add github action (manually squash)
yl-1993
yl-1993
parent
4e6875d44e
commit
71e3b86ed3
.github
workflows
configs/imagenet
requirements
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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
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# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
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name: build
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on: [push, pull_request]
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jobs:
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build:
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runs-on: ubuntu-latest
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env:
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CUDA: 10.1.105-1
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CUDA_SHORT: 10.1
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UBUNTU_VERSION: ubuntu1804
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FORCE_CUDA: 1
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CUDA_ARCH: ${{matrix.cuda_arch}}
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strategy:
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matrix:
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python-version: [3.6, 3.7]
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torch: [1.3.0, 1.5.0]
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include:
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- torch: 1.3.0
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torchvision: 0.4.2
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cuda_arch: "6.0"
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- torch: 1.5.0
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torchvision: 0.6.0
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cuda_arch: "7.0"
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install Pillow
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run: pip install Pillow==6.2.2
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if: ${{matrix.torchvision < 0.5}}
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- name: Install PyTorch
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run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
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- name: Install mmcls dependencies
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run: |
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pip install -r requirements.txt
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- name: Lint with flake8
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run: flake8 .
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- name: Lint with isort
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run: isort --recursive --check-only --diff mmcls/ tools/ configs/ tests/
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- name: Format python codes with yapf
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run: yapf -r -d mmcls/ tools/ configs/ tests/
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- name: Build and install
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run: |
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rm -rf .eggs
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python setup.py check -m -s
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TORCH_CUDA_ARCH_LIST=${CUDA_ARCH} python setup.py build_ext --inplace
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- name: Run unittests and generate coverage report
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run: |
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coverage run --branch --source mmcls -m pytest tests/
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coverage xml
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coverage report -m --omit="mmcls/utils/*","mmcls/apis/*"
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# Only upload coverage report for python3.7 && pytorch1.5
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- name: Upload coverage to Codecov
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if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}}
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uses: codecov/codecov-action@v1.0.10
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with:
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file: ./coverage.xml
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flags: unittests
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env_vars: OS,PYTHON
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name: codecov-umbrella
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fail_ci_if_error: false
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@ -1,98 +0,0 @@
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import os.path as osp
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# run with 8 GPUs
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearHead',
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num_classes=1000,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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))
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# dataset settings
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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memcached_root = '/mnt/lustre/share/memcached_client/'
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train_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args=dict(
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backend='memcached',
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server_list_cfg=osp.join(memcached_root, 'server_list.conf'),
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client_cfg=osp.join(memcached_root, 'client.conf'))),
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dict(type='ToPIL'),
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomHorizontalFlip', p=0.5),
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dict(type='ToNumpy'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_labels']),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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test_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args=dict(
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backend='memcached',
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server_list_cfg=osp.join(memcached_root, 'server_list.conf'),
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client_cfg=osp.join(memcached_root, 'client.conf'))),
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dict(type='ToPIL'),
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dict(type='Resize', size=256),
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dict(type='CenterCrop', size=224),
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dict(type='ToNumpy'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_labels']),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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data = dict(
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samples_per_gpu=64,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_prefix='data/imagenet/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
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optimizer_config = dict(grad_clip=None)
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# learning policy
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lr_config = dict(policy='step', step=[30, 60, 90])
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# checkpoint saving
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=100,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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# runtime settings
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total_epochs = 100
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/imagenet_resnet50_batch256'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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@ -1,2 +1,2 @@
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mmcv
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numpy
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-r requirements/runtime.txt
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-r requirements/tests.txt
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@ -0,0 +1 @@
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numpy
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@ -0,0 +1,2 @@
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mmcv
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numpy
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asynctest
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codecov
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flake8
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interrogate
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isort==4.3.21
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pytest
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xdoctest >= 0.10.0
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yapf
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96
setup.py
96
setup.py
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@ -79,11 +79,80 @@ def get_version():
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return locals()['__version__']
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def get_requirements(filename='requirements.txt'):
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here = os.path.dirname(os.path.realpath(__file__))
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with open(os.path.join(here, filename), 'r') as f:
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requires = [line.replace('\n', '') for line in f.readlines()]
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return requires
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def parse_requirements(fname='requirements.txt', with_version=True):
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"""Parse the package dependencies listed in a requirements file but strips
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specific versioning information.
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Args:
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fname (str): path to requirements file
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with_version (bool, default=False): if True include version specs
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Returns:
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List[str]: list of requirements items
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CommandLine:
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python -c "import setup; print(setup.parse_requirements())"
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"""
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import re
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import sys
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from os.path import exists
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require_fpath = fname
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def parse_line(line):
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"""Parse information from a line in a requirements text file."""
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if line.startswith('-r '):
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# Allow specifying requirements in other files
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target = line.split(' ')[1]
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for info in parse_require_file(target):
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yield info
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else:
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info = {'line': line}
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if line.startswith('-e '):
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info['package'] = line.split('#egg=')[1]
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else:
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# Remove versioning from the package
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pat = '(' + '|'.join(['>=', '==', '>']) + ')'
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parts = re.split(pat, line, maxsplit=1)
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parts = [p.strip() for p in parts]
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info['package'] = parts[0]
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if len(parts) > 1:
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op, rest = parts[1:]
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if ';' in rest:
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# Handle platform specific dependencies
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# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
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version, platform_deps = map(str.strip,
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rest.split(';'))
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info['platform_deps'] = platform_deps
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else:
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version = rest # NOQA
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info['version'] = (op, version)
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yield info
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def parse_require_file(fpath):
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with open(fpath, 'r') as f:
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for line in f.readlines():
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line = line.strip()
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if line and not line.startswith('#'):
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for info in parse_line(line):
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yield info
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def gen_packages_items():
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if exists(require_fpath):
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for info in parse_require_file(require_fpath):
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parts = [info['package']]
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if with_version and 'version' in info:
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parts.extend(info['version'])
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if not sys.version.startswith('3.4'):
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# apparently package_deps are broken in 3.4
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platform_deps = info.get('platform_deps')
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if platform_deps is not None:
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parts.append(';' + platform_deps)
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item = ''.join(parts)
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yield item
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packages = list(gen_packages_items())
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return packages
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if __name__ == '__main__':
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@ -91,8 +160,12 @@ if __name__ == '__main__':
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setup(
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name='mmcls',
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version=get_version(),
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description='A template for pytorch projects.',
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description='OpenMMLab Image Classification Toolbox and Benchmark',
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long_description=readme(),
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author='OpenMMLab',
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author_email='yangleidev@gmail.com',
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keywords='computer vision, image classification',
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url='https://github.com/open-mmlab/mmclassification',
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packages=find_packages(exclude=('configs', 'tools', 'demo')),
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package_data={'mmcls.ops': ['*/*.so']},
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classifiers=[
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@ -105,7 +178,12 @@ if __name__ == '__main__':
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'Programming Language :: Python :: 3.7',
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],
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license='Apache License 2.0',
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setup_requires=['pytest-runner', 'cython', 'numpy'],
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tests_require=['pytest', 'xdoctest'],
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install_requires=get_requirements(),
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setup_requires=parse_requirements('requirements/build.txt'),
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tests_require=parse_requirements('requirements/tests.txt'),
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install_requires=parse_requirements('requirements/runtime.txt'),
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extras_require={
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'all': parse_requirements('requirements.txt'),
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'tests': parse_requirements('requirements/tests.txt'),
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'build': parse_requirements('requirements/build.txt'),
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},
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zip_safe=False)
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