r\n", | |
| "iopath 0.1.9\r\n", | |
| "cv2 4.5.3\r\n", | |
| "---------------------- --------------------------------------------------------------------------------\r\n", | |
| "PyTorch built with:\r\n", | |
| " - GCC 7.3\r\n", | |
| " - C++ Version: 201402\r\n", | |
| " - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\r\n", | |
| " - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\r\n", | |
| " - OpenMP 201511 (a.k.a. OpenMP 4.5)\r\n", | |
| " - NNPACK is enabled\r\n", | |
| " - CPU capability usage: NO AVX\r\n", | |
| " - CUDA Runtime 10.2\r\n", | |
| " - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\r\n", | |
| " - CuDNN 7.6.5\r\n", | |
| " - Magma 2.5.2\r\n", | |
| " - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \r\n", | |
| "\r\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "337e8ac4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "# packages in environment at /mnt/stockage/Developp/EnvPLFlash:\r\n", | |
| "#\r\n", | |
| "# Name Version Build Channel\r\n", | |
| "_libgcc_mutex 0.1 conda_forge conda-forge\r\n", | |
| "_openmp_mutex 4.5 1_llvm conda-forge\r\n", | |
| "absl-py 0.14.1 pypi_0 pypi\r\n", | |
| "aiohttp 3.7.4.post0 pypi_0 pypi\r\n", | |
| "albumentations 0.5.2 pypi_0 pypi\r\n", | |
| "antlr4-python3-runtime 4.8 pypi_0 pypi\r\n", | |
| "anyio 3.3.3 pypi_0 pypi\r\n", | |
| "argon2-cffi 21.1.0 pypi_0 pypi\r\n", | |
| "async-timeout 3.0.1 pypi_0 pypi\r\n", | |
| "attrs 21.2.0 pypi_0 pypi\r\n", | |
| "backcall 0.2.0 pypi_0 pypi\r\n", | |
| "blas 1.0 mkl \r\n", | |
| "bleach 4.1.0 pypi_0 pypi\r\n", | |
| "blis 0.7.4 pypi_0 pypi\r\n", | |
| "bzip2 1.0.8 h7f98852_4 conda-forge\r\n", | |
| "ca-certificates 2021.10.8 ha878542_0 conda-forge\r\n", | |
| "cachetools 4.2.4 pypi_0 pypi\r\n", | |
| "catalogue 2.0.6 pypi_0 pypi\r\n", | |
| "certifi 2021.10.8 pypi_0 pypi\r\n", | |
| "cffi 1.15.0 pypi_0 pypi\r\n", | |
| "chardet 4.0.0 pypi_0 pypi\r\n", | |
| "charset-normalizer 2.0.7 pypi_0 pypi\r\n", | |
| "click 8.0.3 pypi_0 pypi\r\n", | |
| "configparser 5.0.2 pypi_0 pypi\r\n", | |
| "cudatoolkit 10.2.89 h8f6ccaa_9 conda-forge\r\n", | |
| "cycler 0.10.0 pypi_0 pypi\r\n", | |
| "cymem 2.0.5 pypi_0 pypi\r\n", | |
| "cython 0.29.24 pypi_0 pypi\r\n", | |
| "dataclasses 0.6 pypi_0 pypi\r\n", | |
| "decorator 5.1.0 pypi_0 pypi\r\n", | |
| "defusedxml 0.7.1 pypi_0 pypi\r\n", | |
| "docker-pycreds 0.4.0 pypi_0 pypi\r\n", | |
| "docstring-parser 0.11 pypi_0 pypi\r\n", | |
| "effdet 0.2.4 pypi_0 pypi\r\n", | |
| "efficientnet-pytorch 0.6.3 pypi_0 pypi\r\n", | |
| "entrypoints 0.3 pypi_0 pypi\r\n", | |
| "fastai 2.3.1 pypi_0 pypi\r\n", | |
| "fastcore 1.3.26 pypi_0 pypi\r\n", | |
| "fastprogress 1.0.0 pypi_0 pypi\r\n", | |
| "ffmpeg 4.4.0 hca11adc_0 conda-forge\r\n", | |
| "fire 0.4.0 pypi_0 pypi\r\n", | |
| "freetype 2.10.4 h0708190_1 conda-forge\r\n", | |
| "fsspec 2021.10.0 pypi_0 pypi\r\n", | |
| "future 0.18.2 pypi_0 pypi\r\n", | |
| "fvcore 0.1.5.post20210924 pypi_0 pypi\r\n", | |
| "gettext 0.21.0 hf68c758_0 \r\n", | |
| "gitdb 4.0.7 pypi_0 pypi\r\n", | |
| "gitpython 3.1.24 pypi_0 pypi\r\n", | |
| "gmp 6.2.1 h58526e2_0 conda-forge\r\n", | |
| "gnutls 3.6.15 he1e5248_0 \r\n", | |
| "google-auth 2.3.0 pypi_0 pypi\r\n", | |
| "google-auth-oauthlib 0.4.6 pypi_0 pypi\r\n", | |
| "grpcio 1.41.0 pypi_0 pypi\r\n", | |
| "icedata 0.4.0 pypi_0 pypi\r\n", | |
| "icevision 0.8.1 pypi_0 pypi\r\n", | |
| "icu 68.1 h58526e2_0 conda-forge\r\n", | |
| "idna 3.3 pypi_0 pypi\r\n", | |
| "imageio 2.9.0 pypi_0 pypi\r\n", | |
| "imgaug 0.4.0 pypi_0 pypi\r\n", | |
| "iopath 0.1.9 pypi_0 pypi\r\n", | |
| "ipykernel 5.5.6 pypi_0 pypi\r\n", | |
| "ipython 7.28.0 pypi_0 pypi\r\n", | |
| "ipython-genutils 0.2.0 pypi_0 pypi\r\n", | |
| "jedi 0.18.0 pypi_0 pypi\r\n", | |
| "jinja2 3.0.2 pypi_0 pypi\r\n", | |
| "joblib 1.1.0 pypi_0 pypi\r\n", | |
| "jpeg 9b h024ee3a_2 \r\n", | |
| "jsonargparse 3.19.4 pypi_0 pypi\r\n", | |
| "jsonschema 4.1.0 pypi_0 pypi\r\n", | |
| "jupyter-client 6.1.12 pypi_0 pypi\r\n", | |
| "jupyter-core 4.8.1 pypi_0 pypi\r\n", | |
| "jupyter-server 1.11.1 pypi_0 pypi\r\n", | |
| "jupyterlab-pygments 0.1.2 pypi_0 pypi\r\n", | |
| "kiwisolver 1.3.2 pypi_0 pypi\r\n", | |
| "kornia 0.5.3 pypi_0 pypi\r\n", | |
| "lame 3.100 h7f98852_1001 conda-forge\r\n", | |
| "lcms2 2.12 h3be6417_0 \r\n", | |
| "ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge\r\n", | |
| "libffi 3.4.2 h9c3ff4c_4 conda-forge\r\n", | |
| "libgcc-ng 11.2.0 h1d223b6_11 conda-forge\r\n", | |
| "libiconv 1.16 h516909a_0 conda-forge\r\n", | |
| "libidn2 2.3.2 h7f98852_0 conda-forge\r\n", | |
| "libpng 1.6.37 h21135ba_2 conda-forge\r\n", | |
| "libstdcxx-ng 11.2.0 he4da1e4_11 conda-forge\r\n", | |
| "libtasn1 4.17.0 h7f98852_0 conda-forge\r\n", | |
| "libtiff 4.2.0 h85742a9_0 \r\n", | |
| "libunistring 0.9.10 h7f98852_0 conda-forge\r\n", | |
| "libuv 1.42.0 h7f98852_0 conda-forge\r\n", | |
| "libwebp-base 1.2.1 h7f98852_0 conda-forge\r\n", | |
| "libxml2 2.9.12 h72842e0_0 conda-forge\r\n", | |
| "libzlib 1.2.11 h36c2ea0_1013 conda-forge\r\n", | |
| "lightning-bolts 0.4.0 pypi_0 pypi\r\n", | |
| "lightning-flash 0.5.0 pypi_0 pypi\r\n", | |
| "llvm-openmp 12.0.1 h4bd325d_1 conda-forge\r\n", | |
| "loguru 0.5.3 pypi_0 pypi\r\n", | |
| "lz4-c 1.9.3 h9c3ff4c_1 conda-forge\r\n", | |
| "markdown 3.3.4 pypi_0 pypi\r\n", | |
| "markupsafe 2.0.1 pypi_0 pypi\r\n", | |
| "matplotlib 3.4.3 pypi_0 pypi\r\n", | |
| "matplotlib-inline 0.1.3 pypi_0 pypi\r\n", | |
| "mistune 0.8.4 pypi_0 pypi\r\n", | |
| "mkl 2021.4.0 h8d4b97c_729 conda-forge\r\n", | |
| "mkl-service 2.4.0 py39h3811e60_0 conda-forge\r\n", | |
| "mkl_fft 1.3.0 py39h42c9631_2 \r\n", | |
| "mkl_random 1.2.2 py39hde0f152_0 conda-forge\r\n", | |
| "multidict 5.2.0 pypi_0 pypi\r\n", | |
| "munch 2.5.0 pypi_0 pypi\r\n", | |
| "murmurhash 1.0.5 pypi_0 pypi\r\n", | |
| "nbclient 0.5.4 pypi_0 pypi\r\n", | |
| "nbconvert 6.2.0 pypi_0 pypi\r\n", | |
| "nbformat 5.1.3 pypi_0 pypi\r\n", | |
| "ncurses 6.2 h58526e2_4 conda-forge\r\n", | |
| "nest-asyncio 1.5.1 pypi_0 pypi\r\n", | |
| "nettle 3.7.3 hbbd107a_1 \r\n", | |
| "networkx 2.6.3 pypi_0 pypi\r\n", | |
| "ninja 1.10.2 h4bd325d_1 conda-forge\r\n", | |
| "nose 1.3.7 pypi_0 pypi\r\n", | |
| "notebook 6.4.4 pypi_0 pypi\r\n", | |
| "numpy 1.21.2 py39h20f2e39_0 \r\n", | |
| "numpy-base 1.21.2 py39h79a1101_0 \r\n", | |
| "oauthlib 3.1.1 pypi_0 pypi\r\n", | |
| "olefile 0.46 pyh9f0ad1d_1 conda-forge\r\n", | |
| "omegaconf 2.1.1 pypi_0 pypi\r\n", | |
| "opencv-python 4.5.3.56 pypi_0 pypi\r\n", | |
| "opencv-python-headless 4.5.3.56 pypi_0 pypi\r\n", | |
| "openh264 2.1.1 h780b84a_0 conda-forge\r\n", | |
| "openjpeg 2.4.0 hb52868f_1 conda-forge\r\n", | |
| "openssl 3.0.0 h7f98852_1 conda-forge\r\n", | |
| "packaging 21.0 pypi_0 pypi\r\n", | |
| "pandas 1.2.5 pypi_0 pypi\r\n", | |
| "pandocfilters 1.5.0 pypi_0 pypi\r\n", | |
| "parso 0.8.2 pypi_0 pypi\r\n", | |
| "pathtools 0.1.2 pypi_0 pypi\r\n", | |
| "pathy 0.6.0 pypi_0 pypi\r\n", | |
| "pexpect 4.8.0 pypi_0 pypi\r\n", | |
| "pickleshare 0.7.5 pypi_0 pypi\r\n", | |
| "pillow 8.3.1 py39h2c7a002_0 \r\n", | |
| "pip 21.3 pyhd8ed1ab_0 conda-forge\r\n", | |
| "portalocker 2.3.2 pypi_0 pypi\r\n", | |
| "preshed 3.0.5 pypi_0 pypi\r\n", | |
| "pretrainedmodels 0.7.4 pypi_0 pypi\r\n", | |
| "prometheus-client 0.11.0 pypi_0 pypi\r\n", | |
| "promise 2.3 pypi_0 pypi\r\n", | |
| "prompt-toolkit 3.0.20 pypi_0 pypi\r\n", | |
| "protobuf 3.18.1 pypi_0 pypi\r\n", | |
| "psutil 5.8.0 pypi_0 pypi\r\n", | |
| "ptyprocess 0.7.0 pypi_0 pypi\r\n", | |
| "pyasn1 0.4.8 pypi_0 pypi\r\n", | |
| "pyasn1-modules 0.2.8 pypi_0 pypi\r\n", | |
| "pycocotools 2.0.2 pypi_0 pypi\r\n", | |
| "pycparser 2.20 pypi_0 pypi\r\n", | |
| "pydantic 1.8.2 pypi_0 pypi\r\n", | |
| "pydeprecate 0.3.1 pypi_0 pypi\r\n", | |
| "pygments 2.10.0 pypi_0 pypi\r\n", | |
| "pyparsing 2.4.7 pypi_0 pypi\r\n", | |
| "pyrsistent 0.18.0 pypi_0 pypi\r\n", | |
| "pystiche 1.0.1 pypi_0 pypi\r\n", | |
| "python 3.9.7 hf930737_3_cpython conda-forge\r\n", | |
| "python-dateutil 2.8.2 pypi_0 pypi\r\n", | |
| "python_abi 3.9 2_cp39 conda-forge\r\n", | |
| "pytorch 1.8.2 py3.9_cuda10.2_cudnn7.6.5_0 pytorch-lts\r\n", | |
| "pytorch-lightning 1.4.9 pypi_0 pypi\r\n", | |
| "pytz 2021.3 pypi_0 pypi\r\n", | |
| "pywavelets 1.1.1 pypi_0 pypi\r\n", | |
| "pyyaml 6.0 pypi_0 pypi\r\n", | |
| "pyzmq 22.3.0 pypi_0 pypi\r\n", | |
| "readline 8.1 h46c0cb4_0 conda-forge\r\n", | |
| "requests 2.26.0 pypi_0 pypi\r\n", | |
| "requests-oauthlib 1.3.0 pypi_0 pypi\r\n", | |
| "requests-unixsocket 0.2.0 pypi_0 pypi\r\n", | |
| "resnest 0.0.6b20211013 pypi_0 pypi\r\n", | |
| "rsa 4.7.2 pypi_0 pypi\r\n", | |
| "scikit-image 0.18.3 pypi_0 pypi\r\n", | |
| "scikit-learn 1.0 pypi_0 pypi\r\n", | |
| "scipy 1.7.1 pypi_0 pypi\r\n", | |
| "segmentation-models-pytorch 0.2.0 pypi_0 pypi\r\n", | |
| "send2trash 1.8.0 pypi_0 pypi\r\n", | |
| "sentry-sdk 1.4.3 pypi_0 pypi\r\n", | |
| "setuptools 58.2.0 py39hf3d152e_0 conda-forge\r\n", | |
| "shapely 1.7.1 pypi_0 pypi\r\n", | |
| "shortuuid 1.0.1 pypi_0 pypi\r\n", | |
| "six 1.16.0 pyh6c4a22f_0 conda-forge\r\n", | |
| "smart-open 5.2.1 pypi_0 pypi\r\n", | |
| "smmap 4.0.0 pypi_0 pypi\r\n", | |
| "sniffio 1.2.0 pypi_0 pypi\r\n", | |
| "spacy 3.1.3 pypi_0 pypi\r\n", | |
| "spacy-legacy 3.0.8 pypi_0 pypi\r\n", | |
| "sqlite 3.36.0 h9cd32fc_2 conda-forge\r\n", | |
| "srsly 2.4.1 pypi_0 pypi\r\n", | |
| "subprocess32 3.5.4 pypi_0 pypi\r\n", | |
| "tabulate 0.8.9 pypi_0 pypi\r\n", | |
| "tbb 2021.3.0 h4bd325d_0 conda-forge\r\n", | |
| "tensorboard 2.7.0 pypi_0 pypi\r\n", | |
| "tensorboard-data-server 0.6.1 pypi_0 pypi\r\n", | |
| "tensorboard-plugin-wit 1.8.0 pypi_0 pypi\r\n", | |
| "termcolor 1.1.0 pypi_0 pypi\r\n", | |
| "terminado 0.12.1 pypi_0 pypi\r\n", | |
| "testpath 0.5.0 pypi_0 pypi\r\n", | |
| "thinc 8.0.10 pypi_0 pypi\r\n", | |
| "threadpoolctl 3.0.0 pypi_0 pypi\r\n", | |
| "tifffile 2021.10.12 pypi_0 pypi\r\n", | |
| "timm 0.4.12 pypi_0 pypi\r\n", | |
| "tk 8.6.11 h27826a3_1 conda-forge\r\n", | |
| "torchaudio 0.8.2 py39 pytorch-lts\r\n", | |
| "torchmetrics 0.5.0 pypi_0 pypi\r\n", | |
| "torchvision 0.9.2 py39_cu102 pytorch-lts\r\n", | |
| "tornado 6.1 pypi_0 pypi\r\n", | |
| "tqdm 4.62.3 pypi_0 pypi\r\n", | |
| "traitlets 5.1.0 pypi_0 pypi\r\n", | |
| "typer 0.4.0 pypi_0 pypi\r\n", | |
| "typing_extensions 3.10.0.2 pyha770c72_0 conda-forge\r\n", | |
| "tzdata 2021c he74cb21_0 conda-forge\r\n", | |
| "urllib3 1.26.7 pypi_0 pypi\r\n", | |
| "voila 0.2.16 pypi_0 pypi\r\n", | |
| "wandb 0.12.4 pypi_0 pypi\r\n", | |
| "wasabi 0.8.2 pypi_0 pypi\r\n", | |
| "wcwidth 0.2.5 pypi_0 pypi\r\n", | |
| "webencodings 0.5.1 pypi_0 pypi\r\n", | |
| "websocket-client 1.2.1 pypi_0 pypi\r\n", | |
| "werkzeug 2.0.2 pypi_0 pypi\r\n", | |
| "wheel 0.37.0 pyhd8ed1ab_1 conda-forge\r\n", | |
| "x264 1!161.3030 h7f98852_1 conda-forge\r\n", | |
| "xz 5.2.5 h516909a_1 conda-forge\r\n", | |
| "yacs 0.1.8 pypi_0 pypi\r\n", | |
| "yarl 1.7.0 pypi_0 pypi\r\n", | |
| "yaspin 2.1.0 pypi_0 pypi\r\n", | |
| "zlib 1.2.11 h36c2ea0_1013 conda-forge\r\n", | |
| "zstd 1.4.9 ha95c52a_0 conda-forge\r\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Note: you may need to restart the kernel to use updated packages.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%conda list" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "id": "d8ccf9e7", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "usage: instance_segmentation [-h] [--config CONFIG]\r\n", | |
| " [--print_config [={comments,skip_null}+]]\r\n", | |
| " [--seed_everything SEED_EVERYTHING]\r\n", | |
| " [--trainer.serve_sanity_check {true,false}]\r\n", | |
| " [--trainer.logger LOGGER]\r\n", | |
| " [--trainer.checkpoint_callback {true,false}]\r\n", | |
| " [--trainer.callbacks CALLBACKS]\r\n", | |
| " [--trainer.default_root_dir DEFAULT_ROOT_DIR]\r\n", | |
| " [--trainer.gradient_clip_val GRADIENT_CLIP_VAL]\r\n", | |
| " [--trainer.gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM]\r\n", | |
| " [--trainer.process_position PROCESS_POSITION]\r\n", | |
| " [--trainer.num_nodes NUM_NODES]\r\n", | |
| " [--trainer.num_processes NUM_PROCESSES]\r\n", | |
| " [--trainer.devices DEVICES] [--trainer.gpus GPUS]\r\n", | |
| " [--trainer.auto_select_gpus {true,false}]\r\n", | |
| " [--trainer.tpu_cores TPU_CORES]\r\n", | |
| " [--trainer.ipus IPUS]\r\n", | |
| " [--trainer.log_gpu_memory LOG_GPU_MEMORY]\r\n", | |
| " [--trainer.progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE]\r\n", | |
| " [--trainer.overfit_batches OVERFIT_BATCHES]\r\n", | |
| " [--trainer.track_grad_norm TRACK_GRAD_NORM]\r\n", | |
| " [--trainer.check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH]\r\n", | |
| " [--trainer.fast_dev_run FAST_DEV_RUN]\r\n", | |
| " [--trainer.accumulate_grad_batches ACCUMULATE_GRAD_BATCHES]\r\n", | |
| " [--trainer.max_epochs MAX_EPOCHS]\r\n", | |
| " [--trainer.min_epochs MIN_EPOCHS]\r\n", | |
| " [--trainer.max_steps MAX_STEPS]\r\n", | |
| " [--trainer.min_steps MIN_STEPS]\r\n", | |
| " [--trainer.max_time MAX_TIME]\r\n", | |
| " [--trainer.limit_train_batches LIMIT_TRAIN_BATCHES]\r\n", | |
| " [--trainer.limit_val_batches LIMIT_VAL_BATCHES]\r\n", | |
| " [--trainer.limit_test_batches LIMIT_TEST_BATCHES]\r\n", | |
| " [--trainer.limit_predict_batches LIMIT_PREDICT_BATCHES]\r\n", | |
| " [--trainer.val_check_interval VAL_CHECK_INTERVAL]\r\n", | |
| " [--trainer.flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS]\r\n", | |
| " [--trainer.log_every_n_steps LOG_EVERY_N_STEPS]\r\n", | |
| " [--trainer.accelerator ACCELERATOR]\r\n", | |
| " [--trainer.sync_batchnorm {true,false}]\r\n", | |
| " [--trainer.precision PRECISION]\r\n", | |
| " [--trainer.weights_summary WEIGHTS_SUMMARY]\r\n", | |
| " [--trainer.weights_save_path WEIGHTS_SAVE_PATH]\r\n", | |
| " [--trainer.num_sanity_val_steps NUM_SANITY_VAL_STEPS]\r\n", | |
| " [--trainer.truncated_bptt_steps TRUNCATED_BPTT_STEPS]\r\n", | |
| " [--trainer.resume_from_checkpoint RESUME_FROM_CHECKPOINT]\r\n", | |
| " [--trainer.profiler PROFILER]\r\n", | |
| " [--trainer.benchmark {true,false}]\r\n", | |
| " [--trainer.deterministic {true,false}]\r\n", | |
| " [--trainer.reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS]\r\n", | |
| " [--trainer.reload_dataloaders_every_epoch {true,false}]\r\n", | |
| " [--trainer.auto_lr_find AUTO_LR_FIND]\r\n", | |
| " [--trainer.replace_sampler_ddp {true,false}]\r\n", | |
| " [--trainer.terminate_on_nan {true,false}]\r\n", | |
| " [--trainer.auto_scale_batch_size AUTO_SCALE_BATCH_SIZE]\r\n", | |
| " [--trainer.prepare_data_per_node {true,false}]\r\n", | |
| " [--trainer.plugins PLUGINS]\r\n", | |
| " [--trainer.amp_backend AMP_BACKEND]\r\n", | |
| " [--trainer.amp_level AMP_LEVEL]\r\n", | |
| " [--trainer.distributed_backend DISTRIBUTED_BACKEND]\r\n", | |
| " [--trainer.move_metrics_to_cpu {true,false}]\r\n", | |
| " [--trainer.multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE]\r\n", | |
| " [--trainer.stochastic_weight_avg {true,false}]\r\n", | |
| " [--model.backbone BACKBONE] [--model.head HEAD]\r\n", | |
| " [--model.pretrained {true,false}]\r\n", | |
| " [--model.optimizer OPTIMIZER]\r\n", | |
| " [--model.optimizer_kwargs OPTIMIZER_KWARGS]\r\n", | |
| " [--model.scheduler SCHEDULER]\r\n", | |
| " [--model.scheduler_kwargs SCHEDULER_KWARGS]\r\n", | |
| " [--model.learning_rate LEARNING_RATE]\r\n", | |
| " [--model.serializer SERIALIZER]\r\n", | |
| " [--model.num_classes NUM_CLASSES]\r\n", | |
| " {from_coco,from_voc,from_files,from_folders,from_datasets,from_pets}\r\n", | |
| " ...\r\n", | |
| "\r\n", | |
| "pytorch-lightning trainer command line tool\r\n", | |
| "\r\n", | |
| "optional arguments:\r\n", | |
| " -h, --help Show this help message and exit.\r\n", | |
| " --config CONFIG Path to a configuration file in json or yaml format.\r\n", | |
| " --print_config [={comments,skip_null}+]\r\n", | |
| " Print configuration and exit.\r\n", | |
| " --seed_everything SEED_EVERYTHING\r\n", | |
| " Set to an int to run seed_everything with this value\r\n", | |
| " before classes instantiation (type: Optional[int],\r\n", | |
| " default: null)\r\n", | |
| "\r\n", | |
| "Customize every aspect of training via flags:\r\n", | |
| " --trainer.serve_sanity_check {true,false}\r\n", | |
| " (type: bool, default: False)\r\n", | |
| " --trainer.logger LOGGER\r\n", | |
| " Logger (or iterable collection of loggers) for\r\n", | |
| " experiment tracking. A ``True`` value uses the default\r\n", | |
| " ``TensorBoardLogger``. ``False`` will disable logging.\r\n", | |
| " If multiple loggers are provided and the `save_dir`\r\n", | |
| " property of that logger is not set, local files\r\n", | |
| " (checkpoints, profiler traces, etc.) are saved in\r\n", | |
| " ``default_root_dir`` rather than in the ``log_dir`` of\r\n", | |
| " any of the individual loggers. (type:\r\n", | |
| " Union[LightningLoggerBase,\r\n", | |
| " Iterable[LightningLoggerBase], bool], default: True)\r\n", | |
| " --trainer.checkpoint_callback {true,false}\r\n", | |
| " If ``True``, enable checkpointing. It will configure a\r\n", | |
| " default ModelCheckpoint callback if there is no user-\r\n", | |
| " defined ModelCheckpoint in :paramref:`~pytorch_lightni\r\n", | |
| " ng.trainer.trainer.Trainer.callbacks`. (type: bool,\r\n", | |
| " default: True)\r\n", | |
| " --trainer.callbacks CALLBACKS\r\n", | |
| " Add a callback or list of callbacks. (type:\r\n", | |
| " Union[List[Callback], Callback, null], default: null)\r\n", | |
| " --trainer.default_root_dir DEFAULT_ROOT_DIR\r\n", | |
| " Default path for logs and weights when no\r\n", | |
| " logger/ckpt_callback passed. Default: ``os.getcwd()``.\r\n", | |
| " Can be remote file paths such as `s3://mybucket/path`\r\n", | |
| " or 'hdfs://path/' (type: Optional[str], default: null)\r\n", | |
| " --trainer.gradient_clip_val GRADIENT_CLIP_VAL\r\n", | |
| " 0 means don't clip. (type: Union[int, float], default:\r\n", | |
| " 0.0)\r\n", | |
| " --trainer.gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM\r\n", | |
| " 'value' means clip_by_value, 'norm' means\r\n", | |
| " clip_by_norm. Default: 'norm' (type: str, default:\r\n", | |
| " norm)\r\n", | |
| " --trainer.process_position PROCESS_POSITION\r\n", | |
| " orders the progress bar when running multiple models\r\n", | |
| " on same machine. (type: int, default: 0)\r\n", | |
| " --trainer.num_nodes NUM_NODES\r\n", | |
| " number of GPU nodes for distributed training. (type:\r\n", | |
| " int, default: 1)\r\n", | |
| " --trainer.num_processes NUM_PROCESSES\r\n", | |
| " number of processes for distributed training with\r\n", | |
| " distributed_backend=\"ddp_cpu\" (type: int, default: 1)\r\n", | |
| " --trainer.devices DEVICES\r\n", | |
| " Will be mapped to either `gpus`, `tpu_cores`,\r\n", | |
| " `num_processes` or `ipus`, based on the accelerator\r\n", | |
| " type. (type: Union[int, str, List[int], null],\r\n", | |
| " default: null)\r\n", | |
| " --trainer.gpus GPUS number of gpus to train on (int) or which GPUs to\r\n", | |
| " train on (list or str) applied per node (type:\r\n", | |
| " Union[int, str, List[int], null], default: null)\r\n", | |
| " --trainer.auto_select_gpus {true,false}\r\n", | |
| " If enabled and `gpus` is an integer, pick available\r\n", | |
| " gpus automatically. This is especially useful when\r\n", | |
| " GPUs are configured to be in \"exclusive mode\", such\r\n", | |
| " that only one process at a time can access them.\r\n", | |
| " (type: bool, default: False)\r\n", | |
| " --trainer.tpu_cores TPU_CORES\r\n", | |
| " How many TPU cores to train on (1 or 8) / Single TPU\r\n", | |
| " to train on [1] (type: Union[int, str, List[int],\r\n", | |
| " null], default: null)\r\n", | |
| " --trainer.ipus IPUS How many IPUs to train on. (type: Optional[int],\r\n", | |
| " default: null)\r\n", | |
| " --trainer.log_gpu_memory LOG_GPU_MEMORY\r\n", | |
| " None, 'min_max', 'all'. Might slow performance (type:\r\n", | |
| " Optional[str], default: null)\r\n", | |
| " --trainer.progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE\r\n", | |
| " How often to refresh progress bar (in steps). Value\r\n", | |
| " ``0`` disables progress bar. Ignored when a custom\r\n", | |
| " progress bar is passed to\r\n", | |
| " :paramref:`~Trainer.callbacks`. Default: None, means a\r\n", | |
| " suitable value will be chosen based on the environment\r\n", | |
| " (terminal, Google COLAB, etc.). (type: Optional[int],\r\n", | |
| " default: null)\r\n", | |
| " --trainer.overfit_batches OVERFIT_BATCHES\r\n", | |
| " Overfit a fraction of training data (float) or a set\r\n", | |
| " number of batches (int). (type: Union[int, float],\r\n", | |
| " default: 0.0)\r\n", | |
| " --trainer.track_grad_norm TRACK_GRAD_NORM\r\n", | |
| " -1 no tracking. Otherwise tracks that p-norm. May be\r\n", | |
| " set to 'inf' infinity-norm. (type: Union[int, float,\r\n", | |
| " str], default: -1)\r\n", | |
| " --trainer.check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH\r\n", | |
| " Check val every n train epochs. (type: int, default:\r\n", | |
| " 1)\r\n", | |
| " --trainer.fast_dev_run FAST_DEV_RUN\r\n", | |
| " runs n if set to ``n`` (int) else 1 if set to ``True``\r\n", | |
| " batch(es) of train, val and test to find any bugs (ie:\r\n", | |
| " a sort of unit test). (type: Union[int, bool],\r\n", | |
| " default: False)\r\n", | |
| " --trainer.accumulate_grad_batches ACCUMULATE_GRAD_BATCHES\r\n", | |
| " Accumulates grads every k batches or as set up in the\r\n", | |
| " dict. (type: Union[int, Dict[int, int], List[list]],\r\n", | |
| " default: 1)\r\n", | |
| " --trainer.max_epochs MAX_EPOCHS\r\n", | |
| " Stop training once this number of epochs is reached.\r\n", | |
| " Disabled by default (None). If both max_epochs and\r\n", | |
| " max_steps are not specified, defaults to\r\n", | |
| " ``max_epochs`` = 1000. (type: Optional[int], default:\r\n", | |
| " 3)\r\n", | |
| " --trainer.min_epochs MIN_EPOCHS\r\n", | |
| " Force training for at least these many epochs.\r\n", | |
| " Disabled by default (None). If both min_epochs and\r\n", | |
| " min_steps are not specified, defaults to\r\n", | |
| " ``min_epochs`` = 1. (type: Optional[int], default:\r\n", | |
| " null)\r\n", | |
| " --trainer.max_steps MAX_STEPS\r\n", | |
| " Stop training after this number of steps. Disabled by\r\n", | |
| " default (None). (type: Optional[int], default: null)\r\n", | |
| " --trainer.min_steps MIN_STEPS\r\n", | |
| " Force training for at least these number of steps.\r\n", | |
| " Disabled by default (None). (type: Optional[int],\r\n", | |
| " default: null)\r\n", | |
| " --trainer.max_time MAX_TIME\r\n", | |
| " Stop training after this amount of time has passed.\r\n", | |
| " Disabled by default (None). The time duration can be\r\n", | |
| " specified in the format DD:HH:MM:SS (days, hours,\r\n", | |
| " minutes seconds), as a :class:`datetime.timedelta`, or\r\n", | |
| " a dictionary with keys that will be passed to\r\n", | |
| " :class:`datetime.timedelta`. (type: Union[str,\r\n", | |
| " timedelta, Dict[str, int], null], default: null)\r\n", | |
| " --trainer.limit_train_batches LIMIT_TRAIN_BATCHES\r\n", | |
| " How much of training dataset to check (float =\r\n", | |
| " fraction, int = num_batches) (type: Union[int, float],\r\n", | |
| " default: 1.0)\r\n", | |
| " --trainer.limit_val_batches LIMIT_VAL_BATCHES\r\n", | |
| " How much of validation dataset to check (float =\r\n", | |
| " fraction, int = num_batches) (type: Union[int, float],\r\n", | |
| " default: 1.0)\r\n", | |
| " --trainer.limit_test_batches LIMIT_TEST_BATCHES\r\n", | |
| " How much of test dataset to check (float = fraction,\r\n", | |
| " int = num_batches) (type: Union[int, float], default:\r\n", | |
| " 1.0)\r\n", | |
| " --trainer.limit_predict_batches LIMIT_PREDICT_BATCHES\r\n", | |
| " How much of prediction dataset to check (float =\r\n", | |
| " fraction, int = num_batches) (type: Union[int, float],\r\n", | |
| " default: 1.0)\r\n", | |
| " --trainer.val_check_interval VAL_CHECK_INTERVAL\r\n", | |
| " How often to check the validation set. Use float to\r\n", | |
| " check within a training epoch, use int to check every\r\n", | |
| " n steps (batches). (type: Union[int, float], default:\r\n", | |
| " 1.0)\r\n", | |
| " --trainer.flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS\r\n", | |
| " How often to flush logs to disk (defaults to every 100\r\n", | |
| " steps). (type: int, default: 100)\r\n", | |
| " --trainer.log_every_n_steps LOG_EVERY_N_STEPS\r\n", | |
| " How often to log within steps (defaults to every 50\r\n", | |
| " steps). (type: int, default: 50)\r\n", | |
| " --trainer.accelerator ACCELERATOR\r\n", | |
| " Previously known as distributed_backend (dp, ddp,\r\n", | |
| " ddp2, etc...). Can also take in an accelerator object\r\n", | |
| " for custom hardware. (type: Union[str, Accelerator,\r\n", | |
| " null], default: null)\r\n", | |
| " --trainer.sync_batchnorm {true,false}\r\n", | |
| " Synchronize batch norm layers between process\r\n", | |
| " groups/whole world. (type: bool, default: False)\r\n", | |
| " --trainer.precision PRECISION\r\n", | |
| " Double precision (64), full precision (32) or half\r\n", | |
| " precision (16). Can be used on CPU, GPU or TPUs.\r\n", | |
| " (type: int, default: 32)\r\n", | |
| " --trainer.weights_summary WEIGHTS_SUMMARY\r\n", | |
| " Prints a summary of the weights when training begins.\r\n", | |
| " (type: Optional[str], default: top)\r\n", | |
| " --trainer.weights_save_path WEIGHTS_SAVE_PATH\r\n", | |
| " Where to save weights if specified. Will override\r\n", | |
| " default_root_dir for checkpoints only. Use this if for\r\n", | |
| " whatever reason you need the checkpoints stored in a\r\n", | |
| " different place than the logs written in\r\n", | |
| " `default_root_dir`. Can be remote file paths such as\r\n", | |
| " `s3://mybucket/path` or 'hdfs://path/' Defaults to\r\n", | |
| " `default_root_dir`. (type: Optional[str], default:\r\n", | |
| " null)\r\n", | |
| " --trainer.num_sanity_val_steps NUM_SANITY_VAL_STEPS\r\n", | |
| " Sanity check runs n validation batches before starting\r\n", | |
| " the training routine. Set it to `-1` to run all\r\n", | |
| " batches in all validation dataloaders. (type: int,\r\n", | |
| " default: 2)\r\n", | |
| " --trainer.truncated_bptt_steps TRUNCATED_BPTT_STEPS\r\n", | |
| " Deprecated in v1.3 to be removed in 1.5. Please use :p\r\n", | |
| " aramref:`~pytorch_lightning.core.lightning.LightningMo\r\n", | |
| " dule.truncated_bptt_steps` instead. (type:\r\n", | |
| " Optional[int], default: null)\r\n", | |
| " --trainer.resume_from_checkpoint RESUME_FROM_CHECKPOINT\r\n", | |
| " Path/URL of the checkpoint from which training is\r\n", | |
| " resumed. If there is no checkpoint file at the path,\r\n", | |
| " start from scratch. If resuming from mid-epoch\r\n", | |
| " checkpoint, training will start from the beginning of\r\n", | |
| " the next epoch. (type: Union[str, Path, null],\r\n", | |
| " default: null)\r\n", | |
| " --trainer.profiler PROFILER\r\n", | |
| " To profile individual steps during training and assist\r\n", | |
| " in identifying bottlenecks. (type: Union[BaseProfiler,\r\n", | |
| " str, null], default: null)\r\n", | |
| " --trainer.benchmark {true,false}\r\n", | |
| " If true enables cudnn.benchmark. (type: bool, default:\r\n", | |
| " False)\r\n", | |
| " --trainer.deterministic {true,false}\r\n", | |
| " If true enables cudnn.deterministic. (type: bool,\r\n", | |
| " default: False)\r\n", | |
| " --trainer.reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS\r\n", | |
| " Set to a non-negative integer to reload dataloaders\r\n", | |
| " every n epochs. Default: 0 (type: int, default: 0)\r\n", | |
| " --trainer.reload_dataloaders_every_epoch {true,false}\r\n", | |
| " Set to True to reload dataloaders every epoch. ..\r\n", | |
| " deprecated:: v1.4 ``reload_dataloaders_every_epoch``\r\n", | |
| " has been deprecated in v1.4 and will be removed in\r\n", | |
| " v1.6. Please use\r\n", | |
| " ``reload_dataloaders_every_n_epochs``. (type: bool,\r\n", | |
| " default: False)\r\n", | |
| " --trainer.auto_lr_find AUTO_LR_FIND\r\n", | |
| " If set to True, will make trainer.tune() run a\r\n", | |
| " learning rate finder, trying to optimize initial\r\n", | |
| " learning for faster convergence. trainer.tune() method\r\n", | |
| " will set the suggested learning rate in self.lr or\r\n", | |
| " self.learning_rate in the LightningModule. To use a\r\n", | |
| " different key set a string instead of True with the\r\n", | |
| " key name. (type: Union[bool, str], default: False)\r\n", | |
| " --trainer.replace_sampler_ddp {true,false}\r\n", | |
| " Explicitly enables or disables sampler replacement. If\r\n", | |
| " not specified this will toggled automatically when DDP\r\n", | |
| " is used. By default it will add ``shuffle=True`` for\r\n", | |
| " train sampler and ``shuffle=False`` for val/test\r\n", | |
| " sampler. If you want to customize it, you can set\r\n", | |
| " ``replace_sampler_ddp=False`` and add your own\r\n", | |
| " distributed sampler. (type: bool, default: True)\r\n", | |
| " --trainer.terminate_on_nan {true,false}\r\n", | |
| " If set to True, will terminate training (by raising a\r\n", | |
| " `ValueError`) at the end of each training batch, if\r\n", | |
| " any of the parameters or the loss are NaN or +/-inf.\r\n", | |
| " (type: bool, default: False)\r\n", | |
| " --trainer.auto_scale_batch_size AUTO_SCALE_BATCH_SIZE\r\n", | |
| " If set to True, will `initially` run a batch size\r\n", | |
| " finder trying to find the largest batch size that fits\r\n", | |
| " into memory. The result will be stored in\r\n", | |
| " self.batch_size in the LightningModule. Additionally,\r\n", | |
| " can be set to either `power` that estimates the batch\r\n", | |
| " size through a power search or `binsearch` that\r\n", | |
| " estimates the batch size through a binary search.\r\n", | |
| " (type: Union[str, bool], default: False)\r\n", | |
| " --trainer.prepare_data_per_node {true,false}\r\n", | |
| " If True, each LOCAL_RANK=0 will call prepare data.\r\n", | |
| " Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare\r\n", | |
| " data (type: bool, default: True)\r\n", | |
| " --trainer.plugins PLUGINS\r\n", | |
| " Plugins allow modification of core behavior like ddp\r\n", | |
| " and amp, and enable custom lightning plugins. (type:\r\n", | |
| " Union[List[Union[Plugin, ClusterEnvironment, str]],\r\n", | |
| " Plugin, ClusterEnvironment, str, null], default: null)\r\n", | |
| " --trainer.amp_backend AMP_BACKEND\r\n", | |
| " The mixed precision backend to use (\"native\" or\r\n", | |
| " \"apex\") (type: str, default: native)\r\n", | |
| " --trainer.amp_level AMP_LEVEL\r\n", | |
| " The optimization level to use (O1, O2, etc...). (type:\r\n", | |
| " str, default: O2)\r\n", | |
| " --trainer.distributed_backend DISTRIBUTED_BACKEND\r\n", | |
| " deprecated. Please use 'accelerator' (type:\r\n", | |
| " Optional[str], default: null)\r\n", | |
| " --trainer.move_metrics_to_cpu {true,false}\r\n", | |
| " Whether to force internal logged metrics to be moved\r\n", | |
| " to cpu. This can save some gpu memory, but can make\r\n", | |
| " training slower. Use with attention. (type: bool,\r\n", | |
| " default: False)\r\n", | |
| " --trainer.multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE\r\n", | |
| " How to loop over the datasets when there are multiple\r\n", | |
| " train loaders. In 'max_size_cycle' mode, the trainer\r\n", | |
| " ends one epoch when the largest dataset is traversed,\r\n", | |
| " and smaller datasets reload when running out of their\r\n", | |
| " data. In 'min_size' mode, all the datasets reload when\r\n", | |
| " reaching the minimum length of datasets. (type: str,\r\n", | |
| " default: max_size_cycle)\r\n", | |
| " --trainer.stochastic_weight_avg {true,false}\r\n", | |
| " Whether to use `Stochastic Weight Averaging (SWA)\r\n", | |
| " <https://pytorch.org/blog/pytorch-1.6-now-includes-\r\n", | |
| " stochastic-weight-averaging/>_` (type: bool, default:\r\n", | |
| " False)\r\n", | |
| "\r\n", | |
| "The ``InstanceSegmentation`` is a :class:`~flash.Task` for detecting objects in images. For more details, see:\r\n", | |
| " --model.backbone BACKBONE\r\n", | |
| " Pretained backbone CNN architecture. Constructs a\r\n", | |
| " model with a ResNet-50-FPN backbone when no backbone\r\n", | |
| " is specified. (type: Optional[str], default:\r\n", | |
| " resnet18_fpn)\r\n", | |
| " --model.head HEAD (type: Optional[str], default: mask_rcnn)\r\n", | |
| " --model.pretrained {true,false}\r\n", | |
| " if true, returns a model pre-trained on COCO train2017\r\n", | |
| " (type: bool, default: True)\r\n", | |
| " --model.optimizer OPTIMIZER\r\n", | |
| " The optimizer to use for training. Can either be the\r\n", | |
| " actual class or the class name. (type:\r\n", | |
| " Type[Optimizer], default: Adam)\r\n", | |
| " --model.optimizer_kwargs OPTIMIZER_KWARGS\r\n", | |
| " Additional kwargs to use when creating the optimizer\r\n", | |
| " (if not passed as an instance). (type:\r\n", | |
| " Optional[Dict[str, Any]], default: null)\r\n", | |
| " --model.scheduler SCHEDULER\r\n", | |
| " The scheduler or scheduler class to use. (type:\r\n", | |
| " Union[Type[_LRScheduler], str, _LRScheduler, null],\r\n", | |
| " default: null)\r\n", | |
| " --model.scheduler_kwargs SCHEDULER_KWARGS\r\n", | |
| " Additional kwargs to use when creating the scheduler\r\n", | |
| " (if not passed as an instance). (type:\r\n", | |
| " Optional[Dict[str, Any]], default: null)\r\n", | |
| " --model.learning_rate LEARNING_RATE\r\n", | |
| " The learning rate to use for training (type: float,\r\n", | |
| " default: 0.0005)\r\n", | |
| " --model.serializer SERIALIZER\r\n", | |
| " (type: Union[Serializer, Mapping[str, Serializer],\r\n", | |
| " null], default: null)\r\n", | |
| " --model.num_classes NUM_CLASSES\r\n", | |
| " the number of classes for detection, including\r\n", | |
| " background (type: Optional[int], default: null)\r\n", | |
| "\r\n", | |
| "subcommands:\r\n", | |
| " For more details of each subcommand add it as argument followed by --help.\r\n", | |
| "\r\n", | |
| " {from_coco,from_voc,from_files,from_folders,from_datasets,from_pets}\r\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!flash instance_segmentation --help" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "4756df52", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.9.7" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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