# @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