fine_tune_cb55_AB1_train_20_run_divanet.yaml

# @package _global_

# to execute this experiment run:
# python run.py +experiment=exp_example_full

defaults:
    - /mode: experiment.yaml
    - /plugins: null
    - /task: semantic_segmentation_RGB.yaml
    - /loss: crossentropyloss_balanced.yaml
    - /metric:
          - iou.yaml
    - /model/backbone: divanet.yaml
    - /model/header: identity.yaml
    - /optimizer: adam.yaml
    - /callbacks:
          - check_compatibility.yaml
          - model_checkpoint.yaml
          - watch_model_wandb.yaml
    - /logger:
          - wandb.yaml # set logger here or use command line (e.g. `python run.py logger=wandb`)
          - csv.yaml
    - _self_

# we override default configurations with nulls to prevent them from loading at all
# instead we define all modules and their paths directly in this config,
# so everything is stored in one place for more readibility

name: "fine_tune_cb55_AB1_train_20_divanet_loss_weights"

train: True
test: True
predict: False

trainer:
    _target_: pytorch_lightning.Trainer
    accelerator: 'gpu'
    devices: -1
    strategy: 'ddp_find_unused_parameters_false'
    min_epochs: 1
    max_epochs: 100
    precision: 16
    check_val_every_n_epoch: 1
    accumulate_grad_batches: 5

task:
    confusion_matrix_log_every_n_epoch: 10
    confusion_matrix_val: True
    confusion_matrix_test: True

datamodule:
    _target_: src.datamodules.RGB.datamodule.DataModuleRGB

    data_dir: /net/research-hisdoc/datasets/semantic_segmentation/datasets/CB55-splits/AB1
    num_workers: 4
    batch_size: 1
    shuffle: True
    drop_last: True
    data_folder_name: data
    gt_folder_name: gt
    train_folder_name: training-20
    val_folder_name: validation

optimizer:
    lr: 1e-3
    betas: [0.9, 0.999]
    eps: 1e-5

callbacks:
    model_checkpoint:
        monitor: "val/jaccard_index"
        mode: "max"
        filename: ${checkpoint_folder_name}fine-tune-cb55-train-20-divanet
#    watch_model:
#        log_freq: 1000

model:
    backbone:
        path_to_weights: /netscratch/experiments_lars_paul/lars/experiments/sem_seg_synthetic_DPC_120_divanet_loss_no_weights/2022-10-25/14-12-09/checkpoints/backbone_last.pth

logger:
    wandb:
        project: "ijdar"
        name: ${name}
        tags: ["divanet", "AB1", "training-20", "4-classes", "baseline", "balanced"]
        group: 'baseline'