#' @title Train a model using Temporal Self-Attention Encoder
#' @name sits_tae
#'
#' @author Charlotte Pelletier, \email{charlotte.pelletier@@univ-ubs.fr}
#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br}
#'
#' @description Implementation of Temporal Attention Encoder (TAE)
#' for satellite image time series classification.
#'
#' This function is based on the paper by Vivien Garnot referenced below
#' and code available on github at
#' https://github.com/VSainteuf/pytorch-psetae.
#'
#' 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.
#'
#' If you use this method, please cite Garnot's and Schneider's work.
#'
#' @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
#'
#' 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.
#' @param samples_validation Time series with the validation samples. if the
#' \code{samples_validation} parameter is provided,
#' the \code{validation_split} parameter is ignored.
#' @param epochs Number of iterations to train the model.
#' @param batch_size Number of samples per gradient update.
#' @param validation_split Number between 0 and 1. Fraction of training data
#' to be used as validation data.
#' @param optimizer Optimizer function to be used.
#' @param opt_hparams Hyperparameters for optimizer:
#' lr : Learning rate of the optimizer
#' eps: Term added to the denominator
#' to improve numerical stability.
#' weight_decay: L2 regularization
#' @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 to reset the patience counter.
#' @param verbose Verbosity mode (TRUE/FALSE). Default is FALSE.
#'
#' @return A fitted model to be used for classification.
#'
#' @examples
#' if (sits_run_examples()) {
#' # create a TAE model
#' torch_model <- sits_train(samples_modis_ndvi, sits_tae())
#' # 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 = "MOD13Q1-6.1",
#' 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_tae <- function(samples = NULL,
samples_validation = NULL,
epochs = 150,
batch_size = 64,
validation_split = 0.2,
optimizer = torch::optim_adamw,
opt_hparams = list(
lr = 0.001,
eps = 1e-08,
weight_decay = 1.0e-06
),
lr_decay_epochs = 1,
lr_decay_rate = 0.95,
patience = 20,
min_delta = 0.01,
verbose = FALSE) {
# set caller for error msg
.check_set_caller("sits_tae")
# Function that trains a torch model based on samples
train_fun <- function(samples) {
# does not support working with DEM or other base data
if (inherits(samples, "sits_base"))
stop(.conf("messages", "sits_train_base_data"), call. = FALSE)
# 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)
.check_int_parameter(batch_size)
.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 (!is.null(names(opt_hparams))) {
.check_lst_parameter(opt_hparams,
msg = .conf("messages", ".check_opt_hparams")
)
.check_chr_within(
x = 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)
.check_num_parameter(lr_decay_rate, exclusive_min = 0, max = 1)
.check_int_parameter(patience)
.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 <- .samples_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 validation samples?
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 PSE-TAE model
pse_tae_model <- torch::nn_module(
classname = "model_pse_tae",
initialize = function(n_bands,
n_labels,
timeline,
dim_input_decoder = 128,
dim_layers_decoder = c(64, 32)) {
# define an spatial encoder
self$spatial_encoder <-
.torch_pixel_spatial_encoder(n_bands = n_bands)
# define a temporal encoder
self$temporal_attention_encoder <-
.torch_temporal_attention_encoder(timeline = timeline)
# 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
self$decoder <- .torch_multi_linear_batch_norm_relu(
dim_input_decoder,
dim_layers_decoder
)
# softmax is done after classification - removed from here
# self$softmax <- torch::nn_softmax(dim = -1)
},
forward = function(x) {
x <- x |>
self$spatial_encoder() |>
self$temporal_attention_encoder() |>
self$decoder()
# softmax is done after classification - removed from here
# self$softmax()
return(x)
}
)
# torch 12.0 not working with Apple MPS
cpu_train <- .torch_cpu_train()
# train the model using luz
torch_model <-
luz::setup(
module = pse_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
)
),
accelerator = luz::accelerator(cpu = cpu_train),
dataloader_options = list(batch_size = batch_size),
verbose = verbose
)
# Serialize model
serialized_model <- .torch_serialize_model(torch_model[["model"]])
# 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)
)
# Get GPU memory
gpu_memory <- sits_env[["gpu_memory"]]
# if CUDA is available and gpu memory is defined, transform values
# to torch dataloader
if (.torch_has_cuda() && .has(gpu_memory)) {
# set the batch size according to the GPU memory
b_size <- 2^gpu_memory
# transfor the input array to a dataset
values <- .as_dataset(values)
# To the data set to a torcj transform in a dataloader to use the batch size
values <- torch::dataloader(values, batch_size = b_size)
# Do GPU classification with dataloader
values <- .try(
stats::predict(object = torch_model, values),
.msg_error = .conf("messages", ".check_gpu_memory_size")
)
} else {
# Do classification without dataloader
values <- stats::predict(object = torch_model, values)
}
# Convert from tensor to array
values <- torch::as_array(values)
# 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)
}
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