Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Last synced: 9 months ago
·
JSON representation
Repository
torchvision C++ library extensions
Basic Info
- Host: GitHub
- Owner: mlverse
- License: other
- Language: C++
- Default Branch: main
- Size: 304 KB
Statistics
- Stars: 9
- Watchers: 3
- Forks: 2
- Open Issues: 1
- Releases: 15
Created over 4 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Changelog
License
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# torchvisionlib
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://github.com/mlverse/torchvisionlib/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=torchvisionlib)
[](https://cran.r-project.org/package=torchvisionlib)
[](https://discord.com/invite/s3D5cKhBkx)
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.
Owner
- Name: mlverse
- Login: mlverse
- Kind: organization
- Repositories: 27
- Profile: https://github.com/mlverse
Open source libraries to scale Data Science
GitHub Events
Total
- Create event: 2
- Release event: 1
- Issues event: 1
- Watch event: 1
- Push event: 40
- Pull request event: 3
- Fork event: 1
Last Year
- Create event: 2
- Release event: 1
- Issues event: 1
- Watch event: 1
- Push event: 40
- Pull request event: 3
- Fork event: 1
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 11
- Total pull requests: 14
- Average time to close issues: 26 days
- Average time to close pull requests: about 2 months
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 0.36
- Average comments per pull request: 0.0
- Merged pull requests: 13
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 4
- Average time to close issues: N/A
- Average time to close pull requests: 5 months
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- dfalbel (10)
- SvenVw (1)
Pull Request Authors
- dfalbel (12)
- bart1 (2)
- skeydan (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 260 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 6
- Total maintainers: 1
cran.r-project.org: torchvisionlib
Additional Operators for Image Models
- Homepage: https://github.com/mlverse/torchvisionlib
- Documentation: http://cran.r-project.org/web/packages/torchvisionlib/torchvisionlib.pdf
- License: MIT + file LICENSE
-
Latest release: 0.6.0
published about 1 year ago
Rankings
Dependent repos count: 24.4%
Dependent packages count: 28.0%
Average: 33.7%
Downloads: 48.6%
Maintainers (1)
Last synced:
10 months ago
Dependencies
DESCRIPTION
cran
- Rcpp * imports
- glue * imports
- rlang * imports
- torch >= 0.8.0 imports
- testthat >= 3.0.0 suggests
.github/workflows/R-CMD-check.yaml
actions
- Jimver/cuda-toolkit v0.2.8 composite
- actions/checkout v2 composite
- actions/download-artifact v3 composite
- actions/upload-artifact main composite
- actions/upload-artifact v3 composite
- jwlawson/actions-setup-cmake v1 composite
- mlverse/torch/.github/actions/install-cudnn main composite
- mlverse/torch/.github/actions/setup-r main composite
- r-lib/actions/check-r-package v2 composite
- r-lib/actions/setup-pandoc v1 composite
- r-lib/actions/setup-r v2 composite
- svenstaro/upload-release-action v2 composite