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defaults:
- model: default
- computer: v100
- dataset: osv5m_emb
- stage: null
- _self_
- exp: ???
model:
val_metrics:
_target_: metrics.distance_based.HaversineMetrics
acc_radiuses:
- 1
- 25
- 200
- 750
- 2500
acc_area: []
test_metrics:
_target_: metrics.distance_based.HaversineMetrics
acc_radiuses:
- 1
- 25
- 200
- 750
- 2500
acc_area: ${areas}
datamodule:
_target_: data.datamodule.ImageDataModule
train_dataset: ${dataset.train_dataset}
val_dataset: ${dataset.val_dataset}
test_dataset: ${dataset.test_dataset}
full_batch_size: ${dataset.full_batch_size}
eval_batch_size: ${dataset.eval_batch_size}
num_workers: ${computer.num_workers}
num_nodes: ${computer.num_nodes}
num_devices: ${computer.devices}
val_proportion: 0.02
trainer:
_target_: pytorch_lightning.Trainer
devices: ${computer.devices}
accelerator: ${computer.accelerator}
strategy: ${computer.strategy}
num_nodes: ${computer.num_nodes}
precision: ${computer.precision}
max_steps: 1000000
val_check_interval: 25000
check_val_every_n_epoch: null
logger:
_target_: pytorch_lightning.loggers.WandbLogger
save_dir: ${root_dir}
name: ${experiment_name}${logger_suffix}
project: diff_plonk
log_model: False
offline: False
checkpoints:
_target_: pytorch_lightning.callbacks.ModelCheckpoint
dirpath: ${root_dir}/checkpoints/${experiment_name}
filename: 'epoch_{epoch}'
monitor: val/loss
save_last: True
save_top_k: 0
every_n_epochs: 1
enable_version_counter: False
progress_bar:
_target_: pytorch_lightning.callbacks.TQDMProgressBar
refresh_rate: ${computer.progress_bar_refresh_rate}
data_dir: ${root_dir}/datasets
root_dir: ${hydra:runtime.cwd}
experiment_name: ${dataset.name}_${model.name}_${experiment_name_suffix}
experiment_name_suffix: base
logger_suffix: ""
mode: train # change that to eval to do the testing
areas: ['country', 'region', 'sub-region', 'city']
class_name: null
streetclip: False
blur: False
text_tuning: False
hydra:
run:
dir: outputs/${hydra.job.name}/${now:%Y-%m-%d_%H-%M-%S}/${experiment_name}
job:
chdir: true
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