Add new quantizer

1 files changed, 149 insertions, 0 deletions

diff --git a/training/conf/experiment/vq_transformer_lines.yaml b/training/conf/experiment/vq_transformer_lines.yaml
new file mode 100644
index 0000000..bbe1178
--- /dev/null
+++ b/training/conf/experiment/vq_transformer_lines.yaml
@@ -0,0 +1,149 @@
+# @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 512
+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
+    
+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:
+  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.CosineAnnealingLR
+    T_max: *epochs
+    eta_min: 1.0e-5
+    last_epoch: -1
+    interval: epoch
+    monitor: val/loss
+
+datamodule:
+  batch_size: 16
+  num_workers: 12
+  train_fraction: 0.9
+  pin_memory: true
+  << : *mapping
+
+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
+
+network:
+  _target_: text_recognizer.networks.vq_transformer.VqTransformer
+  input_dims: [1, 56, 1024]
+  hidden_dim: *hidden_dim
+  num_classes: *num_classes
+  pad_index: *ignore_index
+  encoder:
+    _target_: text_recognizer.networks.encoders.efficientnet.EfficientNet
+    arch: b1
+    stochastic_dropout_rate: 0.2
+    bn_momentum: 0.99
+    bn_eps: 1.0e-3
+  decoder:
+    depth: 6
+    _target_: text_recognizer.networks.transformer.layers.Decoder
+    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.ScaleNorm
+      normalized_shape: *hidden_dim
+    ff: 
+      _target_: text_recognizer.networks.transformer.mlp.FeedForward
+      dim: *hidden_dim
+      dim_out: null
+      expansion_factor: 4
+      glu: true
+      dropout_rate: *dropout_rate
+    pre_norm: true
+  pixel_pos_embedding:
+    _target_: text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbedding
+    dim: *hidden_dim
+    shape: [1, 32]
+  quantizer:
+    _target_: text_recognizer.networks.quantizer.quantizer.VectorQuantizer
+    input_dim: 512
+    codebook:
+      _target_: text_recognizer.networks.quantizer.codebook.CosineSimilarityCodebook
+      dim: 16
+      codebook_size: 4096
+      kmeans_init: true
+      kmeans_iters: 10
+      decay: 0.8
+      eps: 1.0e-5
+      threshold_dead: 2
+    commitment: 1.0
+
+model:
+  _target_: text_recognizer.models.vq_transformer.VqTransformerLitModel
+  << : *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