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seed: 4711
network:
desc: Configuration of the PyTorch neural network.
type: VQVAE
args:
in_channels: 1
channels: [32, 64, 64, 96, 96]
kernel_sizes: [4, 4, 4, 4, 4]
strides: [2, 2, 2, 2, 2]
num_residual_layers: 2
embedding_dim: 512
num_embeddings: 1024
upsampling: null
beta: 0.25
activation: leaky_relu
dropout_rate: 0.2
model:
desc: Configuration of the PyTorch Lightning model.
type: LitVQVAEModel
args:
optimizer:
type: MADGRAD
args:
lr: 1.0e-3
momentum: 0.9
weight_decay: 0
eps: 1.0e-6
lr_scheduler:
type: OneCycleLR
args:
interval: &interval step
max_lr: 1.0e-3
three_phase: true
epochs: 64
steps_per_epoch: 633 # num_samples / batch_size
criterion:
type: MSELoss
args:
reduction: mean
monitor: val_loss
mapping: sentence_piece
data:
desc: Configuration of the training/test data.
type: IAMExtendedParagraphs
args:
batch_size: 32
num_workers: 12
train_fraction: 0.8
augment: true
callbacks:
- type: ModelCheckpoint
args:
monitor: val_loss
mode: min
save_last: true
- type: StochasticWeightAveraging
args:
swa_epoch_start: 0.8
swa_lrs: 0.05
annealing_epochs: 10
annealing_strategy: cos
device: null
- type: LearningRateMonitor
args:
logging_interval: *interval
# - type: EarlyStopping
# args:
# monitor: val_loss
# mode: min
# patience: 10
trainer:
desc: Configuration of the PyTorch Lightning Trainer.
args:
stochastic_weight_avg: true
auto_scale_batch_size: binsearch
gradient_clip_val: 0
fast_dev_run: false
gpus: 1
precision: 16
max_epochs: 64
terminate_on_nan: true
weights_summary: top
load_checkpoint: null
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