cb55_select_train30_unet_finetune_rotnet.yaml

# @package _global_

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

defaults:
    - /mode: experiment.yaml
    - /plugins: null
    - /task: semantic_segmentation_HisDB_cropped.yaml
    - /loss: crossentropyloss.yaml
    - /metric:
          - hisdbiou.yaml
    - /model/backbone: unet.yaml
    - /model/header: unet_segmentation.yaml
    - /optimizer: adam.yaml
    - /callbacks:
          - check_compatibility.yaml
          - model_checkpoint.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: "semantic_segmentation_cb55_full_unet_finetune_rotnet"

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: 50
    precision: 16
    check_val_every_n_epoch: 4

task:
    confusion_matrix_log_every_n_epoch: 10
    confusion_matrix_val: True
    confusion_matrix_test: True

datamodule:
    _target_: src.datamodules.DivaHisDB.datamodule_cropped.DivaHisDBDataModuleCropped

    data_dir: /net/research-hisdoc/datasets/semantic_segmentation/datasets_cropped/CB55
    crop_size: 256
    num_workers: 4
    batch_size: 100
    shuffle: True
    drop_last: False
    data_folder_name: data
    gt_folder_name: gt

callbacks:
    model_checkpoint:
        monitor: "val/hisdbiou"
        mode: "max"
        filename: ${checkpoint_folder_name}task
#    watch_model:
#        log_freq: 1000

logger:
    wandb:
        name: ${name}
        tags: ["best_model", "USL", "finetune"]
        group: 'finetune'