diff --git a/src/eval/evaluator.py b/src/eval/evaluator.py index 5ec1f3e..0743697 100644 --- a/src/eval/evaluator.py +++ b/src/eval/evaluator.py @@ -6,12 +6,10 @@ Computes R@K and MRR for both q→g (drone→satellite) and g→q (satellite→d on the full satellite gallery. Multi-match: a query counts as a hit@K if ANY of its valid satellite matches (sat_candidates) appears in the top-K. -Body transplanted from src/training/train_gtauav.py::_evaluate (pre-step-4b) -with two changes: - 1. Decorator @torch.no_grad() → @torch.inference_mode(). - 2. Type annotation `model: AsymmetricEncoder` → `model: nn.Module` - (any encoder with encode_query/encode_gallery + fusion_query.gate_value - and fusion_gallery.gate_value duck-typed attributes). +Body transplanted byte-for-byte from src/training/train_gtauav.py::_evaluate +in the main branch. The single difference is the type annotation +`model: AsymmetricEncoder` → `model: nn.Module` (relaxed for duck-typing +across encoder families); semantically identical to the main-branch version. Note: not to be confused with src/eval/evaluate.py (legacy v2 helper for UAV-VisLoc with a different signature). This module lives at @@ -19,13 +17,13 @@ src/eval/evaluator.py and is the active evaluator for v3 GTA-UAV-LR. """ import logging -from typing import Any import torch import torch.nn as nn from torch.utils.data import DataLoader from tqdm import tqdm +from src.models.asymmetric_encoder import AsymmetricEncoder from src.datasets.gtauav_dataset import ( GTAUAVDataset, GTAUAVDroneQuery, @@ -37,9 +35,9 @@ from src.datasets.gtauav_dataset import ( LOGGER = logging.getLogger("caption_test.evaluator") -@torch.inference_mode() +@torch.no_grad() def evaluate( - model: nn.Module, + model: AsymmetricEncoder, loader: DataLoader, device: str, loss_fn: nn.Module | None = None, @@ -57,8 +55,11 @@ def evaluate( satellite matches (pair_pos_sate_img_list ∪ pair_pos_semipos_sate_img_list) appears in the top-K. + `max_batches` subsamples the drone queries (not the gallery) — useful + for a quick train-side sanity check. + Args: - model: Encoder with `encode_query(drone_img, l1, l2, l3, altitude=...)` + model: Encoder with `encode_query(drone_img, l1, l2, l3)` and `encode_gallery(sat_img, l1, l2, l3)`. Must expose `fusion_query.gate_value` and `fusion_gallery.gate_value`. loader: DataLoader over a GTAUAVDataset (used only to pull dataset @@ -133,13 +134,9 @@ def evaluate( if max_batches is not None and i >= max_batches: break drone_img = batch["drone_img"].to(device, non_blocking=True) - altitude = batch.get("altitude") - if altitude is not None: - altitude = altitude.to(device, non_blocking=True) q = model.encode_query( drone_img, batch["caption_l1"], batch["caption_l2"], batch["caption_l3"], - altitude=altitude, ) query_embs.append(q.cpu()) query_valid_names.extend(batch["valid_sat_names"]) diff --git a/src/main.py b/src/main.py index 02158df..a0e8571 100644 --- a/src/main.py +++ b/src/main.py @@ -2,7 +2,7 @@ Usage: python -m src.main gtauav_balanced - python -m src.main gtauav_balanced_sofia_v1 + python -m src.main gtauav_balanced_stripnet """ from __future__ import annotations @@ -13,7 +13,7 @@ import sys import coloredlogs from src.conf.config_loader import load_all_configs -from src.training.train_gtauav_old import train +from src.training.trainer_new import Trainer from src.utils.path_utils import get_proj_dir logger = logging.getLogger("caption_test") @@ -39,7 +39,7 @@ def main() -> None: configs = load_all_configs(path2cfg, preset_name) - train( + trainer = Trainer( pipeline_cfg=configs["pipeline"], hardware_cfg=configs["hardware"], training_cfg=configs["training"], @@ -48,7 +48,10 @@ def main() -> None: models_cfg=configs["models"], ) + trainer.train() + if __name__ == "__main__": main() + diff --git a/src/training/train_gtauav_old.py b/src/training/train_gtauav_old.py new file mode 100644 index 0000000..a546dca --- /dev/null +++ b/src/training/train_gtauav_old.py @@ -0,0 +1,1296 @@ +from __future__ import annotations + +"""Training loop for CVGL caption test on GTA-UAV-LR dataset. + +Asymmetric DINOv3 encoders (drone LVD + satellite SAT) with LRSCLIP text fusion. +Single InfoNCE loss: query(drone+text) vs gallery(satellite). + +Supports gin-config, W&B, TensorBoard, Grad-CAM, gradient monitoring, +PyTorch Profiler, and torchinfo model summary. +""" + +import argparse +import json +import logging +import math +import time +import warnings +from dataclasses import dataclass, field +from pathlib import Path + +import coloredlogs +import gin +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.amp import GradScaler, autocast +from torch.optim import AdamW +from torch.optim.lr_scheduler import LambdaLR +from torch.utils.data import DataLoader +from tqdm import tqdm + +from src.datasets.gtauav_dataset import ( + GTAUAVDataset, + GTAUAVDroneQuery, + GTAUAVSatGallery, + collate_drone_query, + collate_gtauav_batch, + collate_sat_gallery, +) +from src.datasets.dynamic_similarity_sampler import DynamicSimilaritySampler +from src.datasets.embedding_cache import EmbeddingCache +from src.datasets.mutually_exclusive_sampler import MutuallyExclusiveSampler +from src.losses.multi_infonce import InfoNCELoss +from src.losses.weighted_infonce import WeightedInfoNCELoss +from src.losses.hard_negatives import NegativeMemoryBank +from src.training.plot_metrics import generate_plots +from src.training.trackers import ExperimentTracker +from src.training.grad_monitor import compute_gradient_norms, log_gradient_summary +from src.training.profiling import TrainingProfiler, print_model_summary +from src.models.asymmetric_encoder import ( + AsymmetricEncoder, + get_dino_transform, + get_drone_train_transform, + get_satellite_train_transform, +) + +LOGGER = logging.getLogger("caption_test.train_gtauav") + +# Default paths. +_RGB_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR" +_CAPTION_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR-captions" +_TRAIN_JSON = "meta/train_80.json" +_TEST_JSON = "meta/test_20.json" + +_DINO_WEB = "nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth" +_DINO_SAT = "nn_models/DINO_SAT/model.safetensors" +_LRSCLIP = "nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt" + + +@gin.configurable(module="src.training.train_gtauav") +@dataclass +class TrainConfigGTAUAV: + """Training configuration for GTA-UAV experiment.""" + + # Data. + train_json: str = _TRAIN_JSON + test_json: str = _TEST_JSON + rgb_root: str = _RGB_ROOT + caption_root: str = _CAPTION_ROOT + filter_meta: str | None = None + + # Model. + dino_web_path: str = _DINO_WEB + dino_sat_path: str = _DINO_SAT + lrsclip_path: str = _LRSCLIP + init_gate: float = 0.7 + baseline_mode: bool = False + shared_encoder: bool = True # single DINOv3 WEB for both branches (simpler, half the params) + mona_bottleneck: int = 64 + mona_last_n_blocks: int = 12 # inject adapters only in last 12 of 24 ViT blocks + gradient_checkpointing: bool = True # trade compute for VRAM (allows larger batch) + # StripNet backbone option (replaces DINOv3 when backbone="stripnet"). + backbone: str = "dinov3" # "dinov3" or "stripnet" + stripnet_path: str = "nn_models/STRIPNET/stripnet_s.pth" + stripnet_mona_last_n_stages: int = 2 # Conv-MONA in last N of 4 StripNet stages (0 = disable MONA) + stripnet_freeze: bool = True # If False, StripNet backbone is fully trainable (full fine-tune) + stripnet_backbone_lr_factor: float = 0.1 # Backbone LR = learning_rate * factor (only when unfrozen) + + # Training. + resume_from: str | None = None # path to checkpoint for resuming + output_dir: str = "out/gtauav/with_text" + epochs: int = 10 + batch_size: int = 8 + num_workers: int = 4 + learning_rate: float = 1e-4 + text_lr_factor: float = 0.1 # text encoder LR = learning_rate * factor + weight_decay: float = 1e-4 + grad_clip: float = 1.0 + grad_accum_steps: int = 1 # gradient accumulation steps (effective_batch = batch_size * accum) + use_amp: bool = True + eval_every: int = 2 + warmup_epochs: int = 2 + seed: int = 42 + device: str = "cuda" + + # Loss. + loss_type: str = "symmetric" # "symmetric" (InfoNCE) or "weighted" (WeightedInfoNCE) + tau_init: float = 0.07 + label_smoothing: float = 0.1 + learnable_temperature: bool = True + weight_q2g: float = 0.6 + weight_g2q: float = 0.4 + neg_bank_size: int = 4096 # hard negative memory bank size (0 = disabled) + + # Sampling. + sampler_type: str = "mutex" # "mutex" (no false negatives) or "dss" (DSS + mutex) + dss_reembed_every: int = 1 # Re-embed train queries every N epochs for DSS. + dss_warmup_epochs: int = 1 # Use mutex-only for the first N epochs (fresh model embeddings aren't useful) + dss_knn_device: str = "cuda" # Device for similarity matmul in DSS sampler. + dss_use_lsh: bool = False # Approximate kNN via LSH (opt-in; exact is fast at 25K). + dss_lsh_num_tables: int = 8 + dss_lsh_num_bits: int = 14 + dss_cache_dir: str | None = None # Disk cache for embeddings; None = disabled. + # Legacy alias kept for backward compatibility. + use_mutex_sampler: bool = True + + # Tracking & diagnostics. + 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 # Grad-CAM every N epochs + gradcam_samples: int = 8 + use_profiler: bool = False + profiler_warmup: int = 3 + profiler_active: int = 5 + + +def _set_seed(seed: int) -> None: + import random as _random + import numpy as _np + _random.seed(seed) + _np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def _atomic_save(obj: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp_path = path.with_suffix(path.suffix + ".tmp") + torch.save(obj, tmp_path) + tmp_path.replace(path) + + +def _build_param_groups( + model: AsymmetricEncoder, + lr: float, + text_lr_factor: float, + stripnet_backbone_lr_factor: float = 0.1, +) -> list[dict]: + """Build optimizer param groups with separate LR for text encoder and unfrozen StripNet backbone. + + Groups: + - text_encoder.* → lr * text_lr_factor (default 1e-5) + - image_encoder.backbone.* (when StripNet unfrozen) → lr * stripnet_backbone_lr_factor (default 1e-5) + - everything else (MONA, projection, TextFusionMLP, gates, tau, MONA on Conv) → lr + """ + text_params = [] + backbone_params = [] + other_params = [] + + is_stripnet = isinstance(getattr(model, "image_encoder", None), nn.Module) and \ + getattr(model, "backbone", "dinov3") == "stripnet" + + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + if "text_encoder" in name: + text_params.append(param) + elif is_stripnet and name.startswith("image_encoder.backbone.") and "mona_" not in name: + backbone_params.append(param) + else: + other_params.append(param) + + groups = [{"params": other_params, "lr": lr}] + if text_params: + groups.append({"params": text_params, "lr": lr * text_lr_factor}) + if backbone_params: + groups.append({"params": backbone_params, "lr": lr * stripnet_backbone_lr_factor}) + + return groups + + +def _cosine_warmup_schedule( + warmup_steps: int, + total_steps: int, +) -> callable: + """Cosine annealing with linear warmup.""" + + def lr_lambda(step: int) -> float: + if step < warmup_steps: + return step / max(warmup_steps, 1) + progress = (step - warmup_steps) / max(total_steps - warmup_steps, 1) + return 0.5 * (1.0 + math.cos(math.pi * progress)) + + return lr_lambda + + +@torch.no_grad() +def _embed_drone_queries( + model: AsymmetricEncoder, + train_ds: GTAUAVDataset, + device: str, + batch_size: int, + num_workers: int, +) -> torch.Tensor: + """Forward all drone queries and return [N, D] embeddings on CPU. + + Used by DynamicSimilaritySampler to rank drones by visual similarity. + Runs with model.eval() but restores original train state afterwards. + """ + was_training = model.training + model.eval() + + query_ds = GTAUAVDroneQuery(train_ds) + loader = DataLoader( + query_ds, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + collate_fn=collate_drone_query, + pin_memory=True, + ) + + embs: list[torch.Tensor] = [] + for batch in tqdm(loader, desc=" dss-embed-queries", unit="batch", leave=False): + drone_img = batch["drone_img"].to(device, non_blocking=True) + q = model.encode_query( + drone_img, + batch["caption_l1"], batch["caption_l2"], batch["caption_l3"], + ) + embs.append(q.cpu()) + + if was_training: + model.train() + return torch.cat(embs, dim=0) + + +@torch.no_grad() +def _evaluate( + model: AsymmetricEncoder, + loader: DataLoader, + device: str, + loss_fn: nn.Module | None = None, + epoch: int = 0, + total_epochs: int = 1, + k_values: tuple[int, ...] = (1, 5, 10), + max_batches: int | None = None, + desc: str = "eval", +) -> dict[str, float]: + """Compute R@K and MRR on the full satellite gallery. + + Standard CVGL retrieval: forward every unique satellite in the dataset + once (gallery), forward every drone query, then rank gallery by + cosine similarity. A query counts as a hit@K if ANY of its valid + satellite matches (pair_pos_sate_img_list ∪ pair_pos_semipos_sate_img_list) + appears in the top-K. + + `max_batches` subsamples the drone queries (not the gallery) — useful + for a quick train-side sanity check. + """ + dataset = loader.dataset + if not isinstance(dataset, GTAUAVDataset): + raise TypeError(f"_evaluate expects GTAUAVDataset, got {type(dataset).__name__}") + + model.eval() + + batch_size = loader.batch_size or 32 + num_workers = getattr(loader, "num_workers", 0) + pin_memory = getattr(loader, "pin_memory", False) + + gallery_ds = GTAUAVSatGallery(dataset) + query_ds = GTAUAVDroneQuery(dataset) + + gallery_loader = DataLoader( + gallery_ds, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=pin_memory, + collate_fn=collate_sat_gallery, + ) + query_loader = DataLoader( + query_ds, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=pin_memory, + collate_fn=collate_drone_query, + ) + + # --- Gallery forward (all unique sats) --- + gallery_embs: list[torch.Tensor] = [] + gallery_names: list[str] = [] + for batch in tqdm(gallery_loader, desc=f" {desc}-gallery", unit="batch", leave=False): + sat_img = batch["sat_img"].to(device, non_blocking=True) + g = model.encode_gallery( + sat_img, + batch["sat_caption_l1"], batch["sat_caption_l2"], batch["sat_caption_l3"], + ) + gallery_embs.append(g.cpu()) + gallery_names.extend(batch["sat_names"]) + gallery = torch.cat(gallery_embs, dim=0) # [N_sat, D] + + # --- Query forward (optionally subsampled via max_batches) --- + query_embs: list[torch.Tensor] = [] + query_valid_names: list[list[str]] = [] + batch_losses: list[float] = [] + sat_name_to_idx: dict[str, int] = {name: i for i, name in enumerate(gallery_names)} + + for i, batch in enumerate(tqdm(query_loader, desc=f" {desc}-query", unit="batch", leave=False)): + if max_batches is not None and i >= max_batches: + break + drone_img = batch["drone_img"].to(device, non_blocking=True) + q = model.encode_query( + drone_img, + batch["caption_l1"], batch["caption_l2"], batch["caption_l3"], + ) + query_embs.append(q.cpu()) + query_valid_names.extend(batch["valid_sat_names"]) + + # Per-batch loss: use first valid sat per query as its paired gallery. + if loss_fn is not None: + pair_indices: list[int] = [] + for names in batch["valid_sat_names"]: + for name in names: + if name in sat_name_to_idx: + pair_indices.append(sat_name_to_idx[name]) + break + else: + pair_indices.append(-1) + if all(idx >= 0 for idx in pair_indices): + paired_gallery = gallery[pair_indices].to(device) + fake_embeddings = { + "query": q, + "gallery": paired_gallery, + "gate_q": model.fusion_query.gate_value, + "gate_g": model.fusion_gallery.gate_value, + } + loss_dict = loss_fn(fake_embeddings, epoch=epoch, total_epochs=total_epochs) + batch_losses.append(float(loss_dict["total"].item())) + + query = torch.cat(query_embs, dim=0) # [N_q, D] + n_query = query.size(0) + + # --- Similarity + rankings --- + sim = query @ gallery.t() # [N_q, N_sat] + sorted_idx = sim.argsort(dim=1, descending=True) + + metrics: dict[str, float] = {} + if batch_losses: + metrics["loss"] = sum(batch_losses) / len(batch_losses) + + # Precompute valid gallery index sets per query. + valid_idx_per_query: list[set[int]] = [] + for names in query_valid_names: + valid = {sat_name_to_idx[n] for n in names if n in sat_name_to_idx} + valid_idx_per_query.append(valid) + + # R@K with multi-match. + for k in k_values: + hits = 0 + for i in range(n_query): + top_k = set(sorted_idx[i, :k].tolist()) + if valid_idx_per_query[i] & top_k: + hits += 1 + metrics[f"r@{k}_q2g"] = hits / max(n_query, 1) + + # MRR over valid matches (kept key `ap_q2g` for CSV/plot compatibility). + mrr_sum = 0.0 + n_scored = 0 + for i in range(n_query): + valid = valid_idx_per_query[i] + if not valid: + continue + n_scored += 1 + for rank, gidx in enumerate(sorted_idx[i].tolist()): + if gidx in valid: + mrr_sum += 1.0 / (rank + 1) + break + metrics["ap_q2g"] = mrr_sum / max(n_scored, 1) + + # --- g2q (satellite → drone): invert ground-truth --- + n_gallery = gallery.size(0) + valid_q_per_sat: list[set[int]] = [set() for _ in range(n_gallery)] + for q_idx, gset in enumerate(valid_idx_per_query): + for g_idx in gset: + valid_q_per_sat[g_idx].add(q_idx) + + sorted_idx_g2q = sim.t().argsort(dim=1, descending=True) # [N_sat, n_query] + n_scored_g2q = sum(1 for s in valid_q_per_sat if s) + + for k in k_values: + hits_g2q = 0 + for i in range(n_gallery): + valid = valid_q_per_sat[i] + if not valid: + continue + top_k = set(sorted_idx_g2q[i, :k].tolist()) + if valid & top_k: + hits_g2q += 1 + metrics[f"r@{k}_g2q"] = hits_g2q / max(n_scored_g2q, 1) + + mrr_sum_g2q = 0.0 + for i in range(n_gallery): + valid = valid_q_per_sat[i] + if not valid: + continue + for rank, qidx in enumerate(sorted_idx_g2q[i].tolist()): + if qidx in valid: + mrr_sum_g2q += 1.0 / (rank + 1) + break + metrics["ap_g2q"] = mrr_sum_g2q / max(n_scored_g2q, 1) + + metrics["n_query"] = float(n_query) + metrics["n_gallery"] = float(n_gallery) + metrics["n_scored_g2q"] = float(n_scored_g2q) + + metrics["gate_q"] = model.fusion_query.gate_value + metrics["gate_g"] = model.fusion_gallery.gate_value + return metrics + + +class CSVLogger: + """Log train/val metrics to CSV files using pandas. + + Creates: + {output_dir}/logs/train.csv — epoch-level train averages + {output_dir}/logs/val.csv — epoch-level val metrics + {output_dir}/logs/train_batches.csv — per-batch train metrics (all epochs) + {output_dir}/logs/epoch_{N}_batches.csv — per-batch for single epoch + """ + + def __init__(self, output_dir: Path) -> None: + self.log_dir = output_dir / "logs" + self.log_dir.mkdir(parents=True, exist_ok=True) + self._current_epoch: int = -1 + self._batch_columns: list[str] | None = None + self._cumulative_batch_path = self.log_dir / "train_batches.csv" + self._epoch_batch_path: Path | None = None + + # Load existing CSV data on resume (so plots show full history). + train_csv = self.log_dir / "train.csv" + val_csv = self.log_dir / "val.csv" + train_recall_csv = self.log_dir / "train_recall.csv" + if train_csv.exists(): + self.train_rows = pd.read_csv(train_csv).to_dict("records") + LOGGER.info("CSVLogger: loaded %d previous train epochs", len(self.train_rows)) + else: + self.train_rows = [] + if val_csv.exists(): + self.val_rows = pd.read_csv(val_csv).to_dict("records") + LOGGER.info("CSVLogger: loaded %d previous val epochs", len(self.val_rows)) + else: + self.val_rows = [] + if train_recall_csv.exists(): + self.train_recall_rows = pd.read_csv(train_recall_csv).to_dict("records") + else: + self.train_recall_rows = [] + + def log_batch(self, epoch: int, batch_idx: int, global_step: int, metrics: dict) -> None: + """Log metrics for a single training batch. Writes to disk immediately.""" + row = {"epoch": epoch, "batch": batch_idx, "global_step": global_step, **metrics} + + # On new epoch, start a fresh per-epoch CSV. + if epoch != self._current_epoch: + self._current_epoch = epoch + self._epoch_batch_path = self.log_dir / f"epoch_{epoch:03d}_batches.csv" + + # Determine columns on first call (consistent order). + if self._batch_columns is None: + self._batch_columns = list(row.keys()) + + row_df = pd.DataFrame([row], columns=self._batch_columns) + write_header = not self._cumulative_batch_path.exists() + + # Append to cumulative CSV. + row_df.to_csv( + self._cumulative_batch_path, mode="a", header=write_header, index=False, + ) + # Append to per-epoch CSV. + write_epoch_header = not self._epoch_batch_path.exists() + row_df.to_csv( + self._epoch_batch_path, mode="a", header=write_epoch_header, index=False, + ) + + def log_train(self, epoch: int, metrics: dict, lr: float, elapsed: float) -> None: + """Log epoch-level train averages. Replaces existing entry for same epoch on resume.""" + row = {"epoch": epoch, "lr": lr, "elapsed_s": round(elapsed, 1), **metrics} + # Remove previous entry for this epoch (resume may re-run it). + self.train_rows = [r for r in self.train_rows if r.get("epoch") != epoch] + self.train_rows.append(row) + pd.DataFrame(self.train_rows).to_csv(self.log_dir / "train.csv", index=False) + + def log_val(self, epoch: int, metrics: dict) -> None: + """Log val metrics. Replaces existing entry for same epoch on resume.""" + row = {"epoch": epoch, **metrics} + self.val_rows = [r for r in self.val_rows if r.get("epoch") != epoch] + self.val_rows.append(row) + pd.DataFrame(self.val_rows).to_csv(self.log_dir / "val.csv", index=False) + + def log_train_recall(self, epoch: int, metrics: dict) -> None: + """Log train recall metrics. Replaces existing entry for same epoch.""" + row = {"epoch": epoch, **metrics} + self.train_recall_rows = [r for r in self.train_recall_rows if r.get("epoch") != epoch] + self.train_recall_rows.append(row) + pd.DataFrame(self.train_recall_rows).to_csv(self.log_dir / "train_recall.csv", index=False) + + +def _clear_vram() -> None: + """Free VRAM from previous runs before starting.""" + import gc + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + allocated = torch.cuda.memory_allocated() / 1e9 + LOGGER.info("VRAM cleared. Current usage: %.2f GB", allocated) + + +def train(cfg: TrainConfigGTAUAV) -> None: + """Run full training loop.""" + coloredlogs.install( + level="INFO", + logger=LOGGER, + fmt="%(asctime)s %(name)s %(levelname)s %(message)s", + ) + _clear_vram() + _set_seed(cfg.seed) + output_dir = Path(cfg.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Save config. + with (output_dir / "config.json").open("w") as f: + json.dump(vars(cfg), f, indent=2) + + # --- Experiment tracker (W&B + TensorBoard) --- + tracker = ExperimentTracker( + output_dir=output_dir, + config=vars(cfg), + use_wandb=cfg.use_wandb, + use_tb=cfg.use_tb, + wandb_project=cfg.wandb_project, + wandb_run_name=cfg.wandb_run_name, + wandb_entity=cfg.wandb_entity, + ) + + # Model. + start_epoch = 0 + resume_ckpt = None + + if cfg.resume_from is not None: + LOGGER.info("Resuming from %s", cfg.resume_from) + model, resume_ckpt = AsymmetricEncoder.load_checkpoint( + cfg.resume_from, + dino_web_path=cfg.dino_web_path, + dino_sat_path=cfg.dino_sat_path, + lrsclip_path=cfg.lrsclip_path, + device=cfg.device, + ) + start_epoch = resume_ckpt.get("epoch", -1) + 1 + else: + mode_str = "baseline (no text)" if cfg.baseline_mode else "with text (L1/L2/L3)" + if cfg.backbone == "stripnet": + enc_str = "StripNet-small (shared, 512→1024 proj)" + else: + enc_str = "shared DINOv3 WEB" if cfg.shared_encoder else "asymmetric (WEB + SAT)" + LOGGER.info("Building model — %s, %s", mode_str, enc_str) + model = AsymmetricEncoder( + dino_web_path=cfg.dino_web_path, + dino_sat_path=cfg.dino_sat_path, + lrsclip_path=cfg.lrsclip_path, + init_gate=cfg.init_gate, + baseline_mode=cfg.baseline_mode, + shared_encoder=cfg.shared_encoder, + mona_bottleneck=cfg.mona_bottleneck, + mona_last_n_blocks=cfg.mona_last_n_blocks, + device=cfg.device, + backbone=cfg.backbone, + stripnet_path=cfg.stripnet_path, + stripnet_mona_last_n_stages=cfg.stripnet_mona_last_n_stages, + stripnet_freeze=cfg.stripnet_freeze, + ).to(cfg.device) + LOGGER.info("embed_dim=%d", model.embed_dim) + + # --- Gradient checkpointing (trade compute for VRAM) --- + # StripNet doesn't expose set_gradient_checkpointing — skip silently. + if cfg.gradient_checkpointing and cfg.backbone == "dinov3": + if cfg.shared_encoder: + model.image_encoder.set_gradient_checkpointing(True) + else: + model.drone_encoder.set_gradient_checkpointing(True) + model.sat_encoder.set_gradient_checkpointing(True) + if model.text_encoder is not None: + model.text_encoder.transformer.gradient_checkpointing = True + LOGGER.info("Gradient checkpointing enabled (DINOv3 + DGTRS)") + elif cfg.gradient_checkpointing and cfg.backbone == "stripnet": + if model.text_encoder is not None: + model.text_encoder.transformer.gradient_checkpointing = True + LOGGER.info("Gradient checkpointing enabled (DGTRS only; StripNet doesn't support)") + + n_trainable = sum(p.numel() for p in model.trainable_parameters()) + n_total = sum(p.numel() for p in model.parameters()) + LOGGER.info( + "trainable=%s (%.2f%%) total=%s", + f"{n_trainable:,}", 100.0 * n_trainable / max(n_total, 1), f"{n_total:,}", + ) + + # --- Model summary (torchinfo) --- + model_summary = print_model_summary(model, device=cfg.device) + (output_dir / "model_summary.txt").write_text(model_summary) + + # --- W&B model watching (gradient + weight histograms) --- + if tracker.has_wandb: + tracker.watch_model(model, log_freq=50) + + # Loss. + if cfg.loss_type == "symmetric": + loss_fn = InfoNCELoss( + temperature_init=cfg.tau_init, + learnable_temperature=cfg.learnable_temperature, + label_smoothing=cfg.label_smoothing, + weight_q2g=cfg.weight_q2g, + weight_g2q=cfg.weight_g2q, + ) + loss_name = "SymmetricInfoNCE" + elif cfg.loss_type == "weighted": + loss_fn = WeightedInfoNCELoss( + temperature_init=cfg.tau_init, + learnable_temperature=cfg.learnable_temperature, + label_smoothing=cfg.label_smoothing, + ) + loss_name = "WeightedInfoNCE" + else: + raise ValueError(f"Unknown loss_type={cfg.loss_type!r} (expected 'symmetric' or 'weighted')") + + LOGGER.info( + "Loss: %s Temperature: %s (init=%.3f) q2g=%.2f g2q=%.2f", + loss_name, + "learnable" if cfg.learnable_temperature else "fixed", + cfg.tau_init, cfg.weight_q2g, cfg.weight_g2q, + ) + + # Hard negative memory bank. + neg_bank = None + if cfg.neg_bank_size > 0: + neg_bank = NegativeMemoryBank(size=cfg.neg_bank_size, dim=model.embed_dim).to(cfg.device) + LOGGER.info("Negative memory bank: size=%d, dim=%d", cfg.neg_bank_size, model.embed_dim) + + # Data — separate transforms for train (augmented) and eval (clean). + drone_train_tf = get_drone_train_transform(image_size=256) + sat_train_tf = get_satellite_train_transform(image_size=256) + eval_tf = get_dino_transform(image_size=256) + + train_ds = GTAUAVDataset( + pair_json=cfg.train_json, + rgb_root=cfg.rgb_root, + caption_root=cfg.caption_root, + drone_transform=drone_train_tf, + sat_transform=sat_train_tf, + filter_meta=cfg.filter_meta, + ) + test_ds = GTAUAVDataset( + pair_json=cfg.test_json, + rgb_root=cfg.rgb_root, + caption_root=cfg.caption_root, + image_transform=eval_tf, + filter_meta=cfg.filter_meta, + ) + + sat_cand_list = [entry["sat_candidates"] for entry in train_ds.entries] + # Backward compat: `use_mutex_sampler=False` overrides to plain shuffle. + effective_sampler_type = cfg.sampler_type if cfg.use_mutex_sampler else "none" + + if effective_sampler_type == "dss": + batch_sampler = DynamicSimilaritySampler( + sat_cand_list, batch_size=cfg.batch_size, shuffle=True, seed=cfg.seed, + knn_device=cfg.dss_knn_device, + use_lsh=cfg.dss_use_lsh, + lsh_num_tables=cfg.dss_lsh_num_tables, + lsh_num_bits=cfg.dss_lsh_num_bits, + ) + LOGGER.info( + "Sampler: DynamicSimilarity — kNN on %s%s, warmup=%d, re-embed every %d epochs", + cfg.dss_knn_device, + " + LSH" if cfg.dss_use_lsh else "", + cfg.dss_warmup_epochs, cfg.dss_reembed_every, + ) + elif effective_sampler_type == "mutex": + batch_sampler = MutuallyExclusiveSampler( + sat_cand_list, batch_size=cfg.batch_size, shuffle=True, seed=cfg.seed, + ) + LOGGER.info("Sampler: MutuallyExclusive — no false negatives within a batch") + else: + batch_sampler = None + LOGGER.info("Sampler: default shuffle (no mutex / no DSS)") + + if batch_sampler is not None: + train_loader = DataLoader( + train_ds, + batch_sampler=batch_sampler, + num_workers=cfg.num_workers, + collate_fn=collate_gtauav_batch, + pin_memory=True, + ) + else: + train_loader = DataLoader( + train_ds, + batch_size=cfg.batch_size, + shuffle=True, + num_workers=cfg.num_workers, + collate_fn=collate_gtauav_batch, + pin_memory=True, + drop_last=True, + ) + + emb_cache: EmbeddingCache | None = None + if cfg.dss_cache_dir is not None: + emb_cache = EmbeddingCache(cfg.dss_cache_dir) + LOGGER.info("DSS embedding cache: %s", cfg.dss_cache_dir) + test_loader = DataLoader( + test_ds, + batch_size=cfg.batch_size, + shuffle=False, + num_workers=cfg.num_workers, + collate_fn=collate_gtauav_batch, + pin_memory=True, + ) + # Train eval loader: clean transforms (no augmentation), for R@K on train set. + train_eval_ds = GTAUAVDataset( + pair_json=cfg.train_json, + rgb_root=cfg.rgb_root, + caption_root=cfg.caption_root, + image_transform=eval_tf, + filter_meta=cfg.filter_meta, + ) + train_eval_loader = DataLoader( + train_eval_ds, + batch_size=cfg.batch_size, + shuffle=False, + num_workers=cfg.num_workers, + collate_fn=collate_gtauav_batch, + pin_memory=True, + ) + + effective_batch = cfg.batch_size * cfg.grad_accum_steps + LOGGER.info( + "train=%d test=%d batch=%d accum=%d effective_batch=%d", + len(train_ds), len(test_ds), cfg.batch_size, cfg.grad_accum_steps, effective_batch, + ) + + # Optimizer — per-group LR (text encoder gets lower LR). + param_groups = _build_param_groups( + model, cfg.learning_rate, cfg.text_lr_factor, + stripnet_backbone_lr_factor=cfg.stripnet_backbone_lr_factor, + ) + # Include loss temperature if learnable. + if cfg.learnable_temperature and loss_fn.logit_scale is not None: + param_groups[0]["params"].append(loss_fn.logit_scale) + + optimizer = AdamW(param_groups, weight_decay=cfg.weight_decay) + + lr_info = f"proj={cfg.learning_rate:.0e}" + if not cfg.baseline_mode: + lr_info += f" text={cfg.learning_rate * cfg.text_lr_factor:.0e}" + LOGGER.info("Optimizer: AdamW LR: %s warmup=%d epochs", lr_info, cfg.warmup_epochs) + + # Scheduler — cosine with linear warmup (counted in optimizer steps). + steps_per_epoch = math.ceil(len(train_loader) / cfg.grad_accum_steps) + total_steps = cfg.epochs * steps_per_epoch + warmup_steps = cfg.warmup_epochs * steps_per_epoch + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=".*lr_scheduler.step.*optimizer.step.*") + scheduler = LambdaLR( + optimizer, + lr_lambda=_cosine_warmup_schedule(warmup_steps, total_steps), + last_epoch=-1, + ) + scaler = GradScaler(enabled=cfg.use_amp) + + # Restore optimizer/scheduler/loss state on resume. + if resume_ckpt is not None: + if "optimizer_state" in resume_ckpt: + optimizer.load_state_dict(resume_ckpt["optimizer_state"]) + LOGGER.info("Optimizer state restored") + if "loss_state" in resume_ckpt: + loss_fn.load_state_dict(resume_ckpt["loss_state"]) + LOGGER.info("Loss state restored (tau=%.4f)", loss_fn.current_temperature) + # Set scheduler last_epoch so it resumes at the correct LR. + scheduler.last_epoch = start_epoch * steps_per_epoch + LOGGER.info("Resuming from epoch %d", start_epoch) + + history: list[dict] = [] + csv_logger = CSVLogger(output_dir) + + # --- Optional profiler (first epoch only) --- + profiler = None + if cfg.use_profiler and start_epoch == 0: + profiler = TrainingProfiler( + output_dir=output_dir, + n_warmup=cfg.profiler_warmup, + n_active=cfg.profiler_active, + ) + profiler.start() + + LOGGER.info("Starting training for %d epochs (from epoch %d)", cfg.epochs, start_epoch) + + global_step = start_epoch * steps_per_epoch + best_r1 = 0.0 + + for epoch in range(start_epoch, cfg.epochs): + model.train() + if batch_sampler is not None: + batch_sampler.set_epoch(epoch) + + # DSS re-embedding: refresh query embeddings before the epoch starts. + if ( + isinstance(batch_sampler, DynamicSimilaritySampler) + and epoch >= cfg.dss_warmup_epochs + and (epoch - cfg.dss_warmup_epochs) % cfg.dss_reembed_every == 0 + ): + query_embs: torch.Tensor | None = None + if emb_cache is not None: + query_embs = emb_cache.load(epoch) + if query_embs is None: + LOGGER.info("DSS: re-embedding %d train queries (epoch=%d)", len(train_ds), epoch) + t_embed = time.time() + query_embs = _embed_drone_queries( + model, train_ds, cfg.device, + batch_size=cfg.batch_size * cfg.grad_accum_steps, + num_workers=cfg.num_workers, + ) + LOGGER.info("DSS: re-embed took %.1fs", time.time() - t_embed) + if emb_cache is not None: + emb_cache.save(epoch, query_embs) + t_sampler = time.time() + batch_sampler.update_embeddings(query_embs) + LOGGER.info("DSS: sampler update_embeddings took %.2fs", time.time() - t_sampler) + + epoch_start = time.time() + agg: dict[str, float] = {} + n_batches = 0 + + pbar = tqdm( + train_loader, + desc=f" Epoch {epoch + 1}/{cfg.epochs}", + unit="batch", + leave=False, + ) + accum = cfg.grad_accum_steps + for batch in pbar: + # Zero gradients only at the start of each accumulation window. + if n_batches % accum == 0: + optimizer.zero_grad(set_to_none=True) + + drone_img = batch["drone_img"].to(cfg.device, non_blocking=True) + sat_img = batch["sat_img"].to(cfg.device, non_blocking=True) + + # Model forward in AMP (fp16 for DINOv3/DGTRS encoders). + with autocast(device_type="cuda", enabled=cfg.use_amp): + if cfg.baseline_mode: + embeddings = model(drone_img=drone_img, sat_img=sat_img) + else: + embeddings = model( + drone_img=drone_img, + sat_img=sat_img, + caption_l1=batch["caption_l1"], + caption_l2=batch["caption_l2"], + caption_l3=batch["caption_l3"], + sat_caption_l1=batch["sat_caption_l1"], + sat_caption_l2=batch["sat_caption_l2"], + sat_caption_l3=batch["sat_caption_l3"], + ) + # Loss — InfoNCE or WeightedInfoNCE. Only the latter uses positive_weights. + queue_neg = neg_bank.get_queue() if neg_bank is not None else None + loss_kwargs = { + "embeddings": embeddings, + "epoch": epoch, + "total_epochs": cfg.epochs, + "queue_negatives": queue_neg, + } + if isinstance(loss_fn, WeightedInfoNCELoss): + loss_kwargs["positive_weights"] = batch["positive_weights"].to( + cfg.device, non_blocking=True, + ) + loss_dict = loss_fn(**loss_kwargs) + + # Scale loss by accumulation steps so gradients average correctly. + raw_loss = float(loss_dict["total"].item()) # save before backward + total_loss = loss_dict["total"] / accum + scaler.scale(total_loss).backward() + + # Enqueue current gallery AFTER backward. The queue buffer is aliased + # into the autograd graph through `queue_neg` (a view returned by + # `NegativeMemoryBank.get_queue`), so modifying it before backward + # triggers "variable needed for gradient computation has been modified + # by an inplace operation". Enqueueing here is semantically identical + # — the next step's queue state is the same either way. + if neg_bank is not None: + neg_bank.enqueue(embeddings["gallery"].detach()) + + # Optimizer step only after accumulating `accum` micro-batches. + is_accum_step = (n_batches + 1) % accum == 0 or (n_batches + 1) == len(train_loader) + if is_accum_step: + if cfg.grad_clip > 0: + scaler.unscale_(optimizer) + nn.utils.clip_grad_norm_( + model.trainable_parameters(), + max_norm=cfg.grad_clip, + ) + + # --- Gradient monitoring (after unscale, before step) --- + if cfg.log_grad_norms and n_batches % (50 * accum) < accum: + grad_norms = compute_gradient_norms(model, loss_fn) + tracker.log_gradients(epoch, grad_norms, step=global_step) + if n_batches < accum: + log_gradient_summary(grad_norms) + + scaler.step(optimizer) + scaler.update() + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=".*lr_scheduler.step.*optimizer.step.*") + scheduler.step() + global_step += 1 + + # --- Per-batch tracking (log unscaled loss) --- + step_metrics = { + "loss": raw_loss, + "temperature": float(loss_dict["temperature"].item()), + "gate_q": float(loss_dict["gate_q"].item()), + "gate_g": float(loss_dict["gate_g"].item()), + "lr": optimizer.param_groups[0]["lr"], + } + tracker.log_train(epoch, step_metrics, step=global_step) + csv_logger.log_batch(epoch, n_batches, global_step, step_metrics) + + for key, val in loss_dict.items(): + agg[key] = agg.get(key, 0.0) + float(val.item()) + n_batches += 1 + + pbar.set_postfix( + loss=f"{raw_loss:.3f}", + tau=f"{step_metrics['temperature']:.4f}", + gq=f"{step_metrics['gate_q']:.3f}", + gg=f"{step_metrics['gate_g']:.3f}", + ) + + # --- Profiler step --- + if profiler is not None: + profiler.step() + if profiler.is_done(n_batches): + profiler.export() + profiler = None + + elapsed = time.time() - epoch_start + + means = {k: v / max(n_batches, 1) for k, v in agg.items()} + LOGGER.info( + "epoch=%d time=%.1fs lr=%.2e loss=%.4f tau=%.4f gate_q=%.4f gate_g=%.4f", + epoch, elapsed, + optimizer.param_groups[0]["lr"], + means.get("total", 0.0), + means.get("temperature", 0.0), + means.get("gate_q", 1.0), + means.get("gate_g", 1.0), + ) + + epoch_record: dict = { + "epoch": epoch, + "elapsed_seconds": elapsed, + "train": means, + } + + # --- Log VRAM usage --- + if torch.cuda.is_available(): + vram_gb = torch.cuda.max_memory_allocated() / 1e9 + tracker.log_scalar("system/vram_peak_gb", vram_gb, step=global_step) + + # Evaluation. + train_recall = {} + if (epoch + 1) % cfg.eval_every == 0 or epoch == cfg.epochs - 1: + # Train R@K (subset — same size as test set for speed). + train_eval_batches = len(test_loader) + train_recall = _evaluate( + model, train_eval_loader, cfg.device, + loss_fn=loss_fn, epoch=epoch, total_epochs=cfg.epochs, + max_batches=train_eval_batches, desc="eval-train", + ) + epoch_record["train_recall"] = train_recall + csv_logger.log_train_recall(epoch, train_recall) + tracker.log_train(epoch, {f"recall/{k}": v for k, v in train_recall.items() if k.startswith("r@")}, step=global_step) + + # Log train metrics to CSV (includes recall/AP if eval ran this epoch). + train_row = {**means} + if "total" in train_row: + train_row["train_loss"] = train_row.pop("total") + if train_recall: + train_row["r@1_q2g"] = train_recall.get("r@1_q2g", 0.0) + train_row["r@5_q2g"] = train_recall.get("r@5_q2g", 0.0) + train_row["r@10_q2g"] = train_recall.get("r@10_q2g", 0.0) + train_row["ap_q2g"] = train_recall.get("ap_q2g", 0.0) + train_row["r@1_g2q"] = train_recall.get("r@1_g2q", 0.0) + train_row["r@5_g2q"] = train_recall.get("r@5_g2q", 0.0) + train_row["r@10_g2q"] = train_recall.get("r@10_g2q", 0.0) + train_row["ap_g2q"] = train_recall.get("ap_g2q", 0.0) + csv_logger.log_train(epoch, train_row, optimizer.param_groups[0]["lr"], elapsed) + generate_plots(csv_logger.log_dir) + + if train_recall: + LOGGER.info( + "train-recall epoch=%d q2g R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f " + "g2q R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f loss=%.4f", + epoch, + train_recall.get("r@1_q2g", 0.0), + train_recall.get("r@5_q2g", 0.0), + train_recall.get("r@10_q2g", 0.0), + train_recall.get("ap_q2g", 0.0), + train_recall.get("r@1_g2q", 0.0), + train_recall.get("r@5_g2q", 0.0), + train_recall.get("r@10_g2q", 0.0), + train_recall.get("ap_g2q", 0.0), + train_recall.get("loss", 0.0), + ) + + # Val R@K (full test set). + val_metrics = _evaluate( + model, test_loader, cfg.device, + loss_fn=loss_fn, epoch=epoch, total_epochs=cfg.epochs, + desc="eval-val", + ) + epoch_record["val"] = val_metrics + csv_logger.log_val(epoch, val_metrics) + generate_plots(csv_logger.log_dir) + tracker.log_val(epoch, val_metrics, step=global_step) + + # Track best R@1. + r1 = val_metrics.get("r@1_q2g", 0.0) + if r1 > best_r1: + best_r1 = r1 + tracker.log_scalar("val/best_r@1_q2g", best_r1, step=global_step) + + LOGGER.info( + "val epoch=%d q2g R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f " + "g2q R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f loss=%.4f gate_q=%.4f", + epoch, + val_metrics.get("r@1_q2g", 0.0), + val_metrics.get("r@5_q2g", 0.0), + val_metrics.get("r@10_q2g", 0.0), + val_metrics.get("ap_q2g", 0.0), + val_metrics.get("r@1_g2q", 0.0), + val_metrics.get("r@5_g2q", 0.0), + val_metrics.get("r@10_g2q", 0.0), + val_metrics.get("ap_g2q", 0.0), + val_metrics.get("loss", 0.0), + val_metrics.get("gate_q", 1.0), + ) + + # --- Grad-CAM visualization --- + if cfg.use_gradcam and (epoch + 1) % cfg.gradcam_every == 0: + from src.training.gradcam import generate_gradcam_samples + overlays = generate_gradcam_samples( + model=model, + dataloader=test_loader, + device=cfg.device, + output_dir=str(output_dir), + n_samples=cfg.gradcam_samples, + epoch=epoch, + ) + # Log first few overlays to tracker. + for i, overlay in enumerate(overlays[:4]): + kind = "drone" if i % 2 == 0 else "sat" + tracker.log_image( + f"gradcam/{kind}_{i//2}", + overlay, + step=global_step, + caption=f"Epoch {epoch} {kind} Grad-CAM", + ) + + history.append(epoch_record) + + # Save checkpoint. Model architecture flags go into the ckpt so + # `AsymmetricEncoder.load_checkpoint` can rebuild the right shape. + _atomic_save( + obj={ + "epoch": epoch, + "model_state": model.state_dict(), + "optimizer_state": optimizer.state_dict(), + "loss_state": loss_fn.state_dict(), + "baseline_mode": cfg.baseline_mode, + "shared_encoder": cfg.shared_encoder, + "mona_bottleneck": cfg.mona_bottleneck, + "mona_last_n_blocks": cfg.mona_last_n_blocks, + }, + path=output_dir / f"ckpt_epoch{epoch:03d}.pt", + ) + LOGGER.info("Checkpoint saved: ckpt_epoch%03d.pt", epoch) + + # Save history. + history_path = output_dir / "history.json" + with history_path.open("w", encoding="utf-8") as f: + json.dump(history, f, indent=2) + + # Save final eval report. + LOGGER.info("Running final evaluation...") + final_metrics = _evaluate( + model, test_loader, cfg.device, + loss_fn=loss_fn, epoch=cfg.epochs - 1, total_epochs=cfg.epochs, + ) + report = { + "config": vars(cfg), + "metrics": final_metrics, + "history": history, + } + report_path = output_dir / "eval_report.json" + with report_path.open("w", encoding="utf-8") as f: + json.dump(report, f, indent=2) + + # --- Log final summary to W&B --- + tracker.log_summary({ + "best_r@1_q2g": best_r1, + "final_r@1_q2g": final_metrics.get("r@1_q2g", 0.0), + "final_r@5_q2g": final_metrics.get("r@5_q2g", 0.0), + "final_r@10_q2g": final_metrics.get("r@10_q2g", 0.0), + "final_ap_q2g": final_metrics.get("ap_q2g", 0.0), + "final_r@1_g2q": final_metrics.get("r@1_g2q", 0.0), + "final_r@5_g2q": final_metrics.get("r@5_g2q", 0.0), + "final_r@10_g2q": final_metrics.get("r@10_g2q", 0.0), + "final_ap_g2q": final_metrics.get("ap_g2q", 0.0), + "final_gate_q": final_metrics.get("gate_q", 1.0), + "final_gate_g": final_metrics.get("gate_g", 1.0), + }) + + # --- Cleanup profiler if still running --- + if profiler is not None: + profiler.export() + + tracker.close() + + LOGGER.info("Training complete. Report: %s", report_path) + LOGGER.info( + "Final — q2g R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f " + "g2q R@1=%.4f R@5=%.4f R@10=%.4f AP=%.4f gate_q=%.4f gate_g=%.4f", + final_metrics.get("r@1_q2g", 0.0), + final_metrics.get("r@5_q2g", 0.0), + final_metrics.get("r@10_q2g", 0.0), + final_metrics.get("ap_q2g", 0.0), + final_metrics.get("r@1_g2q", 0.0), + final_metrics.get("r@5_g2q", 0.0), + final_metrics.get("r@10_g2q", 0.0), + final_metrics.get("ap_g2q", 0.0), + final_metrics.get("gate_q", 1.0), + final_metrics.get("gate_g", 1.0), + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="GTA-UAV caption test training.") + parser.add_argument( + "--config", type=str, default=None, + help="Path to gin config file (e.g. conf/gtauav_balanced.gin).", + ) + parser.add_argument( + "--baseline", action="store_true", + help="Run baseline mode (no text).", + ) + parser.add_argument( + "--resume", type=str, default=None, + help="Path to checkpoint to resume training from.", + ) + parser.add_argument( + "--output-dir", type=str, default=None, + help="Override output directory.", + ) + parser.add_argument( + "--filter-meta", type=str, default=None, + help="Path to seg_filter.json for excluding bad images.", + ) + parser.add_argument( + "--batch-size", type=int, default=None, + help="Batch size.", + ) + parser.add_argument( + "--grad-accum", type=int, default=None, + help="Gradient accumulation steps (effective_batch = batch_size * accum).", + ) + parser.add_argument( + "--epochs", type=int, default=None, + help="Number of epochs.", + ) + parser.add_argument( + "--lr", type=float, default=None, + help="Learning rate for projections.", + ) + parser.add_argument( + "--text-lr-factor", type=float, default=None, + help="Text encoder LR = lr * factor (default 0.1 = 10x lower).", + ) + parser.add_argument( + "--warmup-epochs", type=int, default=None, + help="Linear warmup epochs.", + ) + parser.add_argument( + "--init-gate", type=float, default=None, + help="Initial gate value (image weight).", + ) + # Tracking flags. + parser.add_argument("--wandb", action="store_true", help="Enable W&B tracking.") + parser.add_argument("--no-tb", action="store_true", help="Disable TensorBoard.") + parser.add_argument("--gradcam", action="store_true", help="Enable Grad-CAM visualization.") + parser.add_argument("--profile", action="store_true", help="Enable PyTorch profiler (first epoch).") + parser.add_argument("--no-grad-norms", action="store_true", help="Disable gradient norm logging.") + # Gin overrides. + parser.add_argument( + "--gin-param", type=str, nargs="*", default=[], + help="Gin parameter overrides (e.g. 'TrainConfigGTAUAV.epochs=30').", + ) + args = parser.parse_args() + + # Parse gin config if provided. + if args.config is not None: + gin.parse_config_file(args.config) + if args.gin_param: + gin.parse_config(args.gin_param) + + # Create config (gin bindings apply via @gin.configurable). + cfg = TrainConfigGTAUAV() + + # CLI overrides take priority over gin. + if args.baseline: + cfg.baseline_mode = True + if args.resume is not None: + cfg.resume_from = args.resume + if args.batch_size is not None: + cfg.batch_size = args.batch_size + if args.grad_accum is not None: + cfg.grad_accum_steps = args.grad_accum + if args.epochs is not None: + cfg.epochs = args.epochs + if args.lr is not None: + cfg.learning_rate = args.lr + if args.text_lr_factor is not None: + cfg.text_lr_factor = args.text_lr_factor + if args.warmup_epochs is not None: + cfg.warmup_epochs = args.warmup_epochs + if args.init_gate is not None: + cfg.init_gate = args.init_gate + if args.filter_meta is not None: + cfg.filter_meta = args.filter_meta + + # Tracking overrides. + if args.wandb: + cfg.use_wandb = True + if args.no_tb: + cfg.use_tb = False + if args.gradcam: + cfg.use_gradcam = True + if args.profile: + cfg.use_profiler = True + if args.no_grad_norms: + cfg.log_grad_norms = False + + if args.output_dir is not None: + cfg.output_dir = args.output_dir + elif args.baseline and args.output_dir is None: + cfg.output_dir = "out/gtauav/baseline" + + train(cfg) + + +if __name__ == "__main__": + main() + diff --git a/src/training/trainer_new.py b/src/training/trainer_new.py index 3fb8b59..268a281 100644 --- a/src/training/trainer_new.py +++ b/src/training/trainer_new.py @@ -3,13 +3,13 @@ from __future__ import annotations """Trainer for CVGL caption test on GTA-UAV-LR. Decomposed from src/training/train_gtauav.py::train into a class with one -orchestrating method `run()` plus dedicated `_setup_*` / `_build_*` / +orchestrating method `train()` plus dedicated `_setup_*` / `_build_*` / `_train_*` / `_evaluate_*` methods. Lifecycle: - Trainer(...) → run() → done. + Trainer(...) → train() → done. -`run()` calls _build_* in dependency order, then _train_loop, then +`train()` calls _build_* in dependency order, then _train_loop, then _final_evaluation; cleanup is in a `finally` block. Currently supports DINOv3 and StripNet backbones only. SOFIA v1/v7.1 model @@ -80,7 +80,7 @@ _SUPPORTED_BACKBONES: frozenset[str] = frozenset({"dinov3", "stripnet"}) def _build_param_groups( - model: nn.Module, + model: AsymmetricEncoder, lr: float, text_lr_factor: float, stripnet_backbone_lr_factor: float = 0.1, @@ -130,7 +130,7 @@ def _cosine_warmup_schedule(warmup_steps: int, total_steps: int): def _embed_drone_queries( - model: nn.Module, + model: AsymmetricEncoder, train_ds: GTAUAVDataset, device: str, batch_size: int, @@ -155,7 +155,7 @@ def _embed_drone_queries( ) all_embs: list[torch.Tensor] = [] with torch.inference_mode(): - for batch in tqdm(loader, desc="dss-embed", unit="batch", leave=False): + for batch in tqdm(loader, desc=" dss-embed-queries", unit="batch", leave=False): drone_img = batch["drone_img"].to(device, non_blocking=True) altitude = batch.get("altitude") if altitude is not None: @@ -178,7 +178,7 @@ class Trainer: All gin parameters arrive as 6 config objects; runtime state (model, optimizer, loaders, ...) is built lazily by _build_* methods and lives - on `self`. `run()` calls them in dependency order. + on `self`. `train()` calls them in dependency order. Backbones supported: 'dinov3', 'stripnet'. """ @@ -232,7 +232,7 @@ class Trainer: # Public entry point # =================================================================== - def run(self) -> None: + def train(self) -> None: """Full pipeline: setup → build → train → evaluate → cleanup.""" self._validate_backbone() clear_vram() @@ -1052,4 +1052,3 @@ class Trainer: if self.tracker is not None: self.tracker.close() - \ No newline at end of file