rm(list = ls())
rstudioapi::restartSession()
devtools::load_all("../utils4ni/")
devtools::load_all("../ni.datasets/")
ni.datasets::set_dataset_dir(dir = "/Volumes/Domingo/dlni_data")
devtools::load_all()
##%######################################################%##
# #
#### Example For Autoencoder ####
#### Convolutional ####
# #
##%######################################################%##
require(neurobase)
load_keras()
##%######################################################%##
# #
#### Data Loading ####
# #
##%######################################################%##
problem <- "brain_extraction"
problem_path <- problem %>% get_dataset()
info <- problem_path %>% get_problem_info(as_autoencoder = TRUE,
interactive = FALSE)
info %>% split_train_test_sets()
##%######################################################%##
# #
#### Network Scheme ####
# #
##%######################################################%##
width <- 7
scheme <- DLscheme$new()
scheme$add(width = width,
is_autoencoder = TRUE,
only_convolutionals = FALSE,
output_width = 3,
num_features = 3,
vol_layers_pattern = list(clf(all = TRUE,
hidden_layers = list(dense(300),
dense(400),
dense(200),
dense(100),
dense(250),
dense(100)))),
vol_dropout = 0.25,
feature_layers = list(dense(10),
dense(5)),
feature_dropout = 0.4,
common_layers = list(clf(all = TRUE,
hidden_layers = list(dense(400),
dense(200),
dense(100)))),
common_dropout = 0.25,
decoder_layers = list(clf(all = TRUE,
hidden_layers = list(dense(400),
dense(200),
dense(100)))),
last_hidden_layers = list(dense(10)),
optimizer = "nadam",
scale = "meanmax")
scheme$add(memory_limit = "4G")
##%######################################################%##
# #
#### Network Instantiation ####
# #
##%######################################################%##
ae_model <- scheme$instantiate(problem_info = info)
ae_model$summary()
##%######################################################%##
# #
#### Model Plotting ####
# #
##%######################################################%##
g <- ae_model$graph()
g %>% plot_graph()
ae_model$plot(to_file = paste0("model_", problem, ".png"))
##%######################################################%##
# #
#### Data Generators ####
# #
##%######################################################%##
# By default, 1024 windows are extracted from each file.
# Use 'use_data' to provide a different number.
target_windows_per_file <- 1024 * 8
ae_model$check_memory()
ae_model$use_data(use = "train",
x_files = info$train$x,
y_files = info$train$y,
target_windows_per_file = target_windows_per_file)
ae_model$use_data(use = "test",
x_files = info$test$x,
y_files = info$test$y,
target_windows_per_file = target_windows_per_file)
##%######################################################%##
# #
#### Fit ####
# #
##%######################################################%##
epochs <- 30
keep_best <- TRUE
saving_path <- "/Volumes/Domingo/dlni_models" # Must exist
dir.create(path = saving_path, showWarnings = FALSE, recursive = TRUE)
saving_prefix <- paste0(problem, "_", format(Sys.time(), "%Y_%m_%d_%H_%M_%S"))
ae_model$fit(epochs = epochs,
keep_best = keep_best,
path = saving_path,
prefix = saving_prefix,
metrics_viewer = TRUE,
verbose = TRUE)
ae_model$plot_history()
saving_prefix <- paste0(saving_prefix, "_final")
ae_model$save(path = saving_path,
prefix = saving_prefix,
comment = "Final model after training")
##%######################################################%##
# #
#### Test Image ####
# #
##%######################################################%##
# Select random test image
test_index <- sample(info$test$subject_indices, size = 1)
input_file_list <- lapply(info$inputs, function(x) x[test_index])
# Load images and ground truth
input_imgs <- read_nifti_batch(file_list = input_file_list)
ground_truth <- input_imgs[[1]]
# ground_truth <- do.call(paste0("scale_", scheme$scale), args = list(ground_truth))
# Infer in the input volume
reconstruction <- ae_model$infer(V = input_imgs)
# Plot Ground Truth results
ortho_plot(x = ground_truth,
text = "Ground Truth",
interactiveness = FALSE)
# Plot Model results
ortho_plot(x = reconstruction * 255,
text = "Predicted",
interactiveness = FALSE)
rec <- map_images(source = reconstruction,
target = ground_truth)
# Plot Model results
ortho_plot(x = rec,
text = "Matched",
interactiveness = FALSE)
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