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# @package _global_
defaults:
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_lines
- override /network: null
- override /model: lit_transformer
- override /lr_scheduler: null
- override /optimizer: null
tags: [lines, vit]
epochs: &epochs 256
ignore_index: &ignore_index 3
# summary: [[1, 1, 56, 1024], [1, 89]]
logger:
wandb:
tags: ${tags}
criterion:
ignore_index: *ignore_index
# label_smoothing: 0.05
decoder:
max_output_len: 89
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_: adan_pytorch.Adan
lr: 3.0e-4
betas: [0.02, 0.08, 0.01]
weight_decay: 0.02
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: 16
train_fraction: 0.95
network:
_target_: text_recognizer.network.vit.VisionTransformer
image_height: 56
image_width: 1024
patch_height: 28
patch_width: 32
dim: &dim 1024
num_classes: &num_classes 58
encoder:
_target_: text_recognizer.network.transformer.encoder.Encoder
dim: *dim
inner_dim: 2048
heads: 16
dim_head: 64
depth: 4
dropout_rate: 0.0
decoder:
_target_: text_recognizer.network.transformer.decoder.Decoder
dim: *dim
inner_dim: 2048
heads: 16
dim_head: 64
depth: 4
dropout_rate: 0.0
token_embedding:
_target_: "text_recognizer.network.transformer.embedding.token.\
TokenEmbedding"
num_tokens: *num_classes
dim: *dim
use_l2: true
pos_embedding:
_target_: "text_recognizer.network.transformer.embedding.absolute.\
AbsolutePositionalEmbedding"
dim: *dim
max_length: 89
use_l2: true
tie_embeddings: true
pad_index: 3
model:
max_output_len: 89
trainer:
fast_dev_run: false
gradient_clip_val: 1.0
max_epochs: *epochs
accumulate_grad_batches: 4
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
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