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-rw-r--r--notebooks/03-look-at-iam-paragraphs.ipynb574
-rw-r--r--notebooks/05c-test-model-end-to-end.ipynb526
2 files changed, 664 insertions, 436 deletions
diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb
index b56e2f6..fe23ab1 100644
--- a/notebooks/03-look-at-iam-paragraphs.ipynb
+++ b/notebooks/03-look-at-iam-paragraphs.ipynb
@@ -2,10 +2,19 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"id": "6ce2519f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
@@ -31,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 6,
"id": "726ac25b",
"metadata": {},
"outputs": [],
@@ -48,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 7,
"id": "ec16e41f-3d12-4da2-bf02-7429b41cf98e",
"metadata": {},
"outputs": [],
@@ -60,7 +69,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 8,
"id": "e9386367-2b49-4633-9936-57081132e59e",
"metadata": {},
"outputs": [
@@ -88,21 +97,20 @@
" log_freq: 100\n",
" upload_code_as_artifact:\n",
" _target_: callbacks.wandb_callbacks.UploadCodeAsArtifact\n",
- " project_dir: ${work_dir}/text_recognizer\n",
+ " project_dir: ${work_dir}/../text_recognizer\n",
" upload_ckpts_as_artifact:\n",
" _target_: callbacks.wandb_callbacks.UploadCheckpointsAsArtifact\n",
" ckpt_dir: checkpoints/\n",
" upload_best_only: true\n",
- " log_text_predictions:\n",
- " _target_: callbacks.wandb_callbacks.LogTextPredictions\n",
+ " log_image_reconstruction:\n",
+ " _target_: callbacks.wandb_callbacks.LogReconstuctedImages\n",
" num_samples: 8\n",
"criterion:\n",
- " _target_: text_recognizer.criterions.label_smoothing.LabelSmoothingLoss\n",
- " smoothing: 0.1\n",
- " ignore_index: 1000\n",
+ " _target_: torch.nn.MSELoss\n",
+ " reduction: mean\n",
"datamodule:\n",
" _target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs\n",
- " batch_size: 4\n",
+ " batch_size: 16\n",
" num_workers: 12\n",
" train_fraction: 0.8\n",
" augment: true\n",
@@ -125,8 +133,8 @@
" _target_: torch.optim.lr_scheduler.OneCycleLR\n",
" max_lr: 0.001\n",
" total_steps: null\n",
- " epochs: 512\n",
- " steps_per_epoch: 4992\n",
+ " epochs: 64\n",
+ " steps_per_epoch: 830\n",
" pct_start: 0.3\n",
" anneal_strategy: cos\n",
" cycle_momentum: true\n",
@@ -138,55 +146,52 @@
" last_epoch: -1\n",
" verbose: false\n",
"mapping:\n",
- " _target_: text_recognizer.data.emnist_mapping.EmnistMapping\n",
+ " _target_: text_recognizer.data.word_piece_mapping.WordPieceMapping\n",
+ " num_features: 1000\n",
+ " tokens: iamdb_1kwp_tokens_1000.txt\n",
+ " lexicon: iamdb_1kwp_lex_1000.txt\n",
+ " data_dir: null\n",
+ " use_words: false\n",
+ " prepend_wordsep: false\n",
+ " special_tokens:\n",
+ " - <s>\n",
+ " - <e>\n",
+ " - <p>\n",
" extra_symbols:\n",
" - '\n",
"\n",
" '\n",
"model:\n",
- " _target_: text_recognizer.models.transformer.TransformerLitModel\n",
+ " _target_: text_recognizer.models.vqvae.VQVAELitModel\n",
" interval: step\n",
" monitor: val/loss\n",
- " max_output_len: 451\n",
- " start_token: <s>\n",
- " end_token: <e>\n",
- " pad_token: <p>\n",
+ " latent_loss_weight: 1.0\n",
"network:\n",
" encoder:\n",
- " _target_: text_recognizer.networks.encoders.efficientnet.EfficientNet\n",
- " arch: b0\n",
- " out_channels: 1280\n",
- " stochastic_dropout_rate: 0.2\n",
- " bn_momentum: 0.99\n",
- " bn_eps: 0.001\n",
+ " _target_: text_recognizer.networks.vqvae.encoder.Encoder\n",
+ " in_channels: 1\n",
+ " hidden_dim: 32\n",
+ " channels_multipliers:\n",
+ " - 1\n",
+ " - 2\n",
+ " - 6\n",
+ " - 8\n",
+ " dropout_rate: 0.25\n",
" decoder:\n",
- " _target_: text_recognizer.networks.transformer.Decoder\n",
- " dim: 96\n",
- " depth: 2\n",
- " num_heads: 8\n",
- " attn_fn: text_recognizer.networks.transformer.attention.Attention\n",
- " attn_kwargs:\n",
- " dim_head: 16\n",
- " dropout_rate: 0.2\n",
- " norm_fn: torch.nn.LayerNorm\n",
- " ff_fn: text_recognizer.networks.transformer.mlp.FeedForward\n",
- " ff_kwargs:\n",
- " dim_out: null\n",
- " expansion_factor: 4\n",
- " glu: true\n",
- " dropout_rate: 0.2\n",
- " cross_attend: true\n",
- " pre_norm: true\n",
- " rotary_emb: null\n",
- " _target_: text_recognizer.networks.conv_transformer.ConvTransformer\n",
- " input_dims:\n",
- " - 1\n",
- " - 576\n",
- " - 640\n",
- " hidden_dim: 96\n",
- " dropout_rate: 0.2\n",
- " num_classes: 1006\n",
- " pad_index: 1002\n",
+ " _target_: text_recognizer.networks.vqvae.decoder.Decoder\n",
+ " out_channels: 1\n",
+ " hidden_dim: 32\n",
+ " channels_multipliers:\n",
+ " - 8\n",
+ " - 6\n",
+ " - 2\n",
+ " - 1\n",
+ " dropout_rate: 0.25\n",
+ " _target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n",
+ " hidden_dim: 256\n",
+ " embedding_dim: 32\n",
+ " num_embeddings: 1024\n",
+ " decay: 0.99\n",
"optimizer:\n",
" _target_: madgrad.MADGRAD\n",
" lr: 0.001\n",
@@ -202,7 +207,7 @@
" fast_dev_run: false\n",
" gpus: 1\n",
" precision: 16\n",
- " max_epochs: 512\n",
+ " max_epochs: 64\n",
" terminate_on_nan: true\n",
" weights_summary: top\n",
" limit_train_batches: 1.0\n",
@@ -218,32 +223,26 @@
"debug: false\n",
"print_config: true\n",
"ignore_warnings: true\n",
+ "summary:\n",
+ "- 1\n",
+ "- 576\n",
+ "- 640\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_text_predictions': {'_target_': 'callbacks.wandb_callbacks.LogTextPredictions', 'num_samples': 8}}, 'criterion': {'_target_': 'text_recognizer.criterions.label_smoothing.LabelSmoothingLoss', 'smoothing': 0.1, 'ignore_index': 1000}, 'datamodule': {'_target_': 'text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs', 'batch_size': 4, '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': 512, '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}, 'mapping': {'_target_': 'text_recognizer.data.emnist_mapping.EmnistMapping', 'extra_symbols': ['\\n']}, 'model': {'_target_': 'text_recognizer.models.transformer.TransformerLitModel', 'interval': 'step', 'monitor': 'val/loss', 'max_output_len': 451, 'start_token': '<s>', 'end_token': '<e>', 'pad_token': '<p>'}, 'network': {'encoder': {'_target_': 'text_recognizer.networks.encoders.efficientnet.EfficientNet', 'arch': 'b0', 'out_channels': 1280, 'stochastic_dropout_rate': 0.2, 'bn_momentum': 0.99, 'bn_eps': 0.001}, 'decoder': {'_target_': 'text_recognizer.networks.transformer.Decoder', 'dim': 96, 'depth': 2, 'num_heads': 8, 'attn_fn': 'text_recognizer.networks.transformer.attention.Attention', 'attn_kwargs': {'dim_head': 16, 'dropout_rate': 0.2}, 'norm_fn': 'torch.nn.LayerNorm', 'ff_fn': 'text_recognizer.networks.transformer.mlp.FeedForward', 'ff_kwargs': {'dim_out': None, 'expansion_factor': 4, 'glu': True, 'dropout_rate': 0.2}, 'cross_attend': True, 'pre_norm': True, 'rotary_emb': None}, '_target_': 'text_recognizer.networks.conv_transformer.ConvTransformer', 'input_dims': [1, 576, 640], 'hidden_dim': 96, 'dropout_rate': 0.2, 'num_classes': 1006, 'pad_index': 1002}, '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': 512, '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': '${hydra:runtime.cwd}', 'debug': False, 'print_config': True, 'ignore_warnings': True}\n"
+ "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_code_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCodeAsArtifact', 'project_dir': '${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"
]
}
],
"source": [
"# context initialization\n",
"with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
- " cfg = compose(config_name=\"config\", overrides=[\"mapping=emnist\"])\n",
+ " cfg = compose(config_name=\"config\", overrides=[\"+experiment=vqvae\"])\n",
" print(OmegaConf.to_yaml(cfg))\n",
" print(cfg)"
]
},
{
"cell_type": "code",
- "execution_count": 41,
- "id": "60b1b9a7-a504-47d5-948a-4f3bd0ce7e1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "cfg.datamodule.word_pieces = False"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
+ "execution_count": 9,
"id": "1c4624d1-6de5-41ab-9208-0988fcdba76d",
"metadata": {},
"outputs": [
@@ -251,8 +250,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2021-08-03 19:11:47.244 | INFO | text_recognizer.data.iam_paragraphs:setup:97 - Loading IAM paragraph regions and lines for None...\n",
- "2021-08-03 19:12:09.949 | INFO | text_recognizer.data.iam_synthetic_paragraphs:setup:68 - IAM Synthetic dataset steup for stage None...\n"
+ "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"
]
},
{
@@ -260,14 +263,14 @@
"output_type": "stream",
"text": [
"IAM Original and Synthetic Paragraphs Dataset\n",
- "Num classes: 84\n",
+ "Num classes: 1006\n",
"Dims: (1, 576, 640)\n",
"Output dims: (682, 1)\n",
- "Train/val/test sizes: 19958, 262, 231\n",
- "Train Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0114), tensor(0.0515), tensor(0.9961))\n",
- "Train Batch y stats: (torch.Size([4, 682]), torch.int64, tensor(1), tensor(83))\n",
- "Test Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0321), tensor(0.0744), tensor(0.8118))\n",
- "Test Batch y stats: (torch.Size([4, 682]), torch.int64, tensor(1), tensor(83))\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"
]
}
@@ -281,176 +284,391 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "c6188bce",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "dataset = IAMExtendedParagraphs(batch_size=1, word_pieces=True)\n",
- "dataset.prepare_data()\n",
- "dataset.setup()\n",
- "print(dataset)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "55b26b5d",
+ "execution_count": 10,
+ "id": "770f29f6-94f3-40c7-80f0-d85bd2d23fef",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "1006"
+ "1245"
]
},
- "execution_count": 15,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "len(datamodule.mapping)"
+ "len(datamodule.train_dataloader())"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "42501428",
+ "execution_count": 9,
+ "id": "e6e8c05b",
"metadata": {},
"outputs": [],
"source": [
- "dataset = IAMParagraphs()\n",
- "dataset.prepare_data()\n",
- "dataset.setup()\n",
- "print(dataset)"
+ "x, y = next(iter(datamodule.train_dataloader()))"
]
},
{
"cell_type": "code",
- "execution_count": 53,
- "id": "e6e8c05b",
+ "execution_count": null,
+ "id": "8bed2170",
"metadata": {},
"outputs": [],
"source": [
- "x, y = next(iter(datamodule.test_dataloader()))"
+ "x.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "8bed2170",
+ "execution_count": 20,
+ "id": "0cf22683",
"metadata": {},
"outputs": [],
"source": [
- "x.shape"
+ "x, y = datamodule.data_train[-3]"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "0cf22683",
+ "execution_count": 21,
+ "id": "074b269f-caff-4ec6-acdc-3f73721d5a05",
"metadata": {},
- "outputs": [],
+ "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])"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "x, y = dataset.data_train[0]"
+ "y"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "8541e6ee",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 576, 640])"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "40447ce6",
+ "execution_count": 23,
+ "id": "4bf9178f-5f36-4083-964c-28c0d1e1be4f",
"metadata": {},
- "outputs": [],
+ "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}"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "y["
+ "datamodule.mapping.inverse_mapping"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "016e8c81",
+ "execution_count": 24,
+ "id": "ec962504-808d-4819-9853-51711c0175f3",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "58"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "len(y)"
+ "len(datamodule.mapping.mapping)"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "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])"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
"id": "bcfc61cc-e6cc-4fb0-91ca-eca02168c6e1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "tensor([3])"
+ "tensor([1004])"
]
},
- "execution_count": 45,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "datamodule.mapping.get_index(\"<p>\")"
+ "datamodule.mapping.get_index(\"#\")"
]
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 27,
"id": "1e657891-45bb-479e-95ba-bdefe3a84ae9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'<s>He rose from his breakfast-nook bench\\nand came into the livingroom, where\\nHeather and Steve stood aghast at\\nhis entrance. He came, almost falling\\nforward in an ungainly shuffle, neck\\nthrust out, arms dangling loosely.\\nThen, abruptly, he drew himself up\\nand walked on the very tips of\\nhis toes. He stretched his arms\\nover his head and yawned agape,\\ndrawing-in great breaths that\\nbecame great sighs of ecstacy.<e>'"
+ "'<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": 54,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "convert_y_label_to_string(y[0], datamodule.mapping, padding_index=3)"
+ "convert_y_label_to_string(y, datamodule.mapping, padding_index=3)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "7aa8c021",
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 576, 640])"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 29,
"id": "7ef93252",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
@@ -462,111 +680,35 @@
}
],
"source": [
- "_plot(x[0, 0], vmax=1, title=datamodule.mapping.get_text(y[0]))"
+ "_plot(x[0], vmax=1, title=datamodule.mapping.get_text(y))"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"id": "edd0e44b-b383-4117-83ca-0bfd7e5235aa",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([1000])"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"datamodule.mapping[\"<p>\"]"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "3480ae5f-9cec-4814-98fe-02082a139add",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([1002, 25, 147, 233, 88, 16, 45, 1, 61, 54, 7, 20,\n",
- " 95, 71, 20, 2, 15, 30, 21, 24, 24, 95, 18, 21,\n",
- " 78, 1001, 14, 779, 7, 1, 218, 3, 1, 36, 23, 64,\n",
- " 23, 21, 46, 54, 24, 24, 16, 4, 1, 542, 1001, 1,\n",
- " 47, 7, 20, 15, 47, 7, 54, 14, 1, 2, 15, 7,\n",
- " 64, 7, 99, 281, 1, 20, 46, 47, 20, 2, 15, 80,\n",
- " 1001, 45, 1, 7, 21, 15, 54, 20, 21, 31, 7, 33,\n",
- " 25, 1, 31, 20, 16, 7, 4, 1, 28, 744, 489, 12,\n",
- " 1001, 35, 362, 11, 67, 1, 41, 21, 46, 20, 23, 21,\n",
- " 36, 13, 1, 2, 47, 41, 71, 71, 36, 7, 4, 1,\n",
- " 120, 155, 1001, 22, 54, 41, 66, 1, 24, 41, 15, 4,\n",
- " 673, 2, 1, 17, 20, 21, 46, 36, 23, 21, 46, 1,\n",
- " 36, 24, 24, 2, 7, 36, 13, 33, 1001, 1, 15, 47,\n",
- " 7, 21, 4, 1, 20, 61, 54, 41, 26, 15, 36, 13,\n",
- " 4, 25, 172, 7, 84, 162, 237, 121, 1001, 14, 34, 28,\n",
- " 95, 9, 42, 3, 1, 351, 1, 15, 23, 26, 2, 10,\n",
- " 1001, 45, 1, 15, 24, 7, 2, 33, 25, 1, 2, 15,\n",
- " 54, 7, 15, 31, 47, 7, 17, 45, 673, 2, 1001, 223,\n",
- " 45, 534, 14, 1, 13, 20, 84, 21, 7, 17, 1, 20,\n",
- " 46, 20, 26, 7, 4, 1001, 1, 17, 54, 20, 84, 23,\n",
- " 21, 46, 30, 23, 21, 229, 1, 61, 54, 7, 20, 15,\n",
- " 47, 2, 22, 19, 1001, 18, 31, 20, 197, 229, 1, 2,\n",
- " 193, 47, 2, 10, 1, 7, 31, 2, 15, 20, 31, 13,\n",
- " 33, 1003, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
- " 1000, 1000, 1000, 1000, 1000, 1000, 1000])"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"y[0]"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"id": "6c62572f",
"metadata": {},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "'int' object is not iterable",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/tmp/ipykernel_9271/3685142356.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatamodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in \u001b[0;36mconvert_y_label_to_string\u001b[0;34m(y, mapping, padding_index)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36mget_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwordpiece_processor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/iam_preprocessor.py\u001b[0m in \u001b[0;36mto_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexicon\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtokens_to_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
- ]
- }
- ],
+ "outputs": [],
"source": [
"_plot(x[0, 0], vmax=1, title=convert_y_label_to_string(y[0], datamodule.mapping))"
]
diff --git a/notebooks/05c-test-model-end-to-end.ipynb b/notebooks/05c-test-model-end-to-end.ipynb
index 913eafd..7996257 100644
--- a/notebooks/05c-test-model-end-to-end.ipynb
+++ b/notebooks/05c-test-model-end-to-end.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "1e40a88b",
"metadata": {},
"outputs": [],
@@ -25,7 +25,294 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
+ "id": "38fb3d9d-a163-4b72-981f-f31b51be39f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hydra import compose, initialize\n",
+ "from omegaconf import OmegaConf\n",
+ "from hydra.utils import instantiate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74780b21-3313-452b-b580-703cac878416",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# context initialization\n",
+ "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n",
+ " cfg = compose(config_name=\"vqvae\")\n",
+ " print(OmegaConf.to_yaml(cfg))\n",
+ " print(cfg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "205a03e8-7aa1-407f-afa5-92693715b677",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "net = instantiate(cfg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c74384f0-754e-4c29-8f06-339372d6e4c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchsummary import summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ebab599-2497-42f8-b54b-1663ee66fde9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "summary(net, (1, 576, 640), device=\"cpu\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6ba3f405-5948-465d-a7b8-459c84345034",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "net = net.cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c998137-0967-488f-a572-a5f5a6b86353",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = torch.randn(16, 1, 576, 640)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "920aeeb2-088c-4ea0-84a2-a2532d4f697a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = x.cuda()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "119ab631-fb3a-47a3-afc2-0e66260ebe7f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xx, l = net(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ccdec29-3952-460d-95b4-820b03aa4997",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xx.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a847084a-a65d-4072-ae1e-ae5d85a1664a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b21480a-707b-41de-b75d-30fb467973a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vq(x)[0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cba1096d-8832-4955-88c9-a8650cf968cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "443a52d9-09f3-4e24-8a23-e0397a65f747",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import glob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78541477-6f02-42da-ad75-4a47bb043e79",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdedced3-e08b-4bec-822c-e5dcd521c6b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list(Path(code_dir).glob(\"**/*.py\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79771541-c474-46a9-afdf-f74e736d6c16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for path in glob.glob(os.path.join(code_dir, \"**/*.py\"), recursive=True):\n",
+ " print(path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a79a2a20-56df-48b3-b964-22a0def52117",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "e = Encoder(1, 64, 32, 0.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a6fd004-6d7c-4a20-9ed4-508a73b329b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d = Decoder(64, 1, 32, 0.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82c18401-ea33-4ab6-ace4-03cb6e2e4435",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "z = e(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64f99b20-fa37-4614-b258-5870b7668959",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xh = d(z)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a81e7de-1203-4ab6-9562-37341e135daf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xh.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "204d167b-dce0-4dd7-b0e1-88a53859fd28",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = [2, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b77a6e8a-070d-46d3-9470-a5729eace57f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a += [1, 1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "741adac8-acc4-4715-afe9-07d3522cab62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "49b894be-5947-4e06-b698-bb990bf2c64c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4371af97-1f3b-4c5e-9812-3fb97d07c1cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "576 // (2 * 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28224cc8-79e0-481f-b24c-85bd0ef69f0a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "16 // 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"id": "d3a6146b-94b1-4618-a4e4-00f8e23ffdb0",
"metadata": {},
"outputs": [],
@@ -37,148 +324,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "764c8736-7d68-4261-a57d-face10ebbf42",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "callbacks:\n",
- " model_checkpoint:\n",
- " _target_: pytorch_lightning.callbacks.ModelCheckpoint\n",
- " monitor: val/loss\n",
- " save_top_k: 1\n",
- " save_last: true\n",
- " mode: min\n",
- " verbose: false\n",
- " dirpath: checkpoints/\n",
- " filename: '{epoch:02d}'\n",
- " learning_rate_monitor:\n",
- " _target_: pytorch_lightning.callbacks.LearningRateMonitor\n",
- " logging_interval: step\n",
- " log_momentum: false\n",
- " watch_model:\n",
- " _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",
- " upload_best_only: true\n",
- " log_image_reconstruction:\n",
- " _target_: callbacks.wandb_callbacks.LogReconstuctedImages\n",
- " num_samples: 8\n",
- "criterion:\n",
- " _target_: torch.nn.MSELoss\n",
- " reduction: mean\n",
- "datamodule:\n",
- " _target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs\n",
- " batch_size: 32\n",
- " num_workers: 12\n",
- " train_fraction: 0.8\n",
- " augment: true\n",
- " pin_memory: false\n",
- " word_pieces: true\n",
- "logger:\n",
- " wandb:\n",
- " _target_: pytorch_lightning.loggers.wandb.WandbLogger\n",
- " project: text-recognizer\n",
- " name: null\n",
- " save_dir: .\n",
- " offline: false\n",
- " id: null\n",
- " log_model: false\n",
- " prefix: ''\n",
- " 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: 624\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",
- " tokens: iamdb_1kwp_tokens_1000.txt\n",
- " lexicon: iamdb_1kwp_lex_1000.txt\n",
- " data_dir: null\n",
- " use_words: false\n",
- " prepend_wordsep: false\n",
- " special_tokens:\n",
- " - <s>\n",
- " - <e>\n",
- " - <p>\n",
- " extra_symbols:\n",
- " - '\n",
- "\n",
- " '\n",
- "model:\n",
- " _target_: text_recognizer.models.vqvae.VQVAELitModel\n",
- " interval: step\n",
- " monitor: val/loss\n",
- " latent_loss_weight: 0.25\n",
- "network:\n",
- " _target_: text_recognizer.networks.vqvae.VQVAE\n",
- " in_channels: 1\n",
- " res_channels: 32\n",
- " num_residual_layers: 2\n",
- " embedding_dim: 64\n",
- " num_embeddings: 512\n",
- " decay: 0.99\n",
- " activation: mish\n",
- "optimizer:\n",
- " _target_: madgrad.MADGRAD\n",
- " lr: 0.01\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",
- " auto_scale_batch_size: binsearch\n",
- " auto_lr_find: false\n",
- " gradient_clip_val: 0\n",
- " fast_dev_run: false\n",
- " gpus: 1\n",
- " precision: 16\n",
- " max_epochs: 64\n",
- " terminate_on_nan: true\n",
- " weights_summary: top\n",
- " limit_train_batches: 1.0\n",
- " limit_val_batches: 1.0\n",
- " limit_test_batches: 1.0\n",
- " resume_from_checkpoint: null\n",
- "seed: 4711\n",
- "tune: false\n",
- "train: true\n",
- "test: true\n",
- "logging: INFO\n",
- "work_dir: ${hydra:runtime.cwd}\n",
- "debug: false\n",
- "print_config: true\n",
- "ignore_warnings: true\n",
- "\n",
- "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_code_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCodeAsArtifact', 'project_dir': '${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': 32, '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': 624, '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': 0.25}, 'network': {'_target_': 'text_recognizer.networks.vqvae.VQVAE', 'in_channels': 1, 'res_channels': 32, 'num_residual_layers': 2, 'embedding_dim': 64, 'num_embeddings': 512, 'decay': 0.99, 'activation': 'mish'}, 'optimizer': {'_target_': 'madgrad.MADGRAD', 'lr': 0.01, '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}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# context initialization\n",
"with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
@@ -189,25 +338,17 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "c1a9aa6b-6405-4ffe-b065-02340762476a",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-08-04 05:07:26.480 | DEBUG | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"mapping = instantiate(cfg.mapping)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "969ba3be-d78f-4b1e-b522-ea8a42669e86",
"metadata": {},
"outputs": [],
@@ -217,7 +358,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "6147cd3e-0ad1-490f-917d-21be9bb8ce1c",
"metadata": {},
"outputs": [],
@@ -227,70 +368,37 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "a0ecea0c-abaf-4d5d-a13d-c085c1e4d282",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.Size([1, 64, 144, 160])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"network.encode(x)[0].shape"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "a7b9f249-7e5e-4f31-bbe1-cfd6d3701cf0",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "torch.Size([512])\n",
- "torch.Size([512])\n",
- "torch.Size([512])\n",
- "torch.Size([512])\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"t, l = network(x)"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "9a9450d2-f45d-4823-adac-68a8ea05ed1d",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(0.0188, grad_fn=<AddBackward0>)"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"l"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "93b8c90f-788a-4095-aa7a-55b34f0ddaaf",
"metadata": {},
"outputs": [],
@@ -300,42 +408,20 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "c9983788-2dae-4375-a821-a64cd1c68edf",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(0.5669, grad_fn=<AddBackward0>)"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"F.mse_loss(x, t) + l"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "29b128ca-80b7-481e-bb3c-44f109c7d292",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.Size([1, 1, 576, 640])"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"t.shape"
]