Add configs_train folder

configs_train/config_modalities.yaml

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+modalities: 
+
+    inputs:
+        AERIAL_RGBI       : True
+        AERIAL-RLT_PAN    : False
+        DEM_ELEV          : False
+        SPOT_RGBI         : False
+        SENTINEL2_TS      : True 
+        SENTINEL1-ASC_TS  : True
+        SENTINEL1-DESC_TS : True
+        
+    inputs_channels:
+        AERIAL_RGBI       : [4,1,2] 
+        SPOT_RGBI         : 
+        SENTINEL2_TS      : [1,2,3,4,5,6,7,8,9,10]
+        SENTINEL1-ASC_TS  : [1,2] 
+        SENTINEL1-DESC_TS : [1,2]
+
+    aux_loss: 
+        AERIAL_RGBI       : True
+        AERIAL-RLT_PAN    : False
+        DEM_ELEV          : False
+        SPOT_RGBI         : False
+        SENTINEL2_TS      : True
+        SENTINEL1-ASC_TS  : True
+        SENTINEL1-DESC_TS : True
+
+    aux_loss_weight: 1 # multiplier before adding to main loss
+
+    modality_dropout: # between 0 (no dropout) and 1 (complete systematic dropout)
+        AERIAL_RGBI       : 0
+        AERIAL-RLT_PAN    : 0
+        DEM_ELEV          : 0
+        SPOT_RGBI         : 0
+        SENTINEL2_TS      : 0
+        SENTINEL1-ASC_TS  : 0
+        SENTINEL1-DESC_TS : 0
+
+    pre_processings: 
+        filter_sentinel2: True 
+        filter_sentinel2_max_cloud : 1 # [0-100]
+        filter_sentinel2_max_snow : 1 # [0-100]
+        filter_sentinel2_max_frac_cover : 0.05 # [0-1]
+        temporal_average_sentinel2 : False  # possible : False, monthly, semi-monthly
+        temporal_average_sentinel1 : False
+
+        calc_elevation : True
+        calc_elevation_stack_dsm : True 
+        
+        use_augmentation: False
+
+    normalization: 
+        norm_type : custom   # possible :  custom, scaling, without
+
+        AERIAL_RGBI_means : [106.59, 105.66, 111.35]
+        AERIAL_RGBI_stds  : [39.78, 52.23, 45.62]
+
+        AERIAL-RLT_PAN_means : [125.92]
+        AERIAL-RLT_PAN_stds  : [38.45]
+
+        SPOT_RGBI_means : [433.26, 508.75, 467.77, 1137.03]
+        SPOT_RGBI_stds  : [312.76, 284.61, 226.02, 543.11]
+
+        DEM_ELEV_means : [311.06, 311.06] # use same for both DSM/DTM to allow keeping differences of elevation
+        DEM_ELEV_stds  : [537.55, 537.55] # use same for both DSM/DTM to allow keeping differences of elevation

configs_train/config_models.yaml

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+models: 
+
+    monotemp_model: # encoder-decoder from SMP
+
+        arch: swin_base_patch4_window12_384-upernet
+        new_channels_init_mode: 'random' 
+
+    multitemp_model: 
+    
+        ref_date: '05-15' # defined for whole dataset    
+        encoder_widths: [64, 64, 64, 128] # last must be equivalent to decoder_widths
+        decoder_widths: [32, 32, 64, 128] # last must be equivalent to encoder_widths
+        out_conv: [32, 19]
+        str_conv_k: 3
+        str_conv_s: 1
+        str_conv_p: 1
+        agg_mode: "att_group"
+        encoder_norm: "group"
+        n_head: 16
+        d_model: 256
+        d_k: 4
+        pad_value: 0
+        padding_mode: "reflect"

configs_train/config_supervision.yaml

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+labels: 
+  - AERIAL_LABEL-COSIA
+
+labels_configs:
+    AERIAL_LABEL-COSIA:
+        task_weight: 1
+        value_name:
+            0  : 'building'
+            1  : 'greenhouse'
+            2  : 'swimming_pool'
+            3  : 'impervious surface'
+            4  : 'pervious surface'
+            5  : 'bare soil'
+            6  : 'water'
+            7  : 'snow'
+            8  : 'herbaceous vegetation'
+            9  : 'agricultural land'
+            10 : 'plowed land'
+            11 : 'vineyard'
+            12 : 'deciduous'
+            13 : 'coniferous'
+            14 : 'brushwood'
+            15 : 'clear cut'
+            16 : 'ligneous'
+            17 : 'mixed'
+            18 : 'undefined'
+        value_weights: 
+            default: 1
+            default_exceptions:
+                15: 0
+                16: 0
+                17: 0
+                18: 0
+            per_modality_exceptions:
+                AERIAL_RGBI: 
+                SENTINEL2_TS: 
+                SENTINEL1-ASC_TS: 
+                SENTINEL1-DESC_TS:
+

configs_train/config_task.yaml

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+# SLURM
+
+paths :   
+
+    out_folder: '../'
+    out_model_name: 'FLAIR-HUB_LC-F_swinbase-upernet'
+
+    train_csv: '../TRAIN_FLAIR-INC.csv'
+    val_csv: '../VALID_FLAIR-INC.csv'
+    test_csv: '../TEST_FLAIR-INC.csv'
+    global_mtd_folder: '../GLOBAL_ALL_MTD/'
+    ckpt_model_path: ''
+
+
+tasks: 
+
+    train: True
+    train_tasks: 
+        init_weights_only_from_ckpt: False
+        resume_training_from_ckpt: False
+
+    predict: True
+    write_files: False
+    georeferencing_output: False
+
+    metrics_only: False
+
+hyperparams:
+
+    num_epochs: 150
+    batch_size: 5
+    seed: 2025
+    learning_rate: 0.00005
+
+    optimizer: adamw  #sgd, adam, adamw
+    optim_weight_decay: 0.01
+    optim_betas: [0.9, 0.999]
+
+    scheduler: one_cycle_lr #  [one_cycle_lr, reduce_on_plateau, cycle_then_plateau]
+    warmup_fraction: 0.2 #if using one_cycle_lr. [0-1]
+    plateau_patience: 5
+
+hardware:
+    accelerator: 'gpu'
+    num_nodes: 6
+    gpus_per_node: 4
+    strategy: 'ddp_find_unused_parameters_true'
+    num_workers: 10
+
+saving:   
+    ckpt_save_also_last: True
+    ckpt_weights_only: False
+    ckpt_monitor: 'val_miou'
+    ckpt_monitor_mode: 'max'
+    ckpt_earlystopping_patience: 20
+    cp_csv_and_conf_to_output: True
+    enable_progress_bar: True
+    progress_rate: 10
+
+    ckpt_verbose: True
+    verbose_config: False