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Diff for: .flake8

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# This is an example .flake8 config, used when developing *Black* itself.
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# Keep in sync with setup.cfg which is used for source packages.
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[flake8]
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ignore = W503, E203, E221, C901
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max-line-length = 100
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max-complexity = 18
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select = B,C,E,F,W,T4,B9
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exclude = build,__init__.py

Diff for: .gitignore

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# output dir
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output
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instant_test_output
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inference_test_output
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*.jpg
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*.png
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*.txt
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# compilation and distribution
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__pycache__
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_ext
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*.pyc
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*.so
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*.egg-info/
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build/
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dist/
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tools/cvpack2_test
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tools/cvpack2_train
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# pytorch/python/numpy formats
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*.pth
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*.pkl
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*.npy
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# ipython/jupyter notebooks
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*.ipynb
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**/.ipynb_checkpoints/
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# Editor temporaries
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*.swn
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*.swo
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*.swp
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*~
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# Pycharm editor settings
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.idea
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# project dir and file
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/datasets
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**/log
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tools/dl_test
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tools/dl_train

Diff for: LICENSE

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Diff for: README.md

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# CenterNet
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This repo is implemented based on my dl_lib, some parts of code in my dl_lib is based on [detectron2](https://github.com/facebookresearch/detectron2).
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## Motivation
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[Objects as Points](https://arxiv.org/abs/1904.07850) is one of my favorite paper in object detection area. However, its [code](https://github.com/xingyizhou/CenterNet/blob/master/README.md) is a little difficult to understand. I believe that CenterNet could get higher pts and implemented in a more elegant way, so I write this repo.
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## Performance
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This repo use less training time to get a better performance, it nearly spend half training time and get 1~2 pts higher mAP compared with the old repo. Here is the table of performance.
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| Backbone | mAP | FPS | trained model |
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|--------------|---------|---------|-------------------|
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|ResNet-18 | 29.8 | 92 | [google drive](https://drive.google.com/open?id=1D3tO95sdlsh9egOjOg0N-2HHmMfqbt5X) |
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|ResNet-50 | 34.9 | 57 | [google drive](https://drive.google.com/open?id=1t5Bw520_fJrn3aeSVxDBYNIgwpNdLR5s) |
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|ResNet-101 | 36.8 | 43 | [google drive](https://drive.google.com/open?id=1762Y93i9QreUTHq-87Ir73R2nNcrHuk0) |
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## What\'s New?
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* **treat config as a object.** You could run your config file and check the config value, which is really helpful for debug.
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* **Common training / testing scripts in default.** you just need to invoke `dl_train/test --num-gpus x` in your playground and your projects only need to include all project-specific configs and network modules.
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* **Performance report is dumped automaticly.** After your training is over, we will evaluate your model automatically and generate a markdown file.
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* **Vectorize some operations.** This improves the speed and efficiency.
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## What\'s comming
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- [ ] Support DLA backbone
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- [ ] Support Hourglass backbone
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- [ ] Support KeyPoints dataset
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## Get started
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### Requirements
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* Python >= 3.6
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* PyTorch >= 1.3
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* torchvision that matches the PyTorch installation.
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* OpenCV
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* pycocotools
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```shell
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pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
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```
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* GCC >= 4.9
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```shell
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gcc --version
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```
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### Installation
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Make sure that your get at least one gpu when compiled. Run:
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```shell
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pip install -e .
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```
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### Training
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For example, if you want to train CenterNet with resnet-18 backbone, run:
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```shell
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cd playground/centernet.res18.coco.512size
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dl_train --num-gpus 8
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```
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After training process, a README.md file will be generated automatically and this file will report your model\'s performance.
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NOTE: For ResNet-18 and ResNet-50 backbone, we suppose your machine has over 150GB Memory for training. If your memory is not enough, please change NUM_WORKER (in config.py) to a smaller value.
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### Testing and Evaluation
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```shell
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dl_test --num-gpus 8
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```
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test downloaded model:
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```shell
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dl_test --num-gpus 8 MODEL.WEIGHTS path/to/your/save_dir/ckpt.pth
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```
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## Acknowledgement
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* [detectron2](https://github.com/facebookresearch/detectron2)
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* [CenterNet](https://github.com/xingyizhou/CenterNet)
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## Coding style
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please refer to [google python coding style](https://zh-google-styleguide.readthedocs.io/en/latest/google-python-styleguide/python_style_rules/)
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## AD time
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I am an intern@[megvii](https://megvii.com/) now, if you are interested in this company/want to get hired/want to be a intern, please send email to [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).

Diff for: dl_lib/__init__.py

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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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from .utils.env import setup_environment
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setup_environment()
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__version__ = "0.1"

Diff for: dl_lib/checkpoint/__init__.py

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# -*- coding: utf-8 -*-
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# File:
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from dl_lib.utils.checkpoint import Checkpointer, PeriodicCheckpointer
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from . import catalog as _UNUSED # register the handler
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from .detection_checkpoint import DetectionCheckpointer
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__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"]

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