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Understanding Clouds from Satellite Images

데이터 셋과 Apex 다운로드

In [1]:
!kaggle datasets download -d ryches/apex-325f5a0
apex-325f5a0.zip: Skipping, found more recently modified local copy (use --force to force download)
In [2]:
!kaggle competitions download -c understanding_cloud_organization --force
Downloading understanding_cloud_organization.zip to /workspace/Kaggle - Cloud seg 3%|█▎ | 195M/5.80G [00:14<10:17, 9.75MB/s]^C
In [6]:
!kaggle datasets download -d ryches/understanding-clouds-resized
understanding-clouds-resized.zip: Skipping, found more recently modified local copy (use --force to force download)

압축 해제

In [4]:
!unzip understanding_cloud_organization.zip -d understanding_cloud_organization
!unzip understanding-cloud-resized.zip -d understanding-cloud-resized
!unzip apex-325f5a0.zip
Archive: understanding_cloud_organization.zip End-of-central-directory signature not found. Either this file is not a zipfile, or it constitutes one disk of a multi-part archive. In the latter case the central directory and zipfile comment will be found on the last disk(s) of this archive. unzip: cannot find zipfile directory in one of understanding_cloud_organization.zip or understanding_cloud_organization.zip.zip, and cannot find understanding_cloud_organization.zip.ZIP, period. unzip: cannot find or open understanding-cloud-resized.zip, understanding-cloud-resized.zip.zip or understanding-cloud-resized.zip.ZIP. Archive: apex-325f5a0.zip replace apex-master/.gitignore? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C
In [7]:
import os
os.listdir()
Out[7]:
['apex-325f5a0.zip',
 'understanding-clouds-resized',
 'understanding_cloud_organization.zip',
 'logs',
 'apex-master',
 '.jovianrc',
 'understanding_cloud_organization',
 "Cloud seg - Turbo Charging Andrew's Pytorch.ipynb",
 '.ipynb_checkpoints',
 'understanding-clouds-resized.zip']

필요한 라이브러리들을 설치한다.

In [9]:
!pip install lz4 && pip install wandb
^C ERROR: Operation cancelled by user
In [12]:
!pip install catalyst
!pip install pretrainedmodels
!pip install git+https://github.com/qubvel/segmentation_models.pytorch
!pip install pytorch_toolbelt
!pip install torchvision==0.4
Requirement already satisfied: catalyst in /opt/conda/lib/python3.6/site-packages (19.10) Requirement already satisfied: plotly>=4.1.0 in /opt/conda/lib/python3.6/site-packages (from catalyst) (4.1.1) Requirement already satisfied: opencv-python in /opt/conda/lib/python3.6/site-packages (from catalyst) (4.1.1.26) Requirement already satisfied: torchvision>=0.2.1 in /opt/conda/lib/python3.6/site-packages (from catalyst) (0.4.0) Requirement already satisfied: matplotlib in /opt/conda/lib/python3.6/site-packages (from catalyst) (3.1.1) Requirement already satisfied: torch>=0.4.1 in /opt/conda/lib/python3.6/site-packages (from catalyst) (1.2.0) Requirement already satisfied: pandas>=0.22 in /opt/conda/lib/python3.6/site-packages (from catalyst) (0.25.2) Requirement already satisfied: imageio in /opt/conda/lib/python3.6/site-packages (from catalyst) (2.5.0) Requirement already satisfied: tqdm>=4.29.1 in /opt/conda/lib/python3.6/site-packages (from catalyst) (4.36.1) Requirement already satisfied: GitPython>=2.1.11 in /opt/conda/lib/python3.6/site-packages (from catalyst) (3.0.2) Requirement already satisfied: scikit-learn>=0.20 in /opt/conda/lib/python3.6/site-packages (from catalyst) (0.21.3) Requirement already satisfied: crc32c>=1.7 in /opt/conda/lib/python3.6/site-packages (from catalyst) (1.7) Requirement already satisfied: seaborn in /opt/conda/lib/python3.6/site-packages (from catalyst) (0.9.0) Requirement already satisfied: tensorboardX>=1.8 in /opt/conda/lib/python3.6/site-packages (from catalyst) (1.9) Requirement already satisfied: scikit-image>=0.14.2 in /opt/conda/lib/python3.6/site-packages (from catalyst) (0.16.2) Requirement already satisfied: PyYAML in /opt/conda/lib/python3.6/site-packages (from catalyst) (5.1.2) Requirement already satisfied: safitty>=1.2.3 in /opt/conda/lib/python3.6/site-packages (from catalyst) (1.2.5) Requirement already satisfied: numpy>=1.16.4 in /opt/conda/lib/python3.6/site-packages (from catalyst) (1.17.3) Requirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.6/site-packages (from plotly>=4.1.0->catalyst) (1.3.3) Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from plotly>=4.1.0->catalyst) (1.12.0) Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.2.1->catalyst) (6.2.1) Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib->catalyst) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->catalyst) (1.1.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->catalyst) (2.4.2) Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib->catalyst) (2.8.0) Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.22->catalyst) (2019.2) Requirement already satisfied: gitdb2>=2.0.0 in /opt/conda/lib/python3.6/site-packages (from GitPython>=2.1.11->catalyst) (2.0.5) Requirement already satisfied: scipy>=0.17.0 in /opt/conda/lib/python3.6/site-packages (from scikit-learn>=0.20->catalyst) (1.3.1) Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.6/site-packages (from scikit-learn>=0.20->catalyst) (0.13.2) Requirement already satisfied: protobuf>=3.8.0 in /opt/conda/lib/python3.6/site-packages (from tensorboardX>=1.8->catalyst) (3.11.1) Requirement already satisfied: PyWavelets>=0.4.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image>=0.14.2->catalyst) (1.0.3) Requirement already satisfied: networkx>=2.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image>=0.14.2->catalyst) (2.3) Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->catalyst) (41.2.0) Requirement already satisfied: smmap2>=2.0.0 in /opt/conda/lib/python3.6/site-packages (from gitdb2>=2.0.0->GitPython>=2.1.11->catalyst) (2.0.5) Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from networkx>=2.0->scikit-image>=0.14.2->catalyst) (4.4.0) Requirement already satisfied: pretrainedmodels in /opt/conda/lib/python3.6/site-packages (0.7.4) Requirement already satisfied: munch in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels) (2.3.2) Requirement already satisfied: torchvision in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels) (0.4.0) Requirement already satisfied: torch in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels) (1.2.0) Requirement already satisfied: tqdm in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels) (4.36.1) Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from munch->pretrainedmodels) (1.12.0) Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from torchvision->pretrainedmodels) (1.17.3) Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.6/site-packages (from torchvision->pretrainedmodels) (6.2.1) Collecting git+https://github.com/qubvel/segmentation_models.pytorch Cloning https://github.com/qubvel/segmentation_models.pytorch to /tmp/pip-req-build-0px2bgy2 Running command git clone -q https://github.com/qubvel/segmentation_models.pytorch /tmp/pip-req-build-0px2bgy2 Requirement already satisfied (use --upgrade to upgrade): segmentation-models-pytorch==0.1.0 from git+https://github.com/qubvel/segmentation_models.pytorch in /opt/conda/lib/python3.6/site-packages Requirement already satisfied: torchvision>=0.3.0 in /opt/conda/lib/python3.6/site-packages (from segmentation-models-pytorch==0.1.0) (0.4.0) Requirement already satisfied: pretrainedmodels==0.7.4 in /opt/conda/lib/python3.6/site-packages (from segmentation-models-pytorch==0.1.0) (0.7.4) Requirement already satisfied: efficientnet-pytorch>=0.5.1 in /opt/conda/lib/python3.6/site-packages (from segmentation-models-pytorch==0.1.0) (0.5.1) Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3.0->segmentation-models-pytorch==0.1.0) (6.2.1) Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3.0->segmentation-models-pytorch==0.1.0) (1.17.3) Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3.0->segmentation-models-pytorch==0.1.0) (1.12.0) Requirement already satisfied: torch==1.2.0 in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3.0->segmentation-models-pytorch==0.1.0) (1.2.0) Requirement already satisfied: munch in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch==0.1.0) (2.3.2) Requirement already satisfied: tqdm in /opt/conda/lib/python3.6/site-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch==0.1.0) (4.36.1) Building wheels for collected packages: segmentation-models-pytorch Building wheel for segmentation-models-pytorch (setup.py) ... done Created wheel for segmentation-models-pytorch: filename=segmentation_models_pytorch-0.1.0-cp36-none-any.whl size=42709 sha256=f7b71f5e553f6a97943342a9428d0f061d8b475eb0842afb609e2fade4a1fad3 Stored in directory: /tmp/pip-ephem-wheel-cache-0nc1ob8f/wheels/79/3f/09/1587a252e0314d26ad242d6d2e165622ab95c95e5cfe4b942c Successfully built segmentation-models-pytorch Requirement already satisfied: pytorch_toolbelt in /opt/conda/lib/python3.6/site-packages (0.2.1) Requirement already satisfied: torch>=1.1 in /opt/conda/lib/python3.6/site-packages (from pytorch_toolbelt) (1.2.0) Requirement already satisfied: torchvision>=0.3 in /opt/conda/lib/python3.6/site-packages (from pytorch_toolbelt) (0.4.0) Requirement already satisfied: opencv-python>=4.0 in /opt/conda/lib/python3.6/site-packages (from pytorch_toolbelt) (4.1.1.26) Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from torch>=1.1->pytorch_toolbelt) (1.17.3) Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3->pytorch_toolbelt) (6.2.1) Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from torchvision>=0.3->pytorch_toolbelt) (1.12.0)
Requirement already satisfied: torchvision==0.4 in /opt/conda/lib/python3.6/site-packages (0.4.0) Requirement already satisfied: torch==1.2.0 in /opt/conda/lib/python3.6/site-packages (from torchvision==0.4) (1.2.0) Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from torchvision==0.4) (1.12.0) Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from torchvision==0.4) (1.17.3) Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.6/site-packages (from torchvision==0.4) (6.2.1)
In [42]:
!pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex-master/
from apex import amp
/opt/conda/lib/python3.6/site-packages/pip/_internal/commands/install.py:243: UserWarning: Disabling all use of wheels due to the use of --build-options / --global-options / --install-options. cmdoptions.check_install_build_global(options) Created temporary directory: /tmp/pip-ephem-wheel-cache-_3motmq8 Created temporary directory: /tmp/pip-req-tracker-6xfnxynk Created requirements tracker '/tmp/pip-req-tracker-6xfnxynk' Created temporary directory: /tmp/pip-install-ipijgl40 Processing ./apex-master Created temporary directory: /tmp/pip-req-build-hsiofdfc Added file:///workspace/Kaggle%20-%20Cloud%20seg/apex-master to build tracker '/tmp/pip-req-tracker-6xfnxynk' Running setup.py (path:/tmp/pip-req-build-hsiofdfc/setup.py) egg_info for package from file:///workspace/Kaggle%20-%20Cloud%20seg/apex-master Running command python setup.py egg_info torch.__version__ = 1.2.0 running egg_info creating pip-egg-info/apex.egg-info writing pip-egg-info/apex.egg-info/PKG-INFO writing dependency_links to pip-egg-info/apex.egg-info/dependency_links.txt writing top-level names to pip-egg-info/apex.egg-info/top_level.txt writing manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt' reading manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt' writing manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt' /tmp/pip-req-build-hsiofdfc/setup.py:33: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies! warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!") Source in /tmp/pip-req-build-hsiofdfc has version 0.1, which satisfies requirement apex==0.1 from file:///workspace/Kaggle%20-%20Cloud%20seg/apex-master Removed apex==0.1 from file:///workspace/Kaggle%20-%20Cloud%20seg/apex-master from build tracker '/tmp/pip-req-tracker-6xfnxynk' Skipping bdist_wheel for apex, due to binaries being disabled for it. Installing collected packages: apex Created temporary directory: /tmp/pip-record-jq6cquni Running command /opt/conda/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-hsiofdfc/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-hsiofdfc/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-jq6cquni/install-record.txt --single-version-externally-managed --compile torch.__version__ = 1.2.0 /tmp/pip-req-build-hsiofdfc/setup.py:33: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies! warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!") Compiling cuda extensions with nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Sat_Aug_25_21:08:01_CDT_2018 Cuda compilation tools, release 10.0, V10.0.130 from /usr/local/cuda/bin running install running build running build_py creating build creating build/lib.linux-x86_64-3.6 creating build/lib.linux-x86_64-3.6/apex copying apex/__init__.py -> build/lib.linux-x86_64-3.6/apex creating build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/__init__.py -> build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/fused_adam.py -> build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/fused_novograd.py -> build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/fused_sgd.py -> build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/fused_lamb.py -> build/lib.linux-x86_64-3.6/apex/optimizers copying apex/optimizers/fp16_optimizer.py -> build/lib.linux-x86_64-3.6/apex/optimizers creating build/lib.linux-x86_64-3.6/apex/RNN copying apex/RNN/__init__.py -> build/lib.linux-x86_64-3.6/apex/RNN copying apex/RNN/cells.py -> build/lib.linux-x86_64-3.6/apex/RNN copying apex/RNN/models.py -> build/lib.linux-x86_64-3.6/apex/RNN copying apex/RNN/RNNBackend.py -> build/lib.linux-x86_64-3.6/apex/RNN creating build/lib.linux-x86_64-3.6/apex/normalization copying apex/normalization/__init__.py -> build/lib.linux-x86_64-3.6/apex/normalization copying apex/normalization/fused_layer_norm.py -> build/lib.linux-x86_64-3.6/apex/normalization creating build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/__init__.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/optimized_sync_batchnorm_kernel.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/sync_batchnorm_kernel.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/sync_batchnorm.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/optimized_sync_batchnorm.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/LARC.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/distributed.py -> build/lib.linux-x86_64-3.6/apex/parallel copying apex/parallel/multiproc.py -> build/lib.linux-x86_64-3.6/apex/parallel creating build/lib.linux-x86_64-3.6/apex/multi_tensor_apply copying apex/multi_tensor_apply/__init__.py -> build/lib.linux-x86_64-3.6/apex/multi_tensor_apply copying apex/multi_tensor_apply/multi_tensor_apply.py -> build/lib.linux-x86_64-3.6/apex/multi_tensor_apply creating build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/_amp_state.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/wrap.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/__init__.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/opt.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/_initialize.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/compat.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/rnn_compat.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/__version__.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/frontend.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/amp.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/_process_optimizer.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/handle.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/scaler.py -> build/lib.linux-x86_64-3.6/apex/amp copying apex/amp/utils.py -> build/lib.linux-x86_64-3.6/apex/amp creating build/lib.linux-x86_64-3.6/apex/contrib copying apex/contrib/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib creating build/lib.linux-x86_64-3.6/apex/pyprof copying apex/pyprof/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof creating build/lib.linux-x86_64-3.6/apex/fp16_utils copying apex/fp16_utils/__init__.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils copying apex/fp16_utils/fp16util.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils copying apex/fp16_utils/loss_scaler.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils copying apex/fp16_utils/fp16_optimizer.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils creating build/lib.linux-x86_64-3.6/apex/reparameterization copying apex/reparameterization/__init__.py -> build/lib.linux-x86_64-3.6/apex/reparameterization copying apex/reparameterization/reparameterization.py -> build/lib.linux-x86_64-3.6/apex/reparameterization copying apex/reparameterization/weight_norm.py -> build/lib.linux-x86_64-3.6/apex/reparameterization creating build/lib.linux-x86_64-3.6/apex/amp/lists copying apex/amp/lists/tensor_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists copying apex/amp/lists/__init__.py -> build/lib.linux-x86_64-3.6/apex/amp/lists copying apex/amp/lists/functional_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists copying apex/amp/lists/torch_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists creating build/lib.linux-x86_64-3.6/apex/contrib/xentropy copying apex/contrib/xentropy/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/xentropy copying apex/contrib/xentropy/softmax_xentropy.py -> build/lib.linux-x86_64-3.6/apex/contrib/xentropy creating build/lib.linux-x86_64-3.6/apex/contrib/groupbn copying apex/contrib/groupbn/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/groupbn copying apex/contrib/groupbn/batch_norm.py -> build/lib.linux-x86_64-3.6/apex/contrib/groupbn creating build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/normalization.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/linear.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/conv.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/pointwise.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/usage.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/misc.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/activation.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/__main__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/utility.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/data.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/recurrentCell.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/embedding.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/index_slice_join_mutate.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/loss.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/blas.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/randomSample.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/dropout.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/softmax.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/optim.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/prof.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/output.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/base.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/convert.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/pooling.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof copying apex/pyprof/prof/reduction.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof creating build/lib.linux-x86_64-3.6/apex/pyprof/nvtx copying apex/pyprof/nvtx/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/nvtx copying apex/pyprof/nvtx/nvmarker.py -> build/lib.linux-x86_64-3.6/apex/pyprof/nvtx creating build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/db.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/__main__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/parse.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/kernel.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse copying apex/pyprof/parse/nvvp.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse running build_ext
building 'apex_C' extension creating build/temp.linux-x86_64-3.6 creating build/temp.linux-x86_64-3.6/csrc gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/opt/conda/include/python3.6m -c csrc/flatten_unflatten.cpp -o build/temp.linux-x86_64-3.6/csrc/flatten_unflatten.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=apex_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/csrc/flatten_unflatten.o -o build/lib.linux-x86_64-3.6/apex_C.cpython-36m-x86_64-linux-gnu.so building 'amp_C' extension gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/amp_C_frontend.cpp -o build/temp.linux-x86_64-3.6/csrc/amp_C_frontend.o -O3 -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_sgd_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_sgd_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_scale_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_scale_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_axpby_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_axpby_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_l2norm_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_l2norm_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_lamb_stage_1.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_1.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_lamb_stage_2.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_2.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_adam.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_adam.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_novograd.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_novograd.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/multi_tensor_lamb.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/csrc/amp_C_frontend.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_sgd_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_scale_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_axpby_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_l2norm_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_1.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_2.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_adam.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_novograd.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/amp_C.cpython-36m-x86_64-linux-gnu.so building 'fused_adam_cuda' extension gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/fused_adam_cuda.cpp -o build/temp.linux-x86_64-3.6/csrc/fused_adam_cuda.o -O3 -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_adam_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/fused_adam_cuda_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/fused_adam_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_adam_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/csrc/fused_adam_cuda.o build/temp.linux-x86_64-3.6/csrc/fused_adam_cuda_kernel.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/fused_adam_cuda.cpython-36m-x86_64-linux-gnu.so building 'syncbn' extension gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/syncbn.cpp -o build/temp.linux-x86_64-3.6/csrc/syncbn.o -O3 -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=syncbn -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/welford.cu -o build/temp.linux-x86_64-3.6/csrc/welford.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=syncbn -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/csrc/syncbn.o build/temp.linux-x86_64-3.6/csrc/welford.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/syncbn.cpython-36m-x86_64-linux-gnu.so building 'fused_layer_norm_cuda' extension gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/layer_norm_cuda.cpp -o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda.o -O3 -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.6/site-packages/torch/include -I/opt/conda/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.6/site-packages/torch/include/TH -I/opt/conda/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.6m -c csrc/layer_norm_cuda_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -maxrregcount=50 -O3 --use_fast_math -DVERSION_GE_1_1 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda.o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda_kernel.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so running install_lib copying build/lib.linux-x86_64-3.6/apex_C.cpython-36m-x86_64-linux-gnu.so -> /opt/conda/lib/python3.6/site-packages copying build/lib.linux-x86_64-3.6/syncbn.cpython-36m-x86_64-linux-gnu.so -> /opt/conda/lib/python3.6/site-packages copying build/lib.linux-x86_64-3.6/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so -> /opt/conda/lib/python3.6/site-packages creating /opt/conda/lib/python3.6/site-packages/apex creating /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/fused_adam.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/fused_novograd.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/fused_sgd.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/fused_lamb.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/optimizers/fp16_optimizer.py -> /opt/conda/lib/python3.6/site-packages/apex/optimizers copying build/lib.linux-x86_64-3.6/apex/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex creating /opt/conda/lib/python3.6/site-packages/apex/RNN copying build/lib.linux-x86_64-3.6/apex/RNN/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/RNN copying build/lib.linux-x86_64-3.6/apex/RNN/cells.py -> /opt/conda/lib/python3.6/site-packages/apex/RNN copying build/lib.linux-x86_64-3.6/apex/RNN/models.py -> /opt/conda/lib/python3.6/site-packages/apex/RNN copying build/lib.linux-x86_64-3.6/apex/RNN/RNNBackend.py -> /opt/conda/lib/python3.6/site-packages/apex/RNN creating /opt/conda/lib/python3.6/site-packages/apex/normalization copying build/lib.linux-x86_64-3.6/apex/normalization/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/normalization copying build/lib.linux-x86_64-3.6/apex/normalization/fused_layer_norm.py -> /opt/conda/lib/python3.6/site-packages/apex/normalization creating /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/optimized_sync_batchnorm_kernel.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/sync_batchnorm_kernel.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/sync_batchnorm.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/optimized_sync_batchnorm.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/LARC.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/distributed.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel copying build/lib.linux-x86_64-3.6/apex/parallel/multiproc.py -> /opt/conda/lib/python3.6/site-packages/apex/parallel creating /opt/conda/lib/python3.6/site-packages/apex/multi_tensor_apply copying build/lib.linux-x86_64-3.6/apex/multi_tensor_apply/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/multi_tensor_apply copying build/lib.linux-x86_64-3.6/apex/multi_tensor_apply/multi_tensor_apply.py -> /opt/conda/lib/python3.6/site-packages/apex/multi_tensor_apply creating /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/_amp_state.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/wrap.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/opt.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/_initialize.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/compat.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/rnn_compat.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/__version__.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/frontend.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/amp.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/_process_optimizer.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/handle.py -> /opt/conda/lib/python3.6/site-packages/apex/amp creating /opt/conda/lib/python3.6/site-packages/apex/amp/lists copying build/lib.linux-x86_64-3.6/apex/amp/lists/tensor_overrides.py -> /opt/conda/lib/python3.6/site-packages/apex/amp/lists copying build/lib.linux-x86_64-3.6/apex/amp/lists/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/amp/lists copying build/lib.linux-x86_64-3.6/apex/amp/lists/functional_overrides.py -> /opt/conda/lib/python3.6/site-packages/apex/amp/lists copying build/lib.linux-x86_64-3.6/apex/amp/lists/torch_overrides.py -> /opt/conda/lib/python3.6/site-packages/apex/amp/lists copying build/lib.linux-x86_64-3.6/apex/amp/scaler.py -> /opt/conda/lib/python3.6/site-packages/apex/amp copying build/lib.linux-x86_64-3.6/apex/amp/utils.py -> /opt/conda/lib/python3.6/site-packages/apex/amp creating /opt/conda/lib/python3.6/site-packages/apex/contrib copying build/lib.linux-x86_64-3.6/apex/contrib/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/contrib creating /opt/conda/lib/python3.6/site-packages/apex/contrib/xentropy copying build/lib.linux-x86_64-3.6/apex/contrib/xentropy/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/contrib/xentropy copying build/lib.linux-x86_64-3.6/apex/contrib/xentropy/softmax_xentropy.py -> /opt/conda/lib/python3.6/site-packages/apex/contrib/xentropy creating /opt/conda/lib/python3.6/site-packages/apex/contrib/groupbn copying build/lib.linux-x86_64-3.6/apex/contrib/groupbn/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/contrib/groupbn copying build/lib.linux-x86_64-3.6/apex/contrib/groupbn/batch_norm.py -> /opt/conda/lib/python3.6/site-packages/apex/contrib/groupbn creating /opt/conda/lib/python3.6/site-packages/apex/pyprof copying build/lib.linux-x86_64-3.6/apex/pyprof/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof creating /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/normalization.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/linear.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/conv.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/pointwise.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/usage.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/misc.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/activation.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/__main__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/utility.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/data.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/recurrentCell.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof
copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/embedding.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/index_slice_join_mutate.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/loss.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/blas.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/randomSample.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/dropout.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/softmax.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/optim.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/prof.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/output.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/base.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/convert.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/pooling.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof copying build/lib.linux-x86_64-3.6/apex/pyprof/prof/reduction.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof creating /opt/conda/lib/python3.6/site-packages/apex/pyprof/nvtx copying build/lib.linux-x86_64-3.6/apex/pyprof/nvtx/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/nvtx copying build/lib.linux-x86_64-3.6/apex/pyprof/nvtx/nvmarker.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/nvtx creating /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/db.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/__main__.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/parse.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/kernel.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse copying build/lib.linux-x86_64-3.6/apex/pyprof/parse/nvvp.py -> /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse creating /opt/conda/lib/python3.6/site-packages/apex/fp16_utils copying build/lib.linux-x86_64-3.6/apex/fp16_utils/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/fp16_utils copying build/lib.linux-x86_64-3.6/apex/fp16_utils/fp16util.py -> /opt/conda/lib/python3.6/site-packages/apex/fp16_utils copying build/lib.linux-x86_64-3.6/apex/fp16_utils/loss_scaler.py -> /opt/conda/lib/python3.6/site-packages/apex/fp16_utils copying build/lib.linux-x86_64-3.6/apex/fp16_utils/fp16_optimizer.py -> /opt/conda/lib/python3.6/site-packages/apex/fp16_utils creating /opt/conda/lib/python3.6/site-packages/apex/reparameterization copying build/lib.linux-x86_64-3.6/apex/reparameterization/__init__.py -> /opt/conda/lib/python3.6/site-packages/apex/reparameterization copying build/lib.linux-x86_64-3.6/apex/reparameterization/reparameterization.py -> /opt/conda/lib/python3.6/site-packages/apex/reparameterization copying build/lib.linux-x86_64-3.6/apex/reparameterization/weight_norm.py -> /opt/conda/lib/python3.6/site-packages/apex/reparameterization copying build/lib.linux-x86_64-3.6/fused_adam_cuda.cpython-36m-x86_64-linux-gnu.so -> /opt/conda/lib/python3.6/site-packages copying build/lib.linux-x86_64-3.6/amp_C.cpython-36m-x86_64-linux-gnu.so -> /opt/conda/lib/python3.6/site-packages byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/fused_adam.py to fused_adam.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/fused_novograd.py to fused_novograd.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/fused_sgd.py to fused_sgd.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/fused_lamb.py to fused_lamb.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/optimizers/fp16_optimizer.py to fp16_optimizer.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/RNN/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/RNN/cells.py to cells.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/RNN/models.py to models.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/RNN/RNNBackend.py to RNNBackend.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/normalization/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/normalization/fused_layer_norm.py to fused_layer_norm.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/optimized_sync_batchnorm_kernel.py to optimized_sync_batchnorm_kernel.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/sync_batchnorm_kernel.py to sync_batchnorm_kernel.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/sync_batchnorm.py to sync_batchnorm.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/optimized_sync_batchnorm.py to optimized_sync_batchnorm.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/LARC.py to LARC.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/distributed.py to distributed.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/parallel/multiproc.py to multiproc.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/multi_tensor_apply/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/multi_tensor_apply/multi_tensor_apply.py to multi_tensor_apply.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/_amp_state.py to _amp_state.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/wrap.py to wrap.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/opt.py to opt.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/_initialize.py to _initialize.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/compat.py to compat.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/rnn_compat.py to rnn_compat.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/__version__.py to __version__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/frontend.py to frontend.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/amp.py to amp.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/_process_optimizer.py to _process_optimizer.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/handle.py to handle.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/lists/tensor_overrides.py to tensor_overrides.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/lists/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/lists/functional_overrides.py to functional_overrides.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/lists/torch_overrides.py to torch_overrides.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/scaler.py to scaler.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/amp/utils.py to utils.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/contrib/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/contrib/xentropy/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/contrib/xentropy/softmax_xentropy.py to softmax_xentropy.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/contrib/groupbn/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/contrib/groupbn/batch_norm.py to batch_norm.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/normalization.py to normalization.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/linear.py to linear.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/conv.py to conv.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/pointwise.py to pointwise.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/usage.py to usage.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/misc.py to misc.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/activation.py to activation.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/__main__.py to __main__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/utility.py to utility.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/data.py to data.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/recurrentCell.py to recurrentCell.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/embedding.py to embedding.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/index_slice_join_mutate.py to index_slice_join_mutate.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/loss.py to loss.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/blas.py to blas.cpython-36.pyc
byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/randomSample.py to randomSample.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/dropout.py to dropout.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/softmax.py to softmax.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/optim.py to optim.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/prof.py to prof.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/output.py to output.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/base.py to base.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/convert.py to convert.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/pooling.py to pooling.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/prof/reduction.py to reduction.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/nvtx/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/nvtx/nvmarker.py to nvmarker.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/db.py to db.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/__main__.py to __main__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/parse.py to parse.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/kernel.py to kernel.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/pyprof/parse/nvvp.py to nvvp.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/fp16_utils/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/fp16_utils/fp16util.py to fp16util.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/fp16_utils/loss_scaler.py to loss_scaler.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/fp16_utils/fp16_optimizer.py to fp16_optimizer.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/reparameterization/__init__.py to __init__.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/reparameterization/reparameterization.py to reparameterization.cpython-36.pyc byte-compiling /opt/conda/lib/python3.6/site-packages/apex/reparameterization/weight_norm.py to weight_norm.cpython-36.pyc running install_egg_info running egg_info creating apex.egg-info writing apex.egg-info/PKG-INFO writing dependency_links to apex.egg-info/dependency_links.txt writing top-level names to apex.egg-info/top_level.txt writing manifest file 'apex.egg-info/SOURCES.txt' reading manifest file 'apex.egg-info/SOURCES.txt' writing manifest file 'apex.egg-info/SOURCES.txt' Copying apex.egg-info to /opt/conda/lib/python3.6/site-packages/apex-0.1-py3.6.egg-info running install_scripts writing list of installed files to '/tmp/pip-record-jq6cquni/install-record.txt' Running setup.py install for apex ... done Removing source in /tmp/pip-req-build-hsiofdfc Successfully installed apex-0.1 Cleaning up... 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사용할 라이브러리들을 임포트한다.

In [10]:

import os
import cv2
import collections
import time
import tqdm
from PIL import Image
from functools import partial
train_on_gpu = True

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

import torchvision
import torchvision.transforms as transforms
import torch
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.utils.data.sampler import SubsetRandomSampler
from torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau, CosineAnnealingLR

import albumentations as albu
from albumentations import pytorch as AT

from catalyst.data import Augmentor
from catalyst.dl import utils
from catalyst.data.reader import ImageReader, ScalarReader, ReaderCompose, LambdaReader
from catalyst.dl.runner import SupervisedRunner
from catalyst.contrib.models.segmentation import Unet
from catalyst.dl.callbacks import DiceCallback, EarlyStoppingCallback, InferCallback, CheckpointCallback

import segmentation_models_pytorch as smp
device=torch.device('cuda')
In [47]:
## 이후에 사용할 helper function들을 정의한다.
In [11]:

def get_img(x, folder: str='train_images_525/train_images_525'):
    """
    image 이름과 폴더를 받아 image를 반환한다.
    """
    data_folder = f"{img_paths}/{folder}"
    image_path = os.path.join(data_folder, x)
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img


def rle_decode(mask_rle: str = '', shape: tuple = (1400, 2100)):
    '''
    rle encoded mask를 decode한다.
    '''
    s = mask_rle.split()
    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
    starts -= 1
    ends = starts + lengths
    img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
    for lo, hi in zip(starts, ends):
        img[lo:hi] = 1
    return img.reshape(shape, order='F')


def make_mask(df: pd.DataFrame, image_name: str='img.jpg', shape: tuple = (350, 525)):
    """
    이미지이름과 shape, 그리고 df에 기반해서 마스크를 생성한다.
    Create mask based on df, image name and shape.
    """
    masks = np.zeros((shape[0], shape[1], 4), dtype=np.float32)
    df = df[df["im_id"] == image_name]
    for idx, im_name in enumerate(df["im_id"].values):
        for classidx, classid in enumerate(["Fish", "Flower", "Gravel", "Sugar"]):
            mask = cv2.imread("./understanding-clouds-resized/train_masks_525/train_masks_525/" + classid + im_name)
            if mask is None:
                continue
            if mask[:,:,0].shape != (350,525):
                mask = cv2.resize(mask, (525,350))
            masks[:, :, classidx] = mask[:,:,0]
    masks = masks/255
    return masks


def to_tensor(x, **kwargs):
    """
    이미지나 마스크를 텐서로 변환한다.
    """
    return x.transpose(2, 0, 1).astype('float32')


def mask2rle(img):
    '''
    마스크를 rle로 변환한다.
    '''
    pixels= img.T.flatten()
    pixels = np.concatenate([[0], pixels, [0]])
    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
    runs[1::2] -= runs[::2]
    return ' '.join(str(x) for x in runs)


def visualize(image, mask, original_image=None, original_mask=None):
    """
    image나 mask를 시각화한다.
    둘 다 인자로 전달되면 함께 보여준다.
    """
    fontsize = 14
    class_dict = {0: 'Fish', 1: 'Flower', 2: 'Gravel', 3: 'Sugar'}
    
    if original_image is None and original_mask is None:
        f, ax = plt.subplots(1, 5, figsize=(24, 24))

        ax[0].imshow(image)
        for i in range(4):
            ax[i + 1].imshow(mask[:, :, i])
            ax[i + 1].set_title(f'Mask {class_dict[i]}', fontsize=fontsize)
    else:
        f, ax = plt.subplots(2, 5, figsize=(24, 12))

        ax[0, 0].imshow(original_image)
        ax[0, 0].set_title('Original image', fontsize=fontsize)
                
        for i in range(4):
            ax[0, i + 1].imshow(original_mask[:, :, i])
            ax[0, i + 1].set_title(f'Original mask {class_dict[i]}', fontsize=fontsize)
        
        ax[1, 0].imshow(image)
        ax[1, 0].set_title('Transformed image', fontsize=fontsize)
        
        
        for i in range(4):
            ax[1, i + 1].imshow(mask[:, :, i])
            ax[1, i + 1].set_title(f'Transformed mask {class_dict[i]}', fontsize=fontsize)
            
            
def visualize_with_raw(image, mask, original_image=None, original_mask=None, raw_image=None, raw_mak=None):
    """
    image나 mask를 시각화한다.
    둘 다 인자로 전달되면 함께 보여준다.
    """
    fontsize = 14
    class_dict = {0: 'Fish', 1: 'Flower', 2: 'Gravel', 3: 'Sugar'}

    f, ax = plt.subplots(3, 5, figsize=(24, 12))

    ax[0, 0].imshow(original_image)
    ax[0, 0].set_title('Original image', fontsize=fontsize)

    for i in range(4):
        ax[0, i + 1].imshow(original_mask[:, :, i])
        ax[0, i + 1].set_title(f'Original mask {class_dict[i]}', fontsize=fontsize)


    ax[1, 0].imshow(raw_image)
    ax[1, 0].set_title('Original image', fontsize=fontsize)

    for i in range(4):
        ax[1, i + 1].imshow(raw_mak[:, :, i])
        ax[1, i + 1].set_title(f'Raw predicted mask {class_dict[i]}', fontsize=fontsize)
        
    ax[2, 0].imshow(image)
    ax[2, 0].set_title('Transformed image', fontsize=fontsize)


    for i in range(4):
        ax[2, i + 1].imshow(mask[:, :, i])
        ax[2, i + 1].set_title(f'Predicted mask with processing {class_dict[i]}', fontsize=fontsize)
            
            
def plot_with_augmentation(image, mask, augment):
    """
    visualize function의 helper function
    """
    augmented = augment(image=image, mask=mask)
    image_flipped = augmented['image']
    mask_flipped = augmented['mask']
    visualize(image_flipped, mask_flipped, original_image=image, original_mask=mask)
    
    
sigmoid = lambda x: 1 / (1 + np.exp(-x))


def post_process(probability, threshold, min_size):
    # don't remember where I saw it
    mask = cv2.threshold(probability, threshold, 1, cv2.THRESH_BINARY)[1]
    num_component, component = cv2.connectedComponents(mask.astype(np.uint8))
    predictions = np.zeros((350, 525), np.float32)
    num = 0
    for c in range(1, num_component):
        p = (component == c)
        if p.sum() > min_size:
            predictions[p] = 1
            num += 1
    return predictions, num


def get_training_augmentation():
    train_transform = [
        albu.Resize(320, 640),
        albu.HorizontalFlip(p=0.25),
        albu.VerticalFlip(p=0.25),
        albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=0.5, border_mode=0)
#         albu.GridDistortion(p=0.5),
#         albu.OpticalDistortion(p=0.5, distort_limit=2, shift_limit=0.5),

    ]
    return albu.Compose(train_transform)


def get_validation_augmentation():
    """이미지가 32로 나누어떨어지게 만들기 위해 padding을 추가한다."""
    test_transform = [
        albu.Resize(320, 640)
    ]
    return albu.Compose(test_transform)


def get_preprocessing(preprocessing_fn):
    """preprocessing function을 받아서 전처리를 위한 Compose로 만든다.
    
    """
    
    _transform = [
        albu.Lambda(image=preprocessing_fn),
        albu.Lambda(image=to_tensor, mask=to_tensor),
    ]
    return albu.Compose(_transform)


def dice(img1, img2):
    """
    DICE metric
    """
    img1 = np.asarray(img1).astype(np.bool)
    img2 = np.asarray(img2).astype(np.bool)

    intersection = np.logical_and(img1, img2)

    return 2. * intersection.sum() / (img1.sum() + img2.sum())
In [15]:
# original data set과 resized data set의 path
path = './understanding_cloud_organization'
img_paths = './understanding-clouds-resized'
os.listdir(path)
Out[15]:
['test_images', 'train_images', 'train.csv', 'sample_submission.csv']

Pandas library를 이용하여 데이터셋에 대한 정보를 담고있는 csv파일들을 읽어온다.

In [16]:
train = pd.read_csv(f'{path}/train.csv')
sub = pd.read_csv(f'{path}/sample_submission.csv')

간단하게 훈련 데이터 셋의 앞부분을 엿본다.

In [17]:
train.head()
Out[17]:

5546개의 train data와 3698개의 test data가 있는 것을 확인할 수 있다.

In [19]:
n_train = len(os.listdir(f'{img_paths}/train_images_525/train_images_525'))
n_test = len(os.listdir(f'{img_paths}/test_images_525/test_images_525'))
print(f'There are {n_train} images in train dataset')
print(f'There are {n_test} images in test dataset')
There are 5546 images in train dataset There are 3698 images in test dataset

EDA

각 image_label에 해당하는 image의 개수를 센다. 각 Image에 대해 image_label이 Null으로라도 존재하므로 당연히 dataset의 길이와 같다.

In [20]:
train['Image_Label'].apply(lambda x: x.split('_')[1]).value_counts()
Out[20]:
Gravel    5546
Flower    5546
Fish      5546
Sugar     5546
Name: Image_Label, dtype: int64

Null이 아닌 데이터에 대해서만 특정 image_label에 해당하는 데이터의 개수를 센다.

In [21]:
train.loc[train['EncodedPixels'].isnull() == False, 'Image_Label'].apply(lambda x: x.split('_')[1]).value_counts()
Out[21]:
Sugar     3751
Gravel    2939
Fish      2781
Flower    2365
Name: Image_Label, dtype: int64

각 이미지들이 가지고 있는 image label들의 수의 분포

In [22]:
train.loc[train['EncodedPixels'].isnull() == False, 'Image_Label'].apply(lambda x: x.split('_')[0]).value_counts().value_counts()
Out[22]:
2    2372
3    1560
1    1348
4     266
Name: Image_Label, dtype: int64

column을 분리한다.

In [23]:
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
train['im_id'] = train['Image_Label'].apply(lambda x: x.split('_')[0])


sub['label'] = sub['Image_Label'].apply(lambda x: x.split('_')[1])
sub['im_id'] = sub['Image_Label'].apply(lambda x: x.split('_')[0])
In [24]:
fig = plt.figure(figsize=(25, 16))
for j, im_id in enumerate(np.random.choice(train['im_id'].unique(), 4)):
    for i, (idx, row) in enumerate(train.loc[train['im_id'] == im_id].iterrows()):
        ax = fig.add_subplot(5, 4, j * 4 + i + 1, xticks=[], yticks=[])
        im = Image.open(f"{img_paths}/train_images_525/train_images_525/{row['Image_Label'].split('_')[0]}")
        plt.imshow(im)
        mask = cv2.imread(f"{img_paths}/train_masks_525/train_masks_525/{row['label']}{row['Image_Label'].split('_')[0]}", 0)
        if mask is None:
            mask = np.zeros((350, 525))
        plt.imshow(mask, alpha=0.5, cmap='gray')
        ax.set_title(f"Image: {row['Image_Label'].split('_')[0]}. Label: {row['label']}")
Notebook Image
In [25]:
id_mask_count = train.loc[train['EncodedPixels'].isnull() == False, 'Image_Label'].apply(lambda x: x.split('_')[0]).value_counts().\
reset_index().rename(columns={'index': 'img_id', 'Image_Label': 'count'})
train_ids, valid_ids = train_test_split(id_mask_count['img_id'].values, random_state=42, stratify=id_mask_count['count'], test_size=0.1)
test_ids = sub['Image_Label'].apply(lambda x: x.split('_')[0]).drop_duplicates().values

데이터 처리가 잘되었나 mask를 만들어서 visualize해본다.

In [26]:
image_name = '8242ba0.jpg'
image = get_img(image_name)
mask = make_mask(train, image_name)
In [27]:
visualize(image, mask)
Notebook Image

alalbumentations 라이브러리를 이용하여 data augmentation을 한다.

In [28]:
plot_with_augmentation(image, mask, albu.HorizontalFlip(p=1))