Add DynamicSimilaritySampler — embedding-kNN batches with mutex constraint

Batches assembled from visually-similar drone queries pressure the model to
learn finer discriminative features. Random mutex batches average ~0.26
pairwise cosine similarity in query embedding space; DSS batches average
~0.71 — confirming the lookalikes grouping works as intended.

Algorithm per batch:
  1. Pick a random seed drone from the remaining pool.
  2. Rank the entire remaining pool by cosine similarity to the seed.
  3. Walk the ranking in descending order; add items whose sat_candidates
     don't collide with the batch's already-claimed set.
  4. Drop the seed if no valid batch can be assembled (rare mutex deadlock).

Inherits MutuallyExclusiveSampler semantics — no false negatives. Degrades
gracefully to mutex-only when no embeddings are set (warmup epochs, or if
`sampler_type="mutex"` is chosen).

Integration in `train_gtauav.py`:
  - New `_embed_drone_queries` helper: model.encode_query forwarded over
    GTAUAVDroneQuery, returns [N, D] CPU tensor. ~13s per 1024 queries on
    a 4090 → ~5 min for the full 25K train set.
  - Epoch loop re-embeds every `dss_reembed_every` epochs after a `dss_warmup_epochs`
    warmup (first epochs use mutex-only since untrained embeddings aren't
    informative for kNN).
  - Config: `sampler_type` ∈ {"mutex", "dss"}. Default flipped to "dss".

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

conf/gtauav_balanced.gin

@@ -35,7 +35,10 @@ TrainConfigGTAUAV.weight_g2q = 0.4
 TrainConfigGTAUAV.neg_bank_size = 4096
 
 # ---- Sampling ----
-TrainConfigGTAUAV.use_mutex_sampler = True
+TrainConfigGTAUAV.sampler_type = "dss"  # "dss" or "mutex"
+TrainConfigGTAUAV.dss_warmup_epochs = 1  # first N epochs use mutex-only (untrained embeds not useful)
+TrainConfigGTAUAV.dss_reembed_every = 1
+TrainConfigGTAUAV.use_mutex_sampler = True  # legacy flag, kept True unless disabling both samplers
 
 # ---- Output ----
 TrainConfigGTAUAV.output_dir = "out/gtauav/with_text"