configuration.py

#!/usr/bin/env python3
"""
script including
class object with global settings
"""
from collections import namedtuple
import os
from os.path import join

ModelConfig = namedtuple(
    "Model",
    [
        "name",  # Name of the model
        "module_name",  # Name of the module the model class is defined in
        "class_name",  # Name of the model class within the module
        "kwargs",  # Dictionary of keyword arguments to supply to the model class
        # during loading
        "model_weights",  # Path to the pretrained model weights
    ],
)

DatasetConfig = namedtuple(
    "Dataset",
    [
        "name",  # Name of the dataset
        "module_name",  # Name of the module the dataset class is defined in
        "class_name",  # Name of the dataset class within the module
        "kwargs",  # Dictionary of keyword arguments to supply to the dataset class
        # during loading
    ],
)

models = dict(
    deeplabv3plus=ModelConfig(
        "deeplabv3plus",
        "src.model.deepv3",
        "DeepWV3Plus",
        dict(num_classes=19),
        "/path/to/the/pretrained/deeplabv3plus/weights.pth",
    )
)

# The constructor of your meta model needs to accept an integer (as first argument) to
# specify the number of input features.
# The output of your meta model is expected to be linear and to have a single node
meta_models = dict(
    meta_nn=ModelConfig(
        "meta_nn", "src.MetaSeg.functions.meta_nn", "MetaNN", {}, "./src/meta_nn.pth"
    )
)

datasets = dict(
    cityscapes=DatasetConfig(
        "cityscapes",
        "src.datasets.cityscapes",
        "Cityscapes",
        dict(root="/data/datasets/semseg/Cityscapes", split="train"),
    ),
    cityscapes_val=DatasetConfig(
        "cityscapes_val",
        "src.datasets.cityscapes",
        "Cityscapes",
        dict(root="/data/datasets/semseg/Cityscapes", split="val"),
    ),
    cityscapes_test=DatasetConfig(
        "cityscapes_test",
        "src.datasets.cityscapes",
        "Cityscapes",
        dict(root="/data/datasets/semseg/Cityscapes", split="test"),
    ),
    cityscapes_laf=DatasetConfig(
        "cityscapes_laf",
        "src.datasets.cityscapes_laf",
        "CityscapesLAF",
        dict(root="/data/datasets/semseg/Cityscapes_lost_and_found", split="train"),
    ),
    a2d2=DatasetConfig(
        "a2d2",
        "src.datasets.a2d2",
        "A2D2",
        dict(root="/data/datasets/semseg/A2D2", cam_positions=["front_center"]),
    ),
    custom_dataset=DatasetConfig(
        "custom_dataset",
        "src.datasets.custom",
        "CustomDataset",
        dict(root="/path/to/your/image/directory", image_file_extension=".png"),
    ),
)


class CONFIG:
    # --------------------- #
    # set necessary paths   #
    # --------------------- #

    # directory with inputs and outputs, i.e. saving and loading data
    metaseg_io_path = "/your/metaseg/input-output/path"

    # ---------------------------- #
    # paths for data preparation   #
    # ---------------------------- #
    # The following path definitions are deprecated but for
    # compatibility reasons still here
    IMG_DIR = "/data/ai_data_and_models/data/DS_20k/test/Input/"
    GT_DIR = "/data/ai_data_and_models/data/DS_20k/test/PNG_13cl/"
    PROBS_DIR = (
        "/data/ai_data_and_models/inference_results/"
        "FRRNA_Softmax_Output/nparrays/softmax/predictions/"
    )

    # ------------------ #
    # select or define   #
    # ------------------ #

    meta_model_types = ["linear", "neural"]

    CLASS_DTYPE = "probs"  # = "probs" ( one of ['one_hot_classes', 'probs'], just
    # leave it like that
    TRAIN_DATASET = datasets[
        "cityscapes"
    ]  # The dataset the semantic segmentation model got trained on
    DATASET = datasets["cityscapes_val"]  # used for input/output folder path
    MODEL_NAME = "deeplabv3plus"  # used for input/output folder path
    META_MODEL_NAME = "meta_nn"
    META_MODEL_TYPE = meta_model_types[1]

    # --------------------------------------------------------------------#
    # select tasks to be executed by setting boolean variable True/False #
    # --------------------------------------------------------------------#

    COMPUTE_METRICS = True
    VISUALIZE_RATING = False
    ANALYZE_METRICS = False

    # ----------- #
    # optionals   #
    # ----------- #

    GPU_ID = 0
    NUM_CORES = 8
    # NUM_IMAGES = 500  # uncomment to only process the first 'NUM_IMAGES' images of
    # the specified dataset
    NUM_AVERAGES = 10  # only used when meta model is 'linear'
    NUM_LASSO_LAMBDAS = 40  # only used when meta model is 'linear'
    CLASSINDEX = None  # if DATASET is set to 'a2d2' and CLASSINDEX is set to an valid
    # integer only images
    # containing the specified class will be processed.

    INPUT_DIR = join(metaseg_io_path, "input", MODEL_NAME, DATASET.name) + "/"
    METRICS_DIR = join(metaseg_io_path, "metrics", MODEL_NAME, DATASET.name) + "/"
    COMPONENTS_DIR = join(metaseg_io_path, "components", MODEL_NAME, DATASET.name) + "/"
    IOU_SEG_VIS_DIR = (
        join(metaseg_io_path, "iou_seg_vis", MODEL_NAME, DATASET.name) + "/"
    )
    RESULTS_DIR = join(metaseg_io_path, "results", MODEL_NAME, DATASET.name) + "/"
    STATS_DIR = join(metaseg_io_path, "stats", MODEL_NAME, DATASET.name) + "/"
    LOG_FILE_PATH = join(metaseg_io_path, "log.txt")

    for p in [INPUT_DIR, METRICS_DIR, COMPONENTS_DIR]:
        if not os.path.exists(p):
            os.makedirs(p, exist_ok=True)