README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# torchvisionlib

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The goal of torchvisionlib is to provide access to C++ opeartions implemented in
[torchvision](https://github.com/pytorch/vision). It provides plain R acesss to
some of those C++ operations but, most importantly it provides full support for 
JIT operators defined in [torchvision](https://github.com/pytorch/vision), allowing
us to load 'scripted' object detection and image segmentation models.

## Installation

torchvisionlib can be installed from CRAN with:

```r
install.packages("torchvisionlib")
```

You can also install the development version of torchvisionlib from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("mlverse/torchvisionlib")
```

## Example

Suppose that we want to load an image detection model implemented in torchvision.
First, in Python, we can save JIT script and then save this model:

```python
import torch
import torchvision

model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)
model.eval()

jit_model = torch.jit.script(model)
torch.jit.save(jit_model, "fasterrcnn_mobilenet_v3_large_320_fpn.pt")
```

We can then load this model in R. Simply loading torchvisionlib will register all
JIT operators, and we can use `torch::jit_load()`.

```{r include=FALSE}
url <- "https://storage.googleapis.com/torch-lantern-builds/testing-models/fasterrcnn_mobilenet_v3_large_320_fpn.pt"
download.file(url, destfile = "fasterrcnn_mobilenet_v3_large_320_fpn.pt", mode = "wb")
```

```{r}
library(torchvisionlib)
model <- torch::jit_load("fasterrcnn_mobilenet_v3_large_320_fpn.pt")
model
```

You can then use this model to make preditions or even fine tuning.