Arabic Sign Language

constants.py

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constants.py

source core configuration constants

File Path: src/core/constants.py

Purpose: System-wide constants, configuration values, and path definitions.

Overview

Central configuration module that defines:

  • Device configuration (CPU/GPU)
  • Directory paths (local vs Kaggle)
  • Model hyperparameters
  • Dataset parameters
  • Enumerations for data types

Path Configuration Logic

graph TD
    A[LOCAL_DEV env var] --> B{LOCAL_DEV == 1?}
    B -->|Yes| C[Use local paths]
    B -->|No| D[Use Kaggle paths]
    
    C --> E[DATA_INPUT_DIR = ./data]
    C --> F[DATA_OUTPUT_DIR = ./data]
    
    D --> G[DATA_INPUT_DIR = /kaggle/input]
    D --> H[DATA_OUTPUT_DIR = /kaggle/working]
    
    E --> I[MODELS_DIR = ./data/models]
    F --> I
    G --> J[MODELS_DIR = /kaggle/working/models]
    H --> J

Environment Variables

USE_CPU

Type: String (“0” or “1”) Default: “0” Purpose: Force CPU execution even if GPU available Used By: DEVICE constant

LOCAL_DEV

Type: String (“0” or “1”) Default: “0” Purpose: Switch between local and Kaggle paths Used By: DATA_INPUT_DIR, DATA_OUTPUT_DIR

Constants

Device Configuration

DEVICE

use_gpu = os.environ.get("USE_CPU", "0") == "0" and cuda_is_available()
DEVICE = ["cpu", "cuda"][int(use_gpu)]

Type: str (“cpu” or “cuda”) Purpose: Execution device for PyTorch/ONNX Used By: All model loading and training functions

Directory Paths

PROJECT_ROOT_DIR

Type: Path string Value: Parent directory of src/ Purpose: Base directory for all relative paths

LOGS_DIR

Type: Path string Value: {PROJECT_ROOT_DIR}/logs Purpose: Application logs storage

LANDMARKERS_DIR

Type: Path string Value: {PROJECT_ROOT_DIR}/landmarkers Purpose: MediaPipe model files (.task files) Used By: mediapipe_utils.py

MODELS_DIR

Type: Path string Value: {DATA_OUTPUT_DIR}/models Purpose: ONNX model storage Used By: main.py lifespan()

TRAIN_CHECKPOINTS_DIR

Type: Path string Value: {DATA_OUTPUT_DIR}/checkpoints Purpose: PyTorch training checkpoints Used By: train.py

DATA_INPUT_DIR

Type: Path string Value: /kaggle/input or {PROJECT_ROOT_DIR}/data (based on LOCAL_DEV) Purpose: Input data directory

DATA_OUTPUT_DIR

Type: Path string Value: /kaggle/working or {PROJECT_ROOT_DIR}/data (based on LOCAL_DEV) Purpose: Output data directory

Dataset Paths

LABELS_PATH

Type: Path string Value: {PROJECT_DATA_DIR}/KARSL-502_Labels.xlsx Purpose: Excel file with sign labels

LABELS_JSON_PATH

Type: Path string Value: {PROJECT_DATA_DIR}/KARSL-502_Labels.json Purpose: JSON version of labels

KARSL_DATA_DIR

Type: Path string Value: {DATA_INPUT_DIR}/karsl-502 Purpose: Raw video dataset directory

NPZ_KPS_DIR

Type: Path string Value: {DATA_INPUT_DIR}/word-level-arabic-sign-language-extrcted-keypoints/karsl-kps Purpose: Extracted keypoints in NPZ format

MMAP_PREPROCESSED_DIR

Type: Path string Value: {DATA_INPUT_DIR}/word-level-arabic-sign-language-preprcsd-keypoints Purpose: Memory-mapped preprocessed keypoints

Model Hyperparameters

SEQ_LEN

Type: int Value: 50 Purpose: Maximum sequence length for model input Used By: All model and dataset code

FEAT_NUM

Type: int Value: 184 Purpose: Number of keypoint features (pose + face + hands) Calculation: POSE_NUM + FACE_NUM + 2 * HAND_NUM

FEAT_DIM

Type: int Value: 4 Purpose: Dimensions per keypoint (x, y, z, visibility)

Performance

MAX_WORKERS

Type: int Value: 4 Purpose: Thread pool size for keypoint extraction Used By: live_processing.py

Timing

MS_30FPS

Type: float Value: 1000 / 30 ≈ 33.33 Purpose: Milliseconds per frame at 30 FPS

MS_30FPS_INT

Type: int Value: 1000 // 30 = 33 Purpose: Integer milliseconds per frame

Enumerations

SplitType

enum dataset

Purpose: Dataset split types

Values:

  • train: Training split
  • val: Validation split
  • test: Test split

Usage:

from core.constants import SplitType
 
split = SplitType.train

DatasetType

enum dataset

Purpose: Dataset implementation types

Values:

  • lazy: Lazy loading dataset
  • mmap: Memory-mapped dataset

Usage:

from core.constants import DatasetType
 
dataset_type = DatasetType.mmap

Type Aliases

KarslDatasetType

Definition:

type KarslDatasetType = "LazyKArSLDataset" | "MmapKArSLDataset"

Purpose: Type hint for dataset classes

Usage Examples

Accessing Paths

from core.constants import MODELS_DIR, LABELS_PATH
 
# Load model
model_path = os.path.join(MODELS_DIR, "model.onnx")
 
# Load labels
labels_df = pd.read_excel(LABELS_PATH)

Device Configuration

from core.constants import DEVICE
import torch
 
# Create tensor on configured device
tensor = torch.zeros(10, 10).to(DEVICE)

Used By:

Conceptual:

Graph View

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