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# @package _global_
defaults:
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_lines
- override /network: conv_transformer
- override /model: lit_transformer
- override /lr_scheduler: null
- override /optimizer: null
epochs: &epochs 512
ignore_index: &ignore_index 3
num_classes: &num_classes 57
max_output_len: &max_output_len 89
summary: [[1, 1, 56, 1024], [1, 89]]
criterion:
ignore_index: *ignore_index
label_smoothing: 0.05
callbacks:
stochastic_weight_averaging:
_target_: pytorch_lightning.callbacks.StochasticWeightAveraging
swa_epoch_start: 0.75
swa_lrs: 1.0e-5
annealing_epochs: 10
annealing_strategy: cos
device: null
optimizer:
_target_: torch.optim.RAdam
lr: 3.0e-4
betas: [0.9, 0.999]
weight_decay: 0
eps: 1.0e-8
parameters: network
lr_scheduler:
_target_: torch.optim.lr_scheduler.OneCycleLR
max_lr: 3.0e-4
total_steps: null
epochs: *epochs
steps_per_epoch: 1354
pct_start: 0.3
anneal_strategy: cos
cycle_momentum: true
base_momentum: 0.85
max_momentum: 0.95
div_factor: 25.0
final_div_factor: 10000.0
three_phase: true
last_epoch: -1
verbose: false
interval: step
monitor: val/cer
datamodule:
batch_size: 8
train_fraction: 0.95
network:
input_dims: [1, 1, 56, 1024]
num_classes: *num_classes
pad_index: *ignore_index
encoder:
depth: 5
decoder:
depth: 6
pixel_embedding:
shape: [3, 64]
model:
max_output_len: *max_output_len
trainer:
gradient_clip_val: 0.5
max_epochs: *epochs
accumulate_grad_batches: 1
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