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135 lines
4.2 KiB
Markdown
135 lines
4.2 KiB
Markdown
English | [简体中文](INSTALL_cn.md)
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# Installation
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This document covers how to install PaddleDetection and its dependencies
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(including PaddlePaddle), together with COCO and Pascal VOC dataset.
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For general information about PaddleDetection, please see [README.md](https://github.com/PaddlePaddle/PaddleDetection/tree/develop).
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## Requirements:
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- PaddlePaddle 2.1
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- OS 64 bit
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- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
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- pip/pip3(9.0.1+), 64 bit
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- CUDA >= 10.1
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- cuDNN >= 7.6
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Dependency of PaddleDetection and PaddlePaddle:
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| PaddleDetection version | PaddlePaddle version | tips |
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| :----------------: | :---------------: | :-------: |
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| release/2.1 | >= 2.1.0 | Dygraph mode is set as default |
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| release/2.0 | >= 2.0.1 | Dygraph mode is set as default |
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| release/2.0-rc | >= 2.0.1 | -- |
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| release/0.5 | >= 1.8.4 | Cascade R-CNN and SOLOv2 depends on 2.0.0.rc |
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| release/0.4 | >= 1.8.4 | PP-YOLO depends on 1.8.4 |
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| release/0.3 | >=1.7 | -- |
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## Instruction
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### 1. Install PaddlePaddle
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```
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# CUDA10.1
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python -m pip install paddlepaddle-gpu==2.1.0.post101 -f https://paddlepaddle.org.cn/whl/mkl/stable.html
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# CPU
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python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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- For more CUDA version or environment to quick install, please refer to the [PaddlePaddle Quick Installation document](https://www.paddlepaddle.org.cn/install/quick)
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- For more installation methods such as conda or compile with source code, please refer to the [installation document](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html)
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Please make sure that your PaddlePaddle is installed successfully and the version is not lower than the required version. Use the following command to verify.
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```
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# check
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>>> import paddle
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>>> paddle.utils.run_check()
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# confirm the paddle's version
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python -c "import paddle; print(paddle.__version__)"
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```
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**Note**
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1. If you want to use PaddleDetection on multi-GPU, please install NCCL at first.
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### 2. Install PaddleDetection
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PaddleDetection can be installed in the following two ways:
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#### 2.1 Install via pip
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**Note:** Installing via pip only supports Python3
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```
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# Install paddledet via pip
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pip install paddledet==2.1.0 -i https://mirror.baidu.com/pypi/simple
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# Download and use the configuration files and code examples in the source code
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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cd PaddleDetection
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```
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#### 2.2 Compile and install from Source code
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```
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# Clone PaddleDetection repository
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cd <path/to/clone/PaddleDetection>
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git clone https://github.com/PaddlePaddle/PaddleDetection.git
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# Compile and install paddledet
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cd PaddleDetection
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python setup.py install
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# Install other dependencies
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pip install -r requirements.txt
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```
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**Note**
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1. If you are working on Windows OS, `pycocotools` installing may failed because of the origin version of cocoapi does not support windows, another version can be used used which only supports Python3:
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```pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI```
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2. If you are using Python <= 3.6, `pycocotools` installing may failed with error like `distutils.errors.DistutilsError: Could not find suitable distribution for Requirement.parse('cython>=0.27.3')`, please install `cython` firstly, for example `pip install cython`
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After installation, make sure the tests pass:
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```shell
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python ppdet/modeling/tests/test_architectures.py
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```
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If the tests are passed, the following information will be prompted:
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```
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.....
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----------------------------------------------------------------------
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Ran 5 tests in 4.280s
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OK
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```
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## Inference demo
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**Congratulation!** Now you have installed PaddleDetection successfully and try our inference demo:
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```
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# Predict an image by GPU
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export CUDA_VISIBLE_DEVICES=0
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python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg
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```
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An image of the same name with the predicted result will be generated under the `output` folder.
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The result is as shown below：
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