diff options
32 files changed, 1097 insertions, 669 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": 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- "'<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>'" + 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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, 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\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" ] diff --git a/text_recognizer/data/emnist_mapping.py b/text_recognizer/data/emnist_mapping.py index 925d214..3e91594 100644 --- a/text_recognizer/data/emnist_mapping.py +++ b/text_recognizer/data/emnist_mapping.py @@ -9,15 +9,23 @@ from text_recognizer.data.emnist import emnist_mapping class EmnistMapping(AbstractMapping): - def __init__(self, extra_symbols: Optional[Set[str]] = None) -> None: + def __init__(self, extra_symbols: Optional[Set[str]] = None, lower: bool = True) -> None: self.extra_symbols = set(extra_symbols) if extra_symbols is not None else None self.mapping, self.inverse_mapping, self.input_size = emnist_mapping( self.extra_symbols ) + if lower: + self._to_lower() super().__init__(self.input_size, self.mapping, self.inverse_mapping) - def __attrs_post_init__(self) -> None: - """Post init configuration.""" + def _to_lower(self) -> None: + """Converts mapping to lowercase letters only.""" + def _filter(x: int) -> int: + if 40 <= x: + return x - 26 + return x + self.inverse_mapping = {v: _filter(k) for k, v in enumerate(self.mapping)} + self.mapping = [c for c in self.mapping if not c.isupper()] def get_token(self, index: Union[int, Tensor]) -> str: if (index := int(index)) <= len(self.mapping): diff --git a/text_recognizer/data/transforms.py b/text_recognizer/data/transforms.py index 047496f..51f52de 100644 --- a/text_recognizer/data/transforms.py +++ b/text_recognizer/data/transforms.py @@ -1,10 +1,11 @@ """Transforms for PyTorch datasets.""" from pathlib import Path -from typing import Optional, Union, Set +from typing import Optional, Union, Type, Set import torch from torch import Tensor +from text_recognizer.data.base_mapping import AbstractMapping from text_recognizer.data.word_piece_mapping import WordPieceMapping diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py index f3ba49d..b1a101e 100644 --- a/text_recognizer/networks/conv_transformer.py +++ b/text_recognizer/networks/conv_transformer.py @@ -4,7 +4,6 @@ from typing import Tuple from torch import nn, Tensor -from text_recognizer.networks.encoders.efficientnet import EfficientNet from text_recognizer.networks.transformer.layers import Decoder from text_recognizer.networks.transformer.positional_encodings import ( PositionalEncoding, @@ -18,15 +17,17 @@ class ConvTransformer(nn.Module): def __init__( self, input_dims: Tuple[int, int, int], + encoder_dim: int, hidden_dim: int, dropout_rate: float, num_classes: int, pad_index: Tensor, - encoder: EfficientNet, + encoder: nn.Module, decoder: Decoder, ) -> None: super().__init__() self.input_dims = input_dims + self.encoder_dim = encoder_dim self.hidden_dim = hidden_dim self.dropout_rate = dropout_rate self.num_classes = num_classes @@ -38,7 +39,7 @@ class ConvTransformer(nn.Module): # positional encoding. self.latent_encoder = nn.Sequential( nn.Conv2d( - in_channels=self.encoder.out_channels, + in_channels=self.encoder_dim, out_channels=self.hidden_dim, kernel_size=1, ), diff --git a/text_recognizer/networks/vq_transformer.py b/text_recognizer/networks/vq_transformer.py index a972565..0433863 100644 --- a/text_recognizer/networks/vq_transformer.py +++ b/text_recognizer/networks/vq_transformer.py @@ -1,16 +1,12 @@ """Vector quantized encoder, transformer decoder.""" -import math from typing import Tuple -from torch import nn, Tensor +import torch +from torch import Tensor -from text_recognizer.networks.encoders.efficientnet import EfficientNet +from text_recognizer.networks.vqvae.vqvae import VQVAE from text_recognizer.networks.conv_transformer import ConvTransformer from text_recognizer.networks.transformer.layers import Decoder -from text_recognizer.networks.transformer.positional_encodings import ( - PositionalEncoding, - PositionalEncoding2D, -) class VqTransformer(ConvTransformer): @@ -19,16 +15,18 @@ class VqTransformer(ConvTransformer): def __init__( self, input_dims: Tuple[int, int, int], + encoder_dim: int, hidden_dim: int, dropout_rate: float, num_classes: int, pad_index: Tensor, - encoder: EfficientNet, + encoder: VQVAE, decoder: Decoder, + pretrained_encoder_path: str, ) -> None: - # TODO: Load pretrained vqvae encoder. super().__init__( input_dims=input_dims, + encoder_dim=encoder_dim, hidden_dim=hidden_dim, dropout_rate=dropout_rate, num_classes=num_classes, @@ -36,24 +34,19 @@ class VqTransformer(ConvTransformer): encoder=encoder, decoder=decoder, ) - # Latent projector for down sampling number of filters and 2d - # positional encoding. - self.latent_encoder = nn.Sequential( - nn.Conv2d( - in_channels=self.encoder.out_channels, - out_channels=self.hidden_dim, - kernel_size=1, - ), - PositionalEncoding2D( - hidden_dim=self.hidden_dim, - max_h=self.input_dims[1], - max_w=self.input_dims[2], - ), - nn.Flatten(start_dim=2), - ) + self.pretrained_encoder_path = pretrained_encoder_path + + # For typing + self.encoder: VQVAE + + def setup_encoder(self) -> None: + """Remove unecessary layers.""" + # TODO: load pretrained vqvae + del self.encoder.decoder + del self.encoder.post_codebook_conv def encode(self, x: Tensor) -> Tensor: - """Encodes an image into a latent feature vector. + """Encodes an image into a discrete (VQ) latent representation. Args: x (Tensor): Image tensor. @@ -69,8 +62,11 @@ class VqTransformer(ConvTransformer): Returns: Tensor: A Latent embedding of the image. """ - z = self.encoder(x) - z = self.latent_encoder(z) + with torch.no_grad(): + z_e = self.encoder.encode(x) + z_q, _ = self.encoder.quantize(z_e) + + z = self.latent_encoder(z_q) # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E] z = z.permute(0, 2, 1) diff --git a/text_recognizer/networks/vqvae/__init__.py b/text_recognizer/networks/vqvae/__init__.py index 7d56bdb..e1f05fa 100644 --- a/text_recognizer/networks/vqvae/__init__.py +++ b/text_recognizer/networks/vqvae/__init__.py @@ -1,2 +1 @@ """VQ-VAE module.""" -from .vqvae import VQVAE diff --git a/text_recognizer/networks/vqvae/attention.py b/text_recognizer/networks/vqvae/attention.py index 5a6b3ce..78a2cc9 100644 --- a/text_recognizer/networks/vqvae/attention.py +++ b/text_recognizer/networks/vqvae/attention.py @@ -7,7 +7,7 @@ import torch.nn.functional as F from text_recognizer.networks.vqvae.norm import Normalize -@attr.s +@attr.s(eq=False) class Attention(nn.Module): """Convolutional attention.""" @@ -63,11 +63,12 @@ class Attention(nn.Module): B, C, H, W = q.shape q = q.reshape(B, C, H * W).permute(0, 2, 1) # [B, HW, C] k = k.reshape(B, C, H * W) # [B, C, HW] - energy = torch.bmm(q, k) * (C ** -0.5) + energy = torch.bmm(q, k) * (int(C) ** -0.5) attention = F.softmax(energy, dim=2) # Compute attention to which values - v = v.reshape(B, C, H * W).permute(0, 2, 1) # [B, HW, C] + v = v.reshape(B, C, H * W) + attention = attention.permute(0, 2, 1) # [B, HW, HW] out = torch.bmm(v, attention) out = out.reshape(B, C, H, W) out = self.proj(out) diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py index fcf768b..f51e0a3 100644 --- a/text_recognizer/networks/vqvae/decoder.py +++ b/text_recognizer/networks/vqvae/decoder.py @@ -1,62 +1,69 @@ """CNN decoder for the VQ-VAE.""" -import attr +from typing import Sequence + from torch import nn from torch import Tensor from text_recognizer.networks.util import activation_function +from text_recognizer.networks.vqvae.norm import Normalize from text_recognizer.networks.vqvae.residual import Residual -@attr.s(eq=False) class Decoder(nn.Module): """A CNN encoder network.""" - in_channels: int = attr.ib() - embedding_dim: int = attr.ib() - out_channels: int = attr.ib() - res_channels: int = attr.ib() - num_residual_layers: int = attr.ib() - activation: str = attr.ib() - decoder: nn.Sequential = attr.ib(init=False) - - def __attrs_post_init__(self) -> None: - """Post init configuration.""" + def __init__(self, out_channels: int, hidden_dim: int, channels_multipliers: Sequence[int], dropout_rate: float, activation: str = "mish") -> None: super().__init__() + self.out_channels = out_channels + self.hidden_dim = hidden_dim + self.channels_multipliers = tuple(channels_multipliers) + self.activation = activation + self.dropout_rate = dropout_rate self.decoder = self._build_decompression_block() def _build_decompression_block(self,) -> nn.Sequential: + in_channels = self.hidden_dim * self.channels_multipliers[0] + decoder = [] + for _ in range(2): + decoder += [ + Residual( + in_channels=in_channels, + out_channels=in_channels, + dropout_rate=self.dropout_rate, + use_norm=True, + ), + ] + activation_fn = activation_function(self.activation) - blocks = [ + out_channels_multipliers = self.channels_multipliers + (1, ) + num_blocks = len(self.channels_multipliers) + + for i in range(num_blocks): + in_channels = self.hidden_dim * self.channels_multipliers[i] + out_channels = self.hidden_dim * out_channels_multipliers[i + 1] + decoder += [ + nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=4, + stride=2, + padding=1, + ), + activation_fn, + ] + + decoder += [ + Normalize(num_channels=self.hidden_dim * out_channels_multipliers[-1]), + nn.Mish(), nn.Conv2d( - in_channels=self.in_channels, - out_channels=self.embedding_dim, - kernel_size=3, - padding=1, - ) - ] - for _ in range(self.num_residual_layers): - blocks.append( - Residual(in_channels=self.embedding_dim, out_channels=self.res_channels) - ) - blocks.append(activation_fn) - blocks += [ - nn.ConvTranspose2d( - in_channels=self.embedding_dim, - out_channels=self.embedding_dim // 2, - kernel_size=4, - stride=2, - padding=1, - ), - activation_fn, - nn.ConvTranspose2d( - in_channels=self.embedding_dim // 2, + in_channels=self.hidden_dim * out_channels_multipliers[-1], out_channels=self.out_channels, - kernel_size=4, - stride=2, + kernel_size=3, + stride=1, padding=1, ), ] - return nn.Sequential(*blocks) + return nn.Sequential(*decoder) def forward(self, z_q: Tensor) -> Tensor: """Reconstruct input from given codes.""" diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py index f086c6b..ad8f950 100644 --- a/text_recognizer/networks/vqvae/encoder.py +++ b/text_recognizer/networks/vqvae/encoder.py @@ -1,7 +1,6 @@ """CNN encoder for the VQ-VAE.""" -from typing import Sequence, Optional, Tuple, Type +from typing import List, Tuple -import attr from torch import nn from torch import Tensor @@ -9,64 +8,59 @@ from text_recognizer.networks.util import activation_function from text_recognizer.networks.vqvae.residual import Residual -@attr.s(eq=False) class Encoder(nn.Module): """A CNN encoder network.""" - in_channels: int = attr.ib() - out_channels: int = attr.ib() - res_channels: int = attr.ib() - num_residual_layers: int = attr.ib() - embedding_dim: int = attr.ib() - activation: str = attr.ib() - encoder: nn.Sequential = attr.ib(init=False) - - def __attrs_post_init__(self) -> None: - """Post init configuration.""" + def __init__(self, in_channels: int, hidden_dim: int, channels_multipliers: List[int], dropout_rate: float, activation: str = "mish") -> None: super().__init__() + self.in_channels = in_channels + self.hidden_dim = hidden_dim + self.channels_multipliers = tuple(channels_multipliers) + self.activation = activation + self.dropout_rate = dropout_rate self.encoder = self._build_compression_block() def _build_compression_block(self) -> nn.Sequential: - activation_fn = activation_function(self.activation) - block = [ + """Builds encoder network.""" + encoder = [ nn.Conv2d( in_channels=self.in_channels, - out_channels=self.out_channels // 2, - kernel_size=4, - stride=2, - padding=1, - ), - activation_fn, - nn.Conv2d( - in_channels=self.out_channels // 2, - out_channels=self.out_channels, - kernel_size=4, - stride=2, - padding=1, - ), - activation_fn, - nn.Conv2d( - in_channels=self.out_channels, - out_channels=self.out_channels, + out_channels=self.hidden_dim, kernel_size=3, + stride=1, padding=1, ), ] - for _ in range(self.num_residual_layers): - block.append( - Residual(in_channels=self.out_channels, out_channels=self.res_channels) - ) + num_blocks = len(self.channels_multipliers) + channels_multipliers = (1, ) + self.channels_multipliers + activation_fn = activation_function(self.activation) - block.append( - nn.Conv2d( - in_channels=self.out_channels, - out_channels=self.embedding_dim, - kernel_size=1, - ) - ) + for i in range(num_blocks): + in_channels = self.hidden_dim * channels_multipliers[i] + out_channels = self.hidden_dim * channels_multipliers[i + 1] + encoder += [ + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=4, + stride=2, + padding=1, + ), + activation_fn, + ] + + for _ in range(2): + encoder += [ + Residual( + in_channels=self.hidden_dim * self.channels_multipliers[-1], + out_channels=self.hidden_dim * self.channels_multipliers[-1], + dropout_rate=self.dropout_rate, + use_norm=True, + ) + ] - return nn.Sequential(*block) + return nn.Sequential(*encoder) def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: """Encodes input into a discrete representation.""" diff --git a/text_recognizer/networks/vqvae/norm.py b/text_recognizer/networks/vqvae/norm.py index df66efc..3e6963a 100644 --- a/text_recognizer/networks/vqvae/norm.py +++ b/text_recognizer/networks/vqvae/norm.py @@ -3,7 +3,7 @@ import attr from torch import nn, Tensor -@attr.s +@attr.s(eq=False) class Normalize(nn.Module): num_channels: int = attr.ib() norm: nn.GroupNorm = attr.ib(init=False) @@ -12,7 +12,7 @@ class Normalize(nn.Module): """Post init configuration.""" super().__init__() self.norm = nn.GroupNorm( - num_groups=32, num_channels=self.num_channels, eps=1.0e-6, affine=True + num_groups=self.num_channels, num_channels=self.num_channels, eps=1.0e-6, affine=True ) def forward(self, x: Tensor) -> Tensor: diff --git a/text_recognizer/networks/vqvae/pixelcnn.py b/text_recognizer/networks/vqvae/pixelcnn.py new file mode 100644 index 0000000..5c580df --- /dev/null +++ b/text_recognizer/networks/vqvae/pixelcnn.py @@ -0,0 +1,165 @@ +"""PixelCNN encoder and decoder. + +Same as in VQGAN among other. Hopefully, better reconstructions... + +TODO: Add num of residual layers. +""" +from typing import Sequence + +from torch import nn +from torch import Tensor + +from text_recognizer.networks.vqvae.attention import Attention +from text_recognizer.networks.vqvae.norm import Normalize +from text_recognizer.networks.vqvae.residual import Residual +from text_recognizer.networks.vqvae.resize import Downsample, Upsample + + +class Encoder(nn.Module): + """PixelCNN encoder.""" + + def __init__( + self, + in_channels: int, + hidden_dim: int, + channels_multipliers: Sequence[int], + dropout_rate: float, + ) -> None: + super().__init__() + self.in_channels = in_channels + self.dropout_rate = dropout_rate + self.hidden_dim = hidden_dim + self.channels_multipliers = tuple(channels_multipliers) + self.encoder = self._build_encoder() + + def _build_encoder(self) -> nn.Sequential: + """Builds encoder.""" + encoder = [ + nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.hidden_dim, + kernel_size=3, + stride=1, + padding=1, + ), + ] + num_blocks = len(self.channels_multipliers) + in_channels_multipliers = (1,) + self.channels_multipliers + for i in range(num_blocks): + in_channels = self.hidden_dim * in_channels_multipliers[i] + out_channels = self.hidden_dim * self.channels_multipliers[i] + encoder += [ + Residual( + in_channels=in_channels, + out_channels=out_channels, + dropout_rate=self.dropout_rate, + use_norm=True, + ), + ] + if i == num_blocks - 1: + encoder.append(Attention(in_channels=out_channels)) + encoder.append(Downsample()) + + for _ in range(2): + encoder += [ + Residual( + in_channels=self.hidden_dim * self.channels_multipliers[-1], + out_channels=self.hidden_dim * self.channels_multipliers[-1], + dropout_rate=self.dropout_rate, + use_norm=True, + ), + Attention(in_channels=self.hidden_dim * self.channels_multipliers[-1]) + ] + + encoder += [ + Normalize(num_channels=self.hidden_dim * self.channels_multipliers[-1]), + nn.Mish(), + nn.Conv2d( + in_channels=self.hidden_dim * self.channels_multipliers[-1], + out_channels=self.hidden_dim * self.channels_multipliers[-1], + kernel_size=3, + stride=1, + padding=1, + ), + ] + return nn.Sequential(*encoder) + + def forward(self, x: Tensor) -> Tensor: + """Encodes input to a latent representation.""" + return self.encoder(x) + + +class Decoder(nn.Module): + """PixelCNN decoder.""" + + def __init__( + self, + hidden_dim: int, + channels_multipliers: Sequence[int], + out_channels: int, + dropout_rate: float, + ) -> None: + super().__init__() + self.hidden_dim = hidden_dim + self.out_channels = out_channels + self.channels_multipliers = tuple(channels_multipliers) + self.dropout_rate = dropout_rate + self.decoder = self._build_decoder() + + def _build_decoder(self) -> nn.Sequential: + """Builds decoder.""" + in_channels = self.hidden_dim * self.channels_multipliers[0] + decoder = [ + Residual( + in_channels=in_channels, + out_channels=in_channels, + dropout_rate=self.dropout_rate, + use_norm=True, + ), + Attention(in_channels=in_channels), + Residual( + in_channels=in_channels, + out_channels=in_channels, + dropout_rate=self.dropout_rate, + use_norm=True, + ), + ] + + out_channels_multipliers = self.channels_multipliers + (1, ) + num_blocks = len(self.channels_multipliers) + + for i in range(num_blocks): + in_channels = self.hidden_dim * self.channels_multipliers[i] + out_channels = self.hidden_dim * out_channels_multipliers[i + 1] + decoder.append( + Residual( + in_channels=in_channels, + out_channels=out_channels, + dropout_rate=self.dropout_rate, + use_norm=True, + ) + ) + if i == 0: + decoder.append( + Attention( + in_channels=out_channels + ) + ) + decoder.append(Upsample()) + + decoder += [ + Normalize(num_channels=self.hidden_dim * out_channels_multipliers[-1]), + nn.Mish(), + nn.Conv2d( + in_channels=self.hidden_dim * out_channels_multipliers[-1], + out_channels=self.out_channels, + kernel_size=3, + stride=1, + padding=1, + ), + ] + return nn.Sequential(*decoder) + + def forward(self, x: Tensor) -> Tensor: + """Decodes latent vector.""" + return self.decoder(x) diff --git a/text_recognizer/networks/vqvae/quantizer.py b/text_recognizer/networks/vqvae/quantizer.py index a4f11f0..6fb57e8 100644 --- a/text_recognizer/networks/vqvae/quantizer.py +++ b/text_recognizer/networks/vqvae/quantizer.py @@ -11,13 +11,15 @@ import torch.nn.functional as F class EmbeddingEMA(nn.Module): + """Embedding for Exponential Moving Average (EMA).""" + def __init__(self, num_embeddings: int, embedding_dim: int) -> None: super().__init__() weight = torch.zeros(num_embeddings, embedding_dim) nn.init.kaiming_uniform_(weight, nonlinearity="linear") self.register_buffer("weight", weight) - self.register_buffer("_cluster_size", torch.zeros(num_embeddings)) - self.register_buffer("_weight_avg", weight) + self.register_buffer("cluster_size", torch.zeros(num_embeddings)) + self.register_buffer("weight_avg", weight.clone()) class VectorQuantizer(nn.Module): @@ -81,16 +83,17 @@ class VectorQuantizer(nn.Module): return quantized_latent def compute_ema(self, one_hot_encoding: Tensor, latent: Tensor) -> None: + """Computes the EMA update to the codebook.""" batch_cluster_size = one_hot_encoding.sum(axis=0) batch_embedding_avg = (latent.t() @ one_hot_encoding).t() - self.embedding._cluster_size.data.mul_(self.decay).add_( + self.embedding.cluster_size.data.mul_(self.decay).add_( batch_cluster_size, alpha=1 - self.decay ) - self.embedding._weight_avg.data.mul_(self.decay).add_( + self.embedding.weight_avg.data.mul_(self.decay).add_( batch_embedding_avg, alpha=1 - self.decay ) - new_embedding = self.embedding._weight_avg / ( - self.embedding._cluster_size + 1.0e-5 + new_embedding = self.embedding.weight_avg / ( + self.embedding.cluster_size + 1.0e-5 ).unsqueeze(1) self.embedding.weight.data.copy_(new_embedding) diff --git a/text_recognizer/networks/vqvae/residual.py b/text_recognizer/networks/vqvae/residual.py index 98109b8..4ed3781 100644 --- a/text_recognizer/networks/vqvae/residual.py +++ b/text_recognizer/networks/vqvae/residual.py @@ -1,18 +1,55 @@ """Residual block.""" +import attr from torch import nn from torch import Tensor +from text_recognizer.networks.vqvae.norm import Normalize + +@attr.s(eq=False) class Residual(nn.Module): - def __init__(self, in_channels: int, out_channels: int,) -> None: + in_channels: int = attr.ib() + out_channels: int = attr.ib() + dropout_rate: float = attr.ib(default=0.0) + use_norm: bool = attr.ib(default=False) + + def __attrs_post_init__(self) -> None: + """Post init configuration.""" super().__init__() - self.block = nn.Sequential( - nn.Mish(inplace=True), - nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False), - nn.Mish(inplace=True), - nn.Conv2d(out_channels, in_channels, kernel_size=1, bias=False), - ) + self.block = self._build_res_block() + if self.in_channels != self.out_channels: + self.conv_shortcut = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=3, stride=1, padding=1) + else: + self.conv_shortcut = None + + def _build_res_block(self) -> nn.Sequential: + """Build residual block.""" + block = [] + if self.use_norm: + block.append(Normalize(num_channels=self.in_channels)) + block += [ + nn.Mish(), + nn.Conv2d( + self.in_channels, + self.out_channels, + kernel_size=3, + padding=1, + bias=False, + ), + ] + if self.dropout_rate: + block += [nn.Dropout(p=self.dropout_rate)] + + if self.use_norm: + block.append(Normalize(num_channels=self.out_channels)) + + block += [ + nn.Mish(), + nn.Conv2d(self.out_channels, self.out_channels, kernel_size=1, bias=False), + ] + return nn.Sequential(*block) def forward(self, x: Tensor) -> Tensor: """Apply the residual forward pass.""" - return x + self.block(x) + residual = self.conv_shortcut(x) if self.conv_shortcut is not None else x + return residual + self.block(x) diff --git a/text_recognizer/networks/vqvae/resize.py b/text_recognizer/networks/vqvae/resize.py index 769d089..8d67d02 100644 --- a/text_recognizer/networks/vqvae/resize.py +++ b/text_recognizer/networks/vqvae/resize.py @@ -8,7 +8,7 @@ class Upsample(nn.Module): def forward(self, x: Tensor) -> Tensor: """Applies upsampling.""" - return F.interpolate(x, scale_factor=2, mode="nearest") + return F.interpolate(x, scale_factor=2.0, mode="nearest") class Downsample(nn.Module): diff --git a/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py index 1585d40..0646119 100644 --- a/text_recognizer/networks/vqvae/vqvae.py +++ b/text_recognizer/networks/vqvae/vqvae.py @@ -1,13 +1,9 @@ """The VQ-VAE.""" from typing import Tuple -import torch from torch import nn from torch import Tensor -import torch.nn.functional as F -from text_recognizer.networks.vqvae.decoder import Decoder -from text_recognizer.networks.vqvae.encoder import Encoder from text_recognizer.networks.vqvae.quantizer import VectorQuantizer @@ -16,93 +12,45 @@ class VQVAE(nn.Module): def __init__( self, - in_channels: int, - res_channels: int, - num_residual_layers: int, + encoder: nn.Module, + decoder: nn.Module, + hidden_dim: int, embedding_dim: int, num_embeddings: int, decay: float = 0.99, - activation: str = "mish", ) -> None: super().__init__() - # Encoders - self.btm_encoder = Encoder( - in_channels=1, - out_channels=embedding_dim, - res_channels=res_channels, - num_residual_layers=num_residual_layers, - embedding_dim=embedding_dim, - activation=activation, + self.encoder = encoder + self.decoder = decoder + self.pre_codebook_conv = nn.Conv2d( + in_channels=hidden_dim, out_channels=embedding_dim, kernel_size=1 ) - - self.top_encoder = Encoder( - in_channels=embedding_dim, - out_channels=embedding_dim, - res_channels=res_channels, - num_residual_layers=num_residual_layers, - embedding_dim=embedding_dim, - activation=activation, + self.post_codebook_conv = nn.Conv2d( + in_channels=embedding_dim, out_channels=hidden_dim, kernel_size=1 ) - - # Quantizers - self.btm_quantizer = VectorQuantizer( - num_embeddings=num_embeddings, embedding_dim=embedding_dim, decay=decay, - ) - - self.top_quantizer = VectorQuantizer( + self.quantizer = VectorQuantizer( num_embeddings=num_embeddings, embedding_dim=embedding_dim, decay=decay, ) - # Decoders - self.top_decoder = Decoder( - in_channels=embedding_dim, - out_channels=embedding_dim, - embedding_dim=embedding_dim, - res_channels=res_channels, - num_residual_layers=num_residual_layers, - activation=activation, - ) - - self.btm_decoder = Decoder( - in_channels=2 * embedding_dim, - out_channels=in_channels, - embedding_dim=embedding_dim, - res_channels=res_channels, - num_residual_layers=num_residual_layers, - activation=activation, - ) - def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]: + def encode(self, x: Tensor) -> Tensor: """Encodes input to a latent code.""" - z_btm = self.btm_encoder(x) - z_top = self.top_encoder(z_btm) - return z_btm, z_top + z_e = self.encoder(x) + return self.pre_codebook_conv(z_e) - def quantize( - self, z_btm: Tensor, z_top: Tensor - ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - q_btm, vq_btm_loss = self.top_quantizer(z_btm) - q_top, vq_top_loss = self.top_quantizer(z_top) - return q_btm, vq_btm_loss, q_top, vq_top_loss + def quantize(self, z_e: Tensor) -> Tuple[Tensor, Tensor]: + z_q, vq_loss = self.quantizer(z_e) + return z_q, vq_loss - def decode(self, q_btm: Tensor, q_top: Tensor) -> Tuple[Tensor, Tensor]: + def decode(self, z_q: Tensor) -> Tensor: """Reconstructs input from latent codes.""" - d_top = self.top_decoder(q_top) - x_hat = self.btm_decoder(torch.cat((d_top, q_btm), dim=1)) - return d_top, x_hat - - def loss_fn( - self, vq_btm_loss: Tensor, vq_top_loss: Tensor, d_top: Tensor, z_btm: Tensor - ) -> Tensor: - """Calculates the latent loss.""" - return 0.5 * (vq_top_loss + vq_btm_loss) + F.mse_loss(d_top, z_btm) + z = self.post_codebook_conv(z_q) + x_hat = self.decoder(z) + return x_hat def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: """Compresses and decompresses input.""" - z_btm, z_top = self.encode(x) - q_btm, vq_btm_loss, q_top, vq_top_loss = self.quantize(z_btm=z_btm, z_top=z_top) - d_top, x_hat = self.decode(q_btm=q_btm, q_top=q_top) - vq_loss = self.loss_fn( - vq_btm_loss=vq_btm_loss, vq_top_loss=vq_top_loss, d_top=d_top, z_btm=z_btm - ) + z_e = self.encode(x) + z_q, vq_loss = self.quantize(z_e) + x_hat = self.decode(z_q) return x_hat, vq_loss diff --git a/training/callbacks/wandb_callbacks.py b/training/callbacks/wandb_callbacks.py index 61d71df..2264750 100644 --- a/training/callbacks/wandb_callbacks.py +++ b/training/callbacks/wandb_callbacks.py @@ -39,8 +39,8 @@ class WatchModel(Callback): class UploadCodeAsArtifact(Callback): """Upload all *.py files to W&B as an artifact, at the beginning of the run.""" - def __init__(self, project_dir: str) -> None: - self.project_dir = Path(project_dir) + def __init__(self) -> None: + self.project_dir = Path(__file__).resolve().parents[2] / "text_recognizer" @rank_zero_only def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None: @@ -49,7 +49,7 @@ class UploadCodeAsArtifact(Callback): experiment = logger.experiment artifact = wandb.Artifact("project-source", type="code") for filepath in self.project_dir.glob("**/*.py"): - artifact.add_file(filepath) + 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 = ckpt_dir + self.ckpt_dir = Path(__file__).resolve().parent / ckpt_dir self.upload_best_only = upload_best_only @rank_zero_only diff --git a/training/conf/callbacks/wandb_code.yaml b/training/conf/callbacks/wandb_code.yaml index 35f6ea3..012cdce 100644 --- a/training/conf/callbacks/wandb_code.yaml +++ b/training/conf/callbacks/wandb_code.yaml @@ -1,3 +1,2 @@ upload_code_as_artifact: _target_: callbacks.wandb_callbacks.UploadCodeAsArtifact - project_dir: ${work_dir}/text_recognizer diff --git a/training/conf/callbacks/wandb_htr.yaml b/training/conf/callbacks/wandb_htr.yaml index 9c9a6da..44adb71 100644 --- a/training/conf/callbacks/wandb_htr.yaml +++ b/training/conf/callbacks/wandb_htr.yaml @@ -3,4 +3,4 @@ defaults: - wandb_watch - wandb_code - wandb_checkpoints - - wandb_ocr_predictions + - wandb_htr_predictions diff --git a/training/conf/callbacks/wandb_vae.yaml b/training/conf/callbacks/wandb_vae.yaml index 609a8e8..c7b09b0 100644 --- a/training/conf/callbacks/wandb_vae.yaml +++ b/training/conf/callbacks/wandb_vae.yaml @@ -1,6 +1,6 @@ defaults: - default - wandb_watch - - wandb_code - wandb_checkpoints - wandb_image_reconstructions + # - wandb_code diff --git a/training/conf/experiment/htr_char.yaml b/training/conf/experiment/htr_char.yaml index 77126ae..e51a116 100644 --- a/training/conf/experiment/htr_char.yaml +++ b/training/conf/experiment/htr_char.yaml @@ -3,10 +3,15 @@ defaults: - override /mapping: characters +datamodule: + word_pieces: false + criterion: ignore_index: 3 network: - num_classes: 89 + num_classes: 58 pad_index: 3 + +model: max_output_len: 682 diff --git a/training/conf/experiment/vqvae.yaml b/training/conf/experiment/vqvae.yaml index 699612e..eb40f3b 100644 --- a/training/conf/experiment/vqvae.yaml +++ b/training/conf/experiment/vqvae.yaml @@ -8,14 +8,16 @@ defaults: trainer: max_epochs: 64 - gradient_clip_val: 0.25 + # gradient_clip_val: 0.25 datamodule: - batch_size: 32 + batch_size: 16 lr_scheduler: epochs: 64 - steps_per_epoch: 624 + steps_per_epoch: 1245 optimizer: lr: 1.0e-3 + +summary: [1, 576, 640] diff --git a/training/conf/model/lit_vqvae.yaml b/training/conf/model/lit_vqvae.yaml index 8837573..409fa0d 100644 --- a/training/conf/model/lit_vqvae.yaml +++ b/training/conf/model/lit_vqvae.yaml @@ -1,4 +1,4 @@ _target_: text_recognizer.models.vqvae.VQVAELitModel interval: step monitor: val/loss -latent_loss_weight: 0.25 +latent_loss_weight: 1.0 diff --git a/training/conf/network/decoder/pixelcnn_encoder.yaml b/training/conf/network/decoder/pixelcnn_encoder.yaml new file mode 100644 index 0000000..47a130d --- /dev/null +++ b/training/conf/network/decoder/pixelcnn_encoder.yaml @@ -0,0 +1,5 @@ +_target_: text_recognizer.networks.vqvae.pixelcnn.Encoder +in_channels: 1 +hidden_dim: 8 +channels_multipliers: [1, 2, 8, 8] +dropout_rate: 0.25 diff --git a/training/conf/network/decoder/vae_decoder.yaml b/training/conf/network/decoder/vae_decoder.yaml new file mode 100644 index 0000000..b2090b3 --- /dev/null +++ b/training/conf/network/decoder/vae_decoder.yaml @@ -0,0 +1,5 @@ +_target_: text_recognizer.networks.vqvae.decoder.Decoder +out_channels: 1 +hidden_dim: 32 +channels_multipliers: [8, 6, 2, 1] +dropout_rate: 0.25 diff --git a/training/conf/network/encoder/pixelcnn_decoder.yaml b/training/conf/network/encoder/pixelcnn_decoder.yaml new file mode 100644 index 0000000..3895164 --- /dev/null +++ b/training/conf/network/encoder/pixelcnn_decoder.yaml @@ -0,0 +1,5 @@ +_target_: text_recognizer.networks.vqvae.pixelcnn.Decoder +out_channels: 1 +hidden_dim: 8 +channels_multipliers: [8, 8, 2, 1] +dropout_rate: 0.25 diff --git a/training/conf/network/encoder/vae_encoder.yaml b/training/conf/network/encoder/vae_encoder.yaml new file mode 100644 index 0000000..5dc6814 --- /dev/null +++ b/training/conf/network/encoder/vae_encoder.yaml @@ -0,0 +1,5 @@ +_target_: text_recognizer.networks.vqvae.encoder.Encoder +in_channels: 1 +hidden_dim: 32 +channels_multipliers: [1, 2, 6, 8] +dropout_rate: 0.25 diff --git a/training/conf/network/vqvae.yaml b/training/conf/network/vqvae.yaml index 5a5c066..835d0b7 100644 --- a/training/conf/network/vqvae.yaml +++ b/training/conf/network/vqvae.yaml @@ -1,8 +1,9 @@ -_target_: text_recognizer.networks.vqvae.VQVAE -in_channels: 1 -res_channels: 32 -num_residual_layers: 2 -embedding_dim: 64 -num_embeddings: 512 +defaults: + - encoder: vae_encoder + - decoder: vae_decoder + +_target_: text_recognizer.networks.vqvae.vqvae.VQVAE +hidden_dim: 256 +embedding_dim: 32 +num_embeddings: 1024 decay: 0.99 -activation: mish diff --git a/training/conf/network/vqvae_pixelcnn.yaml b/training/conf/network/vqvae_pixelcnn.yaml new file mode 100644 index 0000000..10200bc --- /dev/null +++ b/training/conf/network/vqvae_pixelcnn.yaml @@ -0,0 +1,9 @@ +defaults: + - encoder: pixelcnn_encoder + - decoder: pixelcnn_decoder + +_target_: text_recognizer.networks.vqvae.vqvae.VQVAE +hidden_dim: 64 +embedding_dim: 32 +num_embeddings: 512 +decay: 0.99 diff --git a/training/conf/optimizer/madgrad.yaml b/training/conf/optimizer/madgrad.yaml index 84626d3..46b2fff 100644 --- a/training/conf/optimizer/madgrad.yaml +++ b/training/conf/optimizer/madgrad.yaml @@ -1,5 +1,5 @@ _target_: madgrad.MADGRAD -lr: 1.0e-3 +lr: 2.0e-4 momentum: 0.9 weight_decay: 0 eps: 1.0e-6 diff --git a/training/conf/trainer/default.yaml b/training/conf/trainer/default.yaml index 0fa9ce1..c665adc 100644 --- a/training/conf/trainer/default.yaml +++ b/training/conf/trainer/default.yaml @@ -8,7 +8,7 @@ gpus: 1 precision: 16 max_epochs: 512 terminate_on_nan: true -weights_summary: full +weights_summary: top limit_train_batches: 1.0 limit_val_batches: 1.0 limit_test_batches: 1.0 diff --git a/training/run.py b/training/run.py index 13a6a82..a2529b0 100644 --- a/training/run.py +++ b/training/run.py @@ -13,6 +13,7 @@ from pytorch_lightning import ( ) from pytorch_lightning.loggers import LightningLoggerBase from torch import nn +from torchsummary import summary from text_recognizer.data.base_mapping import AbstractMapping import utils @@ -37,6 +38,9 @@ def run(config: DictConfig) -> Optional[float]: log.info(f"Instantiating network <{config.network._target_}>") network: nn.Module = hydra.utils.instantiate(config.network) + if config.summary: + summary(network, tuple(config.summary), device="cpu") + log.info(f"Instantiating criterion <{config.criterion._target_}>") loss_fn: Type[nn.Module] = hydra.utils.instantiate(config.criterion) |