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online/A_control/offline_baseline_stage1_ga32.yaml

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+# Offline tracking stage (DVIS_Plus_offline) at GA=32, 2/3 training budget.
+#
+# See configs/full/online/final_run/baseline_stage1_ga32.yaml for the full derivation.
+#     effective_batch = IMS_PER_BATCH * OPT_GA_STEPS = 8 * 32 = 256   (was 64 at GA=8)
+#     optimizer_steps = 106656 / 32 = 3333                            (2/3 of 5000)
+#
+# MODEL.WEIGHTS chains from the GA=32 ONLINE run (not the GA=8 one), keeping the
+# cascade internally consistent: stage-1 segmenter -> online_ga32 -> offline_ga32.
+# This run therefore cannot start until full_online_ga32/model_final.pth exists.
+#
+# DATALOADER.NUM_WORKERS 16 -> 32: the GA=8 offline run spent ~61% of wall-clock
+# blocked on the dataloader (median data_time 1.324s of a 2.155s iter), reading
+# clips from the HDFS FUSE mount. PROJECTED speedup, not yet benchmarked.
+_BASE_: baseline_stage1.yaml
+
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32/model_final.pth"
+
+SOLVER:
+  MAX_ITER: 106656
+  STEPS: (74656, 96000)
+  WARMUP_ITERS: 12000
+  CHECKPOINT_PERIOD: 10000   # ~7h between saves; see the online ga32 config
+
+DATALOADER:
+  NUM_WORKERS: 32
+
+OPT_GA: True
+OPT_GA_STEPS: 32
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline_ga32'
+
+# In-training AQ / STQ / mIoU on the 120-clip val cache (dvis_Plus/aq_eval_hook.py).
+# Period is in OPTIMIZER steps -> ~16 evals over the 3333-step run, ~60s each.
+# NOTE: this is a FRONT center-crop subset, so its absolute AQ reads low (~0.15 vs
+# the 0.386 in aq_500, which averages 5 cameras x 5 crops). Use it as a RELATIVE
+# signal for whether association is improving.
+OPT_AQ_EVAL_EVERY: 200

online/A_control/online_baseline_stage1_ga32.yaml

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+# Online tracking stage (DVIS_Plus_online) at GA=32, 2/3 training budget.
+#
+# MAX_ITER counts MICRO-iterations (one forward/backward per GAAMPTrainer.run_step,
+# train_video.py:4763), NOT optimizer steps. Optimizer steps = MAX_ITER / OPT_GA_STEPS.
+#
+#     effective_batch = IMS_PER_BATCH * OPT_GA_STEPS = 8 * 32 = 256   (was 64 at GA=8)
+#     optimizer_steps = 106656 / 32 = 3333                            (2/3 of the 5000
+#                                                                      used by the GA=8 run)
+#
+# Schedule keys are all in micro-iterations, so they are set to hold the same
+# optimizer-step fractions as the GA=8 baseline:
+#     STEPS  (74656, 96000)  -> opt steps 2333 / 3000  = 0.70 / 0.90 of the run
+#     WARMUP_ITERS 12000     -> 375 warmup opt steps, same ABSOLUTE count as the
+#                               GA=8 baseline (warmup is about update count, so it
+#                               is deliberately NOT scaled down with the budget)
+#     CHECKPOINT_PERIOD 10000 -> a save every 10000 micro-iters (= 312 optimizer
+#                               steps, ~7h wall-clock), 10 checkpoints + final.
+#                               NOT scaled to preserve the GA=8 checkpoint COUNT:
+#                               this run is ~4x longer in wall-clock, so keeping
+#                               the old count would mean ~14h between saves. This
+#                               keeps the ~7h cadence of the GA=8 run instead.
+#
+# BASE_LR is UNCHANGED at 0.000283, following the existing GA sweep
+# (final_propose_stage2_no_beta_ce_ga16/_ga32/_ga64 all held BASE_LR constant
+# despite raising the effective batch).
+#
+# DATALOADER.NUM_WORKERS 16 -> 32. The GA=8 runs spent ~50% of wall-clock blocked
+# on next(data_loader) (median data_time 1.287s of a 2.586s iter). Training data
+# lives on the HDFS FUSE mount (dvis_Plus/data_video/datasets/waymo.py:2011), so
+# workers block on network I/O rather than CPU; 8 GPUs x 32 = 256 worker procs on
+# 192 cores is intentional oversubscription to hide that latency.
+# NOTE: this speedup is PROJECTED, not yet benchmarked on this host.
+_BASE_: baseline_stage1.yaml
+
+SOLVER:
+  MAX_ITER: 106656
+  STEPS: (74656, 96000)
+  WARMUP_ITERS: 12000
+  CHECKPOINT_PERIOD: 10000
+
+DATALOADER:
+  NUM_WORKERS: 32
+
+OPT_GA: True
+OPT_GA_STEPS: 32
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32'
+
+# In-training AQ / STQ / mIoU on the 120-clip val cache (dvis_Plus/aq_eval_hook.py).
+# Period is in OPTIMIZER steps -> ~16 evals over the 3333-step run, ~60s each.
+# NOTE: this is a FRONT center-crop subset, so its absolute AQ reads low (~0.15 vs
+# the 0.386 in aq_500, which averages 5 cameras x 5 crops). Use it as a RELATIVE
+# signal for whether association is improving.
+OPT_AQ_EVAL_EVERY: 200

online/B_proposeinit/offline_baseline_stage1_ga32_proposeinit.yaml

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+# OFFLINE stage for ARM B (the init-swap arm).
+#
+# Cascade: stage-1 segmenter (propose_ga32) -> online arm B -> THIS.
+#
+# Identical to the offline control (baseline_stage1_ga32.yaml) in every respect --
+# same GA, same 3333 optimizer steps, same LR, no teacher, no hard-region loss --
+# except MODEL.WEIGHTS, which chains from arm B's online output instead of arm A's.
+#
+#   offline control init : full_online_ga32/model_final.pth              (from arm A)
+#   THIS init            : full_online_ga32_proposeinit/model_final.pth  (from arm B)
+#
+# So (offline B) - (offline control) asks whether the arm-B advantage -- if any --
+# SURVIVES the refiner, or whether the refiner washes it out. That matters: the refiner
+# is where almost all of the association gain lives (AQ 0.386 -> 0.503 at equal mIoU on
+# the GA=8 runs), so a segmenter-level improvement is only interesting if it is still
+# visible after refinement.
+#
+# BLOCKED until arm B produces full_online_ga32_proposeinit/model_final.pth.
+_BASE_: baseline_stage1_ga32.yaml
+
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_proposeinit/model_final.pth"
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline_ga32_proposeinit'

online/B_proposeinit/online_baseline_stage1_ga32_proposeinit.yaml

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+# ARM B: online tracker at GA=32, initialised from the PROPOSE (hard-region) segmenter.
+#
+# Identical to arm A (baseline_stage1_ga32.yaml) in EVERY respect -- same GA, same
+# 3333 optimizer steps, same LR, same data, same schedule, no teacher, no hard-region
+# loss in the online stage -- except ONE thing: MODEL.WEIGHTS.
+#
+#   arm A init : baseline_stage1/model_final.pth
+#                the VANILLA stage-1 segmenter.
+#   arm B init : final_propose_stage2_no_beta_ce_ga32/model_final.pth
+#                the same segmenter further trained with the alpha/beta hard-region
+#                method (propose_ga32: OPT_TRAIN_LOSS=KD, DualMaskHeads decoder,
+#                plain XOR hard region, teacher = baseline_stage1).
+#
+# So B - A answers exactly one question:
+#     "Does a better (hard-region-trained) SEGMENTER produce a better TRACKER?"
+# It does NOT add any hard-region loss to the online stage itself -- the tracker is
+# trained with the plain loss_ce + loss_mask + loss_dice in both arms. The hard-region
+# loss inside the online stage is a separate arm (baseline_stage1_ga32_hardregion.yaml).
+#
+# NOTE on the checkpoint: propose_ga32 was trained with the DualMaskHeads decoder, so
+# its state_dict carries the extra beta params (boundary_decoder_norm.*, boundary_embed.*).
+# The online stage uses the plain VideoMultiScaleMaskedTransformerDecoder_dvisPlus, so
+# those keys are simply not consumed at load. The shared alpha weights -- decoder layers,
+# decoder_norm, mask_embed, class_embed, pixel decoder, backbone -- load normally. That
+# is intended: we want the segmenter that the hard-region training produced, not its
+# train-only beta head (which is unused at inference anyway).
+_BASE_: baseline_stage1_ga32.yaml
+
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/final_propose_stage2_no_beta_ce_ga32/model_final.pth"
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_proposeinit'

online/C_hardregion/offline_baseline_stage1_ga32_hardregion.yaml

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+# ARM B (OFFLINE): refiner at GA=32 WITH the teacher-guided hard-region loss.
+#
+# A/B against configs/full/offline/final_run/baseline_stage1_ga32.yaml. Inherits
+# everything from it, so the two differ ONLY in the OPT_TRACKER_KD block.
+#
+#   student init : full_online_ga32/model_final.pth   (the arm-B-consistent cascade:
+#                  stage1 -> online_ga32_hardregion -> offline_ga32_hardregion)
+#   TEACHER      : full_offline/model_final.pth       (the finished offline refiner,
+#                  AQ 0.503 on aq_500) -- a full DVIS_Plus_offline, so its REFINER is
+#                  run too and the hard region reflects REFINED masks.
+#   hard region  : hard = GT XOR (teacher_mask > 0)   -- plain XOR, as in propose_ga32.
+#
+# NOTE the offline layout: the criterion flattens the T frames into one tall image
+# ('b q t h w -> b q () (t h) w'). GT, student and teacher are all flattened the same
+# way, and the XOR is shape-agnostic, so the hard region is computed consistently.
+#
+# BLOCKED until the arm-B online run produces full_online_ga32/model_final.pth.
+_BASE_: baseline_stage1_ga32.yaml
+
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_hardregion/model_final.pth"
+
+OPT_TRACKER_KD: True
+OPT_TRACKER_BETA_HEAD: False
+OPT_TRACKER_HR_CLASS_AWARE: False
+
+OPT_ALPHA_MASK_WEIGHT: 0.1
+OPT_ALPHA_DICE_WEIGHT: 1.0
+OPT_BETA_MASK_WEIGHT: 1.0
+OPT_BETA_DICE_WEIGHT: 3.0
+OPT_HC_NUM_POINTS_DIVIDER: 5
+
+OPT_TEACHER_CONFIG: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline/config.yaml"
+OPT_TEACHER_WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline/model_final.pth"
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline_ga32_hardregion'

online/C_hardregion/online_baseline_stage1_ga32_hardregion.yaml

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+# ARM C: online tracker at GA=32 with the teacher-guided hard-region loss (NO beta head).
+#
+# Arm C+ (baseline_stage1_ga32_hardregion_beta.yaml) inherits from this file and turns
+# the beta head ON. C keeps it OFF, so C is architecturally IDENTICAL to arm A (zero
+# extra params) and C - A isolates the LOSS alone; C+ - C then isolates the BETA HEAD.
+#
+# Inherits everything from arm A (baseline_stage1_ga32.yaml) -- same init, data,
+# schedule, batch, LR, checkpointing -- and adds only the OPT_TRACKER_KD block below.
+#
+#   student init : baseline_stage1/model_final.pth   (SAME as arm A)
+#   TEACHER      : full_online/model_final.pth       (the finished GA=8 online tracker,
+#                  AQ 0.386 on aq_500) -- a full DVIS_Plus_online, so its TRACKER is run
+#                  too and the hard region reflects TRACKED masks, not segmenter masks.
+#   hard region  : hard = GT XOR (teacher_mask > 0)   -- plain, class-blind XOR, matching
+#                  propose_stage2_no_beta_ce_ga32.yaml (which sets neither
+#                  OPT_HARD_REGION_CLASS_AWARE nor OPT_HARD_REGION_SEMANTIC).
+#   losses       : loss2_alpha_mask/dice on the tracker's existing mask head
+#   weights      : copied verbatim from propose_ga32 (alpha 0.1/1.0; beta 1.0/3.0 are
+#                  set here too but unused unless OPT_TRACKER_BETA_HEAD is on -- arm C+).
+#
+# C - A isolates the hard-region LOSS in the online stage.
+# (B - A is the separate init-swap arm; see online_offline_exp.md.)
+#
+# BETA HEAD CAVEAT -- relevant to arm C+, which flips OPT_TRACKER_BETA_HEAD on.
+# In the segmenter, alpha and beta are two QUERY STREAMS through shared decoder layers;
+# they diverge because each builds its own PIXEL ATTENTION MASK from its own mask logits,
+# which then steers its own query evolution. The tracker's queries cross-attend to FRAME
+# EMBEDDINGS and every one of its attention calls passes memory_mask=None
+# (tracker.py:315, 336, 373, 394, ...), so there is no such mask and a cloned second
+# stream would be BIT-IDENTICAL to alpha. Beta here is therefore a second OUTPUT HEAD
+# (boundary_decoder_norm + boundary_embed) on the shared final query state.
+# What survives: beta still trains only on the hard region, and its gradients still flow
+# back through the shared transformer stack -- the mechanism by which beta shapes the
+# trunk in the segmenter. What is lost: beta cannot specialise its own queries.
+# Expect a weaker effect than the segmenter's beta.
+# See dvis_Plus/tracker_hard_region.py.
+_BASE_: baseline_stage1_ga32.yaml
+
+OPT_TRACKER_KD: True
+OPT_TRACKER_BETA_HEAD: False          # arm C+ overrides this to True
+OPT_TRACKER_HR_CLASS_AWARE: False     # plain XOR, as in propose_ga32
+
+OPT_ALPHA_MASK_WEIGHT: 0.1
+OPT_ALPHA_DICE_WEIGHT: 1.0
+OPT_BETA_MASK_WEIGHT: 1.0
+OPT_BETA_DICE_WEIGHT: 3.0
+OPT_HC_NUM_POINTS_DIVIDER: 5          # hard-region point budget = TRAIN_NUM_POINTS / 5
+
+OPT_TEACHER_CONFIG: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online/config.yaml"
+OPT_TEACHER_WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online/model_final.pth"
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_hardregion'

online/Cplus_hardregion_beta/offline_baseline_stage1_ga32_hardregion_beta.yaml

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+# OFFLINE stage for ARM C+ (hard-region loss + beta head).
+#
+# Cascade: stage-1 segmenter -> online arm C+ -> THIS.
+#
+# Identical to the offline hard-region arm (baseline_stage1_ga32_hardregion.yaml)
+# except it turns the beta head on, mirroring what arm C+ does in the online stage --
+# so each offline run carries the SAME treatment as the online arm it chains from.
+#
+#   init    : full_online_ga32_hardregion_beta/model_final.pth   (from arm C+)
+#   TEACHER : full_offline/model_final.pth  -- the finished GA=8 offline refiner
+#             (AQ 0.503 on aq_500). A full DVIS_Plus_offline, so its REFINER is run too
+#             and the hard region reflects REFINED masks, not tracker masks.
+#   losses  : loss2_alpha_mask/dice on the refiner's existing mask head
+#             loss2_beta_mask/dice  on the refiner's beta head (hard region only)
+#
+# The beta head on the refiner has the same limitation as on the tracker: it is a second
+# OUTPUT head on the shared query state, not a second query stream, because there is no
+# pixel attention mask for two streams to diverge on. See dvis_Plus/tracker_hard_region.py.
+#
+# Checkpoint note: arm C+'s online model_final carries tracker beta params
+# (tracker.boundary_*). The offline stage loads the tracker and freezes it; with
+# OPT_TRACKER_BETA_HEAD on, those keys are consumed. Beta is train-only in any case --
+# inference reads the alpha head -- so it does not affect the offline tracker's masks.
+#
+# BLOCKED until arm C+ produces full_online_ga32_hardregion_beta/model_final.pth.
+_BASE_: baseline_stage1_ga32_hardregion.yaml
+
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_hardregion_beta/model_final.pth"
+
+OPT_TRACKER_BETA_HEAD: True
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_offline_ga32_hardregion_beta'

online/Cplus_hardregion_beta/online_baseline_stage1_ga32_hardregion_beta.yaml

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+# ARM C: online tracker at GA=32, hard-region loss + the BETA HEAD.
+#
+# Identical to arm B (baseline_stage1_ga32_hardregion.yaml) except it turns the beta
+# head on, so the three online arms isolate two variables:
+#
+#   arm A  baseline_stage1_ga32.yaml             control: no teacher, no hard region
+#   arm B  ..._ga32_hardregion.yaml              + hard-region loss   (loss2_alpha_*)
+#   arm C  ..._ga32_hardregion_beta.yaml         + hard-region loss + beta head
+#                                                  (loss2_alpha_* AND loss2_beta_*)
+#
+# B - A  isolates the LOSS (A and B are architecturally identical).
+# C - B  isolates the BETA HEAD (same loss, extra head + extra params).
+#
+# On the beta head here: it is a second OUTPUT head (boundary_decoder_norm +
+# boundary_embed) reading the same final query state, not a second query stream.
+# The segmenter's beta gets its own query stream because each stream builds its own
+# pixel attention mask from its own mask logits; the tracker's queries cross-attend to
+# frame embeddings with memory_mask=None, so there is no such mask and a cloned stream
+# would be bit-identical to alpha. Beta still trains only on the hard region and its
+# gradients still flow back through the shared transformer stack -- the mechanism that
+# shapes the trunk in the segmenter -- it just cannot specialise its own queries.
+# Expect a smaller effect than the segmenter's beta. See dvis_Plus/tracker_hard_region.py.
+#
+# Beta weights are propose_ga32's (1.0 mask / 3.0 dice).
+_BASE_: baseline_stage1_ga32_hardregion.yaml
+
+OPT_TRACKER_BETA_HEAD: True
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online_ga32_hardregion_beta'

online/Cplus_hardregion_beta/online_model_0009999_early_aq0.16.pth

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+size 3223722042

online/Cplus_hardregion_beta/online_model_final_aq0.238.pth

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online/ga_8_baseline/online_baseline_stage1_ga8.yaml

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+# Online tracking stage (DVIS_Plus_online), machine-adapted for this host.
+# Mirrors configs/full/mask2former/final_run/baseline_stage1.yaml (the segmenter
+# stage-1 config) but trains the online tracker on top of the trained segmenter:
+#   - MODEL.WEIGHTS  -> the trained stage-1 segmenter (backbone + sem_seg_head are
+#                       loaded and frozen; the tracker module is trained).
+#   - same dataset / data pipeline as the segmenter (waymo_train_all_5_views).
+#   - OUTPUT_DIR     -> .../new_waymo_config1/full_online
+# META_ARCHITECTURE: DVIS_Plus_online comes from the _BASE_ below.
+_BASE_: ../DVIS_Plus_Online_R50.yaml
+MODEL:
+  WEIGHTS: "/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/baseline_stage1/model_final.pth"
+  BACKBONE:
+    NAME: "D2VitAdapterDinoV2"
+  VIT_ADAPTER:
+    FINETUNE: False
+    NAME: 'vitl'
+    VIT_WEIGHT: '/mnt/hdfs/andrew.estornell/multi_modal/repo_backup/weights/work_dirs/dinov2_vitl14_pretrain_.pth'
+    FREEZE_VIT: True
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
+    NO_OBJECT_WEIGHT: 0.1
+    TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder_dvisPlus"
+  CL_PLUGIN:
+    NUM_NEGATIVES: 199
+  SEM_SEG_HEAD:
+    NUM_CLASSES: 29
+SOLVER:
+  IMS_PER_BATCH: 8
+  OPTIMIZER: "ADAMW"
+  BASE_LR: 0.000283
+  MAX_ITER: 40000
+  STEPS: (28000, 36000)
+  GAMMA: 0.1
+  CHECKPOINT_PERIOD: 8000
+  WEIGHT_DECAY: 0.05
+  WARMUP_FACTOR: 0.001
+  WARMUP_ITERS: 3000
+
+OPT_FLAG_ABSENT_IDS: True
+
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: False
+  NUM_WORKERS: 16
+INPUT:
+  MIN_SIZE_TRAIN: (960, 1120, 1280)
+  MAX_SIZE_TRAIN: 1920
+  SAMPLING_FRAME_NUM: 5
+  SAMPLING_FRAME_RANGE: 5
+  MIN_SIZE_TEST: 1280
+OPT_IF_ADD_RANDOM_CROP: True
+OPT_IF_ADD_RANDOM_FLIP: True
+
+OPT_GA: True
+OPT_GA_STEPS: 8
+
+OPT_DATASET: "waymo_train_all_5_views"
+OPT_VAL_DATASET: "waymo_train_5_views"
+OPT_ADD_DAUG: "add_data_aug"
+OPT_ATTN_MASK_METHOD: "attn_mask"
+
+OUTPUT_DIR: '/mnt/hdfs/andrew.estornell/multi_modal/DVIS_Plus/new_output_dir/new_waymo_config1/full_online'

online/ga_8_baseline/online_model_final_ga8_aq0.386.pth

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