diff options
| -rw-r--r-- | training/conf/experiment/conv_transformer_lines.yaml | 29 | ||||
| -rw-r--r-- | training/conf/experiment/conv_transformer_paragraphs.yaml | 16 |
2 files changed, 25 insertions, 20 deletions
diff --git a/training/conf/experiment/conv_transformer_lines.yaml b/training/conf/experiment/conv_transformer_lines.yaml index 2631e81..e0e426c 100644 --- a/training/conf/experiment/conv_transformer_lines.yaml +++ b/training/conf/experiment/conv_transformer_lines.yaml @@ -5,7 +5,6 @@ defaults: - override /callbacks: htr - override /datamodule: iam_lines - override /network: null - # - override /network: conv_transformer - override /model: lit_transformer - override /lr_scheduler: null - override /optimizer: null @@ -23,7 +22,7 @@ logger: criterion: ignore_index: *ignore_index - # label_smoothing: 0.1 + # label_smoothing: 0.05 callbacks: stochastic_weight_averaging: @@ -36,7 +35,7 @@ callbacks: optimizer: _target_: adan_pytorch.Adan - lr: 3.0e-4 + lr: 1.0e-3 betas: [0.02, 0.08, 0.01] weight_decay: 0.02 @@ -55,42 +54,43 @@ lr_scheduler: monitor: val/cer datamodule: - batch_size: 8 + batch_size: 16 train_fraction: 0.95 network: _target_: text_recognizer.networks.ConvTransformer input_dims: [1, 1, 56, 1024] - hidden_dim: &hidden_dim 128 + hidden_dim: &hidden_dim 384 num_classes: 58 pad_index: 3 encoder: _target_: text_recognizer.networks.convnext.ConvNext dim: 16 - dim_mults: [2, 4, 8] + dim_mults: [2, 4, 24] depths: [3, 3, 6] downsampling_factors: [[2, 2], [2, 2], [2, 2]] attn: _target_: text_recognizer.networks.convnext.TransformerBlock attn: _target_: text_recognizer.networks.convnext.Attention - dim: 128 + dim: *hidden_dim heads: 4 dim_head: 64 scale: 8 ff: _target_: text_recognizer.networks.convnext.FeedForward - dim: 128 - mult: 4 + dim: *hidden_dim + mult: 2 decoder: _target_: text_recognizer.networks.transformer.Decoder depth: 6 + dim: *hidden_dim block: - _target_: text_recognizer.networks.transformer.DecoderBlock + _target_: text_recognizer.networks.transformer.decoder_block.DecoderBlock self_attn: _target_: text_recognizer.networks.transformer.Attention dim: *hidden_dim - num_heads: 12 + num_heads: 8 dim_head: 64 dropout_rate: &dropout_rate 0.2 causal: true @@ -100,7 +100,7 @@ network: cross_attn: _target_: text_recognizer.networks.transformer.Attention dim: *hidden_dim - num_heads: 12 + num_heads: 8 dim_head: 64 dropout_rate: *dropout_rate causal: false @@ -119,7 +119,7 @@ network: AxialPositionalEmbeddingImage" dim: *hidden_dim axial_shape: [7, 128] - axial_dims: [64, 64] + axial_dims: [192, 192] token_pos_embedding: _target_: "text_recognizer.networks.transformer.embeddings.fourier.\ PositionalEncoding" @@ -134,3 +134,6 @@ trainer: gradient_clip_val: 1.0 max_epochs: *epochs accumulate_grad_batches: 1 + limit_train_batches: 1.0 + limit_val_batches: 1.0 + limit_test_batches: 1.0 diff --git a/training/conf/experiment/conv_transformer_paragraphs.yaml b/training/conf/experiment/conv_transformer_paragraphs.yaml index 60898da..60ff1bf 100644 --- a/training/conf/experiment/conv_transformer_paragraphs.yaml +++ b/training/conf/experiment/conv_transformer_paragraphs.yaml @@ -13,13 +13,12 @@ tags: [paragraphs] epochs: &epochs 600 num_classes: &num_classes 58 ignore_index: &ignore_index 3 -max_output_len: &max_output_len 682 +# max_output_len: &max_output_len 682 # summary: [[1, 1, 576, 640], [1, 682]] logger: wandb: tags: ${tags} - id: 8je5lxmx criterion: ignore_index: *ignore_index @@ -67,9 +66,9 @@ network: encoder: _target_: text_recognizer.networks.convnext.ConvNext dim: 16 - dim_mults: [2, 4, 8, 8] - depths: [3, 3, 6, 6] - downsampling_factors: [[2, 2], [2, 2], [2, 2], [2, 1]] + dim_mults: [1, 2, 4, 8, 8] + depths: [3, 3, 3, 3, 6] + downsampling_factors: [[2, 2], [2, 2], [2, 1], [2, 1], [2, 1]] attn: _target_: text_recognizer.networks.convnext.TransformerBlock attn: @@ -118,7 +117,7 @@ network: _target_: "text_recognizer.networks.transformer.embeddings.axial.\ AxialPositionalEmbeddingImage" dim: *hidden_dim - axial_shape: [36, 80] + axial_shape: [18, 160] axial_dims: [64, 64] token_pos_embedding: _target_: "text_recognizer.networks.transformer.embeddings.fourier.\ @@ -130,4 +129,7 @@ network: trainer: gradient_clip_val: 1.0 max_epochs: *epochs - accumulate_grad_batches: 6 + accumulate_grad_batches: 8 + limit_train_batches: 1.0 + limit_val_batches: 1.0 + limit_test_batches: 1.0 |