# GTA-UAV Baseline: no text fusion (gate forced to 1.0).
# query = drone_only -> InfoNCE vs satellite
# Reference R@1 for delta computation.
#
# Diagnostic mode (2026-04-24): DSS and hard-negative mining disabled after
# the previous run collapsed (R@1 = 0.6% at epoch 8, train loss growing).
# Hypothesis: DSS packs visually-identical drones at bs=8 and the hard-mining
# queue amplifies that hardness — together they prevent convergence from a
# nearly-random start. Run with mutex-only sampling and the full queue as
# uniform negatives first, restore the extras incrementally once baseline
# converges.

include 'conf/gtauav_balanced.gin'

TrainConfigGTAUAV.baseline_mode = True
TrainConfigGTAUAV.output_dir = "out/gtauav/baseline_inbatch"
TrainConfigGTAUAV.use_gradcam = False

# ---- Diagnostic overrides ----
# Previous mutex-only run still collapsed at epoch 1 (val loss locked at log(8)).
# Hypothesis refined: the MoCo-style queue stays stale because we have no
# momentum encoder, and with reduced trainable surface (MONA-12) the model
# can't reconcile fresh representations against 4096 stale negatives —
# mode collapse. Disable the queue entirely so InfoNCE sees only the 8
# fresh in-batch negatives, matching the OLD run's effective setup.
TrainConfigGTAUAV.sampler_type = "mutex"  # was "dss"
TrainConfigGTAUAV.neg_bank_size = 0  # was 4096 — disable MoCo queue (no momentum encoder)
InfoNCELoss.hard_mining_k = 0  # was 512 — irrelevant when queue is empty