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# @package _global_
defaults:
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_extended_paragraphs
- override /network: conv_transformer
- override /model: lit_transformer
- override /lr_scheduler: null
- override /optimizer: null
tags: [paragraphs]
epochs: &epochs 600
num_classes: &num_classes 58
ignore_index: &ignore_index 3
max_output_len: &max_output_len 682
summary: [[1, 1, 576, 640], [1, 682]]
logger:
wandb:
tags: ${tags}
id: 8je5lxmx
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
lr_scheduler:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
mode: min
factor: 0.8
patience: 10
threshold: 1.0e-4
threshold_mode: rel
cooldown: 0
min_lr: 1.0e-5
eps: 1.0e-8
verbose: false
interval: epoch
monitor: val/cer
datamodule:
batch_size: 8
train_fraction: 0.95
network:
input_dims: [1, 1, 576, 640]
num_classes: *num_classes
pad_index: *ignore_index
pixel_embedding:
shape: [18, 79]
model:
max_output_len: *max_output_len
trainer:
gradient_clip_val: 1.0
max_epochs: *epochs
accumulate_grad_batches: 1
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