# specify directory as data io info
BASEDIR = "/content/"

# final DATADIR is BASEDIR / DATADIR if not accessing from gcs
# if accessing from gcs, the given path is the DATADIR
# training, testing and val data dir are then
#  DATADIR / "training",  DATADIR / "testing",  DATADIR / "validation" resp.
DATADIR = "datasets"

# OUTPUT_DIR from BASEDIR
# The OUTPUT_DIR is BASEDIR / OUTPUT_DIR
OUTPUT_DIR = "output"
MODEL_NAME = "aces"
MODEL_CHECKPOINT_NAME = "modelCheckpoint"
MODEL_DIR_NAME = "unet_v1"

# Auto generate model dir name
# if so it is generated as trial_MODELTYPE + datetime.now() + _v + version
AUTO_MODEL_DIR_NAME = False

# specify features and labels
# keep them in this format for reading
FEATURES = "red_before
green_before
blue_before
nir_before
red_during
green_during
blue_during
nir_during"

# For model training, USE_ELEVATION extends FEATURES with "elevation" & "slope"
# USE_S1 extends FEATURES with "vv_asc_before", "vh_asc_before", "vv_asc_during", "vh_asc_during",
# "vv_desc_before", "vh_desc_before", "vv_desc_during", "vh_desc_during"
# In case these are not useful and you have other bands in your training data, you can do set
# USE_ELEVATION and USE_S1 to False and update FEATURES to include needed bands
USE_ELEVATION = True
USE_S1 = True

DERIVE_FEATURES = False
ADDED_FEATURES = "ndvi_before
ndvi_during
evi_before
evi_during
ndwi_before
ndwi_during
savi_before
savi_during
msavi_before
msavi_during
mtvi2_before
mtvi2_during
vari_before
vari_during
tgi_before
tgi_during"

LABELS = ["class"]

# Scale of computation
SCALE = 10

# use seed for reproducibility
USE_SEED = True
SEED = 42

# print info of a single dataset while loading
PRINT_INFO = True

# patch size for training
PATCH_SHAPE = (256, 256)
# buffer for prediction purpose
# Half this will extend on the sides of each patch.
# if zero; does not do buffer
# else specify the size as tuple (e.g. 72 x 72)
KERNEL_BUFFER = 0

# Sizes of the training and evaluation datasets.
TRAIN_SIZE = 8531
TEST_SIZE = 1222
VAL_SIZE = 2404

# Specify model training parameters.
BATCH_SIZE = 64
EPOCHS = 5
RAMPUP_EPOCHS = 25
SUSTAIN_EPOCHS = 20

# Rates
USE_ADJUSTED_LR = False
MAX_LR = 1E-3
MID_LR = 3E-4
MIN_LR = 1E-4
DROPOUT_RATE = 0.2

# other params w/ notes
CALLBACK_PARAMETER = "val_loss"
EARLY_STOPPING = False
# choices are: cnn, dnn, unet,
MODEL_TYPE = "unet"
# Needs fixing;
# using GCP AI Platform needs a wrapped model; wrapped and original model are obtained but inference not working
USE_AI_PLATFORM = False
# dnn does not do augmentation
TRANSFORM_DATA = True

# ACTIVATION_FN autogenerated in the main script
# if n_class = 1: activation_fn = "sigmoid"
# else: activation_fn = "softmax"
ACTIVATION_FN = "softmax"
OPTIMIZER = "adam"
# standard or custom available
# current custom available are: "custom_focal_tversky_loss"
LOSS = "categorical_crossentropy"

# "cropland_etc", "rice", "forest", "urban", "others_water_etc"
OUT_CLASS_NUM = 5

# Needs fixing
USE_BEST_MODEL_FOR_INFERENCE = False

# EE settings
USE_SERVICE_ACCOUNT = False
EE_SERVICE_CREDENTIALS = "your-service-credentials.json"
# where the prediction output will be stored
EE_OUTPUT_ASSET = "projects/aces/prediction"
# output prediction name for both asset, locally (in TF Format) and gcs output (in TFRecord format)
OUTPUT_NAME = "prediction_unet"

# GCS settings
GCS_PROJECT = "mygcs-aces"
GCS_BUCKET = "mygcs-aces"
# prediction image directory
GCS_IMAGE_DIR = "prediction_images"
# prediction image prefix
GCS_IMAGE_PREFIX = "image_2021"

# Vertex AI Settings
# Vertex AI Model Save Directory
GCS_VERTEX_MODEL_SAVE_DIR = "model_save"
# Region
GCS_REGION = "us-central1"
GCS_VERTEX_CONTAINER_IMAGE = "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-11:latest"

# Get your machine type here: https://cloud.google.com/vertex-ai/docs/predictions/configure-compute
GCP_MACHINE_TYPE = "c2-standard-60"