Reformatting with attrs, config for encoder and decoder

1 files changed, 397 insertions, 0 deletions

diff --git a/notebooks/05c-test-model-end-to-end.ipynb b/notebooks/05c-test-model-end-to-end.ipynb
new file mode 100644
index 0000000..a0b4ee9
--- /dev/null
+++ b/notebooks/05c-test-model-end-to-end.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1e40a88b",
+   "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\n",
+    "from torch import nn\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,
+   "id": "2ab9ac7a-a288-45bc-bfb7-8579a3a38d93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ecab65ba-5aa0-45f0-99d7-e837464185ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function torch.nn.functional.softmax(input: torch.Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[int] = None) -> torch.Tensor>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "F.softmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3e812a1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import attr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a42a7988",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@attr.s\n",
+    "class C(object):\n",
+    "    d = {2: \"hej\"}\n",
+    "    x: F.softmax = attr.ib(init=False, default=F.softmax)\n",
+    "    @x.validator\n",
+    "    def check(self, attribute, value):\n",
+    "        print(attribute)\n",
+    "        print(self.x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "660a7b1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attribute(name='x', default=<function softmax at 0x7fb624839ca0>, validator=<function C.check at 0x7fb622ce2040>, repr=True, eq=True, eq_key=None, order=True, order_key=None, hash=None, init=False, metadata=mappingproxy({}), type=<function softmax at 0x7fb624839ca0>, converter=None, kw_only=False, inherited=False, on_setattr=None)\n",
+      "<function softmax at 0x7fb624839ca0>\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = C()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9c3d1163",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function torch.nn.functional.softmax(input: torch.Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[int] = None) -> torch.Tensor>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b3c8879c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2f5f6b75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = nn.ModuleList([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9938ec53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = nn.Linear(10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fc49db78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(10):\n",
+    "    l.append(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "e799a9dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ModuleList(\n",
+       "  (0): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (2): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (3): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (4): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (5): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (6): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (7): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (8): Linear(in_features=10, out_features=10, bias=True)\n",
+       "  (9): Linear(in_features=10, out_features=10, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "17213dfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Linear' object has no attribute 'copy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_31696/2302067867.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m   1131\u001b[0m             type(self).__name__, name))\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Linear' object has no attribute 'copy'"
+     ]
+    }
+   ],
+   "source": [
+    "ff = f.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "60277c26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from copy import deepcopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cf86534a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ff = deepcopy(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "2a260dc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140011688939472"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(ff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6dcf5f63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140011688936544"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "74958f8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140011688936544"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(l[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "bcceabd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140011688936544"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(l[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "191a0b03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'nn'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\".\".join(\"nn.LayerNorm\".split(\".\")[:-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "4ff8ae08",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'str' object has no attribute 'LayerNorm'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_31696/162121485.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"torch.nn\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LayerNorm\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'LayerNorm'"
+     ]
+    }
+   ],
+   "source": [
+    "getattr(\"torch.nn\", \"LayerNorm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d536bf2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}