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