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 Brain Multi-Machine ####
# #
##%######################################################%##
require(neurobase)
load_keras()
##%######################################################%##
# #
#### Data Loading ####
# #
##%######################################################%##
problem <- "multi-machine"
problem_path <- problem %>% get_dataset()
info <- problem_path %>% get_problem_info()
info %>% split_train_test_sets()
##%######################################################%##
# #
#### Network Scheme ####
# #
##%######################################################%##
width <- 32
scheme <- DLscheme$new()
scheme$add(width = width,
only_convolutionals = TRUE,
output_width = width,
num_features = 3,
vol_layers_pattern = segnet(depth = as.integer(log2(width) - 1),
mode = "convolutional",
initial_filters = 4),
vol_dropout = 0,
feature_layers = list(),
feature_dropout = 0,
common_layers = list(),
common_dropout = 0,
last_hidden_layers = list(),
optimizer = "nadam",
scale = "z",
scale_y = "none")
scheme$add(memory_limit = "2G")
##%######################################################%##
# #
#### Network Instantiation ####
# #
##%######################################################%##
modality_model <- scheme$instantiate(problem_info = info)
modality_model$summary()
##%######################################################%##
# #
#### Model Plotting ####
# #
##%######################################################%##
g <- modality_model$graph
g %>% plot_graph()
modality_model$plot(to_file = paste0("model_", problem, ".png"))
##%######################################################%##
# #
#### Data Generators ####
# #
##%######################################################%##
# By default, 1024 windows are extracted from each file.
# Use 'use_data' to provide a different number.
target_windows_per_file <- 1024
modality_model$check_memory()
modality_model$use_data(use = "train",
x_files = info$train$x,
y_files = info$train$y,
target_windows_per_file = target_windows_per_file)
modality_model$use_data(use = "test",
x_files = info$test$x,
y_files = info$test$y,
target_windows_per_file = target_windows_per_file)
##%######################################################%##
# #
#### Fit ####
# #
##%######################################################%##
epochs <- 15
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"))
modality_model$fit(epochs = epochs,
keep_best = keep_best,
path = saving_path,
prefix = saving_prefix)
modality_model$plot_history()
saving_prefix <- paste0(saving_prefix, "_final")
modality_model$save(path = saving_path,
prefix = saving_prefix,
comment = "Final model after training")
##%######################################################%##
# #
#### Test Image ####
# #
##%######################################################%##
# Select random test subject
test_index <- sample(info$test$subject_indices, size = 1)
input_file_list <- lapply(info$inputs, function(x) x[test_index])
# Read images and ground truth
input_imgs <- read_nifti_batch(file_list = input_file_list)
ground_truth <- read_nifti_to_array(info$outputs[test_index])
# Predict on the inputs
modality <- modality_model$infer(V = input_imgs, speed = "faster")
# Plot
ortho_plot(x = input_imgs[[1]], text = "Original image", interactiveness = FALSE)
ortho_plot(x = ground_truth, text = "Ground Truth", interactiveness = FALSE)
ortho_plot(x = modality, text = "Predicted", interactiveness = FALSE)
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