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fastai

R interface to fast.ai

https://github.com/eagerai/fastai

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audio collaborative-filtering darknet darknet-image-classification fastai medical object-detection r tabular text vision
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R interface to fast.ai

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audio collaborative-filtering darknet darknet-image-classification fastai medical object-detection r tabular text vision
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README.md

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

Lifecycle: stable CRAN status Last month downloads

Continuous Build Status

| Build | Status | | ----------------- | ------------------------------------------------------------------------------ | | Bionic | ubuntu_18 | | Focal | ubuntu_20 | | Mac OS | mac_os | | Windows | 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:

reticulate::use_condaenv('r-reticulate',required = TRUE) fastai::install_fastai(gpu = FALSE, cuda_version = '11.6', 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

URLsADULTSAMPLE()

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: Loss function: FlattenedLoss of CrossEntropyLoss()

Callbacks: - TrainEvalCallback - Recorder - ProgressCallback ```

Before fitting try to find optimal learning rate:

``` model %>% lr_find()

model %>% plotlrfind(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

See training process:

train

Get confusion matrix:

model %>% get_confusion_matrix()

``` <50k >=50k <50k 407 22

=50k 68 64 ```

Plot it:

``` interp = ClassificationInterpretationfromlearner(model)

interp %>% plotconfusionmatrix(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 ```

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/americanpitbullterrier129.jpg ```

Dataloader:

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

Show batch for visualization:

dls %>% show_batch()

Pets

Model architecture:

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

And fit:

``` learn %>% fitonecycle(n_epoch = 2)

epoch trainloss validloss 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 %>% getconfusionmatrix()

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

Pets

Note that the plot is built with highcharter.

Plot top losses:

``` interp = ClassificationInterpretationfromlearner(learn)

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

Pets

Alternatively, load images from folders:

```

get sample data

URLsMNISTSAMPLE()

transformations

path = 'mnist_sample' bs = 20

load into memory

data = ImageDataLoadersfromfolder(path, 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(timmlistmodels()[1:10])) List of 10 $ : chr "advinceptionv3" $ : chr "cspdarknet53" $ : chr "cspdarknet53iabn" $ : chr "cspresnet50" $ : chr "cspresnet50d" $ : chr "cspresnet50w" $ : chr "cspresnext50" $ : chr "cspresnext50iabn" $ : 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 = getimagefiles(path_img)

dls = ImageDataLoadersfromnamere( path, fnames, pat='(.+)\d+.jpg$', itemtfms=Resize(size = 460), bs = 10, batchtfms=list(Normalizefromstats( imagenet_stats() ) ) )

learn = timmlearner(dls, 'cspdarknet53', metrics = list(accuracy, errorrate))

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: 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 trainloss validloss 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):

``` URLsLSUNBEDROOMS()

path = 'bedroom' ```

Dataloader function:

``` getdls <- function(bs, size) { dblock = DataBlock(blocks = list(TransformBlock(), ImageBlock()), getx = generatenoise(), getitems = getimagefiles(), splitter = IndexSplitter(c()), itemtfms = Resize(size, method = "crop"), batchtfms = Normalizefromstats(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 = basicgenerator(outsize = 64, nchannels = 3, nextralayers = 1) critic = basiccritic(insize = 64, nchannels = 3, nextralayers = 1, actcls = partial(nn$LeakyReLU, negativeslope = 0.2))

```

Model:

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

And fit:

``` learn$recorder$trainmetrics = TRUE learn$recorder$validmetrics = 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

Unet example

Call libraries:

library(fastai) library(magrittr)

Get data

URLs_CAMVID()

Specify folders:

``` path = 'camvid' fnames = getimagefiles(paste(path,'images',sep = '/')) lblnames = getimagefiles(paste(path,'labels',sep = '/')) codes = data.table::fread(paste(path,'codes.txt',sep = '/'), header = FALSE)[['V1']] validfnames = 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)), getitems = getimagefiles, splitter = FileSplitter('camvid/valid.txt'), gety = function(x) {paste('camvid/labels/',x$stem,'P',x$suffix,sep = '')}, batchtfms = list(Normalizefromstats( 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:

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

attr(acccamvid, "pyfunctionname") <- 'acccamvid' ```

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 %>% fitonecycle(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:

``` moviebias = learn %>% getbias(topmovies, isitem = TRUE)

result = data.table(bias = moviebias, title = topmovies)

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(moview, cor = TRUE)) %>% highcharter::hclegend(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:

``` imdblm = DataBlock(blocks=list(TextBlockfromfolder(path, islm = TRUE)), getitems = partial(gettext_files(), folders = c('train', 'test', 'unsup')), splitter = RandomSplitter(0.1))

dbunchlm = imdblm %>% dataloaders(source = path, path = path, bs = bs, seq_len = 80) ```

Load a pretrained model and fit:

``` learn = languagemodellearner(dbunchlm, AWDLSTM(), drop_mult = 0.3, metrics = list(accuracy, Perplexity()))

learn %>% fitonecycle(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:

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

library(zeallot) c(fig, axs[[2]]) %<-% 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::matrixrasterplot(img %>% getdcmmatrix(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 %>% maskfromblur(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('uniformblur2d', 'gaussblur2d') 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/ylim functions from ggplot if(i==2) { p = nandb::matrixrasterplot(img %>% getdcm_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::matrixrasterplot(img %>% getdcmmatrix(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(lrmin=0.017378008365631102, lrsteep=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 = getimagefiles('camvid/images') cam_fn = capture.output(fns[0])

mask

maskfn = 'camvid/labels/0016E501110P.png' camimg = Imagecreate(camfn)

create mask

tmask = Transform(Maskcreate()) mask = tmask(maskfn)

visualize

mask %>% tomatrix() %>% nandb::matrixraster_plot(colours = viridis::plasma(3)) + theme(legend.position = "none") ```

Mask

TensorPoints

Load Tiny Mnist:

```

download

URLsMNISTTINY()

black and white img

timg = Transform(ImageBWcreate) mnistfn = "mnisttiny/valid/3/9007.png" mnistimg = timg(mnist_fn)

resize img

pntimg = TensorImage(mnistimg %>% Image_resize(size = list(28,35)))

visualize

library(ggplot2) pntimg %>% tomatrix() %>% nandb::matrixrasterplot(colours = c('white','black')) + geompoint(aes(x=0, y=0),size=2, colour="red")+ geompoint(aes(x=0, y=35),size=2, colour="red")+ geompoint(aes(x=28, y=0),size=2, colour="red")+ geompoint(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)

URLsCOCOTINY()

c(images, lblbbox) %<-% getannotations('cocotiny/train.json') timg = Transform(ImageBWcreate) idx = 49 c(cocofn,bbox) %<-% list(paste('cocotiny/train',images[[idx]],sep = '/'), lblbbox[[idx]]) cocoimg = 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(cocofn,bbox) %<-% list(paste('cocotiny/train',images[[idx]],sep = '/'), lbl_bbox[[idx]])

cocobb = function(x) { TensorBBoxcreate(bbox[[1]]) }

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

cocodsrc = Datasets(c(rep(cocofn,10)), list(Imagecreate(), list(cocobb), list( cocolbl, MultiCategorize(addna = TRUE) ) ), n_inp = 1)

cocotdl = TfmdDL(cocodsrc, bs = 9, afteritem = list(BBoxLabeler(), PointScaler(), ToTensor()), afterbatch = list(IntToFloatTensor()) )

cocotdl %>% showbatch(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)) )

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.

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Dependencies

DESCRIPTION cran
  • generics * imports
  • ggplot2 * imports
  • ggpubr * imports
  • glue * imports
  • png * imports
  • reticulate * imports
  • R.utils * suggests
  • curl * suggests
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  • rmarkdown * suggests
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  • testthat * suggests
  • utils * suggests
  • vctrs * suggests
  • viridis * suggests
  • zeallot * suggests
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