clean up to baseline

src/conf/config_loader.py

@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-import logging
-from pathlib import Path
-from typing import Any
-
-import gin
-
-from src.conf.hardware_conf import HardwareConfig
-from src.conf.models_conf import ModelsConfig
-from src.conf.pipeline_conf import PipelineConfig
-from src.conf.tracking_conf import TrackingConfig
-from src.conf.training_conf import TrainingConfig
-
-logger = logging.getLogger(__name__)
-
-
-def load_all_configs(path2cfg: str) -> dict[str, Any]:
-    """Parse ALL .gin files in path2cfg and return all config objects.
-
-    This is the PRODUCTION entry point — main() calls this once. Individual
-    get_*_cfg() loaders exist only for unit tests / notebooks.
-
-    Args:
-        path2cfg: Path to config directory (WITH trailing slash).
-
-    Returns:
-        Dict with config objects keyed by name:
-            {
-                "pipeline": PipelineConfig,
-                "hardware": HardwareConfig,
-                "models":   ModelsConfig,
-                "training": TrainingConfig,
-                "tracking": TrackingConfig,
-            }
-
-    Raises:
-        FileNotFoundError: If path2cfg contains no .gin files.
-    """
-    cfg_dir = Path(path2cfg)
-    gin_files = sorted(cfg_dir.glob("*.gin"))
-    if not gin_files:
-        raise FileNotFoundError(f"No .gin files found in {cfg_dir}")
-
-    # MANDATORY: reset gin global state before parsing — without clear_config(),
-    # parameters from previous parses accumulate (gin holds global bindings).
-    gin.clear_config()
-    gin.parse_config_files_and_bindings(
-        config_files=[str(f) for f in gin_files],
-        bindings=[],
-    )
-    logger.info("Loaded %d gin files from %s", len(gin_files), cfg_dir)
-
-    # Instantiate AFTER all bindings are parsed.
-    return {
-        "pipeline": PipelineConfig(),
-        "hardware": HardwareConfig(),
-        "models":   ModelsConfig(),
-        "training": TrainingConfig(),
-        "tracking": TrackingConfig(),
-    }
-

src/conf/hardware_conf.py

@@ -1,43 +0,0 @@
-from __future__ import annotations
-
-import gin
-
-
-@gin.configurable
-class HardwareConfig:
-    """GPU profile + memory/compute optimisation flags.
-
-    Everything that changes when you switch hardware (4090 → A100 → Jetson)
-    lives here. batch_size and grad_accum_steps are hardware-bound: they
-    determine VRAM footprint, not the training recipe.
-    """
-
-    def __init__(
-        self,
-        device: str = "cuda",
-        batch_size: int = 8,
-        grad_accum_steps: int = 1,
-        num_workers: int = 4,
-        use_amp: bool = True,
-        gradient_checkpointing: bool = True,
-        reserve_gb: float = 2.0,
-    ) -> None:
-        self.device = device
-        self.batch_size = batch_size
-        self.grad_accum_steps = grad_accum_steps
-        self.num_workers = num_workers
-        self.use_amp = use_amp
-        self.gradient_checkpointing = gradient_checkpointing
-        self.reserve_gb = reserve_gb
-        # Derived (RTX 4090 default; override per profile):
-        self.total_vram_gb = 24.0
-        self.available_vram_gb = self.total_vram_gb - self.reserve_gb
-        self.effective_batch_size = self.batch_size * self.grad_accum_steps
-
-
-def get_hardware_cfg(path2cfg: str) -> HardwareConfig:
-    """Load ONLY hardware config (TESTING ONLY — use load_all_configs in production)."""
-    gin.clear_config()
-    gin.parse_config_file(f"{path2cfg}hardware.gin")
-    return HardwareConfig()
-

src/conf/models_conf.py

@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-import gin
-
-
-@gin.configurable
-class ModelsConfig:
-    """Model checkpoints + architecture switches.
-
-    Default checkpoint paths are relative to the project root (matching the
-    repository layout: nn_models/DINO_WEB/, nn_models/DINO_SAT/, etc.).
-    These are gitignored and must be downloaded separately — see README.
-    """
-
-    def __init__(
-        self,
-        # Checkpoints — relative to project root, defaults match repo layout.
-        dino_web_path: str = "nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth",
-        dino_sat_path: str = "nn_models/DINO_SAT/model.safetensors",
-        lrsclip_path: str = "nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt",
-        stripnet_path: str = "nn_models/STRIPNET/stripnet_s.pth",
-        # Backbone selection.
-        backbone: str = "dinov3",
-        shared_encoder: bool = True,
-        baseline_mode: bool = False,
-        # Fusion.
-        init_gate: float = 0.7,
-        # MONA (DINOv3).
-        mona_bottleneck: int = 64,
-        mona_last_n_blocks: int = 12,
-        # StripNet-specific.
-        stripnet_freeze: bool = True,
-        stripnet_mona_last_n_stages: int = 2,
-    ) -> None:
-        self.dino_web_path = dino_web_path
-        self.dino_sat_path = dino_sat_path
-        self.lrsclip_path = lrsclip_path
-        self.stripnet_path = stripnet_path
-        self.backbone = backbone
-        self.shared_encoder = shared_encoder
-        self.baseline_mode = baseline_mode
-        self.init_gate = init_gate
-        self.mona_bottleneck = mona_bottleneck
-        self.mona_last_n_blocks = mona_last_n_blocks
-        self.stripnet_freeze = stripnet_freeze
-        self.stripnet_mona_last_n_stages = stripnet_mona_last_n_stages
-
-
-def get_models_cfg(path2cfg: str) -> ModelsConfig:
-    """Load ONLY models config (TESTING ONLY — use load_all_configs in production)."""
-    gin.clear_config()
-    gin.parse_config_file(f"{path2cfg}models.gin")
-    return ModelsConfig()
-

src/conf/pipeline_conf.py

@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-import gin
-
-
-@gin.configurable
-class PipelineConfig:
-    """Pipeline orchestration: data IO, training schedule, output, resume.
-
-    Defaults match the current `belka_refactor` HEAD: hardcoded servml paths
-    are preserved verbatim. To switch to a different machine, override in
-    pipeline.gin — never edit defaults here.
-    """
-
-    def __init__(
-        self,
-        # Data paths (defaults match servml workstation).
-        train_json: str = "meta/train_80.json",
-        test_json: str = "meta/test_20.json",
-        rgb_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR",
-        caption_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR-captions",
-        filter_meta: str | None = None,
-        # Training schedule.
-        epochs: int = 10,
-        warmup_epochs: int = 2,
-        eval_every: int = 1,
-        # Reproducibility & output.
-        seed: int = 42,
-        output_dir: str = "out/gtauav/with_text",
-        resume_from: str | None = None,
-    ) -> None:
-        self.train_json = train_json
-        self.test_json = test_json
-        self.rgb_root = rgb_root
-        self.caption_root = caption_root
-        self.filter_meta = filter_meta
-        self.epochs = epochs
-        self.warmup_epochs = warmup_epochs
-        self.eval_every = eval_every
-        self.seed = seed
-        self.output_dir = output_dir
-        self.resume_from = resume_from
-
-
-def get_pipeline_cfg(path2cfg: str) -> PipelineConfig:
-    """Load ONLY pipeline config (TESTING ONLY — use load_all_configs in production).
-
-    Args:
-        path2cfg: Path to config directory (with trailing slash).
-
-    Returns:
-        Instantiated PipelineConfig.
-    """
-    gin.clear_config()
-    gin.parse_config_file(f"{path2cfg}pipeline.gin")
-    return PipelineConfig()
-

src/conf/tracking_conf.py

@@ -1,48 +0,0 @@
-from __future__ import annotations
-
-import gin
-
-
-@gin.configurable
-class TrackingConfig:
-    """Experiment tracking + diagnostics.
-
-    Independent axis: changing these flags does not affect training results,
-    only what is observed/recorded.
-    """
-
-    def __init__(
-        self,
-        use_wandb: bool = False,
-        use_tb: bool = True,
-        wandb_project: str = "caption-test-gtauav",
-        wandb_run_name: str | None = None,
-        wandb_entity: str | None = None,
-        log_grad_norms: bool = True,
-        use_gradcam: bool = False,
-        gradcam_every: int = 5,
-        gradcam_samples: int = 8,
-        use_profiler: bool = False,
-        profiler_warmup: int = 3,
-        profiler_active: int = 5,
-    ) -> None:
-        self.use_wandb = use_wandb
-        self.use_tb = use_tb
-        self.wandb_project = wandb_project
-        self.wandb_run_name = wandb_run_name
-        self.wandb_entity = wandb_entity
-        self.log_grad_norms = log_grad_norms
-        self.use_gradcam = use_gradcam
-        self.gradcam_every = gradcam_every
-        self.gradcam_samples = gradcam_samples
-        self.use_profiler = use_profiler
-        self.profiler_warmup = profiler_warmup
-        self.profiler_active = profiler_active
-
-
-def get_tracking_cfg(path2cfg: str) -> TrackingConfig:
-    """Load ONLY tracking config (TESTING ONLY — use load_all_configs in production)."""
-    gin.clear_config()
-    gin.parse_config_file(f"{path2cfg}tracking.gin")
-    return TrackingConfig()
-

src/conf/training_conf.py

@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-import gin
-
-
-@gin.configurable
-class TrainingConfig:
-    """Training recipe: loss + optimizer + sampler.
-
-    These three move together when you tune learning. Changing tau usually
-    pairs with changing lr; switching sampler_type usually pairs with
-    re-tuning loss weights. Keeping them in one config matches the actual
-    workflow of running ablations.
-    """
-
-    def __init__(
-        self,
-        # --- Loss ---
-        loss_type: str = "symmetric",
-        tau_init: float = 0.07,
-        tau_min: float = 0.01,
-        tau_max: float = 0.1,
-        learnable_temperature: bool = True,
-        label_smoothing: float = 0.1,
-        weight_q2g: float = 0.6,
-        weight_g2q: float = 0.4,
-        hard_mining_k: int = 0,
-        neg_bank_size: int = 0,
-        # --- Optimizer ---
-        learning_rate: float = 1e-4,
-        text_lr_factor: float = 0.1,
-        stripnet_backbone_lr_factor: float = 0.1,
-        weight_decay: float = 1e-4,
-        grad_clip: float = 1.0,
-        # --- Sampler ---
-        sampler_type: str = "mutex",
-        dss_warmup_epochs: int = 1,
-        dss_reembed_every: int = 1,
-        dss_knn_device: str = "cuda",
-        dss_use_lsh: bool = False,
-        dss_lsh_num_tables: int = 8,
-        dss_lsh_num_bits: int = 14,
-        dss_cache_dir: str | None = None,
-    ) -> None:
-        # Loss.
-        self.loss_type = loss_type
-        self.tau_init = tau_init
-        self.tau_min = tau_min
-        self.tau_max = tau_max
-        self.learnable_temperature = learnable_temperature
-        self.label_smoothing = label_smoothing
-        self.weight_q2g = weight_q2g
-        self.weight_g2q = weight_g2q
-        self.hard_mining_k = hard_mining_k
-        self.neg_bank_size = neg_bank_size
-        # Optimizer.
-        self.learning_rate = learning_rate
-        self.text_lr_factor = text_lr_factor
-        self.stripnet_backbone_lr_factor = stripnet_backbone_lr_factor
-        self.weight_decay = weight_decay
-        self.grad_clip = grad_clip
-        # Sampler.
-        self.sampler_type = sampler_type
-        self.dss_warmup_epochs = dss_warmup_epochs
-        self.dss_reembed_every = dss_reembed_every
-        self.dss_knn_device = dss_knn_device
-        self.dss_use_lsh = dss_use_lsh
-        self.dss_lsh_num_tables = dss_lsh_num_tables
-        self.dss_lsh_num_bits = dss_lsh_num_bits
-        self.dss_cache_dir = dss_cache_dir
-
-
-def get_training_cfg(path2cfg: str) -> TrainingConfig:
-    """Load ONLY training config (TESTING ONLY — use load_all_configs in production)."""
-    gin.clear_config()
-    gin.parse_config_file(f"{path2cfg}training.gin")
-    return TrainingConfig()
-
-