a499fcfd65
Six critical fixes to the caption-test training/eval stack:
1. **IndentationError blocker** (train_gtauav.py:765-766)
Unparseable file — train-recall LOGGER.info block was orphaned outside
its `if eval_every` guard. Wrapped in `if train_recall:` so val eval
and Grad-CAM only run on eval epochs.
2. **Full satellite gallery in `_evaluate`**
Old code assembled gallery from DataLoader batches (one random sat per
drone), producing an incomplete gallery of size ≈ N_query instead of
N_unique_sat. Metrics were inflated because retrieval was against a
subset that always contained the target.
New `GTAUAVSatGallery` / `GTAUAVDroneQuery` iterate all unique tiles
and queries independently; full-gallery multi-match R@K + MRR.
3. **Per-sample caption mask** (`AsymmetricEncoder._fuse_with_mask`)
Mixed batches (some samples have captions, some don't) previously
encoded empty strings through DGTRS and mixed the noise output into
every sample via scalar gate. New `encode_query`/`encode_gallery` use
`torch.where` to fall back to pure image features for empty-caption
samples. Training `forward()` routes through the same helper so
training and eval share code.
4. **Symmetric InfoNCE as primary loss** (multi_infonce.InfoNCELoss)
Switched gin default from `WeightedInfoNCELoss` (adaptive label
smoothing — not the Game4Loc soft-IoU target it claimed) to the
existing symmetric InfoNCE with q2g=0.6/g2q=0.4 weighting. Loss type
now selectable via `cfg.loss_type ∈ {"symmetric", "weighted"}`.
5. **MutuallyExclusiveSampler** (new file)
BatchSampler that greedily packs drones whose `sat_candidates` sets
are pairwise disjoint within a batch. Eliminates false negatives from
the semi-positive graph without needing soft-label losses.
At bs=8 keeps 100% of 24,891 train entries; at bs=64 keeps 92.6%.
`set_epoch()` for reproducibility + different batches per epoch.
6. **Temperature clamp [0.01, 0.1]** (both loss modules)
Old tau_max=0.5 allowed the logit distribution to collapse into a
near-uniform softmax. Tightened to the CLIP-standard range.
Also:
- Added `scripts/smoke_eval.py` / `scripts/smoke_train.py` for fast
regression checks (eval in ~2 min, 2 train steps in ~1 min on RTX 4090).
- CLAUDE.md updated to reflect the new pipeline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
"""Minimal training smoke test: 2 batches forward+backward.
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Verifies end-to-end that MutuallyExclusiveSampler + InfoNCELoss +
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per-sample caption masking compose correctly for training.
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"""
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import torch
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from torch.utils.data import DataLoader
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from src.datasets.gtauav_dataset import GTAUAVDataset, collate_gtauav_batch
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from src.datasets.mutually_exclusive_sampler import MutuallyExclusiveSampler
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from src.losses.multi_infonce import InfoNCELoss
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from src.models.asymmetric_encoder import AsymmetricEncoder, get_dino_transform
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CKPT = "out/gtauav/with_text/ckpt_epoch005.pt"
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def main() -> None:
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model, _ = AsymmetricEncoder.load_checkpoint(CKPT, device="cuda")
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model.train()
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tf = get_dino_transform(image_size=256)
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ds = GTAUAVDataset(
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pair_json="meta/train_80.json",
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filter_meta="meta/seg_filter.json",
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drone_transform=tf,
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sat_transform=tf,
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)
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sampler = MutuallyExclusiveSampler(
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[e["sat_candidates"] for e in ds.entries],
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batch_size=8, shuffle=True, seed=42,
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)
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sampler.set_epoch(0)
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loader = DataLoader(
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ds, batch_sampler=sampler, num_workers=2,
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collate_fn=collate_gtauav_batch, pin_memory=True,
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)
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loss_fn = InfoNCELoss(
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temperature_init=0.07, learnable_temperature=True,
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label_smoothing=0.1, weight_q2g=0.6, weight_g2q=0.4,
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tau_min=0.01, tau_max=0.1,
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).to("cuda")
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trainable = [p for p in model.trainable_parameters()] + list(loss_fn.parameters())
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opt = torch.optim.AdamW(trainable, lr=1e-4)
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it = iter(loader)
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for step in range(2):
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batch = next(it)
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opt.zero_grad()
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emb = model(
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drone_img=batch["drone_img"].to("cuda", non_blocking=True),
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sat_img=batch["sat_img"].to("cuda", non_blocking=True),
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caption_l1=batch["caption_l1"], caption_l2=batch["caption_l2"], caption_l3=batch["caption_l3"],
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sat_caption_l1=batch["sat_caption_l1"], sat_caption_l2=batch["sat_caption_l2"], sat_caption_l3=batch["sat_caption_l3"],
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)
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out = loss_fn(emb, epoch=0, total_epochs=10)
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out["total"].backward()
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opt.step()
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# Verify mutual exclusion in batch
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batch_sats = [set(ds.entries[i]["sat_candidates"]) for i in batch.get("__indices__", range(8))]
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# We can also check via sat_names (one sat per drone sampled)
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sat_names = batch["sat_names"]
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print(
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f" step {step}: loss={out['total'].item():.4f} "
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f"tau={out['temperature'].item():.4f} "
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f"gate_q={out['gate_q'].item():.3f} gate_g={out['gate_g'].item():.3f} "
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f"n_drone_caps={sum(1 for t in batch['caption_l1'] if t)} "
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f"n_sat_caps={sum(1 for t in batch['sat_caption_l1'] if t)}"
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)
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assert torch.isfinite(out["total"]).all(), "Loss not finite!"
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print("OK: 2 train steps completed with finite loss")
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if __name__ == "__main__":
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main()
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