To_fp16()

[Cdk29]

There is one callback describe here : https://docs.fast.ai/callback.fp16.html
Details here : https://docs.fast.ai/callback.fp16.html#Learner.to_fp16

It allow to use less GPU when training a CNN.
The book describe the use of the method when creating the learner, but it can also be used on the learner directly.
Running it in R however create a bug, and I don't see how to use it.

Precisely :

learnR <- dataloader %>% cnn_learner(xresnet50(), metrics = accuracy,  model_dir="fastai_model/") #prettier
learnR$to_fp16()

Then running :
learnR %>% lr_find()

Error in py_call_impl(callable, dots$args, dots$keywords) : AttributeError: 'str' object has no attribute 'type'`

Because now the learner is a string.

[turgut090]

Hi.
This works for me:

library(fastai)
library(magrittr)

URLs_PETS()

path = 'oxford-iiit-pet'
path_anno = 'oxford-iiit-pet/annotations'
path_img = 'oxford-iiit-pet/images'
fnames = get_image_files(path_img)

item_tfms = list(Flip(), Dihedral(),Zoom_())

dls = ImageDataLoaders_from_name_re(
  path, fnames, pat='(.+)_\\d+.jpg$',
  item_tfms=Resize(300), bs = 10,
  batch_tfms=list(aug_transforms(size = 224, min_scale = 0.75,max_rotate = 180,
                                 max_zoom = 10, max_lighting = 0.9,max_warp = 0.8,
                                 flip_vert = TRUE),
                  Normalize_from_stats( imagenet_stats() )
  )
)

learn = cnn_learner(dls, xresnet50(), metrics = list(error_rate, accuracy),model_dir="fastai_model/")
learn$to_fp16()
lrs = learn %>% lr_find()
SuggestedLRs(lr_min=7.585775847473997e-08, lr_steep=2.2908675418875646e-06)

[Cdk29]

@henry090 that is odd.

I got it to works. Below to full code from rmarkdown that created the bug :

path_img = 'cassava-leaf-disease-classification/train_images/'

#library(data.table)

labels<-read_csv('cassava-leaf-disease-classification//train.csv')
head(labels)

dataloader <- fastai::ImageDataLoaders_from_df(df=labels, path=path_img, bs=8, seed=6, device = 'cuda', 
                                               num_workers=0, 
                                               item_tfms = Resize(448),
                                               batch_tfms = aug_transforms(size=224, min_scale=0.75))

num_workers=0 is mandatory to not have the error "RuntimeError: DataLoader worker (pid(s) 482) exited unexpectedly".

dataloader %>% show_batch()

learnR <- dataloader %>% cnn_learner(xresnet50(), metrics = accuracy,  model_dir="fastai_model/") #prettier

To save computation power :

learnR$to_fp16()

learnR$freeze()

learnR %>% lr_find()

learnR %>% plot_lr_find(dpi = 200)

Here what was creating the bug :

device = 'cuda', inside the call to create dataloader.
Removing it make the code works as intended.

[turgut090]

Could you share session info as well, please?

[Cdk29]

@henry090 here it is :

R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.10

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
[1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
[5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8 LC_PAPER=fr_FR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] fastai_2.0.3 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[8] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0

loaded via a namespace (and not attached):
[1] httr_1.4.2 pkgload_1.1.0 jsonlite_1.7.2 modelr_0.1.8 assertthat_0.2.1 cellranger_1.1.0
[7] yaml_2.2.1 remotes_2.2.0 sessioninfo_1.1.1 pillar_1.4.7 backports_1.2.1 lattice_0.20-41
[13] glue_1.4.2 reticulate_1.18-9000 digest_0.6.27 rvest_0.3.6 colorspace_2.0-0 htmltools_0.5.0
[19] Matrix_1.2-18 pkgconfig_2.0.3 devtools_2.3.2 broom_0.7.2 haven_2.3.1 scales_1.1.1
[25] processx_3.4.5 generics_0.1.0 usethis_2.0.0 ellipsis_0.3.1 withr_2.3.0 cli_2.2.0
[31] magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1 memoise_1.1.0 evaluate_0.14 ps_1.5.0
[37] fs_1.5.0 fansi_0.4.1 xml2_1.3.2 pkgbuild_1.1.0 tools_4.0.2 prettyunits_1.1.1
[43] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 callr_3.5.1 compiler_4.0.2
[49] rlang_0.4.9 grid_4.0.2 rstudioapi_0.13 rappdirs_0.3.1 rmarkdown_2.5 testthat_3.0.0
[55] gtable_0.3.0 curl_4.3 DBI_1.1.0 R6_2.5.0 lubridate_1.7.9.2 knitr_1.30
[61] rprojroot_2.0.2 desc_1.2.0 stringi_1.5.3 Rcpp_1.0.5 vctrs_0.3.5 png_0.1-7
[67] dbplyr_2.0.0 tidyselect_1.1.0 xfun_0.19

[turgut090]

Could you install reticulate from Github (then restart RStudio). Then remove num_workers argument and run again, please? Kevin from RStudio fixed that issue with reticulate.

[Cdk29]

Okay. With or without device = 'cuda' ?

[turgut090]

If you have Cuda, skip that as well.

[Cdk29]

Okay. I wait for the current test to finish and then I will run it with the requested changes on more epoch.
I will add the link to the md document with the session infos when it will have finish running.

[Cdk29]

@henry090 it works perfectly : https://github.com/Cdk29/Cassava/blob/main/trying-resnext-with-r-and-fastai.md

[turgut090]

I close this issue, then. If you face an issue, again, please reopen.