summaryrefslogtreecommitdiff
path: root/training/conf/experiment/vqgan.yaml
diff options
context:
space:
mode:
Diffstat (limited to 'training/conf/experiment/vqgan.yaml')
-rw-r--r--training/conf/experiment/vqgan.yaml36
1 files changed, 21 insertions, 15 deletions
diff --git a/training/conf/experiment/vqgan.yaml b/training/conf/experiment/vqgan.yaml
index 9224bc7..6c78deb 100644
--- a/training/conf/experiment/vqgan.yaml
+++ b/training/conf/experiment/vqgan.yaml
@@ -2,7 +2,7 @@
defaults:
- override /network: vqvae
- - override /criterion: vqgan_loss
+ - override /criterion: null
- override /model: lit_vqgan
- override /callbacks: wandb_vae
- override /optimizers: null
@@ -11,7 +11,7 @@ defaults:
criterion:
_target_: text_recognizer.criterions.vqgan_loss.VQGANLoss
reconstruction_loss:
- _target_: torch.nn.L1Loss
+ _target_: torch.nn.MSELoss
reduction: mean
discriminator:
_target_: text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator
@@ -21,35 +21,41 @@ criterion:
vq_loss_weight: 0.25
discriminator_weight: 1.0
discriminator_factor: 1.0
- discriminator_iter_start: 2.0e4
+ discriminator_iter_start: 5e2
datamodule:
- batch_size: 6
+ batch_size: 8
+ resize: [288, 320]
-lr_schedulers: null
+lr_schedulers:
+ generator:
+ _target_: torch.optim.lr_scheduler.CosineAnnealingLR
+ T_max: 128
+ eta_min: 4.5e-6
+ last_epoch: -1
-# lr_schedulers:
-# generator:
+ interval: epoch
+ monitor: val/loss
# _target_: torch.optim.lr_scheduler.OneCycleLR
# max_lr: 3.0e-4
# total_steps: null
# epochs: 100
-# steps_per_epoch: 3369
+# steps_per_epoch: 2496
# pct_start: 0.1
# anneal_strategy: cos
# cycle_momentum: true
# base_momentum: 0.85
# max_momentum: 0.95
-# div_factor: 1.0e3
+# 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
@@ -79,7 +85,7 @@ optimizers:
parameters: loss_fn.discriminator
trainer:
- max_epochs: 64
- # gradient_clip_val: 1.0e1
-
-summary: null
+ max_epochs: 128
+ limit_train_batches: 0.1
+ limit_val_batches: 0.1
+ # gradient_clip_val: 100