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.

fastai

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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 = '10.1', overwrite = FALSE)

4. Restart RStudio!

fast.ai extensions:

  1. NLP, Transformers
  2. Object Detection
  3. Time-series
  4. CycleGAN
  5. Audio

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)

lr

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)

lr

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()

bs

See training process:

train

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))

Pets

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)
Shap

Dependence plot:

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

Summary plot:

exp %>% summary_plot()
Shap

Waterfall plot:

exp %>% waterfall_plot(row_idx=10)

Shap

Force (JS) plot:

exp %>% force_plot(class_id = 0)

Shap

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)

Pets

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))

Pets

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))

Pets

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)

Mnist

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!

Mnist

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)

Mnist

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()

Mnist

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

Mnist

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)

lr

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)

PCA

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)

dicom

Apply different cmaps:

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

dicom

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)

dicom

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])
                  )

dicom2

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 ))

Learning_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]])

Batch

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")

Mask

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")

Mnist_3

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"
  )
}

Annotation

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)

Annotation_

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:

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)

Detection

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)
)

Detection

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:

Annotation

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:

Annotation

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.