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# lightning.pytorch==2.1.3
seed_everything: 1009
trainer:
accelerator: auto
strategy: auto
devices: auto
num_nodes: 1
precision: null
logger: null
callbacks:
- class_path: callbacks.SaveImageCallback
init_args:
save_interval: 0
final_dir: out
fast_dev_run: false
max_epochs: 10
min_epochs: 10
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: 50
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: null
check_val_every_n_epoch: 1
num_sanity_val_steps: null
log_every_n_steps: 3
enable_checkpointing: null
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: null
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: null
model:
transform: tanh
width: 512
depth: 2
bias: true
alpha: 0.75
data:
val_size: 10000
train_size: 10000
batch_size: 256
num_workers: 3
ckpt_path: null
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.001
betas:
- 0.9
- 0.999
eps: 1.0e-08
weight_decay: 0.01
amsgrad: false
maximize: false
foreach: null
capturable: false
differentiable: false
fused: null
lr_scheduler:
class_path: lightning.pytorch.cli.ReduceLROnPlateau
init_args:
monitor: hp_metric
mode: min
factor: 0.05
patience: 5
threshold: 0.0001
threshold_mode: rel
cooldown: 10
min_lr: 0.0
eps: 1.0e-08
verbose: true