{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ce2519f",
   "metadata": {},
   "outputs": [],
   "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": 49,
   "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": 3,
   "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": 31,
   "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_text_predictions:\n",
      "    _target_: callbacks.wandb_callbacks.LogTextPredictions\n",
      "    num_samples: 8\n",
      "criterion:\n",
      "  _target_: text_recognizer.criterions.label_smoothing.LabelSmoothingLoss\n",
      "  smoothing: 0.1\n",
      "  ignore_index: 1000\n",
      "datamodule:\n",
      "  _target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs\n",
      "  batch_size: 4\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: 512\n",
      "  steps_per_epoch: 4992\n",
      "  pct_start: 0.3\n",
      "  anneal_strategy: cos\n",
      "  cycle_momentum: true\n",
      "  base_momentum: 0.85\n",
      "  max_momentum: 0.95\n",
      "  div_factor: 25.0\n",
      "  final_div_factor: 10000.0\n",
      "  three_phase: true\n",
      "  last_epoch: -1\n",
      "  verbose: false\n",
      "mapping:\n",
      "  _target_: text_recognizer.data.emnist_mapping.EmnistMapping\n",
      "  extra_symbols:\n",
      "  - '\n",
      "\n",
      "    '\n",
      "model:\n",
      "  _target_: text_recognizer.models.transformer.TransformerLitModel\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",
      "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",
      "  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",
      "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: 512\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_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"
     ]
    }
   ],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"config\", overrides=[\"mapping=emnist\"])\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,
   "id": "1c4624d1-6de5-41ab-9208-0988fcdba76d",
   "metadata": {},
   "outputs": [
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IAM Original and Synthetic Paragraphs Dataset\n",
      "Num classes: 84\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",
      "\n"
     ]
    }
   ],
   "source": [
    "datamodule = instantiate(cfg.datamodule, mapping=cfg.mapping)\n",
    "datamodule.prepare_data()\n",
    "datamodule.setup()\n",
    "print(datamodule)"
   ]
  },
  {
   "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",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1006"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datamodule.mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42501428",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = IAMParagraphs()\n",
    "dataset.prepare_data()\n",
    "dataset.setup()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e6e8c05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(iter(datamodule.test_dataloader()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bed2170",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf22683",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = dataset.data_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8541e6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40447ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y["
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016e8c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bcfc61cc-e6cc-4fb0-91ca-eca02168c6e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamodule.mapping.get_index(\"<p>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1e657891-45bb-479e-95ba-bdefe3a84ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<s>He rose from his breakfast-nook bench\\nand came into the livingroom, where\\nHeather and Steve stood aghast at\\nhis entrance. He came, almost falling\\nforward in an ungainly shuffle, neck\\nthrust out, arms dangling loosely.\\nThen, abruptly, he drew himself up\\nand walked on the very tips of\\nhis toes. He stretched his arms\\nover his head and yawned agape,\\ndrawing-in great breaths that\\nbecame great sighs of ecstacy.<e>'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_y_label_to_string(y[0], datamodule.mapping, padding_index=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aa8c021",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7ef93252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMDRfwDsLV/2DhyLCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_plot(x[0, 0], vmax=1, title=datamodule.mapping.get_text(y[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "edd0e44b-b383-4117-83ca-0bfd7e5235aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1000])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamodule.mapping[\"<p>\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3480ae5f-9cec-4814-98fe-02082a139add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1002,   25,  147,  233,   88,   16,   45,    1,   61,   54,    7,   20,\n",
       "          95,   71,   20,    2,   15,   30,   21,   24,   24,   95,   18,   21,\n",
       "          78, 1001,   14,  779,    7,    1,  218,    3,    1,   36,   23,   64,\n",
       "          23,   21,   46,   54,   24,   24,   16,    4,    1,  542, 1001,    1,\n",
       "          47,    7,   20,   15,   47,    7,   54,   14,    1,    2,   15,    7,\n",
       "          64,    7,   99,  281,    1,   20,   46,   47,   20,    2,   15,   80,\n",
       "        1001,   45,    1,    7,   21,   15,   54,   20,   21,   31,    7,   33,\n",
       "          25,    1,   31,   20,   16,    7,    4,    1,   28,  744,  489,   12,\n",
       "        1001,   35,  362,   11,   67,    1,   41,   21,   46,   20,   23,   21,\n",
       "          36,   13,    1,    2,   47,   41,   71,   71,   36,    7,    4,    1,\n",
       "         120,  155, 1001,   22,   54,   41,   66,    1,   24,   41,   15,    4,\n",
       "         673,    2,    1,   17,   20,   21,   46,   36,   23,   21,   46,    1,\n",
       "          36,   24,   24,    2,    7,   36,   13,   33, 1001,    1,   15,   47,\n",
       "           7,   21,    4,    1,   20,   61,   54,   41,   26,   15,   36,   13,\n",
       "           4,   25,  172,    7,   84,  162,  237,  121, 1001,   14,   34,   28,\n",
       "          95,    9,   42,    3,    1,  351,    1,   15,   23,   26,    2,   10,\n",
       "        1001,   45,    1,   15,   24,    7,    2,   33,   25,    1,    2,   15,\n",
       "          54,    7,   15,   31,   47,    7,   17,   45,  673,    2, 1001,  223,\n",
       "          45,  534,   14,    1,   13,   20,   84,   21,    7,   17,    1,   20,\n",
       "          46,   20,   26,    7,    4, 1001,    1,   17,   54,   20,   84,   23,\n",
       "          21,   46,   30,   23,   21,  229,    1,   61,   54,    7,   20,   15,\n",
       "          47,    2,   22,   19, 1001,   18,   31,   20,  197,  229,    1,    2,\n",
       "         193,   47,    2,   10,    1,    7,   31,    2,   15,   20,   31,   13,\n",
       "          33, 1003, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n",
       "        1000, 1000, 1000, 1000, 1000, 1000, 1000])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6c62572f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_9271/3685142356.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatamodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in \u001b[0;36mconvert_y_label_to_string\u001b[0;34m(y, mapping, padding_index)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36mget_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwordpiece_processor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/iam_preprocessor.py\u001b[0m in \u001b[0;36mto_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexicon\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtokens_to_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "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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