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https://github.com/eagerai/fastai
R interface to fast.ai
https://github.com/eagerai/fastai
audio collaborative-filtering darknet darknet-image-classification fastai medical object-detection r tabular text vision
Last synced: 3 days ago
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R interface to fast.ai
- Host: GitHub
- URL: https://github.com/eagerai/fastai
- Owner: EagerAI
- License: apache-2.0
- Created: 2020-08-03T10:53:27.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-04-11T17:37:14.000Z (9 months ago)
- Last Synced: 2024-12-17T00:12:56.535Z (10 days ago)
- Topics: audio, collaborative-filtering, darknet, darknet-image-classification, fastai, medical, object-detection, r, tabular, text, vision
- Language: HTML
- Homepage: https://eagerai.github.io/fastai/
- Size: 69 MB
- Stars: 118
- Watchers: 6
- Forks: 13
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
## R interface to fastai
The fastai package provides R wrappers to
[fastai](https://github.com/fastai/fastai).
The fastai library simplifies training fast and accurate neural nets using
modern best practices. See the
[fastai website](https://eagerai.github.io/fastai/) 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.
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://CRAN.R-project.org/package=fastai)
[](https://cran.r-project.org/package=fastai)
## Continuous Build Status
| Build | Status |
| ----------------- | ------------------------------------------------------------------------------ |
| **Bionic** |  |
| **Focal** |  |
| **Mac OS** |  |
| **Windows** |  |
## Installation
**1. Install miniconda and activate environment:**
```
reticulate::install_miniconda()
reticulate::conda_create('r-reticulate')
```
**2. The dev version:**
```
devtools::install_github('eagerai/fastai')
```
**3. Later, you need to install the python module `fastai`:**
```
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](https://github.com/ohmeow/blurr)
2. [Object Detection](https://github.com/airctic/icevision)
3. [Time-series](https://github.com/tcapelle/timeseries_fastai)
4. [CycleGAN](https://github.com/tmabraham/UPIT)
5. [Audio](https://github.com/fastaudio/fastaudio)
## Kaggle
We currently prepare the examples of usage of the fastai from R in Kaggle competitions:
- [Introduction](https://www.kaggle.com/henry090/r-interface-to-fastai)
- [MNIST with Pytorch and fastai](https://www.kaggle.com/henry090/r-and-fastai)
- [NLP Binary Classification](https://www.kaggle.com/henry090/r-fastai-and-transformers)
- [Audio classification](https://www.kaggle.com/henry090/fast-ai-from-r)
- [CycleGAN](https://www.kaggle.com/henry090/r-fast-ai-and-cyclegan)
- [Fastai on Colab TPUs](https://colab.research.google.com/drive/1PiBECDM552No-5apVIB8LqUSdSqqJSi-?usp=sharing)
> 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:
Loss function: FlattenedLoss of CrossEntropyLoss()
Callbacks:
- TrainEvalCallback
- Recorder
- ProgressCallback
```
Before fitting try to find optimal learning rate:
```
model %>% lr_find()
model %>% plot_lr_find(dpi = 200)
```
Run:
```
model %>% fit(5, lr = 10^-1)
```
```
epoch train_loss valid_loss accuracy time
0 0.360149 0.329587 0.846702 00:04
1 0.352106 0.345761 0.828877 00:04
2 0.368743 0.340913 0.844920 00:05
3 0.347277 0.333084 0.852050 00:04
4 0.348969 0.350707 0.830660 00:04
```
Plot loss history:
```
model %>% plot_loss(dpi = 200)
```
See training process:
Get confusion matrix:
```
model %>% get_confusion_matrix()
```
```
<50k >=50k
<50k 407 22
>=50k 68 64
```
Plot it:
```
interp = ClassificationInterpretation_from_learner(model)
interp %>% plot_confusion_matrix(dpi = 90,figsize = c(6,6))
```
Get predictions on new data:
```
> model %>% predict(df[10:15,])
<50k >=50k classes
1 0.5108562 0.4891439 0
2 0.4827824 0.5172176 1
3 0.4873166 0.5126833 1
4 0.5013804 0.4986197 0
5 0.4964157 0.5035844 1
6 0.5111378 0.4888622 0
```
## 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(Normalize_from_stats( imagenet_stats() )
)
)
```
Show batch for visualization:
```
dls %>% show_batch()
```
Model architecture:
```
learn = cnn_learner(dls, resnet34(), metrics = error_rate)
```
And fit:
```
learn %>% fit_one_cycle(n_epoch = 2)
epoch train_loss valid_loss error_rate time
0 0.904872 0.317927 0.105548 00:35
1 0.694395 0.239520 0.083897 00:36
```
Get confusion matrix and plot:
```
conf = learn %>% get_confusion_matrix()
library(highcharter)
hchart(conf, label = TRUE) %>%
hc_yAxis(title = list(text = 'Actual')) %>%
hc_xAxis(title = list(text = 'Predicted'),
labels = list(rotation = -90))
```
> Note that the plot is built with highcharter.
Plot top losses:
```
interp = ClassificationInterpretation_from_learner(learn)
interp %>% plot_top_losses(k = 9, figsize = c(15,11))
```
Alternatively, load images from folders:
```
# get sample data
URLs_MNIST_SAMPLE()
# transformations
path = 'mnist_sample'
bs = 20
#load into memory
data = ImageDataLoaders_from_folder(path, size = 26, bs = bs)
# Visualize and train
data %>% show_batch(dpi = 150)
learn = cnn_learner(data, resnet18(), metrics = accuracy)
learn %>% fit(2)
```
**What about the implementation of the latest
[Computer Vision models](https://github.com/huggingface/pytorch-image-models)?**
There is a function in fastai `timm_learner` which originally written by
[Zachary Mueller](https://github.com/walkwithfastai/walkwithfastai.github.io/).
It helps to quickly load the pretrained models from
[timm library](https://github.com/huggingface/pytorch-image-models).
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(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:
Loss function: FlattenedLoss of CrossEntropyLoss()
Model frozen up to parameter group #1
Callbacks:
- TrainEvalCallback
- Recorder
- ProgressCallback
```
And finally, fit:
```
learn %>% fit_one_cycle(3)
```
```
epoch train_loss valid_loss accuracy error_rate time
------ ----------- ----------- --------- ----------- ------
0 1.206384 0.518956 0.847091 0.152909 01:00
1 0.841627 0.411970 0.890392 0.109608 00:58
2 0.657220 0.328548 0.899188 0.100812 00:59
```
See results:
```
learn %>% show_results()
```
Impressive!
### GAN example
Get data (4,4 GB):
```
URLs_LSUN_BEDROOMS()
path = 'bedroom'
```
Dataloader function:
```
get_dls <- function(bs, size) {
dblock = DataBlock(blocks = list(TransformBlock(), ImageBlock()),
get_x = generate_noise(),
get_items = get_image_files(),
splitter = IndexSplitter(c()),
item_tfms = Resize(size, method = "crop"),
batch_tfms = Normalize_from_stats(c(0.5,0.5,0.5), c(0.5,0.5,0.5))
)
dblock %>% dataloaders(source = path, path = path,bs = bs)
}
dls = get_dls(128, 64)
```
Generator and discriminator:
```
generator = basic_generator(out_size = 64, n_channels = 3, n_extra_layers = 1)
critic = basic_critic(in_size = 64, n_channels = 3, n_extra_layers = 1,
act_cls = partial(nn$LeakyReLU, negative_slope = 0.2))
```
Model:
```
learn = GANLearner_wgan(dls, generator, critic, opt_func = partial(Adam(), mom=0.))
```
And fit:
```
learn$recorder$train_metrics = TRUE
learn$recorder$valid_metrics = FALSE
learn %>% fit(1, 2e-4, wd = 0)
```
```
epoch train_loss gen_loss crit_loss time
0 -0.555554 0.516327 -0.967604 05:06
```
This is the result for 1 epoch.
```
learn %>% show_results(max_n = 16, figsize = c(8,8), ds_idx=0)
```
## 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(Normalize_from_stats( imagenet_stats() )
)
)
# prefix and suffix of the name of the file
x$stem; x$suffix
```
Dataloader object and list of labels:
```
dls = camvid %>% dataloaders(source = "camvid/images", bs = bs, path = path)
dls %>% show_batch()
void_code = which(codes == "Void")
dls$vocab = codes
name2id = as.list(1:(length(codes)))
names(name2id) = codes
```
```
str(name2id)
List of 32
$ Animal : int 1
$ Archway : int 2
$ Bicyclist : int 3
$ Bridge : int 4
$ Building : int 5
$ Car : int 6
$ CartLuggagePram : int 7
$ Child : int 8
$ Column_Pole : int 9
$ Fence : int 10
$ LaneMkgsDriv : int 11
$ LaneMkgsNonDriv : int 12
$ Misc_Text : int 13
$ MotorcycleScooter: int 14
$ OtherMoving : int 15
$ ParkingBlock : int 16
$ Pedestrian : int 17
$ Road : int 18
$ RoadShoulder : int 19
$ Sidewalk : int 20
$ SignSymbol : int 21
$ Sky : int 22
$ SUVPickupTruck : int 23
$ TrafficCone : int 24
$ TrafficLight : int 25
$ Train : int 26
$ Tree : int 27
$ Truck_Bus : int 28
$ Tunnel : int 29
$ VegetationMisc : int 30
$ Void : int 31
$ Wall : int 32
```
Custom accuracy function:
```
acc_camvid <- function(input, target) {
target = target$squeeze(1L)
# exclude/filter void label
mask = target != void_code
return(
(input$argmax(dim=1L)[mask]$eq(target[mask])) %>%
float() %>% mean()
)
}
attr(acc_camvid, "py_function_name") <- 'acc_camvid'
```
Debug acc_camvid manually
```
batch = dls %>% one_batch(convert = FALSE)
```
```
[[1]]
TensorImage([[[[-1.4419e+00, -1.3117e+00, -1.1976e+00, ..., 2.2489e+00,
2.2238e+00, 2.0948e+00],
[-1.5401e+00, -1.5213e+00, -1.4010e+00, ..., 1.9834e+00,
2.2378e+00, 2.2173e+00],
[-1.6401e+00, -1.5477e+00, -1.5588e+00, ..., 9.1953e-01,
1.9501e+00, 1.1138e+00],
...,
[-1.6852e+00, -1.5440e+00, -1.5132e+00, ..., -1.0596e+00,
-1.0711e+00, -1.0674e+00],
[-1.5265e+00, -1.6030e+00, -1.5804e+00, ..., -1.0268e+00,
-1.0946e+00, -1.1181e+00],
[-1.5423e+00, -1.5516e+00, -1.6014e+00, ..., -1.1734e+00,
-1.1293e+00, -1.0777e+00]],
[[-1.3446e+00, -1.2023e+00, -1.0470e+00, ..., 2.4286e+00,
2.4090e+00, 2.2977e+00],
[-1.4481e+00, -1.4276e+00, -1.2930e+00, ..., 2.1422e+00,
2.4158e+00, 2.3778e+00],
[-1.5607e+00, -1.4584e+00, -1.4641e+00, ..., 1.0026e+00,
2.0258e+00, 1.1376e+00],
...,
[-1.5809e+00, -1.4399e+00, -1.4133e+00, ..., -7.8931e-01,
-7.9807e-01, -7.9637e-01],
[-1.4161e+00, -1.4909e+00, -1.4646e+00, ..., -8.0615e-01,
-8.5201e-01, -8.5311e-01],
[-1.4472e+00, -1.4567e+00, -1.5077e+00, ..., -9.4607e-01,
-8.9744e-01, -8.2074e-01]],
[[-1.1164e+00, -1.0162e+00, -9.1189e-01, ..., 2.6257e+00,
2.5726e+00, 2.4016e+00],
[-1.2195e+00, -1.1752e+00, -1.0595e+00, ..., 2.3488e+00,
2.6271e+00, 2.5764e+00],
[-1.3316e+00, -1.2451e+00, -1.2400e+00, ..., 1.0476e+00,
2.1812e+00, 1.3635e+00],
...,
[-1.2881e+00, -1.1393e+00, -1.1035e+00, ..., -3.8940e-01,
-4.0598e-01, -3.9861e-01],
[-1.1427e+00, -1.2167e+00, -1.1906e+00, ..., -3.6462e-01,
-4.3055e-01, -4.5333e-01],
[-1.1525e+00, -1.1651e+00, -1.2190e+00, ..., -4.8259e-01,
-4.3712e-01, -4.1413e-01]]],
[[[-2.0552e-01, 3.9563e-01, 4.0691e-01, ..., -9.7342e-01,
-7.8957e-01, -7.6035e-01],
[-3.8852e-01, 4.2912e-01, 4.4469e-01, ..., -1.0449e+00,
-8.5347e-01, -7.5299e-01],
[ 3.5939e-01, 3.6353e-01, 4.7028e-01, ..., -9.3101e-01,
-8.7398e-01, -7.9327e-01],
...,
[-1.0510e+00, -1.0661e+00, -9.6690e-01, ..., -1.3688e+00,
-1.4543e+00, -1.4645e+00],
[-1.0578e+00, -1.0939e+00, -9.3117e-01, ..., -1.3939e+00,
-1.4033e+00, -1.4209e+00],
[-9.9012e-01, -1.0312e+00, -1.0074e+00, ..., -1.4274e+00,
-1.3829e+00, -1.3758e+00]],
[[ 6.0090e-02, 7.8124e-01, 7.5145e-01, ..., -8.2881e-01,
-6.7773e-01, -6.3718e-01],
[-1.7114e-01, 7.8613e-01, 7.8531e-01, ..., -9.0003e-01,
-7.3661e-01, -5.8707e-01],
[ 7.3440e-01, 7.5691e-01, 8.2297e-01, ..., -8.0694e-01,
-7.5451e-01, -6.2783e-01],
...,
[-7.8971e-01, -7.8585e-01, -7.4870e-01, ..., -1.2630e+00,
-1.3108e+00, -1.3046e+00],
[-7.8414e-01, -7.9617e-01, -7.2847e-01, ..., -1.2297e+00,
-1.2414e+00, -1.2594e+00],
[-7.3135e-01, -7.7442e-01, -7.4849e-01, ..., -1.2259e+00,
-1.1889e+00, -1.2022e+00]],
[[ 4.4920e-01, 1.2392e+00, 1.3399e+00, ..., -6.0991e-01,
-4.5250e-01, -4.4251e-01],
[ 2.7577e-01, 1.2913e+00, 1.3755e+00, ..., -6.8060e-01,
-5.1114e-01, -3.7442e-01],
[ 1.0632e+00, 1.3052e+00, 1.3774e+00, ..., -5.8343e-01,
-5.2787e-01, -3.9803e-01],
...,
[-4.4165e-01, -4.4558e-01, -3.8942e-01, ..., -8.7048e-01,
-9.2835e-01, -9.2750e-01],
[-4.4233e-01, -4.6348e-01, -3.7176e-01, ..., -8.6960e-01,
-8.8080e-01, -8.9788e-01],
[-3.8967e-01, -4.3118e-01, -3.8587e-01, ..., -8.7933e-01,
-8.4775e-01, -8.5052e-01]]],
[[[ 1.2805e+00, 2.2139e+00, 9.9765e-01, ..., 6.6338e-01,
-4.0192e-01, 2.8007e-01],
[ 1.0171e+00, 1.8849e+00, 1.1654e+00, ..., -1.0001e+00,
1.1788e+00, 2.0717e+00],
[ 2.8709e-01, 1.9494e+00, 2.1978e+00, ..., -6.7389e-01,
3.2762e-01, 4.5549e-01],
...,
[-4.3609e-01, -4.2635e-01, -4.6298e-01, ..., 7.7548e-02,
3.6271e-02, -3.1759e-02],
[-3.7265e-01, -4.3453e-01, -4.4666e-01, ..., -7.5601e-02,
5.3570e-03, -2.9393e-02],
[-3.7581e-01, -4.0105e-01, -4.2908e-01, ..., 8.5172e-03,
-3.3988e-03, -1.8303e-02]],
[[ 1.3276e+00, 2.3720e+00, 1.0603e+00, ..., 8.6043e-01,
-1.1662e-01, 5.2147e-01],
[ 1.0938e+00, 2.0233e+00, 1.2629e+00, ..., -9.1610e-01,
1.3807e+00, 2.2914e+00],
[ 3.8840e-01, 2.1078e+00, 2.3635e+00, ..., -5.8584e-01,
5.2653e-01, 7.8300e-01],
...,
[-3.1636e-01, -3.0640e-01, -3.4385e-01, ..., 1.3784e-01,
9.5460e-02, 2.5607e-02],
[-2.5150e-01, -3.1476e-01, -3.2716e-01, ..., -1.9409e-02,
6.3717e-02, 2.8037e-02],
[-2.5473e-01, -2.8054e-01, -3.0920e-01, ..., 6.6963e-02,
5.4727e-02, 3.9424e-02]],
[[ 1.8118e+00, 2.6126e+00, 1.5284e+00, ..., 1.3408e+00,
3.8263e-01, 9.4347e-01],
[ 1.4345e+00, 2.2263e+00, 1.5055e+00, ..., -4.0407e-01,
1.9165e+00, 2.5325e+00],
[ 6.9120e-01, 2.3214e+00, 2.5724e+00, ..., -5.9273e-02,
7.6707e-01, 9.8036e-01],
...,
[-3.2707e-02, -2.5592e-02, -6.5520e-02, ..., 3.1733e-01,
2.8317e-01, 2.2166e-01],
[ 1.6474e-02, -4.1773e-02, -5.1314e-02, ..., 1.6267e-01,
2.4836e-01, 2.1449e-01],
[ 2.4832e-02, 1.0270e-02, -1.5259e-02, ..., 2.3768e-01,
2.2930e-01, 2.2220e-01]]],
...,
[[[-1.5176e-02, -1.9729e-02, -5.4177e-02, ..., 2.0812e+00,
2.2489e+00, 2.2242e+00],
[-1.0897e-02, 3.5695e-02, 2.3053e-03, ..., 2.1605e+00,
2.0372e+00, 2.1403e+00],
[-2.8262e-02, -3.0313e-02, -3.4347e-02, ..., 2.2136e+00,
2.2489e+00, 1.2613e+00],
...,
[-1.2644e+00, -1.2548e+00, -1.2313e+00, ..., -1.3335e+00,
-1.3230e+00, -1.2787e+00],
[-1.1986e+00, -1.2068e+00, -1.1631e+00, ..., -1.2694e+00,
-1.2973e+00, -1.2696e+00],
[-1.2508e+00, -1.2447e+00, -1.2294e+00, ..., -1.0572e+00,
-1.0660e+00, -1.0694e+00]],
[[ 2.2227e-01, 2.1430e-01, 2.1605e-01, ..., 2.3389e+00,
2.4286e+00, 2.4286e+00],
[ 2.0176e-01, 2.4693e-01, 2.4092e-01, ..., 2.3745e+00,
2.2931e+00, 2.3820e+00],
[ 1.8103e-01, 1.7892e-01, 1.7477e-01, ..., 2.4036e+00,
2.4286e+00, 1.4878e+00],
...,
[-1.0710e+00, -1.0613e+00, -1.0374e+00, ..., -1.2492e+00,
-1.2385e+00, -1.2225e+00],
[-1.0040e+00, -1.0124e+00, -9.6780e-01, ..., -1.1836e+00,
-1.2122e+00, -1.2193e+00],
[-1.0572e+00, -1.0510e+00, -1.0354e+00, ..., -9.5631e-01,
-9.6512e-01, -9.6444e-01]],
[[ 5.4786e-01, 5.5583e-01, 5.3839e-01, ..., 2.5781e+00,
2.6400e+00, 2.6400e+00],
[ 5.3558e-01, 5.8483e-01, 5.6649e-01, ..., 2.5895e+00,
2.5283e+00, 2.6400e+00],
[ 5.2345e-01, 5.2294e-01, 5.1033e-01, ..., 2.6400e+00,
2.6400e+00, 1.7087e+00],
...,
[-8.1354e-01, -8.0387e-01, -7.9721e-01, ..., -1.0014e+00,
-9.9075e-01, -9.5806e-01],
[-7.4687e-01, -7.5518e-01, -7.2870e-01, ..., -9.4173e-01,
-9.6991e-01, -9.5030e-01],
[-7.9981e-01, -7.9358e-01, -7.9630e-01, ..., -7.3474e-01,
-7.4333e-01, -7.3628e-01]]],
[[[ 6.8056e-01, 6.8056e-01, 6.9105e-01, ..., -3.6921e-01,
-3.1641e-01, -3.3400e-01],
[ 6.9991e-01, 7.1771e-01, 6.8056e-01, ..., -3.3319e-01,
-3.4023e-01, -3.8674e-01],
[ 6.9781e-01, 7.1034e-01, 6.9885e-01, ..., -2.9567e-01,
-3.0638e-01, -2.8775e-01],
...,
[-1.4393e+00, -1.4183e+00, -1.4183e+00, ..., -1.3420e+00,
-1.4022e+00, -1.3872e+00],
[-1.4436e+00, -1.4326e+00, -1.4335e+00, ..., -1.3950e+00,
-1.3800e+00, -1.3734e+00],
[-1.4509e+00, -1.4539e+00, -1.4533e+00, ..., -1.3681e+00,
-1.4340e+00, -1.3650e+00]],
[[ 2.0471e+00, 2.0471e+00, 2.0603e+00, ..., -6.5347e-02,
2.6326e-02, 3.4833e-02],
[ 2.0525e+00, 2.0750e+00, 2.0818e+00, ..., -4.7675e-02,
-5.2935e-03, -2.6855e-02],
[ 2.0976e+00, 2.1136e+00, 2.1051e+00, ..., 1.8606e-02,
4.1052e-02, 8.5274e-02],
...,
[-1.2304e+00, -1.2244e+00, -1.2219e+00, ..., -1.2425e+00,
-1.3041e+00, -1.2836e+00],
[-1.2239e+00, -1.2107e+00, -1.2107e+00, ..., -1.2967e+00,
-1.2813e+00, -1.2746e+00],
[-1.2210e+00, -1.2154e+00, -1.2157e+00, ..., -1.2695e+00,
-1.3401e+00, -1.2696e+00]],
[[ 2.6400e+00, 2.6400e+00, 2.6400e+00, ..., 3.4950e-01,
4.4111e-01, 4.1667e-01],
[ 2.6400e+00, 2.6400e+00, 2.6400e+00, ..., 3.3850e-01,
3.8055e-01, 3.7792e-01],
[ 2.6400e+00, 2.6400e+00, 2.6400e+00, ..., 4.4053e-01,
4.5217e-01, 4.8598e-01],
...,
[-8.2900e-01, -8.1651e-01, -8.1498e-01, ..., -9.5577e-01,
-1.0173e+00, -9.9684e-01],
[-8.3432e-01, -8.2192e-01, -8.2227e-01, ..., -1.0234e+00,
-1.0080e+00, -1.0014e+00],
[-8.3237e-01, -8.2912e-01, -8.2936e-01, ..., -1.0039e+00,
-1.0649e+00, -9.9452e-01]]],
[[[ 2.0699e+00, 1.9477e+00, 2.0700e+00, ..., -1.5310e+00,
-1.6490e+00, -1.6860e+00],
[ 1.8292e+00, 2.1599e+00, 1.8882e+00, ..., -1.6536e+00,
-1.6374e+00, -1.6022e+00],
[ 2.0288e+00, 1.7863e+00, 2.0564e+00, ..., -1.6149e+00,
-1.6315e+00, -1.5586e+00],
...,
[-1.4481e+00, -1.3921e+00, -1.4195e+00, ..., -1.5045e+00,
-1.5133e+00, -1.5381e+00],
[-1.4223e+00, -1.3757e+00, -1.3943e+00, ..., -1.5238e+00,
-1.5371e+00, -1.5453e+00],
[-1.4134e+00, -1.4104e+00, -1.4300e+00, ..., -1.5163e+00,
-1.5862e+00, -1.5565e+00]],
[[ 1.5571e+00, 1.4284e+00, 1.8346e+00, ..., -1.4521e+00,
-1.6496e+00, -1.6908e+00],
[ 1.2790e+00, 1.6710e+00, 1.3942e+00, ..., -1.5838e+00,
-1.6467e+00, -1.6069e+00],
[ 1.4661e+00, 1.2568e+00, 1.7123e+00, ..., -1.5898e+00,
-1.6761e+00, -1.6212e+00],
...,
[-1.2567e+00, -1.2393e+00, -1.2457e+00, ..., -1.4077e+00,
-1.4073e+00, -1.4286e+00],
[-1.2191e+00, -1.2129e+00, -1.2214e+00, ..., -1.4193e+00,
-1.4265e+00, -1.4403e+00],
[-1.2213e+00, -1.2350e+00, -1.2495e+00, ..., -1.4075e+00,
-1.4811e+00, -1.4504e+00]],
[[ 1.1398e+00, 1.0327e+00, 1.4135e+00, ..., -1.2147e+00,
-1.4180e+00, -1.4598e+00],
[ 8.6931e-01, 1.2768e+00, 1.0129e+00, ..., -1.3449e+00,
-1.3906e+00, -1.3518e+00],
[ 1.1199e+00, 9.0534e-01, 1.2758e+00, ..., -1.3922e+00,
-1.4662e+00, -1.4051e+00],
...,
[-8.5999e-01, -8.2594e-01, -8.6729e-01, ..., -1.0699e+00,
-1.0976e+00, -1.1388e+00],
[-8.4630e-01, -8.2145e-01, -8.4266e-01, ..., -1.1058e+00,
-1.1325e+00, -1.1478e+00],
[-8.5198e-01, -8.5977e-01, -8.7435e-01, ..., -1.1186e+00,
-1.1739e+00, -1.1579e+00]]]], device='cuda:0')
[[2]]
TensorMask([[[ 4, 4, 4, ..., 4, 4, 4],
[ 4, 4, 4, ..., 4, 4, 4],
[ 4, 4, 4, ..., 4, 4, 4],
...,
[19, 19, 19, ..., 17, 17, 17],
[19, 19, 19, ..., 17, 17, 17],
[19, 19, 19, ..., 17, 17, 17]],
[[ 4, 4, 4, ..., 4, 4, 4],
[ 4, 4, 4, ..., 4, 4, 4],
[ 4, 4, 4, ..., 4, 4, 4],
...,
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17]],
[[26, 21, 26, ..., 26, 26, 26],
[26, 21, 26, ..., 26, 26, 26],
[26, 21, 21, ..., 26, 26, 26],
...,
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17]],
...,
[[ 4, 4, 4, ..., 26, 26, 26],
[ 4, 4, 4, ..., 26, 26, 26],
[ 4, 4, 4, ..., 26, 26, 26],
...,
[17, 17, 17, ..., 19, 19, 19],
[17, 17, 17, ..., 19, 19, 19],
[17, 17, 17, ..., 19, 19, 19]],
[[21, 21, 21, ..., 4, 4, 4],
[21, 21, 21, ..., 4, 4, 4],
[21, 21, 21, ..., 4, 4, 4],
...,
[17, 17, 17, ..., 19, 19, 19],
[17, 17, 17, ..., 19, 19, 19],
[17, 17, 17, ..., 19, 19, 19]],
[[ 4, 4, 4, ..., 30, 30, 30],
[ 4, 4, 4, ..., 30, 30, 30],
[ 4, 4, 4, ..., 30, 30, 30],
...,
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17],
[17, 17, 17, ..., 17, 17, 17]]], device='cuda:0')
```
The shape of the tensors:
```
batch[[1]]$shape;batch[[2]]$shape
```
```
torch.Size([8, 3, 200, 266])
torch.Size([8, 200, 266])
```
Define input and target:
```
input = batch[[1]]
target = batch[[2]]
```
Filter Void class:
```
mask = target != void_code
```
`31` will be filtered as `False`:
```
TensorMask([[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]],
[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]],
[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]],
...,
[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]],
[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]],
[[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]]], device='cuda:0')
```
```
> (input$argmax(dim=1L)[mask] == target[mask])
tensor([False, False, False, ..., False, False, False], device='cuda:0')
```
```
> (input$argmax(dim=1L)[mask] == target[mask]) %>%
float()
tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0')
```
```
> (input$argmax(dim=1L)[mask]==target[mask]) %>%
float() %>% mean()
tensor(0.0011, device='cuda:0')
```
Resnet34 model architecture for unet:
```
learn = unet_learner(dls, resnet34(), metrics = acc_camvid)
```
And finally, fit:
```
lr = 3e-3
wd = 1e-2
learn %>% fit_one_cycle(2, slice(lr), pct_start = 0.9, wd = wd)
```
```
epoch train_loss valid_loss acc_camvid time
0 1.367869 1.239496 0.666145 00:25
1 0.929434 0.661407 0.839969 00:23
```
```
learn %>% show_results(max_n = 1, figsize = c(20,10), vmin = 1, vmax = 30)
```
## Collab (Collaborative filtering)
Call libraries:
```
library(zeallot)
library(magrittr)
```
Get data:
```
URLs_MOVIE_LENS_ML_100k()
```
Specify column names:
```
c(user,item,title) %<-% list('userId','movieId','title')
```
Read datasets:
```
ratings = fread('ml-100k/u.data', col.names = c(user,item,'rating','timestamp'))
movies = fread('ml-100k/u.item', col.names = c(item, 'title', 'date', 'N', 'url',
paste('g',1:19,sep = '')))
```
Left join on item:
```
rating_movie = ratings[movies[, .SD, .SDcols=c(item,title)], on = item]
```
Load data from dataframe (R):
```
dls = CollabDataLoaders_from_df(rating_movie, seed=42, valid_pct=0.1, bs=64, item_name=title, path='ml-100k')
```
Build model:
```
learn = collab_learner(dls, n_factors = 40, y_range=c(0, 5.5))
```
Start learning:
```
learn %>% fit_one_cycle(1, 5e-3, wd = 1e-1)
```
Get top 1,000 movies:
```
top_movies = head(unique(rating_movie[ , count := .N, by = .(title)]
[order(count,decreasing = T)]
[, c('title','count')]),
1e3)[['title']]
```
Find mean ratings for the films:
```
mean_ratings = unique(rating_movie[ , .(mean = mean(rating)), by = title])
```
```
title mean
1: Toy Story (1995) 3.878319
2: GoldenEye (1995) 3.206107
3: Four Rooms (1995) 3.033333
4: Get Shorty (1995) 3.550239
5: Copycat (1995) 3.302326
---
1660: Sweet Nothing (1995) 3.000000
1661: Mat' i syn (1997) 1.000000
1662: B. Monkey (1998) 3.000000
1663: You So Crazy (1994) 3.000000
1664: Scream of Stone (Schrei aus Stein) (1991) 3.000000
```
Extract bias:
```
movie_bias = learn %>% get_bias(top_movies, is_item = TRUE)
result = data.table(bias = movie_bias,
title = top_movies)
res = merge(result, mean_ratings, all.y = FALSE)
res[order(bias, decreasing = TRUE)]
```
```
title bias mean
1: Star Wars (1977) 0.29479960 4.358491
2: Fargo (1996) 0.25264889 4.155512
3: Godfather, The (1972) 0.23247446 4.283293
4: Silence of the Lambs, The (1991) 0.22765337 4.289744
5: Titanic (1997) 0.22353025 4.245714
---
996: Children of the Corn: The Gathering (1996) -0.05671900 1.315789
997: Jungle2Jungle (1997) -0.05957306 2.439394
998: Leave It to Beaver (1997) -0.06268980 1.840909
999: Speed 2: Cruise Control (1997) -0.06567496 2.131579
1000: Island of Dr. Moreau, The (1996) -0.07530680 2.157895
```
Get weights:
```
movie_w = learn %>% get_weights(top_movies, is_item = TRUE, convert = TRUE)
```
Visualize with highcharter:
```
rownames(movie_w) = res$title
highcharter::hchart(princomp(movie_w, cor = TRUE)) %>% highcharter::hc_legend(enabled = FALSE)
```
## Text data
Grab data:
```
URLs_IMDB()
```
Specify path and small batch_size because it consumes a lot of GPU:
```
path = 'imdb'
bs = 20
```
Create datablock and iterator:
```
imdb_lm = DataBlock(blocks=list(TextBlock_from_folder(path, is_lm = TRUE)),
get_items = partial(get_text_files(),
folders = c('train', 'test', 'unsup')),
splitter = RandomSplitter(0.1))
dbunch_lm = imdb_lm %>% dataloaders(source = path, path = path, bs = bs, seq_len = 80)
```
Load a pretrained model and fit:
```
learn = language_model_learner(dbunch_lm, AWD_LSTM(), drop_mult = 0.3,
metrics = list(accuracy, Perplexity()))
learn %>% fit_one_cycle(1, 2e-2, moms = c(0.8, 0.7, 0.8))
```
> Note:
> [AWD_LSTM() can throw an error](https://github.com/fastai/fastai/issues/1439).
> In this case find and clean ".fastai" folder.
## Medical data
[Import dicom data](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/overview):
```
img = dcmread('hemorrhage.dcm')
```
Visualize data with different
[windowing effects](https://radiopaedia.org/articles/windowing-ct):
```
dicom_windows = dicom_windows()
scale = list(FALSE, TRUE, dicom_windows$brain, dicom_windows$subdural)
titles = c('raw','normalized','brain windowed','subdural windowed')
library(zeallot)
c(fig, axs[[2]]) %<-% subplots()
for (i in 1:4) {
img %>% show(scale = scale[[i]],
ax = axs[[i]],
title=titles[i])
}
img %>% plot(dpi = 250)
```
Apply different cmaps:
```
img %>% show(cmap = cm()$gist_ncar, figsize = c(6,6))
img %>% plot()
```
Or get dcm matrix and plot with ggplot:
```
types = c('raw', 'normalized', 'brain', 'subdural')
p_ = list()
for ( i in 1:length(types)) {
p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i]))
p_[[i]] = p
}
ggpubr::ggarrange(p_[[1]], p_[[2]], p_[[3]], p_[[4]], labels = types)
```
Let's try a relatively complex example:
```
library(ggplot2)
# crop parameters
img = dcmread('hemorrhage.dcm')
res = img %>% mask_from_blur(win_brain()) %>%
mask2bbox()
types = c('raw', 'normalized', 'brain', 'subdural')
# colors for matrix filling
colors = list(viridis::inferno(30), viridis::magma(30),
viridis::plasma(30), viridis::cividis(30))
scan_ = c('uniform_blur2d', 'gauss_blur2d')
p_ = list()
for ( i in 1:length(types)) {
if(i == 3) {
scan = scan_[1]
} else if (i==4) {
scan = scan_[2]
} else {
scan = ''
}
# crop with x/y_lim functions from ggplot
if(i==2) {
p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
scan = scan),
colours = colors[[i]])
p = p + ylim(c(res[[1]][[1]],res[[2]][[1]])) + xlim(c(res[[1]][[2]],res[[2]][[2]]))
# zoom image (25 %)
} else if (i==4) {
img2 = img
img2 %>% zoom(0.25)
p = nandb::matrix_raster_plot(img2 %>% get_dcm_matrix(type = types[i],
scan = scan),
colours = colors[[i]])
} else {
p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
scan = scan),
colours = colors[[i]])
}
p_[[i]] = p
}
ggpubr::ggarrange(p_[[1]],
p_[[2]],
p_[[3]],
p_[[4]],
labels = paste(types[1:4],
paste(c('','',scan_))[1:4])
)
```
## Additional features
### Find optimal learning rate
Get optimal learning rate and then fit:
```
data = model %>% lr_find()
data
# SuggestedLRs(lr_min=0.017378008365631102, lr_steep=0.0020892962347716093)
```
```
lr_rates losses
1 0.0000001000000 5.349157
2 0.0000001202264 5.231493
3 0.0000001445440 5.087494
4 0.0000001737801 5.068282
5 0.0000002089296 5.043181
6 0.0000002511886 5.023340
```
Visualize:
```
highcharter::hchart(data, "line", highcharter::hcaes(y = losses, x = lr_rates ))
```
### Visualize batch
Visualize tensor(s):
```
# get batch
batch = dls %>% one_batch(convert = TRUE)
# visualize img 9 with transformations
magick::image_read(batch[[1]][[9]])
```
### Mask
Visualize mask:
```
library(magrittr)
library(fastai)
# original image
fns = get_image_files('camvid/images')
cam_fn = capture.output(fns[0])
# mask
mask_fn = 'camvid/labels/0016E5_01110_P.png'
cam_img = Image_create(cam_fn)
# create mask
tmask = Transform(Mask_create())
mask = tmask(mask_fn)
# visualize
mask %>% to_matrix() %>%
nandb::matrix_raster_plot(colours = viridis::plasma(3)) + theme(legend.position = "none")
```
### TensorPoints
Load Tiny Mnist:
```
# download
URLs_MNIST_TINY()
# black and white img
timg = Transform(ImageBW_create)
mnist_fn = "mnist_tiny/valid/3/9007.png"
mnist_img = timg(mnist_fn)
# resize img
pnt_img = TensorImage(mnist_img %>% Image_resize(size = list(28,35)))
# visualize
library(ggplot2)
pnt_img %>% to_matrix() %>% nandb::matrix_raster_plot(colours = c('white','black')) +
geom_point(aes(x=0, y=0),size=2, colour="red")+
geom_point(aes(x=0, y=35),size=2, colour="red")+
geom_point(aes(x=28, y=0),size=2, colour="red")+
geom_point(aes(x=28, y=35),size=2, colour="red")+
geom_point(aes(x=9, y=17),size=2, colour="red")+
theme(legend.position = "none")
```
### Annotations on Tiny COCO
```
library(magrittr)
library(zeallot)
library(fastai)
URLs_COCO_TINY()
c(images, lbl_bbox) %<-% get_annotations('coco_tiny/train.json')
timg = Transform(ImageBW_create)
idx = 49
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
lbl_bbox[[idx]])
coco_img = timg(coco_fn)
tbbox = LabeledBBox(TensorBBox(bbox[[1]]), bbox[[2]])
```
```
(#2) [TensorBBox([[ 91.3000, 77.9400, 102.4300, 82.4700],
[ 27.5800, 77.6500, 40.7600, 82.3400]]),['tv', 'tv']]
```
Visualize:
```
library(imager)
coco = imager::load.image(coco_fn)
plot(coco,axes=F)
for ( i in 1:length(bbox[[1]])) {
rect(bbox[[1]][[i]][[1]],bbox[[1]][[i]][[2]],
bbox[[1]][[i]][[3]],bbox[[1]][[i]][[4]],
border = "white", lwd = 2)
text(bbox[[1]][[i]][[3]]-2.5,bbox[[1]][[i]][[4]]+2.5, labels = bbox[[2]][i],
offset = 2,
pos = 2,
cex = 1,
col = "white"
)
}
```
Alternatively, we could see batch via dataloader:
```
idx = 3
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
lbl_bbox[[idx]])
coco_bb = function(x) {
TensorBBox_create(bbox[[1]])
}
coco_lbl = function(x) {
bbox[[2]]
}
coco_dsrc = Datasets(c(rep(coco_fn,10)),
list(Image_create(), list(coco_bb),
list( coco_lbl, MultiCategorize(add_na = TRUE) )
), n_inp = 1)
coco_tdl = TfmdDL(coco_dsrc, bs = 9,
after_item = list(BBoxLabeler(), PointScaler(),
ToTensor()),
after_batch = list(IntToFloatTensor())
)
coco_tdl %>% show_batch(dpi = 200)
```
### NN module
To build a custom sequential model and pass it to learner:
```
nn$Sequential() +
nn$Conv2d(1L,20L,5L) +
nn$Conv2d(1L,20L,5L) +
nn$Conv2d(1L,20L,5L)
```
```
Sequential(
(0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)
```
To specify the name of the layers, one has to pass layer within lists, because
torch layers have no `name` argument:
```
nn$Sequential() +
nn$Conv2d(1L,20L,5L) +
list('my_conv2',nn$Conv2d(1L,20L,5L)) +
nn$Conv2d(1L,20L,5L) +
list('my_conv4',nn$Conv2d(1L,20L,5L))
```
```
Sequential(
(0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(my_conv2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
(my_conv4): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)
```
## Code of Conduct
Please note that the fastai project is released with a
[Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.