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# @package _global_
defaults:
- override /mapping: null
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_lines
- override /network: null
- override /model: null
- override /lr_schedulers: null
- override /optimizers: null
epochs: &epochs 620
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: 0.1
mapping: &mapping
mapping:
_target_: text_recognizer.data.mappings.emnist.EmnistMapping
callbacks:
stochastic_weight_averaging:
_target_: pytorch_lightning.callbacks.StochasticWeightAveraging
swa_epoch_start: 0.75
swa_lrs: 1.0e-4
annealing_epochs: 10
annealing_strategy: cos
device: null
optimizers:
madgrad:
_target_: madgrad.MADGRAD
lr: 3.0e-4
momentum: 0.9
weight_decay: 0
eps: 1.0e-6
parameters: network
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: *epochs
eta_min: 1.0e-4
last_epoch: -1
interval: epoch
monitor: val/loss
datamodule:
batch_size: 16
num_workers: 12
train_fraction: 0.9
pin_memory: true
<< : *mapping
rotary_embedding: &rotary_embedding
rotary_embedding:
_target_: text_recognizer.networks.transformer.embeddings.rotary.RotaryEmbedding
dim: 64
attn: &attn
dim: &hidden_dim 256
num_heads: 4
dim_head: 64
dropout_rate: &dropout_rate 0.5
network:
_target_: text_recognizer.networks.conv_transformer.ConvTransformer
input_dims: [1, 56, 1024]
hidden_dim: *hidden_dim
num_classes: *num_classes
pad_index: *ignore_index
encoder:
_target_: text_recognizer.networks.encoders.efficientnet.EfficientNet
arch: b1
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
decoder:
depth: 6
_target_: text_recognizer.networks.transformer.layers.Decoder
self_attn:
_target_: text_recognizer.networks.transformer.attention.Attention
<< : *attn
causal: true
<< : *rotary_embedding
cross_attn:
_target_: text_recognizer.networks.transformer.attention.Attention
<< : *attn
causal: false
norm:
_target_: text_recognizer.networks.transformer.norm.ScaleNorm
normalized_shape: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.mlp.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 4
glu: true
dropout_rate: *dropout_rate
pre_norm: true
pixel_pos_embedding:
_target_: text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbedding
dim: *hidden_dim
shape: &shape [3, 64]
axial_encoder:
_target_: text_recognizer.networks.transformer.axial_attention.encoder.AxialEncoder
dim: *hidden_dim
heads: 4
shape: *shape
depth: 2
dim_head: 64
dim_index: 1
model:
_target_: text_recognizer.models.transformer.TransformerLitModel
<< : *mapping
max_output_len: *max_output_len
start_token: <s>
end_token: <e>
pad_token: <p>
trainer:
_target_: pytorch_lightning.Trainer
stochastic_weight_avg: true
auto_scale_batch_size: binsearch
auto_lr_find: false
gradient_clip_val: 0.5
fast_dev_run: false
gpus: 1
precision: 16
max_epochs: *epochs
terminate_on_nan: true
weights_summary: null
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
resume_from_checkpoint: null
accumulate_grad_batches: 1
overfit_batches: 0
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