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# @package _global_
defaults:
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_extended_paragraphs
- override /network: null
- 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}
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_: 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: 2
train_fraction: 0.95
network:
_target_: text_recognizer.networks.ConvTransformer
input_dims: [1, 1, 576, 640]
hidden_dim: &hidden_dim 128
num_classes: *num_classes
pad_index: 3
encoder:
_target_: text_recognizer.networks.convnext.ConvNext
dim: 16
dim_mults: [1, 2, 4, 8, 8]
depths: [3, 3, 3, 3, 6]
downsampling_factors: [[2, 2], [2, 2], [2, 1], [2, 1], [2, 1]]
attn:
_target_: text_recognizer.networks.convnext.TransformerBlock
attn:
_target_: text_recognizer.networks.convnext.Attention
dim: 128
heads: 4
dim_head: 64
scale: 8
ff:
_target_: text_recognizer.networks.convnext.FeedForward
dim: 128
mult: 4
decoder:
_target_: text_recognizer.networks.transformer.Decoder
depth: 6
dim: *hidden_dim
block:
_target_: text_recognizer.networks.transformer.decoder_block.DecoderBlock
self_attn:
_target_: text_recognizer.networks.transformer.Attention
dim: *hidden_dim
num_heads: 12
dim_head: 64
dropout_rate: &dropout_rate 0.2
causal: true
rotary_embedding:
_target_: text_recognizer.networks.transformer.RotaryEmbedding
dim: 64
cross_attn:
_target_: text_recognizer.networks.transformer.Attention
dim: *hidden_dim
num_heads: 12
dim_head: 64
dropout_rate: *dropout_rate
causal: false
norm:
_target_: text_recognizer.networks.transformer.RMSNorm
dim: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 2
glu: true
dropout_rate: *dropout_rate
pixel_embedding:
_target_: "text_recognizer.networks.transformer.embeddings.axial.\
AxialPositionalEmbeddingImage"
dim: *hidden_dim
axial_shape: [18, 160]
axial_dims: [64, 64]
token_pos_embedding:
_target_: "text_recognizer.networks.transformer.embeddings.fourier.\
PositionalEncoding"
dim: *hidden_dim
dropout_rate: 0.1
max_len: 89
trainer:
gradient_clip_val: 1.0
max_epochs: *epochs
accumulate_grad_batches: 8
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
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