## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE, error = FALSE)

## -----------------------------------------------------------------------------
# spec <- platypus_spec("run.yaml")
# fit <- platypus_fit(spec)
# evaluate(fit)

## -----------------------------------------------------------------------------
# fit <- platypus_fit(platypus_spec("run.yaml"))
# evaluate(fit)            # one row per model
# training_history(fit)    # one row per model per epoch

## -----------------------------------------------------------------------------
# segmentation_data("data/all", split = list(fractions = c(0.8, 0.2), group_by = NULL))
# segmentation_data("data/train", validation = "data/validation")
# segmentation_data("data/all", validation = FALSE)

## -----------------------------------------------------------------------------
# u_net(
#   "augmented",
#   input_shape = c(256, 256),
#   epochs = 40,
#   loss = loss_dice(),
#   metrics = list(metric_iou(), metric_dice()),
#   optimizer = optimizer_adam(learning_rate = 0.001),
#   callbacks = list(
#     callback_early_stopping(monitor = "val_loss", patience = 10),
#     callback_model_checkpoint(monitor = "val_iou", path = "runs/best.safetensors")
#   ),
#   augmentation = list(
#     augmentation_step("HorizontalFlip", p = 0.5),
#     augmentation_step("RandomBrightnessContrast", p = 0.3)
#   )
# )

## -----------------------------------------------------------------------------
# spec <- platypus_spec(
#   data = instance_data("data/train", "data/validation"),
#   models = list(
#     u_net(
#       "rim",
#       input_shape = c(256, 256),
#       separation = separation_rim(width = 2),
#       loss = loss_cce_dice(),
#       decode = list(decode_nearest_seed(), decode_connected_components())
#     ),
#     u_net(
#       "flows",
#       input_shape = c(256, 256),
#       separation = separation_flows(loss = loss_mse(where = "foreground"))
#     )
#   )
# )
# fit <- platypus_fit(spec)
# evaluate(fit)

## -----------------------------------------------------------------------------
# spec <- platypus_spec("run.yaml")
# 
# spec <- platypus_spec(
#   data = segmentation_data(
#     "data/train",
#     validation = "data/validation",
#     colormap = list(c(0, 0, 0), c(255, 255, 255))
#   ),
#   models = list(u_net("first_try", input_shape = c(256, 256)))
# )

## -----------------------------------------------------------------------------
# yaml::write_yaml(as.list(spec), "run.yaml")