Remove VQVAE stuff, did not work...

1 files changed, 0 insertions, 149 deletions

diff --git a/training/conf/experiment/vq_transformer_lines.yaml b/training/conf/experiment/vq_transformer_lines.yaml
deleted file mode 100644
index bbe1178..0000000
--- a/training/conf/experiment/vq_transformer_lines.yaml
+++ /dev/null
@@ -1,149 +0,0 @@
-# @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