{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "from torch import nn\n",
    "from torchsummary import summary\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from omegaconf import OmegaConf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"../training/configs/vqvae.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = OmegaConf.load(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(OmegaConf.to_yaml(conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.networks import VQVAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae = VQVAE(**conf.network.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datum = torch.randn([2, 1, 576, 640])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae.encoder(datum)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae(datum)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.networks.backbones.efficientnet import EfficientNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "en = EfficientNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datum = torch.randn([2, 1, 576, 640])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trg = torch.randint(0, 1000, [2, 682])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datum = torch.randn([2, 1, 224, 224])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "en(datum).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"../training/configs/cnn_transformer.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = OmegaConf.load(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed: 4711\n",
      "network:\n",
      "  desc: Configuration of the PyTorch neural network.\n",
      "  type: CNNTransformer\n",
      "  args:\n",
      "    encoder:\n",
      "      type: EfficientNet\n",
      "      args: null\n",
      "    num_decoder_layers: 4\n",
      "    hidden_dim: 256\n",
      "    num_heads: 4\n",
      "    expansion_dim: 1024\n",
      "    dropout_rate: 0.1\n",
      "    transformer_activation: glu\n",
      "model:\n",
      "  desc: Configuration of the PyTorch Lightning model.\n",
      "  type: LitTransformerModel\n",
      "  args:\n",
      "    optimizer:\n",
      "      type: MADGRAD\n",
      "      args:\n",
      "        lr: 0.001\n",
      "        momentum: 0.9\n",
      "        weight_decay: 0\n",
      "        eps: 1.0e-06\n",
      "    lr_scheduler:\n",
      "      type: OneCycleLR\n",
      "      args:\n",
      "        interval: step\n",
      "        max_lr: 0.001\n",
      "        three_phase: true\n",
      "        epochs: 512\n",
      "        steps_per_epoch: 1246\n",
      "    criterion:\n",
      "      type: CrossEntropyLoss\n",
      "      args:\n",
      "        weight: None\n",
      "        ignore_index: -100\n",
      "        reduction: mean\n",
      "    monitor: val_loss\n",
      "    mapping: sentence_piece\n",
      "data:\n",
      "  desc: Configuration of the training/test data.\n",
      "  type: IAMExtendedParagraphs\n",
      "  args:\n",
      "    batch_size: 16\n",
      "    num_workers: 12\n",
      "    train_fraction: 0.8\n",
      "    augment: true\n",
      "callbacks:\n",
      "- type: ModelCheckpoint\n",
      "  args:\n",
      "    monitor: val_loss\n",
      "    mode: min\n",
      "    save_last: true\n",
      "- type: StochasticWeightAveraging\n",
      "  args:\n",
      "    swa_epoch_start: 0.8\n",
      "    swa_lrs: 0.05\n",
      "    annealing_epochs: 10\n",
      "    annealing_strategy: cos\n",
      "    device: null\n",
      "- type: LearningRateMonitor\n",
      "  args:\n",
      "    logging_interval: step\n",
      "- type: EarlyStopping\n",
      "  args:\n",
      "    monitor: val_loss\n",
      "    mode: min\n",
      "    patience: 10\n",
      "trainer:\n",
      "  desc: Configuration of the PyTorch Lightning Trainer.\n",
      "  args:\n",
      "    stochastic_weight_avg: true\n",
      "    auto_scale_batch_size: binsearch\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: true\n",
      "load_checkpoint: null\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(OmegaConf.to_yaml(conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.networks.cnn_transformer import CNNTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = CNNTransformer(input_shape=(1, 576, 640), output_shape=(682, 1), **conf.network.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.encode(datum).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 682, 1004])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t(datum, trg).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "b, n = 16, 128\n",
    "device = \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = lambda: torch.ones((b, n), device=device).bool()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 128])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 128])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones((b, n), device=device).bool().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.randn(1, 1, 576, 640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "576 // 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "640 // 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.randn(1, 1, 144, 160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from einops import rearrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "patch_size=16\n",
    "p = rearrange(x, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1440, 256])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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