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defaults:
- override /mapping: null
- override /criterion: null
- override /datamodule: null
- 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 58
max_output_len: &max_output_len 89
criterion:
_target_: text_recognizer.criterions.label_smoothing.LabelSmoothingLoss
smoothing: 0.1
ignore_index: *ignore_index
mapping:
mapping: &mapping
_target_: text_recognizer.data.emnist_mapping.EmnistMapping
callbacks:
stochastic_weight_averaging:
_target_: pytorch_lightning.callbacks.StochasticWeightAveraging
swa_epoch_start: 0.75
swa_lrs: 5.0e-5
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.OneCycleLR
max_lr: 3.0e-4
total_steps: null
epochs: *epochs
steps_per_epoch: 90
pct_start: 0.1
anneal_strategy: cos
cycle_momentum: true
base_momentum: 0.85
max_momentum: 0.95
div_factor: 25
final_div_factor: 1.0e4
three_phase: false
last_epoch: -1
verbose: false
# Non-class arguments
interval: step
monitor: val/loss
datamodule:
_target_: text_recognizer.data.iam_lines.IAMLines
batch_size: 32
num_workers: 12
train_fraction: 0.8
augment: true
pin_memory: true
word_pieces: false
<<: *mapping
network:
_target_: text_recognizer.networks.conv_transformer.ConvTransformer
input_dims: [1, 56, 1024]
hidden_dim: &hidden_dim 128
encoder_dim: 1280
dropout_rate: 0.2
<<: *mapping
num_classes: *num_classes
pad_index: *ignore_index
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: *hidden_dim
depth: 3
num_heads: 4
attn_fn: text_recognizer.networks.transformer.attention.Attention
attn_kwargs:
dim_head: 32
dropout_rate: 0.2
norm_fn: text_recognizer.networks.transformer.norm.ScaleNorm
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:
_target_: text_recognizer.networks.transformer.positional_encodings.rotary_embedding.RotaryEmbedding
dim: 32
pixel_pos_embedding:
_target_: text_recognizer.networks.transformer.positional_encodings.PositionalEncoding2D
hidden_dim: *hidden_dim
max_h: 1
max_w: 32
token_pos_embedding:
_target_: text_recognizer.networks.transformer.positional_encodings.PositionalEncoding
hidden_dim: *hidden_dim
dropout_rate: 0.2
max_len: *max_output_len
model:
_target_: text_recognizer.models.transformer.TransformerLitModel
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: 4
overfit_batches: 0
summary: [[1, 1, 56, 1024], [1, 89]]
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