R language support (#22417)

* R-language inference support

r/Dockerfile

+FROM ubuntu:18.04
+MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
+
+WORKDIR /workspace
+
+ENV PATH /opt/python3/bin:/root/.local/bin:$PATH
+ENV LD_LIBRARY_PATH $LD_LIBRARY_PATH:/opt/python3/lib
+
+# Install Python
+ADD https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-4.7.12.1-Linux-x86_64.sh miniconda3.sh
+RUN /bin/bash miniconda3.sh -b -p /opt/python3/ && \
+    rm -f miniconda3.sh
+
+RUN mkdir -p ~/.pip && \
+    echo "[global]" >> ~/.pip/pip.conf && \
+    echo "trusted-host =  mirrors.aliyun.com" >> ~/.pip/pip.conf && \
+    echo "index-url = https://mirrors.aliyun.com/pypi/simple" >> ~/.pip/pip.conf
+
+RUN echo "channels:" >> ~/.condarc && \
+    echo "  - conda-forge" >> ~/.condarc && \
+    echo "  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/" >> ~/.condarc && \
+    echo "  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/" >> ~/.condarc && \
+    echo "  - defaults" >> ~/.condarc && \
+    echo "custom_channels:" >> ~/.condarc && \
+    echo "  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud" >> ~/.condarc && \
+    echo "show_channel_urls: true" >> ~/.condarc && \
+    echo "channel_priority: strict" >> ~/.condarc
+    
+# Install R
+RUN conda install -y r -c conda-forge
+
+# Install PaddlePaddle
+RUN /opt/python3/bin/python -m pip install \
+    https://paddle-wheel.bj.bcebos.com/0.0.0-cpu-mkl/paddlepaddle-0.0.0-cp37-cp37m-linux_x86_64.whl
+
+# Install reticulate, R interface to Python
+RUN Rscript -e 'install.packages("reticulate", repos="https://cran.rstudio.com")'
+
+COPY example example
+RUN cd example && \
+    curl -O https://paddle-inference-dist.cdn.bcebos.com/mobilenet-test-model-data.tar.gz && \
+    tar -zxvf mobilenet-test-model-data.tar.gz && rm mobilenet-test-model-data.tar.gz 

r/README.md

+# R support
+
+English | [简体中文](./README_cn.md)
+
+Use paddle in R.
+
+## Install
+### Use docker
+Download [`Dockerfile`](./Dockerfile), run
+``` bash
+docker build -t paddle-rapi:latest .
+```
+
+### Local installation
+First, make sure `Python` is installed, assuming that the path is `/opt/python3.7`.
+
+``` bash
+python -m pip install paddlepaddle # CPU version
+python -m pip install paddlepaddle-gpu # GPU version
+```
+
+Install the R libraries needed to use paddle.
+``` r
+install.packages("reticulate") # call Python in R
+install.packages("RcppCNPy") # use numpy.ndarray in R
+```
+
+## Use Paddle inference in R
+First, load PaddlePaddle in R.
+``` r
+library(reticulate)
+library(RcppCNPy)
+
+use_python("/opt/python3.7/bin/python3.7")
+paddle <- import("paddle.fluid.core")
+```
+
+Create an `AnalysisConfig`, which is the configuration of the paddle inference engine.
+``` r
+config <- paddle$AnalysisConfig("")
+```
+
+Set model path.
+``` r
+config$set_model("model/__model__", "model/__params__")
+```
+
+Use zero copy inference.
+``` r
+config$switch_use_feed_fetch_ops(FALSE)
+config$switch_specify_input_names(TRUE)
+```
+
+Other configuration options and descriptions are as fallows.
+``` r
+config$enable_profile() # turn on inference profile
+config$enable_use_gpu(gpu_memory_mb, gpu_id) # use GPU
+config$disable_gpu() # disable GPU 
+config$gpu_device_id() # get GPU id
+config$switch_ir_optim(TRUE) # turn on IR optimize(default is TRUE)
+config$enable_tensorrt_engine(workspace_size,
+                              max_batch_size,
+                              min_subgraph_size,
+                              paddle$AnalysisConfig$Precision$FLOAT32,
+                              use_static,
+                              use_calib_mode
+                              ) # use TensorRT
+config$enable_mkldnn() # use MKLDNN
+config$delete_pass(pass_name) # delete IR pass
+```
+
+Create inference engine.
+``` r
+predictor <- paddle$create_paddle_predictor(config)
+```
+
+Get input tensor(assume single input), and set input data
+``` r
+input_names <- predictor$get_input_names()
+input_tensor <- predictor$get_input_tensor(input_names[1])
+input_shape <- as.integer(c(1, 3, 300, 300)) # shape has integer type
+input_data <- np_array(data, dtype="float32")$reshape(input_shape)
+input_tensor$copy_from_cpu(input_data)
+```
+
+Run inference.
+``` r
+predictor$zero_copy_run()
+```
+
+Get output tensor(assume single output).
+``` r
+output_names <- predictor$get_output_names()
+output_tensor <- predictor$get_output_tensor(output_names[1])
+```
+
+Parse output data, and convert to `numpy.ndarray`
+``` r
+output_data <- output_tensor$copy_to_cpu()
+output_data <- np_array(output_data)
+```
+
+Click to see the full [R mobilenet example](./example/mobilenet.r) and the corresponding [Python mobilenet example](./example/mobilenet.py) the above. For more examples, see [R inference example](./example).
+
+## Quick start
+Download [Dockerfile](./Dockerfile) and [example](./example) to local directory, and build docker image
+``` bash
+docker build -t paddle-rapi:latest .
+```
+
+Create and enter container
+``` bash
+docker run --rm -it paddle-rapi:latest bash
+```
+
+Run the following command in th container
+```
+cd example
+chmod +x mobilenet.r
+./mobilenet.r
+```

r/README_cn.md

+# R 语言支持
+在 R 中使用 PaddlePaddle
+
+## 环境安装
+首先确保已安装Python，假设路径为`/opt/python3.7`
+
+使用Python安装Paddle
+``` bash
+/opt/python3.7/bin/python3.7 -m pip install paddlepaddle # CPU
+/opt/python3.7/bin/python3.7 -m pip install paddlepaddle-gpu # GPU
+```
+
+安装r运行paddle预测所需要的库
+``` r
+install.packages("reticulate") # 调用Paddle
+```
+
+## 在 R 中使用Paddle预测
+首先在 R 中引入paddle预测环境
+
+``` r
+library(reticulate)
+use_python("/opt/python3.7/bin/python")
+
+paddle <- import("paddle.fluid.core")
+```
+
+创建一个AnalysisConfig，用于设置预测引擎的各选项
+
+``` r
+config <- paddle$AnalysisConfig("")
+```
+
+禁用feed和fetch OP，以使用 zero copy 预测
+``` r
+config$switch_use_feed_fetch_ops(FALSE)
+config$switch_specify_input_names(TRUE)
+```
+
+设置模型路径有两种形式：
+- `model` 目录中存在一个模型文件和多个参数文件
+- `model` 目录中存在一个模型文件`__model__`和一个参数文件`__params__`
+
+分别对应如下设置
+
+``` r
+config$set_model("model")
+config$set_model("model/__model__", "model/__params__")
+```
+
+其他一些配置选项及说明如下
+``` r
+config$enable_profile() # 打开预测profile
+config$enable_use_gpu(gpu_memory_mb, gpu_id) # 开启GPU预测
+config$disable_gpu() # 禁用GPU
+config$gpu_device_id() # 返回使用的GPU ID
+config$switch_ir_optim(TRUE) # 开启IR优化(默认开启)
+config$enable_tensorrt_engine(workspace_size,
+                              max_batch_size,
+                              min_subgraph_size,
+                              paddle$AnalysisConfig$Precision$Float32,
+                              use_static,
+                              use_calib_mode
+                              ) # 开启TensorRT
+config$enable_mkldnn() # 开启MKLDNN
+config$disable_glog_info() # 禁用预测中的glog日志
+config$delete_pass(pass_name) # 预测的时候删除指定的pass
+
+```
+
+创建预测引擎
+``` r
+predictor <- paddle$create_paddle_predictor(config)
+```
+
+获取输入tensor(为简单起见，此处假设只有一个输入)，并设置输入tensor中的数据(注意需要使用np_array以传入numpy.ndarray类型的数据)
+``` r
+input_names <- predictor$get_input_names()
+input_tensor <- predictor$get_input_tensor(input_names[1])
+
+input_shape <- as.integer(c(1, 3, 300, 300)) # shape 为int类型
+input_data <- np_array(data, dtype="float32")$reshape(input_shape)
+input_tensor$copy_from_cpu(input_data)
+```
+
+运行预测引擎
+``` r
+predictor$zero_copy_run()
+```
+
+获取输出tensor(为简单起见，此处假设只有一个输出)
+``` r
+output_names <- predictor$get_output_names()
+output_tensor <- predictor$get_output_tensor(output_names[1])
+```
+
+获取输出tensor中的数据，注意需要转为numpy.ndarray
+``` r
+output_data <- output_tensor$copy_to_cpu()
+output_data <- np_array(output_data)
+```
+
+点击查看完整的[R预测示例](./example/mobilenet.r)及对应的[python预测示例](./example/mobilenet.py)
+
+### 快速运行
+将[Dockerfile](./Dockerfile)和[example](./example)下载到本地，使用以下命令构建docker镜像
+``` bash
+docker build -t paddle-rapi:latest .
+```
+
+启动一个容器
+``` bash
+docker run --rm -it paddle-rapi:latest bash
+```
+
+运行示例
+``` bash
+cd example && chmod +x mobilenet.r
+./mobilenet.r
+```

r/example/mobilenet.py

+#!/usr/bin/env python3.7
+# pylint: skip-file
+
+import functools
+import numpy as np
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import AnalysisPredictor
+from paddle.fluid.core import create_paddle_predictor
+
+
+def main():
+    config = set_config()
+    predictor = create_paddle_predictor(config)
+
+    data, result = parse_data()
+
+    input_names = predictor.get_input_names()
+    input_tensor = predictor.get_input_tensor(input_names[0])
+    shape = (1, 3, 300, 300)
+    input_data = data[:-4].astype(np.float32).reshape(shape)
+    input_tensor.copy_from_cpu(input_data)
+
+    predictor.zero_copy_run()
+
+    output_names = predictor.get_output_names()
+    output_tensor = predictor.get_output_tensor(output_names[0])
+    output_data = output_tensor.copy_to_cpu()
+
+
+def set_config():
+    config = AnalysisConfig("")
+    config.set_model("model/__model__", "model/__params__")
+    config.switch_use_feed_fetch_ops(False)
+    config.switch_specify_input_names(True)
+    config.enable_profile()
+
+    return config
+
+
+def parse_data():
+    """ parse input and output data """
+    with open('data/data.txt', 'r') as fr:
+        data = np.array([float(_) for _ in fr.read().split()])
+
+    with open('data/result.txt', 'r') as fr:
+        result = np.array([float(_) for _ in fr.read().split()])
+
+    return (data, result)
+
+
+if __name__ == "__main__":
+    main()

r/example/mobilenet.r

+#!/usr/bin/env Rscript
+
+library(reticulate) # call Python library
+
+use_python("/opt/python3.7/bin/python")
+
+np <- import("numpy")
+paddle <- import("paddle.fluid.core")
+
+set_config <- function() {
+    config <- paddle$AnalysisConfig("")
+    config$set_model("data/model/__model__", "data/model/__params__")
+    config$switch_use_feed_fetch_ops(FALSE)
+    config$switch_specify_input_names(TRUE)
+    config$enable_profile()
+
+    return(config)
+}
+
+zero_copy_run_mobilenet <- function() {
+    data <- np$loadtxt("data/data.txt")
+    data <- data[0:(length(data) - 4)]
+    result <- np$loadtxt("data/result.txt")
+    result <- result[0:(length(result) - 4)]
+
+    config <- set_config()
+    predictor <- paddle$create_paddle_predictor(config)
+
+    input_names <- predictor$get_input_names()
+    input_tensor <- predictor$get_input_tensor(input_names[1])
+    input_data <- np_array(data, dtype="float32")$reshape(as.integer(c(1, 3, 300, 300)))
+    input_tensor$copy_from_cpu(input_data)
+
+    predictor$zero_copy_run()
+
+    output_names <- predictor$get_output_names()
+    output_tensor <- predictor$get_output_tensor(output_names[1])
+    output_data <- output_tensor$copy_to_cpu()
+    output_data <- np_array(output_data)$reshape(as.integer(-1))
+    #all.equal(output_data, result)
+}
+
+if (!interactive()) {
+    zero_copy_run_mobilenet()
+}