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# @package _global_
defaults:
- override /network: vqvae
- override /criterion: null
- override /model: lit_vqgan
- override /callbacks: wandb_vae
- override /optimizers: null
- override /lr_schedulers: null
criterion:
_target_: text_recognizer.criterions.vqgan_loss.VQGANLoss
reconstruction_loss:
_target_: torch.nn.MSELoss
reduction: mean
discriminator:
_target_: text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator
in_channels: 1
num_channels: 64
num_layers: 3
vq_loss_weight: 0.25
discriminator_weight: 1.0
discriminator_factor: 1.0
discriminator_iter_start: 5e2
datamodule:
batch_size: 8
resize: [288, 320]
lr_schedulers:
generator:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: 128
eta_min: 4.5e-6
last_epoch: -1
interval: epoch
monitor: val/loss
# _target_: torch.optim.lr_scheduler.OneCycleLR
# max_lr: 3.0e-4
# total_steps: null
# epochs: 100
# steps_per_epoch: 2496
# pct_start: 0.1
# anneal_strategy: cos
# cycle_momentum: true
# base_momentum: 0.85
# max_momentum: 0.95
# div_factor: 25
# final_div_factor: 1.0e4
# three_phase: true
# last_epoch: -1
# verbose: false
# # Non-class arguments
# interval: step
# monitor: val/loss
# discriminator:
# _target_: torch.optim.lr_scheduler.CosineAnnealingLR
# T_max: 64
# eta_min: 0.0
# last_epoch: -1
#
# interval: epoch
# monitor: val/loss
optimizers:
generator:
_target_: madgrad.MADGRAD
lr: 1.0e-4
momentum: 0.5
weight_decay: 0
eps: 1.0e-7
parameters: network
discriminator:
_target_: madgrad.MADGRAD
lr: 4.5e-6
momentum: 0.5
weight_decay: 0
eps: 1.0e-6
parameters: loss_fn.discriminator
trainer:
max_epochs: 128
limit_train_batches: 0.1
limit_val_batches: 0.1
# gradient_clip_val: 100
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