R interface to fast.ai

R interface to fastai

The fastai package provides R wrappers to fastai.

The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.

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Mac OS
Windows

Installation

1. Install miniconda and activate environment:

reticulate::install_miniconda()
reticulate::conda_create('r-reticulate')

2. The dev version:

devtools::install_github('eagerai/fastai')

3. Later, you need to install the python module fastai:

fastai::install_fastai(gpu = FALSE, cuda_version = '10', overwrite = FALSE)

4. Restart RStudio!

fast.ai extensions:

Kaggle

We currently prepare the examples of usage of the fastai from R in Kaggle competitions:

Contributions are very welcome!

Tabular data

library(magrittr)
library(fastai)

# download
URLs_ADULT_SAMPLE()

# read data
df = data.table::fread('adult_sample/adult.csv')

Variables:

dep_var = 'salary'
cat_names = c('workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race')
cont_names = c('age', 'fnlwgt', 'education-num')

Preprocess strategy:

procs = list(FillMissing(),Categorify(),Normalize())

Prepare:

dls = TabularDataTable(df, procs, cat_names, cont_names,
      y_names = dep_var, splits = list(c(1:32000),c(32001:32561))) %>%
      dataloaders(bs = 64)

Summary:

model = dls %>% tabular_learner(layers=c(200,100), metrics=accuracy)
model %>% summary()

TabularModel (Input shape: ['64 x 7', '64 x 3'])
================================================================
Layer (type)         Output Shape         Param #    Trainable
================================================================
Embedding            64 x 6               60         True
________________________________________________________________
Embedding            64 x 8               136        True
________________________________________________________________
Embedding            64 x 5               40         True
________________________________________________________________
Embedding            64 x 8               136        True
________________________________________________________________
Embedding            64 x 5               35         True
________________________________________________________________
Embedding            64 x 4               24         True
________________________________________________________________
Embedding            64 x 3               9          True
________________________________________________________________
Dropout              64 x 39              0          False
________________________________________________________________
BatchNorm1d          64 x 3               6          True
________________________________________________________________
BatchNorm1d          64 x 42              84         True
________________________________________________________________
Linear               64 x 200             8,400      True
________________________________________________________________
ReLU                 64 x 200             0          False
________________________________________________________________
BatchNorm1d          64 x 200             400        True
________________________________________________________________
Linear               64 x 100             20,000     True
________________________________________________________________
ReLU                 64 x 100             0          False
________________________________________________________________
Linear               64 x 2               202        True
________________________________________________________________

Total params: 29,532
Total trainable params: 29,532
Total non-trainable params: 0

Optimizer used: <function Adam at 0x7fa246283598>
Loss function: FlattenedLoss of CrossEntropyLoss()

Callbacks:
  - TrainEvalCallback
  - Recorder
  - ProgressCallback

Before fitting try to find optimal learning rate:

model %>% lr_find()

model %>% plot_lr_find(dpi = 200)

Run:

model %>% fit(5, lr = 10^-1)

epoch     train_loss  valid_loss  accuracy  time
0         0.360149    0.329587    0.846702  00:04
1         0.352106    0.345761    0.828877  00:04
2         0.368743    0.340913    0.844920  00:05
3         0.347277    0.333084    0.852050  00:04
4         0.348969    0.350707    0.830660  00:04

Plot loss history:

model %>% plot_loss(dpi = 200)

At the same time, users can find optimal batch size.

Implementation of OpenAI paper "An Empirical Model of Large-Batch Training" for Fastai was done by hal-314:

bss = model %>% bs_find(lr=1e-3)

model %>% plot_bs_find()

See training process:

Get confusion matrix:

model %>% get_confusion_matrix()

       <50k  >=50k
<50k   407    22
>=50k   68    64

Plot it:

interp = ClassificationInterpretation_from_learner(model)

interp %>% plot_confusion_matrix(dpi = 90,figsize = c(6,6))

Get predictions on new data:

> model %>% predict(df[10:15,])

       <50k     >=50k classes
1 0.5108562 0.4891439       0
2 0.4827824 0.5172176       1
3 0.4873166 0.5126833       1
4 0.5013804 0.4986197       0
5 0.4964157 0.5035844       1
6 0.5111378 0.4888622       0

Fastinference by Zachary Mueller has ShapInterpretation function that allows to utilize various methods within the SHAP interpretation library. Currently summary_plot, dependence_plot, waterfall_plot, force_plot, and decision_plot are supported.

First, get explanation object:

exp = ShapInterpretation(model,n_samples = 20)
# 100%|██████████| 20/20 [02:49<00:00,  8.46s/it]

Then, visualize decision plot:

exp %>% decision_plot(class_id = 1, row_idx = 2)

Dependence plot:

exp %>% dependence_plot('age', class_id = 0)

Summary plot:

exp %>% summary_plot()

Waterfall plot:

exp %>% waterfall_plot(row_idx=10)

Force (JS) plot:

exp %>% force_plot(class_id = 0)

Image data

Get Pets dataset:

URLs_PETS()

Define path to folders:

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

See one of examples:

fnames[1]

oxford-iiit-pet/images/american_pit_bull_terrier_129.jpg

Dataloader:

dls = ImageDataLoaders_from_name_re(
  path, fnames, pat='(.+)_\\d+.jpg$',
  item_tfms=Resize(size = 460), bs = 10,
  batch_tfms=list(aug_transforms(size = 224, min_scale = 0.75),
                  Normalize_from_stats( imagenet_stats() )
                  )
)

Show batch for visualization:

dls %>% show_batch(dpi = 150)

Model architecture:

learn = cnn_learner(dls, resnet34(), metrics = error_rate)

And fit:

learn %>% fit_one_cycle(n_epoch = 2)

epoch     train_loss  valid_loss  error_rate  time
0         0.904872    0.317927    0.105548    00:35
1         0.694395    0.239520    0.083897    00:36

Get confusion matrix and plot:

conf = learn %>% get_confusion_matrix()

library(highcharter)
hchart(conf, label = TRUE) %>%
    hc_yAxis(title = list(text = 'Actual')) %>%
    hc_xAxis(title = list(text = 'Predicted'),
             labels = list(rotation = -90))

Note that the plot is built with highcharter.

Plot top losses:

interp = ClassificationInterpretation_from_learner(learn)

interp %>% plot_top_losses(k = 9, figsize = c(15,11))

Alternatively, load images from folders:

# get sample data
URLs_MNIST_SAMPLE()

# transformations
tfms = aug_transforms(do_flip = FALSE)
path = 'mnist_sample'
bs = 20

#load into memory
data = ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs)

# Visualize and train
data %>% show_batch(dpi = 150)

learn = cnn_learner(data, resnet18(), metrics = accuracy)
learn %>% fit(2)

What about the implementation of the latest Computer Vision models?

There is a function in fastai timm_learner which originally written by Zachary Mueller. It helps to quickly load the pretrained models from timm library.

First, lets's see the list of available models (TOP 10):

> str(as.list(timm_list_models()[1:10]))
List of 10
 $ : chr "adv_inception_v3"
 $ : chr "cspdarknet53"
 $ : chr "cspdarknet53_iabn"
 $ : chr "cspresnet50"
 $ : chr "cspresnet50d"
 $ : chr "cspresnet50w"
 $ : chr "cspresnext50"
 $ : chr "cspresnext50_iabn"
 $ : chr "darknet53"
 $ : chr "densenet121"

Exciting!

Now, load and train pets dataset:

library(magrittr)
library(fastai)

path = 'oxford-iiit-pet'

path_img = 'oxford-iiit-pet/images'

fnames = get_image_files(path_img)

dls = ImageDataLoaders_from_name_re(
  path, fnames, pat='(.+)_\\d+.jpg$',
  item_tfms=Resize(size = 460), bs = 10,
  batch_tfms=list(aug_transforms(size = 224, min_scale = 0.75),
                  Normalize_from_stats( imagenet_stats() )
  )
)

learn = timm_learner(dls, 'cspdarknet53', metrics = list(accuracy, error_rate))

learn %>% summary()

Model summary

Sequential (Input shape: ['10 x 3 x 224 x 224'])
================================================================
Layer (type)         Output Shape         Param #    Trainable
================================================================
Conv2d               10 x 32 x 224 x 224  864        False
________________________________________________________________
LeakyReLU            10 x 32 x 224 x 224  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  18,432     False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 128 x 112 x 11  8,192      False
________________________________________________________________
LeakyReLU            10 x 128 x 112 x 11  0          False
________________________________________________________________
Conv2d               10 x 32 x 112 x 112  2,048      False
________________________________________________________________
LeakyReLU            10 x 32 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  18,432     False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  8,192      False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   73,728     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    36,864     False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    36,864     False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   294,912    False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   1,179,648  False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    4,718,592  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    1,048,576  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    1,048,576  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
AdaptiveAvgPool2d    10 x 1024 x 1 x 1    0          False
________________________________________________________________
AdaptiveMaxPool2d    10 x 1024 x 1 x 1    0          False
________________________________________________________________
Flatten              10 x 2048            0          False
________________________________________________________________
BatchNorm1d          10 x 2048            4,096      True
________________________________________________________________
Dropout              10 x 2048            0          False
________________________________________________________________
Linear               10 x 512             1,048,576  True
________________________________________________________________
ReLU                 10 x 512             0          False
________________________________________________________________
BatchNorm1d          10 x 512             1,024      True
________________________________________________________________
Dropout              10 x 512             0          False
________________________________________________________________
Linear               10 x 37              18,944     True
________________________________________________________________

Total params: 27,654,496
Total trainable params: 1,072,640
Total non-trainable params: 26,581,856

Optimizer used: <function Adam at 0x7fc1cfc16f28>
Loss function: FlattenedLoss of CrossEntropyLoss()

Model frozen up to parameter group #1

Callbacks:
  - TrainEvalCallback
  - Recorder
  - ProgressCallback

And finally, fit:

learn %>% fit_one_cycle(3)

epoch   train_loss   valid_loss   accuracy   error_rate   time
------  -----------  -----------  ---------  -----------  ------
0       1.206384     0.518956     0.847091   0.152909     01:00
1       0.841627     0.411970     0.890392   0.109608     00:58
2       0.657220     0.328548     0.899188   0.100812     00:59

See results:

learn %>% show_results()

Impressive!

GAN example

Get data (4,4 GB):

URLs_LSUN_BEDROOMS()

path = 'bedroom'

Dataloader function:

get_dls <- function(bs, size) {
  dblock = DataBlock(blocks = list(TransformBlock(), ImageBlock()),
                     get_x = generate_noise(),
                     get_items = get_image_files(),
                     splitter = IndexSplitter(c()),
                     item_tfms = Resize(size, method = "crop"),
                     batch_tfms = Normalize_from_stats(c(0.5,0.5,0.5), c(0.5,0.5,0.5))
  )
  dblock %>% dataloaders(source = path, path = path,bs = bs)
}

dls = get_dls(128, 64)

Generator and discriminator:

generator = basic_generator(out_size = 64, n_channels = 3, n_extra_layers = 1)
critic    = basic_critic(in_size = 64, n_channels = 3, n_extra_layers = 1,
                                    act_cls = partial(nn$LeakyReLU, negative_slope = 0.2))

Model:

learn = GANLearner_wgan(dls, generator, critic, opt_func = partial(Adam(), mom=0.))

And fit:

learn$recorder$train_metrics = TRUE
learn$recorder$valid_metrics = FALSE

learn %>% fit(1, 2e-4, wd = 0)

epoch     train_loss  gen_loss  crit_loss  time
0         -0.555554   0.516327  -0.967604  05:06

This is the result for 1 epoch.

learn %>% show_results(max_n = 16, figsize = c(8,8), ds_idx=0)

CycleGAN

CycleGAN package by Tanishq Abraham makes building and training a CycleGAN model very easy

Get data:

URLs_HORSE_2_ZEBRA()

Prepare data:

horse2zebra = 'horse2zebra'


trainA_path = file.path(horse2zebra,'trainA')
trainB_path = file.path(horse2zebra,'trainB')
testA_path = file.path(horse2zebra,'testA')
testB_path = file.path(horse2zebra,'testB')

dls = get_dls(trainA_path, trainB_path, num_A = 130,load_size = 270,crop_size = 144,bs=4)

Build model:

cycle_gan = CycleGAN(3,3,64)
learn = cycle_learner(dls, cycle_gan)

And fit:

learn %>% fit_flat_lin(4,4,2e-4)

epoch   train_loss   id_loss_A   id_loss_B   gen_loss_A   gen_loss_B   cyc_loss_A   cyc_loss_B   D_A_loss   D_B_loss   time
------  -----------  ----------  ----------  -----------  -----------  -----------  -----------  ---------  ---------  -----
0       10.500859    1.551905    1.678394    0.375322     0.385088     3.266770     3.509404     0.367762   0.367762   00:19
1       9.547493     1.267837    1.453950    0.301558     0.298583     2.698074     3.106223     0.253554   0.253554   00:19
2       8.938786     1.234537    1.250279    0.328651     0.328309     2.618085     2.713281     0.237375   0.237375   00:19
3       8.391484     1.066745    1.227453    0.327970     0.336748     2.285323     2.669749     0.240033   0.240033   00:19
4       7.642654     0.941413    1.057014    0.327448     0.350729     1.980680     2.274255     0.246695   0.246695   00:19
5       7.478543     0.966484    1.111054    0.291666     0.384912     2.119692     2.446879     0.251393   0.251393   00:18
6       7.190168     0.961237    1.034505    0.315916     0.397697     1.990408     2.182239     0.222851   0.222851   00:19
7       6.902316     0.891176    1.001932    0.343578     0.386471     1.848317     2.137690     0.215832   0.215832   00:19

Get predicitons and see results:

learn %>% get_preds_cyclegan(testA_path, './h2z-preds')

learn %>% show_results()

Unet example

Call libraries:

library(fastai)
library(magrittr)

Get data

URLs_CAMVID()

Specify folders:

path = 'camvid'
fnames = get_image_files(paste(path,'images',sep = '/'))
lbl_names = get_image_files(paste(path,'labels',sep = '/'))
codes = data.table::fread(paste(path,'codes.txt',sep = '/'), header = FALSE)[['V1']]
valid_fnames = data.table::fread(paste(path,'valid.txt',sep = '/'),header = FALSE)[['V1']]
# batch size
bs = 8

Define a loader object:

camvid = DataBlock(blocks = c(ImageBlock(), MaskBlock(codes)),
                   get_items = get_image_files,
                   splitter = FileSplitter('camvid/valid.txt'),
                   get_y = function(x) {paste('camvid/labels/',x$stem,'_P',x$suffix,sep = '')},
                   batch_tfms = list(aug_transforms(size = list(200,266)),
                                     Normalize_from_stats( imagenet_stats() )
                   )
)

# prefix and suffix of the name of the file
x$stem; x$suffix

Dataloader object and list of labels:

dls = camvid %>% dataloaders(source = "camvid/images", bs = bs, path = path)

dls %>% show_batch()

void_code = which(codes == "Void")

dls$vocab = codes

name2id = as.list(1:(length(codes)))
names(name2id) = codes

str(name2id)
List of 32
 $ Animal           : int 1
 $ Archway          : int 2
 $ Bicyclist        : int 3
 $ Bridge           : int 4
 $ Building         : int 5
 $ Car              : int 6
 $ CartLuggagePram  : int 7
 $ Child            : int 8
 $ Column_Pole      : int 9
 $ Fence            : int 10
 $ LaneMkgsDriv     : int 11
 $ LaneMkgsNonDriv  : int 12
 $ Misc_Text        : int 13
 $ MotorcycleScooter: int 14
 $ OtherMoving      : int 15
 $ ParkingBlock     : int 16
 $ Pedestrian       : int 17
 $ Road             : int 18
 $ RoadShoulder     : int 19
 $ Sidewalk         : int 20
 $ SignSymbol       : int 21
 $ Sky              : int 22
 $ SUVPickupTruck   : int 23
 $ TrafficCone      : int 24
 $ TrafficLight     : int 25
 $ Train            : int 26
 $ Tree             : int 27
 $ Truck_Bus        : int 28
 $ Tunnel           : int 29
 $ VegetationMisc   : int 30
 $ Void             : int 31
 $ Wall             : int 32

Custom accuracy function:

acc_camvid <- function(input, target) {
  target = target$squeeze(1L)
  # exclude/filter void label
  mask = target != void_code
  return(
    (input$argmax(dim=1L)[mask]$eq(target[mask])) %>%
      float() %>% mean()
  )
}

attr(acc_camvid, "py_function_name") <- 'acc_camvid'

Debug acc_camvid manually

batch = dls %>% one_batch(convert = FALSE)

[[1]]
TensorImage([[[[-1.4419e+00, -1.3117e+00, -1.1976e+00,  ...,  2.2489e+00,
            2.2238e+00,  2.0948e+00],
          [-1.5401e+00, -1.5213e+00, -1.4010e+00,  ...,  1.9834e+00,
            2.2378e+00,  2.2173e+00],
          [-1.6401e+00, -1.5477e+00, -1.5588e+00,  ...,  9.1953e-01,
            1.9501e+00,  1.1138e+00],
          ...,
          [-1.6852e+00, -1.5440e+00, -1.5132e+00,  ..., -1.0596e+00,
           -1.0711e+00, -1.0674e+00],
          [-1.5265e+00, -1.6030e+00, -1.5804e+00,  ..., -1.0268e+00,
           -1.0946e+00, -1.1181e+00],
          [-1.5423e+00, -1.5516e+00, -1.6014e+00,  ..., -1.1734e+00,
           -1.1293e+00, -1.0777e+00]],

         [[-1.3446e+00, -1.2023e+00, -1.0470e+00,  ...,  2.4286e+00,
            2.4090e+00,  2.2977e+00],
          [-1.4481e+00, -1.4276e+00, -1.2930e+00,  ...,  2.1422e+00,
            2.4158e+00,  2.3778e+00],
          [-1.5607e+00, -1.4584e+00, -1.4641e+00,  ...,  1.0026e+00,
            2.0258e+00,  1.1376e+00],
          ...,
          [-1.5809e+00, -1.4399e+00, -1.4133e+00,  ..., -7.8931e-01,
           -7.9807e-01, -7.9637e-01],
          [-1.4161e+00, -1.4909e+00, -1.4646e+00,  ..., -8.0615e-01,
           -8.5201e-01, -8.5311e-01],
          [-1.4472e+00, -1.4567e+00, -1.5077e+00,  ..., -9.4607e-01,
           -8.9744e-01, -8.2074e-01]],

         [[-1.1164e+00, -1.0162e+00, -9.1189e-01,  ...,  2.6257e+00,
            2.5726e+00,  2.4016e+00],
          [-1.2195e+00, -1.1752e+00, -1.0595e+00,  ...,  2.3488e+00,
            2.6271e+00,  2.5764e+00],
          [-1.3316e+00, -1.2451e+00, -1.2400e+00,  ...,  1.0476e+00,
            2.1812e+00,  1.3635e+00],
          ...,
          [-1.2881e+00, -1.1393e+00, -1.1035e+00,  ..., -3.8940e-01,
           -4.0598e-01, -3.9861e-01],
          [-1.1427e+00, -1.2167e+00, -1.1906e+00,  ..., -3.6462e-01,
           -4.3055e-01, -4.5333e-01],
          [-1.1525e+00, -1.1651e+00, -1.2190e+00,  ..., -4.8259e-01,
           -4.3712e-01, -4.1413e-01]]],


        [[[-2.0552e-01,  3.9563e-01,  4.0691e-01,  ..., -9.7342e-01,
           -7.8957e-01, -7.6035e-01],
          [-3.8852e-01,  4.2912e-01,  4.4469e-01,  ..., -1.0449e+00,
           -8.5347e-01, -7.5299e-01],
          [ 3.5939e-01,  3.6353e-01,  4.7028e-01,  ..., -9.3101e-01,
           -8.7398e-01, -7.9327e-01],
          ...,
          [-1.0510e+00, -1.0661e+00, -9.6690e-01,  ..., -1.3688e+00,
           -1.4543e+00, -1.4645e+00],
          [-1.0578e+00, -1.0939e+00, -9.3117e-01,  ..., -1.3939e+00,
           -1.4033e+00, -1.4209e+00],
          [-9.9012e-01, -1.0312e+00, -1.0074e+00,  ..., -1.4274e+00,
           -1.3829e+00, -1.3758e+00]],

         [[ 6.0090e-02,  7.8124e-01,  7.5145e-01,  ..., -8.2881e-01,
           -6.7773e-01, -6.3718e-01],
          [-1.7114e-01,  7.8613e-01,  7.8531e-01,  ..., -9.0003e-01,
           -7.3661e-01, -5.8707e-01],
          [ 7.3440e-01,  7.5691e-01,  8.2297e-01,  ..., -8.0694e-01,
           -7.5451e-01, -6.2783e-01],
          ...,
          [-7.8971e-01, -7.8585e-01, -7.4870e-01,  ..., -1.2630e+00,
           -1.3108e+00, -1.3046e+00],
          [-7.8414e-01, -7.9617e-01, -7.2847e-01,  ..., -1.2297e+00,
           -1.2414e+00, -1.2594e+00],
          [-7.3135e-01, -7.7442e-01, -7.4849e-01,  ..., -1.2259e+00,
           -1.1889e+00, -1.2022e+00]],

         [[ 4.4920e-01,  1.2392e+00,  1.3399e+00,  ..., -6.0991e-01,
           -4.5250e-01, -4.4251e-01],
          [ 2.7577e-01,  1.2913e+00,  1.3755e+00,  ..., -6.8060e-01,
           -5.1114e-01, -3.7442e-01],
          [ 1.0632e+00,  1.3052e+00,  1.3774e+00,  ..., -5.8343e-01,
           -5.2787e-01, -3.9803e-01],
          ...,
          [-4.4165e-01, -4.4558e-01, -3.8942e-01,  ..., -8.7048e-01,
           -9.2835e-01, -9.2750e-01],
          [-4.4233e-01, -4.6348e-01, -3.7176e-01,  ..., -8.6960e-01,
           -8.8080e-01, -8.9788e-01],
          [-3.8967e-01, -4.3118e-01, -3.8587e-01,  ..., -8.7933e-01,
           -8.4775e-01, -8.5052e-01]]],


        [[[ 1.2805e+00,  2.2139e+00,  9.9765e-01,  ...,  6.6338e-01,
           -4.0192e-01,  2.8007e-01],
          [ 1.0171e+00,  1.8849e+00,  1.1654e+00,  ..., -1.0001e+00,
            1.1788e+00,  2.0717e+00],
          [ 2.8709e-01,  1.9494e+00,  2.1978e+00,  ..., -6.7389e-01,
            3.2762e-01,  4.5549e-01],
          ...,
          [-4.3609e-01, -4.2635e-01, -4.6298e-01,  ...,  7.7548e-02,
            3.6271e-02, -3.1759e-02],
          [-3.7265e-01, -4.3453e-01, -4.4666e-01,  ..., -7.5601e-02,
            5.3570e-03, -2.9393e-02],
          [-3.7581e-01, -4.0105e-01, -4.2908e-01,  ...,  8.5172e-03,
           -3.3988e-03, -1.8303e-02]],

         [[ 1.3276e+00,  2.3720e+00,  1.0603e+00,  ...,  8.6043e-01,
           -1.1662e-01,  5.2147e-01],
          [ 1.0938e+00,  2.0233e+00,  1.2629e+00,  ..., -9.1610e-01,
            1.3807e+00,  2.2914e+00],
          [ 3.8840e-01,  2.1078e+00,  2.3635e+00,  ..., -5.8584e-01,
            5.2653e-01,  7.8300e-01],
          ...,
          [-3.1636e-01, -3.0640e-01, -3.4385e-01,  ...,  1.3784e-01,
            9.5460e-02,  2.5607e-02],
          [-2.5150e-01, -3.1476e-01, -3.2716e-01,  ..., -1.9409e-02,
            6.3717e-02,  2.8037e-02],
          [-2.5473e-01, -2.8054e-01, -3.0920e-01,  ...,  6.6963e-02,
            5.4727e-02,  3.9424e-02]],

         [[ 1.8118e+00,  2.6126e+00,  1.5284e+00,  ...,  1.3408e+00,
            3.8263e-01,  9.4347e-01],
          [ 1.4345e+00,  2.2263e+00,  1.5055e+00,  ..., -4.0407e-01,
            1.9165e+00,  2.5325e+00],
          [ 6.9120e-01,  2.3214e+00,  2.5724e+00,  ..., -5.9273e-02,
            7.6707e-01,  9.8036e-01],
          ...,
          [-3.2707e-02, -2.5592e-02, -6.5520e-02,  ...,  3.1733e-01,
            2.8317e-01,  2.2166e-01],
          [ 1.6474e-02, -4.1773e-02, -5.1314e-02,  ...,  1.6267e-01,
            2.4836e-01,  2.1449e-01],
          [ 2.4832e-02,  1.0270e-02, -1.5259e-02,  ...,  2.3768e-01,
            2.2930e-01,  2.2220e-01]]],


        ...,


        [[[-1.5176e-02, -1.9729e-02, -5.4177e-02,  ...,  2.0812e+00,
            2.2489e+00,  2.2242e+00],
          [-1.0897e-02,  3.5695e-02,  2.3053e-03,  ...,  2.1605e+00,
            2.0372e+00,  2.1403e+00],
          [-2.8262e-02, -3.0313e-02, -3.4347e-02,  ...,  2.2136e+00,
            2.2489e+00,  1.2613e+00],
          ...,
          [-1.2644e+00, -1.2548e+00, -1.2313e+00,  ..., -1.3335e+00,
           -1.3230e+00, -1.2787e+00],
          [-1.1986e+00, -1.2068e+00, -1.1631e+00,  ..., -1.2694e+00,
           -1.2973e+00, -1.2696e+00],
          [-1.2508e+00, -1.2447e+00, -1.2294e+00,  ..., -1.0572e+00,
           -1.0660e+00, -1.0694e+00]],

         [[ 2.2227e-01,  2.1430e-01,  2.1605e-01,  ...,  2.3389e+00,
            2.4286e+00,  2.4286e+00],
          [ 2.0176e-01,  2.4693e-01,  2.4092e-01,  ...,  2.3745e+00,
            2.2931e+00,  2.3820e+00],
          [ 1.8103e-01,  1.7892e-01,  1.7477e-01,  ...,  2.4036e+00,
            2.4286e+00,  1.4878e+00],
          ...,
          [-1.0710e+00, -1.0613e+00, -1.0374e+00,  ..., -1.2492e+00,
           -1.2385e+00, -1.2225e+00],
          [-1.0040e+00, -1.0124e+00, -9.6780e-01,  ..., -1.1836e+00,
           -1.2122e+00, -1.2193e+00],
          [-1.0572e+00, -1.0510e+00, -1.0354e+00,  ..., -9.5631e-01,
           -9.6512e-01, -9.6444e-01]],

         [[ 5.4786e-01,  5.5583e-01,  5.3839e-01,  ...,  2.5781e+00,
            2.6400e+00,  2.6400e+00],
          [ 5.3558e-01,  5.8483e-01,  5.6649e-01,  ...,  2.5895e+00,
            2.5283e+00,  2.6400e+00],
          [ 5.2345e-01,  5.2294e-01,  5.1033e-01,  ...,  2.6400e+00,
            2.6400e+00,  1.7087e+00],
          ...,
          [-8.1354e-01, -8.0387e-01, -7.9721e-01,  ..., -1.0014e+00,
           -9.9075e-01, -9.5806e-01],
          [-7.4687e-01, -7.5518e-01, -7.2870e-01,  ..., -9.4173e-01,
           -9.6991e-01, -9.5030e-01],
          [-7.9981e-01, -7.9358e-01, -7.9630e-01,  ..., -7.3474e-01,
           -7.4333e-01, -7.3628e-01]]],


        [[[ 6.8056e-01,  6.8056e-01,  6.9105e-01,  ..., -3.6921e-01,
           -3.1641e-01, -3.3400e-01],
          [ 6.9991e-01,  7.1771e-01,  6.8056e-01,  ..., -3.3319e-01,
           -3.4023e-01, -3.8674e-01],
          [ 6.9781e-01,  7.1034e-01,  6.9885e-01,  ..., -2.9567e-01,
           -3.0638e-01, -2.8775e-01],
          ...,
          [-1.4393e+00, -1.4183e+00, -1.4183e+00,  ..., -1.3420e+00,
           -1.4022e+00, -1.3872e+00],
          [-1.4436e+00, -1.4326e+00, -1.4335e+00,  ..., -1.3950e+00,
           -1.3800e+00, -1.3734e+00],
          [-1.4509e+00, -1.4539e+00, -1.4533e+00,  ..., -1.3681e+00,
           -1.4340e+00, -1.3650e+00]],

         [[ 2.0471e+00,  2.0471e+00,  2.0603e+00,  ..., -6.5347e-02,
            2.6326e-02,  3.4833e-02],
          [ 2.0525e+00,  2.0750e+00,  2.0818e+00,  ..., -4.7675e-02,
           -5.2935e-03, -2.6855e-02],
          [ 2.0976e+00,  2.1136e+00,  2.1051e+00,  ...,  1.8606e-02,
            4.1052e-02,  8.5274e-02],
          ...,
          [-1.2304e+00, -1.2244e+00, -1.2219e+00,  ..., -1.2425e+00,
           -1.3041e+00, -1.2836e+00],
          [-1.2239e+00, -1.2107e+00, -1.2107e+00,  ..., -1.2967e+00,
           -1.2813e+00, -1.2746e+00],
          [-1.2210e+00, -1.2154e+00, -1.2157e+00,  ..., -1.2695e+00,
           -1.3401e+00, -1.2696e+00]],

         [[ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  3.4950e-01,
            4.4111e-01,  4.1667e-01],
          [ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  3.3850e-01,
            3.8055e-01,  3.7792e-01],
          [ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  4.4053e-01,
            4.5217e-01,  4.8598e-01],
          ...,
          [-8.2900e-01, -8.1651e-01, -8.1498e-01,  ..., -9.5577e-01,
           -1.0173e+00, -9.9684e-01],
          [-8.3432e-01, -8.2192e-01, -8.2227e-01,  ..., -1.0234e+00,
           -1.0080e+00, -1.0014e+00],
          [-8.3237e-01, -8.2912e-01, -8.2936e-01,  ..., -1.0039e+00,
           -1.0649e+00, -9.9452e-01]]],


        [[[ 2.0699e+00,  1.9477e+00,  2.0700e+00,  ..., -1.5310e+00,
           -1.6490e+00, -1.6860e+00],
          [ 1.8292e+00,  2.1599e+00,  1.8882e+00,  ..., -1.6536e+00,
           -1.6374e+00, -1.6022e+00],
          [ 2.0288e+00,  1.7863e+00,  2.0564e+00,  ..., -1.6149e+00,
           -1.6315e+00, -1.5586e+00],
          ...,
          [-1.4481e+00, -1.3921e+00, -1.4195e+00,  ..., -1.5045e+00,
           -1.5133e+00, -1.5381e+00],
          [-1.4223e+00, -1.3757e+00, -1.3943e+00,  ..., -1.5238e+00,
           -1.5371e+00, -1.5453e+00],
          [-1.4134e+00, -1.4104e+00, -1.4300e+00,  ..., -1.5163e+00,
           -1.5862e+00, -1.5565e+00]],

         [[ 1.5571e+00,  1.4284e+00,  1.8346e+00,  ..., -1.4521e+00,
           -1.6496e+00, -1.6908e+00],
          [ 1.2790e+00,  1.6710e+00,  1.3942e+00,  ..., -1.5838e+00,
           -1.6467e+00, -1.6069e+00],
          [ 1.4661e+00,  1.2568e+00,  1.7123e+00,  ..., -1.5898e+00,
           -1.6761e+00, -1.6212e+00],
          ...,
          [-1.2567e+00, -1.2393e+00, -1.2457e+00,  ..., -1.4077e+00,
           -1.4073e+00, -1.4286e+00],
          [-1.2191e+00, -1.2129e+00, -1.2214e+00,  ..., -1.4193e+00,
           -1.4265e+00, -1.4403e+00],
          [-1.2213e+00, -1.2350e+00, -1.2495e+00,  ..., -1.4075e+00,
           -1.4811e+00, -1.4504e+00]],

         [[ 1.1398e+00,  1.0327e+00,  1.4135e+00,  ..., -1.2147e+00,
           -1.4180e+00, -1.4598e+00],
          [ 8.6931e-01,  1.2768e+00,  1.0129e+00,  ..., -1.3449e+00,
           -1.3906e+00, -1.3518e+00],
          [ 1.1199e+00,  9.0534e-01,  1.2758e+00,  ..., -1.3922e+00,
           -1.4662e+00, -1.4051e+00],
          ...,
          [-8.5999e-01, -8.2594e-01, -8.6729e-01,  ..., -1.0699e+00,
           -1.0976e+00, -1.1388e+00],
          [-8.4630e-01, -8.2145e-01, -8.4266e-01,  ..., -1.1058e+00,
           -1.1325e+00, -1.1478e+00],
          [-8.5198e-01, -8.5977e-01, -8.7435e-01,  ..., -1.1186e+00,
           -1.1739e+00, -1.1579e+00]]]], device='cuda:0')

[[2]]
TensorMask([[[ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         ...,
         [19, 19, 19,  ..., 17, 17, 17],
         [19, 19, 19,  ..., 17, 17, 17],
         [19, 19, 19,  ..., 17, 17, 17]],

        [[ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]],

        [[26, 21, 26,  ..., 26, 26, 26],
         [26, 21, 26,  ..., 26, 26, 26],
         [26, 21, 21,  ..., 26, 26, 26],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]],

        ...,

        [[ 4,  4,  4,  ..., 26, 26, 26],
         [ 4,  4,  4,  ..., 26, 26, 26],
         [ 4,  4,  4,  ..., 26, 26, 26],
         ...,
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19]],

        [[21, 21, 21,  ...,  4,  4,  4],
         [21, 21, 21,  ...,  4,  4,  4],
         [21, 21, 21,  ...,  4,  4,  4],
         ...,
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19]],

        [[ 4,  4,  4,  ..., 30, 30, 30],
         [ 4,  4,  4,  ..., 30, 30, 30],
         [ 4,  4,  4,  ..., 30, 30, 30],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]]], device='cuda:0')

The shape of the tensors:

batch[[1]]$shape;batch[[2]]$shape

torch.Size([8, 3, 200, 266])
torch.Size([8, 200, 266])

Define input and target:

input = batch[[1]]
target = batch[[2]]

Filter Void class:

mask = target != void_code

31 will be filtered as False:

TensorMask([[[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        ...,

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]]], device='cuda:0')

> (input$argmax(dim=1L)[mask] == target[mask])
tensor([False, False, False,  ..., False, False, False], device='cuda:0')

> (input$argmax(dim=1L)[mask] == target[mask]) %>%
              float()
tensor([0., 0., 0.,  ..., 0., 0., 0.], device='cuda:0')

> (input$argmax(dim=1L)[mask]==target[mask]) %>%
              float() %>% mean()
tensor(0.0011, device='cuda:0')

Resnet34 model architecture for unet:

learn = unet_learner(dls, resnet34(), metrics = acc_camvid)

And finally, fit:

lr = 3e-3
wd = 1e-2

learn %>% fit_one_cycle(2, slice(lr), pct_start = 0.9, wd = wd)

epoch     train_loss  valid_loss  acc_camvid  time
0         1.367869    1.239496    0.666145    00:25
1         0.929434    0.661407    0.839969    00:23

learn %>% show_results(max_n = 1, figsize = c(20,10), vmin = 1, vmax = 30)

Collab (Collaborative filtering)

Call libraries:

library(zeallot)
library(magrittr)

Get data:

URLs_MOVIE_LENS_ML_100k()

Specify column names:

c(user,item,title)  %<-% list('userId','movieId','title')

Read datasets:

ratings = fread('ml-100k/u.data', col.names = c(user,item,'rating','timestamp'))
movies = fread('ml-100k/u.item', col.names = c(item, 'title', 'date', 'N', 'url',
                                                           paste('g',1:19,sep = '')))

Left join on item:

rating_movie = ratings[movies[, .SD, .SDcols=c(item,title)], on = item]

Load data from dataframe (R):

dls = CollabDataLoaders_from_df(rating_movie, seed=42, valid_pct=0.1, bs=64, item_name=title, path='ml-100k')

Build model:

learn = collab_learner(dls, n_factors = 40, y_range=c(0, 5.5))

Start learning:

learn %>% fit_one_cycle(1, 5e-3,  wd = 1e-1)

Get top 1,000 movies:

top_movies = head(unique(rating_movie[ , count := .N, by = .(title)]
                    [order(count,decreasing = T)]
                    [, c('title','count')]),
                   1e3)[['title']]

Find mean ratings for the films:

mean_ratings = unique(rating_movie[ , .(mean = mean(rating)), by = title])

                                          title     mean
   1:                          Toy Story (1995) 3.878319
   2:                          GoldenEye (1995) 3.206107
   3:                         Four Rooms (1995) 3.033333
   4:                         Get Shorty (1995) 3.550239
   5:                            Copycat (1995) 3.302326
  ---
1660:                      Sweet Nothing (1995) 3.000000
1661:                         Mat' i syn (1997) 1.000000
1662:                          B. Monkey (1998) 3.000000
1663:                       You So Crazy (1994) 3.000000
1664: Scream of Stone (Schrei aus Stein) (1991) 3.000000

Extract bias:

movie_bias = learn %>% get_bias(top_movies, is_item = TRUE)

result = data.table(bias = movie_bias,
           title = top_movies)

res = merge(result, mean_ratings, all.y = FALSE)

res[order(bias, decreasing = TRUE)]

                                           title        bias     mean
   1:                           Star Wars (1977)  0.29479960 4.358491
   2:                               Fargo (1996)  0.25264889 4.155512
   3:                      Godfather, The (1972)  0.23247446 4.283293
   4:           Silence of the Lambs, The (1991)  0.22765337 4.289744
   5:                             Titanic (1997)  0.22353025 4.245714
  ---
 996: Children of the Corn: The Gathering (1996) -0.05671900 1.315789
 997:                       Jungle2Jungle (1997) -0.05957306 2.439394
 998:                  Leave It to Beaver (1997) -0.06268980 1.840909
 999:             Speed 2: Cruise Control (1997) -0.06567496 2.131579
1000:           Island of Dr. Moreau, The (1996) -0.07530680 2.157895

Get weights:

movie_w = learn %>% get_weights(top_movies, is_item = TRUE, convert = TRUE)

Visualize with highcharter:

rownames(movie_w) = res$title

highcharter::hchart(princomp(movie_w, cor = TRUE)) %>% highcharter::hc_legend(enabled = FALSE)

Text data

Grab data:

URLs_IMDB()

Specify path and small batch_size because it consumes a lot of GPU:

path = 'imdb'
bs = 20

Create datablock and iterator:

imdb_lm = DataBlock(blocks=list(TextBlock_from_folder(path, is_lm = TRUE)),
                    get_items = partial(get_text_files(),
                    folders = c('train', 'test', 'unsup')),
                    splitter = RandomSplitter(0.1))

dbunch_lm = imdb_lm %>% dataloaders(source = path, path = path, bs = bs, seq_len = 80)

Load a pretrained model and fit:

learn = language_model_learner(dbunch_lm, AWD_LSTM(), drop_mult = 0.3,
                               metrics = list(accuracy, Perplexity()))

learn %>% fit_one_cycle(1, 2e-2, moms = c(0.8, 0.7, 0.8))

Note: AWD_LSTM() can throw an error. In this case find and clean ".fastai" folder.

Medical data

Import dicom data:

img = dcmread('hemorrhage.dcm')

Visualize data with different windowing effects:

dicom_windows = dicom_windows()
scale = list(FALSE, TRUE, dicom_windows$brain, dicom_windows$subdural)
titles = c('raw','normalized','brain windowed','subdural windowed')

library(zeallot)
c(fig, axs) %<-% subplots()

for (i in 1:4) {
  img %>% show(scale = scale[[i]],
               ax = axs[[i]],
               title=titles[i])
}

img %>% plot(dpi = 250)

Apply different cmaps:

img %>% show(cmap = cm()$gist_ncar, figsize = c(6,6))
img %>% plot()

Or get dcm matrix and plot with ggplot:

types = c('raw', 'normalized', 'brain', 'subdural')
p_ = list()
for ( i in 1:length(types)) {
  p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i]))
  p_[[i]] = p
}

ggpubr::ggarrange(p_[[1]], p_[[2]], p_[[3]], p_[[4]], labels = types)

Let's try a relatively complex example:

library(ggplot2)

# crop parameters
img = dcmread('hemorrhage.dcm')
res = img %>% mask_from_blur(win_brain()) %>%
  mask2bbox()

types = c('raw', 'normalized', 'brain', 'subdural')

# colors for matrix filling
colors = list(viridis::inferno(30), viridis::magma(30),
              viridis::plasma(30), viridis::cividis(30))
scan_ = c('uniform_blur2d', 'gauss_blur2d')
p_ = list()

for ( i in 1:length(types)) {
  if(i == 3) {
    scan = scan_[1]
  } else if (i==4) {
    scan = scan_[2]
  } else {
    scan = ''
  }

  # crop with x/y_lim functions from ggplot
  if(i==2) {
    p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
                                                         scan = scan),
                                                         colours = colors[[i]])
    p = p + ylim(c(res[[1]][[1]],res[[2]][[1]])) + xlim(c(res[[1]][[2]],res[[2]][[2]]))

  # zoom image (25 %)
  } else if (i==4) {

    img2 = img
    img2 %>% zoom(0.25)
    p = nandb::matrix_raster_plot(img2 %>% get_dcm_matrix(type = types[i],
                                                          scan = scan),
                                                          colours = colors[[i]])
  } else {
    p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
                                                         scan = scan),
                                                         colours = colors[[i]])
  }

  p_[[i]] = p
}

ggpubr::ggarrange(p_[[1]],
                  p_[[2]],
                  p_[[3]],
                  p_[[4]],
                  labels = paste(types[1:4],
                                 paste(c('','',scan_))[1:4])
                  )

Additional features

Find optimal learning rate

Get optimal learning rate and then fit:

data = model %>% lr_find()
data

# SuggestedLRs(lr_min=0.017378008365631102, lr_steep=0.0020892962347716093)

         lr_rates   losses
1 0.0000001000000 5.349157
2 0.0000001202264 5.231493
3 0.0000001445440 5.087494
4 0.0000001737801 5.068282
5 0.0000002089296 5.043181
6 0.0000002511886 5.023340

Visualize:

highcharter::hchart(data, "line", highcharter::hcaes(y = losses, x = lr_rates ))

Visualize batch

Visualize tensor(s):

# get batch
batch = dls %>% one_batch(convert = TRUE)

# visualize img 9 with transformations
magick::image_read(batch[[1]][[9]])

Mask

Visualize mask:

library(magrittr)
library(fastai)

# original image
fns = get_image_files('camvid/images')
cam_fn = capture.output(fns[0])

# mask
mask_fn = 'camvid/labels/0016E5_01110_P.png'
cam_img = Image_create(cam_fn)

# create mask
tmask = Transform(Mask_create())
mask = tmask(mask_fn)

# visualize
mask %>% to_matrix() %>%
  nandb::matrix_raster_plot(colours = viridis::plasma(3)) + theme(legend.position = "none")

TensorPoints

Load Tiny Mnist:

# download
URLs_MNIST_TINY()

# black and white img
timg = Transform(ImageBW_create)
mnist_fn = "mnist_tiny/valid/3/9007.png"
mnist_img = timg(mnist_fn)

# resize img
pnt_img = TensorImage(mnist_img %>% Image_resize(size = list(28,35)))

# visualize
library(ggplot2)
pnt_img %>% to_matrix() %>% nandb::matrix_raster_plot(colours = c('white','black')) +
  geom_point(aes(x=0, y=0),size=2, colour="red")+
  geom_point(aes(x=0, y=35),size=2, colour="red")+
  geom_point(aes(x=28, y=0),size=2, colour="red")+
  geom_point(aes(x=28, y=35),size=2, colour="red")+
  geom_point(aes(x=9, y=17),size=2, colour="red")+
  theme(legend.position = "none")

Annotations on Tiny COCO

library(magrittr)
library(zeallot)
library(fastai)

URLs_COCO_TINY()

c(images, lbl_bbox) %<-% get_annotations('coco_tiny/train.json')
timg = Transform(ImageBW_create)
idx = 49
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
                       lbl_bbox[[idx]])
coco_img = timg(coco_fn)

tbbox = LabeledBBox(TensorBBox(bbox[[1]]), bbox[[2]])

(#2) [TensorBBox([[ 91.3000,  77.9400, 102.4300,  82.4700],
        [ 27.5800,  77.6500,  40.7600,  82.3400]]),['tv', 'tv']]

Visualize:

library(imager)
coco = imager::load.image(coco_fn)
plot(coco,axes=F)

for ( i in 1:length(bbox[[1]])) {
  rect(bbox[[1]][[i]][[1]],bbox[[1]][[i]][[2]],
       bbox[[1]][[i]][[3]],bbox[[1]][[i]][[4]],
       border = "white", lwd = 2)

  text(bbox[[1]][[i]][[3]]-2.5,bbox[[1]][[i]][[4]]+2.5, labels = bbox[[2]][i],
       offset = 2,
       pos = 2,
       cex = 1,
       col = "white"
  )
}

Alternatively, we could see batch via dataloader:

idx = 3
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
                          lbl_bbox[[idx]])

coco_bb = function(x) {
 TensorBBox_create(bbox[[1]])
}

coco_lbl = function(x) {
  bbox[[2]]
}

coco_dsrc = Datasets(c(rep(coco_fn,10)),
                     list(Image_create(), list(coco_bb),
                     list( coco_lbl, MultiCategorize(add_na = TRUE) )
                          ), n_inp = 1)

coco_tdl = TfmdDL(coco_dsrc, bs = 9,
                  after_item = list(BBoxLabeler(), PointScaler(),
                                 ToTensor()),
                  after_batch = list(IntToFloatTensor(), aug_transforms())
                  )

coco_tdl %>% show_batch(dpi = 200)

NN module

To build a custom sequential model and pass it to learner:

nn$Sequential() +
  nn$Conv2d(1L,20L,5L) +
  nn$Conv2d(1L,20L,5L) +
  nn$Conv2d(1L,20L,5L)

Sequential(
  (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)

To specify the name of the layers, one has to pass layer within lists, because torch layers have no name argument:

nn$Sequential() +
  nn$Conv2d(1L,20L,5L) +
  list('my_conv2',nn$Conv2d(1L,20L,5L)) +
  nn$Conv2d(1L,20L,5L) +
  list('my_conv4',nn$Conv2d(1L,20L,5L))

Sequential(
  (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (my_conv2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (my_conv4): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)

Icevision

Icevision module has a great set of tools for object detection tasks:

Note: First, install icevision module: reticulate::py_install('icevision', pip = TRUE)

library(fastai)
library(magrittr)

# get file
url = "https://cvbp.blob.core.windows.net/public/datasets/object_detection/odFridgeObjects.zip"
download.file(url,destfile = odFridgeObjects.zip)

# Parser
class_map = icevision_ClassMap(c("milk_bottle", "carton", "can", "water_bottle"))
parser = parsers_voc(annotations_dir= "odFridgeObjects/annotations/",
                     images_dir= "odFridgeObjects/images",
                     class_map=class_map)
records = parser$parse()

# Records
train_records = records[[1]]
valid_records = records[[2]]

# Transforms
train_tfms = icevision_Adapter(list(icevision_aug_tfms(size=384, presize=512),
                               icevision_Normalize()))
valid_tfms = icevision_Adapter(list(icevision_resize_and_pad(384),icevision_Normalize()))

# Datasets
train_ds = icevision_Dataset(train_records, train_tfms)
valid_ds = icevision_Dataset(valid_records, valid_tfms)

# See batch

train_ds %>% show_samples(idx=c(5,10,20,50,100,99), class_map=class_map,
                          denormalize_fn=denormalize_imagenet(),ncols = 3)

Next, we create data loader and fastai training:

# DataLoaders
train_dl = efficientdet_train_dl(train_ds, batch_size=16, num_workers=4, shuffle=T)
valid_dl = efficientdet_valid_dl(valid_ds, batch_size=16, num_workers=4, shuffle=F)

# Model and Metrics
model = efficientdet_model(model_name="tf_efficientdet_lite0", num_classes=5, img_size=384)
metrics = list(COCOMetric())

# Training using Fastai
learn = efficientdet_learner(dls=list(train_dl, valid_dl), model=model, metrics=metrics)
res = learn %>% fine_tune(10, 1e-2, freeze_epochs=10)

epoch   train_loss   valid_loss   COCOMetric   time  
------  -----------  -----------  -----------  ------
0       1.711512     1.264311     0.001845     00:05 
1       1.568228     1.247132     0.009066     00:03 
2       1.496242     1.222866     0.010658     00:03 
3       1.433081     1.137174     0.030878     00:03 
4       1.349961     1.060559     0.114889     00:03 
5       1.260490     0.937396     0.180179     00:03 
6       1.169536     0.998160     0.174759     00:04 
7       1.097816     0.929107     0.208315     00:03 
8       1.042755     0.821408     0.230876     00:03 
9       0.987962     0.840447     0.297297     00:03 
epoch   train_loss   valid_loss   COCOMetric   time  
------  -----------  -----------  -----------  ------
0       0.629509     0.715716     0.325585     00:04 
1       0.599518     0.684150     0.382239     00:03 
2       0.585920     0.631303     0.480204     00:04 
3       0.554550     0.615111     0.516641     00:04 
4       0.539868     0.622383     0.459974     00:04 
5       0.521756     0.609013     0.556388     00:04 
6       0.498118     0.491549     0.611895     00:04 
7       0.477656     0.469396     0.685497     00:04 
8       0.459495     0.407668     0.713956     00:04 
9       0.446206     0.383890     0.729540     00:04

Predictions:

# Inference
# DataLoader
infer_dl = efficientdet_infer_dl(valid_ds, batch_size=8)
# Predict
res <- efficientdet_predict_dl(model, infer_dl)

show_preds(
  res,
  c(10,20,25,12,16),
  class_map=class_map,
  denormalize_fn=denormalize_imagenet(),
  ncols=5,
  figsize=c(19,10)
)

Kaggle

Kaggle API is fantastic because it simplifies all the necessary steps for participating in a competition! Using the API it is possible to directly download/submit files, check leader board and etc. If you want to use this functionality, then it is important to place your kaggle.json to .kaggle folder:

Let's participate in a Titanic competition:

library(fastai)
library(magrittr)

com_nm = 'titanic'

titanic_files = competition_list_files(com_nm)
titanic_files = lapply(1:length(titanic_files),
                      function(x) as.character(titanic_files[[x]]))

str(titanic_files)

if(!dir.exists(com_nm)) {
  dir.create(com_nm)
}

# download via api
competition_download_files(competition = com_nm, path = com_nm, unzip = TRUE)

train = data.table::fread(paste(com_nm, 'train.csv', sep = '/'))

train[['Survived']] = as.factor(train[['Survived']])
train[['Name']] <- NULL
train[['PassengerId']] <- NULL

str(train)

Classes ‘data.table’ and 'data.frame':	595 obs. of  10 variables:
 $ Survived: Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
 $ Pclass  : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Sex     : chr  "male" "female" "female" "female" ...
 $ Age     : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp   : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch   : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket  : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare    : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin   : chr  "" "C85" "" "C123" ...
 $ Embarked: chr  "S" "C" "S" "S" ...
 - attr(*, ".internal.selfref")=<externalptr>

Preprocess:

dep_var = 'Survived'
cont_names = c('Fare','Parch', 'SibSp', 'Pclass')
cat_names = setdiff(names(train),c(cont_names,dep_var))


tot = 1:nrow(train)
tr_idx = sample(nrow(train), 0.8 * nrow(train))
ts_idx = tot[!tot %in% tr_idx]

Dataloader:

procs = list(FillMissing(),Categorify(),Normalize())

dls = TabularDataTable(train, procs, cat_names, cont_names,
                       y_names = dep_var, splits = list(tr_idx, ts_idx) ) %>%
  dataloaders(bs = 30)


model = dls %>% tabular_learner(layers=c(200,100),
                                config = tabular_config(embed_p = 0.3, use_bn = FALSE),
                                metrics = list(accuracy, RocAucBinary(),
                                               Precision(), Recall()))

Fit:

model %>% lr_find()

model %>% plot_lr_find()

res = model %>% fit(4, lr = 1e-3)

Prepare test dataset and submit:

test = data.table::fread(paste(com_nm, 'test.csv', sep = '/'))

test$Fare[is.na(test$Fare)] = median(test$Fare, na.rm = TRUE)

submission = model %>% predict(test)

head(submission)

          0         1 class
1 0.7636479 0.2363522     0
2 0.7594652 0.2405347     0
3 0.6516959 0.3483041     0
4 0.7734003 0.2265998     0
5 0.5761551 0.4238448     0
6 0.7645358 0.2354642     0

# add col names
submission = data.frame(PassengerId = test$PassengerId,
                        Survived = submission$class)

dest = paste(com_nm, 'submission.csv',sep = '/')

# write
data.table::fwrite(submission, dest)

# submit via api
competition_submit(dest, 'sumbission from R!', competition = com_nm)

100%|██████████| 9.27k/9.27k [00:04<00:00, 2.02kB/s]
Successfully submitted to Titanic: Machine Learning from Disaster

Enter Kaggle.com and see if everything works fine:

Code of Conduct

Please note that the fastai project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Potential error in TextBlock_from_df ?
Hi. I am not sure why but I got trouble running TextBlock_from_df(). I have a data.table looking like the following :

Id | text_article | label

d0fa7568-7d8e-4db9-870f-f9c6f668c17b | What is this study about? This study used data from the National Education Longitudinal Study (NELS:88) to examine the effects of dual enrollment programs for high school students on college degree attainment | national education longitudinal study

I am trying to run :

data_block = DataBlock( blocks = list(TextBlock_from_df(text_cols="text_article")), #List() is required on the R side. get_x=ColReader("text_article"), get_y=ColReader('label'))

According to the example here https://docs.fast.ai/text.data.html#TextBlock.from_df and here : https://github.com/EagerAI/fastai#text-data and the fastai books.

But I also tried directly :

data_block = DataBlock(blocks = list(TextBlock_from_df(text_cols="text_article"))) #from fastai doc

Both gave me the following error :

Error: $ operator is invalid for atomic vectors Traceback:

DataBlock(blocks = list(TextBlock_from_df(text_cols = "text_article")))

TextBlock_from_df(text_cols = "text_article")

do.call(text()$TextBlock$from_df, args)

I put in bold what attract my suspicion. I am not really at home here, I am doing NLP with fastai for the first time, both in R and fastai as well. I am putting the name of the column directly as a string, "text_article" as I am supposed to do. Based on the examples the dataframe with the text is supposed to be provided after. For what I understand the fastai wrapper is not happy about the text()$TextBlock$from_df).
opened by Cdk29 27
To_fp16()
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.
opened by Cdk29 10
Error in py_call_impl

Hello, I followed this tutorial: https://henry090.github.io/fastai/articles/multilabel.html

And hit an error when I ran this line: learn %>% summary()

Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: not enough values to unpack (expected 5, got 4)

Detailed traceback: File "/mnt/hdd/Z_NLP_Classifier/07_Test_03/02_Code/renv/python/r-reticulate/lib/python3.6/site-packages/blurr/modeling/core.py", line 120, in blurr_summary res = blurr_module_summary(self, *xb) File "/mnt/hdd/Z_NLP_Classifier/07_Test_03/02_Code/renv/python/r-reticulate/lib/python3.6/site-packages/blurr/modeling/core.py", line 98, in blurr_module_summary for typ,np,trn,sz,chnged in infos:

Lastly, not sure if this is relevant, I modified the creation of blocks to be the following (whilst tutorial only include hf_arch and hf_tokenizer): blocks = list( HF_TextBlock(hf_arch, hf_config, hf_tokenizer, hf_model), MultiCategoryBlock(encoded=TRUE, vocab=lbl_cols))

Thank you very much in advance!

opened by jesuiskelly 8
Can't download using URLs_ADULT_SAMPLE

Hi, I receive the following error when I try to download the data using URLs_ADULT_SAMPLE().

Error: $ operator is invalid for atomic vectors

Can somebody please assist

opened by stevebit 7
Issues with package in Kaggle

Thanks again for doing this package. Since you seem to use Kaggle notebooks yourself, do you know what's up with the installed R package that is now part of the standard R Kaggle set-up? I tried a few things and just could not get it to work. It looks to me like the python fastai package is missing in the background (I also posted this on Kaggle), but my attempt to simply use fastai::install_fastai(gpu = FALSE, cuda_version = '10.1', overwrite = FALSE) failed. Do you know how to get this to work (great if you could share that)? I would of course also understand if you feel that fixing Kaggle's setup is not your problem.

opened by bjoernholzhauer 5
New vision models

Here the news models.

I have tried some locally, it works fine a priori.

I took the freedom to rearrange a bit the order of inside R/vision_models.R to see more easily what was missing from https://docs.fast.ai/vision.models.xresnet.html. It will ease the long term maintenance too I think.

opened by Cdk29 5
load_learner does not seems to exist

load_learner() is the function that allow to load a model that has been exported. It does not seems to exist in the wrapper :

Source : https://docs.fast.ai/learner.html#load_learner
enhancement

opened by Cdk29 4
Installation error

Hi there,

I am getting the following error when I try to run this part. Any thoughts?

fastai::install_fastai(gpu = FALSE, cuda_version = '10', overwrite = FALSE)

r-miniconda/envs/r-reticulate/python.exe" -m pip install --upgrade "torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html" "IPython" "pydicom" "kornia" "opencv-python" "scikit-image" "timm" "fastinference[interp]" ERROR: Invalid requirement: 'torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html'

Thank you very much for your time.

Kind regards,

M

opened by mubashirqasim 3
AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config'
I am using this tutorial for super resolution Unet model. https://cran.r-project.org/web/packages/fastai/vignettes/super_res_gan.html. However, the create_gen_learner function resulted in Error in py_get_attr_impl(x, name, silent) : AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config' error. The basic Unet without unet_config argument works fine. https://github.com/henry090/fastai#unet-example

create_gen_learner = function() { unet_learner(dls_gen, arch, loss_func = loss_gen, config = unet_config(blur=TRUE, norm_type = "Weight", self_attention = TRUE, y_range = y_range)) } learn_gen = create_gen_learner() # Error in py_get_attr_impl(x, name, silent) : # AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config'
opened by houndcl 3
TPU and the fastai wrapper

Hi,

I was thinking on the how to train xse-resnext-50 in R without going out of memory. Huge networks like this seems to be trained in Pytorch using TPU on Kaggle.

Fastai does not handle directly TPU but there is some wrapper : https://github.com/butchland/fastai_xla_extensions Example here : https://www.kaggle.com/johnyquest/tpu-fastai-notebook

I put this as a feature request and a reminder to myself to look into this. As far as I can see it does not seems to much difficult to use TPU with fastai (four pip install and a methods on the learner). But I don't have your experience with the wrapper/TPU (As far as I know you are the only person that created a kernel with TPU inside R on kaggle :p )

Is it as "easy" as it seems or is there a problem I do not see ?
enhancement ➕

opened by Cdk29 3

Error when using model$export()

When training a model like in this notebook or here (md doc to got the session infos).

I got the following error from python :


Quitting from lines 106-107 (Bug_method_export.Rmd) 
Error in py_call_impl(callable, dots$args, dots$keywords) : 
  AttributeError: Can't pickle local object 'make_python_function.<locals>.python_function'

Detailed traceback: 
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastai/learner.py", line 542, in export
    torch.save(self, self.path/fname, pickle_protocol=pickle_protocol)
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/torch/serialization.py", line 372, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/torch/serialization.py", line 476, in _save
    pickler.dump(obj)
Calls: <Anonymous> ... withVisible -> eval -> eval -> <Anonymous> -> py_call_impl
Exécution arrêtée

When I am running :

learnR$export(fname = export.pkl)

The error message came from the first notebook. The second md document provide the session infos().

bug

opened by Cdk29 3

Export in the wrong place ?

I am not 100 % sure it is a bug.

Running code from here : https://github.com/Cdk29/Cassava/blob/main/Xresnet.md

Particularly :

I create the learner using :

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

But fastai export it elsewhere :

erolland@erolland-GL75-9SE:~/Bureau$ locate xresnet_50.pkl

/home/erolland/Bureau/Cassava/cassava-leaf-disease-classification/train_images/xresnet_50.pkl

I am not sure but I don't think it is the normal behavior of the export of fastai to use the directory of the training set and not the model_dir to export its model.
question

opened by Cdk29 3
Error related to magma during training

@henry090 : I am trying to train a xse_resnet50.

During training I got the following error :

R: /opt/conda/conda-bld/magma-cuda101_1583546950098/work/interface_cuda/interface.cpp:901: void magma_queue_create_from_cuda_internal(magma_device_t, cudaStream_t, cublasHandle_t, cusparseHandle_t, magma_queue**, const char*, const char*, int): Assertion `queue->dCarray__ != __null' failed.

It is not a simple out of memory error, it seems to be some kind of memory leak related to magma, similar to related here.

But : I did not find any mention of this bug occurring with fastai, which I would have expect if this thing occurred recurrently, except for this message on this thread : https://forums.fast.ai/t/a-walk-with-fastai2-vision-study-group-and-online-lectures-megathread/59929/1293 :

The only “new” thing I am doing is that I am encapsulating most of my code for training the model in a try/except block in a while loop.

I wonder if the memory leak is not somehow due to using a function as a wrapper or reticulate. Link towards the code and error : https://www.kaggle.com/cdk292/magma-error-xse-resnext50-with-r?scriptVersionId=50229515

The last version is still compiling but you can see in the log of execution of version 4 the error, and will probably shown up again in V6.

PS : merry Christmas.
bug

opened by Cdk29 15

Potenial ploting issue inside Rstudio

Hi again.

Running the following :

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,
                                               num_workers=0, 
                                               item_tfms = Resize(448),
                                               batch_tfms = aug_transforms(size=224, min_scale=0.75))

dataloader %>% show_batch()

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

learnR$to_fp16()

learnR$freeze()

learnR %>% lr_find()

learnR %>% plot_lr_find(dpi = 200)

I can knit and got a md document with the graphs. However, running the cells of the rmarkdown document will not output anything below the line Or inside the plots panels. (while it will happen with keras for example).

I do not know for sure if this is a local config problem, or a change introduced by the commit 8594699e357d7fdb8e9260022dd9269f114bb56f.