Bug fix

1 files changed, 598 insertions, 0 deletions

diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb
new file mode 100644
index 0000000..d50fd59
--- /dev/null
+++ b/notebooks/00-scratch-pad.ipynb
@@ -0,0 +1,598 @@
+{
+ "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": [
+    {
+     "data": {
+      "text/plain": [
+       "ModuleList(\n",
+       "  (0): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (1): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (2): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (3): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (4): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (5): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (6): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (7): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (8): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       "  (9): ModuleList(\n",
+       "    (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nn.ModuleList([nn.ModuleList([nn.Linear(10, 10)]) for _ in range(10)])"
+   ]
+  },
+  {
+   "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
+}