path: root/notebooks/03-look-at-iam-paragraphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ce2519f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
    "import random\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "\n",
    "from text_recognizer.data.iam_paragraphs import IAMParagraphs\n",
    "from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs\n",
    "from text_recognizer.data.iam_extended_paragraphs import IAMExtendedParagraphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "726ac25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _plot(image, figsize=(12,12), title='', vmin=0, vmax=255):\n",
    "    plt.figure(figsize=figsize)\n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    plt.imshow(image, cmap='gray', vmin=vmin, vmax=vmax)\n",
    "\n",
    "def convert_y_label_to_string(y, mapping, padding_index=3):\n",
    "    return ''.join([mapping[int(i)] for i in y if i != padding_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec16e41f-3d12-4da2-bf02-7429b41cf98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import compose, initialize\n",
    "from omegaconf import OmegaConf\n",
    "from hydra.utils import instantiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9386367-2b49-4633-9936-57081132e59e",
   "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: 16\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: 830\n",
      "  pct_start: 0.3\n",
      "  anneal_strategy: cos\n",
      "  cycle_momentum: true\n",
      "  base_momentum: 0.85\n",
      "  max_momentum: 0.95\n",
      "  div_factor: 25.0\n",
      "  final_div_factor: 10000.0\n",
      "  three_phase: true\n",
      "  last_epoch: -1\n",
      "  verbose: false\n",
      "mapping:\n",
      "  _target_: text_recognizer.data.word_piece_mapping.WordPieceMapping\n",
      "  num_features: 1000\n",
      "  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: 1.0\n",
      "network:\n",
      "  encoder:\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.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",
      "  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: ${hydra:runtime.cwd}\n",
      "debug: false\n",
      "print_config: true\n",
      "ignore_warnings: true\n",
      "summary:\n",
      "- 1\n",
      "- 576\n",
      "- 640\n",
      "\n",
      "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_code_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCodeAsArtifact', 'project_dir': '${work_dir}/../text_recognizer'}, 'upload_ckpts_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCheckpointsAsArtifact', 'ckpt_dir': 'checkpoints/', 'upload_best_only': True}, 'log_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=[\"+experiment=vqvae\"])\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c4624d1-6de5-41ab-9208-0988fcdba76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-06 01:28:48.099 | DEBUG    | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
      "2021-08-06 01:28:48.299 | INFO     | text_recognizer.data.iam_paragraphs:setup:97 - Loading IAM paragraph regions and lines for None...\n",
      "2021-08-06 01:29:08.361 | DEBUG    | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
      "2021-08-06 01:29:11.692 | DEBUG    | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n",
      "2021-08-06 01:29:11.797 | INFO     | text_recognizer.data.iam_synthetic_paragraphs:setup:68 - IAM Synthetic dataset steup for stage None...\n",
      "2021-08-06 01:29:24.065 | DEBUG    | text_recognizer.data.word_piece_mapping:__init__:37 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IAM Original and Synthetic Paragraphs Dataset\n",
      "Num classes: 1006\n",
      "Dims: (1, 576, 640)\n",
      "Output dims: (682, 1)\n",
      "Train/val/test sizes: 19911, 262, 231\n",
      "Train Batch x stats: (torch.Size([16, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0165), tensor(0.0767), tensor(1.))\n",
      "Train Batch y stats: (torch.Size([16, 451]), torch.int64, tensor(1), tensor(1003))\n",
      "Test Batch x stats: (torch.Size([16, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0312), tensor(0.0817), tensor(0.9294))\n",
      "Test Batch y stats: (torch.Size([16, 451]), torch.int64, tensor(1), tensor(1003))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "datamodule = instantiate(cfg.datamodule, mapping=cfg.mapping)\n",
    "datamodule.prepare_data()\n",
    "datamodule.setup()\n",
    "print(datamodule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "770f29f6-94f3-40c7-80f0-d85bd2d23fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1245"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datamodule.train_dataloader())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e6e8c05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(iter(datamodule.train_dataloader()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bed2170",
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   "outputs": [],
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    "x.shape"
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   "execution_count": 20,
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       " '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": [
    "datamodule.mapping.inverse_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec962504-808d-4819-9853-51711c0175f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datamodule.mapping.mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b0c4625b-b864-4c9d-b865-d9cc29f87298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1002,    3,  573,   10,  338,  119,  531,   18,    1,    2,   24,   36,\n",
       "          64,    7,   17,   33,    1,   37,   15,   47,    7,   54,    7,   71,\n",
       "          24,   54,    7,    1,    2,  743,    1,  511,   13,    7,    1,  742,\n",
       "        1000,    1,    2,  370,    3,  125,  112,   12,   11,    3,   91,   86,\n",
       "          20,    1,   26,   20,   36,   20,   31,    7,    4,  100,  508,   48,\n",
       "        1000,  116,   29,   67,    1,    7,   20,    2,   15,    7,   54,   36,\n",
       "          13,    1,   17,   54,   23,   71,   15,    1,  653, 1000,  953,    8,\n",
       "           1,   36,   24,   64,    7,   37,   33, 1000,   91,   35,    3,  507,\n",
       "         369,   12,  316,    1,   47,   20,   21,   17,   33, 1000,    1,  469,\n",
       "         324,   33,    1,   54,    7,   46,   54,    7,    2,    2,   23,   24,\n",
       "          21,    1,    7,    2,   15,   23,   16,   20,   15,    7,    2,   10,\n",
       "           3,  263,   26,  182,   23,  480,   42, 1000,    3,  260,   40,  100,\n",
       "         127,  149,    6,    1,   71,   23,   46,   16,    7,   21,   15,   10,\n",
       "        1000,    6,  522,    1,  852,    2,    1,  465,   88,   16,    6,  460,\n",
       "         423,    1,   64,   23,   36,   36,   20,   46,    7,    4, 1001, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003, 1003,\n",
       "        1003, 1003, 1003, 1003, 1003, 1003, 1003])"
      ]
     },
     "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([1004])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamodule.mapping.get_index(\"#\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e657891-45bb-479e-95ba-bdefe3a84ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<s>▁problem▁of▁life▁cannot▁be▁solved.▁\"therefore▁shall▁1ye▁lay\\n▁since▁meeting▁in▁doria▁palace,▁no▁word▁had\\n▁there▁is▁an▁easterly▁drift▁special\\n▁someone▁to▁love\".\\n▁do▁for▁world▁using▁my▁hand.\\n▁78.▁regression▁estimates▁of▁expenditure▁on\\n▁play▁was▁no▁more▁than▁a▁figment▁of\\n▁a▁few▁minutes▁later▁from▁a▁nearby▁village,<e><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p><p>'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_y_label_to_string(y, datamodule.mapping, padding_index=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7aa8c021",
   "metadata": {
    "scrolled": true
   },
   "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": 29,
   "id": "7ef93252",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_plot(x[0], vmax=1, title=datamodule.mapping.get_text(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edd0e44b-b383-4117-83ca-0bfd7e5235aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "datamodule.mapping[\"<p>\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3480ae5f-9cec-4814-98fe-02082a139add",
   "metadata": {},
   "outputs": [],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c62572f",
   "metadata": {},
   "outputs": [],
   "source": [
    "_plot(x[0, 0], vmax=1, title=convert_y_label_to_string(y[0], datamodule.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7778ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_train))\n",
    "    x, y = dataset.data_train[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf845a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from einops import rearrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe4bfb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = dataset.data_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ba4dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "_plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34348d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 32\n",
    "patches = rearrange(x.unsqueeze(0), 'b c (h p1) (w p2) -> b c (h w) p1 p2', p1 = p, p2 = p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77bded74",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(20, 20))\n",
    "for i in range(15):\n",
    "    ax = fig.add_subplot(1, 15, i + 1)\n",
    "    ax.imshow(patches[0, 0, i + 160, :, :].squeeze(0), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d11ca56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_test))\n",
    "    x, y = dataset.data_test[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548d10da",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = IAMSyntheticParagraphs()\n",
    "dataset.prepare_data()\n",
    "dataset.setup()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "627730b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_train))\n",
    "    x, y = dataset.data_train[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4150722e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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