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|
_target_: text_recognizer.networks.ConvTransformer
input_dims: [1, 1, 576, 640]
hidden_dim: &hidden_dim 128
num_classes: 58
pad_index: 3
encoder:
_target_: text_recognizer.networks.convnext.ConvNext
dim: 16
dim_mults: [2, 4, 8]
depths: [3, 3, 6]
downsampling_factors: [[2, 2], [2, 2], [2, 2]]
decoder:
_target_: text_recognizer.networks.transformer.Decoder
dim: *hidden_dim
depth: 10
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: [7, 128]
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
|
