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-rw-r--r--README.md1
-rw-r--r--notebooks/03-look-at-iam-paragraphs.ipynb460
-rw-r--r--text_recognizer/models/base.py10
-rw-r--r--training/callbacks/wandb_callbacks.py12
-rw-r--r--training/conf/callbacks/wandb_code.yaml2
-rw-r--r--training/conf/callbacks/wandb_config.yaml2
-rw-r--r--training/conf/callbacks/wandb_htr.yaml2
-rw-r--r--training/conf/callbacks/wandb_vae.yaml2
-rw-r--r--training/conf/criterion/vqgan_loss.yaml2
-rw-r--r--training/conf/experiment/vqgan.yaml40
-rw-r--r--training/conf/lr_schedulers/one_cycle.yaml2
-rw-r--r--training/conf/network/decoder/vae_decoder.yaml4
-rw-r--r--training/conf/network/encoder/vae_encoder.yaml4
-rw-r--r--training/conf/network/vqvae.yaml6
14 files changed, 187 insertions, 362 deletions
diff --git a/README.md b/README.md
index ef99b4a..ea85e92 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@ python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kw
- [ ] attr refactor
- [ ] Refactor once more
- [ ] fix linting
+- [ ] Fix artifact uploading to wandb
## Run Sweeps
diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb
index fe23ab1..866e612 100644
--- a/notebooks/03-look-at-iam-paragraphs.ipynb
+++ b/notebooks/03-look-at-iam-paragraphs.ipynb
@@ -2,19 +2,10 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"id": "6ce2519f",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
@@ -40,7 +31,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"id": "726ac25b",
"metadata": {},
"outputs": [],
@@ -57,7 +48,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"id": "ec16e41f-3d12-4da2-bf02-7429b41cf98e",
"metadata": {},
"outputs": [],
@@ -69,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "e9386367-2b49-4633-9936-57081132e59e",
"metadata": {},
"outputs": [
@@ -95,9 +86,6 @@
" _target_: callbacks.wandb_callbacks.WatchModel\n",
" log: all\n",
" log_freq: 100\n",
- " upload_code_as_artifact:\n",
- " _target_: callbacks.wandb_callbacks.UploadCodeAsArtifact\n",
- " project_dir: ${work_dir}/../text_recognizer\n",
" upload_ckpts_as_artifact:\n",
" _target_: callbacks.wandb_callbacks.UploadCheckpointsAsArtifact\n",
" ckpt_dir: checkpoints/\n",
@@ -106,11 +94,22 @@
" _target_: callbacks.wandb_callbacks.LogReconstuctedImages\n",
" num_samples: 8\n",
"criterion:\n",
- " _target_: torch.nn.MSELoss\n",
- " reduction: mean\n",
+ " _target_: text_recognizer.criterions.vqgan_loss.VQGANLoss\n",
+ " reconstruction_loss:\n",
+ " _target_: torch.nn.L1Loss\n",
+ " reduction: mean\n",
+ " discriminator:\n",
+ " _target_: text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator\n",
+ " in_channels: 1\n",
+ " num_channels: 64\n",
+ " num_layers: 3\n",
+ " vq_loss_weight: 0.25\n",
+ " discriminator_weight: 1.0\n",
+ " discriminator_factor: 1.0\n",
+ " discriminator_iter_start: 20000.0\n",
"datamodule:\n",
" _target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs\n",
- " batch_size: 16\n",
+ " batch_size: 14\n",
" num_workers: 12\n",
" train_fraction: 0.8\n",
" augment: true\n",
@@ -129,22 +128,6 @@
" job_type: train\n",
" group: ''\n",
" tags: []\n",
- "lr_scheduler:\n",
- " _target_: torch.optim.lr_scheduler.OneCycleLR\n",
- " max_lr: 0.001\n",
- " total_steps: null\n",
- " epochs: 64\n",
- " steps_per_epoch: 830\n",
- " pct_start: 0.3\n",
- " anneal_strategy: cos\n",
- " cycle_momentum: true\n",
- " base_momentum: 0.85\n",
- " max_momentum: 0.95\n",
- " div_factor: 25.0\n",
- " final_div_factor: 10000.0\n",
- " three_phase: true\n",
- " last_epoch: -1\n",
- " verbose: false\n",
"mapping:\n",
" _target_: text_recognizer.data.word_piece_mapping.WordPieceMapping\n",
" num_features: 1000\n",
@@ -162,10 +145,7 @@
"\n",
" '\n",
"model:\n",
- " _target_: text_recognizer.models.vqvae.VQVAELitModel\n",
- " interval: step\n",
- " monitor: val/loss\n",
- " latent_loss_weight: 1.0\n",
+ " _target_: text_recognizer.models.vqgan.VQGANLitModel\n",
"network:\n",
" encoder:\n",
" _target_: text_recognizer.networks.vqvae.encoder.Encoder\n",
@@ -173,31 +153,25 @@
" hidden_dim: 32\n",
" channels_multipliers:\n",
" - 1\n",
- " - 2\n",
- " - 6\n",
- " - 8\n",
- " dropout_rate: 0.25\n",
+ " - 4\n",
+ " - 4\n",
+ " - 4\n",
+ " dropout_rate: 0.0\n",
" decoder:\n",
" _target_: text_recognizer.networks.vqvae.decoder.Decoder\n",
" out_channels: 1\n",
" hidden_dim: 32\n",
" channels_multipliers:\n",
- " - 8\n",
- " - 6\n",
+ " - 4\n",
+ " - 4\n",
" - 2\n",
" - 1\n",
- " dropout_rate: 0.25\n",
+ " dropout_rate: 0.0\n",
" _target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n",
- " hidden_dim: 256\n",
+ " hidden_dim: 128\n",
" embedding_dim: 32\n",
- " num_embeddings: 1024\n",
+ " num_embeddings: 2048\n",
" decay: 0.99\n",
- "optimizer:\n",
- " _target_: madgrad.MADGRAD\n",
- " lr: 0.001\n",
- " momentum: 0.9\n",
- " weight_decay: 0\n",
- " eps: 1.0e-06\n",
"trainer:\n",
" _target_: pytorch_lightning.Trainer\n",
" stochastic_weight_avg: false\n",
@@ -207,7 +181,7 @@
" fast_dev_run: false\n",
" gpus: 1\n",
" precision: 16\n",
- " max_epochs: 64\n",
+ " max_epochs: 256\n",
" terminate_on_nan: true\n",
" weights_summary: top\n",
" limit_train_batches: 1.0\n",
@@ -221,28 +195,66 @@
"logging: INFO\n",
"work_dir: ${hydra:runtime.cwd}\n",
"debug: false\n",
- "print_config: true\n",
+ "print_config: false\n",
"ignore_warnings: true\n",
- "summary:\n",
- "- 1\n",
- "- 576\n",
- "- 640\n",
+ "summary: null\n",
+ "lr_schedulers:\n",
+ " generator: null\n",
+ " _target_: torch.optim.lr_scheduler.OneCycleLR\n",
+ " max_lr: 0.0005\n",
+ " total_steps: null\n",
+ " epochs: 200\n",
+ " steps_per_epoch: 4992\n",
+ " pct_start: 0.3\n",
+ " anneal_strategy: cos\n",
+ " cycle_momentum: true\n",
+ " base_momentum: 0.85\n",
+ " max_momentum: 0.95\n",
+ " div_factor: 25.0\n",
+ " final_div_factor: 10000.0\n",
+ " three_phase: true\n",
+ " last_epoch: -1\n",
+ " verbose: false\n",
+ " interval: step\n",
+ " monitor: val/loss\n",
+ " discriminator:\n",
+ " _target_: torch.optim.lr_scheduler.CosineAnnealingLR\n",
+ " T_max: 256\n",
+ " eta_min: 0.0\n",
+ " last_epoch: -1\n",
+ " interval: epoch\n",
+ " monitor: val/loss\n",
+ "optimizers:\n",
+ " generator:\n",
+ " _target_: madgrad.MADGRAD\n",
+ " lr: 0.0003\n",
+ " momentum: 0.5\n",
+ " weight_decay: 0\n",
+ " eps: 1.0e-06\n",
+ " parameters: network\n",
+ " discriminator:\n",
+ " _target_: madgrad.MADGRAD\n",
+ " lr: 4.5e-06\n",
+ " momentum: 0.5\n",
+ " weight_decay: 0\n",
+ " eps: 1.0e-06\n",
+ " parameters: loss_fn.discriminator\n",
"\n",
- "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_code_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCodeAsArtifact', 'project_dir': '${work_dir}/../text_recognizer'}, 'upload_ckpts_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCheckpointsAsArtifact', 'ckpt_dir': 'checkpoints/', 'upload_best_only': True}, 'log_image_reconstruction': {'_target_': 'callbacks.wandb_callbacks.LogReconstuctedImages', 'num_samples': 8}}, 'criterion': {'_target_': 'torch.nn.MSELoss', 'reduction': 'mean'}, 'datamodule': {'_target_': 'text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs', 'batch_size': 16, 'num_workers': 12, 'train_fraction': 0.8, 'augment': True, 'pin_memory': False, 'word_pieces': True}, 'logger': {'wandb': {'_target_': 'pytorch_lightning.loggers.wandb.WandbLogger', 'project': 'text-recognizer', 'name': None, 'save_dir': '.', 'offline': False, 'id': None, 'log_model': False, 'prefix': '', 'job_type': 'train', 'group': '', 'tags': []}}, 'lr_scheduler': {'_target_': 'torch.optim.lr_scheduler.OneCycleLR', 'max_lr': 0.001, 'total_steps': None, 'epochs': 64, 'steps_per_epoch': 830, 'pct_start': 0.3, 'anneal_strategy': 'cos', 'cycle_momentum': True, 'base_momentum': 0.85, 'max_momentum': 0.95, 'div_factor': 25.0, 'final_div_factor': 10000.0, 'three_phase': True, 'last_epoch': -1, 'verbose': False}, 'mapping': {'_target_': 'text_recognizer.data.word_piece_mapping.WordPieceMapping', 'num_features': 1000, 'tokens': 'iamdb_1kwp_tokens_1000.txt', 'lexicon': 'iamdb_1kwp_lex_1000.txt', 'data_dir': None, 'use_words': False, 'prepend_wordsep': False, 'special_tokens': ['<s>', '<e>', '<p>'], 'extra_symbols': ['\\n']}, 'model': {'_target_': 'text_recognizer.models.vqvae.VQVAELitModel', 'interval': 'step', 'monitor': 'val/loss', 'latent_loss_weight': 1.0}, 'network': {'encoder': {'_target_': 'text_recognizer.networks.vqvae.encoder.Encoder', 'in_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [1, 2, 6, 8], 'dropout_rate': 0.25}, 'decoder': {'_target_': 'text_recognizer.networks.vqvae.decoder.Decoder', 'out_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [8, 6, 2, 1], 'dropout_rate': 0.25}, '_target_': 'text_recognizer.networks.vqvae.vqvae.VQVAE', 'hidden_dim': 256, 'embedding_dim': 32, 'num_embeddings': 1024, 'decay': 0.99}, 'optimizer': {'_target_': 'madgrad.MADGRAD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0, 'eps': 1e-06}, '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': 64, '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': None}, 'seed': 4711, 'tune': False, 'train': True, 'test': True, 'logging': 'INFO', 'work_dir': '${hydra:runtime.cwd}', 'debug': False, 'print_config': True, 'ignore_warnings': True, 'summary': [1, 576, 640]}\n"
+ "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_ckpts_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCheckpointsAsArtifact', 'ckpt_dir': 'checkpoints/', 'upload_best_only': True}, 'log_image_reconstruction': {'_target_': 'callbacks.wandb_callbacks.LogReconstuctedImages', 'num_samples': 8}}, 'criterion': {'_target_': 'text_recognizer.criterions.vqgan_loss.VQGANLoss', 'reconstruction_loss': {'_target_': 'torch.nn.L1Loss', 'reduction': 'mean'}, 'discriminator': {'_target_': 'text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator', 'in_channels': 1, 'num_channels': 64, 'num_layers': 3}, 'vq_loss_weight': 0.25, 'discriminator_weight': 1.0, 'discriminator_factor': 1.0, 'discriminator_iter_start': 20000.0}, 'datamodule': {'_target_': 'text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs', 'batch_size': 14, 'num_workers': 12, 'train_fraction': 0.8, 'augment': True, 'pin_memory': False, 'word_pieces': True}, 'logger': {'wandb': {'_target_': 'pytorch_lightning.loggers.wandb.WandbLogger', 'project': 'text-recognizer', 'name': None, 'save_dir': '.', 'offline': False, 'id': None, 'log_model': False, 'prefix': '', 'job_type': 'train', 'group': '', 'tags': []}}, 'mapping': {'_target_': 'text_recognizer.data.word_piece_mapping.WordPieceMapping', 'num_features': 1000, 'tokens': 'iamdb_1kwp_tokens_1000.txt', 'lexicon': 'iamdb_1kwp_lex_1000.txt', 'data_dir': None, 'use_words': False, 'prepend_wordsep': False, 'special_tokens': ['<s>', '<e>', '<p>'], 'extra_symbols': ['\\n']}, 'model': {'_target_': 'text_recognizer.models.vqgan.VQGANLitModel'}, 'network': {'encoder': {'_target_': 'text_recognizer.networks.vqvae.encoder.Encoder', 'in_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [1, 4, 4, 4], 'dropout_rate': 0.0}, 'decoder': {'_target_': 'text_recognizer.networks.vqvae.decoder.Decoder', 'out_channels': 1, 'hidden_dim': 32, 'channels_multipliers': [4, 4, 2, 1], 'dropout_rate': 0.0}, '_target_': 'text_recognizer.networks.vqvae.vqvae.VQVAE', 'hidden_dim': 128, 'embedding_dim': 32, 'num_embeddings': 2048, 'decay': 0.99}, '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': 256, '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': None}, 'seed': 4711, 'tune': False, 'train': True, 'test': True, 'logging': 'INFO', 'work_dir': '${hydra:runtime.cwd}', 'debug': False, 'print_config': False, 'ignore_warnings': True, 'summary': None, 'lr_schedulers': {'generator': None, '_target_': 'torch.optim.lr_scheduler.OneCycleLR', 'max_lr': 0.0005, 'total_steps': None, 'epochs': 200, 'steps_per_epoch': 4992, 'pct_start': 0.3, 'anneal_strategy': 'cos', 'cycle_momentum': True, 'base_momentum': 0.85, 'max_momentum': 0.95, 'div_factor': 25.0, 'final_div_factor': 10000.0, 'three_phase': True, 'last_epoch': -1, 'verbose': False, 'interval': 'step', 'monitor': 'val/loss', 'discriminator': {'_target_': 'torch.optim.lr_scheduler.CosineAnnealingLR', 'T_max': 256, 'eta_min': 0.0, 'last_epoch': -1, 'interval': 'epoch', 'monitor': 'val/loss'}}, 'optimizers': {'generator': {'_target_': 'madgrad.MADGRAD', 'lr': 0.0003, 'momentum': 0.5, 'weight_decay': 0, 'eps': 1e-06, 'parameters': 'network'}, 'discriminator': {'_target_': 'madgrad.MADGRAD', 'lr': 4.5e-06, 'momentum': 0.5, 'weight_decay': 0, 'eps': 1e-06, 'parameters': 'loss_fn.discriminator'}}}\n"
]
}
],
"source": [
"# context initialization\n",
"with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
- " cfg = compose(config_name=\"config\", overrides=[\"+experiment=vqvae\"])\n",
+ " cfg = compose(config_name=\"config\", overrides=[\"+experiment=vqgan\"])\n",
" print(OmegaConf.to_yaml(cfg))\n",
" print(cfg)"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "1c4624d1-6de5-41ab-9208-0988fcdba76d",
"metadata": {},
"outputs": [
@@ -250,28 +262,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2021-08-06 01:28:48.099 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
- "2021-08-06 01:28:48.299 | INFO | text_recognizer.data.iam_paragraphs:setup:97 - Loading IAM paragraph regions and lines for None...\n",
- "2021-08-06 01:29:08.361 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
- "2021-08-06 01:29:11.692 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
- "2021-08-06 01:29:11.797 | INFO | text_recognizer.data.iam_synthetic_paragraphs:setup:68 - IAM Synthetic dataset steup for stage None...\n",
- "2021-08-06 01:29:24.065 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "IAM Original and Synthetic Paragraphs Dataset\n",
- "Num classes: 1006\n",
- "Dims: (1, 576, 640)\n",
- "Output dims: (682, 1)\n",
- "Train/val/test sizes: 19911, 262, 231\n",
- "Train Batch x stats: (torch.Size([16, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0165), tensor(0.0767), tensor(1.))\n",
- "Train Batch y stats: (torch.Size([16, 451]), torch.int64, tensor(1), tensor(1003))\n",
- "Test Batch x stats: (torch.Size([16, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0312), tensor(0.0817), tensor(0.9294))\n",
- "Test Batch y stats: (torch.Size([16, 451]), torch.int64, tensor(1), tensor(1003))\n",
- "\n"
+ "2021-08-22 16:36:19.395 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
+ "2021-08-22 16:36:19.526 | INFO | text_recognizer.data.iam_paragraphs:setup:97 - Loading IAM paragraph regions and lines for None...\n",
+ "2021-08-22 16:36:39.935 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n"
]
}
],
@@ -284,28 +277,17 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "770f29f6-94f3-40c7-80f0-d85bd2d23fef",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1245"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"len(datamodule.train_dataloader())"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"id": "e6e8c05b",
"metadata": {},
"outputs": [],
@@ -315,22 +297,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "8bed2170",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([8, 1, 576, 640])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 53,
"id": "0cf22683",
"metadata": {},
"outputs": [],
"source": [
- "x, y = datamodule.data_train[-3]"
+ "x, y = datamodule.data_train[0]"
]
},
{
@@ -393,284 +386,112 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "id": "8541e6ee",
+ "execution_count": 27,
+ "id": "1e657891-45bb-479e-95ba-bdefe3a84ae9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "torch.Size([1, 576, 640])"
+ "'<s>▁problem▁of▁life▁cannot▁be▁solved.▁\"therefore▁shall▁1ye▁lay\\n▁since▁meeting▁in▁doria▁palace,▁no▁word▁had\\n▁there▁is▁an▁easterly▁drift▁special\\n▁someone▁to▁love\".\\n▁do▁for▁world▁using▁my▁hand.\\n▁78.▁regression▁estimates▁of▁expenditure▁on\\n▁play▁was▁no▁more▁than▁a▁figment▁of\\n▁a▁few▁minutes▁later▁from▁a▁nearby▁village,<e><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p>'"
]
},
- "execution_count": 22,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "x.shape"
+ "convert_y_label_to_string(y, datamodule.mapping, padding_index=3)"
]
},
{
"cell_type": "code",
- "execution_count": 23,
- "id": "4bf9178f-5f36-4083-964c-28c0d1e1be4f",
- "metadata": {},
+ "execution_count": 28,
+ "id": "7aa8c021",
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"data": {
"text/plain": [
- "{'<b>': 0,\n",
- " '<s>': 1,\n",
- " '<e>': 2,\n",
- " '<p>': 3,\n",
- " '0': 4,\n",
- " '1': 5,\n",
- " '2': 6,\n",
- " '3': 7,\n",
- " '4': 8,\n",
- " '5': 9,\n",
- " '6': 10,\n",
- " '7': 11,\n",
- " '8': 12,\n",
- " '9': 13,\n",
- " 'A': 14,\n",
- " 'B': 15,\n",
- " 'C': 16,\n",
- " 'D': 17,\n",
- " 'E': 18,\n",
- " 'F': 19,\n",
- " 'G': 20,\n",
- " 'H': 21,\n",
- " 'I': 22,\n",
- " 'J': 23,\n",
- " 'K': 24,\n",
- " 'L': 25,\n",
- " 'M': 26,\n",
- " 'N': 27,\n",
- " 'O': 28,\n",
- " 'P': 29,\n",
- " 'Q': 30,\n",
- " 'R': 31,\n",
- " 'S': 32,\n",
- " 'T': 33,\n",
- " 'U': 34,\n",
- " 'V': 35,\n",
- " 'W': 36,\n",
- " 'X': 37,\n",
- " 'Y': 38,\n",
- " 'Z': 39,\n",
- " 'a': 14,\n",
- " 'b': 15,\n",
- " 'c': 16,\n",
- " 'd': 17,\n",
- " 'e': 18,\n",
- " 'f': 19,\n",
- " 'g': 20,\n",
- " 'h': 21,\n",
- " 'i': 22,\n",
- " 'j': 23,\n",
- " 'k': 24,\n",
- " 'l': 25,\n",
- " 'm': 26,\n",
- " 'n': 27,\n",
- " 'o': 28,\n",
- " 'p': 29,\n",
- " 'q': 30,\n",
- " 'r': 31,\n",
- " 's': 32,\n",
- " 't': 33,\n",
- " 'u': 34,\n",
- " 'v': 35,\n",
- " 'w': 36,\n",
- " 'x': 37,\n",
- " 'y': 38,\n",
- " 'z': 39,\n",
- " ' ': 40,\n",
- " '!': 41,\n",
- " '\"': 42,\n",
- " '#': 43,\n",
- " '&': 44,\n",
- " \"'\": 45,\n",
- " '(': 46,\n",
- " ')': 47,\n",
- " '*': 48,\n",
- " '+': 49,\n",
- " ',': 50,\n",
- " '-': 51,\n",
- " '.': 52,\n",
- " '/': 53,\n",
- " ':': 54,\n",
- " ';': 55,\n",
- " '?': 56,\n",
- " '\\n': 57}"
+ "torch.Size([1, 576, 640])"
]
},
- "execution_count": 23,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "datamodule.mapping.inverse_mapping"
+ "x.shape"
]
},
{
"cell_type": "code",
- "execution_count": 24,
- "id": "ec962504-808d-4819-9853-51711c0175f3",
+ "execution_count": 54,
+ "id": "7ef93252",
"metadata": {},
"outputs": [
{
"data": {
+ "image/png": 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"text/plain": [
- "58"
+ "<Figure size 864x864 with 1 Axes>"
]
},
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(datamodule.mapping.mapping)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "b0c4625b-b864-4c9d-b865-d9cc29f87298",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([1002, 3, 573, 10, 338, 119, 531, 18, 1, 2, 24, 36,\n",
- " 64, 7, 17, 33, 1, 37, 15, 47, 7, 54, 7, 71,\n",
- " 24, 54, 7, 1, 2, 743, 1, 511, 13, 7, 1, 742,\n",
- " 1000, 1, 2, 370, 3, 125, 112, 12, 11, 3, 91, 86,\n",
- " 20, 1, 26, 20, 36, 20, 31, 7, 4, 100, 508, 48,\n",
- " 1000, 116, 29, 67, 1, 7, 20, 2, 15, 7, 54, 36,\n",
- " 13, 1, 17, 54, 23, 71, 15, 1, 653, 1000, 953, 8,\n",
- " 1, 36, 24, 64, 7, 37, 33, 1000, 91, 35, 3, 507,\n",
- " 369, 12, 316, 1, 47, 20, 21, 17, 33, 1000, 1, 469,\n",
- " 324, 33, 1, 54, 7, 46, 54, 7, 2, 2, 23, 24,\n",
- " 21, 1, 7, 2, 15, 23, 16, 20, 15, 7, 2, 10,\n",
- " 3, 263, 26, 182, 23, 480, 42, 1000, 3, 260, 40, 100,\n",
- " 127, 149, 6, 1, 71, 23, 46, 16, 7, 21, 15, 10,\n",
- " 1000, 6, 522, 1, 852, 2, 1, 465, 88, 16, 6, 460,\n",
- " 423, 1, 64, 23, 36, 36, 20, 46, 7, 4, 1001, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
- " 1003, 1003, 1003, 1003, 1003, 1003, 1003])"
- ]
+ "metadata": {
+ "needs_background": "light"
},
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "y"
+ "_plot(x[0], vmax=1, title=datamodule.mapping.get_text(y))"
]
},
{
"cell_type": "code",
- "execution_count": 26,
- "id": "bcfc61cc-e6cc-4fb0-91ca-eca02168c6e1",
+ "execution_count": 11,
+ "id": "be988c75-84f0-4e4d-99c8-878ee9ed36d4",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([1004])"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "datamodule.mapping.get_index(\"#\")"
+ "import torch.nn.functional as F"
]
},
{
"cell_type": "code",
- "execution_count": 27,
- "id": "1e657891-45bb-479e-95ba-bdefe3a84ae9",
+ "execution_count": 55,
+ "id": "d3c7b58a-9c87-4287-bdb1-1dbbc9c5c76f",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'<s>▁problem▁of▁life▁cannot▁be▁solved.▁\"therefore▁shall▁1ye▁lay\\n▁since▁meeting▁in▁doria▁palace,▁no▁word▁had\\n▁there▁is▁an▁easterly▁drift▁special\\n▁someone▁to▁love\".\\n▁do▁for▁world▁using▁my▁hand.\\n▁78.▁regression▁estimates▁of▁expenditure▁on\\n▁play▁was▁no▁more▁than▁a▁figment▁of\\n▁a▁few▁minutes▁later▁from▁a▁nearby▁village,<e><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p>'"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "convert_y_label_to_string(y, datamodule.mapping, padding_index=3)"
+ "xx = F.avg_pool2d(x, kernel_size=2, stride=2)"
]
},
{
"cell_type": "code",
- "execution_count": 28,
- "id": "7aa8c021",
- "metadata": {
- "scrolled": true
- },
+ "execution_count": 56,
+ "id": "18632487-8534-45da-9347-1570c6241e91",
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "torch.Size([1, 576, 640])"
+ "<matplotlib.image.AxesImage at 0x7fc3595fad00>"
]
},
- "execution_count": 28,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
- }
- ],
- "source": [
- "x.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "7ef93252",
- "metadata": {},
- "outputs": [
+ },
{
"data": {
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"text/plain": [
- "<Figure size 864x864 with 1 Axes>"
+ "<Figure size 2304x2304 with 1 Axes>"
]
},
"metadata": {
@@ -680,17 +501,8 @@
}
],
"source": [
- "_plot(x[0], vmax=1, title=datamodule.mapping.get_text(y))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "edd0e44b-b383-4117-83ca-0bfd7e5235aa",
- "metadata": {},
- "outputs": [],
- "source": [
- "datamodule.mapping[\"<p>\"]"
+ "plt.figure(figsize=(32,32))\n",
+ "plt.imshow(xx[0], cmap='gray')"
]
},
{
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 94dbde5..56d4ca5 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -1,5 +1,5 @@
"""Base PyTorch Lightning model."""
-from typing import Any, Dict, List, Tuple, Type
+from typing import Any, Dict, List, Optional, Tuple, Type
import attr
import hydra
@@ -25,7 +25,7 @@ class BaseLitModel(LightningModule):
mapping: Type[AbstractMapping] = attr.ib()
loss_fn: Type[nn.Module] = attr.ib()
optimizer_configs: DictConfig = attr.ib()
- lr_scheduler_configs: DictConfig = attr.ib()
+ lr_scheduler_configs: Optional[DictConfig] = attr.ib()
train_acc: torchmetrics.Accuracy = attr.ib(
init=False, default=torchmetrics.Accuracy()
)
@@ -55,9 +55,7 @@ class BaseLitModel(LightningModule):
del optimizer_config.parameters
log.info(f"Instantiating optimizer <{optimizer_config._target_}>")
optimizers.append(
- hydra.utils.instantiate(
- optimizer_config, params=module.parameters()
- )
+ hydra.utils.instantiate(optimizer_config, params=module.parameters())
)
return optimizers
@@ -65,6 +63,8 @@ class BaseLitModel(LightningModule):
self, optimizers: List[Type[torch.optim.Optimizer]]
) -> List[Dict[str, Any]]:
"""Configures the lr scheduler."""
+ if None in self.lr_scheduler_configs:
+ return []
schedulers = []
for optimizer, lr_scheduler_config in zip(
optimizers, self.lr_scheduler_configs.values()
diff --git a/training/callbacks/wandb_callbacks.py b/training/callbacks/wandb_callbacks.py
index 2264750..11d0936 100644
--- a/training/callbacks/wandb_callbacks.py
+++ b/training/callbacks/wandb_callbacks.py
@@ -36,19 +36,19 @@ class WatchModel(Callback):
logger.watch(model=trainer.model, log=self.log, log_freq=self.log_freq)
-class UploadCodeAsArtifact(Callback):
+class UploadConfigAsArtifact(Callback):
"""Upload all *.py files to W&B as an artifact, at the beginning of the run."""
def __init__(self) -> None:
- self.project_dir = Path(__file__).resolve().parents[2] / "text_recognizer"
+ self.config_dir = Path(".hydra/")
@rank_zero_only
def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Uploads project code as an artifact."""
logger = get_wandb_logger(trainer)
experiment = logger.experiment
- artifact = wandb.Artifact("project-source", type="code")
- for filepath in self.project_dir.glob("**/*.py"):
+ artifact = wandb.Artifact("experiment-config", type="config")
+ for filepath in self.config_dir.rglob("*.yaml"):
artifact.add_file(str(filepath))
experiment.use_artifact(artifact)
@@ -60,7 +60,7 @@ class UploadCheckpointsAsArtifact(Callback):
def __init__(
self, ckpt_dir: str = "checkpoints/", upload_best_only: bool = False
) -> None:
- self.ckpt_dir = Path(__file__).resolve().parent / ckpt_dir
+ self.ckpt_dir = Path(ckpt_dir)
self.upload_best_only = upload_best_only
@rank_zero_only
@@ -73,7 +73,7 @@ class UploadCheckpointsAsArtifact(Callback):
if self.upload_best_only:
ckpts.add_file(trainer.checkpoint_callback.best_model_path)
else:
- for ckpt in (self.ckpt_dir).glob("**/*.ckpt"):
+ for ckpt in (self.ckpt_dir).rglob("**/*.ckpt"):
ckpts.add_file(ckpt)
experiment.use_artifact(ckpts)
diff --git a/training/conf/callbacks/wandb_code.yaml b/training/conf/callbacks/wandb_code.yaml
deleted file mode 100644
index 012cdce..0000000
--- a/training/conf/callbacks/wandb_code.yaml
+++ /dev/null
@@ -1,2 +0,0 @@
-upload_code_as_artifact:
- _target_: callbacks.wandb_callbacks.UploadCodeAsArtifact
diff --git a/training/conf/callbacks/wandb_config.yaml b/training/conf/callbacks/wandb_config.yaml
new file mode 100644
index 0000000..747a7c6
--- /dev/null
+++ b/training/conf/callbacks/wandb_config.yaml
@@ -0,0 +1,2 @@
+upload_code_as_artifact:
+ _target_: callbacks.wandb_callbacks.UploadConfigAsArtifact
diff --git a/training/conf/callbacks/wandb_htr.yaml b/training/conf/callbacks/wandb_htr.yaml
index 44adb71..f8c1ef7 100644
--- a/training/conf/callbacks/wandb_htr.yaml
+++ b/training/conf/callbacks/wandb_htr.yaml
@@ -1,6 +1,6 @@
defaults:
- default
- wandb_watch
- - wandb_code
+ - wandb_config
- wandb_checkpoints
- wandb_htr_predictions
diff --git a/training/conf/callbacks/wandb_vae.yaml b/training/conf/callbacks/wandb_vae.yaml
index c7b09b0..ffc467f 100644
--- a/training/conf/callbacks/wandb_vae.yaml
+++ b/training/conf/callbacks/wandb_vae.yaml
@@ -3,4 +3,4 @@ defaults:
- wandb_watch
- wandb_checkpoints
- wandb_image_reconstructions
- # - wandb_code
+ - wandb_config
diff --git a/training/conf/criterion/vqgan_loss.yaml b/training/conf/criterion/vqgan_loss.yaml
index a1c886e..f983f6f 100644
--- a/training/conf/criterion/vqgan_loss.yaml
+++ b/training/conf/criterion/vqgan_loss.yaml
@@ -1,6 +1,6 @@
_target_: text_recognizer.criterions.vqgan_loss.VQGANLoss
reconstruction_loss:
- _target_: torch.nn.L1Loss
+ _target_: torch.nn.MSELoss
reduction: mean
discriminator:
_target_: text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator
diff --git a/training/conf/experiment/vqgan.yaml b/training/conf/experiment/vqgan.yaml
index 40af15a..34d8f84 100644
--- a/training/conf/experiment/vqgan.yaml
+++ b/training/conf/experiment/vqgan.yaml
@@ -16,29 +16,41 @@ criterion:
discriminator:
_target_: text_recognizer.criterions.n_layer_discriminator.NLayerDiscriminator
in_channels: 1
- num_channels: 32
+ num_channels: 64
num_layers: 3
- vq_loss_weight: 0.8
- discriminator_weight: 0.8
+ vq_loss_weight: 0.25
+ discriminator_weight: 1.0
discriminator_factor: 1.0
- discriminator_iter_start: 2e4
+ discriminator_iter_start: 2.0e4
datamodule:
- batch_size: 8
+ batch_size: 12
lr_schedulers:
generator:
- _target_: torch.optim.lr_scheduler.CosineAnnealingLR
- T_max: 256
- eta_min: 0.0
+ _target_: torch.optim.lr_scheduler.OneCycleLR
+ max_lr: 3.0e-4
+ total_steps: null
+ epochs: 64
+ steps_per_epoch: 1685
+ pct_start: 0.1
+ anneal_strategy: cos
+ cycle_momentum: true
+ base_momentum: 0.85
+ max_momentum: 0.95
+ div_factor: 1.0e2
+ final_div_factor: 1.0e4
+ three_phase: true
last_epoch: -1
+ verbose: false
- interval: epoch
+ # Non-class arguments
+ interval: step
monitor: val/loss
discriminator:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
- T_max: 256
+ T_max: 64
eta_min: 0.0
last_epoch: -1
@@ -48,10 +60,10 @@ lr_schedulers:
optimizers:
generator:
_target_: madgrad.MADGRAD
- lr: 4.5e-6
+ lr: 1.0e-4
momentum: 0.5
weight_decay: 0
- eps: 1.0e-6
+ eps: 1.0e-7
parameters: network
@@ -65,7 +77,7 @@ optimizers:
parameters: loss_fn.discriminator
trainer:
- max_epochs: 256
- # gradient_clip_val: 0.25
+ max_epochs: 64
+ # gradient_clip_val: 1.0e1
summary: null
diff --git a/training/conf/lr_schedulers/one_cycle.yaml b/training/conf/lr_schedulers/one_cycle.yaml
index c60577a..801a01f 100644
--- a/training/conf/lr_schedulers/one_cycle.yaml
+++ b/training/conf/lr_schedulers/one_cycle.yaml
@@ -1,4 +1,4 @@
-onc_cycle:
+one_cycle:
_target_: torch.optim.lr_scheduler.OneCycleLR
max_lr: 1.0e-3
total_steps: null
diff --git a/training/conf/network/decoder/vae_decoder.yaml b/training/conf/network/decoder/vae_decoder.yaml
index a5e7286..7558ff0 100644
--- a/training/conf/network/decoder/vae_decoder.yaml
+++ b/training/conf/network/decoder/vae_decoder.yaml
@@ -1,5 +1,5 @@
_target_: text_recognizer.networks.vqvae.decoder.Decoder
out_channels: 1
hidden_dim: 32
-channels_multipliers: [8, 8, 4, 1]
-dropout_rate: 0.25
+channels_multipliers: [4, 4, 2, 1]
+dropout_rate: 0.0
diff --git a/training/conf/network/encoder/vae_encoder.yaml b/training/conf/network/encoder/vae_encoder.yaml
index 099c36a..b32f425 100644
--- a/training/conf/network/encoder/vae_encoder.yaml
+++ b/training/conf/network/encoder/vae_encoder.yaml
@@ -1,5 +1,5 @@
_target_: text_recognizer.networks.vqvae.encoder.Encoder
in_channels: 1
hidden_dim: 32
-channels_multipliers: [1, 4, 8, 8]
-dropout_rate: 0.25
+channels_multipliers: [1, 2, 4, 4]
+dropout_rate: 0.0
diff --git a/training/conf/network/vqvae.yaml b/training/conf/network/vqvae.yaml
index 835d0b7..936e575 100644
--- a/training/conf/network/vqvae.yaml
+++ b/training/conf/network/vqvae.yaml
@@ -3,7 +3,7 @@ defaults:
- decoder: vae_decoder
_target_: text_recognizer.networks.vqvae.vqvae.VQVAE
-hidden_dim: 256
-embedding_dim: 32
-num_embeddings: 1024
+hidden_dim: 128
+embedding_dim: 64
+num_embeddings: 2048
decay: 0.99
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-25 19:38:05 +0000