# SEGMENTATION MODEL CONFIG

import argparse

import utils

# test: get inference on new data

# precision: compute MS COCO mean average precision

def get_config_pars(stage):

    parser_ = argparse.ArgumentParser(

        description='arguments for training Mask R-CNN segmentation for 2 classes '

                    '(Ground Glass Opacity and Consolidation) on CNCB dataset')

    parser_.add_argument("--backbone_name", type=str, default='resnet50', help="One of resnet50, resnet34, reesnet18")

    parser_.add_argument("--truncation", type=str, default="0", help="One of 0,1,2 for no truncation, last block, two last blocks")

    parser_.add_argument("--device", type=str, default='cpu')

    parser_.add_argument("--model_name", type=str, default=None)

    parser_.add_argument("--rpn_nms_th", type=float, default=0.75, help="Both at train and test stages.")

    parser_.add_argument("--mask_type", type=str, default=None, help="currently accepts three types: both"

                                                                     "for 2 classes, GGO and C, ggo for only "

                                                                     "GGO mask, and merge for merged GGO and C "

                                                                     "classes. These inputs are used both in the "

                                                                     "dataset and model interfaces.")

    if stage == "trainval":

        parser_.add_argument("--start_epoch", type=str, default=0)

        parser_.add_argument("--pretrained_model", type=str, help="Pretrained model, must be a checkpoint with keys:"

                                                       "model_weights, anchor_generator, optimizer_state, model_name",

                             default=None)

        parser_.add_argument("--num_epochs", type=int, default=50)

        parser_.add_argument("--use_pretrained_resnet_backbone", type=utils.str_to_bool, default=False,

                             help="Use the ResNetw/FPN weights from Torchvision repository")

        parser_.add_argument("--save_dir", type=str, default="saved_models",

                             help="Directory to save checkpoints")

        parser_.add_argument("--train_data_dir", type=str, default='../covid_data/train',

                             help="Path to the training data. Must contain images and binary masks")

        parser_.add_argument("--val_data_dir", type=str, default='../covid_data/test',

                             help="Path to the validation data. Must contain images and binary masks")

        parser_.add_argument("--gt_dir", type=str, default='masks',

                             help="Path to directory with binary masks. Must be in the data directory.")

        parser_.add_argument("--imgs_dir", type=str, default='imgs')

        parser_.add_argument("--batch_size", type=int, default=1, help="Implemented only for batch size = 1")

        parser_.add_argument("--save_every", type=int, default=10)

        parser_.add_argument("--lrate", type=float, default=1e-5, help="Learning rate")

    elif stage == "test" or stage == "precision":

        parser_.add_argument("--ckpt", type=str,

                             help="Checkpoint file in .pth format. "

                                  "Must contain the following keys: model_weights, optimizer_state, anchor_generator")

        parser_.add_argument("--test_data_dir", type=str, default='../covid_data/test',

                             help="Path to the test data. Must contain images and may contain binary masks")

        parser_.add_argument("--test_imgs_dir", type=str, default='imgs', help="Directory with images. "

                                                                               "Must be in the test data directory.")

        parser_.add_argument("--save_dir", type=str, default='eval_imgs',

                             help="Directory to save segmentation results.")

        parser_.add_argument("--confidence_th", type=float, default=0.05, help="Lower confidence score threshold on "

                                                                               "positive prediction from RoI.")

        parser_.add_argument("--mask_logits_th", type=float, default=0.5, help="Lower threshold for positve mask "

                                                                               "prediction at "

                                                                               "pixel level.")

        parser_.add_argument("--gt_dir", type=str, default='masks',

                             help="Path to directory with binary masks. Must be in the data directory. If the stage == "

                                  "'precision', this path must be provided.")

        parser_.add_argument("--roi_nms_th", type=float, default=0.5, help="Only at test stage.")

    model_args = parser_.parse_args()

    return model_args