- Notifications
You must be signed in to change notification settings - Fork13
EagerAI/fastai
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
The fastai package provides R wrappers tofastai.
The fastai library simplifies training fast and accurate neural nets usingmodern best practices. See thefastai website to get started. The libraryis based on research into deep learning best practices undertaken atfast.ai,and includes "out of the box" support forvision,text,tabular, andcollab(collaborative filtering) models.
| Build | Status |
|---|---|
| Bionic |  |
| Focal |  |
| Mac OS |  |
| Windows |  |
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 modulefastai:
reticulate::use_condaenv('r-reticulate',required = TRUE)fastai::install_fastai(gpu = FALSE, cuda_version = '11.6', overwrite = FALSE)4. Restart RStudio!
We currently prepare the examples of usage of the fastai from R in Kaggle competitions:
- Introduction
- MNIST with Pytorch and fastai
- NLP Binary Classification
- Audio classification
- CycleGAN
- Fastai on Colab TPUs
Contributions are very welcome!
library(magrittr)library(fastai)# downloadURLs_ADULT_SAMPLE()# read datadf = 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: 29,532Total trainable params: 29,532Total non-trainable params: 0Optimizer used: <function Adam at 0x7fa246283598>Loss function: FlattenedLoss of CrossEntropyLoss()Callbacks: - TrainEvalCallback - Recorder - ProgressCallbackBefore 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 time0 0.360149 0.329587 0.846702 00:041 0.352106 0.345761 0.828877 00:042 0.368743 0.340913 0.844920 00:053 0.347277 0.333084 0.852050 00:044 0.348969 0.350707 0.830660 00:04Plot loss history:
model %>% plot_loss(dpi = 200)
See training process:
Get confusion matrix:
model %>% get_confusion_matrix() <50k >=50k<50k 407 22>=50k 68 64Plot 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 classes1 0.5108562 0.4891439 02 0.4827824 0.5172176 13 0.4873166 0.5126833 14 0.5013804 0.4986197 05 0.4964157 0.5035844 16 0.5111378 0.4888622 0Get 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.jpgDataloader:
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 time0 0.904872 0.317927 0.105548 00:351 0.694395 0.239520 0.083897 00:36Get 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 dataURLs_MNIST_SAMPLE()# transformationspath = 'mnist_sample'bs = 20#load into memorydata = ImageDataLoaders_from_folder(path, size = 26, bs = bs)# Visualize and traindata %>% show_batch(dpi = 150)learn = cnn_learner(data, resnet18(), metrics = accuracy)learn %>% fit(2)
What about the implementation of the latestComputer Vision models?
There is a function in fastaitimm_learner which originally written byZachary Mueller.It helps to quickly load the pretrained models fromtimm library.
First, lets's see the list of available models (TOP 10):
> str(as.list(timm_list_models()[1:10]))List of 10 $ : chr "adv_inception_v3" $ : chr "cspdarknet53" $ : chr "cspdarknet53_iabn" $ : chr "cspresnet50" $ : chr "cspresnet50d" $ : chr "cspresnet50w" $ : chr "cspresnext50" $ : chr "cspresnext50_iabn" $ : chr "darknet53" $ : chr "densenet121"Exciting!
Now, load and train pets dataset:
library(magrittr)library(fastai)path = 'oxford-iiit-pet'path_img = 'oxford-iiit-pet/images'fnames = get_image_files(path_img)dls = ImageDataLoaders_from_name_re( path, fnames, pat='(.+)_\\d+.jpg$', item_tfms=Resize(size = 460), bs = 10, batch_tfms=list(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: 27,654,496Total trainable params: 1,072,640Total non-trainable params: 26,581,856Optimizer used: <function Adam at 0x7fc1cfc16f28>Loss function: FlattenedLoss of CrossEntropyLoss()Model frozen up to parameter group #1Callbacks: - TrainEvalCallback - Recorder - ProgressCallbackAnd 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 01:001 0.841627 0.411970 0.890392 0.109608 00:582 0.657220 0.328548 0.899188 0.100812 00:59See results:
learn %>% show_results()Impressive!
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 = TRUElearn$recorder$valid_metrics = FALSElearn %>% fit(1, 2e-4, wd = 0)epoch train_loss gen_loss crit_loss time0 -0.555554 0.516327 -0.967604 05:06This is the result for 1 epoch.
learn %>% show_results(max_n = 16, figsize = c(8,8), ds_idx=0)
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 sizebs = 8Define 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 filex$stem; x$suffixDataloader 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 = codesname2id = 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 32Custom 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]]$shapetorch.Size([8, 3, 200, 266])torch.Size([8, 200, 266])Define input and target:
input = batch[[1]]target = batch[[2]]Filter Void class:
mask = target != void_code31 will be filtered asFalse:
TensorMask([[[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]], [[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]], [[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]], ..., [[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]], [[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]], [[True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], ..., [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True], [True, True, True, ..., True, True, True]]], device='cuda:0')> (input$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-3wd = 1e-2learn %>% fit_one_cycle(2, slice(lr), pct_start = 0.9, wd = wd)epoch train_loss valid_loss acc_camvid time0 1.367869 1.239496 0.666145 00:251 0.929434 0.661407 0.839969 00:23learn %>% show_results(max_n = 1, figsize = c(20,10), vmin = 1, vmax = 30)
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) 3.0000001661: Mat' i syn (1997) 1.0000001662: B. Monkey (1998) 3.0000001663: You So Crazy (1994) 3.0000001664: Scream of Stone (Schrei aus Stein) (1991) 3.000000Extract 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 2.1315791000: Island of Dr. Moreau, The (1996) -0.07530680 2.157895Get weights:
movie_w = learn %>% get_weights(top_movies, is_item = TRUE, convert = TRUE)Visualize with highcharter:
rownames(movie_w) = res$titlehighcharter::hchart(princomp(movie_w, cor = TRUE)) %>% highcharter::hc_legend(enabled = FALSE)
Grab data:
URLs_IMDB()Specify path and small batch_size because it consumes a lot of GPU:
path = 'imdb'bs = 20Create datablock and iterator:
imdb_lm = DataBlock(blocks=list(TextBlock_from_folder(path, is_lm = TRUE)), get_items = partial(get_text_files(), folders = c('train', 'test', 'unsup')), splitter = RandomSplitter(0.1))dbunch_lm = imdb_lm %>% dataloaders(source = path, path = path, bs = bs, seq_len = 80)Load a pretrained model and fit:
learn = language_model_learner(dbunch_lm, AWD_LSTM(), drop_mult = 0.3, metrics = list(accuracy, Perplexity()))learn %>% fit_one_cycle(1, 2e-2, moms = c(0.8, 0.7, 0.8))Note:AWD_LSTM() can throw an error.In this case find and clean ".fastai" folder.
img = dcmread('hemorrhage.dcm')Visualize data with differentwindowing effects:
dicom_windows = dicom_windows()scale = list(FALSE, TRUE, dicom_windows$brain, dicom_windows$subdural)titles = c('raw','normalized','brain windowed','subdural windowed')library(zeallot)c(fig, axs[[2]]) %<-% subplots()for (i in 1:4) { img %>% show(scale = scale[[i]], ax = axs[[i]], title=titles[i])}img %>% plot(dpi = 250)
Apply different cmaps:
img %>% show(cmap = cm()$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 parametersimg = dcmread('hemorrhage.dcm')res = img %>% mask_from_blur(win_brain()) %>% mask2bbox()types = c('raw', 'normalized', 'brain', 'subdural')# colors for matrix fillingcolors = 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]) )
Get optimal learning rate and then fit:
data = model %>% lr_find()data# SuggestedLRs(lr_min=0.017378008365631102, lr_steep=0.0020892962347716093) lr_rates losses1 0.0000001000000 5.3491572 0.0000001202264 5.2314933 0.0000001445440 5.0874944 0.0000001737801 5.0682825 0.0000002089296 5.0431816 0.0000002511886 5.023340Visualize:
highcharter::hchart(data, "line", highcharter::hcaes(y = losses, x = lr_rates ))
Visualize tensor(s):
# get batchbatch = dls %>% one_batch(convert = TRUE)# visualize img 9 with transformationsmagick::image_read(batch[[1]][[9]])
Visualize mask:
library(magrittr)library(fastai)# original imagefns = get_image_files('camvid/images')cam_fn = capture.output(fns[0])# maskmask_fn = 'camvid/labels/0016E5_01110_P.png'cam_img = Image_create(cam_fn)# create masktmask = Transform(Mask_create())mask = tmask(mask_fn)# visualizemask %>% to_matrix() %>% nandb::matrix_raster_plot(colours = viridis::plasma(3)) + theme(legend.position = "none")
Load Tiny Mnist:
# downloadURLs_MNIST_TINY()# black and white imgtimg = Transform(ImageBW_create)mnist_fn = "mnist_tiny/valid/3/9007.png"mnist_img = timg(mnist_fn)# resize imgpnt_img = TensorImage(mnist_img %>% Image_resize(size = list(28,35)))# visualizelibrary(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")
library(magrittr)library(zeallot)library(fastai)URLs_COCO_TINY()c(images, lbl_bbox) %<-% get_annotations('coco_tiny/train.json')timg = Transform(ImageBW_create)idx = 49c(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 = 3c(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)
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, becausetorch layers have noname 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)))Please note that the fastai project is released with aContributor Code of Conduct.By contributing to this project, you agree to abide by its terms.
About
R interface to fast.ai
Topics
Resources
License
Code of conduct
Stars
Watchers
Forks
Packages0
No packages published