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morpho_cb55_B22_doc_ufcn.yaml
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# @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.yaml
- /metric:
- iou.yaml
- /model/backbone: doc_ufcn.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: "morpho_cb55_B22_doc_ufcn_loss_no_weights"
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: 200
check_val_every_n_epoch: 1
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/self-supervised/CB55/morphology_960_1344/B22_dataset_60
num_workers: 4
batch_size: 1
shuffle: True
drop_last: True
data_folder_name: data
gt_folder_name: gt
train_folder_name: train
val_folder_name: val
optimizer:
lr: 1e-3
betas: [0.9, 0.999]
eps: 1e-5
callbacks:
model_checkpoint:
monitor: "val/crossentropyloss"
mode: "min"
filename: ${checkpoint_folder_name}cb55-ssl-full-doc_ufcn
# watch_model
# log_freq: 1000
logger:
wandb:
project: "ijdar"
name: ${name}
tags: ["best_model", "Morpho", "pre-training", "doc_ufcn", "B22-60", "CB55-ssl-set", "200-epochs"]
group: 'pre-training'