dev_rotnet_cnn_basic_cb55_10.yaml

# @package _global_

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

defaults:
    - /mode: development.yaml
    - /plugins: null
    - /task: classification.yaml
    - /loss: crossentropyloss.yaml
    - /metric:
          - accuracy.yaml
    - /model/backbone: cnn_basic.yaml
    - /model/header: single_layer.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

seed: 42

train: True
test: False
predict: False

trainer:
    _target_: pytorch_lightning.Trainer
    accelerator: 'gpu'
    devices: -1
    strategy: 'ddp_find_unused_parameters_false'
    min_epochs: 1
    max_epochs: 3
    enable_model_summary: full
    precision: 16

task:
    confusion_matrix_log_every_n_epoch: 1
    confusion_matrix_val: False
    confusion_matrix_test: False

model:
    header:
        in_channels: 109512

datamodule:
    _target_: src.datamodules.RotNet.datamodule_cropped.RotNetDivaHisDBDataModuleCropped

    data_dir: /net/research-hisdoc/datasets/semantic_segmentation/datasets_cropped/CB55-10-segmentation
    crop_size: 256
    num_workers: 4
    batch_size: 16
    shuffle: True
    drop_last: True
    data_folder_name: data

callbacks:
    model_checkpoint:
        filename: ${checkpoint_folder_name}dev-rotnet-basic-cnn-cb55-10
    watch_model:
        log_freq: 1

logger:
    wandb:
        name: 'dev-rotnet-basic-cnn-cb55-10'
        tags: [ "best_model", "USL" ]
        group: 'dev-runs'
        notes: "Testing"