_target_: text_recognizer.networks.ConvTransformer
input_dims: [1, 1, 576, 640]
hidden_dim: &hidden_dim 144
num_classes: 58
pad_index: 3
encoder:
  _target_: text_recognizer.networks.EfficientNet
  arch: b0
  stochastic_dropout_rate: 0.2
  bn_momentum: 0.99
  bn_eps: 1.0e-3
  depth: 3
  out_channels: 128
decoder:
  _target_: text_recognizer.networks.transformer.Decoder
  depth: 6
  block:
    _target_: text_recognizer.networks.transformer.DecoderBlock
    self_attn:
      _target_: text_recognizer.networks.transformer.Attention
      dim: *hidden_dim
      num_heads: 8
      dim_head: 64
      dropout_rate: &dropout_rate 0.4
      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: 8
      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.AxialPositionalEmbedding
  dim: *hidden_dim
  shape: [72, 80]