1 files changed, 0 insertions, 547 deletions
diff --git a/notebooks/00-testing-stuff-out.ipynb b/notebooks/00-testing-stuff-out.ipynb
deleted file mode 100644
index 12c5145..0000000
--- a/notebooks/00-testing-stuff-out.ipynb
+++ /dev/null
@@ -1,547 +0,0 @@
-{
- "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": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}