defaults: - override /network: null - override /criterion: null - override /datamodule: 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.BCEWithLogitsLoss reduction: mean discriminator: _target_: text_recognizer.criterions.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: 1.5e4 datamodule: _target_: text_recognizer.data.iam_lines.IAMLines batch_size: 24 num_workers: 12 train_fraction: 0.8 augment: true pin_memory: false lr_schedulers: generator: _target_: torch.optim.lr_scheduler.CosineAnnealingLR T_max: 64 eta_min: 4.5e-6 last_epoch: -1 interval: epoch 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 network: _target_: text_recognizer.networks.vqvae.vqvae.VQVAE hidden_dim: 256 embedding_dim: 32 num_embeddings: 512 decay: 0.99 encoder: _target_: text_recognizer.networks.vqvae.encoder.Encoder in_channels: 1 hidden_dim: 32 channels_multipliers: [1, 4, 8] dropout_rate: 0.0 activation: mish use_norm: true num_residuals: 2 residual_channels: 32 decoder: _target_: text_recognizer.networks.vqvae.decoder.Decoder out_channels: 1 hidden_dim: 32 channels_multipliers: [8, 4, 1] dropout_rate: 0.0 activation: mish use_norm: true num_residuals: 2 residual_channels: 32 trainer: max_epochs: 64 # limit_train_batches: 0.1 # limit_val_batches: 0.1 # gradient_clip_val: 100 # tune: false # train: true # test: false summary: [2, 1, 56, 1024]