claude_refactor_v3: Added .py-confs and all presets (nx5 .gin files). TODO: common gins-mapping and prepare to next step

belka_refactor_30_04_table.md

@@ -0,0 +1,25 @@
+# Сводная таблица всех 15 пресетов
+## Изменения
+- scr/conf/*.py конфиги
+- presets/*/*.gin (по 5 файлов на пресет: [pipeline, hardware, models, tracking, trainig]) 
+
+## TODO: Сейчас конфиги пресетов разделены, но много дублирующихся общих пресетов файлов: сделать маппинг (Dict) общих файлов [pipeline, hardware, models, tracking, trainig] на свои пресеты
+---
+
+| Пресет                              | backbone  | baseline_mode | shared_encoder | init_gate |
+| ----------------------------------- | --------- | ------------- | -------------- | --------- |
+| `gtauav_balanced`                   | dinov3    | False         | True           | 0.7       |
+| `gtauav_baseline`                   | dinov3    | True          | True           | 0.7       |
+| `gtauav_balanced_asym`              | dinov3    | False         | False          | 0.7       |
+| `gtauav_baseline_asym`              | dinov3    | True          | False          | 0.7       |
+| `gtauav_text_heavy`                 | dinov3    | False         | True           | **0.3**   |
+| `gtauav_image_heavy`                | dinov3    | False         | True           | **0.9**   |
+| `gtauav_balanced_stripnet`          | stripnet  | False         | —              | 0.7       |
+| `gtauav_balanced_stripnet_unfrozen` | stripnet  | False         | —              | 0.7       |
+| `gtauav_baseline_stripnet`          | stripnet  | True          | —              | 0.7       |
+| `gtauav_baseline_stripnet_unfrozen` | stripnet  | True          | —              | 0.7       |
+| `gtauav_balanced_sofia`             | sofia_v71 | False         | —              | 0.7       |
+| `gtauav_baseline_sofia`             | sofia_v71 | True          | —              | 0.7       |
+| `gtauav_balanced_sofia_v1`          | sofia_v1  | False         | —              | 0.7       |
+| `gtauav_baseline_sofia_v1`          | sofia_v1  | True          | —              | 0.7       |
+| `preprocess`                        | —         | —             | —              | —         |

presets/gtauav_balanced/hardware.gin

@@ -1 +1,9 @@
-
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0

presets/gtauav_balanced/models.gin

@@ -1 +1,12 @@
+# DINOv3 shared encoder + MONA-12 + DGTRS-CLIP with text.
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = True
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 12
+DINOv3ModelsConfig.lora_rank = 4

presets/gtauav_balanced/pipeline.gin

@@ -1 +1,14 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
 
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/with_text'
+PipelineConfig.resume_from = None

presets/gtauav_balanced/tracking.gin

@@ -1 +1,15 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
 
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5

presets/gtauav_balanced/training.gin

@@ -1 +1,31 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
 
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True

presets/gtauav_balanced_asym/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_balanced_asym/models.gin

@@ -1 +1,11 @@
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = False            
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 24           
+DINOv3ModelsConfig.lora_rank = 4

presets/gtauav_balanced_asym/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/balanced_asym'
+PipelineConfig.resume_from = None
 

presets/gtauav_balanced_asym/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_balanced_asym/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_balanced_sofia/hardware.gin

@@ -1 +1,9 @@
-
+# SOFIA v7.1 from-scratch — keep activations live (no gradient checkpointing).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = False
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0

presets/gtauav_balanced_sofia/models.gin

@@ -1 +1,91 @@
+# SOFIA v7.1 Tiny preset (~5M params) with text fusion (Text-FiLM mid-level
+# in SAT and UAV heads + GatedFusion late-level on descriptors).
+#
+# Tiny-specific notes:
+#   - num_heads_s3/s4 = 4 (channels 176/224 not divisible by 8)
+#   - mamba_headdim = 16 (channels not divisible by default 64)
+#   - mamba_variant = 'mamba1' (Mamba-2 torch fallback bug for these dims)
+#   - d_descriptor = 1024 (override from preset M default 512)
+#   - text fusion enabled (override from preset M default disabled)
 
+ModelsCommonConfig.backbone = 'sofia_v71'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+# Variant label (informational).
+SOFIAv71ModelsConfig.variant_label = 'Tiny'
+
+# Input.
+SOFIAv71ModelsConfig.input_size = 256
+SOFIAv71ModelsConfig.in_channels = 3
+
+# Stem (Tiny dims).
+SOFIAv71ModelsConfig.stem_mid = 16
+SOFIAv71ModelsConfig.stem_out = 32
+
+# Backbone dimensions (Tiny).
+SOFIAv71ModelsConfig.embed_dims = [48, 96, 176, 224]
+SOFIAv71ModelsConfig.depths = [2, 3, 4, 2]
+
+# Stage 1-2 block params (default).
+SOFIAv71ModelsConfig.mbconv_expand = 4
+SOFIAv71ModelsConfig.se_ratio = 16
+SOFIAv71ModelsConfig.strip_kernel_s1 = 7
+SOFIAv71ModelsConfig.strip_kernel_s2 = 5
+SOFIAv71ModelsConfig.mix_kernels = [3, 5, 7]
+SOFIAv71ModelsConfig.use_dcn_strip = True
+
+# Stage 3-4 (MambaVision). Tiny: mamba1 to bypass torch fallback bug.
+SOFIAv71ModelsConfig.mamba_d_state = 16
+SOFIAv71ModelsConfig.mamba_dt_rank = None
+SOFIAv71ModelsConfig.mamba_backend = 'auto'
+SOFIAv71ModelsConfig.mamba_variant = 'mamba1'
+
+# Mamba-2 tunables (used when mamba_variant='mamba2'; Tiny would need
+# headdim=16 because 176 % 64 != 0 and 224 % 64 != 0).
+SOFIAv71ModelsConfig.mamba_d_state_mamba2 = 64
+SOFIAv71ModelsConfig.mamba_headdim = 16
+SOFIAv71ModelsConfig.mamba_expand = 2
+SOFIAv71ModelsConfig.mamba_d_conv = 4
+SOFIAv71ModelsConfig.mamba_n_directions = 2
+
+# Heads / attention (Tiny: heads=4).
+SOFIAv71ModelsConfig.num_heads_s3 = 4
+SOFIAv71ModelsConfig.num_heads_s4 = 4
+SOFIAv71ModelsConfig.use_strip_branch_s3 = True
+SOFIAv71ModelsConfig.use_strip_branch_s4 = False
+SOFIAv71ModelsConfig.ffn_expand = 4
+
+# EVSS bridge (off by default).
+SOFIAv71ModelsConfig.use_evss_bridge = False
+SOFIAv71ModelsConfig.evss_bridge_locations = ['pre_stage3']
+
+# Neck (Tiny).
+SOFIAv71ModelsConfig.neck_channels = 128
+
+# CVGL Head.
+SOFIAv71ModelsConfig.d_descriptor = 1024
+SOFIAv71ModelsConfig.use_asymmetric_heads = True
+SOFIAv71ModelsConfig.chp_rings = 8
+SOFIAv71ModelsConfig.chp_angles = 16
+SOFIAv71ModelsConfig.chp_harmonics = 4
+SOFIAv71ModelsConfig.use_film_altitude = True
+SOFIAv71ModelsConfig.altitude_norm = 500.0
+SOFIAv71ModelsConfig.ring_count = 4
+SOFIAv71ModelsConfig.use_ring_aux = True
+
+# Text fusion enabled.
+SOFIAv71ModelsConfig.return_normalized = True
+SOFIAv71ModelsConfig.use_text_film_sat = True
+SOFIAv71ModelsConfig.use_text_film_uav = True
+SOFIAv71ModelsConfig.text_film_dim = 1024
+SOFIAv71ModelsConfig.text_film_hidden = 256
+
+# Sharing / KD / deploy.
+SOFIAv71ModelsConfig.share_stages_1_2 = True
+SOFIAv71ModelsConfig.enable_kd_taps = True
+SOFIAv71ModelsConfig.precision = 'fp16'
+
+# LoRA.
+SOFIAv71ModelsConfig.lora_rank = 4

presets/gtauav_balanced_sofia/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/with_text_sofia'
+PipelineConfig.resume_from = None
 

presets/gtauav_balanced_sofia/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_balanced_sofia/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_balanced_sofia_v1/hardware.gin

@@ -1 +1,8 @@
-
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = False
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0

presets/gtauav_balanced_sofia_v1/models.gin

@@ -1 +1,30 @@
+# SOFIA v1 'tiny' variant (~1M params) with text fusion (Text-FiLM mid-level
+# in SAT and UAV heads + AltitudeFiLM in UAV head + GatedFusion late-level).
 
+ModelsCommonConfig.backbone = 'sofia_v1'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+# Backbone.
+SOFIAv1ModelsConfig.variant_label = 'tiny'
+SOFIAv1ModelsConfig.in_channels = 3
+SOFIAv1ModelsConfig.input_size = 256
+SOFIAv1ModelsConfig.dcn_variant = 'v2'
+
+# Heads.
+SOFIAv1ModelsConfig.d_descriptor = 1024
+SOFIAv1ModelsConfig.return_normalized = False
+
+# Altitude-FiLM.
+SOFIAv1ModelsConfig.use_film_altitude = True
+SOFIAv1ModelsConfig.altitude_norm = 500.0
+
+# Text-FiLM.
+SOFIAv1ModelsConfig.use_text_film_uav = True
+SOFIAv1ModelsConfig.use_text_film_sat = True
+SOFIAv1ModelsConfig.text_film_dim = 1024
+SOFIAv1ModelsConfig.text_film_hidden = 256
+
+# LoRA on DGTRS-CLIP.
+SOFIAv1ModelsConfig.lora_rank = 4

presets/gtauav_balanced_sofia_v1/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/with_text_sofia_v1'
+PipelineConfig.resume_from = None
 

presets/gtauav_balanced_sofia_v1/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_balanced_sofia_v1/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_balanced_stripnet/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_balanced_stripnet/models.gin

@@ -1 +1,11 @@
+# StripNet small backbone (frozen) + Conv-MONA on last 2 stages.
+ModelsCommonConfig.backbone = 'stripnet'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+StripNetModelsConfig.stripnet_path = 'nn_models/STRIPNET/stripnet_s.pth'
+StripNetModelsConfig.stripnet_freeze = True
+StripNetModelsConfig.stripnet_mona_last_n_stages = 2
+StripNetModelsConfig.stripnet_backbone_lr_factor = 0.1
+StripNetModelsConfig.lora_rank = 4

presets/gtauav_balanced_stripnet/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/balanced_stripnet'
+PipelineConfig.resume_from = None
 

presets/gtauav_balanced_stripnet/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_balanced_stripnet/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_balanced_stripnet_unfrozen/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_balanced_stripnet_unfrozen/models.gin

@@ -1 +1,11 @@
+# StripNet small backbone (frozen) + Conv-MONA on last 2 stages.
+ModelsCommonConfig.backbone = 'stripnet'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+StripNetModelsConfig.stripnet_path = 'nn_models/STRIPNET/stripnet_s.pth'
+StripNetModelsConfig.stripnet_freeze = False
+StripNetModelsConfig.stripnet_mona_last_n_stages = 2
+StripNetModelsConfig.stripnet_backbone_lr_factor = 0.1
+StripNetModelsConfig.lora_rank = 4

presets/gtauav_balanced_stripnet_unfrozen/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/balanced_stripnet_unfrozen'
+PipelineConfig.resume_from = None
 

presets/gtauav_balanced_stripnet_unfrozen/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_balanced_stripnet_unfrozen/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_baseline/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_baseline/models.gin

@@ -1 +1,12 @@
+# DINOv3 shared encoder + MONA-12 + DGTRS-CLIP with text.
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = True
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 12
+DINOv3ModelsConfig.lora_rank = 4

presets/gtauav_baseline/pipeline.gin

@@ -1 +1,14 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
 
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_inbatch'
+PipelineConfig.resume_from = None

presets/gtauav_baseline/tracking.gin

@@ -1 +1,15 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
 
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5

presets/gtauav_baseline/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_baseline_asym/hardware.gin

@@ -1 +1,9 @@
-
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0

presets/gtauav_baseline_asym/models.gin

@@ -1 +1,12 @@
+# DINOv3 shared encoder + MONA-12 + DGTRS-CLIP with text.
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = False
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 24
+DINOv3ModelsConfig.lora_rank = 4

presets/gtauav_baseline_asym/pipeline.gin

@@ -1 +1,14 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
 
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_asym'
+PipelineConfig.resume_from = None

presets/gtauav_baseline_asym/tracking.gin

@@ -1 +1,15 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
 
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5

presets/gtauav_baseline_asym/training.gin

@@ -1 +1,31 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
 
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True

presets/gtauav_baseline_sofia/hardware.gin

@@ -1 +1,10 @@
+# SOFIA v7.1 from-scratch — keep activations live (no gradient checkpointing).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = False
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_baseline_sofia/models.gin

@@ -1 +1,92 @@
+# SOFIA v7.1 Tiny preset (~5M params) with text fusion (Text-FiLM mid-level
+# in SAT and UAV heads + GatedFusion late-level on descriptors).
+#
+# Tiny-specific notes:
+#   - num_heads_s3/s4 = 4 (channels 176/224 not divisible by 8)
+#   - mamba_headdim = 16 (channels not divisible by default 64)
+#   - mamba_variant = 'mamba1' (Mamba-2 torch fallback bug for these dims)
+#   - d_descriptor = 1024 (override from preset M default 512)
+#   - text fusion enabled (override from preset M default disabled)
+
+ModelsCommonConfig.backbone = 'sofia_v71'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+# Variant label (informational).
+SOFIAv71ModelsConfig.variant_label = 'Tiny'
+
+# Input.
+SOFIAv71ModelsConfig.input_size = 256
+SOFIAv71ModelsConfig.in_channels = 3
+
+# Stem (Tiny dims).
+SOFIAv71ModelsConfig.stem_mid = 16
+SOFIAv71ModelsConfig.stem_out = 32
+
+# Backbone dimensions (Tiny).
+SOFIAv71ModelsConfig.embed_dims = [48, 96, 176, 224]
+SOFIAv71ModelsConfig.depths = [2, 3, 4, 2]
+
+# Stage 1-2 block params (default).
+SOFIAv71ModelsConfig.mbconv_expand = 4
+SOFIAv71ModelsConfig.se_ratio = 16
+SOFIAv71ModelsConfig.strip_kernel_s1 = 7
+SOFIAv71ModelsConfig.strip_kernel_s2 = 5
+SOFIAv71ModelsConfig.mix_kernels = [3, 5, 7]
+SOFIAv71ModelsConfig.use_dcn_strip = True
+
+# Stage 3-4 (MambaVision). Tiny: mamba1 to bypass torch fallback bug.
+SOFIAv71ModelsConfig.mamba_d_state = 16
+SOFIAv71ModelsConfig.mamba_dt_rank = None
+SOFIAv71ModelsConfig.mamba_backend = 'auto'
+SOFIAv71ModelsConfig.mamba_variant = 'mamba1'
+
+# Mamba-2 tunables (used when mamba_variant='mamba2'; Tiny would need
+# headdim=16 because 176 % 64 != 0 and 224 % 64 != 0).
+SOFIAv71ModelsConfig.mamba_d_state_mamba2 = 64
+SOFIAv71ModelsConfig.mamba_headdim = 16
+SOFIAv71ModelsConfig.mamba_expand = 2
+SOFIAv71ModelsConfig.mamba_d_conv = 4
+SOFIAv71ModelsConfig.mamba_n_directions = 2
+
+# Heads / attention (Tiny: heads=4).
+SOFIAv71ModelsConfig.num_heads_s3 = 4
+SOFIAv71ModelsConfig.num_heads_s4 = 4
+SOFIAv71ModelsConfig.use_strip_branch_s3 = True
+SOFIAv71ModelsConfig.use_strip_branch_s4 = False
+SOFIAv71ModelsConfig.ffn_expand = 4
+
+# EVSS bridge (off by default).
+SOFIAv71ModelsConfig.use_evss_bridge = False
+SOFIAv71ModelsConfig.evss_bridge_locations = ['pre_stage3']
+
+# Neck (Tiny).
+SOFIAv71ModelsConfig.neck_channels = 128
+
+# CVGL Head.
+SOFIAv71ModelsConfig.d_descriptor = 1024
+SOFIAv71ModelsConfig.use_asymmetric_heads = True
+SOFIAv71ModelsConfig.chp_rings = 8
+SOFIAv71ModelsConfig.chp_angles = 16
+SOFIAv71ModelsConfig.chp_harmonics = 4
+SOFIAv71ModelsConfig.use_film_altitude = True
+SOFIAv71ModelsConfig.altitude_norm = 500.0
+SOFIAv71ModelsConfig.ring_count = 4
+SOFIAv71ModelsConfig.use_ring_aux = True
+
+# Text fusion enabled.
+SOFIAv71ModelsConfig.return_normalized = True
+SOFIAv71ModelsConfig.use_text_film_sat = True
+SOFIAv71ModelsConfig.use_text_film_uav = True
+SOFIAv71ModelsConfig.text_film_dim = 1024
+SOFIAv71ModelsConfig.text_film_hidden = 256
+
+# Sharing / KD / deploy.
+SOFIAv71ModelsConfig.share_stages_1_2 = True
+SOFIAv71ModelsConfig.enable_kd_taps = True
+SOFIAv71ModelsConfig.precision = 'fp16'
+
+# LoRA.
+SOFIAv71ModelsConfig.lora_rank = 4
 

presets/gtauav_baseline_sofia/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_sofia'
+PipelineConfig.resume_from = None
 

presets/gtauav_baseline_sofia/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_baseline_sofia/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_baseline_sofia_v1/hardware.gin

@@ -1 +1,9 @@
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = False
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_baseline_sofia_v1/models.gin

@@ -1 +1,31 @@
+# SOFIA v1 'tiny' variant (~1M params) with text fusion (Text-FiLM mid-level
+# in SAT and UAV heads + AltitudeFiLM in UAV head + GatedFusion late-level).
+
+ModelsCommonConfig.backbone = 'sofia_v1'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+# Backbone.
+SOFIAv1ModelsConfig.variant_label = 'tiny'
+SOFIAv1ModelsConfig.in_channels = 3
+SOFIAv1ModelsConfig.input_size = 256
+SOFIAv1ModelsConfig.dcn_variant = 'v2'
+
+# Heads.
+SOFIAv1ModelsConfig.d_descriptor = 1024
+SOFIAv1ModelsConfig.return_normalized = False
+
+# Altitude-FiLM.
+SOFIAv1ModelsConfig.use_film_altitude = True
+SOFIAv1ModelsConfig.altitude_norm = 500.0
+
+# Text-FiLM.
+SOFIAv1ModelsConfig.use_text_film_uav = True
+SOFIAv1ModelsConfig.use_text_film_sat = True
+SOFIAv1ModelsConfig.text_film_dim = 1024
+SOFIAv1ModelsConfig.text_film_hidden = 256
+
+# LoRA on DGTRS-CLIP.
+SOFIAv1ModelsConfig.lora_rank = 4
 

presets/gtauav_baseline_sofia_v1/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_sofia_v1'
+PipelineConfig.resume_from = None
 

presets/gtauav_baseline_sofia_v1/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_baseline_sofia_v1/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_baseline_stripnet/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_baseline_stripnet/models.gin

@@ -1 +1,12 @@
+# StripNet small backbone (frozen) + Conv-MONA on last 2 stages.
+ModelsCommonConfig.backbone = 'stripnet'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+StripNetModelsConfig.stripnet_path = 'nn_models/STRIPNET/stripnet_s.pth'
+StripNetModelsConfig.stripnet_freeze = True
+StripNetModelsConfig.stripnet_mona_last_n_stages = 2
+StripNetModelsConfig.stripnet_backbone_lr_factor = 0.1
+StripNetModelsConfig.lora_rank = 4
 

presets/gtauav_baseline_stripnet/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_stripnet'
+PipelineConfig.resume_from = None
 

presets/gtauav_baseline_stripnet/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_baseline_stripnet/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_baseline_stripnet_unfrozen/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_baseline_stripnet_unfrozen/models.gin

@@ -1 +1,11 @@
+# StripNet small backbone (unfrozen) + Conv-MONA on last 2 stages.
+ModelsCommonConfig.backbone = 'stripnet'
+ModelsCommonConfig.baseline_mode = True
+ModelsCommonConfig.init_gate = 0.7
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
 
+StripNetModelsConfig.stripnet_path = 'nn_models/STRIPNET/stripnet_s.pth'
+StripNetModelsConfig.stripnet_freeze = False
+StripNetModelsConfig.stripnet_mona_last_n_stages = 2
+StripNetModelsConfig.stripnet_backbone_lr_factor = 0.1
+StripNetModelsConfig.lora_rank = 4

presets/gtauav_baseline_stripnet_unfrozen/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/baseline_stripnet_unfrozen'
+PipelineConfig.resume_from = None
 

presets/gtauav_baseline_stripnet_unfrozen/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_baseline_stripnet_unfrozen/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_image_heavy/hardware.gin

@@ -1 +1,10 @@
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0
 

presets/gtauav_image_heavy/models.gin

@@ -1 +1,13 @@
+# DINOv3 shared encoder + MONA-12 + DGTRS-CLIP with text.
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.9
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = True
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 12
+DINOv3ModelsConfig.lora_rank = 4
 

presets/gtauav_image_heavy/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/image_heavy'
+PipelineConfig.resume_from = None
 

presets/gtauav_image_heavy/tracking.gin

@@ -1 +1,16 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
+
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5
 

presets/gtauav_image_heavy/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/gtauav_text_heavy/hardware.gin

@@ -1 +1,10 @@
 
+# RTX 4090 profile, shared encoder (DINOv3).
+HardwareConfig.device = 'cuda'
+HardwareConfig.batch_size = 8
+HardwareConfig.grad_accum_steps = 8
+HardwareConfig.num_workers = 4
+HardwareConfig.use_amp = True
+HardwareConfig.gradient_checkpointing = True
+HardwareConfig.reserve_gb = 2.0
+HardwareConfig.total_vram_gb = 24.0

presets/gtauav_text_heavy/models.gin

@@ -1 +1,13 @@
+# DINOv3 shared encoder + MONA-12 + DGTRS-CLIP with text.
+ModelsCommonConfig.backbone = 'dinov3'
+ModelsCommonConfig.baseline_mode = False
+ModelsCommonConfig.init_gate = 0.3
+ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
+
+DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
+DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
+DINOv3ModelsConfig.shared_encoder = True
+DINOv3ModelsConfig.mona_bottleneck = 64
+DINOv3ModelsConfig.mona_last_n_blocks = 12
+DINOv3ModelsConfig.lora_rank = 4
 

presets/gtauav_text_heavy/pipeline.gin

@@ -1 +1,15 @@
+# Pipeline: GTA-UAV-LR with text captions, servml workstation paths.
+PipelineConfig.train_json = 'meta/train_80.json'
+PipelineConfig.test_json = 'meta/test_20.json'
+PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
+PipelineConfig.filter_meta = 'meta/seg_filter.json'
+
+PipelineConfig.epochs = 10
+PipelineConfig.warmup_epochs = 2
+PipelineConfig.eval_every = 1
+PipelineConfig.seed = 42
+
+PipelineConfig.output_dir = 'out/gtauav/text_heavy'
+PipelineConfig.resume_from = None
 

presets/gtauav_text_heavy/tracking.gin

@@ -1 +1,15 @@
+# Default tracking: TensorBoard on, W&B off, no Grad-CAM, no profiler.
+TrackingConfig.use_wandb = False
+TrackingConfig.use_tb = True
+TrackingConfig.wandb_project = 'caption-test-gtauav'
+TrackingConfig.wandb_run_name = None
+TrackingConfig.wandb_entity = None
 
+TrackingConfig.log_grad_norms = True
+TrackingConfig.use_gradcam = False
+TrackingConfig.gradcam_every = 5
+TrackingConfig.gradcam_samples = 8
+
+TrackingConfig.use_profiler = False
+TrackingConfig.profiler_warmup = 3
+TrackingConfig.profiler_active = 5

presets/gtauav_text_heavy/training.gin

@@ -1 +1,32 @@
+# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
+TrainingConfig.loss_type = 'symmetric'
+TrainingConfig.tau_init = 0.07
+TrainingConfig.tau_min = 0.01
+TrainingConfig.tau_max = 0.1
+TrainingConfig.learnable_temperature = True
+TrainingConfig.label_smoothing = 0.1
+
+TrainingConfig.tau_final = 0.01
+TrainingConfig.weight_q2g = 0.6
+TrainingConfig.weight_g2q = 0.4
+TrainingConfig.hard_mining_k = 0
+TrainingConfig.neg_bank_size = 0
+
+# WeightedInfoNCE-only (unused when loss_type='symmetric').
+TrainingConfig.weighted_loss_k = 5.0
+
+TrainingConfig.learning_rate = 1e-4
+TrainingConfig.text_lr_factor = 0.1
+TrainingConfig.weight_decay = 1e-4
+TrainingConfig.grad_clip = 1.0
+
+TrainingConfig.sampler_type = 'mutex'
+TrainingConfig.dss_warmup_epochs = 1
+TrainingConfig.dss_reembed_every = 1
+TrainingConfig.dss_knn_device = 'cuda'
+TrainingConfig.dss_use_lsh = False
+TrainingConfig.dss_lsh_num_tables = 8
+TrainingConfig.dss_lsh_num_bits = 14
+TrainingConfig.dss_cache_dir = None
+TrainingConfig.use_mutex_sampler = True
 

presets/preprocess/preprocess.gin

@@ -1 +1,20 @@
+# Preprocessing config used by scripts/make_split.py and
+# scripts/filter_segmentation.py. Independent from training pipeline.
 
+# Inputs.
+PreprocessConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
+PreprocessConfig.segm_root = '/home/servml/Документы/datasets/GTA-UAV-LR-aug/segm'
+
+# make_split.py — 80/20 split with seed=42.
+PreprocessConfig.split_ratio = 0.8
+PreprocessConfig.split_seed = 42
+PreprocessConfig.split_input_train = 'cross-area-drone2sate-train.json'
+PreprocessConfig.split_input_test = 'cross-area-drone2sate-test.json'
+PreprocessConfig.split_output_dir = 'meta'
+PreprocessConfig.split_output_train = 'train_80.json'
+PreprocessConfig.split_output_test = 'test_20.json'
+
+# filter_segmentation.py — exclude images with >=90% background+water.
+PreprocessConfig.seg_threshold = 0.90
+PreprocessConfig.seg_exclude_classes = [0, 4]
+PreprocessConfig.seg_filter_output = 'meta/seg_filter.json'

refactor_v3_plan.md

@@ -1,942 +0,0 @@
-# Шаг 2 — План разделения с диффами
-
-> Решения, на которых строится этот план:
-> 1. **Плоские `.gin`** без `include` — каждый эксперимент = самодостаточный набор файлов
-> 2. **`TrainConfigGTAUAV` разделяем сразу**
-> 3. **Отдельные `ModelsConfig`-классы на каждый бэкбон** (DINOv3, StripNet, SOFIAv1, SOFIAv71)
-> 4. **Скрипты переводим на gin**
->
-> Этот документ — **план**, не финальный полный набор диффов. Он отвечает на вопрос «какие конфиг-классы будут, какие гин-файлы, какая иерархия пресетов». Для каждого нового файла — содержимое. Для каждого правимого файла — diff. Это ответ на вопрос «что делать с конфигом», без переписывания `Trainer` (это будет следующим шагом).
-
----
-
-## Часть A — Раскладка `TrainConfigGTAUAV` по 4 «универсальным» конфигам
-
-`TrainConfigGTAUAV` содержит 50+ полей. Разделяю их по принципу из «Рекомендуемые_gin-config_категории.md» (одна ось изменчивости = один конфиг):
-
-| Поле текущего `TrainConfigGTAUAV` | Куда едет |
-|---|---|
-| `train_json`, `test_json`, `rgb_root`, `caption_root`, `filter_meta`, `output_dir`, `resume_from`, `epochs`, `eval_every`, `warmup_epochs`, `seed` | **`PipelineConfig`** |
-| `batch_size`, `num_workers`, `grad_accum_steps`, `use_amp`, `gradient_checkpointing`, `device` | **`HardwareConfig`** |
-| `loss_type`, `tau_init`, `label_smoothing`, `learnable_temperature`, `weight_q2g`, `weight_g2q`, `neg_bank_size`, `learning_rate`, `text_lr_factor`, `weight_decay`, `grad_clip`, `sampler_type`, `dss_*`, `use_mutex_sampler` | **`TrainingConfig`** |
-| `use_wandb`, `use_tb`, `wandb_project`, `wandb_run_name`, `wandb_entity`, `log_grad_norms`, `use_gradcam`, `gradcam_every`, `gradcam_samples`, `use_profiler`, `profiler_warmup`, `profiler_active` | **`TrackingConfig`** |
-| `dino_web_path`, `dino_sat_path`, `lrsclip_path`, `init_gate`, `baseline_mode`, `shared_encoder`, `mona_bottleneck`, `mona_last_n_blocks`, `backbone`, `stripnet_path`, `stripnet_mona_last_n_stages`, `stripnet_freeze`, `stripnet_backbone_lr_factor` | **`Models*Config`** (см. Часть B) |
-
-**Итого 4 «универсальных» конфига** (Pipeline / Hardware / Training / Tracking) + плюс семейство Models-классов из Части B.
-
----
-
-## Часть B — Семейство `Models*Config` (по бэкбону)
-
-Каждый бэкбон → собственный gin-configurable класс. **Один на эксперимент** — какой именно загружается, определяется тем, какой `models_*.gin` положен в директорию пресета.
-
-### `ModelsCommonConfig` — общие поля
-
-Поля, нужные **всем** бэкбонам:
-
-```python
-@gin.configurable
-class ModelsCommonConfig:
-    """Common architecture switches shared by all backbones."""
-    def __init__(
-        self,
-        backbone: str = "dinov3",            # 'dinov3' | 'stripnet' | 'sofia_v1' | 'sofia_v71'
-        baseline_mode: bool = False,         # text disabled, gate forced 1.0
-        init_gate: float = 0.7,
-        lrsclip_path: str = "nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt",
-    ) -> None:
-        self.backbone = backbone
-        self.baseline_mode = baseline_mode
-        self.init_gate = init_gate
-        self.lrsclip_path = lrsclip_path
-```
-
-### `DINOv3ModelsConfig`
-
-```python
-@gin.configurable
-class DINOv3ModelsConfig:
-    def __init__(
-        self,
-        dino_web_path: str = "nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth",
-        dino_sat_path: str = "nn_models/DINO_SAT/model.safetensors",
-        shared_encoder: bool = True,
-        mona_bottleneck: int = 64,
-        mona_last_n_blocks: int = 12,
-    ) -> None:
-        self.dino_web_path = dino_web_path
-        self.dino_sat_path = dino_sat_path
-        self.shared_encoder = shared_encoder
-        self.mona_bottleneck = mona_bottleneck
-        self.mona_last_n_blocks = mona_last_n_blocks
-```
-
-### `StripNetModelsConfig`
-
-```python
-@gin.configurable
-class StripNetModelsConfig:
-    def __init__(
-        self,
-        stripnet_path: str = "nn_models/STRIPNET/stripnet_s.pth",
-        stripnet_freeze: bool = True,
-        stripnet_mona_last_n_stages: int = 2,
-        stripnet_backbone_lr_factor: float = 0.1,
-    ) -> None:
-        self.stripnet_path = stripnet_path
-        self.stripnet_freeze = stripnet_freeze
-        self.stripnet_mona_last_n_stages = stripnet_mona_last_n_stages
-        self.stripnet_backbone_lr_factor = stripnet_backbone_lr_factor
-```
-
-### `SOFIAv1ModelsConfig`
-
-Покрывает всё, что сейчас в `src/models/sofia_v1/config.py::SOFIAv1Config`:
-
-```python
-@gin.configurable
-class SOFIAv1ModelsConfig:
-    def __init__(
-        self,
-        # Backbone.
-        variant: str = "small",              # 'tiny_tiny' | 'tiny' | 'small' | 'small_v2'
-        in_channels: int = 3,
-        input_size: int = 256,
-        dcn_variant: str = "v2",             # 'v2' (stable) | 'v4' (faster, leaks)
-        # Heads.
-        d_descriptor: int = 1024,
-        return_normalized: bool = False,
-        # Altitude-FiLM.
-        use_film_altitude: bool = True,
-        altitude_norm: float = 500.0,
-        # Text-FiLM.
-        use_text_film_uav: bool = True,
-        use_text_film_sat: bool = True,
-        text_film_dim: int = 1024,
-        text_film_hidden: int = 256,
-    ) -> None:
-        self.variant = variant
-        self.in_channels = in_channels
-        self.input_size = input_size
-        self.dcn_variant = dcn_variant
-        self.d_descriptor = d_descriptor
-        self.return_normalized = return_normalized
-        self.use_film_altitude = use_film_altitude
-        self.altitude_norm = altitude_norm
-        self.use_text_film_uav = use_text_film_uav
-        self.use_text_film_sat = use_text_film_sat
-        self.text_film_dim = text_film_dim
-        self.text_film_hidden = text_film_hidden
-```
-
-> **`SOFIAv1Config` (`@dataclass`) в `src/models/sofia_v1/config.py` остаётся** — это внутренняя структура для модели. В `Trainer._build_model` создаём `SOFIAv1Config(...)` из полей `models_cfg` (где `models_cfg: SOFIAv1ModelsConfig`). Один источник правды — gin, а dataclass это просто адаптер на границе модельного слоя.
-
-### `SOFIAv71ModelsConfig`
-
-Покрывает поля из `src/models/sofia_v71/config.py`. По README вижу:
-
-```python
-@gin.configurable
-class SOFIAv71ModelsConfig:
-    def __init__(
-        self,
-        # Preset.
-        preset: str = "M",                   # 'M' | 'L' | 'Tiny'
-        # Mamba backend.
-        mamba_variant: str = "mamba2",       # 'mamba1' | 'mamba2' | 'efficient_vmamba'
-        mamba_backend: str = "auto",         # 'auto' | 'torch'
-        # Heads.
-        d_descriptor: int = 512,
-        # Altitude-FiLM.
-        use_film_altitude: bool = True,
-        altitude_norm: float = 500.0,
-        # KD taps.
-        return_features: bool = False,
-        # Quantization (PTQ/QAT — for production deploy, not training).
-        # Not adding here unless an experiment toggles them; can be added later.
-    ) -> None:
-        self.preset = preset
-        self.mamba_variant = mamba_variant
-        self.mamba_backend = mamba_backend
-        self.d_descriptor = d_descriptor
-        self.use_film_altitude = use_film_altitude
-        self.altitude_norm = altitude_norm
-        self.return_features = return_features
-```
-
-> ⚠️ Точные поля `SOFIAv71` нужно сверить с `src/models/sofia_v71/config.py` (я его не открыл целиком). Это **открытый пункт** — заполняется при создании файла.
-
-### Как загружать «правильный» Models config?
-
-Ровно один `models.gin` лежит в директории пресета и определяет один из 4 классов. `config_loader` ветвится:
-
-```python
-def load_all_configs(path2cfg: str) -> dict[str, Any]:
-    cfg_dir = Path(path2cfg)
-    gin.clear_config()
-    gin.parse_config_files_and_bindings(
-        config_files=[str(f) for f in sorted(cfg_dir.glob("*.gin"))],
-        bindings=[],
-    )
-
-    # Build common first to learn which backbone to use.
-    common = ModelsCommonConfig()
-    backbone_to_cls = {
-        "dinov3":    DINOv3ModelsConfig,
-        "stripnet":  StripNetModelsConfig,
-        "sofia_v1":  SOFIAv1ModelsConfig,
-        "sofia_v71": SOFIAv71ModelsConfig,
-    }
-    if common.backbone not in backbone_to_cls:
-        raise ValueError(
-            f"Unknown backbone={common.backbone!r}; expected one of {list(backbone_to_cls)}",
-        )
-    models_specific = backbone_to_cls[common.backbone]()  # gin fills it
-
-    return {
-        "pipeline": PipelineConfig(),
-        "hardware": HardwareConfig(),
-        "models_common": common,
-        "models": models_specific,         # one of 4 classes
-        "training": TrainingConfig(),
-        "tracking": TrackingConfig(),
-    }
-```
-
-**В `models.gin` пресета** прописаны биндинги **только** для активного бэкбона + `ModelsCommonConfig`. Биндинги для других бэкбонов в этот файл не попадают (плоский стиль).
-
----
-
-## Часть C — Конфиг для скриптов препроцессинга (Часть 4 ваших ответов)
-
-Скрипты `make_split.py` и `filter_segmentation.py` переводятся на gin. Возникает **5-й универсальный конфиг**:
-
-### `PreprocessConfig`
-
-```python
-@gin.configurable
-class PreprocessConfig:
-    """Preprocessing utilities: train/test split + segmentation filter.
-
-    Used only by scripts/make_split.py and scripts/filter_segmentation.py.
-    Not consumed by the training pipeline directly.
-    """
-    def __init__(
-        self,
-        # Inputs (read from PipelineConfig if you want consistency, but having
-        # them here lets preprocess run independently).
-        rgb_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR",
-        segm_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR-aug/segm",
-        # make_split params.
-        split_ratio: float = 0.8,
-        split_seed: int = 42,
-        split_input_train: str = "cross-area-drone2sate-train.json",
-        split_input_test: str = "cross-area-drone2sate-test.json",
-        split_output_dir: str = "meta",
-        split_output_train: str = "train_80.json",
-        split_output_test: str = "test_20.json",
-        # filter_segmentation params.
-        seg_threshold: float = 0.90,
-        seg_exclude_classes: list[int] | None = None,  # default [0, 4]
-        seg_filter_output: str = "meta/seg_filter.json",
-    ) -> None:
-        self.rgb_root = rgb_root
-        self.segm_root = segm_root
-        self.split_ratio = split_ratio
-        self.split_seed = split_seed
-        self.split_input_train = split_input_train
-        self.split_input_test = split_input_test
-        self.split_output_dir = split_output_dir
-        self.split_output_train = split_output_train
-        self.split_output_test = split_output_test
-        self.seg_threshold = seg_threshold
-        self.seg_exclude_classes = seg_exclude_classes or [0, 4]
-        self.seg_filter_output = seg_filter_output
-```
-
-> **Вопрос дизайна:** держать `rgb_root` отдельно в `PreprocessConfig` (как выше) или брать из `PipelineConfig.rgb_root`?
->
-> **Решение:** держать **отдельно**, потому что `PreprocessConfig` живёт в **другом** наборе .gin (отдельный пресет `preprocess/`). Это согласуется с принципом плоских конфигов без ссылок между файлами. Дублирование одного пути на 2 файла — приемлемая цена за изоляцию.
-
----
-
-## Часть D — Что и где будет лежать (структура каталогов)
-
-```
-caption-test/
-├── in/
-│   └── config_files/                  # АКТИВНЫЙ пресет, копируется из presets/
-│       ├── pipeline.gin
-│       ├── hardware.gin
-│       ├── models.gin                 # биндинги ModelsCommonConfig + одного из Models*Config
-│       ├── training.gin
-│       └── tracking.gin
-├── presets/
-│   ├── gtauav_balanced/               # все 5 файлов, БЕЗ include
-│   │   ├── pipeline.gin
-│   │   ├── hardware.gin
-│   │   ├── models.gin                 # backbone='dinov3', shared, mona_12
-│   │   ├── training.gin
-│   │   └── tracking.gin
-│   ├── gtauav_baseline/               # 5 файлов, baseline_mode=True
-│   ├── gtauav_balanced_asym/          # shared_encoder=False, mona_24
-│   ├── gtauav_baseline_asym/
-│   ├── gtauav_balanced_stripnet/      # backbone='stripnet'
-│   ├── gtauav_balanced_stripnet_unfrozen/
-│   ├── gtauav_baseline_stripnet/
-│   ├── gtauav_baseline_stripnet_unfrozen/
-│   ├── gtauav_text_heavy/             # init_gate=0.3
-│   ├── gtauav_image_heavy/            # init_gate=0.9
-│   ├── gtauav_balanced_sofia/         # backbone='sofia_v71'
-│   ├── gtauav_balanced_sofia_v1/      # backbone='sofia_v1'
-│   ├── gtauav_baseline_sofia/
-│   ├── gtauav_baseline_sofia_v1/
-│   └── preprocess/                    # отдельный пресет для скриптов
-│       └── preprocess.gin             # PreprocessConfig.* — одиночный файл достаточен
-├── src/
-│   └── conf/
-│       ├── __init__.py
-│       ├── pipeline_conf.py           # PipelineConfig + get_pipeline_cfg
-│       ├── hardware_conf.py           # HardwareConfig + get_hardware_cfg
-│       ├── training_conf.py           # TrainingConfig + get_training_cfg
-│       ├── tracking_conf.py           # TrackingConfig + get_tracking_cfg
-│       ├── models_common_conf.py      # ModelsCommonConfig + get_models_common_cfg
-│       ├── models_dinov3_conf.py      # DINOv3ModelsConfig + get_models_dinov3_cfg
-│       ├── models_stripnet_conf.py    # StripNetModelsConfig + get_models_stripnet_cfg
-│       ├── models_sofia_v1_conf.py    # SOFIAv1ModelsConfig + get_models_sofia_v1_cfg
-│       ├── models_sofia_v71_conf.py   # SOFIAv71ModelsConfig + get_models_sofia_v71_cfg
-│       ├── preprocess_conf.py         # PreprocessConfig + get_preprocess_cfg
-│       └── config_loader.py           # load_all_configs() с разводкой по backbone
-```
-
----
-
-## Часть E — Содержимое `presets/gtauav_balanced/` (точно)
-
-Это эталонный пресет — остальные диффятся от него.
-
-### `presets/gtauav_balanced/pipeline.gin`
-
-```gin
-# What to train on, where to save, schedule.
-PipelineConfig.train_json = 'meta/train_80.json'
-PipelineConfig.test_json = 'meta/test_20.json'
-PipelineConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
-PipelineConfig.caption_root = '/home/servml/Документы/datasets/GTA-UAV-LR-captions'
-PipelineConfig.filter_meta = None
-PipelineConfig.output_dir = 'out/gtauav/with_text'
-PipelineConfig.resume_from = None
-PipelineConfig.epochs = 10
-PipelineConfig.warmup_epochs = 2
-PipelineConfig.eval_every = 1
-PipelineConfig.seed = 42
-```
-
-### `presets/gtauav_balanced/hardware.gin`
-
-```gin
-# RTX 4090 profile.
-HardwareConfig.device = 'cuda'
-HardwareConfig.batch_size = 8
-HardwareConfig.grad_accum_steps = 8
-HardwareConfig.num_workers = 4
-HardwareConfig.use_amp = True
-HardwareConfig.gradient_checkpointing = True
-HardwareConfig.reserve_gb = 2.0
-```
-
-### `presets/gtauav_balanced/models.gin`
-
-```gin
-# DINOv3 shared encoder + MONA in last 12 of 24 blocks + DGTRS-CLIP text.
-ModelsCommonConfig.backbone = 'dinov3'
-ModelsCommonConfig.baseline_mode = False
-ModelsCommonConfig.init_gate = 0.7
-ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
-
-DINOv3ModelsConfig.dino_web_path = 'nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth'
-DINOv3ModelsConfig.dino_sat_path = 'nn_models/DINO_SAT/model.safetensors'
-DINOv3ModelsConfig.shared_encoder = True
-DINOv3ModelsConfig.mona_bottleneck = 64
-DINOv3ModelsConfig.mona_last_n_blocks = 12
-```
-
-### `presets/gtauav_balanced/training.gin`
-
-```gin
-# Loss + optimizer + sampler.
-TrainingConfig.loss_type = 'symmetric'
-TrainingConfig.tau_init = 0.07
-TrainingConfig.label_smoothing = 0.1
-TrainingConfig.learnable_temperature = True
-TrainingConfig.weight_q2g = 0.6
-TrainingConfig.weight_g2q = 0.4
-TrainingConfig.neg_bank_size = 0
-
-TrainingConfig.learning_rate = 1e-4
-TrainingConfig.text_lr_factor = 0.1
-TrainingConfig.weight_decay = 1e-4
-TrainingConfig.grad_clip = 1.0
-
-TrainingConfig.sampler_type = 'mutex'
-TrainingConfig.dss_warmup_epochs = 1
-TrainingConfig.dss_reembed_every = 1
-TrainingConfig.dss_knn_device = 'cuda'
-TrainingConfig.dss_use_lsh = False
-TrainingConfig.dss_lsh_num_tables = 8
-TrainingConfig.dss_lsh_num_bits = 14
-TrainingConfig.dss_cache_dir = None
-TrainingConfig.use_mutex_sampler = True
-```
-
-### `presets/gtauav_balanced/tracking.gin`
-
-```gin
-TrackingConfig.use_wandb = False
-TrackingConfig.use_tb = True
-TrackingConfig.wandb_project = 'caption-test-gtauav'
-TrackingConfig.wandb_run_name = None
-TrackingConfig.wandb_entity = None
-TrackingConfig.log_grad_norms = True
-TrackingConfig.use_gradcam = False
-TrackingConfig.gradcam_every = 5
-TrackingConfig.gradcam_samples = 8
-TrackingConfig.use_profiler = False
-TrackingConfig.profiler_warmup = 3
-TrackingConfig.profiler_active = 5
-```
-
----
-
-## Часть F — Дельты остальных пресетов от `gtauav_balanced/`
-
-Каждый пресет — **полная копия** `gtauav_balanced/` с точечными изменениями в указанных файлах. Никакого `include`.
-
-### `gtauav_baseline/`
-
-**Дельта от `gtauav_balanced/`:**
-
-`pipeline.gin`:
-```gin
-PipelineConfig.output_dir = 'out/gtauav/baseline_inbatch'
-```
-
-`models.gin`:
-```gin
-ModelsCommonConfig.baseline_mode = True
-```
-
-`training.gin`:
-```gin
-TrainingConfig.sampler_type = 'mutex'   # was already, kept explicit per diagnostic notes in old conf/gtauav_baseline.gin
-TrainingConfig.neg_bank_size = 0        # explicitly disabled (no MoCo queue)
-```
-
-(Остальные 3 файла — побайтовая копия из `gtauav_balanced/`.)
-
-### `gtauav_balanced_asym/`
-
-`pipeline.gin`:
-```gin
-PipelineConfig.output_dir = 'out/gtauav/balanced_asym'
-```
-
-`models.gin`:
-```gin
-DINOv3ModelsConfig.shared_encoder = False
-DINOv3ModelsConfig.mona_last_n_blocks = 24
-```
-
-### `gtauav_baseline_asym/`
-
-Объединяет дельту `gtauav_baseline/` и `gtauav_balanced_asym/`:
-
-`pipeline.gin`: `output_dir = 'out/gtauav/baseline_asym'`
-`models.gin`: `baseline_mode = True`, `shared_encoder = False`, `mona_last_n_blocks = 24`
-
-### `gtauav_balanced_stripnet/`
-
-`pipeline.gin`: `output_dir = 'out/gtauav/balanced_stripnet'`
-
-`models.gin` (полностью):
-```gin
-ModelsCommonConfig.backbone = 'stripnet'
-ModelsCommonConfig.baseline_mode = False
-ModelsCommonConfig.init_gate = 0.7
-ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
-
-StripNetModelsConfig.stripnet_path = 'nn_models/STRIPNET/stripnet_s.pth'
-StripNetModelsConfig.stripnet_freeze = True
-StripNetModelsConfig.stripnet_mona_last_n_stages = 2
-StripNetModelsConfig.stripnet_backbone_lr_factor = 0.1
-```
-
-(Биндинги `DINOv3ModelsConfig.*` НЕ попадают в этот файл — другой бэкбон.)
-
-### `gtauav_balanced_stripnet_unfrozen/`
-
-Дельта от `gtauav_balanced_stripnet/`:
-
-`pipeline.gin`: `output_dir = 'out/gtauav/balanced_stripnet_unfrozen'`
-`models.gin`: `StripNetModelsConfig.stripnet_freeze = False`
-
-### `gtauav_baseline_stripnet/`, `gtauav_baseline_stripnet_unfrozen/`
-
-Аналогично — `baseline_mode = True` поверх stripnet-вариантов.
-
-### `gtauav_text_heavy/`, `gtauav_image_heavy/`
-
-`pipeline.gin`: соответствующие `output_dir`
-`models.gin`: `ModelsCommonConfig.init_gate = 0.3` (text-heavy) или `0.9` (image-heavy)
-
-### `gtauav_balanced_sofia_v1/`
-
-`pipeline.gin`: `output_dir = 'out/gtauav/balanced_sofia_v1'`
-
-`models.gin` (полностью):
-```gin
-ModelsCommonConfig.backbone = 'sofia_v1'
-ModelsCommonConfig.baseline_mode = False
-ModelsCommonConfig.init_gate = 0.7
-ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
-
-SOFIAv1ModelsConfig.variant = 'small'
-SOFIAv1ModelsConfig.in_channels = 3
-SOFIAv1ModelsConfig.input_size = 256
-SOFIAv1ModelsConfig.dcn_variant = 'v2'
-SOFIAv1ModelsConfig.d_descriptor = 1024
-SOFIAv1ModelsConfig.return_normalized = False
-SOFIAv1ModelsConfig.use_film_altitude = True
-SOFIAv1ModelsConfig.altitude_norm = 500.0
-SOFIAv1ModelsConfig.use_text_film_uav = True
-SOFIAv1ModelsConfig.use_text_film_sat = True
-SOFIAv1ModelsConfig.text_film_dim = 1024
-SOFIAv1ModelsConfig.text_film_hidden = 256
-```
-
-### `gtauav_balanced_sofia/` (= sofia_v71)
-
-`pipeline.gin`: `output_dir = 'out/gtauav/balanced_sofia'`
-
-`models.gin`:
-```gin
-ModelsCommonConfig.backbone = 'sofia_v71'
-ModelsCommonConfig.baseline_mode = False
-ModelsCommonConfig.init_gate = 0.7
-ModelsCommonConfig.lrsclip_path = 'nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt'
-
-SOFIAv71ModelsConfig.preset = 'M'
-SOFIAv71ModelsConfig.mamba_variant = 'mamba2'
-SOFIAv71ModelsConfig.mamba_backend = 'auto'
-SOFIAv71ModelsConfig.d_descriptor = 512
-SOFIAv71ModelsConfig.use_film_altitude = True
-SOFIAv71ModelsConfig.altitude_norm = 500.0
-SOFIAv71ModelsConfig.return_features = False
-```
-
-> ⚠️ Точные дефолты для sofia_v71 пресета зависят от того, как сейчас выглядит `gtauav_balanced_sofia.gin` в локальной копии. **Нужны сами файлы**, чтобы воспроизвести один-в-один.
-
-### `gtauav_baseline_sofia/`, `gtauav_baseline_sofia_v1/`
-
-`baseline_mode = True` поверх sofia-вариантов.
-
----
-
-## Часть G — `presets/preprocess/preprocess.gin`
-
-Один файл (одиночный, потому что один класс):
-
-```gin
-PreprocessConfig.rgb_root = '/home/servml/Документы/datasets/GTA-UAV-LR'
-PreprocessConfig.segm_root = '/home/servml/Документы/datasets/GTA-UAV-LR-aug/segm'
-
-PreprocessConfig.split_ratio = 0.8
-PreprocessConfig.split_seed = 42
-PreprocessConfig.split_input_train = 'cross-area-drone2sate-train.json'
-PreprocessConfig.split_input_test = 'cross-area-drone2sate-test.json'
-PreprocessConfig.split_output_dir = 'meta'
-PreprocessConfig.split_output_train = 'train_80.json'
-PreprocessConfig.split_output_test = 'test_20.json'
-
-PreprocessConfig.seg_threshold = 0.90
-PreprocessConfig.seg_exclude_classes = [0, 4]
-PreprocessConfig.seg_filter_output = 'meta/seg_filter.json'
-```
-
----
-
-## Часть H — Диффы для существующих файлов
-
-> Здесь только то, что нужно поменять **в конфигурационной части**. Внутренности `Trainer`, `_evaluate`, `CSVLogger` — не трогаются на этом шаге.
-
-### Файл: `src/training/train_gtauav.py`
-
-**Полностью убрать `TrainConfigGTAUAV` и module-level path constants.** Функция `train()` получает не `cfg: TrainConfigGTAUAV`, а пять config-объектов.
-
-```diff
-  from __future__ import annotations
-  ...
-- import argparse
-  ...
-- from dataclasses import dataclass, field
-- from pathlib import Path
-+ from pathlib import Path
-
-- import gin
-  ...
-- # 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
--     # ... ВСЕ 50+ полей удаляются ...
--     profiler_active: int = 5
--
--
-- def train(cfg: TrainConfigGTAUAV) -> None:
-+ def train(
-+     pipeline_cfg: "PipelineConfig",
-+     hardware_cfg: "HardwareConfig",
-+     models_common_cfg: "ModelsCommonConfig",
-+     models_cfg: "DINOv3ModelsConfig | StripNetModelsConfig | SOFIAv1ModelsConfig | SOFIAv71ModelsConfig",
-+     training_cfg: "TrainingConfig",
-+     tracking_cfg: "TrackingConfig",
-+ ) -> None:
-      """Run full training loop."""
-      # Inside the function body, every `cfg.<field>` reference is rewritten to
-      # the appropriate <kind>_cfg.<field>. Mapping:
-      #   cfg.train_json     → pipeline_cfg.train_json
-      #   cfg.batch_size     → hardware_cfg.batch_size
-      #   cfg.tau_init       → training_cfg.tau_init
-      #   cfg.use_wandb      → tracking_cfg.use_wandb
-      #   cfg.dino_web_path  → models_cfg.dino_web_path        (when DINOv3)
-      #   cfg.stripnet_path  → models_cfg.stripnet_path        (when StripNet)
-      #   cfg.backbone       → models_common_cfg.backbone
-      #   cfg.baseline_mode  → models_common_cfg.baseline_mode
-      #   cfg.init_gate      → models_common_cfg.init_gate
-      #   cfg.lrsclip_path   → models_common_cfg.lrsclip_path
-      ...
-
-- def main() -> None:
--     parser = argparse.ArgumentParser(description="GTA-UAV caption test training.")
--     parser.add_argument("--config", type=str, default=None, ...)
--     parser.add_argument("--baseline", action="store_true", ...)
--     # ... все 15 argparse флагов удаляются ...
--     args = parser.parse_args()
--
--     if args.config is not None:
--         gin.parse_config_file(args.config)
--     if args.gin_param:
--         gin.parse_config(args.gin_param)
--
--     cfg = TrainConfigGTAUAV()
--
--     if args.baseline:
--         cfg.baseline_mode = True
--     # ... все CLI overrides удаляются ...
--     train(cfg)
-+ def main() -> None:
-+     """Entry point: read configs from in/config_files/ and run training."""
-+     from src.conf.config_loader import load_all_configs
-+     from src.utils.path_utils import get_proj_dir
-+
-+     proj_dir = get_proj_dir()
-+     path2cfg = f"{proj_dir}in/config_files/"
-+     configs = load_all_configs(path2cfg)
-+
-+     train(
-+         pipeline_cfg=configs["pipeline"],
-+         hardware_cfg=configs["hardware"],
-+         models_common_cfg=configs["models_common"],
-+         models_cfg=configs["models"],
-+         training_cfg=configs["training"],
-+         tracking_cfg=configs["tracking"],
-+     )
-```
-
-### Файл: `src/losses/multi_infonce.py`
-
-Снять `@gin.configurable` (см. Шаг 1, Нарушение #2). Параметры будут приходить из `TrainingConfig` через явный вызов в `train()`:
-
-```diff
-  from __future__ import annotations
-  ...
-  import math
-
-- import gin
-  import torch
-  ...
-
-- @gin.configurable
-  class InfoNCELoss(nn.Module):
-      """Symmetric InfoNCE with learnable or scheduled temperature.
-+
-+     Note: NOT @gin.configurable. Parameters arrive explicitly from
-+     train() via TrainingConfig.* — single source of truth.
-      ...
-      """
-```
-
-### Файл: `src/losses/weighted_infonce.py`
-
-Аналогично:
-
-```diff
-  ...
-- import gin
-  import torch
-  ...
-
-- @gin.configurable
-  class WeightedInfoNCELoss(nn.Module):
-      """Weighted InfoNCE with adaptive per-sample label smoothing.
-+
-+     Note: NOT @gin.configurable. Parameters arrive explicitly from
-+     train() via TrainingConfig.* — single source of truth.
-      ...
-      """
-```
-
-### Файл: `src/datasets/visloc_with_captions.py` (legacy v2)
-
-Аналогично — снять `@gin.configurable` с `GeoLocCaptionDataset`. Если v2 удаляется как ветка — этот файл удаляется целиком, дифф не нужен.
-
-```diff
-- import gin
-  ...
-
-- @gin.configurable
-  class GeoLocCaptionDataset(Dataset):
-      ...
-```
-
-### Файл: `src/datasets/gtauav_dataset.py`
-
-Убрать module-level пути. `rgb_root` и `caption_root` становятся обязательными в `__init__` — они придут из `pipeline_cfg.rgb_root` / `pipeline_cfg.caption_root` в `train()`.
-
-```diff
-  ...
-- # Default paths.
-- _RGB_ROOT = Path("/home/servml/Документы/datasets/GTA-UAV-LR")
-- _CAPTION_ROOT = Path("/home/servml/Документы/datasets/GTA-UAV-LR-captions")
-  _EMPTY_CAPTION = ""
-  ...
-  class GTAUAVDataset(Dataset):
-      def __init__(
-          self,
-          pair_json: str,
--         rgb_root: str = str(_RGB_ROOT),
--         caption_root: str = str(_CAPTION_ROOT),
-+         rgb_root: str,
-+         caption_root: str,
-          drone_transform: Callable | None = None,
-          ...
-      ) -> None:
-```
-
-### Файл: `scripts/make_split.py`
-
-Полностью переписывается на gin: argparse → `get_preprocess_cfg`, module-level пути → поля конфига.
-
-```diff
-  from __future__ import annotations
-  ...
-- import argparse
-  import json
-  import logging
-  import random
-  from pathlib import Path
-
-  import coloredlogs
-
-+ from src.conf.preprocess_conf import get_preprocess_cfg
-+ from src.utils.path_utils import get_proj_dir
-
-  LOGGER = logging.getLogger("caption_test.make_split")
-
-- _RGB_ROOT = Path("/home/servml/Документы/datasets/GTA-UAV-LR")
--
--
-  def main() -> None:
--     parser = argparse.ArgumentParser(description="Create 80/20 split for GTA-UAV-LR.")
--     parser.add_argument("--ratio", type=float, default=0.8, help="Train ratio (default 0.8).")
--     parser.add_argument("--seed", type=int, default=42, help="Random seed.")
--     parser.add_argument("--output-dir", type=str, default="meta", help="Output directory.")
--     args = parser.parse_args()
--
--     coloredlogs.install(level="INFO", logger=LOGGER, ...)
-+     coloredlogs.install(level="INFO", logger=LOGGER, ...)
-+
-+     # Load config from a separate preprocess preset directory.
-+     proj_dir = get_proj_dir()
-+     cfg = get_preprocess_cfg(f"{proj_dir}presets/preprocess/")
-+
-+     rgb_root = Path(cfg.rgb_root)
-+     train_path = rgb_root / cfg.split_input_train
-+     test_path = rgb_root / cfg.split_input_test
-
--     train_path = _RGB_ROOT / "cross-area-drone2sate-train.json"
--     test_path = _RGB_ROOT / "cross-area-drone2sate-test.json"
--
-      LOGGER.info("📂 Loading %s", train_path.name)
-      with open(train_path) as f:
-          part1 = json.load(f)
-      ...
-
--     rng = random.Random(args.seed)
-+     rng = random.Random(cfg.split_seed)
-      rng.shuffle(all_pairs)
-
--     n_train = int(len(all_pairs) * args.ratio)
-+     n_train = int(len(all_pairs) * cfg.split_ratio)
-      ...
-
--     out_dir = Path(args.output_dir)
-+     out_dir = Path(cfg.split_output_dir)
-      out_dir.mkdir(parents=True, exist_ok=True)
--     train_out = out_dir / "train_80.json"
--     test_out = out_dir / "test_20.json"
-+     train_out = out_dir / cfg.split_output_train
-+     test_out = out_dir / cfg.split_output_test
-      ...
-```
-
-### Файл: `scripts/filter_segmentation.py`
-
-Аналогично:
-
-```diff
-  from __future__ import annotations
-  ...
-- import argparse
-  import json
-  import logging
-  from pathlib import Path
-
-  import coloredlogs
-  import numpy as np
-  from PIL import Image
-  from tqdm import tqdm
-
-+ from src.conf.preprocess_conf import get_preprocess_cfg
-+ from src.utils.path_utils import get_proj_dir
-
-  LOGGER = logging.getLogger("caption_test.filter_seg")
-
-- SEGM_ROOT = Path("/home/servml/Документы/datasets/GTA-UAV-LR-aug/segm")
-- EXCLUDE_CLASSES = {0, 4}  # background, water
-- DEFAULT_THRESHOLD = 0.90
--
-  ...
-
-  def main() -> None:
--     parser = argparse.ArgumentParser(...)
--     parser.add_argument("--segm-root", ...)
--     parser.add_argument("--threshold", ...)
--     parser.add_argument("--output", ...)
--     args = parser.parse_args()
-+     coloredlogs.install(level="INFO", logger=LOGGER, ...)
-+
-+     proj_dir = get_proj_dir()
-+     cfg = get_preprocess_cfg(f"{proj_dir}presets/preprocess/")
-+
-+     segm_root = Path(cfg.segm_root)
-+     exclude_classes = set(cfg.seg_exclude_classes)
-+     threshold = cfg.seg_threshold
-+     output_path = Path(cfg.seg_filter_output)
-
--     coloredlogs.install(level="INFO", logger=LOGGER, ...)
--     LOGGER.info("🚀 Starting segmentation filter (threshold=%.2f)", args.threshold)
--     segm_root = Path(args.segm_root)
--     results = scan_masks(segm_root, EXCLUDE_CLASSES, args.threshold)
-+     LOGGER.info("🚀 Starting segmentation filter (threshold=%.2f)", threshold)
-+     results = scan_masks(segm_root, exclude_classes, threshold)
-      ...
--     output_path = Path(args.output)
-      output_path.parent.mkdir(parents=True, exist_ok=True)
-
-      out = {
--         "threshold": args.threshold,
--         "exclude_classes": sorted(EXCLUDE_CLASSES),
-+         "threshold": threshold,
-+         "exclude_classes": sorted(exclude_classes),
-          ...
-      }
-      ...
-```
-
-### Файл: `src/training/train.py` (legacy v2)
-
-Если v2 оставляем — снять `@gin.configurable` с `TrainConfig` и переписать на 5 конфигов аналогично `train_gtauav.py`.
-Если удаляем — файл уходит вместе с веткой.
-
-> **Я бы советовал удалить v2** — он создаёт двойную работу при каждом изменении. Но это **отдельный** разговор, не блокер для текущего шага.
-
-### Файл: `conf/` (старые .gin) — удаляются после миграции
-
-После того как **все 14 пресетов в `presets/`** созданы и проверены — старая директория `conf/` удаляется целиком:
-
-```diff
-- conf/balanced.gin
-- conf/baseline_no_text.gin
-- conf/text_heavy.gin
-- conf/gtauav_balanced.gin
-- conf/gtauav_baseline.gin
-- conf/gtauav_balanced_asym.gin
-- conf/gtauav_baseline_asym.gin
-- conf/gtauav_balanced_stripnet.gin
-- conf/gtauav_balanced_stripnet_unfrozen.gin
-- conf/gtauav_baseline_stripnet.gin
-- conf/gtauav_baseline_stripnet_unfrozen.gin
-- conf/gtauav_text_heavy.gin
-- conf/gtauav_image_heavy.gin
-- conf/gtauav_balanced_sofia.gin                    # из локальной копии
-- conf/gtauav_balanced_sofia_v1.gin                 # из локальной копии
-- conf/gtauav_baseline_sofia.gin                    # из локальной копии
-- conf/gtauav_baseline_sofia_v1.gin                 # из локальной копии
-```
-
----
-
-## Часть I — Что НЕ делаем на этом шаге
-
-Чтобы шаг был обозримым, **не трогаем**:
-
-- ❌ Декомпозиция `train()` (1296 строк) на `Trainer.run()` + методы — **отдельный шаг**
-- ❌ Перенос `_evaluate` в `src/eval/evaluator.py` — **отдельный шаг**
-- ❌ Перенос `CSVLogger` в `src/training/csv_logger.py` — **отдельный шаг**
-- ❌ Замена `@torch.no_grad()` на `@torch.inference_mode()` — **отдельный шаг (косметика)**
-- ❌ `_atomic_save` cleanup на ошибке — **отдельный шаг (косметика)**
-- ❌ Логика sofia_v1/v71 моделей и их `dataclass`-конфиги — **внутренний слой не трогаем**
-
-После этого шага получаем: **гин-конфиг разделён, никаких `@gin.configurable + @dataclass`, никаких `@gin.configurable` на бизнес-классах, никаких argparse, плоские пресеты с дублированием полных биндингов вместо `include`**. Структура `train()` остаётся прежней (одна большая функция), но получает 6 объектов конфига вместо одного `cfg`.
-
----
-
-## Часть J — Открытые вопросы для уточнения
-
-1. **SOFIAv71 fields** — точный список полей `SOFIAv71Config` (`@dataclass` в `src/models/sofia_v71/config.py`) для построения `SOFIAv71ModelsConfig`. Я выписал поля по README, но в `config.py` могут быть ещё (mamba `headdim`, `d_state`, `kernel_size`, `num_bins` для квантизации). Нужно открыть файл и составить полный список.
-
-2. **`gtauav_*_sofia*.gin` локальные пресеты** — содержимое 4 sofia-гинов из локальной копии (на скриншоте видны, в репо ещё нет). Нужны как эталон для воспроизведения дефолтов один-в-один.
-
-3. **`use_mutex_sampler`** в `TrainingConfig` — текущий код помечает поле как «legacy alias». Сохранить ли его на этапе разделения, или сразу убрать (тогда `effective_sampler_type` берётся напрямую из `sampler_type`)?
-
-4. **Legacy v2** (`train.py`, `visloc_with_captions.py`, `conf/balanced.gin`/`baseline_no_text.gin`/`text_heavy.gin`) — удаляем или приводим к новому стилю? Я склоняюсь к удалению. Если оставлять — добавляется ещё 3 пресета и переписывание `TrainConfig` на 5 конфигов.
-
-5. **Расположение `presets/`** — в корне проекта или внутри `in/` (как `in/presets/`)? У вас сейчас лежит в корне (`presets/gtauav_balanced/` рядом с `src/`). Оставляем там же.
-
-После ответов на эти 5 вопросов план становится готов к реализации без новых развилок.

src/conf/__init__.py

@@ -1 +1,54 @@
+"""Gin-configurable settings for the caption-test project.
 
+Five universal axes of variability:
+  - PipelineConfig     — paths, training schedule, output, resume
+  - HardwareConfig     — batch size, accumulation, AMP, gradient checkpointing
+  - TrainingConfig     — loss + optimizer + sampler (the recipe)
+  - TrackingConfig     — wandb / tensorboard / gradcam / profiler
+
+Plus a model-family config: ModelsCommonConfig describes the active backbone,
+and one of {DINOv3, StripNet, SOFIAv1, SOFIAv71} ModelsConfig classes
+parameterises it.
+
+Plus PreprocessConfig used only by scripts/make_split.py and
+scripts/filter_segmentation.py.
+
+All configs are loaded together via load_all_configs(path2cfg) — see
+config_loader.py.
+"""
+
+from src.conf.config_loader import load_all_configs
+from src.conf.hardware_conf import HardwareConfig, get_hardware_cfg
+from src.conf.models_common_conf import ModelsCommonConfig, get_models_common_cfg
+from src.conf.models_dinov3_conf import DINOv3ModelsConfig, get_models_dinov3_cfg
+from src.conf.models_sofia_v1_conf import SOFIAv1ModelsConfig, get_models_sofia_v1_cfg
+from src.conf.models_sofia_v71_conf import SOFIAv71ModelsConfig, get_models_sofia_v71_cfg
+from src.conf.models_stripnet_conf import StripNetModelsConfig, get_models_stripnet_cfg
+from src.conf.pipeline_conf import PipelineConfig, get_pipeline_cfg
+from src.conf.preprocess_conf import PreprocessConfig, get_preprocess_cfg
+from src.conf.tracking_conf import TrackingConfig, get_tracking_cfg
+from src.conf.training_conf import TrainingConfig, get_training_cfg
+
+__all__ = [
+    "DINOv3ModelsConfig",
+    "HardwareConfig",
+    "ModelsCommonConfig",
+    "PipelineConfig",
+    "PreprocessConfig",
+    "SOFIAv1ModelsConfig",
+    "SOFIAv71ModelsConfig",
+    "StripNetModelsConfig",
+    "TrackingConfig",
+    "TrainingConfig",
+    "get_hardware_cfg",
+    "get_models_common_cfg",
+    "get_models_dinov3_cfg",
+    "get_models_sofia_v1_cfg",
+    "get_models_sofia_v71_cfg",
+    "get_models_stripnet_cfg",
+    "get_pipeline_cfg",
+    "get_preprocess_cfg",
+    "get_tracking_cfg",
+    "get_training_cfg",
+    "load_all_configs",
+]

src/conf/config_loader.py

@@ -1 +1,90 @@
+"""Single entry point for loading all configs in a training run.
+
+Reads every .gin file in path2cfg (one preset directory) and instantiates
+the 4 universal configs + ModelsCommonConfig + the family-specific Models
+config selected by ModelsCommonConfig.backbone.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any
+
+import gin
+
+from src.conf.hardware_conf import HardwareConfig
+from src.conf.models_common_conf import ModelsCommonConfig
+from src.conf.models_dinov3_conf import DINOv3ModelsConfig
+from src.conf.models_sofia_v1_conf import SOFIAv1ModelsConfig
+from src.conf.models_sofia_v71_conf import SOFIAv71ModelsConfig
+from src.conf.models_stripnet_conf import StripNetModelsConfig
+from src.conf.pipeline_conf import PipelineConfig
+from src.conf.tracking_conf import TrackingConfig
+from src.conf.training_conf import TrainingConfig
+
+logger = logging.getLogger(__name__)
+
+# Maps ModelsCommonConfig.backbone → family-specific config class.
+_BACKBONE_TO_MODELS_CLS = {
+    "dinov3":    DINOv3ModelsConfig,
+    "stripnet":  StripNetModelsConfig,
+    "sofia_v1":  SOFIAv1ModelsConfig,
+    "sofia_v71": SOFIAv71ModelsConfig,
+}
+
+
+def load_all_configs(path2cfg: str) -> dict[str, Any]:
+    """Parse every .gin in path2cfg and instantiate all configs.
+
+    Args:
+        path2cfg: Path to a preset directory ending with '/', e.g.
+                  '/home/user/caption-test/in/config_files/'.
+
+    Returns:
+        Dict with keys:
+            'pipeline'      → PipelineConfig
+            'hardware'      → HardwareConfig
+            'training'      → TrainingConfig
+            'tracking'      → TrackingConfig
+            'models_common' → ModelsCommonConfig
+            'models'        → DINOv3 | StripNet | SOFIAv1 | SOFIAv71 ModelsConfig
+                              (selected by models_common.backbone)
+
+    Raises:
+        FileNotFoundError: If path2cfg contains no .gin files.
+        ValueError: If models_common.backbone is not one of the known values.
+    """
+    cfg_dir = Path(path2cfg)
+    gin_files = sorted(cfg_dir.glob("*.gin"))
+    if not gin_files:
+        raise FileNotFoundError(f"No .gin files found in {cfg_dir}")
+
+    # MANDATORY: reset gin global state — without clear_config(), bindings
+    # from previous parses accumulate.
+    gin.clear_config()
+    gin.parse_config_files_and_bindings(
+        config_files=[str(f) for f in gin_files],
+        bindings=[],
+    )
+    logger.info("Loaded %d gin files from %s", len(gin_files), cfg_dir)
+
+    # Build common first to learn which backbone to use.
+    models_common = ModelsCommonConfig()
+    if models_common.backbone not in _BACKBONE_TO_MODELS_CLS:
+        raise ValueError(
+            f"Unknown backbone={models_common.backbone!r}; "
+            f"expected one of {sorted(_BACKBONE_TO_MODELS_CLS)}",
+        )
+    models_specific = _BACKBONE_TO_MODELS_CLS[models_common.backbone]()
+
+    return {
+        "pipeline":      PipelineConfig(),
+        "hardware":      HardwareConfig(),
+        "training":      TrainingConfig(),
+        "tracking":      TrackingConfig(),
+        "models_common": models_common,
+        "models":        models_specific,
+    }
+
 

src/conf/hardware_conf.py

@@ -1 +1,46 @@
+"""GPU profile + memory/compute optimisation flags."""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class HardwareConfig:
+    """Hardware-bound parameters: VRAM footprint and throughput.
+
+    These do not change the training recipe (loss/optimizer/sampler), only
+    how many samples fit on the device.
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        batch_size: int = 8,
+        grad_accum_steps: int = 8,
+        num_workers: int = 4,
+        use_amp: bool = True,
+        gradient_checkpointing: bool = True,
+        reserve_gb: float = 2.0,
+        total_vram_gb: float = 24.0,
+    ) -> None:
+        self.device = device
+        self.batch_size = batch_size
+        self.grad_accum_steps = grad_accum_steps
+        self.num_workers = num_workers
+        self.use_amp = use_amp
+        self.gradient_checkpointing = gradient_checkpointing
+        self.reserve_gb = reserve_gb
+        self.total_vram_gb = total_vram_gb
+        # Derived.
+        self.available_vram_gb = self.total_vram_gb - self.reserve_gb
+        self.effective_batch_size = self.batch_size * self.grad_accum_steps
+
+
+def get_hardware_cfg(path2cfg: str) -> HardwareConfig:
+    """Load ONLY hardware config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}hardware.gin")
+    return HardwareConfig()
+
 

src/conf/models_common_conf.py

@@ -1,12 +1,38 @@
+"""Backbone-agnostic model parameters.
+
+`backbone` selects which family-specific Models config is loaded by
+config_loader.load_all_configs.
+"""
+
+from __future__ import annotations
+
 import gin
 
+
 @gin.configurable
 class ModelsCommonConfig:
-    """Common architecture switches shared by all backbones."""
+    """Shared model fields across all backbones.
+
+    `backbone` is the dispatch key — one of:
+      - 'dinov3'    → DINOv3ModelsConfig
+      - 'stripnet'  → StripNetModelsConfig
+      - 'sofia_v1'  → SOFIAv1ModelsConfig
+      - 'sofia_v71' → SOFIAv71ModelsConfig
+
+    `baseline_mode=True` disables text fusion entirely (gates locked at 1.0,
+    DGTRS-CLIP not loaded, TextFusionMLP not built). Used for Δ R@1 baselines.
+
+    `init_gate` controls the initial sigmoid value of GatedFusion gates
+    (0.7 = 70% image, 30% text by default; 0.3 = text-heavy; 0.9 = image-heavy).
+
+    `lrsclip_path` is the path to the DGTRS-CLIP checkpoint (only loaded when
+    text fusion is active).
+    """
+
     def __init__(
         self,
-        backbone: str = "dinov3",            # 'dinov3' | 'stripnet' | 'sofia_v1' | 'sofia_v71'
+        backbone: str = "dinov3",
-        baseline_mode: bool = False,         # text disabled, gate forced 1.0
+        baseline_mode: bool = False,
         init_gate: float = 0.7,
         lrsclip_path: str = "nn_models/LRSCLIP/DGTRS-CLIP-ViT-L-14.pt",
     ) -> None:
@@ -16,3 +42,9 @@ class ModelsCommonConfig:
         self.lrsclip_path = lrsclip_path
 
 
+def get_models_common_cfg(path2cfg: str) -> ModelsCommonConfig:
+    """Load ONLY models_common config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}models.gin")
+    return ModelsCommonConfig()
+

src/conf/models_dinov3_conf.py

@@ -1,7 +1,24 @@
+"""DINOv3 backbone configuration: encoders + MONA adapters."""
+
+from __future__ import annotations
+
 import gin
 
+
 @gin.configurable
 class DINOv3ModelsConfig:
+    """DINOv3 ViT-L/16 with MONA adapters (CVPR 2025).
+
+    `shared_encoder=True` uses a single DINOv3 WEB encoder for both drone and
+    satellite branches (default; ~432M params total). When False, separate WEB
+    (drone) + SAT (satellite) encoders are built (~733M params total, +4-5GB
+    VRAM).
+
+    MONA adapters are injected in the LAST `mona_last_n_blocks` of the 24
+    ViT blocks (default: 12 = top half). Set to 24 for full-capacity asymmetric
+    setup.
+    """
+
     def __init__(
         self,
         dino_web_path: str = "nn_models/DINO_WEB/dinov3-vitl16-pretrain-lvd1689m.pth",
@@ -9,11 +26,19 @@ class DINOv3ModelsConfig:
         shared_encoder: bool = True,
         mona_bottleneck: int = 64,
         mona_last_n_blocks: int = 12,
+        lora_rank: int = 4,
     ) -> None:
         self.dino_web_path = dino_web_path
         self.dino_sat_path = dino_sat_path
         self.shared_encoder = shared_encoder
         self.mona_bottleneck = mona_bottleneck
         self.mona_last_n_blocks = mona_last_n_blocks
+        self.lora_rank = lora_rank
+
+
+def get_models_dinov3_cfg(path2cfg: str) -> DINOv3ModelsConfig:
+    """Load ONLY DINOv3 models config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}models.gin")
+    return DINOv3ModelsConfig()
 
-        

src/conf/models_sofia_v1_conf.py

@@ -1,37 +1,69 @@
-# import gin
+"""SOFIA v1 backbone configuration: 4-stage StripNet+DCNv4 + GGeM heads."""
 
-# @gin.configurable
+from __future__ import annotations
-# class SOFIAv1ModelsConfig:
+
-#     def __init__(
+import gin
-#         self,
-#         # Backbone.
-#         variant: str = "small",              # 'tiny_tiny' | 'tiny' | 'small' | 'small_v2'
-#         in_channels: int = 3,
-#         input_size: int = 256,
-#         dcn_variant: str = "v2",             # 'v2' (stable) | 'v4' (faster, leaks)
-#         # Heads.
-#         d_descriptor: int = 1024,
-#         return_normalized: bool = False,
-#         # Altitude-FiLM.
-#         use_film_altitude: bool = True,
-#         altitude_norm: float = 500.0,
-#         # Text-FiLM.
-#         use_text_film_uav: bool = True,
-#         use_text_film_sat: bool = True,
-#         text_film_dim: int = 1024,
-#         text_film_hidden: int = 256,
-#     ) -> None:
-#         self.variant = variant
-#         self.in_channels = in_channels
-#         self.input_size = input_size
-#         self.dcn_variant = dcn_variant
-#         self.d_descriptor = d_descriptor
-#         self.return_normalized = return_normalized
-#         self.use_film_altitude = use_film_altitude
-#         self.altitude_norm = altitude_norm
-#         self.use_text_film_uav = use_text_film_uav
-#         self.use_text_film_sat = use_text_film_sat
-#         self.text_film_dim = text_film_dim
-#         self.text_film_hidden = text_film_hidden
 
 
+@gin.configurable
+class SOFIAv1ModelsConfig:
+    """SOFIA v1: lightweight StripNet+DCNv4 backbone + heads.
+
+    `variant_label` chooses backbone size (architecture dimensions are
+    resolved inside the model code from this label):
+        tiny_tiny: dims [16, 32, 80, 128]   (~0.4M)
+        tiny     : dims [32, 64, 128, 256]  (~1M)
+        small    : dims [64, 128, 320, 512] (~3M, default in code)
+        small_v2 : dims [64, 128, 256, 384] (~2M)
+
+    `dcn_variant`: 'v2' = torchvision DeformConv2d (stable). 'v4' = OpenGVLab
+    DCNv4 (faster but ~9 MB / forward leak from C++ extension).
+
+    Text fusion is two-level:
+      - Mid-level: Text-FiLM modulates feature maps before GGeM (when
+        use_text_film_uav / use_text_film_sat = True).
+      - Late-level: GatedFusion on descriptors (handled outside this config).
+    """
+
+    def __init__(
+        self,
+        # ---- Backbone ----
+        variant_label: str = "small",      # 'tiny_tiny' | 'tiny' | 'small' | 'small_v2'
+        in_channels: int = 3,
+        input_size: int = 256,
+        dcn_variant: str = "v2",           # 'v2' | 'v4'
+        # ---- Heads ----
+        d_descriptor: int = 1024,
+        return_normalized: bool = False,
+        # ---- Altitude-FiLM (UAV head) ----
+        use_film_altitude: bool = True,
+        altitude_norm: float = 500.0,
+        # ---- Text-FiLM (mid-level fusion) ----
+        use_text_film_uav: bool = True,
+        use_text_film_sat: bool = True,
+        text_film_dim: int = 1024,
+        text_film_hidden: int = 256,
+        # ---- LoRA on DGTRS-CLIP text encoder ----
+        lora_rank: int = 4,
+    ) -> None:
+        self.variant_label = variant_label
+        self.in_channels = in_channels
+        self.input_size = input_size
+        self.dcn_variant = dcn_variant
+        self.d_descriptor = d_descriptor
+        self.return_normalized = return_normalized
+        self.use_film_altitude = use_film_altitude
+        self.altitude_norm = altitude_norm
+        self.use_text_film_uav = use_text_film_uav
+        self.use_text_film_sat = use_text_film_sat
+        self.text_film_dim = text_film_dim
+        self.text_film_hidden = text_film_hidden
+        self.lora_rank = lora_rank
+
+
+def get_models_sofia_v1_cfg(path2cfg: str) -> SOFIAv1ModelsConfig:
+    """Load ONLY SOFIA v1 models config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}models.gin")
+    return SOFIAv1ModelsConfig()
+

src/conf/models_sofia_v71_conf.py

@@ -0,0 +1,178 @@
+"""SOFIA v7.1 backbone: 4-stage StripDCN + MambaVision + CVGL-Aware Head."""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class SOFIAv71ModelsConfig:
+    """SOFIA v7.1 student model.
+
+    Mirrors src/models/sofia_v71/config.py::SOFIAConfig with one
+    difference: `mamba_extra_kwargs` (a dict in the dataclass) is flattened
+    into 5 explicit fields here, and reassembled into a dict for downstream
+    code.
+
+    Variant scale presets (see model code):
+        Tiny: stem=16/32, dims=[48, 96, 176, 224],  depths=[2, 3, 4, 2]   (~5M)
+        M   : stem=64/128, dims=[256, 512, 1280, 1536], depths=[3, 4, 15, 3] (~500M, default)
+        L   : stem=64/128, dims=[256, 512, 1536, 2048], depths=[3, 4, 20, 3] (~1B)
+
+    For the active experiment (Tiny preset, see `presets/gtauav_balanced_sofia/`)
+    you can override individual fields directly without resorting to a
+    'preset' string parameter — every architectural dimension is bindable.
+
+    Tiny needs `num_heads_*=4` (channels 176/224 not divisible by 8) and
+    `mamba_headdim=16` (channels not divisible by 64).
+    """
+
+    def __init__(
+        self,
+        # ---- Variant label (informational, used in logs/checkpoints) ----
+        variant_label: str = "M",                # 'M' | 'L' | 'Tiny'
+        # ---- Input ----
+        input_size: int = 256,
+        in_channels: int = 3,
+        # ---- Stem ----
+        stem_mid: int = 64,
+        stem_out: int = 128,
+        # ---- Backbone dimensions (per stage s1..s4) ----
+        # Lists default to None; concrete defaults are filled in __init__ to
+        # avoid the def f(x=[]) anti-pattern.
+        embed_dims: list[int] | None = None,     # default [256, 512, 1280, 1536] (M)
+        depths: list[int] | None = None,         # default [3, 4, 15, 3] (M)
+        # ---- Stage 1-2 block params ----
+        mbconv_expand: int = 4,
+        se_ratio: int = 16,
+        strip_kernel_s1: int = 7,
+        strip_kernel_s2: int = 5,
+        mix_kernels: list[int] | None = None,    # default [3, 5, 7]
+        use_dcn_strip: bool = True,
+        # ---- Stage 3-4 (MambaVision) ----
+        mamba_d_state: int = 16,
+        mamba_dt_rank: int | None = None,        # auto = max(1, C // 16)
+        mamba_backend: str = "auto",             # 'auto' | 'torch' | 'mamba_ssm'
+        mamba_variant: str = "mamba2",           # 'mamba1' | 'mamba2' | 'efficient_vmamba'
+        # mamba_extra_kwargs flattened (assembled back into a dict in __init__):
+        mamba_d_state_mamba2: int = 64,
+        mamba_headdim: int = 64,
+        mamba_expand: int = 2,
+        mamba_d_conv: int = 4,
+        mamba_n_directions: int = 2,
+        # ---- Heads / attention ----
+        num_heads_s3: int = 8,
+        num_heads_s4: int = 8,
+        use_strip_branch_s3: bool = True,
+        use_strip_branch_s4: bool = False,
+        ffn_expand: int = 4,
+        # ---- EVSS bridge ----
+        use_evss_bridge: bool = False,
+        evss_bridge_locations: list[str] | None = None,   # default ['pre_stage3']
+        # ---- Neck ----
+        neck_channels: int = 192,
+        # ---- CVGL-Aware Head v7.1-α ----
+        d_descriptor: int = 512,
+        use_asymmetric_heads: bool = True,
+        chp_rings: int = 8,
+        chp_angles: int = 16,
+        chp_harmonics: int = 4,
+        use_film_altitude: bool = True,
+        altitude_norm: float = 500.0,
+        ring_count: int = 4,
+        use_ring_aux: bool = True,
+        # ---- Text fusion ----
+        return_normalized: bool = True,
+        use_text_film_sat: bool = False,
+        use_text_film_uav: bool = False,
+        text_film_dim: int = 1024,
+        text_film_hidden: int = 256,
+        # ---- Weight-sharing ----
+        share_stages_1_2: bool = True,
+        # ---- KD taps ----
+        enable_kd_taps: bool = True,
+        # ---- Deployment ----
+        precision: str = "fp16",                 # 'fp32' | 'fp16' | 'int8_mixed'
+        # ---- LoRA on DGTRS-CLIP text encoder ----
+        lora_rank: int = 4,
+    ) -> None:
+        # Variant label.
+        self.variant_label = variant_label
+        # Input.
+        self.input_size = input_size
+        self.in_channels = in_channels
+        # Stem.
+        self.stem_mid = stem_mid
+        self.stem_out = stem_out
+        # Backbone dimensions.
+        self.embed_dims = embed_dims if embed_dims is not None else [256, 512, 1280, 1536]
+        self.depths = depths if depths is not None else [3, 4, 15, 3]
+        # Stage 1-2.
+        self.mbconv_expand = mbconv_expand
+        self.se_ratio = se_ratio
+        self.strip_kernel_s1 = strip_kernel_s1
+        self.strip_kernel_s2 = strip_kernel_s2
+        self.mix_kernels = mix_kernels if mix_kernels is not None else [3, 5, 7]
+        self.use_dcn_strip = use_dcn_strip
+        # Stage 3-4.
+        self.mamba_d_state = mamba_d_state
+        self.mamba_dt_rank = mamba_dt_rank
+        self.mamba_backend = mamba_backend
+        self.mamba_variant = mamba_variant
+        self.mamba_d_state_mamba2 = mamba_d_state_mamba2
+        self.mamba_headdim = mamba_headdim
+        self.mamba_expand = mamba_expand
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_n_directions = mamba_n_directions
+        # Heads.
+        self.num_heads_s3 = num_heads_s3
+        self.num_heads_s4 = num_heads_s4
+        self.use_strip_branch_s3 = use_strip_branch_s3
+        self.use_strip_branch_s4 = use_strip_branch_s4
+        self.ffn_expand = ffn_expand
+        # EVSS.
+        self.use_evss_bridge = use_evss_bridge
+        self.evss_bridge_locations = (
+            evss_bridge_locations if evss_bridge_locations is not None else ["pre_stage3"]
+        )
+        # Neck.
+        self.neck_channels = neck_channels
+        # CVGL Head.
+        self.d_descriptor = d_descriptor
+        self.use_asymmetric_heads = use_asymmetric_heads
+        self.chp_rings = chp_rings
+        self.chp_angles = chp_angles
+        self.chp_harmonics = chp_harmonics
+        self.use_film_altitude = use_film_altitude
+        self.altitude_norm = altitude_norm
+        self.ring_count = ring_count
+        self.use_ring_aux = use_ring_aux
+        # Text fusion.
+        self.return_normalized = return_normalized
+        self.use_text_film_sat = use_text_film_sat
+        self.use_text_film_uav = use_text_film_uav
+        self.text_film_dim = text_film_dim
+        self.text_film_hidden = text_film_hidden
+        # Sharing / KD / deploy.
+        self.share_stages_1_2 = share_stages_1_2
+        self.enable_kd_taps = enable_kd_taps
+        self.precision = precision
+        # LoRA.
+        self.lora_rank = lora_rank
+        # Derived: assemble mamba_extra_kwargs back for downstream consumers.
+        self.mamba_extra_kwargs = {
+            "d_state_mamba2": self.mamba_d_state_mamba2,
+            "headdim": self.mamba_headdim,
+            "expand": self.mamba_expand,
+            "d_conv": self.mamba_d_conv,
+            "n_directions": self.mamba_n_directions,
+        }
+
+
+def get_models_sofia_v71_cfg(path2cfg: str) -> SOFIAv71ModelsConfig:
+    """Load ONLY SOFIA v71 models config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}models.gin")
+    return SOFIAv71ModelsConfig()
+
+

src/conf/models_stripnet_conf.py

@@ -1,16 +1,40 @@
+"""StripNet backbone configuration."""
+
+from __future__ import annotations
+
 import gin
 
+
 @gin.configurable
 class StripNetModelsConfig:
+    """StripNet-small encoder with Conv-MONA adaptation.
+
+    `stripnet_freeze=True` keeps the backbone frozen and only trains MONA on
+    the last `stripnet_mona_last_n_stages` of 4 stages.
+
+    `stripnet_freeze=False` (full fine-tune) makes the backbone trainable; in
+    that case backbone params get a separate LR group at
+    `learning_rate * stripnet_backbone_lr_factor` (typically 0.1).
+    """
+
     def __init__(
         self,
         stripnet_path: str = "nn_models/STRIPNET/stripnet_s.pth",
         stripnet_freeze: bool = True,
         stripnet_mona_last_n_stages: int = 2,
         stripnet_backbone_lr_factor: float = 0.1,
+        lora_rank: int = 4,
     ) -> None:
         self.stripnet_path = stripnet_path
         self.stripnet_freeze = stripnet_freeze
         self.stripnet_mona_last_n_stages = stripnet_mona_last_n_stages
         self.stripnet_backbone_lr_factor = stripnet_backbone_lr_factor
-        
+        self.lora_rank = lora_rank
+
+
+def get_models_stripnet_cfg(path2cfg: str) -> StripNetModelsConfig:
+    """Load ONLY StripNet models config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}models.gin")
+    return StripNetModelsConfig()
+

src/conf/pipeline_conf.py

@@ -1 +1,53 @@
 
+"""Pipeline orchestration: data IO, training schedule, output, resume."""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class PipelineConfig:
+    """What to train on, where to save, and how long.
+
+    All paths are absolute or relative to the project root. Defaults match
+    the servml workstation; override in pipeline.gin for other machines.
+    """
+
+    def __init__(
+        self,
+        # Data inputs.
+        train_json: str = "meta/train_80.json",
+        test_json: str = "meta/test_20.json",
+        rgb_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR",
+        caption_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR-captions",
+        filter_meta: str | None = None,
+        # Training schedule.
+        epochs: int = 10,
+        warmup_epochs: int = 2,
+        eval_every: int = 1,
+        # Reproducibility & output.
+        seed: int = 42,
+        output_dir: str = "out/gtauav/with_text",
+        resume_from: str | None = None,
+    ) -> None:
+        self.train_json = train_json
+        self.test_json = test_json
+        self.rgb_root = rgb_root
+        self.caption_root = caption_root
+        self.filter_meta = filter_meta
+        self.epochs = epochs
+        self.warmup_epochs = warmup_epochs
+        self.eval_every = eval_every
+        self.seed = seed
+        self.output_dir = output_dir
+        self.resume_from = resume_from
+
+
+def get_pipeline_cfg(path2cfg: str) -> PipelineConfig:
+    """Load ONLY pipeline config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}pipeline.gin")
+    return PipelineConfig()
+
+    

src/conf/preprocess_conf.py

@@ -1 +1,56 @@
+"""Preprocessing configuration: train/test split + segmentation filter."""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class PreprocessConfig:
+    """Used only by scripts/make_split.py and scripts/filter_segmentation.py.
+
+    Lives in a separate preset (presets/preprocess/preprocess.gin) — it is
+    not consumed by the training pipeline. Held independently from
+    PipelineConfig so that preprocess can run without a training preset
+    being active.
+    """
+
+    def __init__(
+        self,
+        # Inputs.
+        rgb_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR",
+        segm_root: str = "/home/servml/Документы/datasets/GTA-UAV-LR-aug/segm",
+        # make_split.py params.
+        split_ratio: float = 0.8,
+        split_seed: int = 42,
+        split_input_train: str = "cross-area-drone2sate-train.json",
+        split_input_test: str = "cross-area-drone2sate-test.json",
+        split_output_dir: str = "meta",
+        split_output_train: str = "train_80.json",
+        split_output_test: str = "test_20.json",
+        # filter_segmentation.py params.
+        seg_threshold: float = 0.90,
+        seg_exclude_classes: list[int] | None = None,   # default [0, 4]: background + water
+        seg_filter_output: str = "meta/seg_filter.json",
+    ) -> None:
+        self.rgb_root = rgb_root
+        self.segm_root = segm_root
+        self.split_ratio = split_ratio
+        self.split_seed = split_seed
+        self.split_input_train = split_input_train
+        self.split_input_test = split_input_test
+        self.split_output_dir = split_output_dir
+        self.split_output_train = split_output_train
+        self.split_output_test = split_output_test
+        self.seg_threshold = seg_threshold
+        self.seg_exclude_classes = seg_exclude_classes if seg_exclude_classes is not None else [0, 4]
+        self.seg_filter_output = seg_filter_output
+
+
+def get_preprocess_cfg(path2cfg: str) -> PreprocessConfig:
+    """Load preprocess config from the preprocess preset directory."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}preprocess.gin")
+    return PreprocessConfig()
+
 

src/conf/tracking_conf.py

@@ -1 +1,51 @@
+"""Experiment tracking + diagnostics.
+
+Independent axis: changing these flags does not affect training results,
+only what is observed/recorded.
+"""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class TrackingConfig:
+    """Wandb / TensorBoard / Grad-CAM / profiler / gradient norms."""
+
+    def __init__(
+        self,
+        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,
+        gradcam_samples: int = 8,
+        use_profiler: bool = False,
+        profiler_warmup: int = 3,
+        profiler_active: int = 5,
+    ) -> None:
+        self.use_wandb = use_wandb
+        self.use_tb = use_tb
+        self.wandb_project = wandb_project
+        self.wandb_run_name = wandb_run_name
+        self.wandb_entity = wandb_entity
+        self.log_grad_norms = log_grad_norms
+        self.use_gradcam = use_gradcam
+        self.gradcam_every = gradcam_every
+        self.gradcam_samples = gradcam_samples
+        self.use_profiler = use_profiler
+        self.profiler_warmup = profiler_warmup
+        self.profiler_active = profiler_active
+
+
+def get_tracking_cfg(path2cfg: str) -> TrackingConfig:
+    """Load ONLY tracking config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}tracking.gin")
+    return TrackingConfig()
+
 

src/conf/training_conf.py

@@ -1 +1,93 @@
+"""Training recipe: loss + optimizer + sampler.
+
+Three concerns kept together because they form one coherent recipe — they
+co-vary across experiments. Splitting Loss vs Optimizer vs Sampler can be
+done later if a need emerges.
+"""
+
+from __future__ import annotations
+
+import gin
+
+
+@gin.configurable
+class TrainingConfig:
+    """Loss + optimizer + sampler.
+
+    Selects between InfoNCELoss and WeightedInfoNCELoss via `loss_type`.
+    Selects between DSS / mutex / plain shuffle via `sampler_type`.
+    """
+
+    def __init__(
+        self,
+        # ---- Loss: shared between InfoNCELoss and WeightedInfoNCELoss ----
+        loss_type: str = "symmetric",            # 'symmetric' | 'weighted'
+        tau_init: float = 0.07,
+        tau_min: float = 0.01,
+        tau_max: float = 0.1,
+        learnable_temperature: bool = True,
+        label_smoothing: float = 0.1,
+        # ---- Loss: InfoNCELoss-only ----
+        tau_final: float = 0.01,                 # cosine-schedule final tau (when not learnable)
+        weight_q2g: float = 0.6,
+        weight_g2q: float = 0.4,
+        hard_mining_k: int = 0,
+        neg_bank_size: int = 0,
+        # ---- Loss: WeightedInfoNCELoss-only ----
+        weighted_loss_k: float = 5.0,            # sigmoid steepness for weight→eps mapping
+        # ---- Optimizer ----
+        learning_rate: float = 1e-4,
+        text_lr_factor: float = 0.1,             # lr * factor for DGTRS-CLIP/LoRA params
+        weight_decay: float = 1e-4,
+        grad_clip: float = 1.0,
+        # ---- Sampler ----
+        sampler_type: str = "mutex",             # 'mutex' | 'dss' | 'none'
+        dss_warmup_epochs: int = 1,
+        dss_reembed_every: int = 1,
+        dss_knn_device: str = "cuda",
+        dss_use_lsh: bool = False,
+        dss_lsh_num_tables: int = 8,
+        dss_lsh_num_bits: int = 14,
+        dss_cache_dir: str | None = None,
+        # Legacy alias (kept until train_gtauav.py is rewritten in step 4).
+        use_mutex_sampler: bool = True,
+    ) -> None:
+        # Loss (shared).
+        self.loss_type = loss_type
+        self.tau_init = tau_init
+        self.tau_min = tau_min
+        self.tau_max = tau_max
+        self.learnable_temperature = learnable_temperature
+        self.label_smoothing = label_smoothing
+        # Loss (InfoNCE-specific).
+        self.tau_final = tau_final
+        self.weight_q2g = weight_q2g
+        self.weight_g2q = weight_g2q
+        self.hard_mining_k = hard_mining_k
+        self.neg_bank_size = neg_bank_size
+        # Loss (WeightedInfoNCE-specific).
+        self.weighted_loss_k = weighted_loss_k
+        # Optimizer.
+        self.learning_rate = learning_rate
+        self.text_lr_factor = text_lr_factor
+        self.weight_decay = weight_decay
+        self.grad_clip = grad_clip
+        # Sampler.
+        self.sampler_type = sampler_type
+        self.dss_warmup_epochs = dss_warmup_epochs
+        self.dss_reembed_every = dss_reembed_every
+        self.dss_knn_device = dss_knn_device
+        self.dss_use_lsh = dss_use_lsh
+        self.dss_lsh_num_tables = dss_lsh_num_tables
+        self.dss_lsh_num_bits = dss_lsh_num_bits
+        self.dss_cache_dir = dss_cache_dir
+        self.use_mutex_sampler = use_mutex_sampler
+
+
+def get_training_cfg(path2cfg: str) -> TrainingConfig:
+    """Load ONLY training config (TESTING ONLY — production uses load_all_configs)."""
+    gin.clear_config()
+    gin.parse_config_file(f"{path2cfg}training.gin")
+    return TrainingConfig()
+