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sits_lighttae.R
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sits_lighttae.R
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#' @title Train a model using Lightweight Temporal Self-Attention Encoder
#' @name sits_lighttae
#'
#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br}
#' @author Charlotte Pelletier, \email{charlotte.pelletier@@univ-ubs.fr}
#'
#' @description Implementation of Light Temporal Attention Encoder (L-TAE)
#' for satellite image time series
#'
#' This function is based on the paper by Vivien Garnot referenced below
#' and code available on github at
#' https://github.com/VSainteuf/lightweight-temporal-attention-pytorch
#' If you use this method, please cite the original TAE and the LTAE paper.
#'
#' We also used the code made available by Maja Schneider in her work with
#' Marco Körner referenced below and available at
#' https://github.com/maja601/RC2020-psetae.
#'
#' @references
#' Vivien Garnot, Loic Landrieu, Sebastien Giordano, and Nesrine Chehata,
#' "Satellite Image Time Series Classification with Pixel-Set Encoders
#' and Temporal Self-Attention",
#' 2020 Conference on Computer Vision and Pattern Recognition.
#' pages 12322-12331.
#' DOI: 10.1109/CVPR42600.2020.01234
#'
#' Vivien Garnot, Loic Landrieu,
#' "Lightweight Temporal Self-Attention for Classifying
#' Satellite Images Time Series",
#' arXiv preprint arXiv:2007.00586, 2020.
#'
#' Schneider, Maja; Körner, Marco,
#' "[Re] Satellite Image Time Series Classification
#' with Pixel-Set Encoders and Temporal Self-Attention."
#' ReScience C 7 (2), 2021.
#' DOI: 10.5281/zenodo.4835356
#'
#' @param samples Time series with the training samples
#' (tibble of class "sits").
#' @param samples_validation Time series with the validation samples
#' (tibble of class "sits").
#' If \code{samples_validation} parameter is provided,
#' \code{validation_split} is ignored.
#' @param epochs Number of iterations to train the model
#' (integer, min = 1, max = 20000).
#' @param batch_size Number of samples per gradient update
#' (integer, min = 16L, max = 2048L)
#' @param validation_split Fraction of training data
#' to be used as validation data.
#' @param optimizer Optimizer function to be used.
#' @param opt_hparams Hyperparameters for optimizer:
#' \code{lr} : Learning rate of the optimizer
#' \code{eps}: Term added to the denominator
#' to improve numerical stability.
#' \code{weight_decay}: L2 regularization rate.
#' @param lr_decay_epochs Number of epochs to reduce learning rate.
#' @param lr_decay_rate Decay factor for reducing learning rate.
#' @param patience Number of epochs without improvements until
#' training stops.
#' @param min_delta Minimum improvement in loss function
#' to reset the patience counter.
#' @param verbose Verbosity mode (TRUE/FALSE). Default is FALSE.
#'
#' @return A fitted model to be used for classification of data cubes.
#'
#'
#' @examples
#' if (sits_run_examples()) {
#' # create a lightTAE model
#' torch_model <- sits_train(samples_modis_ndvi, sits_lighttae())
#' # plot the model
#' plot(torch_model)
#' # create a data cube from local files
#' data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
#' cube <- sits_cube(
#' source = "BDC",
#' collection = "MOD13Q1-6",
#' data_dir = data_dir
#' )
#' # classify a data cube
#' probs_cube <- sits_classify(
#' data = cube, ml_model = torch_model, output_dir = tempdir()
#' )
#' # plot the probability cube
#' plot(probs_cube)
#' # smooth the probability cube using Bayesian statistics
#' bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
#' # plot the smoothed cube
#' plot(bayes_cube)
#' # label the probability cube
#' label_cube <- sits_label_classification(
#' bayes_cube,
#' output_dir = tempdir()
#' )
#' # plot the labelled cube
#' plot(label_cube)
#' }
#' @export
sits_lighttae <- function(samples = NULL,
samples_validation = NULL,
epochs = 150L,
batch_size = 128L,
validation_split = 0.2,
optimizer = torch::optim_adamw,
opt_hparams = list(
lr = 0.005,
eps = 1e-08,
weight_decay = 1e-06
),
lr_decay_epochs = 50L,
lr_decay_rate = 1.0,
patience = 20L,
min_delta = 0.01,
verbose = FALSE) {
# set caller for error msg
.check_set_caller("sits_lighttae")
# Function that trains a torch model based on samples
train_fun <- function(samples) {
# Avoid add a global variable for 'self'
self <- NULL
# Verifies if 'torch' and 'luz' packages is installed
.check_require_packages(c("torch", "luz"))
# Pre-conditions:
.check_samples_train(samples)
.check_int_parameter(epochs, min = 1L, max = 20000L)
.check_int_parameter(batch_size, min = 16L, max = 2048L)
.check_null_parameter(optimizer)
# Check validation_split parameter if samples_validation is not passed
if (is.null(samples_validation)) {
.check_num_parameter(validation_split, exclusive_min = 0, max = 0.5)
}
# Check opt_hparams
# Get parameters list and remove the 'param' parameter
optim_params_function <- formals(optimizer)[-1]
if (.has(opt_hparams)) {
.check_lst_parameter(opt_hparams)
.check_chr_within(names(opt_hparams),
within = names(optim_params_function),
msg = .conf("messages", ".check_opt_hparams")
)
optim_params_function <- utils::modifyList(
x = optim_params_function, val = opt_hparams
)
}
# Other pre-conditions:
.check_int_parameter(lr_decay_epochs, min = 1)
.check_num_parameter(lr_decay_rate, exclusive_min = 0, max = 1.0)
.check_int_parameter(patience, min = 1)
.check_num_parameter(min_delta, min = 0)
.check_lgl_parameter(verbose)
# Samples labels
labels <- .samples_labels(samples)
# Samples bands
bands <- .samples_bands(samples)
# Samples timeline
timeline <- sits_timeline(samples)
# Create numeric labels vector
code_labels <- seq_along(labels)
names(code_labels) <- labels
# Number of labels, bands, and number of samples (used below)
n_labels <- length(labels)
n_bands <- length(bands)
n_times <- .samples_ntimes(samples)
# Data normalization
ml_stats <- .samples_stats(samples)
train_samples <- .predictors(samples)
train_samples <- .pred_normalize(pred = train_samples, stats = ml_stats)
# Post condition: is predictor data valid?
.check_predictors(pred = train_samples, samples = samples)
# Are there additional samples for validation?
if (!is.null(samples_validation)) {
.check_samples_validation(
samples_validation = samples_validation, labels = labels,
timeline = timeline, bands = bands
)
# Test samples are extracted from validation data
test_samples <- .predictors(samples_validation)
test_samples <- .pred_normalize(
pred = test_samples, stats = ml_stats
)
} else {
# Split the data into training and validation data sets
# Create partitions different splits of the input data
test_samples <- .pred_sample(
pred = train_samples, frac = validation_split
)
# Remove the lines used for validation
sel <- !train_samples[["sample_id"]] %in%
test_samples[["sample_id"]]
train_samples <- train_samples[sel, ]
}
n_samples_train <- nrow(train_samples)
n_samples_test <- nrow(test_samples)
# Shuffle the data
train_samples <- train_samples[sample(
nrow(train_samples), nrow(train_samples)
), ]
test_samples <- test_samples[sample(
nrow(test_samples), nrow(test_samples)
), ]
# Organize data for model training
train_x <- array(
data = as.matrix(.pred_features(train_samples)),
dim = c(n_samples_train, n_times, n_bands)
)
train_y <- unname(code_labels[.pred_references(train_samples)])
# Create the test data
test_x <- array(
data = as.matrix(.pred_features(test_samples)),
dim = c(n_samples_test, n_times, n_bands)
)
test_y <- unname(code_labels[.pred_references(test_samples)])
# Set torch seed
torch::torch_manual_seed(sample.int(10^5, 1))
# Define the L-TAE architecture
light_tae_model <- torch::nn_module(
classname = "model_ltae",
initialize = function(n_bands,
n_labels,
timeline,
layers_spatial_encoder = c(32, 64, 128),
n_heads = 16,
n_neurons = c(256, 128),
dropout_rate = 0.2,
dim_input_decoder = 128,
dim_layers_decoder = c(64, 32)) {
# define an spatial encoder
self$spatial_encoder <-
.torch_pixel_spatial_encoder(
n_bands = n_bands,
layers_spatial_encoder = layers_spatial_encoder
)
# number of input channels == last layer of mlp2
in_channels <-
layers_spatial_encoder[length(layers_spatial_encoder)]
# define a temporal encoder
self$temporal_encoder <-
.torch_light_temporal_attention_encoder(
timeline = timeline,
in_channels = in_channels,
n_heads = n_heads,
n_neurons = n_neurons,
dropout_rate = dropout_rate
)
# add a final layer to the decoder
# with a dimension equal to the number of layers
dim_layers_decoder[length(dim_layers_decoder) + 1] <- n_labels
# decode the tensor
self$decoder <- .torch_multi_linear_batch_norm_relu(
dim_input_decoder,
dim_layers_decoder
)
# classify using softmax
self$softmax <- torch::nn_softmax(dim = -1)
},
forward = function(input) {
out <- self$spatial_encoder(input)
out <- self$temporal_encoder(out)
out <- self$decoder(out)
out <- self$softmax(out)
return(out)
}
)
# Train the model using luz
torch_model <-
luz::setup(
module = light_tae_model,
loss = torch::nn_cross_entropy_loss(),
metrics = list(luz::luz_metric_accuracy()),
optimizer = optimizer
) |>
luz::set_hparams(
n_bands = n_bands,
n_labels = n_labels,
timeline = timeline
) |>
luz::set_opt_hparams(
!!!optim_params_function
) |>
luz::fit(
data = list(train_x, train_y),
epochs = epochs,
valid_data = list(test_x, test_y),
callbacks = list(
luz::luz_callback_early_stopping(
monitor = "valid_loss",
mode = "min",
patience = patience,
min_delta = min_delta
),
luz::luz_callback_lr_scheduler(
torch::lr_step,
step_size = lr_decay_epochs,
gamma = lr_decay_rate
)
),
dataloader_options = list(batch_size = batch_size),
verbose = verbose
)
# Serialize model
serialized_model <- .torch_serialize_model(torch_model[["model"]])
# Retrieve attention mask
# Get the encoder
# encoder <- torch_model$model$temporal_encoder
# Retrieve the attention mask from the encoder
# attn_mask <- encoder$attention_heads$attention$attention_mask
# Function that predicts labels of input values
predict_fun <- function(values) {
# Verifies if torch package is installed
.check_require_packages("torch")
# Set torch threads to 1
# Note: function does not work on MacOS
suppressWarnings(torch::torch_set_num_threads(1))
# Unserialize model
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
# Used to check values (below)
input_pixels <- nrow(values)
# Transform input into a 3D tensor
# Reshape the 2D matrix into a 3D array
n_samples <- nrow(values)
n_times <- .samples_ntimes(samples)
n_bands <- length(bands)
# Performs data normalization
values <- .pred_normalize(pred = values, stats = ml_stats)
values <- array(
data = as.matrix(values), dim = c(n_samples, n_times, n_bands)
)
# if CUDA is available, transform to torch data set
# Load into GPU
if (torch::cuda_is_available()) {
values <- .as_dataset(values)
# We need to transform in a dataloader to use the batch size
values <- torch::dataloader(
values, batch_size = 2^15
)
# Do GPU classification
values <- .try(
stats::predict(object = torch_model, values),
.msg_error = .conf("messages", ".check_gpu_memory_size")
)
} else {
# Do CPU classification
values <- stats::predict(object = torch_model, values)
}
# Convert to tensor CPU
values <- torch::as_array(
x = torch::torch_tensor(values, device = "cpu")
)
# Are the results consistent with the data input?
.check_processed_values(
values = values, input_pixels = input_pixels
)
# Update the columns names to labels
colnames(values) <- labels
return(values)
}
# Set model class
predict_fun <- .set_class(
predict_fun, "torch_model", "sits_model", class(predict_fun)
)
return(predict_fun)
}
# If samples is informed, train a model and return a predict function
# Otherwise give back a train function to train model further
result <- .factory_function(samples, train_fun)
return(result)
}