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# @package _global_
defaults:
- override /mapping: null
- override /criterion: null
- override /datamodule: null
- override /network: null
- override /model: null
- override /lr_schedulers: null
- override /optimizers: null
criterion:
_target_: torch.nn.CrossEntropyLoss
ignore_index: 3
mapping:
_target_: text_recognizer.data.emnist_mapping.EmnistMapping
# extra_symbols: [ "\n" ]
optimizers:
madgrad:
_target_: madgrad.MADGRAD
lr: 1.0e-4
momentum: 0.9
weight_decay: 0
eps: 1.0e-6
parameters: network
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: 1024
eta_min: 4.5e-6
last_epoch: -1
interval: epoch
monitor: val/loss
datamodule:
_target_: text_recognizer.data.iam_lines.IAMLines
batch_size: 24
num_workers: 12
train_fraction: 0.8
augment: true
pin_memory: false
network:
_target_: text_recognizer.networks.conv_transformer.ConvTransformer
input_dims: [1, 56, 1024]
hidden_dim: 128
encoder_dim: 1280
dropout_rate: 0.2
num_classes: 58
pad_index: 3
encoder:
_target_: text_recognizer.networks.encoders.efficientnet.EfficientNet
arch: b0
out_channels: 1280
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
decoder:
_target_: text_recognizer.networks.transformer.Decoder
dim: 128
depth: 3
num_heads: 4
attn_fn: text_recognizer.networks.transformer.attention.Attention
attn_kwargs:
dim_head: 32
dropout_rate: 0.2
norm_fn: torch.nn.LayerNorm
ff_fn: text_recognizer.networks.transformer.mlp.FeedForward
ff_kwargs:
dim_out: null
expansion_factor: 4
glu: true
dropout_rate: 0.2
cross_attend: true
pre_norm: true
rotary_emb: null
model:
_target_: text_recognizer.models.transformer.TransformerLitModel
max_output_len: 89
start_token: <s>
end_token: <e>
pad_token: <p>
trainer:
_target_: pytorch_lightning.Trainer
stochastic_weight_avg: false
auto_scale_batch_size: binsearch
auto_lr_find: false
gradient_clip_val: 0
fast_dev_run: false
gpus: 1
precision: 16
max_epochs: 1024
terminate_on_nan: true
weights_summary: top
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
resume_from_checkpoint: null
accumulate_grad_batches: 4
overfit_batches: 0.0
# summary: [[1, 1, 56, 1024], [1, 89]]
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