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# @package _global_
defaults:
- override /network: vqvae
- override /criterion: null
- override /model: lit_vqgan
- override /callbacks: vae
- override /optimizers: null
- override /lr_schedulers: null
epochs: &epochs 100
ignore_index: &ignore_index 3
num_classes: &num_classes 58
max_output_len: &max_output_len 682
summary: [[1, 1, 576, 640]]
criterion:
_target_: text_recognizer.criterion.vqgan_loss.VQGANLoss
reconstruction_loss:
_target_: torch.nn.BCEWithLogitsLoss
reduction: mean
discriminator:
_target_: text_recognizer.criterion.n_layer_discriminator.NLayerDiscriminator
in_channels: 1
num_channels: 64
num_layers: 3
commitment_weight: 0.25
discriminator_weight: 0.8
discriminator_factor: 1.0
discriminator_iter_start: 8.0e4
mapping: &mapping
mapping:
_target_: text_recognizer.data.mappings.emnist.EmnistMapping
extra_symbols: [ "\n" ]
datamodule:
_target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs
batch_size: 4
num_workers: 12
train_fraction: 0.9
pin_memory: true
<< : *mapping
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: *epochs
eta_min: 1.0e-5
last_epoch: -1
interval: epoch
monitor: val/loss
discriminator:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: *epochs
eta_min: 1.0e-5
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:
_target_: pytorch_lightning.Trainer
stochastic_weight_avg: false
auto_scale_batch_size: binsearch
auto_lr_find: false
gradient_clip_val: 0
fast_dev_run: false
gpus: 1
precision: 16
max_epochs: *epochs
terminate_on_nan: true
weights_summary: null
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
resume_from_checkpoint: null
accumulate_grad_batches: 2
overfit_batches: 0
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