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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 200
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-5
annealing_epochs: 10
annealing_strategy: cos
device: null
optimizers:
radam:
_target_: torch.optim.RAdam
lr: 3.0e-4
betas: [0.9, 0.999]
weight_decay: 0
eps: 1.0e-8
parameters: network
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
mode: min
factor: 0.5
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/loss
datamodule:
batch_size: 16
num_workers: 12
train_fraction: 0.9
pin_memory: true
<< : *mapping
encoder: &encoder
_target_: text_recognizer.networks.efficientnet.efficientnet.EfficientNet
arch: b0
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
depth: 5
rotary_embedding: &rotary_embedding
rotary_embedding:
_target_: >
text_recognizer.networks.transformer.embeddings.rotary.RotaryEmbedding
dim: 64
attn: &attn
dim: &hidden_dim 512
num_heads: 4
dim_head: 64
dropout_rate: &dropout_rate 0.4
decoder: &decoder
_target_: text_recognizer.networks.transformer.decoder.Decoder
depth: 6
has_pos_emb: true
block:
_target_: text_recognizer.networks.transformer.decoder.DecoderBlock
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.RMSNorm
dim: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.mlp.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 2
glu: true
dropout_rate: *dropout_rate
pixel_pos_embedding: &pixel_pos_embedding
_target_: >
text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbedding
dim: *hidden_dim
shape: &shape [3, 64]
network:
_target_: text_recognizer.networks.conv_transformer.ConvTransformer
input_dims: [1, 1, 56, 1024]
hidden_dim: *hidden_dim
num_classes: *num_classes
pad_index: *ignore_index
encoder:
<< : *encoder
decoder:
<< : *decoder
pixel_pos_embedding:
<< : *pixel_pos_embedding
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
|
