From 58ae7154aa945cfe5a46592cc1dfb28f0a4e51b3 Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Sat, 1 May 2021 23:53:50 +0200 Subject: Working on new attention module --- notebooks/02a-sentence-generator.ipynb | 98 -- notebooks/02c-image-patches.ipynb | 525 --------- notebooks/03-look-at-iam-paragraphs.ipynb | 35 +- notebooks/03a-line-prediction.ipynb | 419 -------- notebooks/04a-look-at-iam-lines.ipynb | 383 ------- notebooks/07-look-at-lexicon.ipynb | 1119 -------------------- notebooks/07-try-gtn.ipynb | 202 ---- notebooks/Untitled.ipynb | 385 ------- notebooks/g1.png | Bin 8590 -> 0 bytes notebooks/g2.png | Bin 5247 -> 0 bytes notebooks/intersect.png | Bin 7953 -> 0 bytes notebooks/intersection.pdf | Bin 10154 -> 0 bytes poetry.toml | 2 + text_recognizer/networks/__init__.py | 2 +- text_recognizer/networks/backbones/__init__.py | 2 - text_recognizer/networks/backbones/efficientnet.py | 145 --- text_recognizer/networks/encoders/__init__.py | 2 + text_recognizer/networks/encoders/efficientnet.py | 145 +++ .../networks/encoders/residual_network.py | 310 ++++++ text_recognizer/networks/encoders/wide_resnet.py | 221 ++++ text_recognizer/networks/residual_network.py | 310 ------ text_recognizer/networks/transformer/attention.py | 119 +-- text_recognizer/networks/transformer/norm.py | 13 + text_recognizer/networks/wide_resnet.py | 221 ---- 24 files changed, 750 insertions(+), 3908 deletions(-) delete mode 100644 notebooks/02a-sentence-generator.ipynb delete mode 100644 notebooks/02c-image-patches.ipynb delete mode 100644 notebooks/03a-line-prediction.ipynb delete mode 100644 notebooks/04a-look-at-iam-lines.ipynb delete mode 100644 notebooks/07-look-at-lexicon.ipynb delete mode 100644 notebooks/07-try-gtn.ipynb delete mode 100644 notebooks/Untitled.ipynb delete mode 100644 notebooks/g1.png delete mode 100644 notebooks/g2.png delete mode 100644 notebooks/intersect.png delete mode 100644 notebooks/intersection.pdf create mode 100644 poetry.toml delete mode 100644 text_recognizer/networks/backbones/__init__.py delete mode 100644 text_recognizer/networks/backbones/efficientnet.py create mode 100644 text_recognizer/networks/encoders/__init__.py create mode 100644 text_recognizer/networks/encoders/efficientnet.py create mode 100644 text_recognizer/networks/encoders/residual_network.py create mode 100644 text_recognizer/networks/encoders/wide_resnet.py delete mode 100644 text_recognizer/networks/residual_network.py delete mode 100644 text_recognizer/networks/wide_resnet.py diff --git a/notebooks/02a-sentence-generator.ipynb b/notebooks/02a-sentence-generator.ipynb deleted file mode 100644 index 99aa56a..0000000 --- a/notebooks/02a-sentence-generator.ipynb +++ /dev/null @@ -1,98 +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\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": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import SentenceGenerator" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "sentence_generator = SentenceGenerator(32)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'broad___________________________'" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sentence_generator.generate()" - ] - }, - { - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/02c-image-patches.ipynb b/notebooks/02c-image-patches.ipynb deleted file mode 100644 index fedea91..0000000 --- a/notebooks/02c-image-patches.ipynb +++ /dev/null @@ -1,525 +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\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, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "emnist_lines = EmnistLinesDataset(train=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-10 17:44:25.666 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:153 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "emnist_lines.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", - " return ''.join([emnist_lines.mapper(int(i)) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office in Arkansas after the______\n", - "in________________________________\n", - "by a oneshot technique____________\n", - "office Incumbent__________________\n", - "of the revolutionary______________\n", - "they______________________________\n", - "the scene but_____________________\n", - "Knox Ky___________________________\n", - "workers wife refused to have______\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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jY2MkEonbspR77rvzeDwUFxezadMm1q1bh9vtJp1O4/P5aGhoYP369axatYrHHnuMqqoqPB4P1lpmZ2fp6urirbfe4sCBAxw+fJipqakrAptkMklvb6+zLxwOs2XLFkpKSpzgJ1dRZa1lfn6eaDSqah8RERERERG5py178OPxeCgqKiIvL4/i4mJWrVpFdXU1eXl5Thj04IMPUlBQQEVFBZWVlZSWlpJOp0kkEkxNTXHkyBF+8YtfcOLECaLRKMXFxXz5y1/mqaeewu/309bWht/vp7a2lt27d9Pa2orL5WJoaIh3332X/fv3093dzcjICF//+tdpbW2lubmZgoICpwF0S0sLJ06coKOjA5/PR1lZmTMNrLm52Ql94JeBT24pc2MMU1NTzM3NATirfpWUlNDc3Mz4+DiDg4PMzMyQSqWor6/H6/Vy//33Y4xhYGCAU6dOsbi4iN/vZ+fOnTz22GO0tLQQCoXIZDJEo1Heeustjh07xszMzG0JfVwuF4WFheTl5ZGfn8/KlStZuXKlM83L7/ezevVqCgsLKSkpoaamhsrKSgASiQRzc3P09fWxf/9+3nrrLTo7O5menr5qlU4mk6Gzs5O+vj6ampoIh8OsWrWKhoYG+vv7ycvLIy8vD4/HQyqVIhaLfWhDbhEREREREZF7wbIGP7n+OM8//zyNjY3U1dWxfv166urqnKlSRUVFPP/8885rcn/Ud3d3c/HiRQ4fPszevXvp7u5mYWGBsrIy7r//fjZt2kQoFMLlclFVVcXmzZtZtWoVmzdvJpPJMD09zalTp3jnnXfYt28fExMTJBIJ9u3bhzGGwsJCgsEgHo8Ht9tNXV0d9fX1+Hw+gsEgZWVlVFdXU1VVRVFRkRP65MaYCzaSySTGGLq6uhgbG8PlctHQ0EBpaSnhcJjm5mZnKtfg4OBlPWuCwSC1tbXU19dTVlbGzMwMhYWF7Nq1iy1btlBeXo7L5SKZTDI0NER3dzexWOy2TO9yuVwUFRXxhS98wQnrGhoa2Lx5s9MnKRwOs3PnzsuqbuLxOF1dXfT19XH+/HmOHj3Ke++9x+Dg4IeGVbkGzy+//DIlJSU88sgj1NfX88QTT3D06FFWrlxJfX09oVCI0dFR9u3bx8jIiCp+RERERERE5J62bMFPbjWs6upqvva1r9Hc3Ow0QH6/XNPmiYkJ2tvbeeONN3j99dcZGxtjcnLSWfkpFApRXl7OAw88wIoVK3C73QA0NjbS0NDgVOG88sor7N27lzfffJOenp7LVr3as2cP586do7CwkNLSUoqLi50VqKqrqyksLGTTpk0888wzzvSzXEi1uLjI1NQUp0+f5vXXX3fCntyUskwmQ0NDA8888wytra34fD5qa2vxer0MDg4yPDzMxMQE8/PzTuPouro6nn76adLpND/72c9Yu3Ytn/70pykvL8fj8ZBMJunq6uLb3/42nZ2dzM/P35bvLxf8vPDCC7S2tjrVUZcGYLnnJZNJJicnGR0d5aWXXmLPnj2MjIwwNTVFIpG47qBqYWGB9vZ2Ojo62Lx5M3l5eWzevJlNmzbx/PPPO6vAxeNxRkZG1N9HRERERERE7nnLPtULcFZ9yoU+7w8PMpkM3d3dnD59mgMHDvDOO+/Q3d3N/Pz8Vf+4zwUQuffJrdKVSCQ4dOgQ3/ve9zh58iSjo6NXrHg1Pz/P9PQ0iUTCeW+Xy0V5eTm7du1i7969rF69mqamJkpLS/F4PM4YZ2dn6e3t5fDhw7z66quMjo4yNzeH2+0mk8lQUlKCx+PB7/dfNtbcNKXZ2Vn27dvHmjVr2Lp1K7W1tXg8HlatWsXzzz/Pli1bnOlSbrebWCzG6Ogop0+f5uDBg7ct9MnJfb65Vdcu3ZZjrWV6eprTp0/T1tbGnj176OjoIJlMkkqlbmhKmrWWvr4+urq6iEQiTs+gQCDA6tWrqaysZGhoiH379vHaa68p+BEREREREZF73rIGP4uLi05vmurqakKhEG6325mec2mIEIvF6O3t5dy5c/T09Hxo8+L3hw+AE/ycPHmSM2fOMD4+zvz8/BVTgXJVQclk8rIeMbmVvIqKiqisrKS8vJy8vDwn8MhV+/T29nL+/Hl6enqYm5tzwodQKEQoFHJee2koFQgEKCwsJD8/n76+Pg4dOkRhYSFlZWV4PB4CgQCVlZXk5+cTDoedPjb9/f20t7dz8uRJpqenb+KbuHGZTIZYLMbBgwepqqpizZo1lwVal/Y7WlhYIBKJONU6c3NzZDKZjzQNa2pqinPnznHq1Cmam5sJhUJs377d+S6i0SgdHR0MDQ3dsnMVERERERER+aS6cl7VbWKtJZVKMTk5yf79+4nFYk4YMDs7y8jICNPT085KWOXl5dTX11NRUeFU0NzIsXLhw4EDBxgZGSGZTF71PXKVOwMDA0QiESeccLvdhEIhampqqKioIBwO4/P5nPe/ePEi7777Lm+//TanTp1iZmbGqUhKpVIEg0Hq6upobm6mvr7+siltLpcLr9eLy+VidnaWtrY2zpw5QzKZxFrrVAWVlpaSn5/vjPHw4cPs2bOHt99+m1gsdjNfxw2z1hKLxThy5Aj9/f1OiJbJZBgcHGRyctLZlpeXR01NDY2NjXg8no8c+gDO1La2tjbnsykqKsLn82GtZW5ujomJiSsquURERERERETuRcta8ZOrrrl0ilIqlWJgYICOjg5nGfWamhrq6urIZDJMTk5y/vx5Tp48ed3hTyaTIZlMMj4+zqlTp5zVtT5oTHNzcwwODhKJRMhkMs40tGAwSH19PSUlJfh8Pie8sdZy9uxZ9u3bx7Fjx+ju7nYCq5zi4mIaGhpYuXKls7LVpeObn58nkUgQj8e5cOEC58+fZ25ujsLCQjwej/OT+4xmZ2c5efIkb7/9thNk3U65kGdhYcH5Hqy1JJNJDh48SFFREQ0NDVRVVREOh1m7di3pdJrXX3+dw4cPf+RpaZlMhrGxMbq6ukin085nYoxhenqaoaEhp5+SiIiIiIiIyL1uWZs7+/1+KioqeO655ygrK8Pr9TI1NcWJEyf4yU9+Qn5+Pp/5zGf4/Oc/TygUYvXq1cTjcbq7u+nq6mJubu66KkfS6TTxeJzJycnrqozJZDLMzc0508ncbjfGGPLy8qioqCAvL++y6WSZTIaBgQF6enqcvj6XcrvdVFZWUldXR0VFBfn5+c6+XFgSjUYZHx8nkUiwsLDA2NgY8Xj8snDjUrngZblWrXK73RQVFfH00087y8pbaxkfH+dHP/oRfr+fRx99lMcee4yVK1dSXl7Otm3b+PSnP8358+edptwfRSqVYn5+nvn5eeezsdbS2dnJ/v37aWtrU/AjIiIiIiIiwjJO9fJ4PASDQWpqatiwYYMzbWpmZoa+vj6nj8uZM2ec6hCv10t5eTktLS1UVlYSCAQ+9BjWWmfqWE9PD4cOHWJ0dPSaY0un0wwMDNDf339ZuOTxeMjPz3dW8cpJpVJMTEwwNTV1WV+fS891xYoVTvVSMBh09mUyGRKJBJFIhOHhYeLx+BWhRe48cj8ul4twOMy6devYtGkTDQ0NV4zp4+b3+6msrGTdunWUlpbidrtZXFxkdHSUc+fOOb2UIpEI6XQal8tFIBBgw4YNzmdwtUDreiQSCcbHx4lEIs42ay0HDx7kF7/4BWNjY7fqNEVEREREREQ+0Zal4sflctHU1MSuXbt46qmnWLNmDcYYkskk7733Hu+88w6dnZ24XC5SqRSf+cxnWL9+PYFAgPr6ep577jkCgQBvvvkmr7/+OpFI5IqpVZeKx+OMjY1d9xLf1lr6+/vp6+sjFosRDoedJs656qF4PE5BQQHGGObn55mYmCCRSGCtvWy1K4/HQ3V1NY8//jjbt2+nsrLyst5A09PTdHR0cP78ecbHx7HW0tDQQGtrK+Xl5U7vmlyFSyAQwO/3U1hYyJe+9CUee+wxjh8/zre+9S3a2tpYXFz82KuAAoEATz75JJ///OfZunUrBQUFpFIpRkdH+elPf0pXVxewtEJaQ0MDtbW11NfX4/V62b17N4lEgnfffZf33nuPjo4OZmZmbuj4qVSKoaEh/vEf/5Hf/u3fxu12s7CwwIULF+jt7VW1j4iIiIiIiEjWsgQ/wWCQBx54gM997nNs3boVv9/P0NAQp0+f5qc//SltbW0kEgmMMVy4cIHvfe97/NZv/RYrV64kGAxSXl7O5z73OVpbW9myZQt///d/z7lz50in01es6JULJC5cuMDAwMB1hwK5qVTv35ZMJkkmkwSDQaf/js/nY8WKFdTW1pJMJjHGsLCwQCAQIBwOU1dX5zRmzgVI8MspZcPDw0xOTpLJZCgqKqKlpYWVK1c6AdHc3JwTXFVUVNDY2IjX6yUcDuNyuZifn+eRRx5heHiY8fFxpyn0x8Hj8VBWVsYXv/hFHn30UYqKipienqa3t5eDBw/ys5/9jEQiAUBfXx8vv/wyfr+fz3/+85SVlREIBNi9ezdbtmzh0Ucf5a233uKVV16hr6/vQ8O7q8lVQAHOVLnZ2dlbfs4iIiIiIiIin1TLEvz4fD4qKipoaGigpKQEgO7ubg4fPkxHRwfRaNT5oz4ej3PkyBF27NiB3++ntraWgoICSkpKCAaD7Ny5k9HRURYXFxkaGnJW/Eqn06TTaSKRCIcOHeLVV1+lvb39uoOfdDrNwsICyWSSdDrtLDNvjCEWixGNRonFYs7Ur7Vr1zI1NUUwGKS/v5/Z2VnKysqora11evv4/f7LVvNKpVLOKlSpVIrq6mo2bdrEV77yFdasWYPX62V6eprjx4/z1ltv0dHRQXV1NS+++CLV1dUEAgGCwSCNjY08//zz5OXl0dnZyfHjx4lEIszNzd3yEMjlchEKhVi1ahVlZWW43W7Gx8c5e/YsR48eZWBgwDleMpmks7OTtrY21q1bh8vloqqqiqKiIvLz852KromJCWKxGJOTk9cV/hhjCIVCrFmzxvleent7iUajNxweiYiIiIiIiNzNliX48Xq9TsWMz+cjlUpx7tw52traGBkZuWzFp3Q6TU9PD3v37mViYoL169ezfv16J0hZuXIlTz75JIlEgqNHj+JyuUgmk0xPT7OwsEBvb6/TK2h8fPy6x5gLZaanp5mfn3eqb8LhMKOjo/T39xMMBvF6vZSUlHDfffcRCoVYt24dY2NjTvBTVVVFcXExtbW1+P1+pyIpnU4zOTnJhQsX6OrqYmZmhpKSEtatW8f27dsJhULMz88zNjbGiRMneP311+nq6qKiooKnnnoKt9vtNIouLCxk48aNWGvp6urC7/dz4cIFhoaGGB4eZmFhgXQ6fUu+O5fLhd/vp7i4GL/f7zS2Pn36NGfOnLms4iaTyRCJRGhra6O4uJjBwUEeffRRqqur8fl8VFZWsmnTJubn5xkZGeHEiRNMTU1dM6jyeDwUFBTQ2NiIMYa5uTlOnjzJ+Pi4pnmJiIiIiIiIXGJZgh9jDG6322nuOzs7y7Fjxzh79izRaPSKP94jkQivvPIKp0+fprW1lbm5ObZt20Y4HKakpIRdu3YRjUaZnp6mp6eHgYEBDh06RCKRoLe31wlWbmQVqVzfnpGREerr6wmHwwBUVVXR2dlJb2+vs6qUz+dj5cqVNDY2OlVCi4uLBAIB8vPz8Xg8l03xyk0ZGxgY4MiRIxw/fpxoNEpNTY1TyeR2u5mdnaW3t5fz589z4cIFZyWs9957j5mZGe6//35qamqcqqM1a9ZQWloKQFNTE+fPn6e9vZ1oNMrIyAgLCws3+9UBS9+f1+vFGEMikaCrq4sTJ07Q3d19xWc8OzvL2bNniUQiHDt2DICHH36YgoIC8vPzaW5uJhQKceLECXp6epiZmblmSBUKhaiqqqKsrAyAqakpDh48qKbOIiIiIiIiIu+zLMFPOp0mkUgwMzNDKBSip6eHAwcOMDQ0dFm1z6XGxsaIRCKMjo4yPDzMxYsXWb9+PY888ghut5tAIIC1loGBAU6cOMH+/ftJpVIkEglmZ2edvjPXK5lMMjw8TFdXF01NTRQUFJDJZCgpKaGgoID+/n4uXryIz+fD5/PR0tKC3+/H6/WSn5/vTAvL/VzKWsvc3By9vb1cuHCB0dFRZ+pSc3OzM31paGiIkydPcvbsWaeaJRqN8u1vf5uysjKeffZZdu7cyerVq50xFhYWUl9fTyKRYHBwkDNnznDu3Dl++MMfcvHixZuuiLHWkkqlmJ6eJh6PMzAwQFtbG2fOnGFycvKqr4nH4/T09DA4OMji4iL9/f20tLSwceNG6urqnM/selb58nq9NDU10draSkFBAQBDQ0O0t7czNTV1U+cmIiIiIiIicre55l/axph64PtAJWCBv7LW/oUx5v8AfhvIzZ/6PWvty9dz0Fy1y+nTp5mZmeHs2bOMjo5esx9NburQ0aNHicVixGIx1q5dS0FBgbPSViKRYHJy8gNDiOuVyWQYHBzktddeo7u7m5KSEjKZDF1dXZw9e5apqSnC4TA9PT2MjY1RWVlJSUnJZcuqf9C55IKTnp4euru7mZycpK6ujpaWFlavXo21lmg0yg9/+EP2799PV1fXZYFNJBIhEokwNDTEj3/8YxoaGti9ezfPPvssRUVFlJWVkZ+fz5o1a1i1ahWPP/44k5OTfPe732Vubu6mPpd0Os3MzAwnT550ppZ1d3czPT19zYqqxcVFTp8+TSqVYnx8nLy8PMLhsBPOXU9FVnFxMQ8//DBf+MIXnJXgXn31Vfr7+0kmkzd1biIiIiIiIiJ3m+up+EkB/85ae8wYEwaOGmNey+77M2vtn97oQWOxGK+99hpHjhwhEAgwOzvL6OjodVWjzM/PMzw8TDQapaOjg/b2dmpqamhvb+fUqVNEIpEbHc5VWWud/jSdnZ34/X6stczMzDA5OemEDBcuXODIkSNMTExQUlKCx+O5osLn/XIB1qlTp7h48SKxWMyZ3pSrhhoZGeHs2bP09fV94EpVs7OzJJNJYrEYyWSSeDxORUUFW7dupbS01AmhksnkLZvmlVtK/Y/+6I8oKChgbm6OSCRCPB6/ribS0WiUo0eP0tnZydGjR1mzZg2BQIBXXnmFkZGRa07zyk3vKygoIJ1O09XVxT/8wz8wMTGh/j4iIiIiIiIi73PN4MdaOwwMZ3+PGWPagdqbOai1llgsRjwexxhDJpO5oT/aM5kMyWSS0dFRXnvtNfx+v1PxcyN9fK4l16tncnLysqbM6XTaGW8ymWR8fJw333yTQCBwzdDn0veORCJOE+qhoSHeffddotEo+fn5dHV1cebMmQ9dqSqTybC4uMj09DRnzpxhaGiIQCBAbW3tZUvHp9NpOjs7b1lFTCqVYmRkhPHxcefzuN6Vw6y1Tv+kY8eOce7cOVwuF5OTk9cMpyoqKvjVX/1Vnn32WQoKCpidneWNN96go6PjlgVbIiIiIiIiIneTG+rxY4xpBDYDB4FdwL8yxvwGcISlqqDrnl91o2HP++V6zUSjUYwxt3TJ8vcf48PCJGstCwsL9PT0XNbA+Vpyy8XPz8871UUnTpxwlqQfGxtjYmKCxcXFDz03ay3pdJq5uTni8Tgul4vR0dErKo8SicQtW9kLuObn8mFyY56dnXWmnl3P92etZXZ2lo6ODsbGxpifn+fw4cPMzs6q2kdERERERETkKsz1BibGmBCwD/hja+2PjTGVQISlvj9/BFRba//FVV73NeBr2Ydbb8mo70BXa+J8LdZaJ/Dw+XwEAgH8fj+wVBE0Nzf3kQINl8v1ocf6pMrPz2fDhg1UV1fj9/tJpVKcO3eO9vZ2BT8iIiIiIiJyLztqrd12tR3XFfwYY7zAHuDn1tr/eJX9jcAea+36a7zPJzt5kGVnjLks2MpkMp/4QEtERERERETkJn1g8HNlacj7mKUylr8G2i8NfYwx1Zc87VeA0zc7SpFryU0Tu9HeQiIiIiIiIiL3omtW/BhjHgbeAU4Bufk0vwf8GrCJpalePcDXs42gP+y9xoE5lqaIicidqwxdpyJ3Ol2nIp8MulZF7ny6TuVu0GCtLb/ajuvu8XOrGGOOfFD5kYjcGXSditz5dJ2KfDLoWhW58+k6lbvdNad6iYiIiIiIiIjIJ5OCHxERERERERGRu9RyBD9/tQzHFJEbo+tU5M6n61Tkk0HXqsidT9ep3NVue48fERERERERERG5PTTVS0RERERERETkLnXbgh9jzDPGmPPGmC5jzO/eruOKyOWMMfXGmDeNMWeNMWeMMf8mu73EGPOaMaYz+9/i7HZjjPlW9to9aYzZsrxnIHJvMca4jTFtxpg92cdNxpiD2WvyfxhjfNnt/uzjruz+xmUduMg9whhTZIz5O2PMOWNMuzFmh+6pInceY8y/zf7b97Qx5r8ZYwK6p8q94rYEP8YYN/CXwLPAfcCvGWPuux3HFpErpIB/Z629D3gI+J+y1+PvAnuttauBvdnHsHTdrs7+fA34L7d/yCL3tH8DtF/y+P8G/sxa2wxMAv8yu/1fApPZ7X+WfZ6IfPz+AnjFWrsOeICl61X3VJE7iDGmFvjXwDZr7XrADbyA7qlyj7hdFT/bgS5r7UVr7QLw34HnbtOxReQS1tpha+2x7O8xlv6BWsvSNfm32af9LfDF7O/PAd+3Sw4ARcaY6ts7apF7kzGmDvgs8N3sYwM8Afxd9invv1Zz1/DfAU9mny8iHxNjTCHwKPDXANbaBWvtFLqnityJPECeMcYD5APD6J4q94jbFfzUAv2XPB7IbhORZZQtW90MHAQqrbXD2V0jQGX2d12/Isvnz4H/DchkH5cCU9baVPbxpdejc61m909nny8iH58mYBz4f7NTMr9rjAmie6rIHcVaOwj8KdDHUuAzDRxF91S5R6i5s8g9yhgTAv4e+F+stTOX7rNLy/1pyT+RZWSM+RwwZq09utxjEZEP5AG2AP/FWrsZmOOX07oA3VNF7gTZPlvPsRTW1gBB4JllHZTIbXS7gp9BoP6Sx3XZbSKyDIwxXpZCn/9qrf1xdvNortw8+9+x7HZdvyLLYxfwBWNMD0tTpJ9gqZdIUbZMHS6/Hp1rNbu/EJi4nQMWuQcNAAPW2oPZx3/HUhCke6rIneUpoNtaO26tXQR+zNJ9VvdUuSfcruDnMLA62zXdx1IjrZ/epmOLyCWy85P/Gmi31v7HS3b9FPhq9vevAi9dsv03siuRPARMX1K+LiIfE2vt/26trbPWNrJ033zDWvsV4E3g+ezT3n+t5q7h57PPV5WByMfIWjsC9Btj1mY3PQmcRfdUkTtNH/CQMSY/+2/h3LWqe6rcE8zt+t+vMeYzLPUqcAPfs9b+8W05sIhcxhjzMPAOcIpf9g35PZb6/PwIWAH0Ar9qrY1mb47/iaVy2Djwz621R277wEXuYcaYTwHfsNZ+zhizkqUKoBKgDfhn1tp5Y0wA+AFLfbuiwAvW2ovLNGSRe4YxZhNLDdh9wEXgn7P0f67qnipyBzHG/AHwJZZWuG0DfoulXj66p8pd77YFPyIiIiIiIiIicnupubOIiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF1KwY+IiIiIiIiIyF3q/wcNmIRtNeS3PAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "num_samples_to_plot = 9\n", - "\n", - "for i in range(num_samples_to_plot):\n", - " plt.figure(figsize=(20, 20))\n", - " data, target = emnist_lines[i]\n", - " sentence = convert_y_label_to_string(target.numpy()) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[8]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from einops.layers.torch import Rearrange\n", - "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 46), stride=(1, 46)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=46, c=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from einops import rearrange" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.unsqueeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1, 28, 952])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "patches = slide(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 34, 784])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# remove batch size\n", - "patches = rearrange(x, 'b t (h w) -> b t h w', h = p, w = p)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "patches = patches.squeeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([20, 1, 28, 46])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "patches.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(5):\n", - " ax = fig.add_subplot(1, 5, i + 1)\n", - " ax.imshow(patches[i].squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the data loader for EmnistLines" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'fetch_data_loaders' from 'text_recognizer.datasets.util' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/datasets/util.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtext_recognizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfetch_data_loaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'fetch_data_loaders' from 'text_recognizer.datasets.util' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/datasets/util.py)" - ] - } - ], - "source": [ - "from text_recognizer.datasets.util import fetch_data_loaders" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-08-30 21:31:41.007 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "dls = fetch_data_loaders([\"train\"], \"EmnistLinesDataset\", {}, batch_size=2, shuffle=True, cuda=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "dl = dls[\"train\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "d, t = next(iter(dl))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "patches = sliding_window(images=d, patch_size=(28, 28), stride=(1, 14))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "might as well stand their_________\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 20))\n", - "sentence = convert_y_label_to_string(t[0].numpy()) \n", - "print(sentence)\n", - "plt.title(sentence)\n", - "plt.imshow(d[0, 0], cmap='gray')\n", - "# plt.imshow(d[0, 0], cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(5):\n", - " ax = fig.add_subplot(1, 5, i + 1)\n", - " ax.imshow(patches[0, i].squeeze(0), cmap='gray')" - ] - } - ], - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb index add0b80..67456f9 100644 --- a/notebooks/03-look-at-iam-paragraphs.ipynb +++ b/notebooks/03-look-at-iam-paragraphs.ipynb @@ -56,8 +56,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2021-04-25 23:17:44.177 | INFO | text_recognizer.data.iam_paragraphs:setup:107 - Loading IAM paragraph regions and lines for None...\n", - "2021-04-25 23:18:00.750 | INFO | text_recognizer.data.iam_synthetic_paragraphs:setup:79 - IAM Synthetic dataset steup for stage None\n" + "2021-05-01 20:11:32.991 | INFO | text_recognizer.data.iam_paragraphs:setup:107 - Loading IAM paragraph regions and lines for None...\n", + "2021-05-01 20:11:49.406 | INFO | text_recognizer.data.iam_synthetic_paragraphs:setup:79 - IAM Synthetic dataset steup for stage None\n" ] }, { @@ -68,9 +68,9 @@ "Num classes: 84\n", "Dims: (1, 576, 640)\n", "Output dims: (682, 1)\n", - "Train/val/test sizes: 19948, 262, 231\n", - "Train Batch x stats: (torch.Size([1, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0109), tensor(0.0499), tensor(0.8314))\n", - "Train Batch y stats: (torch.Size([1, 682]), torch.int64, tensor(1), tensor(83))\n", + "Train/val/test sizes: 19925, 262, 231\n", + "Train Batch x stats: (torch.Size([1, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0064), tensor(0.0435), tensor(0.9176))\n", + "Train Batch y stats: (torch.Size([1, 682]), torch.int64, tensor(1), tensor(78))\n", "Test Batch x stats: (torch.Size([1, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0372), tensor(0.0767), tensor(0.8118))\n", "Test Batch y stats: (torch.Size([1, 682]), torch.int64, tensor(1), tensor(83))\n", "\n" @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "0cf22683", "metadata": {}, "outputs": [], @@ -131,27 +131,6 @@ "x, y = dataset.data_train[1]" ] }, - { - "cell_type": "code", - "execution_count": 6, - "id": "af7747a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([682])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, { "cell_type": "code", "execution_count": 7, @@ -424,7 +403,7 @@ { "cell_type": "code", "execution_count": null, - "id": "25a074df", + "id": "4150722e", "metadata": {}, "outputs": [], "source": [] diff --git a/notebooks/03a-line-prediction.ipynb b/notebooks/03a-line-prediction.ipynb deleted file mode 100644 index 13f4ff1..0000000 --- a/notebooks/03a-line-prediction.ipynb +++ /dev/null @@ -1,419 +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\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, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models import CRNNModel\n", - "from text_recognizer.networks import ConvolutionalRecurrentNetwork" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-04 21:35:35.605 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "emnist_lines = EmnistLinesDataset(train=False)\n", - "emnist_lines.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", - " return ''.join([emnist_lines.mapper(int(i)) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([34])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-04 21:37:05.918 | DEBUG | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m----------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\"patch_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\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 14\u001b[0m \"stride\": [1, 14],}\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mline_ctc_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCRNNModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ConvolutionalRecurrentNetwork\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IamLinesDataset\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, network_args)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/models/crnn_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, network_fn, dataset, network_args, dataset_args, metrics, criterion, criterion_args, optimizer, optimizer_args, lr_scheduler, lr_scheduler_args, swa_args, device)\u001b[0m\n\u001b[1;32m 49\u001b[0m )\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"args\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pad_token\"\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 52\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;32mis\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 53\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEmnistMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\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;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] - } - ], - "source": [ - "network_args = {\n", - " \"encoder\": \"ResidualNetworkEncoder\",\n", - " \"encoder_args\": {\n", - " \"in_channels\": 1,\n", - " \"num_classes\": 80,\n", - " \"depths\": 2,\n", - " \"block_sizes\": 128,\n", - " \"activation\": \"leaky_relu\"},\n", - " \"flatten\": True,\n", - " \"input_size\": 128,\n", - " \"hidden_size\": 128,\n", - " \"num_classes\": 80,\n", - " \"patch_size\": [28, 28],\n", - " \"stride\": [1, 14],}\n", - "line_ctc_model = CRNNModel(\"ConvolutionalRecurrentNetwork\", \"IamLinesDataset\") #, network_args)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office in Arkansas after the______\n", - "in________________________________\n", - "by a oneshot technique____________\n", - "office Incumbent__________________\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "num_samples_to_plot = 4\n", - "\n", - "for i in range(num_samples_to_plot):\n", - " plt.figure(figsize=(20, 20))\n", - " data, target = emnist_lines[i]\n", - " sentence = convert_y_label_to_string(target.numpy()) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office Incumbent__________________\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 20))\n", - "sentence = convert_y_label_to_string(target.numpy()) \n", - "print(sentence)\n", - "plt.title(sentence)\n", - "plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.to(\"cuda:0\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('offiee ineumbent', 0.19405342638492584)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "line_ctc_model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p, _ = line_ctc_model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = line_ctc_model.swa_network(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p, _ = p.max(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.exp(p.sum()).item()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models.metrics import cer, wer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.unsqueeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cer(p, target.unsqueeze(0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wer(p, target.unsqueeze(0))" - ] - }, - { - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/04a-look-at-iam-lines.ipynb b/notebooks/04a-look-at-iam-lines.ipynb deleted file mode 100644 index de59a85..0000000 --- a/notebooks/04a-look-at-iam-lines.ipynb +++ /dev/null @@ -1,383 +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\n", - "from torch import nn\n", - "\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 text_recognizer.datasets import IamLinesDataset, AddTokens" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToPILImage\", \"args\": None}, \n", - " #{\"type\": \"RandomResizeCrop\", \"args\": None}, \n", - " {\"type\": \"RandomRotation\", \"args\": {\"degrees\": 0.8, \"fill\": 0}}, \n", - " {\"type\": \"ColorJitter\", \"args\": {\"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": 0.5, \"hue\": 0.5}}, \n", - " {\"type\": \"ToTensor\", \"args\": None}, \n", - " {\"type\": \"Normalize\", \"args\": {\"mean\": [0.912], \"std\": 0.168}},\n", - " #{\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.98, 1.0]}}\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'ToPILImage', 'args': None},\n", - " {'type': 'RandomRotation', 'args': {'degrees': 0.8, 'fill': 0}},\n", - " {'type': 'ColorJitter',\n", - " 'args': {'brightness': 0.5, 'contrast': 0.5, 'saturation': 0.5, 'hue': 0.5}},\n", - " {'type': 'ToTensor', 'args': None},\n", - " {'type': 'Normalize', 'args': {'mean': [0.912], 'std': 0.168}}]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transform" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Lines Dataset\n", - "Number classes: 54\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f', 16: 'g', 17: 'h', 18: 'i', 19: 'j', 20: 'k', 21: 'l', 22: 'm', 23: 'n', 24: 'o', 25: 'p', 26: 'q', 27: 'r', 28: 's', 29: 't', 30: 'u', 31: 'v', 32: 'w', 33: 'x', 34: 'y', 35: 'z', 36: ' ', 37: '!', 38: '\"', 39: '#', 40: '&', 41: \"'\", 42: '(', 43: ')', 44: '*', 45: '+', 46: ',', 47: '-', 48: '.', 49: '/', 50: ':', 51: ';', 52: '?', 53: '_'}\n", - "Data: (1861, 28, 952)\n", - "Targets: (1861, 97)\n", - "\n" - ] - } - ], - "source": [ - "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform, lower=True)\n", - "dataset.load_or_generate_data()\n", - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(28, 952)" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.input_shape" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(97, 54)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.output_shape" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.transforms import ToPILImage" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, dataset=dataset):\n", - " return ''.join([dataset.mapper(int(i)) for i in y])\n", - "\n", - "# convert_y_label_to_string(dataset.targets[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "but since starting salaries would depend on grade a______________________________________________\n", - "or b in the finals next may, and since mating____________________________________________________\n", - "prospects would depend upon salaries, scholarship for____________________________________________\n", - "these fine young people was closely geared to____________________________________________________\n", - "economic and biological ends which, essentially,_________________________________________________\n", - "were really means. so, seeing them revolve in____________________________________________________\n", - "circles, harry had the feeling that moke (or what________________________________________________\n", - "moke consciously or unconsciously symbolised, any-_______________________________________________\n", - "way in harry's mind) had these splendid young____________________________________________________\n", - "people by the short hairs, and was diverting them ...____________________________________________\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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NT09n5cqVenZRcnLyJ5pd5H+fXHfddSxbtox33nmHuro6nE4ncXFxzJ49m9LSUpqamujv7485QPFJ3YvRfsZEEtiG8T72eDxhg7ehMnRC9TNYnwPvM03TztmnMbgTLpAaTX+M+w12rwshhBBCCCFCizlok5SUREpKCh0dHXR1dTE2NoZSipkzZ7JixQoWLVqE0+mkpqaGGTNmcO2112KxWGhqaqK5uZnU1FQWL14MeAfPDQ0NZGdnM23aNMrKyiguLmb37t2kpqZitVrp7OxkYGCA5ORkSktLWbduHZWVlXqmT2dnJxA6uBFNYMMoISGBadOmUVJSwtmzZzGbzcyZM4fS0lLi4+Pp6urCZrNRVFTEvHnziI+Pp7q6GrvdzsBAsOLw57Z/yy23kJaWxtDQEKdPn2b//v2MjIyEzD4J7PdEBrWRvgGP1GawgV8k0WQ5GfsQjWDbh7r2/qlPgQPShIQEKioqsNvtjI6O6oPJaLOmYl0WS5/DtRnLlBo/q9XKkiVL8Hg89Pb2MjAwoAdazydjnywWC3l5edxwww1UVVVRWVlJa2srHo8Hm83GVVddhcPhwOl0fiJ9u9iFCorGEiSJJkssmn0Gay/SvRsY7DHuz/hem2gwWQghhBBCiEtJrDVtyMnJ0WtP9Pf3o5QiOzubZcuWkZmZicfjwePxkJWVxcaNGykvL+fZZ5+lpqaG0dFRysvLWbNmDR9//DGzZs2irq6O+fPnU1hYyPLly1m4cCEej4fk5GRqa2upra2lp6eH/Px8NmzYQGZmJlarFYCPP/6YM2fOxBTECDc9RSnF9OnTKSkpwe12s2fPHvLz81m2bBlZWVmcPn0au91Oeno6ubm5rF27lunTp/Paa6/R39+vB22UUqSnp6NpGoODg/pA1GKxMH/+fK6//nrsdjunT5/myJEjnD17Nmi2TrA+BxsA+bN9opnyFEykQMpk2jjfAs8H/He/u7u7cbvdJCQk6OdI0zScTictLS309vZOqt+RglGTaTvUIDqWNk0mE8nJyUyfPp13332XzMxMOjs7w2YXxSJScCshIYH09HTy8/NZtGgRy5Yt43e/+51ew2Z0dJTU1FSuuuoq3n33Xfr6+sK+D6aiT5MRKegZzb5DvacCs1AC24qLiyM9PZ34+Hiam5vHteXfJlTWX2A/ozmmYP0IFhA3LjP232QyMWPGDFwulx4s9C+7EJl6QgghhBBCfFrFnGnj/0O8o6MDl8tFamoqV199NWVlZbz88svk5OQwY8YMbDYbZWVlfPWrX+XUqVOMjY2RmppKcXEx69evZ8GCBRw+fJg5c+aQn59Pc3MzIyMj5ObmsmLFCn784x/z0UcfMTIygslkIjc3l9WrV3P69GkqKyvZs2cPXV1deiFT8A4cQg1aQtViMTKZTKxcuZJFixZx7Ngx+vv7ufnmm+nr62PHjh10dXUxa9YsrrvuOrq7uzl48CD5+fl6vQ5/H5KTk1m7di0ej0fP6PB4PGRmZvKVr3yF9vZ2bDYbBw8eZP/+/fp2gaIZ2MTFxZGUlER3d3fUUyYiLY8mWymagbFSCovFgslk0jNaQrUbKqso2PJog0ltbW0opcjPz8disTAyMoLVauXMmTOA93qPjY3FdGwTGWxOdoAay71hvIb+DJc///nP+vTCqawbEun8T58+nY0bN3LzzTezYMEC6uvrWb9+vZ6l19PTg9lsZtq0aezcuTOqTLXJ9imUWAIvofYZajohoH8+RJOlFXh/+4PlV199NUVFRTz66KMA4z7rjNlkgfvwL/MHdkP1wVijJlSfgvXX/7NxXbPZzG233UZ7ezsHDx7k+PHjQQOs/vUnG6wTQgghhBDiL1XMQRuTyUR8fDzx8fHk5ubymc98hvXr1/Otb32L9vZ2Tp8+zcyZM0lNTeWhhx7SAzYA8+fPZ/HixWRmZpKUlERycjKvv/46Tz/9NBkZGdx4442cOnWKH/zgBxw5ckTfpz874ujRo6SlpXHs2DGGhoYwmUwkJCTo/YmLi6O7u5vh4WG9jolxMBHsG17j76mpqRQUFOB2u6mpqeHhhx/Gbrfz8ssvMzg4yObNm9m4cSMej4eMjAyuuOIKfv7zn1NRUUFfXx9ms5mMjAzuu+8+kpKSUEpRVFTE4cOHaWtrY+vWrSxYsID33nuPjo4OamtrMZvNFBUV0dDQMK5PxhoXwSilSExM5Prrr2fhwoX827/9G/39/UHXC/WayWTSB0yTGeyGkpGRweWXX052djZvvfWWXph6Mu3G8vrIyAijo6OYzWYSExOJj4/nmmuuoaenB5fLhcPhoLGxEZfLNa6NWKYfhVon2qBWrFNEYg0AKaVwOByUlZXR1tY26eyiWLS3t3Py5Emqq6tJS0vjscceo6WlRX9vTps2jauuuoq3336b5ubmoAG0T0qwwEs0Qk2rC2zH+D4ON/UpcNqQ/zOurKyMO++8k5deegmr1TruXIWrgWNcFixwZAzohDu2wIBWYFZPsPu/oaEBk8mkZ0ZOZIqlEEIIIYQQlzoV49QiraysjG9+85tkZ2fT0dGBxWLh4Ycfpr6+Hk3TiI+Px2q1opRiaGgITdP0AUZ8fDwJCQmkpKQwbdo0Ghoa6OrqwuVyYTabSUhIwGaz0dnZec4gwp81cPvtt7NmzRr6+/sZHh6mt7eXrq4uWltb+eijj6isrGR4ePicb37DfQvuH3CsW7eO22+/naVLl9Lb28uePXv4yU9+Qk9PD5mZmdx3332sWLGCt99+m7Nnz7J//34GBwdJSEjA5XKRkZHB1q1bmTNnDs899xy33347H3zwAS0tLRQUFLBo0SKKi4uJj4/nRz/6EX19fZSXl1NcXMzjjz+un6crrriCVatW4XQ6eemll8YN0Pz9TktLo7y8nLvuuot//dd/pbm5WR/4+aeogfcpLf7CsyMjI3om0KZNm5g/fz5FRUXs3LmTPXv20NbWBkBKSgrr168H4MSJE5w9e3ZcH/xTbpxOp75NoMLCQlasWMGmTZsYHByku7ubJ598MmTgJtr6PYFZANnZ2VgsFsbGxhgbGyM9PZ158+ZRUlJCaWkpiYmJ2O12jhw5wj333MP+/ftpampi7dq1TJs2jRMnTvD973+f3t7ekH0JloUUmNGyfPlysrOz6e7u5vjx4+dkPvm381+PW2+9lbKyMnp7e6mpqeHYsWNUV1cH7YPxmCNlGQX202KxkJiYyMyZM2loaKCvr+8TGyxbrVbWrVvHnXfeyfPPP8+ePXsYHR3V78E1a9Zw11138fDDD1NVVRWynVD3wUQCY58Gxqwfs9nMZz7zGbZu3UphYSHbtm2jq6tr3HvcL9Q0yUjHbzKZzpm25J/iFNgnv2AFkwPv06ysLFJTUxkcHKS1tTXs1ChNk6dHCSGEEEKIS95HmqYtD3wx5kybpqYmXn31VYqLi+nr6+PgwYM0NTXpf9D7sxvg3G9vR0dHcbvd9PX10dHRwcjIiB4McLvdel2YwG/clVK43W5aW1t57rnn2LFjh77N6Ogoo6OjuFwuvX5MNAGAYL/X1dXx/vvvU11dTWVlJcePH9czEwYGBhgeHtazaaxWKwsWLGBkZISenh4cDgd5eXncddddNDY28o1vfIPc3Fyampr0qWSzZ8+mtbWV4eFhOjo6SE9PJy4ujo6ODsD7aPS1a9eyefNmjh8/zgcffBD0XFitVsrKyvj85z/PoUOHcDqdlJaWsmnTJsbGxjh8+DBHjhxh+fLlbN26lZkzZ/LOO++we/du+vv7+exnP0tXVxevv/46N998M6mpqWRmZuJ0OsnNzeWrX/0qZrMZpRR5eXnjpjeYTCZuv/12pk+fzu7du6moqGDGjBnMmTOH1157jcHBQZRSLFu2jDVr1nD69GkAmpub9ce6Bwp3XcKtb7FYuOmmm5g/fz42m42BgQFcLhdut5uTJ09SV1fH6OgopaWlfP3rX+fFF1+kpaWFq666Ss/2ysvLIzMzM2wGSqT+LFmyhJtuuons7GwSEhIYGxvjoYceorm5GU3TiIuLY+bMmSxZsoQ1a9YwZ84cOjs7eemllxgZGWH+/PnMmDGDM2fOBB24KqWw2WzMnTuXVatWkZmZyf79+/n444/1Qtx+FotF77M/g2rFihUcPXqUwcFBvT3/e/N81RbxT0srLi4mLi6OEydO4HK59Os7b9485s6dy7Fjx/SAY+B5jouLY/78+SxZsoTGxkaOHTumP2Ut2kDMRAI2oe7BUMHeUAG9SNsGCyoHBj5KSkrYsGED06ZN04OegZ+rwYI0xmw9s9mMyWTC7XaHrcsTqU1jX4OtbzwOj8eD0+nU31dKKT0o7w9oftqCaUIIIYQQQlwIMQdtRkZG+PDDDzl58iRut5vm5uZxA81wf4wbBxtDQ0NBBwbh6kq43W7a29txOp3jpvWEG4gEGygFG3RomkZ7ezt79uzRAynGbImRkRF2795NV1cXJpOJvr4+ent76enpYWhoiNTUVD140NXVRUNDAyUlJaSlpeFyueju7sZut1NbW0tfXx99fX0kJyeTmJjI7Nmz2bRpExkZGVxzzTU0NDRQWVmpD/r9fffLycmhpKSE1NRUjhw5wtq1a1myZAlXXnklnZ2d2Gw2li1bRnFxMbW1tWRnZ1NaWorNZtODGK+//jqLFi0iMzOTw4cP43Q6ycnJ4fbbbycpKYnDhw+zdOlSVq9ejclkorq6mrGxMcrLy7n66qs5ceIEixYtoqysjOHhYa6++mr27t2LUorCwkKWLl1KXl4eVVVVHDp0SH+SU6zC1drxeDx89NFHNDY2YrVa9YDh8PAw7e3tDA4OkpKSomfdlJeXk5mZSVtbG62trYyMjNDU1ERXV1fUfQkmLS0Nh8NBZWUlCQkJXHnllWzZsoWnnnqKpKQkrr32WubMmUNBQQGzZs0iIyODtrY2CgsLUUpRUFCgZ4cF7istLY3S0lKuuuoq/R7Kzs5m4cKFDA0NsXfvXn39pUuXsmjRIkwmEz09PXR1ddHb20tfXx/9/f3ExcWRm5uLxWLhzJkz5wz+w9UviZWmaeTn55OUlERNTQ3d3d36tYyPj6ekpESfNjc2NkZycjIDAwN6pkhmZibLli2jqKiIlJQU5s6dS2dnJ+3t7edtsB9N0MUfNPVP7/RnBjqdzojnI7At47JgdWTMZjOrVq3CarWya9cuDh8+zNjYWNigjf9npRRxcXFMmzaNa6+9FpvNxnvvvcfJkycZGhoKe/zBjjuwb8b9GKdQGdf199VkMpGRkcGKFSuoq6vTC05H+vwWQgghhBBCTCBoo2kabW1tIafFRCOabIpwA8epqH0RbP/Dw8M0NDQEHbx5PB6OHz9OS0sLubm5DA4O4nA46O/vx+PxMG3aNNLT03n11Vepqqri1KlTpKSkUF1dTUtLC319fezatUsPBLlcLpxOJw6Hg/z8fP2x5yUlJbz++uucPn06ZJAjNzeXoqIiTCYTpaWllJaW4na7yczMJDs7G5vNhtPpxG63s2vXLrKzs0lPT9eDNP7ATWZmJvX19foTumbPns3mzZuprKzEYrEwODjIvHnzmDdvHklJSbhcLlauXElZWRlnzpzRM6V6e3tJSUnhs5/9LN3d3aSkpFBUVKTXGDpx4kTQgWZCQgKlpaV64MRf6yQS4/WpqKg4pwirsY2RkRHq6+t566236OjooL29nZqaGj3o5g9sBLZr/N1kMpGdnU1PTw8jIyPnTEHp6emhqqqK06dP6/Vz1q5dyzPPPMP8+fNZunQpSim6u7sZGhqiqqoKu92uB2I6Ojqoqqo65762WCyUlpZyzTXXkJeXR1NTE01NTSxevJi0tDSSkpL0dbOzs7npppsA71OzkpKSKC0tpba2lvfffx+lFOXl5RQWFuqZYenp6aSkpGC32+nv79czoZTyPv1s+vTpDA0N6fd5rPxFqHt6esYFdrOzs/WsJIvFwqZNm0hMTGTPnj04HA5SUlJYuHAhq1evxu12k5iYqAcAIk0PCxTt9Cir1cr06dMxmUw0NDSMK3BuDKpkZmZSWFhIbm4uaWlppKam0trayptvvjmp2lCBgRt/ptJll12Gw+Hgrbfe0oOLoYIrRv7sriuuuIK5c+fi8XhYu3YtDofjnAChcf+xnl/j9qH6k5GRwYIFC7jyyiupq6sbV1sncBqWEEIIIYQQYrwJBW2iEfgNrL9ugrGNaNqazLf/Ew0MhdpueHiY5uZmmpqazmmnubkZh8PBm2++idvtxmw2097eTnt7u15H5L/+679ITk4mPT2d0dFRWlpa2Lt3L3V1dYA3OFBRUcGRI0f0KVPBpiH4iy8DfP7zn+fIkSO88sorzJ49m/Lycmw2G3v37uUPf/gDQ0NDerAhOztbn4azYcMG/vCHP/DHP/5Rn2ITFxdHfHw8iYmJLFy4kOrqakpKSkhMTCQ9PZ2uri59IJ+RkcH+/fvZv38/brebkpISbr31Vo4cOUJraytut5uuri4aGxvHBWz85zYuLo68vDy2bNnC9u3b9W/fQ2U5hBPuyTPd3d28//77nDhxgpGRETo7OyN+ux84eM7NzWX27NmcOHFCDz4Y78uGhgZSU1Pp7e3FarVit9uxWCwopfRMiQMHDtDd3U1JSQlNTU10d3ezceNGDh06xMGDB9m7d+85fUpPT2fp0qWsXLmSF198kVOnTnHZZZeRm5tLZWUldrsdQK+ps2HDBp588kkqKyuZNm0aq1evZnh4mLa2NubMmcOWLVsAOHr0KHPnzmXmzJnMnDmTDz/8kIqKCrq6ulDKW+B62bJlXHvttZw+fZq3336bgYGBmAfy3d3dDA4OUlpaSlpamp6lVlJSQnp6OomJiaxZs4bly5dTWFjI4OAgH3zwAfPmzWPz5s0UFBRQU1NDfn4+zz77rF7Y1j/IHxkZ0Wv2jI2NjZua6RcpC89fkyg7O5sbbrgBu92Ow+EgLi4Om82GUorBwUFcLhcJCQksXbqUBQsWkJmZic1mIz09HYfDwa5du/QnpPnbTEpKwmw2j5sOFHjvBU4r8v9uMplYsWIFmqZRU1PDqVOn9OWZmZmMjIzgcrnweDxYLBZsNhsul0ufgpaXl8eKFStYsWIF+/btA+C6667jjTfeCJqtFK7WTLjzFyyDyBjsNJvNzJo1i40bN5KQkEBHR4f+/wP/VL5g2UNCCCGEEEIIr5iDNtEKN/g+X+nwwb6tDtWHSGItYKppGm63W89WcLvdej0Xv7GxMXp6esYV4+3s7KSnp4f8/HxuvPFGHnnkEVpaWsLWV6msrMTtdtPd3a1nkWiaxpNPPgmAw+HA6XTqA7i9e/eSn59Peno6Ho+Hf/mXf6G9vV2v/2M2m4mPj6e7u5uHHnqI5ORk3n33XYaHh2lpaSErKwuLxUJvby/PPfccf/rTn6iqqtKzYwAee+wxfvGLX1BWVsbMmTNpbm7m7NmzfPzxx3q/jeLj4ykqKtLrz4yMjOjnPfB4jddkIoaGhmhubo55O38A4wtf+AIfffSRPkgOvDf8mWcWi4UFCxYwZ84ctm/fjtvtpq2tjQULFrBo0SL6+/uZNWsWmzZtYvfu3Tz++OPs37+f9vZ2xsbGxg12PR4PCxcuJD8/n8bGRlJSUvjOd75DYmIi27dvZ8+ePTQ2NmKz2UhMTOTee+9l3759eDweVq5cCcArr7zCyZMnUUrxwAMP6MWIFy1axDXXXKMX/73//vt5+umnOXjwIBaLhYULF/KP//iP2Gw2zp49q5+LWN+3dXV1FBcXc91113HjjTfy6quvMjw8THZ2NkVFRUybNo3W1lYee+wx7rnnHiwWC+np6axevZotW7boT7v63ve+R1tbG263m+TkZNLS0oiLi+Ps2bMUFBSwbt06Ojs7qampoaGhAY/HEzIbL1iwITs7mw0bNrBlyxa2bdum16tasGABcXFxetCvrKyMe++9l6NHj2K328nJyWFkZITnn39e35//aUm5ubmsX7+e1NRUXn75ZQYGBvSaMv4++J/EFxcXNy4IA5CUlMRf//Vfs3PnTj1TTSlvAecvfOELnDx5kqqqKlwuFwUFBcybN4/9+/fjcDiwWCxce+21lJeX09DQwJEjR/jGN75BTU0NIyMjegDdP4VK0zS9kHewjJlwWTTGQI0xMO8/Fzk5OVxxxRUsW7aMRx55hN7eXiwWCykpKRQWFjI2NkZdXd2EMrmEEEIIIYS4FMT89Ciz2XweuzNxUzG4v1AKCgq4++67sVgsPP744yFrTgQT+OjdUOLj4/nc5z7HnXfeSVdXF3a7nbGxMQYGBhgaGqKzs5O6ujoqKiro7e2d9JNcAgN0oYJ4NpstbK2bC3lds7KyuOmmmzCbzfzyl78855xYLBZ9ClBaWhpLliyhuLiYjo4Otm/fztjYGCkpKXz5y19m6dKlJCQkALBy5Uruv/9+Kioq9PpMiYmJJCcnk5OTw8DAAK2trWRnZ3PHHXewbds2zpw5w9GjRxkZGSEjI0PPLunu7qanp4fNmzdjs9l466232L59OwcOHNCDg1arlV//+tcopdizZw9//vOfqa6uxuVyYbPZeOKJJ4iPj2fPnj20tLTwla98hbi4OL75zW9SWVnJ4ODghAKt/uO67LLL2LZtGwB/+tOfOHbsmD6t0O12M2vWLJ566inuv/9+enp6+NznPseiRYv0J6xZrVa9cHF5eTnz58/HarXy4osv8sUvfhGAyspKTp48qWe8/fnPfw4ZuDE+ZSk+Pp6NGzfywAMP8Pjjj9PR0cG2bdswm818+OGH7N69m/r6eoqKinj00UfJysri8OHD7Nu3j2PHjmG32xkZGdGfzGaz2Vi5ciU33HADLS0t7N69m+7ublatWkVtbS2dnZ1Mnz6dtrY2XC4X//zP/0xubi41NTXs2rWLXbt2oZRi9erV3H333Tz11FN6LZvExETuuOMONmzYwMcff0x9fT3l5eWsXLmSAwcO8PTTTzM8PMy0adN48MEHueyyy6isrGT69On6cn+g1WazUVhYyJo1a5g/fz4/+9nPaGxsZHh4OOppXpqmYbVa9WyftLQ0CgoKqK6u1gM5W7duZfHixTgcDn7605+SkpLCbbfdxtq1aykoKKCuro6XX36ZP/7xjwwMDMR8jwkhhBBCCPEXZGqeHhVoKgbVE/kWP1Qb0fQh3DfHnxT/t9JpaWnMnz+fhQsX8sADD8RcrDdUwCbwurhcLnbu3Mnx48eZO3cuLpeLyspKnE6n/lQv/3/Bpk7424kk1usYy/FGe30jtQGRj8VqtZKZmUlRURFPPfXUOQGbhIQELr/8cv7u7/6O/v5+2tvbOXr0KG+88QYOh0MPGPT39/P0009jtVr1IMHy5ctZuXIlq1evpr29Xa+109LSwsmTJ+nq6mJ0dJTW1laeeuopdu/ezdy5c2lubqarq4uOjg76+voYGhrSr9e///u/Y7PZ9Ol4xqeouVwutm3bhlJKDzC43W48Hg+Dg4P80z/9EyaTiZUrV7Jlyxby8/P53e9+R01NDUNDQ1FNJQu2jqZpDA4OUlVVxcMPP4zFYtFr5/gDA3FxccTFxfHuu+/qxZPr6+u58soreeGFF/RzWF1dzeHDhzlw4ADDw8OsXbuWjIwMvva1r/GlL32JuXPnkp+fT319PRkZGbz//vvjMleMjE/OKigooKioCIvFwurVq1mzZg2/+tWv+OCDD7Db7QwNDeHxeBgYGKC+vh673c6OHTuoqKjA7XYTHx9PamoqWVlZ5OTkcNVVVzF37lx6enr49a9/TWFhITfffDMdHR3MmzeP0dFRDh06xMjICN/97nd5/fXXaWxs5PLLL2fJkiW0tLRw5swZZsyYwYkTJ/QCzv7phH//93/PD3/4QxYvXsxtt91GfX09P/rRjzh27Bi9vb3MnDmTW2+9lUWLFmE2mxkeHubZZ59lz549DA8P61MolyxZQklJCR0dHWRkZIyrM2O8fsEKgRt/93g82Gw2Vq1axbJly8ZN88vNzWXJkiUAHDhwgI0bN3LzzTdjt9v5+c9/ztVXX01qaioul0uKEQshhBBCCBHCpIM2gTUZJiLSttEOtMPVZIh2sB8poDOZGjtG/m+nS0tLWbp0KTt37sTpdE754MXYz76+Pk6dOqVPv+rv72d0dPS81JMIV7cj2DrGdaOZohHYxlRfp6ysLLKzs/njH/9Id3f3OctdLhfV1dV6QMdfO6i3t1cPmPj7bcwgMJlM7Nmzh5qaGiwWC8PDw/oj610uF0NDQ3qAyOPx0NzcTHd3N0eOHMHlcjE6OqoHXfzTWYzTqvwBkUD+ejXBzqM/MHDgwAFaW1uZM2cO1113Hbfddhu/+c1vgh6/XzTvXbfbTU9Pjz7INxodHeX06dP84he/oKOjg9HRUX2az4wZMzCbzXR1ddHe3k5nZydKKT788EN27NhBQ0MDDoeD559/nry8PFwuFxaLhXvvvReLxTLuEePB+INWHR0dNDc343K5+NGPfqQHM41BH6fTye9//3vuuece7r77bpxOp17EuqWlhbq6Ov2x9ikpKaSlpbFt2zba2trYtWsXjY2NJCYmopSip6eHWbNmkZaWpme2+T8H7rzzTn74wx/q2W/Dw8OMjY3pU6msVit/8zd/Q3NzMzt27ODYsWPU19fT29uLUgq73c4rr7xCUlISy5YtY8aMGSxcuJDi4mIKCgoYGBhgYGCAmpoadu/eTX5+PnV1dbS0tITMrgs8h8YpZkop5s+fz9y5c1FKcebMGX29zMxMUlNTueyyy8jKysJsNvPqq69SW1uL2WzWn2bnn6YnhBBCCCGEONekgjYX+tvRiQ7MY10/moDBRDKOioqK9Edx7927V6+HE2y/kYIS0VwLj8fD8PBwzNk8sQTDzkcAL5rrHCl4418WqdiqcfnQ0FDQR2ODtz6R0+mkt7cXTdOCFlsO1iePx0N3d/e4R2CH48+MCRc4iVa4fXk8Hjo7O+nt7cVut1NQUMDKlSvZsWOHXkh3spl0wfbv8Xjo7++nv79fX97R0UFvby+1tbUA4+q9KKVoa2vTn66mlOLo0aPYbDZ9Oto777xDXFxcVNO6+vr69KwZp9PJkSNH9Owao9HRUSoqKti5cydZWVl6IMoY9Onr62Pfvn309vaSlpZGZ2cn9fX1VFRU0NPTg8lkQinvI8MLCwtJTU3lr/7qrxgeHiY9PZ2Ojg7sdjujo6M0NDSQkZGh13pyu910dnby05/+FKvVqk9lbGlp0TNVTCYTAwMDVFdX8/vf/57W1lZmzZpFQkICmqbR1NREc3Mzra2ttLS00NHRQXZ2Nna7ncHBQT0IGCxAA+dm3oC3oHhJSQlut5uDBw/idDr1c9fX10dtbS0mk4mOjg7q6uo4dOgQXV1d5OTkcPz4cUZHR/XixEIIIYQQQohzTaqmTaTBdLhixNGsG279cO0HC6YES+0P15ZxeawDikjH6J/ucP3111NWVkZLSwsvvPBC2EyUqQjaRDo3oQRbL9y20Uw/CxXwika4axPtsnD9BUhJSSEpKQmHwxF1ceRw93Ckc23MQgl2TadyCp9SSn9yj/+JR8ZlCQkJ3HjjjWzevJkHH3xQz8yaaDZTqLpLximN4d7Lxu2NWVvB+uQvcN3Z2Ul3d3dUWWTGYF5ggCLw+Px1jAA9yOHxePT9JCQk6Bkmra2tdHV1nXN+rVYreXl5fOc738FsNjM0NERjYyNVVVUcP36cpqYm0tLSSEtLo62tTa//459eFx8fr2fgBGZb+feVkJBASUkJCxYsIDU1lY6ODioqKmhvbx+XvQPoT7szHkeka+c/lrS0NK677jp6e3vZu3ev/pQxpRRJSUksWbKEjIwMHA4H1dXVekAsOTmZwsJC3G43tbW1eDweXC5XxGslhBBCCCHEX7CgNW2mrBBxuIF9iLaCbh9h/2H3HWnbcEGkUAP8cIPKaNsKtl5JSQl33nknQ0NDPP/88zgcDn15NMGuSOc70jSjSAGtiQR3wvUvmn1NhVBTOYy/G/cfbZvBtok28BeqP+GOfaLZW9FITEwkKyuLgYEBfUqefwBvsViYPn06Dz/8MLt27dIfxx4oUt+MxzjV19jfbizvzcDtJrKeMYhjDBr561MFO2Z/sCXwHPmLV+fk5NDb28vAwAAul0vPtgsWwPVfI//voe5r/6O2jf0NzAQLLDZs3D6azxG/9PR0cnJy6O/vx+Fw6Nv7+2CslaNp2rggk3+Zv28StBFCCCGEEJe481OIOJxosjCCrTuRzIuJZOpE249Y+hPNumazmTvuuIOhoSH27ds3LmAzVUINqkP9HM5kAwbRBLGmalAfqZ1g2RmRsrCCCbdOtNlhkYJAUxmo8YuLi6OsrIzNmzfz6KOPAt77MTs7m9HRUbKysrjvvvuwWq289tprER/FHE0fz8fUl0jvzVAZItEEekItD/Va4FOqQmXMBb7unyZnsViCZrlMNPBlzEoKFSAMF3SLtC/je8g41c/PGIzxP8reZDLp58m/rvH383GPCCGEEEII8Zcg5qBNsEGvXzSZB8ZvqQO3i5SBECpbYzI+6cGCUopVq1aRlZXFq6++yoEDB6LeNpZBnDETIVQ7/vWi3W+4/YRqK9K39uf7/EcKjASK9t6KdC9erIPQlJQUioqKKCsrY9OmTYyNjbF8+XIuv/xyNE3T69d87Wtfi1hH53wElWIVeJ/7z7s/wyXa4Itx3UjZT+Ey2EIJXO4PrPiLR4dry58V4w+GKKX0YEhgRmBgP0P1ObBvoZZHe1yB+/JnH/n5M2yMx3kx3D9CCCGEEEJc7Kb0kd+BA9lw3+76hQsATXQgETgAi3Vaz0QH9tEM1OPj47nlllt44403qKqq0h/ZHCoDJprgVbDtQrVjDPiEOs5I1ywak53eM9HMq1imHBnbn8w0nqkK2EzmfAW2EWr77u5uKioqyMrKYvHixVitVhwOB8888wy5ubmkp6fzxhtv0NnZGXY/F8uAezKfE7GuGypQN5kAXajpU+ECHMYAjnGdUH0LFUz1eDzntBOLcMGvwM+QYIGcwH4KIYQQQgghzhVz0CbWwXS49SNNU/G/NtlB+0QG5dEeW6RAlHG5xWJh0aJFuFwuzpw5o2c1xLrfcCIN2icboDAKNdA8n9N8opm6EWq7cCJlE0VaJxbRZEkF7i9UH6KdiuVfd2xsjJaWFt5++23S09OxWCz09vbidDpJTEzEZrPR2Ng46cfAn++gTmBw4JMc9E/1+ybY8mCfgaGCLMasmmACC1z7t4l2GqBxGlOw+zDUZ1iwz4dgGU0SsBFCCCGEECK0KalpE2yKTLA/3CcyjWCq/qCfygF3NG0GW2axWCgvL+ejjz6ivb2d0dHRCe870r4CB0ifJOO1jzQ1wy/SNKvAtifap6k2FZk5U9FmtO0qpRgeHqaxsZHGxsZxy7u6uia0vwuZdXO+r+snfWzhMusCGQsgBwvQBQuKRJOZFEtgPjBgZPw9WGDI2CcJ1gghhBBCCBHZpII2oab0RLtN4B/7U2Eq2plIJkekdZRSmM1m8vLyeP311+nv74+5r+EGZtGuH6p/EzGV7U/FlKzz6UL3KdxAO9bgXKgAq/G1UOteLILdLxe6j7HWQzJuF+mzMNh0p3DXKnD9UIGYYJ/H4e6HSMcyVesKIYQQQgghvCZUiHiq1o80EI1mWbDfIbqiuOH2FUvWRzSDRZPJRFxcHDU1NTidznOeOGMUaopXONFmMcUysJ1IMCBwgDjVgaJoXIh9ng/R1ue5UMc5kTpFF/qaxDItKNZ2I7UX6vMn0uedf7vAJ2IZM/WCBWqC9S3ccQU+njuw/8E+d0PV1gm271DHeaGDbUIIIYQQQlzMVCyDKKXUp3sUfJ6YTKaIgQKTyURCQgJKKQYGBvTXw2U9TJXAqUfRDDAD+xdLHyMFzGIVKigXKqMp2H4nc34vVBAo0jGGOrZw/Y00xe58DaDPZ8Am2r6fz2OcbNvGa2Z8ZHagSMsi1SIKDL4EEyloYww0BXu/hcruMR5bsGXGp2gJIYQQQghxCfpI07TlgS9OSU2bS100RVs9Hg99fX2fQG/+MkUz2DSaygDBhcgOmYq6PhdDZovfxdCP830+JhOwMfbL+HlivO89Hk/Yz5pgy4Ids7EwsTGQEmy7UEGZcMdi/NfPmMUT+G+sGYBCCCGEEEJcSiRoIz4VIg0WxeQYB+6hahVdrGKd8neh+xAoXDbbZO/7cNMsAwNFwTK2lFLnBIMCAzPG5f6sQ38NL03TzglCRbMPIYQQQgghhJcEbYQQ+qA9MBPiYsrW+bSLJltsMlMLY71WgbWnAjNeItWfMQZ6jH31txUq8yfU60IIIYQQQohzSdBGCAHE9rhpcX4EC2jEUuR4otfLn9ETbJqWMRATLFMn8DX/o8iDBXUm00chhBBCCCEuRRK0EUKIT8ClMsVPpjoJIYQQQggxdc6tQCmEEEIIIYQQQgghLrhYM206gIbz0REhhBBCCCGEEEKIS9TMYC+qSyFdXwghhBBCCCGEEOLTRqZHCSGEEEIIIYQQQlyEJGgjhBBCCCGEEEIIcRGSoI0QQgghhBBCCCHERUiCNkIIIYQQQgghhBAXIQnaCCGEEEIIIYQQQlyEJGgjhBBCCCGEEEIIcRGSoI0QQgghhBBCCCHERUiCNkIIIYQQQgghhBAXIQnaCCGEEEIIIYQQQlyE/n8owmiOqk33hgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(190, 200):\n", - " plt.figure(figsize=(20, 20))\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " data, target = dataset[i]\n", - " target = [x - 26 if x > 35 else x for x in target]\n", - " sentence = convert_y_label_to_string(target, dataset) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0).numpy(), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.util import sliding_window" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches = sliding_window(data.unsqueeze(0), (28, 46), (1, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches = patches.squeeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(6):\n", - " ax = fig.add_subplot(1, 6, i + 1)\n", - " ax.imshow(patches[i].squeeze(0), cmap='gray')" - ] - }, - { - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/07-look-at-lexicon.ipynb b/notebooks/07-look-at-lexicon.ipynb deleted file mode 100644 index b7a5a0e..0000000 --- a/notebooks/07-look-at-lexicon.ipynb +++ /dev/null @@ -1,1119 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "from pathlib import Path\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": 15, - "metadata": {}, - "outputs": [], - "source": [ - "path = Path(\"../\").resolve().parent / \"data\" / \"processed\" / \"iam_lines\" / \"iamdb_1kwp_lex_1000.txt\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/processed/iam_lines/iamdb_1kwp_lex_1000.txt')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "with open(path, \"r\") as f:\n", - " lex = (line.strip().split() for line in f)\n", - " lex = {line[0]: line[1:] for line in lex}\n", - " #print(len(lex))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'!': ['▁', '!'],\n", - " '\"': ['▁', '\"'],\n", - " '&': ['▁', '&'],\n", - " \"'\": ['▁', \"'\"],\n", - " \"'30s\": ['▁', \"'\", '3', '0', 's'],\n", - " \"'61\": ['▁', \"'\", '6', '1'],\n", - " \"'d\": ['▁', \"'\", 'd'],\n", - " \"'ll\": ['▁', \"'\", 'll'],\n", - " \"'m\": ['▁', \"'\", 'm'],\n", - " \"'re\": ['▁', \"'\", 're'],\n", - " \"'s\": ['▁', \"'\", 's'],\n", - " \"'ve\": ['▁', \"'\", 've'],\n", - " '(': ['▁', '('],\n", - " ')': ['▁', ')'],\n", - " '*': ['▁', '*'],\n", - " '+2.8': ['▁', '+', '2', '.', '8'],\n", - " '+3.6': ['▁', '+', '3', '.', '6'],\n", - " ',': ['▁', ','],\n", - " '-': ['▁', '-'],\n", - " '-2.6': ['▁', '-', '2', '.', '6'],\n", - " '-5.4': ['▁', '-', '5', '.', '4'],\n", - " '.': ['▁', '.'],\n", - " '...': ['▁', '.', '.', '.'],\n", - " '0m': ['▁', '0', 'm'],\n", - " '1': ['▁', '1'],\n", - " '1,157': ['▁', '1', ',', '1', '5', '7'],\n", - " '1,400': ['▁', '1', ',', '4', '0', '0'],\n", - " '1,500': ['▁', '1', ',', '5', '0', '0'],\n", - " '1-2': ['▁', '1', '-', '2'],\n", - " '1.8': ['▁', '1', '.', '8'],\n", - " '1/2': ['▁', '1', '/', '2'],\n", - " '1/2-in.-long': ['▁', '1', '/', '2', '-', 'in', '.', '-', 'long'],\n", - " '1/4': ['▁', '1', '/', '4'],\n", - " '10': ['▁', '10'],\n", - " '10,000': ['▁', '10', ',', '0', '0', '0'],\n", - " '100': ['▁', '10', '0'],\n", - " '100,000,000': ['▁', '10', '0', ',', '0', '00,000'],\n", - " '104': ['▁', '10', '4'],\n", - " '11': ['▁', '1', '1'],\n", - " '12': ['▁', '1', '2'],\n", - " '12,000-word': ['▁', '1', '2', ',', '0', '0', '0', '-', 'word'],\n", - " '125': ['▁', '1', '2', '5'],\n", - " '13': ['▁', '1', '3'],\n", - " '13,000': ['▁', '1', '3', ',', '0', '0', '0'],\n", - " '14': ['▁', '1', '4'],\n", - " '15': ['▁', '1', '5'],\n", - " '15,000,000': ['▁', '1', '5', ',', '0', '00,000'],\n", - " '15-17': ['▁', '1', '5', '-', '1', '7'],\n", - " '15-nation': ['▁', '1', '5', '-', 'n', 'ation'],\n", - " '15-year-olds': ['▁', '1', '5', '-', 'year', '-', 'old', 's'],\n", - " '150,000,000': ['▁', '1', '5', '0', ',', '0', '00,000'],\n", - " '16': ['▁', '1', '6'],\n", - " '16,000': ['▁', '1', '6', ',', '0', '0', '0'],\n", - " '160': ['▁', '1', '6', '0'],\n", - " '163,000,000': ['▁', '1', '6', '3', ',', '0', '00,000'],\n", - " '167': ['▁', '1', '6', '7'],\n", - " '17': ['▁', '1', '7'],\n", - " '18': ['▁', '1', '8'],\n", - " '18.1': ['▁', '1', '8', '.', '1'],\n", - " '1830': ['▁', '1', '8', '3', '0'],\n", - " \"1830's\": ['▁', '1', '8', '3', '0', \"'\", 's'],\n", - " '1834': ['▁', '1', '8', '3', '4'],\n", - " '1897': ['▁', '1', '8', '9', '7'],\n", - " '19': ['▁', '1', '9'],\n", - " '19.5': ['▁', '1', '9', '.', '5'],\n", - " '1910': ['▁', '1', '9', '10'],\n", - " '1913': ['▁', '1', '9', '1', '3'],\n", - " '1914': ['▁', '1', '9', '1', '4'],\n", - " '1914-18': ['▁', '1', '9', '1', '4', '-', '1', '8'],\n", - " '1918': ['▁', '1', '9', '1', '8'],\n", - " '1920': ['▁', '1', '9', '2', '0'],\n", - " '1930': ['▁', '1', '9', '3', '0'],\n", - " '1931': ['▁', '1', '9', '3', '1'],\n", - " '1932': ['▁', '1', '9', '3', '2'],\n", - " '1934': ['▁', '1', '9', '3', '4'],\n", - " '1936': ['▁', '1', '9', '3', '6'],\n", - " '1939': ['▁', '1', '9', '3', '9'],\n", - " '1943': ['▁', '1', '9', '4', '3'],\n", - " '1944': ['▁', '1', '9', '4', '4'],\n", - " '1950': ['▁', '1', '9', '5', '0'],\n", - " '1951': ['▁', '1', '9', '5', '1'],\n", - " '1952': ['▁', '1', '9', '5', '2'],\n", - " '1953': ['▁', '1', '9', '5', '3'],\n", - " '1954': ['▁', '1', '9', '5', '4'],\n", - " '1956': ['▁', '1', '9', '5', '6'],\n", - " '1957': ['▁', '1', '9', '5', '7'],\n", - " '1958': ['▁', '1', '9', '5', '8'],\n", - " '1959': ['▁', '1', '9', '5', '9'],\n", - " '1960': ['▁', '1960'],\n", - " '1960s': ['▁', '1960', 's'],\n", - " '1961': ['▁', '1', '9', '6', '1'],\n", - " '1963': ['▁', '1', '9', '6', '3'],\n", - " '19th': ['▁', '1', '9', 'th'],\n", - " '1superceded': ['▁', '1', 'superceded'],\n", - " \"1tho'\": ['▁', '1', 'tho', \"'\"],\n", - " '2': ['▁', '2'],\n", - " '2,000': ['▁', '2', ',', '0', '0', '0'],\n", - " '2,415,000,000': ['▁', '2', ',', '4', '1', '5', ',', '0', '00,000'],\n", - " '20': ['▁', '2', '0'],\n", - " '20-month-old': ['▁', '2', '0', '-', 'month', '-', 'old'],\n", - " '200': ['▁', '2', '0', '0'],\n", - " '20th-century': ['▁', '2', '0', 'th', '-', 'cent', 'ur', 'y'],\n", - " '21': ['▁', '2', '1'],\n", - " '210million': ['▁', '2', '10', 'million'],\n", - " '22': ['▁', '2', '2'],\n", - " '23.1': ['▁', '2', '3', '.', '1'],\n", - " '24': ['▁', '2', '4'],\n", - " '24-strong': ['▁', '2', '4', '-', 'strong'],\n", - " '25': ['▁', '2', '5'],\n", - " '27': ['▁', '2', '7'],\n", - " '28.5': ['▁', '2', '8', '.', '5'],\n", - " '280,000': ['▁', '2', '8', '0', ',', '0', '0', '0'],\n", - " '287': ['▁', '2', '8', '7'],\n", - " '288': ['▁', '2', '8', '8'],\n", - " '2bhoys': ['▁', '2', 'b', 'ho', 'y', 's'],\n", - " '2ole': ['▁', '2', 'o', 'le'],\n", - " '2pianna': ['▁', '2', 'p', 'i', 'an', 'n', 'a'],\n", - " '2skint': ['▁', '2', 's', 'k', 'in', 't'],\n", - " '3': ['▁', '3'],\n", - " '3,000': ['▁', '3', ',', '0', '0', '0'],\n", - " '3.6': ['▁', '3', '.', '6'],\n", - " '3/0': ['▁', '3', '/', '0'],\n", - " '3/4': ['▁', '3', '/', '4'],\n", - " '30': ['▁', '3', '0'],\n", - " '30-day': ['▁', '3', '0', '-', 'day'],\n", - " '30-minute': ['▁', '3', '0', '-', 'minute'],\n", - " '300,000': ['▁', '3', '00,000'],\n", - " '32': ['▁', '3', '2'],\n", - " '33': ['▁', '3', '3'],\n", - " '34': ['▁', '3', '4'],\n", - " '35': ['▁', '3', '5'],\n", - " '357million': ['▁', '3', '5', '7', 'million'],\n", - " '36': ['▁', '3', '6'],\n", - " '37,000,000': ['▁', '3', '7', ',', '0', '00,000'],\n", - " '37.2': ['▁', '3', '7', '.', '2'],\n", - " '38': ['▁', '3', '8'],\n", - " '4': ['▁', '4'],\n", - " '4.8': ['▁', '4', '.', '8'],\n", - " '40': ['▁', '4', '0'],\n", - " '400': ['▁', '4', '0', '0'],\n", - " '400,000': ['▁', '4', '00,000'],\n", - " '420000': ['▁', '4', '2', '0', '0', '0', '0'],\n", - " '43': ['▁', '4', '3'],\n", - " '450': ['▁', '4', '5', '0'],\n", - " '5': ['▁', '5'],\n", - " '5,000': ['▁', '5', ',', '0', '0', '0'],\n", - " '5.30': ['▁', '5', '.', '3', '0'],\n", - " '5/8': ['▁', '5', '/', '8'],\n", - " '50': ['▁', '5', '0'],\n", - " '50,000': ['▁', '5', '0', ',', '0', '0', '0'],\n", - " '500': ['▁', '5', '0', '0'],\n", - " '53-year-old': ['▁', '5', '3', '-', 'year', '-', 'old'],\n", - " '55': ['▁', '5', '5'],\n", - " '550,000': ['▁', '5', '5', '0', ',', '0', '0', '0'],\n", - " '58': ['▁', '5', '8'],\n", - " '6': ['▁', '6'],\n", - " '6,000': ['▁', '6', ',', '0', '0', '0'],\n", - " '60': ['▁', '6', '0'],\n", - " '600': ['▁', '6', '0', '0'],\n", - " '600,000': ['▁', '6', '00,000'],\n", - " '61-year-old': ['▁', '6', '1', '-', 'year', '-', 'old'],\n", - " '68': ['▁', '6', '8'],\n", - " '6al': ['▁', '6', 'al'],\n", - " '6tic': ['▁', '6', 'tic'],\n", - " '7.30': ['▁', '7', '.', '3', '0'],\n", - " '7.42': ['▁', '7', '.', '4', '2'],\n", - " '70': ['▁', '7', '0'],\n", - " '70,000,000': ['▁', '7', '0', ',', '0', '00,000'],\n", - " '707': ['▁', '7', '0', '7'],\n", - " '73': ['▁', '7', '3'],\n", - " '750': ['▁', '7', '5', '0'],\n", - " '8': ['▁', '8'],\n", - " '8,000,000': ['▁', '8', ',', '0', '00,000'],\n", - " '8.25': ['▁', '8', '.', '2', '5'],\n", - " '8.4': ['▁', '8', '.', '4'],\n", - " '80': ['▁', '8', '0'],\n", - " '800': ['▁', '8', '0', '0'],\n", - " '800,000': ['▁', '8', '00,000'],\n", - " '86': ['▁', '8', '6'],\n", - " '88': ['▁', '8', '8'],\n", - " '88-year-old': ['▁', '8', '8', '-', 'year', '-', 'old'],\n", - " '89': ['▁', '8', '9'],\n", - " '89-year-old': ['▁', '8', '9', '-', 'year', '-', 'old'],\n", - " '9.30': ['▁', '9', '.', '3', '0'],\n", - " '9.40': ['▁', '9', '.', '4', '0'],\n", - " '90-day': ['▁', '9', '0', '-', 'day'],\n", - " '90-minute': ['▁', '9', '0', '-', 'minute'],\n", - " '91': ['▁', '9', '1'],\n", - " '950': ['▁', '9', '5', '0'],\n", - " '97.5': ['▁', '9', '7', '.', '5'],\n", - " ':': ['▁', ':'],\n", - " ';': ['▁', ';'],\n", - " '?': ['▁', '?'],\n", - " 'a': ['▁', 'a'],\n", - " 'abandon': ['▁', 'a', 'b', 'and', 'on'],\n", - " 'abandoned': ['▁', 'a', 'b', 'and', 'on', 'ed'],\n", - " 'abandoning': ['▁', 'a', 'b', 'and', 'on', 'ing'],\n", - " 'abashed': ['▁', 'a', 'bas', 'he', 'd'],\n", - " 'ability': ['▁', 'a', 'b', 'il', 'ity'],\n", - " 'able': ['▁', 'able'],\n", - " 'able-bodied': ['▁', 'able', '-', 'bo', 'die', 'd'],\n", - " 'abolish': ['▁', 'a', 'bo', 'l', 'ish'],\n", - " 'abolished': ['▁', 'a', 'bo', 'l', 'ish', 'ed'],\n", - " 'abolition': ['▁', 'a', 'bo', 'li', 'tion'],\n", - " 'abortion': ['▁', 'a', 'b', 'or', 'tion'],\n", - " 'abou': ['▁', 'a', 'bo', 'u'],\n", - " 'about': ['▁', 'about'],\n", - " 'about-': ['▁', 'about', '-'],\n", - " 'above': ['▁', 'a', 'bo', 've'],\n", - " 'abreast': ['▁', 'a', 'br', 'east'],\n", - " 'abroad': ['▁', 'a', 'b', 'ro', 'ad'],\n", - " 'absence': ['▁', 'a', 'b', 's', 'ence'],\n", - " 'absent': ['▁', 'a', 'b', 's', 'ent'],\n", - " 'absolutely': ['▁', 'a', 'b', 'solut', 'e', 'ly'],\n", - " 'abstraction': ['▁', 'a', 'b', 's', 'tr', 'action'],\n", - " 'abundance': ['▁', 'a', 'b', 'un', 'd', 'ance'],\n", - " 'ac-': ['▁', 'ac', '-'],\n", - " 'academic': ['▁', 'ac', 'a', 'de', 'm', 'ic'],\n", - " 'accent': ['▁', 'ac', 'cent'],\n", - " 'accents': ['▁', 'ac', 'cent', 's'],\n", - " 'accept': ['▁', 'accept'],\n", - " 'acceptable': ['▁', 'accept', 'able'],\n", - " 'accepted': ['▁', 'accept', 'ed'],\n", - " 'accepting': ['▁', 'accept', 'ing'],\n", - " 'accessories': ['▁', 'ac', 'ce', 's', 'so', 'ries'],\n", - " 'accident': ['▁', 'ac', 'c', 'id', 'ent'],\n", - " 'accidental': ['▁', 'ac', 'c', 'id', 'ent', 'al'],\n", - " 'accommodate': ['▁', 'ac', 'com', 'mo', 'date'],\n", - " 'accommodation': ['▁', 'ac', 'com', 'mo', 'd', 'ation'],\n", - " 'accompanied': ['▁', 'ac', 'com', 'pan', 'i', 'ed'],\n", - " 'accompanist': ['▁', 'ac', 'com', 'pan', 'is', 't'],\n", - " 'accompany': ['▁', 'ac', 'com', 'p', 'any'],\n", - " 'accomplished': ['▁', 'ac', 'com', 'p', 'l', 'ish', 'ed'],\n", - " 'accomplishments': ['▁', 'ac', 'com', 'p', 'l', 'ish', 'ment', 's'],\n", - " 'according': ['▁', 'ac', 'c', 'or', 'd', 'ing'],\n", - " 'account': ['▁', 'ac', 'count'],\n", - " 'accountancy': ['▁', 'ac', 'count', 'an', 'c', 'y'],\n", - " 'accra': ['▁', 'ac', 'c', 'ra'],\n", - " \"accra's\": ['▁', 'ac', 'c', 'ra', \"'\", 's'],\n", - " 'accuracy': ['▁', 'ac', 'cur', 'ac', 'y'],\n", - " 'accurate': ['▁', 'ac', 'cur', 'ate'],\n", - " 'accurately': ['▁', 'ac', 'cur', 'ate', 'ly'],\n", - " 'accused': ['▁', 'ac', 'c', 'used'],\n", - " 'achieved': ['▁', 'a', 'ch', 'i', 'e', 'v', 'ed'],\n", - " 'achievement': ['▁', 'a', 'ch', 'i', 'e', 've', 'ment'],\n", - " 'acquaintance': ['▁', 'ac', 'q', 'u', 'a', 'in', 't', 'ance'],\n", - " 'acquaintances': ['▁', 'ac', 'q', 'u', 'a', 'in', 't', 'ance', 's'],\n", - " 'acres': ['▁', 'ac', 're', 's'],\n", - " 'across': ['▁', 'a', 'cross'],\n", - " 'act': ['▁', 'act'],\n", - " 'acting': ['▁', 'act', 'ing'],\n", - " 'action': ['▁', 'action'],\n", - " 'actions': ['▁', 'action', 's'],\n", - " 'active': ['▁', 'act', 'ive'],\n", - " 'activists': ['▁', 'act', 'i', 'vi', 'st', 's'],\n", - " 'activities': ['▁', 'act', 'i', 'v', 'it', 'ies'],\n", - " 'activity': ['▁', 'act', 'i', 'v', 'ity'],\n", - " 'acton': ['▁', 'act', 'on'],\n", - " 'actor': ['▁', 'act', 'or'],\n", - " 'actress': ['▁', 'act', 're', 's', 's'],\n", - " 'acts': ['▁', 'act', 's'],\n", - " 'actual': ['▁', 'act', 'ual'],\n", - " 'actually': ['▁', 'act', 'ual', 'ly'],\n", - " 'adamafio': ['▁', 'ad', 'a', 'ma', 'f', 'i', 'o'],\n", - " 'adaptation': ['▁', 'ad', 'ap', 't', 'ation'],\n", - " 'adapted': ['▁', 'ad', 'ap', 'ted'],\n", - " 'adapting': ['▁', 'ad', 'ap', 't', 'ing'],\n", - " 'add': ['▁', 'ad', 'd'],\n", - " 'added': ['▁', 'ad', 'd', 'ed'],\n", - " 'adding': ['▁', 'adding'],\n", - " 'addition': ['▁', 'ad', 'd', 'it', 'ion'],\n", - " 'additions': ['▁', 'ad', 'd', 'it', 'ion', 's'],\n", - " 'address': ['▁', 'ad', 'dr', 'es', 's'],\n", - " 'addressed': ['▁', 'ad', 'dr', 'es', 's', 'ed'],\n", - " 'addresses': ['▁', 'ad', 'dr', 'es', 'se', 's'],\n", - " 'addressing': ['▁', 'ad', 'dr', 'es', 's', 'ing'],\n", - " 'adenauer': ['▁', 'adenauer'],\n", - " \"adenauer's\": ['▁', 'adenauer', \"'\", 's'],\n", - " 'adequate': ['▁', 'ad', 'equa', 'te'],\n", - " 'adhem': ['▁', 'ad', 'he', 'm'],\n", - " 'adjust': ['▁', 'ad', 'just'],\n", - " 'adjustment': ['▁', 'ad', 'just', 'ment'],\n", - " 'administration': ['▁', 'ad', 'ministr', 'ation'],\n", - " \"administration's\": ['▁', 'ad', 'ministr', 'ation', \"'\", 's'],\n", - " 'administrative': ['▁', 'ad', 'ministr', 'at', 'ive'],\n", - " 'admiralty': ['▁', 'ad', 'm', 'i', 'r', 'al', 'ty'],\n", - " 'admire': ['▁', 'ad', 'm', 'i', 're'],\n", - " 'admit': ['▁', 'ad', 'm', 'it'],\n", - " 'admitted': ['▁', 'ad', 'm', 'it', 'ted'],\n", - " 'admitting': ['▁', 'ad', 'm', 'it', 't', 'ing'],\n", - " 'adopted': ['▁', 'a', 'do', 'p', 'ted'],\n", - " 'adopting': ['▁', 'a', 'do', 'p', 't', 'ing'],\n", - " 'adoption': ['▁', 'a', 'do', 'p', 'tion'],\n", - " 'adult': ['▁', 'ad', 'ul', 't'],\n", - " 'advance': ['▁', 'ad', 'v', 'ance'],\n", - " 'advanced': ['▁', 'ad', 'v', 'ance', 'd'],\n", - " 'advancing': ['▁', 'ad', 'v', 'an', 'c', 'ing'],\n", - " 'advantage': ['▁', 'advantage'],\n", - " 'advantages': ['▁', 'advantage', 's'],\n", - " 'advertisement': ['▁', 'ad', 'ver', 't', 'is', 'e', 'ment'],\n", - " 'advertisements': ['▁', 'ad', 'ver', 't', 'is', 'ements'],\n", - " 'advice': ['▁', 'advi', 'ce'],\n", - " 'advisability': ['▁', 'advi', 's', 'a', 'b', 'il', 'ity'],\n", - " 'advise': ['▁', 'advise'],\n", - " 'advised': ['▁', 'advise', 'd'],\n", - " 'advisers': ['▁', 'advise', 'r', 's'],\n", - " 'advocate': ['▁', 'ad', 'v', 'o', 'c', 'ate'],\n", - " 'af-': ['▁', 'a', 'f', '-'],\n", - " 'affairs': ['▁', 'a', 'f', 'f', 'air', 's'],\n", - " 'affected': ['▁', 'a', 'f', 'fe', 'c', 'ted'],\n", - " 'affection': ['▁', 'a', 'f', 'fe', 'c', 'tion'],\n", - " 'affilia-': ['▁', 'a', 'f', 'f', 'il', 'i', 'a', '-'],\n", - " 'affiliations': ['▁', 'a', 'f', 'f', 'il', 'i', 'ation', 's'],\n", - " 'affluence': ['▁', 'a', 'f', 'f', 'l', 'u', 'ence'],\n", - " 'affluent': ['▁', 'a', 'f', 'f', 'l', 'u', 'ent'],\n", - " 'afford': ['▁', 'a', 'f', 'for', 'd'],\n", - " 'afraid': ['▁', 'a', 'fr', 'a', 'id'],\n", - " 'africa': ['▁', 'africa'],\n", - " \"africa's\": ['▁', 'africa', \"'\", 's'],\n", - " 'african': ['▁', 'african'],\n", - " 'africans': ['▁', 'african', 's'],\n", - " 'after': ['▁', 'after'],\n", - " 'afternoon': ['▁', 'after', 'no', 'on'],\n", - " 'afterwards': ['▁', 'after', 'ward', 's'],\n", - " 'again': ['▁', 'again'],\n", - " 'against': ['▁', 'against'],\n", - " 'age': ['▁', 'age'],\n", - " 'age-structure': ['▁', 'age', '-', 's', 'tru', 'c', 'ture'],\n", - " 'aged': ['▁', 'aged'],\n", - " 'ageing': ['▁', 'age', 'ing'],\n", - " 'agent': ['▁', 'a', 'g', 'ent'],\n", - " 'agents': ['▁', 'a', 'g', 'ent', 's'],\n", - " 'ages': ['▁', 'age', 's'],\n", - " 'agitation': ['▁', 'a', 'g', 'it', 'ation'],\n", - " 'ago': ['▁', 'a', 'go'],\n", - " 'agree': ['▁', 'agree'],\n", - " 'agreed': ['▁', 'agree', 'd'],\n", - " 'agreement': ['▁', 'agree', 'ment'],\n", - " 'agreements': ['▁', 'agree', 'ment', 's'],\n", - " 'agriculture': ['▁', 'a', 'gr', 'ic', 'ul', 'ture'],\n", - " 'ahead': ['▁', 'a', 'head'],\n", - " 'aid': ['▁', 'a', 'id'],\n", - " 'aide': ['▁', 'a', 'i', 'de'],\n", - " 'aided': ['▁', 'a', 'id', 'ed'],\n", - " 'aides': ['▁', 'a', 'id', 'es'],\n", - " 'aim': ['▁', 'a', 'im'],\n", - " 'aimed': ['▁', 'a', 'im', 'ed'],\n", - " 'aiming': ['▁', 'a', 'im', 'ing'],\n", - " 'air': ['▁', 'air'],\n", - " 'aircraft': ['▁', 'air', 'craft'],\n", - " 'aired': ['▁', 'air', 'ed'],\n", - " \"airliner's\": ['▁', 'air', 'line', 'r', \"'\", 's'],\n", - " 'airmen': ['▁', 'air', 'men'],\n", - " 'airport': ['▁', 'air', 'port'],\n", - " 'akin': ['▁', 'a', 'k', 'in'],\n", - " \"aladdin's\": ['▁', 'al', 'ad', 'd', 'in', \"'\", 's'],\n", - " 'alan': ['▁', 'al', 'an'],\n", - " 'alarm': ['▁', 'al', 'arm'],\n", - " 'alarmed': ['▁', 'al', 'arm', 'ed'],\n", - " 'alas': ['▁', 'al', 'as'],\n", - " 'alcoholic': ['▁', 'al', 'co', 'ho', 'li', 'c'],\n", - " 'algeria': ['▁', 'al', 'g', 'er', 'i', 'a'],\n", - " 'alike': ['▁', 'a', 'like'],\n", - " 'alive': ['▁', 'a', 'live'],\n", - " 'all': ['▁', 'all'],\n", - " 'all-regular': ['▁', 'all', '-', 'regular'],\n", - " 'alleged': ['▁', 'al', 'leg', 'ed'],\n", - " 'allen': ['▁', 'all', 'en'],\n", - " 'alleviation': ['▁', 'alleviation'],\n", - " 'alley': ['▁', 'al', 'le', 'y'],\n", - " 'alliance': ['▁', 'all', 'i', 'ance'],\n", - " 'alliances': ['▁', 'all', 'i', 'ance', 's'],\n", - " 'allied': ['▁', 'all', 'i', 'ed'],\n", - " 'allies': ['▁', 'all', 'ies'],\n", - " 'allow': ['▁', 'allow'],\n", - " 'allowance': ['▁', 'allow', 'ance'],\n", - " 'allowances': ['▁', 'allow', 'ance', 's'],\n", - " 'allowed': ['▁', 'allow', 'ed'],\n", - " 'allowing': ['▁', 'allow', 'ing'],\n", - " 'ally': ['▁', 'al', 'ly'],\n", - " 'almost': ['▁', 'al', 'most'],\n", - " 'alone': ['▁', 'al', 'one'],\n", - " 'along': ['▁', 'a', 'long'],\n", - " 'alongside': ['▁', 'a', 'long', 'side'],\n", - " 'aloud': ['▁', 'a', 'lo', 'ud'],\n", - " 'already': ['▁', 'al', 'read', 'y'],\n", - " 'also': ['▁', 'also'],\n", - " 'alter': ['▁', 'al', 'ter'],\n", - " 'alternative': ['▁', 'al', 'ter', 'n', 'at', 'ive'],\n", - " 'alternatively': ['▁', 'al', 'ter', 'n', 'at', 'ive', 'ly'],\n", - " 'alternatives': ['▁', 'al', 'ter', 'n', 'at', 'ive', 's'],\n", - " 'although': ['▁', 'al', 'though'],\n", - " 'altogether': ['▁', 'al', 'together'],\n", - " 'altos': ['▁', 'al', 'to', 's'],\n", - " 'always': ['▁', 'always'],\n", - " 'am': ['▁', 'am'],\n", - " 'amateur': ['▁', 'am', 'ate', 'ur'],\n", - " 'amazed': ['▁', 'a', 'ma', 'z', 'ed'],\n", - " 'amazing': ['▁', 'a', 'ma', 'z', 'ing'],\n", - " 'ambassador': ['▁', 'am', 'bas', 's', 'ad', 'or'],\n", - " 'amber': ['▁', 'a', 'mber'],\n", - " 'ambition': ['▁', 'am', 'b', 'it', 'ion'],\n", - " 'ambitious': ['▁', 'am', 'b', 'it', 'i', 'ous'],\n", - " 'ambulance': ['▁', 'am', 'b', 'ul', 'ance'],\n", - " 'ambulances': ['▁', 'am', 'b', 'ul', 'ance', 's'],\n", - " 'america': ['▁', 'america'],\n", - " \"america's\": ['▁', 'america', \"'\", 's'],\n", - " 'american': ['▁', 'american'],\n", - " 'american-born': ['▁', 'american', '-', 'b', 'or', 'n'],\n", - " 'americans': ['▁', 'american', 's'],\n", - " 'amid': ['▁', 'am', 'id'],\n", - " 'ammunition': ['▁', 'am', 'm', 'un', 'it', 'ion'],\n", - " 'among': ['▁', 'among'],\n", - " 'amount': ['▁', 'a', 'mo', 'un', 't'],\n", - " 'ample': ['▁', 'amp', 'le'],\n", - " 'amusement': ['▁', 'am', 'use', 'ment'],\n", - " 'amusing': ['▁', 'am', 'us', 'ing'],\n", - " 'an': ['▁', 'an'],\n", - " 'analogy': ['▁', 'an', 'a', 'lo', 'g', 'y'],\n", - " 'analysed': ['▁', 'an', 'a', 'ly', 's', 'ed'],\n", - " 'anchor': ['▁', 'an', 'ch', 'or'],\n", - " 'ancient': ['▁', 'an', 'c', 'i', 'ent'],\n", - " 'and': ['▁', 'and'],\n", - " 'andrei': ['▁', 'and', 're', 'i'],\n", - " 'andrew': ['▁', 'and', 're', 'w'],\n", - " 'anecdotal': ['▁', 'an', 'e', 'c', 'do', 't', 'al'],\n", - " 'angel': ['▁', 'ang', 'el'],\n", - " 'angeles': ['▁', 'ang', 'el', 'es'],\n", - " 'angelo': ['▁', 'ang', 'e', 'lo'],\n", - " 'anger': ['▁', 'ang', 'er'],\n", - " 'anglais': ['▁', 'ang', 'la', 'is'],\n", - " 'angle': ['▁', 'ang', 'le'],\n", - " 'anglesey': ['▁', 'anglesey'],\n", - " \"anglesey's\": ['▁', 'anglesey', \"'\", 's'],\n", - " 'anglesey-road': ['▁', 'anglesey', '-', 'ro', 'ad'],\n", - " 'angola': ['▁', 'an', 'go', 'la'],\n", - " 'angrily': ['▁', 'an', 'gr', 'i', 'ly'],\n", - " 'angry': ['▁', 'ang', 'ry'],\n", - " 'ann': ['▁', 'an', 'n'],\n", - " 'anna': ['▁', 'an', 'n', 'a'],\n", - " 'announced': ['▁', 'an', 'no', 'un', 'c', 'ed'],\n", - " 'announcement': ['▁', 'an', 'no', 'un', 'ce', 'ment'],\n", - " 'announcing': ['▁', 'an', 'no', 'un', 'c', 'ing'],\n", - " 'annoyed': ['▁', 'an', 'no', 'y', 'ed'],\n", - " 'annual': ['▁', 'an', 'n', 'ual'],\n", - " 'another': ['▁', 'another'],\n", - " 'answer': ['▁', 'answer'],\n", - " 'answered': ['▁', 'answer', 'ed'],\n", - " 'answering': ['▁', 'answer', 'ing'],\n", - " 'antagonism': ['▁', 'ant', 'a', 'g', 'on', 'is', 'm'],\n", - " 'anthony': ['▁', 'an', 'th', 'on', 'y'],\n", - " 'anti-apartheid': ['▁', 'ant', 'i', '-', 'a', 'part', 'he', 'id'],\n", - " 'anti-bomb': ['▁', 'ant', 'i', '-', 'bomb'],\n", - " 'anti-german': ['▁', 'ant', 'i', '-', 'german'],\n", - " 'anti-nato': ['▁', 'ant', 'i', '-', 'nato'],\n", - " 'anti-negro': ['▁', 'ant', 'i', '-', 'negro'],\n", - " 'anti-nuclear': ['▁', 'ant', 'i', '-', 'nuclear'],\n", - " 'anti-soviet': ['▁', 'ant', 'i', '-', 'soviet'],\n", - " 'anti-tory': ['▁', 'ant', 'i', '-', 'tory'],\n", - " 'anticipation': ['▁', 'an', 'tic', 'ip', 'ation'],\n", - " 'antonioni': ['▁', 'ant', 'on', 'ion', 'i'],\n", - " \"antonioni's\": ['▁', 'ant', 'on', 'ion', 'i', \"'\", 's'],\n", - " 'any': ['▁', 'any'],\n", - " 'any-': ['▁', 'any', '-'],\n", - " 'anybody': ['▁', 'any', 'body'],\n", - " \"anybody's\": ['▁', 'any', 'body', \"'\", 's'],\n", - " 'anyone': ['▁', 'any', 'one'],\n", - " 'anything': ['▁', 'any', 'thing'],\n", - " 'anyway': ['▁', 'any', 'way'],\n", - " 'apart': ['▁', 'a', 'part'],\n", - " 'apartheid': ['▁', 'a', 'part', 'he', 'id'],\n", - " 'apathetic': ['▁', 'a', 'pa', 'the', 'tic'],\n", - " 'apathy': ['▁', 'a', 'pa', 'th', 'y'],\n", - " 'apex': ['▁', 'ap', 'ex'],\n", - " 'apocalypse': ['▁', 'a', 'po', 'c', 'a', 'ly', 'p', 'se'],\n", - " 'apologising': ['▁', 'a', 'po', 'lo', 'g', 'is', 'ing'],\n", - " 'appalled': ['▁', 'app', 'all', 'ed'],\n", - " 'appalling': ['▁', 'app', 'all', 'ing'],\n", - " 'apparatus': ['▁', 'app', 'ar', 'at', 'us'],\n", - " 'apparent': ['▁', 'app', 'ar', 'ent'],\n", - " 'apparently': ['▁', 'app', 'ar', 'ent', 'ly'],\n", - " 'appeal': ['▁', 'appeal'],\n", - " 'appealing': ['▁', 'appeal', 'ing'],\n", - " 'appeals': ['▁', 'appeal', 's'],\n", - " 'appear': ['▁', 'appear'],\n", - " 'appearance': ['▁', 'appear', 'ance'],\n", - " 'appeared': ['▁', 'appear', 'ed'],\n", - " 'appears': ['▁', 'appear', 's'],\n", - " 'appeasement': ['▁', 'app', 'e', 'a', 'se', 'ment'],\n", - " 'applauding': ['▁', 'app', 'la', 'ud', 'ing'],\n", - " 'appliances': ['▁', 'app', 'li', 'ance', 's'],\n", - " 'application': ['▁', 'app', 'li', 'c', 'ation'],\n", - " 'applications': ['▁', 'app', 'li', 'c', 'ation', 's'],\n", - " 'applied': ['▁', 'app', 'li', 'ed'],\n", - " 'apply': ['▁', 'app', 'ly'],\n", - " 'appointed': ['▁', 'ap', 'point', 'ed'],\n", - " 'appointment': ['▁', 'ap', 'point', 'ment'],\n", - " 'appreciable': ['▁', 'app', 're', 'c', 'i', 'able'],\n", - " 'appreciably': ['▁', 'app', 're', 'c', 'i', 'ably'],\n", - " 'appreciated': ['▁', 'app', 're', 'c', 'i', 'at', 'ed'],\n", - " 'appreciation': ['▁', 'app', 're', 'c', 'i', 'ation'],\n", - " 'apprenticeships': ['▁', 'app', 'r', 'ent', 'i', 'ce', 'ship', 's'],\n", - " 'approach': ['▁', 'ap', 'pro', 'a', 'ch'],\n", - " 'approached': ['▁', 'ap', 'pro', 'a', 'ch', 'ed'],\n", - " 'approaches': ['▁', 'ap', 'pro', 'a', 'che', 's'],\n", - " 'appropriate': ['▁', 'ap', 'pro', 'pri', 'ate'],\n", - " 'appropriated': ['▁', 'ap', 'pro', 'pri', 'at', 'ed'],\n", - " 'approval': ['▁', 'ap', 'pro', 'val'],\n", - " 'approximately': ['▁', 'ap', 'pro', 'x', 'im', 'ate', 'ly'],\n", - " 'april': ['▁', 'a', 'pri', 'l'],\n", - " 'archbishop': ['▁', 'ar', 'ch', 'b', 'is', 'hop'],\n", - " 'arches': ['▁', 'ar', 'che', 's'],\n", - " 'archipelago': ['▁', 'ar', 'ch', 'i', 'pe', 'la', 'go'],\n", - " 'architect': ['▁', 'ar', 'ch', 'it', 'e', 'c', 't'],\n", - " 'architecture': ['▁', 'ar', 'ch', 'it', 'e', 'c', 'ture'],\n", - " 'are': ['▁', 'are'],\n", - " 'area': ['▁', 'are', 'a'],\n", - " 'areas': ['▁', 'are', 'as'],\n", - " \"aren't\": ['▁', 'are', 'n', \"'\", 't'],\n", - " 'arguably': ['▁', 'ar', 'gu', 'ably'],\n", - " 'argued': ['▁', 'ar', 'gu', 'ed'],\n", - " 'argues': ['▁', 'ar', 'gu', 'es'],\n", - " 'arguing': ['▁', 'ar', 'gu', 'ing'],\n", - " 'argument': ['▁', 'ar', 'gu', 'ment'],\n", - " 'arguments': ['▁', 'ar', 'gu', 'ment', 's'],\n", - " 'arise': ['▁', 'a', 'rise'],\n", - " 'arises': ['▁', 'a', 'rise', 's'],\n", - " 'arm': ['▁', 'arm'],\n", - " 'armament': ['▁', 'arm', 'a', 'ment'],\n", - " 'armaments': ['▁', 'arm', 'a', 'ment', 's'],\n", - " 'armed': ['▁', 'arm', 'ed'],\n", - " 'armoured': ['▁', 'arm', 'our', 'ed'],\n", - " 'arms': ['▁', 'arm', 's'],\n", - " \"arms'\": ['▁', 'arm', 's', \"'\"],\n", - " 'army': ['▁', 'arm', 'y'],\n", - " 'arnold': ['▁', 'ar', 'n', 'old'],\n", - " 'arose': ['▁', 'a', 'ro', 'se'],\n", - " 'around': ['▁', 'a', 'round'],\n", - " 'aroused': ['▁', 'ar', 'ous', 'ed'],\n", - " 'arrange': ['▁', 'ar', 'range'],\n", - " 'arranged': ['▁', 'ar', 'range', 'd'],\n", - " 'arrangement': ['▁', 'ar', 'range', 'ment'],\n", - " 'arrangements': ['▁', 'ar', 'range', 'ment', 's'],\n", - " 'arranging': ['▁', 'ar', 'r', 'ang', 'ing'],\n", - " 'arrears': ['▁', 'ar', 're', 'ar', 's'],\n", - " 'arrested': ['▁', 'ar', 'rest', 'ed'],\n", - " 'arrival': ['▁', 'ar', 'r', 'i', 'val'],\n", - " 'arrive': ['▁', 'ar', 'r', 'ive'],\n", - " 'arrived': ['▁', 'arrived'],\n", - " 'arrives': ['▁', 'ar', 'r', 'ive', 's'],\n", - " 'arrogant': ['▁', 'ar', 'ro', 'g', 'ant'],\n", - " 'art': ['▁', 'ar', 't'],\n", - " 'arthur': ['▁', 'ar', 'th', 'ur'],\n", - " 'article': ['▁', 'ar', 'tic', 'le'],\n", - " 'articles': ['▁', 'ar', 'tic', 'le', 's'],\n", - " 'articulation': ['▁', 'ar', 'tic', 'ul', 'ation'],\n", - " 'artistic': ['▁', 'ar', 'tist', 'ic'],\n", - " 'artistically': ['▁', 'ar', 'tist', 'ical', 'ly'],\n", - " 'artistry': ['▁', 'ar', 'tist', 'ry'],\n", - " 'artists': ['▁', 'ar', 'tist', 's'],\n", - " 'as': ['▁', 'as'],\n", - " 'ascents': ['▁', 'as', 'cent', 's'],\n", - " 'ash': ['▁', 'as', 'h'],\n", - " 'ashen': ['▁', 'as', 'he', 'n'],\n", - " 'ask': ['▁', 'as', 'k'],\n", - " 'asked': ['▁', 'asked'],\n", - " 'asking': ['▁', 'asking'],\n", - " 'aspect': ['▁', 'a', 'spect'],\n", - " 'aspects': ['▁', 'a', 'spect', 's'],\n", - " 'aspiring': ['▁', 'as', 'p', 'i', 'r', 'ing'],\n", - " 'assault': ['▁', 'as', 's', 'a', 'ul', 't'],\n", - " 'assembler': ['▁', 'as', 'se', 'm', 'bl', 'er'],\n", - " 'assembly': ['▁', 'as', 'se', 'm', 'b', 'ly'],\n", - " 'assess': ['▁', 'as', 'se', 's', 's'],\n", - " 'assessment': ['▁', 'as', 'se', 's', 's', 'ment'],\n", - " 'assistance': ['▁', 'as', 's', 'istance'],\n", - " 'assistant': ['▁', 'as', 's', 'is', 't', 'ant'],\n", - " 'assistants': ['▁', 'as', 's', 'is', 't', 'ant', 's'],\n", - " 'associate': ['▁', 'associat', 'e'],\n", - " 'associated': ['▁', 'associat', 'ed'],\n", - " 'associates': ['▁', 'associat', 'es'],\n", - " 'association': ['▁', 'associat', 'ion'],\n", - " 'assortment': ['▁', 'as', 's', 'or', 't', 'ment'],\n", - " 'assumption': ['▁', 'assumption'],\n", - " 'assurance': ['▁', 'as', 's', 'ur', 'ance'],\n", - " 'astronaut': ['▁', 'as', 'tr', 'on', 'a', 'u', 't'],\n", - " 'astute': ['▁', 'a', 'st', 'u', 'te'],\n", - " 'at': ['▁', 'at'],\n", - " 'ately': ['▁', 'ate', 'ly'],\n", - " 'atkinson': ['▁', 'at', 'k', 'in', 's', 'on'],\n", - " 'atlantic': ['▁', 'at', 'l', 'an', 'tic'],\n", - " 'atmosphere': ['▁', 'atmospher', 'e'],\n", - " 'atmospheric': ['▁', 'atmospher', 'ic'],\n", - " 'atomic': ['▁', 'a', 'to', 'm', 'ic'],\n", - " 'atoms': ['▁', 'a', 'to', 'm', 's'],\n", - " 'attach': ['▁', 'at', 't', 'a', 'ch'],\n", - " 'attached': ['▁', 'at', 't', 'a', 'ch', 'ed'],\n", - " 'attack': ['▁', 'at', 't', 'a', 'ck'],\n", - " 'attacked': ['▁', 'at', 't', 'a', 'ck', 'ed'],\n", - " 'attacks': ['▁', 'at', 't', 'a', 'ck', 's'],\n", - " 'attainable': ['▁', 'at', 'tain', 'able'],\n", - " 'attempt': ['▁', 'attempt'],\n", - " 'attempted': ['▁', 'attempt', 'ed'],\n", - " 'attempting': ['▁', 'attempt', 'ing'],\n", - " 'attempts': ['▁', 'attempt', 's'],\n", - " 'atten-': ['▁', 'at', 'ten', '-'],\n", - " 'attend': ['▁', 'at', 't', 'end'],\n", - " 'attendance': ['▁', 'at', 't', 'end', 'ance'],\n", - " 'attended': ['▁', 'at', 't', 'end', 'ed'],\n", - " 'attending': ['▁', 'at', 't', 'end', 'ing'],\n", - " 'attention': ['▁', 'at', 'ten', 'tion'],\n", - " 'attitude': ['▁', 'at', 't', 'it', 'u', 'de'],\n", - " 'attitudes': ['▁', 'at', 't', 'it', 'ud', 'es'],\n", - " 'attracted': ['▁', 'at', 'tr', 'act', 'ed'],\n", - " 'attractive': ['▁', 'at', 'tr', 'act', 'ive'],\n", - " 'aubrey': ['▁', 'a', 'u', 'b', 're', 'y'],\n", - " 'audacity': ['▁', 'a', 'ud', 'ac', 'ity'],\n", - " 'auden': ['▁', 'a', 'ud', 'en'],\n", - " 'audience': ['▁', 'a', 'ud', 'i', 'ence'],\n", - " 'audio-tv': ['▁', 'a', 'ud', 'i', 'o', '-', 't', 'v'],\n", - " 'audited': ['▁', 'a', 'ud', 'it', 'ed'],\n", - " 'august': ['▁', 'a', 'ug', 'u', 'st'],\n", - " 'auntie': ['▁', 'a', 'un', 't', 'i', 'e'],\n", - " 'austerity': ['▁', 'a', 'u', 'ster', 'ity'],\n", - " 'australia': ['▁', 'a', 'us', 'tr', 'al', 'i', 'a'],\n", - " 'austria': ['▁', 'a', 'us', 'tri', 'a'],\n", - " 'austrian': ['▁', 'a', 'us', 'tri', 'an'],\n", - " 'authentic': ['▁', 'a', 'u', 'then', 'tic'],\n", - " 'author': ['▁', 'author'],\n", - " 'authorised': ['▁', 'author', 'is', 'ed'],\n", - " 'authorities': ['▁', 'author', 'it', 'ies'],\n", - " 'authority': ['▁', 'author', 'ity'],\n", - " 'automatically': ['▁', 'a', 'u', 'to', 'm', 'at', 'ical', 'ly'],\n", - " 'automation': ['▁', 'a', 'u', 'to', 'm', 'ation'],\n", - " 'autumn': ['▁', 'a', 'u', 't', 'um', 'n'],\n", - " 'available': ['▁', 'a', 'v', 'a', 'il', 'able'],\n", - " 'avenue': ['▁', 'a', 've', 'n', 'ue'],\n", - " 'average': ['▁', 'a', 'ver', 'age'],\n", - " 'averages': ['▁', 'a', 'ver', 'age', 's'],\n", - " 'avert': ['▁', 'a', 'ver', 't'],\n", - " 'aviation': ['▁', 'a', 'vi', 'ation'],\n", - " 'avoid': ['▁', 'a', 'v', 'o', 'id'],\n", - " 'avoided': ['▁', 'a', 'v', 'o', 'id', 'ed'],\n", - " 'avon': ['▁', 'a', 'v', 'on'],\n", - " 'awake': ['▁', 'a', 'w', 'a', 'ke'],\n", - " 'awarded': ['▁', 'a', 'ward', 'ed'],\n", - " 'awards': ['▁', 'a', 'ward', 's'],\n", - " 'aware': ['▁', 'a', 'w', 'are'],\n", - " 'awareness': ['▁', 'a', 'w', 'are', 'ness'],\n", - " 'away': ['▁', 'a', 'way'],\n", - " 'awful': ['▁', 'a', 'w', 'ful'],\n", - " 'awfully': ['▁', 'a', 'w', 'ful', 'ly'],\n", - " 'b': ['▁', 'b'],\n", - " 'b.': ['▁', 'b', '.'],\n", - " 'b.b.c.': ['▁', 'b', '.', 'b', '.', 'c', '.'],\n", - " 'babe': ['▁', 'b', 'a', 'be'],\n", - " 'babel': ['▁', 'b', 'a', 'be', 'l'],\n", - " 'bably': ['▁', 'b', 'ably'],\n", - " 'baby': ['▁', 'b', 'a', 'by'],\n", - " \"baby's\": ['▁', 'b', 'a', 'by', \"'\", 's'],\n", - " 'back': ['▁', 'back'],\n", - " 'backbone': ['▁', 'back', 'b', 'one'],\n", - " 'backed': ['▁', 'back', 'ed'],\n", - " 'backers': ['▁', 'back', 'ers'],\n", - " 'background': ['▁', 'back', 'ground'],\n", - " 'backing': ['▁', 'back', 'ing'],\n", - " 'backstage': ['▁', 'back', 'st', 'age'],\n", - " 'backward': ['▁', 'back', 'ward'],\n", - " 'bad': ['▁', 'b', 'ad'],\n", - " 'badly': ['▁', 'b', 'ad', 'ly'],\n", - " 'baffled': ['▁', 'b', 'a', 'f', 'f', 'led'],\n", - " 'bag': ['▁', 'b', 'a', 'g'],\n", - " 'bagaya': ['▁', 'b', 'a', 'gay', 'a'],\n", - " 'baker': ['▁', 'b', 'a', 'k', 'er'],\n", - " 'balance': ['▁', 'b', 'al', 'ance'],\n", - " 'balance-sheet': ['▁', 'b', 'al', 'ance', '-', 'she', 'e', 't'],\n", - " 'balances': ['▁', 'b', 'al', 'ance', 's'],\n", - " 'bald': ['▁', 'b', 'al', 'd'],\n", - " 'ball': ['▁', 'b', 'all'],\n", - " 'balloon': ['▁', 'b', 'all', 'o', 'on'],\n", - " 'ballyhoo': ['▁', 'b', 'al', 'ly', 'ho', 'o'],\n", - " 'baltic': ['▁', 'b', 'al', 'tic'],\n", - " 'ban': ['▁', 'b', 'an'],\n", - " 'ban-': ['▁', 'b', 'an', '-'],\n", - " 'ban-the-': ['▁', 'b', 'an', '-', 'the', '-'],\n", - " 'ban-the-bomb': ['▁', 'b', 'an', '-', 'the', '-', 'bomb'],\n", - " 'bank': ['▁', 'bank'],\n", - " \"bank's\": ['▁', 'bank', \"'\", 's'],\n", - " 'banking': ['▁', 'bank', 'ing'],\n", - " 'bankrupt': ['▁', 'bank', 'r', 'up', 't'],\n", - " 'banks': ['▁', 'bank', 's'],\n", - " \"banks'\": ['▁', 'bank', 's', \"'\"],\n", - " 'banned': ['▁', 'b', 'an', 'n', 'ed'],\n", - " 'banzie': ['▁', 'b', 'an', 'z', 'i', 'e'],\n", - " 'bar': ['▁', 'b', 'ar'],\n", - " 'barb': ['▁', 'b', 'ar', 'b'],\n", - " 'barbara': ['▁', 'b', 'ar', 'b', 'ar', 'a'],\n", - " 'barbarously': ['▁', 'b', 'ar', 'b', 'ar', 'ous', 'ly'],\n", - " 'barclay': ['▁', 'b', 'ar', 'clay'],\n", - " 'bare': ['▁', 'b', 'are'],\n", - " 'bargain': ['▁', 'b', 'ar', 'g', 'a', 'in'],\n", - " 'bargaining': ['▁', 'b', 'ar', 'g', 'a', 'in', 'ing'],\n", - " 'bark': ['▁', 'b', 'ar', 'k'],\n", - " 'barrier': ['▁', 'b', 'ar', 'r', 'i', 'er'],\n", - " 'barriers': ['▁', 'b', 'ar', 'r', 'i', 'ers'],\n", - " 'barry': ['▁', 'b', 'a', 'rry'],\n", - " 'base': ['▁', 'base'],\n", - " 'based': ['▁', 'bas', 'ed'],\n", - " 'bases': ['▁', 'base', 's'],\n", - " 'basic': ['▁', 'bas', 'ic'],\n", - " 'basin': ['▁', 'bas', 'in'],\n", - " 'basing': ['▁', 'bas', 'ing'],\n", - " 'basis': ['▁', 'bas', 'is'],\n", - " 'baskerville': ['▁', 'bas', 'k', 'er', 'v', 'il', 'le'],\n", - " 'basses': ['▁', 'bas', 'se', 's'],\n", - " 'basting': ['▁', 'bas', 't', 'ing'],\n", - " 'bathing': ['▁', 'b', 'a', 'thing'],\n", - " 'bats': ['▁', 'b', 'at', 's'],\n", - " 'batsman': ['▁', 'b', 'at', 's', 'man'],\n", - " 'battalions': ['▁', 'b', 'at', 't', 'al', 'ion', 's'],\n", - " 'batting': ['▁', 'b', 'at', 't', 'ing'],\n", - " 'battle': ['▁', 'b', 'a', 'ttle'],\n", - " 'bavaria': ['▁', 'b', 'a', 'v', 'ar', 'i', 'a'],\n", - " 'bavarian': ['▁', 'b', 'a', 'v', 'ar', 'i', 'an'],\n", - " 'bavarians': ['▁', 'b', 'a', 'v', 'ar', 'i', 'an', 's'],\n", - " 'bay': ['▁', 'b', 'a', 'y'],\n", - " 'be': ['▁', 'be'],\n", - " 'beach': ['▁', 'b', 'each'],\n", - " 'beaches': ['▁', 'b', 'each', 'es'],\n", - " 'beacon': ['▁', 'be', 'a', 'con'],\n", - " 'beaks': ['▁', 'be', 'a', 'k', 's'],\n", - " 'bean': ['▁', 'be', 'an'],\n", - " 'bear': ['▁', 'be', 'ar'],\n", - " 'bearer': ['▁', 'be', 'are', 'r'],\n", - " 'bears': ['▁', 'be', 'ar', 's'],\n", - " 'beastly': ['▁', 'b', 'east', 'ly'],\n", - " 'beasts': ['▁', 'b', 'east', 's'],\n", - " 'beaten': ['▁', 'be', 'a', 'ten'],\n", - " 'beautiful': ['▁', 'be', 'a', 'u', 't', 'i', 'ful'],\n", - " 'beautifully': ['▁', 'be', 'a', 'u', 't', 'i', 'ful', 'ly'],\n", - " 'beauty': ['▁', 'be', 'a', 'u', 'ty'],\n", - " 'became': ['▁', 'be', 'came'],\n", - " 'because': ['▁', 'because'],\n", - " 'beckoning': ['▁', 'be', 'ck', 'on', 'ing'],\n", - " 'become': ['▁', 'be', 'come'],\n", - " 'becomes': ['▁', 'be', 'come', 's'],\n", - " 'becoming': ['▁', 'be', 'com', 'ing'],\n", - " 'bed': ['▁', 'b', 'ed'],\n", - " 'bedlam': ['▁', 'b', 'ed', 'la', 'm'],\n", - " 'beds': ['▁', 'b', 'ed', 's'],\n", - " 'bedspreads': ['▁', 'b', 'ed', 's', 'p', 'read', 's'],\n", - " 'beech': ['▁', 'be', 'e', 'ch'],\n", - " 'been': ['▁', 'been'],\n", - " 'before': ['▁', 'before'],\n", - " 'befriended': ['▁', 'be', 'friend', 'ed'],\n", - " 'began': ['▁', 'be', 'g', 'an'],\n", - " 'begin': ['▁', 'be', 'g', 'in'],\n", - " 'beginner': ['▁', 'be', 'g', 'in', 'n', 'er'],\n", - " 'beginning': ['▁', 'be', 'g', 'in', 'n', 'ing'],\n", - " 'begins': ['▁', 'be', 'g', 'in', 's'],\n", - " 'begun': ['▁', 'be', 'g', 'un'],\n", - " 'behan': ['▁', 'be', 'h', 'an'],\n", - " 'behave': ['▁', 'be', 'have'],\n", - " 'behaviour': ['▁', 'be', 'h', 'a', 'vi', 'our'],\n", - " 'behind': ['▁', 'behind'],\n", - " 'beier': ['▁', 'be', 'i', 'er'],\n", - " 'being': ['▁', 'being'],\n", - " 'belgian': ['▁', 'be', 'l', 'g', 'i', 'an'],\n", - " 'belgium': ['▁', 'be', 'l', 'giu', 'm'],\n", - " 'belgrade': ['▁', 'be', 'l', 'gr', 'a', 'de'],\n", - " 'belief': ['▁', 'be', 'li', 'e', 'f'],\n", - " 'believe': ['▁', 'believe'],\n", - " 'believed': ['▁', 'believed'],\n", - " 'believes': ['▁', 'believe', 's'],\n", - " 'bell': ['▁', 'be', 'll'],\n", - " \"bell's\": ['▁', 'be', 'll', \"'\", 's'],\n", - " 'belmondo': ['▁', 'be', 'l', 'mon', 'do'],\n", - " 'belonged': ['▁', 'be', 'long', 'ed'],\n", - " 'belongs': ['▁', 'be', 'long', 's'],\n", - " 'below': ['▁', 'be', 'low'],\n", - " 'belt': ['▁', 'be', 'l', 't'],\n", - " 'ben': ['▁', 'be', 'n'],\n", - " 'bench': ['▁', 'be', 'n', 'ch'],\n", - " 'benches': ['▁', 'be', 'n', 'che', 's'],\n", - " 'bend': ['▁', 'b', 'end'],\n", - " 'bending': ['▁', 'b', 'end', 'ing'],\n", - " 'benefits': ['▁', 'be', 'ne', 'f', 'its'],\n", - " 'bent': ['▁', 'b', 'ent'],\n", - " 'ber': ['▁', 'be', 'r'],\n", - " 'berlin': ['▁', 'berlin'],\n", - " \"berlin's\": ['▁', 'berlin', \"'\", 's'],\n", - " 'bernhard': ['▁', 'be', 'r', 'n', 'hard'],\n", - " 'berry': ['▁', 'be', 'rry'],\n", - " 'bertrand': ['▁', 'bert', 'r', 'and'],\n", - " 'beset': ['▁', 'be', 'set'],\n", - " 'beside': ['▁', 'be', 'side'],\n", - " 'best': ['▁', 'best'],\n", - " 'best-seller': ['▁', 'best', '-', 's', 'ell', 'er'],\n", - " 'bet': ['▁', 'be', 't'],\n", - " 'betjeman': ['▁', 'be', 't', 'je', 'man'],\n", - " 'betrayal': ['▁', 'be', 'tr', 'a', 'y', 'al'],\n", - " 'betrayed': ['▁', 'be', 'tr', 'a', 'y', 'ed'],\n", - " 'better': ['▁', 'better'],\n", - " 'better-': ['▁', 'better', '-'],\n", - " \"betti's\": ['▁', 'be', 't', 't', 'i', \"'\", 's'],\n", - " 'between': ['▁', 'between'],\n", - " 'bevel': ['▁', 'be', 've', 'l'],\n", - " 'bevelled': ['▁', 'be', 'v', 'ell', 'ed'],\n", - " 'beware': ['▁', 'be', 'w', 'are'],\n", - " 'bewildered': ['▁', 'be', 'w', 'il', 'd', 'er', 'ed'],\n", - " 'beyond': ['▁', 'beyond'],\n", - " 'bidet': ['▁', 'b', 'i', 'de', 't'],\n", - " 'big': ['▁', 'big'],\n", - " 'bigger': ['▁', 'big', 'g', 'er'],\n", - " 'biggest': ['▁', 'big', 'g', 'est'],\n", - " 'bill': ['▁', 'b', 'ill'],\n", - " 'bills': ['▁', 'b', 'ill', 's'],\n", - " 'binding': ['▁', 'b', 'in', 'd', 'ing'],\n", - " 'biological': ['▁', 'b', 'i', 'o', 'lo', 'g', 'ical'],\n", - " 'bird': ['▁', 'b', 'i', 'r', 'd'],\n", - " 'birds': ['▁', 'b', 'i', 'r', 'd', 's'],\n", - " 'bishop': ['▁', 'b', 'is', 'hop'],\n", - " 'bit': ['▁', 'b', 'it'],\n", - " 'bite': ['▁', 'b', 'it', 'e'],\n", - " 'bits': ['▁', 'b', 'its'],\n", - " 'bitter-sweet': ['▁', 'b', 'it', 'ter', '-', 's', 'we', 'e', 't'],\n", - " 'bitterest': ['▁', 'b', 'it', 'ter', 'est'],\n", - " 'bitterly': ['▁', 'b', 'it', 'ter', 'ly'],\n", - " 'bituminized': ['▁', 'b', 'it', 'um', 'in', 'i', 'z', 'ed'],\n", - " 'black': ['▁', 'bl', 'a', 'ck'],\n", - " 'black-': ['▁', 'bl', 'a', 'ck', '-'],\n", - " 'black-listed': ['▁', 'bl', 'a', 'ck', '-', 'li', 'st', 'ed'],\n", - " 'blackbird': ['▁', 'bl', 'a', 'ck', 'b', 'i', 'r', 'd'],\n", - " 'blacks': ['▁', 'bl', 'a', 'ck', 's'],\n", - " 'blame': ['▁', 'bl', 'a', 'me'],\n", - " 'blamed': ['▁', 'bl', 'am', 'ed'],\n", - " 'blander': ['▁', 'bl', 'and', 'er'],\n", - " 'blank': ['▁', 'bl', 'an', 'k'],\n", - " 'blend': ['▁', 'bl', 'end'],\n", - " 'blight': ['▁', 'b', 'light'],\n", - " 'blind': ['▁', 'bl', 'in', 'd'],\n", - " 'blinked': ['▁', 'bl', 'in', 'k', 'ed'],\n", - " 'block': ['▁', 'block'],\n", - " 'blocks': ['▁', 'block', 's'],\n", - " 'bloem-': ['▁', 'b', 'lo', 'e', 'm', '-'],\n", - " 'blond': ['▁', 'bl', 'on', 'd'],\n", - " 'blood': ['▁', 'b', 'lo', 'od'],\n", - " 'bloodstained': ['▁', 'b', 'lo', 'od', 's', 'tain', 'ed'],\n", - " 'bloody': ['▁', 'b', 'lo', 'od', 'y'],\n", - " 'blouse': ['▁', 'b', 'lo', 'use'],\n", - " 'blouses': ['▁', 'bl', 'ous', 'es'],\n", - " 'blow': ['▁', 'b', 'low'],\n", - " 'blowflies': ['▁', 'b', 'low', 'f', 'l', 'ies'],\n", - " 'blown': ['▁', 'bl', 'own'],\n", - " 'blue': ['▁', 'bl', 'ue'],\n", - " 'blunt': ['▁', 'bl', 'un', 't'],\n", - " 'bluntly': ['▁', 'bl', 'un', 't', 'ly'],\n", - " 'bluster': ['▁', 'bl', 'u', 'ster'],\n", - " 'board': ['▁', 'board'],\n", - " 'boat': ['▁', 'bo', 'at'],\n", - " 'boat-train': ['▁', 'bo', 'at', '-', 'train'],\n", - " 'bobby': ['▁', 'bo', 'b', 'by'],\n", - " 'bodies': ['▁', 'bo', 'd', 'ies'],\n", - " 'body': ['▁', 'body'],\n", - " 'boeing': ['▁', 'bo', 'e', 'ing'],\n", - " 'bogy': ['▁', 'bo', 'g', 'y'],\n", - " 'boiled': ['▁', 'bo', 'il', 'ed'],\n", - " 'boils': ['▁', 'bo', 'il', 's'],\n", - " 'bold': ['▁', 'b', 'old'],\n", - " 'boldly': ['▁', 'b', 'old', 'ly'],\n", - " 'bolt': ['▁', 'bo', 'l', 't'],\n", - " 'bolted': ['▁', 'bo', 'l', 'ted'],\n", - " 'bomb': ['▁', 'bomb'],\n", - " 'bombay': ['▁', 'bomb', 'a', 'y'],\n", - " 'bombed': ['▁', 'bomb', 'ed'],\n", - " 'bombers': ['▁', 'bomb', 'ers'],\n", - " 'bonded': ['▁', 'b', 'on', 'd', 'ed'],\n", - " 'bone': ['▁', 'b', 'one'],\n", - " 'bones': ['▁', 'b', 'one', 's'],\n", - " 'bonn': ['▁', 'b', 'on', 'n'],\n", - " \"bonn's\": ['▁', 'b', 'on', 'n', \"'\", 's'],\n", - " 'book': ['▁', 'book'],\n", - " 'booklet': ['▁', 'book', 'le', 't'],\n", - " 'books': ['▁', 'book', 's'],\n", - " 'booming': ['▁', 'bo', 'o', 'm', 'ing'],\n", - " 'border': ['▁', 'b', 'order'],\n", - " 'bore': ['▁', 'bo', 're'],\n", - " 'bored': ['▁', 'b', 'or', 'ed'],\n", - " 'boredom': ['▁', 'bo', 're', 'do', 'm'],\n", - " 'bores': ['▁', 'bo', 're', 's'],\n", - " 'born': ['▁', 'b', 'or', 'n'],\n", - " 'borough': ['▁', 'bo', 'rough'],\n", - " 'borrow': ['▁', 'b', 'or', 'ro', 'w'],\n", - " 'borstal': ['▁', 'b', 'or', 'st', 'al'],\n", - " 'bosoms': ['▁', 'bo', 'so', 'm', 's'],\n", - " 'bossed': ['▁', 'bo', 's', 's', 'ed'],\n", - " 'bosses': ['▁', 'bo', 's', 'se', 's'],\n", - " 'both': ['▁', 'both'],\n", - " 'bottle': ['▁', 'bo', 'ttle'],\n", - " 'bottom': ['▁', 'bo', 't', 'to', 'm'],\n", - " 'bought': ['▁', 'bo', 'ug', 'h', 't'],\n", - " 'boun': ['▁', 'bo', 'un'],\n", - " 'bound': ['▁', 'b', 'ound'],\n", - " 'boutiques': ['▁', 'b', 'out', 'i', 'q', 'ue', 's'],\n", - " 'bow': ['▁', 'bo', 'w'],\n", - " 'bow-street': ['▁', 'bo', 'w', '-', 'st', 're', 'e', 't'],\n", - " 'bowed': ['▁', 'bo', 'w', 'ed'],\n", - " 'bowing': ['▁', 'bo', 'w', 'ing'],\n", - " 'bows': ['▁', 'bo', 'w', 's'],\n", - " 'box': ['▁', 'bo', 'x'],\n", - " 'boxes': ['▁', 'bo', 'x', 'es'],\n", - " 'boxing': ['▁', 'bo', 'x', 'ing'],\n", - " 'boy': ['▁', 'bo', 'y'],\n", - " 'boycotted': ['▁', 'bo', 'y', 'cott', 'ed'],\n", - " 'boycotting': ['▁', 'bo', 'y', 'cott', 'ing'],\n", - " 'boyd-orr': ['▁', 'bo', 'y', 'd', '-', 'or', 'r'],\n", - " 'boyle': ['▁', 'bo', 'y', 'le'],\n", - " 'boys': ['▁', 'bo', 'y', 's'],\n", - " 'braces': ['▁', 'br', 'a', 'ce', 's'],\n", - " 'brain': ['▁', 'b', 'rain'],\n", - " 'brain-activity': ['▁', 'b', 'rain', '-', 'act', 'i', 'v', 'ity'],\n", - " 'brain-children': ['▁', 'b', 'rain', '-', 'children'],\n", - " 'brains': ['▁', 'b', 'rain', 's'],\n", - " 'brandy': ['▁', 'br', 'and', 'y'],\n", - " 'brash': ['▁', 'br', 'as', 'h'],\n", - " 'brass': ['▁', 'br', 'as', 's'],\n", - " 'brauchitsch': ['▁', 'br', 'a', 'u', 'ch', 'its', 'ch'],\n", - " 'breach': ['▁', 'br', 'each'],\n", - " 'bread-and-butter': ['▁', 'b', 'read', '-', 'and', '-', 'but', 'ter'],\n", - " 'break': ['▁', 'b', 're', 'a', 'k'],\n", - " 'breaking': ['▁', 'b', 're', 'a', 'k', 'ing'],\n", - " 'breaks': ['▁', 'b', 're', 'a', 'k', 's'],\n", - " 'breath': ['▁', 'b', 're', 'a', 'th'],\n", - " 'breathing': ['▁', 'b', 're', 'a', 'thing'],\n", - " 'breathless': ['▁', 'b', 're', 'a', 'th', 'less'],\n", - " 'breeding': ['▁', 'b', 're', 'ed', 'ing'],\n", - " 'breezily': ['▁', 'b', 're', 'e', 'z', 'i', 'ly'],\n", - " 'brehm': ['▁', 'b', 're', 'h', 'm'],\n", - " 'brella': ['▁', 'br', 'ell', 'a'],\n", - " 'brenda': ['▁', 'br', 'end', 'a'],\n", - " 'brendan': ['▁', 'br', 'end', 'an'],\n", - " \"brendan's\": ['▁', 'br', 'end', 'an', \"'\", 's'],\n", - " 'brentano': ['▁', 'br', 'ent', 'a', 'no'],\n", - " 'brezhnev': ['▁', 'b', 're', 'z', 'h', 'ne', 'v'],\n", - " 'brian': ['▁', 'br', 'i', 'an'],\n", - " 'bridal': ['▁', 'br', 'id', 'al'],\n", - " 'bride': ['▁', 'br', 'i', 'de'],\n", - " 'brief': ['▁', 'brief'],\n", - " 'brief-': ['▁', 'brief', '-'],\n", - " 'briefcase': ['▁', 'brief', 'case'],\n", - " 'briefing': ['▁', 'brief', 'ing'],\n", - " 'brigadiers': ['▁', 'br', 'i', 'g', 'ad', 'i', 'ers'],\n", - " 'bright': ['▁', 'b', 'right'],\n", - " 'brighter': ['▁', 'b', 'right', 'er'],\n", - " 'brightly': ['▁', 'b', 'right', 'ly'],\n", - " \"brighton's\": ['▁', 'b', 'right', 'on', \"'\", 's'],\n", - " 'brilliant': ['▁', 'br', 'ill', 'i', 'ant'],\n", - " 'brilliantly': ['▁', 'br', 'ill', 'i', 'ant', 'ly'],\n", - " 'bring': ['▁', 'br', 'ing'],\n", - " 'brings': ['▁', 'br', 'ing', 's'],\n", - " 'bristled': ['▁', 'br', 'is', 't', 'led'],\n", - " 'bristol': ['▁', 'br', 'is', 'to', 'l'],\n", - " 'britain': ['▁', 'britain'],\n", - " \"britain's\": ['▁', 'britain', \"'\", 's'],\n", - " 'british': ['▁', 'british'],\n", - " 'british-owned': ['▁', 'british', '-', 'own', 'ed'],\n", - " 'britishers': ['▁', 'british', 'ers'],\n", - " 'brittle': ['▁', 'br', 'i', 'ttle'],\n", - " 'broad': ['▁', 'b', 'ro', 'ad'],\n", - " 'broadcast': ['▁', 'b', 'ro', 'ad', 'c', 'a', 'st'],\n", - " 'broadcasting': ['▁', 'b', 'ro', 'ad', 'c', 'a', 'st', 'ing'],\n", - " 'broke': ['▁', 'b', 'ro', 'ke'],\n", - " 'broken': ['▁', 'b', 'ro', 'k', 'en'],\n", - " 'bronx': ['▁', 'br', 'on', 'x'],\n", - " \"brook's\": ['▁', 'b', 'ro', 'o', 'k', \"'\", 's'],\n", - " 'brother': ['▁', 'brother'],\n", - " 'brother-': ['▁', 'brother', '-'],\n", - " 'brother-in-law': ['▁', 'brother', '-', 'in', '-', 'law'],\n", - " 'brought': ['▁', 'brought'],\n", - " 'brown': ['▁', 'brown'],\n", - " \"brown's\": ['▁', 'brown', \"'\", 's'],\n", - " 'bru\"cke': ['▁', 'br', 'u', '\"', 'ck', 'e'],\n", - " 'bruce': ['▁', 'br', 'u', 'ce'],\n", - " 'bruno': ['▁', 'br', 'un', 'o'],\n", - " 'brunswick': ['▁', 'br', 'un', 's', 'w', 'i', 'ck'],\n", - " 'brussels': ['▁', 'br', 'us', 's', 'el', 's'],\n", - " 'brutal': ['▁', 'br', 'u', 't', 'al'],\n", - " 'bryan': ['▁', 'br', 'y', 'an'],\n", - " 'bu\"ckerei': ['▁', 'b', 'u', '\"', 'ck', 'e', 're', 'i'],\n", - " 'buck': ['▁', 'b', 'u', 'ck'],\n", - " 'buckingham': ['▁', 'b', 'u', 'ck', 'ing', 'h', 'am'],\n", - " 'buckley': ['▁', 'b', 'u', 'ck', 'le', 'y'],\n", - " 'budge': ['▁', 'b', 'ud', 'g', 'e'],\n", - " 'budgerigar': ['▁', 'b', 'ud', 'g', 'er', 'i', 'g', 'ar'],\n", - " 'budget': ['▁', 'budget'],\n", - " 'budgetary': ['▁', 'budget', 'ary'],\n", - " 'budgette': ['▁', 'budget', 'te'],\n", - " 'buganda': ['▁', 'b', 'ug', 'and', 'a'],\n", - " 'build': ['▁', 'b', 'u', 'il', 'd'],\n", - " 'building': ['▁', 'building'],\n", - " ...}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lex" - ] - } - ], - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/07-try-gtn.ipynb b/notebooks/07-try-gtn.ipynb deleted file mode 100644 index 4ef444b..0000000 --- a/notebooks/07-try-gtn.ipynb +++ /dev/null @@ -1,202 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import gtn\n", - "from IPython.display import display, Image" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Make some graphs:\n", - "g1 = gtn.Graph()\n", - "g1.add_node(True) # Add a start node\n", - "g1.add_node() # Add an internal node\n", - "g1.add_node(False, True) # Add an accepting node\n", - "\n", - "\n", - "# Add arcs with (src node, dst node, label):\n", - "g1.add_arc(0, 1, 1)\n", - "g1.add_arc(0, 1, 2)\n", - "g1.add_arc(1, 2, 1)\n", - "g1.add_arc(1, 2, 0)\n", - "\n", - "\n", - "g2 = gtn.Graph()\n", - "g2.add_node(True, True)\n", - "g2.add_arc(0, 0, 1)\n", - "g2.add_arc(0, 0, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gtn.draw(g1, \"g1.png\")\n", - "gtn.draw(g2, \"g2.png\")\n", - "display(Image(\"g1.png\"), Image(\"g2.png\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intersect = gtn.intersect(g1, g2)\n", - "gtn.draw(intersect, \"intersect.png\")\n", - "Image(\"intersect.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 0.0, 1.0, 0.0]\n" - ] - } - ], - "source": [ - "score = gtn.viterbi_score(intersect)\n", - "gtn.backward(score)\n", - "\n", - "# print gradients of arc weights \n", - "print(g1.grad().weights_to_list()) " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 0.0, 0.5, 0.5]\n" - ] - } - ], - "source": [ - "import gtn\n", - "\n", - "# Make some graphs:\n", - "g1 = gtn.Graph()\n", - "g1.add_node(True) # Add a start node\n", - "g1.add_node() # Add an internal node\n", - "g1.add_node(False, True) # Add an accepting node\n", - "\n", - "# Add arcs with (src node, dst node, label):\n", - "g1.add_arc(0, 1, 1)\n", - "g1.add_arc(0, 1, 2)\n", - "g1.add_arc(1, 2, 1)\n", - "g1.add_arc(1, 2, 0)\n", - "\n", - "g2 = gtn.Graph()\n", - "g2.add_node(True, True)\n", - "g2.add_arc(0, 0, 1)\n", - "g2.add_arc(0, 0, 0)\n", - "\n", - "# Compute a function of the graphs:\n", - "intersection = gtn.intersect(g1, g2)\n", - "score = gtn.forward_score(intersection)\n", - "\n", - "# Visualize the intersected graph:\n", - "gtn.draw(intersection, \"intersection.pdf\")\n", - "\n", - "# Backprop:\n", - "gtn.backward(score)\n", - "\n", - "# Print gradients of arc weights \n", - "print(g1.grad().weights_to_list()) # [1.0, 0.0, 1.0, 0.0]" - ] - }, - { - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb deleted file mode 100644 index 841a37d..0000000 --- a/notebooks/Untitled.ipynb +++ /dev/null @@ -1,385 +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\n", - "from torch import nn\n", - "\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 text_recognizer.datasets import IamLinesDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToPILImage\", \"args\": None}, \n", - " #{\"type\": \"RandomResizeCrop\", \"args\": None}, \n", - " {\"type\": \"RandomRotation\", \"args\": {\"degrees\": 0.8, \"fill\": 0}}, \n", - " {\"type\": \"ColorJitter\", \"args\": {\"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": 0.5, \"hue\": 0.5}}, \n", - " {\"type\": \"ToTensor\", \"args\": None}, \n", - " {\"type\": \"Normalize\", \"args\": {\"mean\": [0.912], \"std\": 0.168}},\n", - " #{\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.98, 1.0]}}\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "target_transforms = [\n", - " {\"type\": \"ToLower\", \"args\": None},\n", - " {\"type\": \"ToCharcters\", \"args\": {\"pad_token\": \"_\", \"eos_token\": \"\"}},\n", - " {\"type\": \"ToWordPieces\", \"args\": {\n", - " \"num_features\": 64, \n", - " \"tokens\": \"iamdb_1kwp_tokens_1000.txt\", \n", - " \"lexicon\": \"iamdb_1kwp_lex_1000.txt\",\n", - " \"use_words\": False,\n", - " \"prepend_wordsep\": False,\n", - " }\n", - " }\n", - " \n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets.transforms import ToText" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-24 21:43:47.687 | DEBUG | text_recognizer.datasets.transforms:__init__:201 - Using data dir: /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb\n" - ] - } - ], - "source": [ - "to_text = ToText(\n", - " num_features= 64, \n", - " tokens=\"iamdb_1kwp_tokens_1000.txt\", \n", - " lexicon=\"iamdb_1kwp_lex_1000.txt\",\n", - " use_words=False,\n", - " prepend_wordsep= False,)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-24 21:42:02.700 | DEBUG | text_recognizer.datasets.transforms:__init__:201 - Using data dir: /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Lines Dataset\n", - "Number classes: 54\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f', 16: 'g', 17: 'h', 18: 'i', 19: 'j', 20: 'k', 21: 'l', 22: 'm', 23: 'n', 24: 'o', 25: 'p', 26: 'q', 27: 'r', 28: 's', 29: 't', 30: 'u', 31: 'v', 32: 'w', 33: 'x', 34: 'y', 35: 'z', 36: ' ', 37: '!', 38: '\"', 39: '#', 40: '&', 41: \"'\", 42: '(', 43: ')', 44: '*', 45: '+', 46: ',', 47: '-', 48: '.', 49: '/', 50: ':', 51: ';', 52: '?', 53: '_'}\n", - "Data: (1861, 28, 952)\n", - "Targets: (1861, 97)\n", - "\n" - ] - } - ], - "source": [ - "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform, target_transform=target_transforms, lower=True)\n", - "dataset.load_or_generate_data()\n", - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "but▁since▁starting▁salaries▁would▁depend▁on▁grade▁a\n", - "or▁b▁in▁the▁finals▁next▁may,▁and▁since▁mating\n", - "prospects▁would▁depend▁upon▁salaries,▁scholarship▁for\n", - "these▁fine▁young▁people▁was▁closely▁geared▁to\n", - "economic▁and▁biological▁ends▁which,▁essentially,\n", - "were▁really▁means.▁so,▁seeing▁them▁revolve▁in\n", - "circles,▁harry▁had▁the▁feeling▁that▁moke▁(or▁what\n", - "moke▁consciously▁or▁unconsciously▁symbolised,▁any-\n", - "way▁in▁harry's▁mind)▁had▁these▁splendid▁young\n", - "people▁by▁the▁short▁hairs,▁and▁was▁diverting▁them▁...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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4AwsLC2hvb8ejjz6K1157DYqioKioCAUFBQgGg1hZWckwZtm8idfQyoPCn8e2sSpKDocDMzMzGecwwUGWZTWk58qVK1hdXc0IRWHXZOcxI5rNh1bIm+hRI57LxBT+PJ65uTnMz88jFovpCkG8MW80GlFQUICdO3fi/PnzGBwcVO+3y+XC1772Nbz66qvo6+vLWAfZvL60tvFj449xOp3YsWMHjh07hrm5OSQSCVy5cgVerxcNDQ2QZRmpVAqyLOPIkSNwuVwYHh7GlStX8NBDD+Hw4cP4wQ9+gPHxcV2RraGhAfPz8+jt7QUAOBwO7N+/H3l5ebhy5QpmZmZQVlaGqqoqvP7664jH45AkCV6vF+l0GsFgUG2vtrYWFRUViMVimJqawp07d1BeXo4jR47AarXi3XffxcjIiPo7YzQa8Qd/8AfYu3cvkskkent74ff7sWvXLjz99NP4y7/8S8zOzqrrX8trho2DVfUKBoOqQKWXGJkXvsVjZFlWryU+6wRBEARBEARBPPjcU3gUj6IoMJvN+OM//mOsrq7i5s2byMvLw44dO+B2u7G0tISrV6/C4/HAbrejtLQUxcXFeO+99zA+Po7Nmzdj8+bNsFgsaGlpQUNDA15//XVEo1FIkgS73Q6v1wu73Y4bN26gvb0diURC03jWM06ZgShJErq6umAymXDjxo2MpL+3b99W872UlJSgpaUFBoMBH3/8sep543K5UFRUhPz8fIyNjSEWiyEcDmcY5DabDd/61rcwOzuLZDKJkpIStLa2ora2Fqurq+jp6VGNbQbzPOHDNbTmesuWLdi5cycmJiYwMDCARCKh5gsR2+ON4j179iAajWJkZASLi4sZYSDseDZPWiFPfD9YH/k8Oe3t7aipqVE9lQoKCpBOp2E2m7Fp0ybMzs7ixRdfxNzcHE6dOpVRHctgMMBms6kiTDweRzQaVdsvLy9XxRheqGN9MhqN2LJlCxobG+F2u/G///u/qhHO7kdVVRX27duH1dVV5OXlYWlpCWNjYygoKEBjYyO2bt0Kg8GAubk5HDt2DC6XC/n5+QiFQhgZGckQz+x2O0pKSrBlyxZ4vV7Mzc2ht7cXi4uLqnjS2NiI/fv3IxqNYmBgABcuXMD8/PyakC6WYFsrjIy/pywsqb29HS+99BIKCgpgNBqRSCRw5swZJBIJdHV1ob+/H5OTk+qa1PKayhUmxvLClizLKCgowNTUlLoO5ubmUFhYiOLiYgSDQUSjUTQ3N6OsrAyDg4MYHh5GRUUF9uzZg1//+teoqanB9u3b0d/fj6GhITgcDrjdbgwPD8Plcqn5cpLJJI4fP47nn38e+fn5iEajmJqagsvlQmlpKU6cOIFIJAKj0Yi2tjZIkoTp6Wl13XR1dWHfvn24efMmwuEwjEYjzGYzurq6cObMGbVdFqLGwjp37tyJ3/zmN0in06irq8O+fftQXFyMt956C1NTUxkCmyiysLW8detWtLa2IpVKIRKJ4NixY2quKq15lmVZnWv228aHZfJrnSAIgiAIgiCILxe/VXiU0WhEXl4e9u3bBwC4ceMGJiYmUFZWhuXlZczMzKgGbVdXl1o1anZ2Fjdu3IDL5UIoFMLy8jIqKytRX1+PUCiEnp4eVFdXo7y8HOXl5WhoaIDJZEIwGMTFixfXVD7SC/8AMpPAGgx3E7iOjY1heHhYzSOiKAoWFxcRCATQ1NSEoqIiLC0tIRQKoaSkBKFQCCaTCbW1tairq4PJZEJ1dTUkScKJEyewsLCAZDIJk8mE4uJi1NfX49///d9hs9nQ0tICu92OhYUFzM/Po7CwMCNUQpZlmM1mKIqCSCQCr9eLlpYWyLKMqakp3Lx5E6urq5BlGXv37sXy8jIWFhYycu8wnE4nrFYrACAajSIUCsFqtaK1tRWDg4Pw+/1qjpGlpaU1niai2CO2z2DGo9lshs/nQ1tbG3p7ezEwMIB0Oo2tW7fCYrEgEokgLy8P9fX1eOyxx3D58mWk02k13Cw/Px+SJKmeQ9PT05iamkI0GoXBcDfhbVdXF8LhMBKJBFZXV2E0GuF2u7GysoLl5WUYjUZUVVVh06ZNGBkZWTOe+vp6tLa2IhqN4vbt26isrITNZkN7ezu8Xi8KCgowPz+PZDIJn8+nemcYDAZMTEzA4/Hg6tWriEQiqKioUJNpR6NRLC8vw+Vywe12Y3V1FUVFRWhqakJzczNmZmYwMzOjXoOFAEqShKKiIlitVgQCATXMy2QyaeYRYsiyjN27d6O6uhq/+MUvsLS0BKPRCJvNhlQqhaamJpw/f14NQSwrK4MkSbh48aIaYqPXth681w37nEgkkJeXp56fSCRU0aO8vBw7d+7E9u3bEQgEcPv2bZhMJuzYsQNzc3MoLy+H0WjE5s2bkZeXh/LycpSWlqKwsBCvvPIKOjs7YTAYEIlE1MTgZrMZN27cwNzcHCYnJ+H1emEymXDhwgVVGKyqqoLf71efZ6PRiM7OTqyurqrhjlarFZs2bUJHRweSySQCgQBCoZAq9lqtVuzduxeDg4OYnp6Gz+eD0+mEJElYXFzEuXPnMgQbJqjw+YpkWUZraysqKiqwuLiIxcVF+Hw+2O121UvG5XLB5XLh1q1balvbtm1DPB5HIBBQnw2tpNm8RxgJOARBEARBEATx5WDDog2ruJROpyHLMurq6lBTU4NTp05hdHQUsVgMs7Oz6O/vx8zMDC5duqQaqk6nE4uLi2qYQmVlJaampjA9PY3GxkYUFRVhcnISHo9H9cjZsmULysrKEIlEYLfbcfTo0YyQBL7iCvsrhksxrwWDwYDS0lIMDAxgdnY24xiWv4KVXz558qTqKTM/Pw+z2YzNmzejsrISd+7cUcWZ0dFRAHeNV+Zlsby8jFu3bqG1tRWlpaWYnp7GqVOnsLKygsbGRtXwNBjuVtqyWCwYHR2F3W7Hzp07UVtbC0VRVIHn6tWrKCoqQl1dHd555x1MTU3BYDDAYrGoQkZJSQkqKythsVgQj8eRSCQwMjKC4uJiSJKEQCCAeDyO6upqxGIx1WDlq/soiqJW82JofWZzbLVa4fV6UVZWhv/4j/+AwWBARUUFqquroSgKAoEA8vPz8dxzz6GxsRGxWAz19fWYn59HMBhUhYrZ2VlIkoSFhYWMkKe2tjZUVlZicHAQTqcTlZWVKCgoQFVVlRri5Ha74fV6YTQaMTk5iaamJkSjUQwPDyMvLw+NjY3w+Xx47733MDQ0hGg0ClmWsW3bNpjNZty6dQuXL19GWVkZ7HY7mpubsXXrVqysrECWZfh8PkSjUfT398Pj8WDXrl3weDw4f/48rl69ipKSElU8qK+vx7Zt2xCNRnH06FEEg0Hs27dPNbKZ0d7Z2amKmul0WhV2RkdHkU6n4Xa74XA4VEEzkUiofZmfn8cHH3yAWCwGWZbVcLz8/HwMDg4ilUqhvr4edXV1mJ6eRm9v7xqPDXYP+b9aMFHCarXC7XajoqIC6XQa9fX1+OCDD1Rvm5WVFdy5cwd1dXX4yle+ApPJhGPHjiEYDKKtrQ3t7e04e/YsysvLEQ6H4fV6YbFY4HQ6kZ+fj+bmZvzsZz/D3r17MTU1hUAggImJCUSjUVy6dAmDg4OqJ5MkSTCbzWrIYX5+PsxmsyrimUwmNQHy4uIiJicnEY/HsWnTJjQ1NaGurg7Ly8s4c+YMQqGQKmIWFhaiuroafX192LJlC0pKSmAwGDA2NoY7d+4gEAhkeOxZLBZIkpQRMun1elFTU4NkMomzZ89ieXkZbrcb5eXl8Pv96jNqs9mwvLyM6elp1StxampKXf8mk0mtfiXeHz40iyAIgiAIgiCIB58NizZutxuNjY2q18CBAwdUg5ixsLCAxcVFNXmpw+HA7du3YTab4XQ6UVdXh9bWViwsLOBnP/sZ7HY7FEXB7OwsVldX8fzzz+Py5cvo6enBlStX0N7eDovFgvPnz0OSJFU0UpS7JbKj0SiAteE9vMHDhAkWwmCz2dR8GABUbwVFUTAxMQFFUdDR0YH33nsPiUQCPp8PoVAIV65cweXLlzEzM4MXXngBW7duVb1bysvL0dzcjE8//RSKosDn82F6ehojIyOIxWKwWq2IRCLYsWMHiouLkZeXh4KCAoTDYbz22mvYuXMnHn30UbzxxhuYmZlBc3Mzjhw5gvHxcWzfvh1zc3NIp9MoKSmBzWZDNBrF0NAQjEYj9u/fj8bGRszMzCAQCMDlcsFkMqGhoQEXLlzA9PQ0ampq0NHRgStXrmQIXEw4YjlyeI8PNp+iFw7zTjCbzUgkEnA4HLBarXjsscfgcrlw4cIF3Lp1CwcOHMBzzz2HUCiE9vZ2OBwOdHd34/Tp0+jr60MgEMDq6mqGN4jBYIDdbseLL76In//85xgcHMSmTZvQ2NiIuro6+Hw+LC4uYnR0FLt27YLD4UAkEsH27dvx8MMPI5VK4R/+4R/g9XrhdDoRDocxMzODvLw8BAIBtLS0wOFw4NatW/jkk09QUFCAI0eO4OjRo9i9ezd8Pp/qnSFJEp544gncvn0bk5OTGB0dVcWS2dlZTE5OAgAKCgpQX18Pg8GAV199FbFYDABw4sQJdUwulwvt7e146qmn8JOf/EQV3nbt2oVt27bhxz/+MRKJBHbu3Inm5mb09fWhv78fCwsLMBqNuHPnDrZs2aJ+jsfjqtcXm0eXy4X6+nrYbDZ8+umnMJvN6vrPy8tDIpFAJBJRw6ZYzhRR0GFiKKvUtHv3brS1tWFkZASdnZ2wWCxqKE8wGMTZs2cxPz+PZ599Fv/93/8Nv9+vPmv5+fnYvn07/vVf/xV79uzBQw89hGAwiJ6eHoyOjuK73/0u3G43PB4Pjh8/jkuXLiESiagV1RhGoxGBQAAzMzPqGi0rK8Pw8DDm5+eRl5cHt9uNZDKJTz/9FH/xF3+BM2fOYHZ2Fna7HS6XC06nE729vRgZGUF+fj6Ki4thMplgtVoxNjaGZ599Frdu3cLZs2cRj8fh8Xjw0UcfqWKmJEnIz89HWVkZrFYrLl++rM7fjh07VMF2ZWUFRUVF8Hq9yMvLQywWw8GDB+Hz+dDb24vdu3fj5z//OaqqqlBYWIjx8XE1mbfJZMKHH36IVCql/laxfEGxWAyRSCQjBJAgCIIgCIIgiAeXDYs2Tz75JKLRKEpLS2G1WvHmm29mCDbAZzlamOcGC3nq7e2Fz+dDVVUVbt26hf7+fvX4ixcvoru7GyMjI5AkSfUkAO6G+uzatQt//dd/jVAopHrn9PX1YXR0FKurq1lDpIDPqhedOXMGVVVVSKfT+PDDDzOS75aXl6s5UDo6OhAMBtW3+3fu3MGdO3cyEpC+8847KCwsxM6dO+FyuTA/P48333wTN27cQDKZRE9PD1588UV89atfxerqqhqC9fHHH+Ps2bN45plnoCgKfv3rXyMSiaCzsxO9vb2YnJyELMvwer2orKyELMuora1VhQu3241EIoGzZ89idHQUJpMJ27Ztw9zcHHp6enD16lWYTCZUVFSgsrIS77//PlKpFIqLiyHLMrq7uzPy2ezfvx+SJOGTTz4BkFlRilU94r2bmIfF7OwsAoEACgoK8KMf/QhLS0t455138Mtf/hJzc3PIy8vD9PQ0IpEIXn/9dbz99tt45plnVM8mFjYihu+YzWZUVlaivLwcdrsdL730EiYnJ3HhwgWcPn0af/M3fwOTyQS73Y6uri6Ul5djYWEBIyMj+Jd/+Re89tprKCsrg9/vx8LCAvbu3YvOzk4Eg0GEw2EsLCxgy5YtKCgoQHl5OWpqavDJJ5/g8uXLuH79Oux2O8LhMMLhsJrfSFEUzM/P43/+539w8OBBPP3006o3DQB4PB7EYjGsrq5qGtQsTO3FF1/ET37yE1y8eBGKoqj5c+rq6vDd734XsizjzJkzMBqN2LVrF5566ikYjUa88cYbakLdRx55BB9++CFu3Lih5g361a9+hWg0ilQqBafTidbWVhiNRiwuLqrCT1dXF8bGxtDd3Y2lpaWMe8znLhKTRLOKbdeuXUNzczN++tOfIplMqs+4oiiwWCxobW1Fd3c3BgcHkUgkkE6ncerUKXR3d6sVo44fP46PPvoIyWQS8Xgc6XQa3//+9xGNRvG9730P4XBYMzEze+bYsw7cFZf4/FANDQ346le/ioqKCoTDYXzve9/D0NAQUqkUlpaWcP36dfzyl7/E008/DZ/Ph4WFBQQCAUxOTmJkZAQ9PT1qxabCwkJYrVYkEgnVK48l1N6+fTvi8TgGBgbUfgBAXV0d+vv7kUwm0d7ejieeeAJ+vx/nzp1DZ2cnWlpaUFhYqD5Pv/nNb9DW1oabN28inU5jz549qKqqwg9+8AMoigKHw4H29na18tvi4iKWlpYwMDCA8+fPq8IgQRAEQRAEQRAPLoaNuNlLkqR0dHSgqakJU1NTuHr1qloJiA9r4d/UA1A9E2RZVsOYWF4XlgiYCT1aFYP4pJwAVCOfT0jMo+c5wBJ+7tmzB62trbDZbPD7/bBarYhGo8jLy1PDYbq7u9Hd3a2KCmKVIEmSkEgk1HEBmaELzJC1WCxqn1n5aRZ2sm3bNiQSCVy7dg0GgwH19fX4/ve/j0gkAr/fj+vXr+P8+fMYHh5WjcWpqSmsrKygtrYWjz/+OH74wx+isbERpaWlOHToED788EP86Ec/QjweR2FhIfbu3Yvjx4/DZDKhs7MT1dXVOH78uJpbZvfu3XC73bh9+za6u7uRTqeRTCbVN/u8Ic+LOSy0SpZlyLIMu92uhpgxLx6Wc6W6uhp+vx/hcBgWiyUjDIvNh5hI2e124+WXX8b09DR+9atfYWxsDOFwGGazGTU1NWpi5a6uLpjNZkxMTGB4eBg2mw3/9m//hu985ztYXFyEJElwOBwoKytDOp3G9PQ0YrEYduzYgerqaiSTSVy8eBF+vz/DCGZ9YqF9X//61zE6OoqZmRm0tbVh3759ePnllxEMBpFKpeBwOPD888+jqakJZ86cQXd3txpex8JiNm/eDIPBgH/+539WxQdW+cjr9SIWi6n98Hq9KC4uhs1mg9vtxlNPPaXmk+nr68MHH3yAoaEhWK1WVFVVZYgXBQUFcDgcSKfTmJ+fR3V1Nf7qr/4KP/7xj3Hp0iWEQqE1Ihwv2oiVzfjniD2fbG0oigK3243m5mbs3bsXP/zhD7GysqJ6ZvHiC1tXerB+iBXQtARZMVSPfeariqXTabWfbBv7DeCfU9YeX52pvLwcVqsV4XAYd+7cAQCUlpZi//796OvrU73n+PlpbGzEgQMHUFpaCr/fj2PHjmFqagqKosDj8ai/BU6nE9/85jfxX//1X3j88ccRCARQUVGBaDSKt956a01lPL6CGPvdTCaTqjcSQRAEQRAEQRAPBBcVRekUN25YtGEVV1KplKZowicv5UNrWE4PABkGo1jdRhR/mEDD5+XQSsLLvvPlc/ltfNsOhwMOhwP5+fnweDxIJBLw+/1q7hBFUTLe+GuVe2bjYOMV84YwY4sZiOxYVtGHJZ9lwpUsy3A4HPB4PEilUojH44hEIohEIohGo2oOjWQyiVQqBbPZrCa4LS4uRkdHB2KxmFqxh72pLysrw8jICFKpFDweD8rLy2GxWDA8PIx0Oo0///M/x9mzZ9HT06Pm51hvHrW+814X4v3kQ2lEbw7Ro4IX7oqLi5FIJLCysqJ6brDktExIs1gsMBqNSCaTiMVian6fqakptW0mLrEKXSyBNvOqYOFCetV9WBWrnTt3oq6uDgBw7do1vP/++4jH42qfi4uLsXXrVtTW1qrPiN/vx/j4OFZXV9Wx833j1wmbDybmMJHTZDKhtLQULS0tCIfDGBgYUCs1sf3xeHzNHDLx65/+6Z/w5ptv4uLFi5ifn1fnTu8Z4YVTtva1trPwx46ODnR1deHo0aOqmJFMJtckPBbFGAa7D1rrgz9Xz5NOFJbE55TfJ3qRsXbZbw0v7vAClcViwde+9jV8+umnCAaDmtWgLBYLHA4HzGYzkskklpeX1RBMfn6ZkBgOh1FVVYVnnnkG4+PjOH36NPx+/xqhix8X/9sTi8VItCEIgiAIgiCIB4fPR7SxWq0ZRpFoJDFDhzcwmEHPe+Gw7XxuFV4s4HOo8MYeK3Ws9+ZdHZiOgcf6xwxOu90OAGpYC2/EAdkTf2oZh6LgIRqhvJjFt8ty7vDt8MfxhiU/FiZilJSUqAmGmUAgSRKsVqvq1WEymWCz2WA2mxEKhXDo0CHYbDacP39eFXb4eRfvFY9YeWo99Lw3tCrhMMGBjZefB/48vm3RG4sfA9vH1h//j7WrVe6cb58JJ263G6lUCtPT0wgGg2u8zBwOB4qKilSxLhQKIRQKqaFAYt/5PvOiAn8MS77LwuJCodAawVRL/PJ4PHjuueeQSCTw7rvvYn5+fo14xs5l28QS76Igwm83GAxoa2tDTU0NUqkUPvroI7XcuFZeJLEdNu9aVct4tARDvi2t9rVEHi0RKZtQxOZCkiTU1dXBZrNhcHAwo2S9iLi22PjE+wzcDZn7+te/DqPRiP7+fty8eXNNaJheNTcWYkiiDUEQBEEQBEE8MGiKNhvOacOLMnqGCzPGmCu/nhAAZJa25Y3zZDK5RtDRYqNv5lmYQTKZRDKZRDQazfCG0RJttOZAa3s2YYcfrx56xjz7zgs+bBwA1HAqsV/JZFIVbBTlbkgSe3NfXFyMyspKHD9+HDMzM5oCgCiyaQksesKVuG0jaAlCWkYvvz3b/eL7z4fGbER0isfj8Pv98Pv9a0QDXoxhpZ71vEo2Cpv/aDSKqampNePS8lgyGO4mPW5ubkZ5eTn+8z//E7Ozs7reRLlcX1x3RqMRRUVFqKqqgizLuHLlCsLhsKbHDN83fg2z7xuZp432n52T7Xck2+8Yw2Aw4Pbt25oeNvxzIa5Htp9vj4ljTU1NKCgoUHN5MTHo81g3BEEQBEEQBEE8GNyTaMN7QOiJElpeJuz4bJ4WbJ8YksQ+8wYQEzL4kt48em/gRa8e1g++n6IRLLYjtqm3Xwu+r/xfsS3RAMzmvcDyh4heKWIZdEW5m2tn+/btmJmZwfj4OJaXl9fMh1boE3+vRKOUDzvh22Btagkx/H5xrfA5U8T54L2S9Nah3hxqCQrr3S9RWNTy/NDyltHqSy4ikVb7vODEr2MtA99ms6GyshIdHR3o7e1Vk/Gytvi/DNEzRGt8bO7Zc9fQ0ACr1YpAIICxsTH1GqKIwV9DfK749bSeoMLPcTbxRlzL2dD6jdL6HZmYmMgo763XR73t/HMuyzI8Hg92796NgYEBjI2NYWVlBYC+qKu1jgmCIAiCIAiCePAxrn/IxuANPPZmn+Wl4UOcgM8Mbj7ZL5/0V8tg42H5ZphXDrBWBOE/s2uJIpIWfJiD2A8+D4/oscHyk7B8JPx8aAkQvPHNjFreWyFX41NvLHyOFNYvVsL5F7/4BZaWljTnnnlAiG0yDyV+HkX4vDS8CMOPla0Jdk0twYxPUiwmtd0IbB753CJ6c6cVPsWvMX5ORK8jvj2tsJVcvXrE+6FVuUtPsDEajaisrERTUxPS6TTefvttANAVkvjzeIFNax9DkiQ4nU50dnZiYmICPT09Gflg+HkQw7xEMYjNk9690XuO+Ta0ELdnE6u0kp/z+xRF0RVs9PouCjbsO0ts/eSTTyIUCqG7uxsLCwtrkphrtUMeOARBEARBEATx5WPDnjbMaOTFAMZ6BhTvHcOS6uZyLm+s8cfx12bJaFl1mo28iRaTIfPn5xJCo3UtZmznaqgripJRKloM98l1POKbeDZHbB7dbjf27duHd999F6FQKCNkhgkbTJRh54nGIgt7E4UyHq31warz8EayLMvq9fi+snvCV7FiQpLW/eJhY9WqZJZtHlm7fO4bvo8AMjxDzGazGl7HxswnrubnS09kEfvNj40/b70xM8rLy/Hwww/DZDLhlVdeQTQaVeeQPXvMU45H9OIS54JtT6VSkGUZjzzyCK5cuYKhoSFEo1F1v1Z7DNamljdLrveUR8/LLds91vJg0kNcwyyBOBNX+X3smqIwLXrUVVVV4eGHH8b09DSOHj265lr8GPSeZYIgCIIgCIIgvjxsSLRhrv3JZDLjzXyu5wKfiRNaCYvZccw44s9jBrGW4cKMr2zlhPX6JL6B5z0asoUisGtpee7oCTa8KMK3y7bzHjaiQJXLXGv1ly/nnJ+fj9LSUiSTSVy/fn2NqMLDhJls3jvZEL1QmHDAzyu7Nn/f2FzwHjp8eBYffqTn7aM176KYpzUG0SDnPcLYOXwf2VjY88COF4UAsQKTmPdlo2j1XVEU2Gw2PP3005iamkJPTw9WV1fV6693v7SEJbYGWPv8vRkbG8PY2JjqJaLndST2UfSWEUWUbB4r2e61nieNeI2NwAtczEuNrUdxvHpVtnihp6amBtXV1ZidncXJkyfXrCtxnOIcic8EQRAEQRAEQRAPPhtSOfhwBvENMtvP/vJGr4hotInGWjZBSO/tOjOGmTEt7uO/i/3l0XrzzgsfDNFrQDT0tRDbFj0YxPnL1o64X++6fL8LCwuRn5+PTz75ZE0olGjY6gkzYmlqvv96bYloeS+JFYwYvFjDJ7HNRYjgRRbWL3G9iTlWxPXN+sCLWOx+s6S063ls6OV34b009M4XRSqxHf64AwcO4M6dO7h58ybm5uZ0wwxFbzUx9Iz3ahLPZW329/cjkUjk7AGkJc6K2/XCo/S8aXIVjLXO17ue3vm8Jxi/nQnJ/D3mQx3ZdrvdjqamJjidTpw8eRKRSCTjOK1nQm+cuTxjBEEQBEEQBEE8GGw4PIo31LMZDrkYFXpv0bU8YAB9YYIdJ7bByFb1KJfPWmPRO07L8GLHZMvHoRUOoefpo2fcasG2WywWJJNJBINBzM3NabbL0EoMvd51tAQnUWjRaocXabSO0+tLNoNdLLEsniNWM2KI4V5iNS8teMNa9CJZ79xs3iFicm3xORDn2e12w+l0YmBgAJOTk4jH4+qx2cah1QfWtla/2LaVlRXNBOC5eGHxiPO13m/KRsQa8dxcEOecv6/iPLKx6s0vf25zczPi8TgGBwcRDAYzBDW935lsa44gCIIgCIIgiC8HGxZtgLXeCTzZhI6NoPdmXu86WqKInuixUaMvl1CEjY5XLwQil/Z4cUhPVOENTuDuGJaXl9XEw9nEFy0jVatNrbnXE0RyHZNoxOqNU2u8/LZs95wfB99H0cuGH4tomGcT6LLB9yOXHDe8Z48eBoMBFRUVmJychN/vV5PmavVRROtZ5u9jNnFWS6zgvZGyjWu99SeS6/Ol55mz0X6w/ex83gtL3KflQacon3ndeTwelJaWYnx8HENDQ4jH45rPfa7iFUEQBEEQBEEQXx7uydMmmzeKlsGUTXDRM8a1Sg/z54plhcV8Iuy49XLvrCcy8eEL4vZsbYoeGFrt8mPh87iIbWXzMOETn7K5Zn1mIR2KoiASiSASiagGOT8vfJvMMJVlWTNMiQ8T4a8lJpbmz2HH8l43/F9+fvlQHT5vDH+vs3md8GPg+6FXQlwrYW42gUTPC0VLcOLXIO+ZkQv8vGndc1FQ8Hg8OHfuHKLRaIaoJOZJykU4E8ML9Y7l76HWOVrCmfh7oCW2ZvPwWQ+9c7XGryeYaAmAWoINADUsUxQw2T9ZltHS0oKpqSlMTk4iHA5nrEetZwxARg6m9YRLgiAIgiAIgiAeXO7J04aFXPCGDDMy+H0GQ2ZS2Y28Pc61UoqYj4OV2tarjqNVDYc/T8vQZCKHXhiP1rjWE7P4c/WSK/N9ZPPLezKwMt7ifLB2ZVnOEAvYGJihyRKrrufxwVfKyTWHCd93AGv6L6JVNlnMk8NEEf7eaokuWrD50hKWtESEbO1owYtH4rVy7aPe9bTWM9+PdDqdIdiwcayXf0ocSy4lpfnx6eUV0nve+b7pCSZfFHrXZmh56emJrvw88b8rbB8TIWVZRmVlJYqKitDb24v5+fmMdvg5zCZe660BEnEIgiAIgiAI4sHHsJH/+BsMhhkAY19cdwiCIAiCIAiCIAiCIL50bFEUpVjcuCHRhiAIgiAIgiAIgiAIgvjdsH6GXYIgCIIgCIIgCIIgCOJ3Dok2BEEQBEEQBEEQBEEQ9yEk2hAEQRAEQRAEQRAEQdyHkGhDEARBEARBEARBEARxH0KiDUEQBEEQBEEQBEEQxH0IiTYEQRAEQRAEQRAEQRD3ISTaEARBEARBEARBEARB3IeQaEMQBEEQBEEQBEEQBHEfQqINQRAEQRAEQRAEQRDEfcj/AdBl/9YrWwG1AAAAAElFTkSuQmCC\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(190, 200):\n", - " plt.figure(figsize=(20, 20))\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " data, target = dataset[i]\n", - "# print(target)\n", - " print(to_text(target))\n", - "# target = [x - 26 if x > 35 else x for x in target]\n", - "# sentence = convert_y_label_to_string(target, dataset) \n", - "# print(target)\n", - "# plt.title(sentence)\n", - " plt.imshow(data.squeeze(0).numpy(), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.target_transform" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.transducer import load_transducer_loss, Transducer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t, i =load_transducer_loss(64, \n", - " 0,\n", - " \"iamdb_1kwp_tokens_1000.txt\", \n", - " \"iamdb_1kwp_lex_1000.txt\",\n", - " \"1kwp_prune_0_0_optblank.bin\",\n", - " \"optional\",\n", - " False,\n", - " False,\n", - " False,\n", - " None,\n", - " \"mean\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t(target, target)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/g1.png b/notebooks/g1.png deleted file mode 100644 index 09dd49e..0000000 Binary files a/notebooks/g1.png and /dev/null differ diff --git a/notebooks/g2.png b/notebooks/g2.png deleted file mode 100644 index a3cf21e..0000000 Binary files a/notebooks/g2.png and /dev/null differ diff --git a/notebooks/intersect.png b/notebooks/intersect.png deleted file mode 100644 index 63b7f2f..0000000 Binary files a/notebooks/intersect.png and /dev/null differ diff --git a/notebooks/intersection.pdf b/notebooks/intersection.pdf deleted file mode 100644 index c425a9f..0000000 Binary files a/notebooks/intersection.pdf and /dev/null differ diff --git a/poetry.toml b/poetry.toml new file mode 100644 index 0000000..3b549d6 --- /dev/null +++ b/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +create = true diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py index 63b43b2..a9117f8 100644 --- a/text_recognizer/networks/__init__.py +++ b/text_recognizer/networks/__init__.py @@ -1,4 +1,4 @@ """Network modules""" -from .backbones import EfficientNet +from .encoders import EfficientNet from .vqvae import VQVAE from .cnn_transformer import CNNTransformer diff --git a/text_recognizer/networks/backbones/__init__.py b/text_recognizer/networks/backbones/__init__.py deleted file mode 100644 index 25aed0e..0000000 --- a/text_recognizer/networks/backbones/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Vision backbones.""" -from .efficientnet import EfficientNet diff --git a/text_recognizer/networks/backbones/efficientnet.py b/text_recognizer/networks/backbones/efficientnet.py deleted file mode 100644 index 61dea77..0000000 --- a/text_recognizer/networks/backbones/efficientnet.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Efficient net b0 implementation.""" -import torch -from torch import nn -from torch import Tensor - - -class ConvNorm(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: int, - stride: int, - padding: int, - groups: int = 1, - ) -> None: - super().__init__() - self.block = nn.Sequential( - nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - groups=groups, - bias=False, - ), - nn.BatchNorm2d(num_features=out_channels), - nn.SiLU(inplace=True), - ) - - def forward(self, x: Tensor) -> Tensor: - return self.block(x) - - -class SqueezeExcite(nn.Module): - def __init__(self, in_channels: int, reduce_dim: int) -> None: - super().__init__() - self.se = nn.Sequential( - nn.AdaptiveAvgPool2d(1), # [C, H, W] -> [C, 1, 1] - nn.Conv2d(in_channels=in_channels, out_channels=reduce_dim, kernel_size=1), - nn.SiLU(), - nn.Conv2d(in_channels=reduce_dim, out_channels=in_channels, kernel_size=1), - nn.Sigmoid(), - ) - - def forward(self, x: Tensor) -> Tensor: - return x * self.se(x) - - -class InvertedResidulaBlock(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: int, - stride: int, - padding: int, - expand_ratio: float, - reduction: int = 4, - survival_prob: float = 0.8, - ) -> None: - super().__init__() - self.survival_prob = survival_prob - self.use_residual = in_channels == out_channels and stride == 1 - hidden_dim = in_channels * expand_ratio - self.expand = in_channels != hidden_dim - reduce_dim = in_channels // reduction - - if self.expand: - self.expand_conv = ConvNorm( - in_channels, hidden_dim, kernel_size=3, stride=1, padding=1 - ) - - self.conv = nn.Sequential( - ConvNorm( - hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim - ), - SqueezeExcite(hidden_dim, reduce_dim), - nn.Conv2d( - in_channels=hidden_dim, - out_channels=out_channels, - kernel_size=1, - bias=False, - ), - nn.BatchNorm2d(num_features=out_channels), - ) - - def stochastic_depth(self, x: Tensor) -> Tensor: - if not self.training: - return x - - binary_tensor = ( - torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.survival_prob - ) - return torch.div(x, self.survival_prob) * binary_tensor - - def forward(self, x: Tensor) -> Tensor: - out = self.expand_conv(x) if self.expand else x - if self.use_residual: - return self.stochastic_depth(self.conv(out)) + x - return self.conv(out) - - -class EfficientNet(nn.Module): - """Efficient net b0 backbone.""" - - def __init__(self) -> None: - super().__init__() - self.base_model = [ - # expand_ratio, channels, repeats, stride, kernel_size - [1, 16, 1, 1, 3], - [6, 24, 2, 2, 3], - [6, 40, 2, 2, 5], - [6, 80, 3, 2, 3], - [6, 112, 3, 1, 5], - [6, 192, 4, 2, 5], - [6, 320, 1, 1, 3], - ] - - self.backbone = self._build_b0() - - def _build_b0(self) -> nn.Sequential: - in_channels = 32 - layers = [ConvNorm(1, in_channels, 3, stride=2, padding=1)] - - for expand_ratio, out_channels, repeats, stride, kernel_size in self.base_model: - for i in range(repeats): - layers.append( - InvertedResidulaBlock( - in_channels, - out_channels, - expand_ratio=expand_ratio, - stride=stride if i == 0 else 1, - kernel_size=kernel_size, - padding=kernel_size // 2, - ) - ) - in_channels = out_channels - layers.append(ConvNorm(in_channels, 256, kernel_size=1, stride=1, padding=0)) - - return nn.Sequential(*layers) - - def forward(self, x: Tensor) -> Tensor: - return self.backbone(x) diff --git a/text_recognizer/networks/encoders/__init__.py b/text_recognizer/networks/encoders/__init__.py new file mode 100644 index 0000000..25aed0e --- /dev/null +++ b/text_recognizer/networks/encoders/__init__.py @@ -0,0 +1,2 @@ +"""Vision backbones.""" +from .efficientnet import EfficientNet diff --git a/text_recognizer/networks/encoders/efficientnet.py b/text_recognizer/networks/encoders/efficientnet.py new file mode 100644 index 0000000..61dea77 --- /dev/null +++ b/text_recognizer/networks/encoders/efficientnet.py @@ -0,0 +1,145 @@ +"""Efficient net b0 implementation.""" +import torch +from torch import nn +from torch import Tensor + + +class ConvNorm(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int, + padding: int, + groups: int = 1, + ) -> None: + super().__init__() + self.block = nn.Sequential( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=False, + ), + nn.BatchNorm2d(num_features=out_channels), + nn.SiLU(inplace=True), + ) + + def forward(self, x: Tensor) -> Tensor: + return self.block(x) + + +class SqueezeExcite(nn.Module): + def __init__(self, in_channels: int, reduce_dim: int) -> None: + super().__init__() + self.se = nn.Sequential( + nn.AdaptiveAvgPool2d(1), # [C, H, W] -> [C, 1, 1] + nn.Conv2d(in_channels=in_channels, out_channels=reduce_dim, kernel_size=1), + nn.SiLU(), + nn.Conv2d(in_channels=reduce_dim, out_channels=in_channels, kernel_size=1), + nn.Sigmoid(), + ) + + def forward(self, x: Tensor) -> Tensor: + return x * self.se(x) + + +class InvertedResidulaBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int, + padding: int, + expand_ratio: float, + reduction: int = 4, + survival_prob: float = 0.8, + ) -> None: + super().__init__() + self.survival_prob = survival_prob + self.use_residual = in_channels == out_channels and stride == 1 + hidden_dim = in_channels * expand_ratio + self.expand = in_channels != hidden_dim + reduce_dim = in_channels // reduction + + if self.expand: + self.expand_conv = ConvNorm( + in_channels, hidden_dim, kernel_size=3, stride=1, padding=1 + ) + + self.conv = nn.Sequential( + ConvNorm( + hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim + ), + SqueezeExcite(hidden_dim, reduce_dim), + nn.Conv2d( + in_channels=hidden_dim, + out_channels=out_channels, + kernel_size=1, + bias=False, + ), + nn.BatchNorm2d(num_features=out_channels), + ) + + def stochastic_depth(self, x: Tensor) -> Tensor: + if not self.training: + return x + + binary_tensor = ( + torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.survival_prob + ) + return torch.div(x, self.survival_prob) * binary_tensor + + def forward(self, x: Tensor) -> Tensor: + out = self.expand_conv(x) if self.expand else x + if self.use_residual: + return self.stochastic_depth(self.conv(out)) + x + return self.conv(out) + + +class EfficientNet(nn.Module): + """Efficient net b0 backbone.""" + + def __init__(self) -> None: + super().__init__() + self.base_model = [ + # expand_ratio, channels, repeats, stride, kernel_size + [1, 16, 1, 1, 3], + [6, 24, 2, 2, 3], + [6, 40, 2, 2, 5], + [6, 80, 3, 2, 3], + [6, 112, 3, 1, 5], + [6, 192, 4, 2, 5], + [6, 320, 1, 1, 3], + ] + + self.backbone = self._build_b0() + + def _build_b0(self) -> nn.Sequential: + in_channels = 32 + layers = [ConvNorm(1, in_channels, 3, stride=2, padding=1)] + + for expand_ratio, out_channels, repeats, stride, kernel_size in self.base_model: + for i in range(repeats): + layers.append( + InvertedResidulaBlock( + in_channels, + out_channels, + expand_ratio=expand_ratio, + stride=stride if i == 0 else 1, + kernel_size=kernel_size, + padding=kernel_size // 2, + ) + ) + in_channels = out_channels + layers.append(ConvNorm(in_channels, 256, kernel_size=1, stride=1, padding=0)) + + return nn.Sequential(*layers) + + def forward(self, x: Tensor) -> Tensor: + return self.backbone(x) diff --git a/text_recognizer/networks/encoders/residual_network.py b/text_recognizer/networks/encoders/residual_network.py new file mode 100644 index 0000000..c33f419 --- /dev/null +++ b/text_recognizer/networks/encoders/residual_network.py @@ -0,0 +1,310 @@ +"""Residual CNN.""" +from functools import partial +from typing import Callable, Dict, List, Optional, Type, Union + +from einops.layers.torch import Rearrange, Reduce +import torch +from torch import nn +from torch import Tensor + +from text_recognizer.networks.util import activation_function + + +class Conv2dAuto(nn.Conv2d): + """Convolution with auto padding based on kernel size.""" + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) + + +def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential: + """3x3 convolution with batch norm.""" + conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,) + return nn.Sequential( + conv3x3(in_channels, out_channels, *args, **kwargs), + nn.BatchNorm2d(out_channels), + ) + + +class IdentityBlock(nn.Module): + """Residual with identity block.""" + + def __init__( + self, in_channels: int, out_channels: int, activation: str = "relu" + ) -> None: + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.blocks = nn.Identity() + self.activation_fn = activation_function(activation) + self.shortcut = nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + residual = x + if self.apply_shortcut: + residual = self.shortcut(x) + x = self.blocks(x) + x += residual + x = self.activation_fn(x) + return x + + @property + def apply_shortcut(self) -> bool: + """Check if shortcut should be applied.""" + return self.in_channels != self.out_channels + + +class ResidualBlock(IdentityBlock): + """Residual with nonlinear shortcut.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + expansion: int = 1, + downsampling: int = 1, + *args, + **kwargs + ) -> None: + """Short summary. + + Args: + in_channels (int): Number of in channels. + out_channels (int): umber of out channels. + expansion (int): Expansion factor of the out channels. Defaults to 1. + downsampling (int): Downsampling factor used in stride. Defaults to 1. + *args (type): Extra arguments. + **kwargs (type): Extra key value arguments. + + """ + super().__init__(in_channels, out_channels, *args, **kwargs) + self.expansion = expansion + self.downsampling = downsampling + + self.shortcut = ( + nn.Sequential( + nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.expanded_channels, + kernel_size=1, + stride=self.downsampling, + bias=False, + ), + nn.BatchNorm2d(self.expanded_channels), + ) + if self.apply_shortcut + else None + ) + + @property + def expanded_channels(self) -> int: + """Computes the expanded output channels.""" + return self.out_channels * self.expansion + + @property + def apply_shortcut(self) -> bool: + """Check if shortcut should be applied.""" + return self.in_channels != self.expanded_channels + + +class BasicBlock(ResidualBlock): + """Basic ResNet block.""" + + expansion = 1 + + def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: + super().__init__(in_channels, out_channels, *args, **kwargs) + self.blocks = nn.Sequential( + conv_bn( + in_channels=self.in_channels, + out_channels=self.out_channels, + bias=False, + stride=self.downsampling, + ), + self.activation_fn, + conv_bn( + in_channels=self.out_channels, + out_channels=self.expanded_channels, + bias=False, + ), + ) + + +class BottleNeckBlock(ResidualBlock): + """Bottleneck block to increase depth while minimizing parameter size.""" + + expansion = 4 + + def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: + super().__init__(in_channels, out_channels, *args, **kwargs) + self.blocks = nn.Sequential( + conv_bn( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=1, + ), + self.activation_fn, + conv_bn( + in_channels=self.out_channels, + out_channels=self.out_channels, + kernel_size=3, + stride=self.downsampling, + ), + self.activation_fn, + conv_bn( + in_channels=self.out_channels, + out_channels=self.expanded_channels, + kernel_size=1, + ), + ) + + +class ResidualLayer(nn.Module): + """ResNet layer.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + block: BasicBlock = BasicBlock, + num_blocks: int = 1, + *args, + **kwargs + ) -> None: + super().__init__() + downsampling = 2 if in_channels != out_channels else 1 + self.blocks = nn.Sequential( + block( + in_channels, out_channels, *args, **kwargs, downsampling=downsampling + ), + *[ + block( + out_channels * block.expansion, + out_channels, + downsampling=1, + *args, + **kwargs + ) + for _ in range(num_blocks - 1) + ] + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + x = self.blocks(x) + return x + + +class ResidualNetworkEncoder(nn.Module): + """Encoder network.""" + + def __init__( + self, + in_channels: int = 1, + block_sizes: Union[int, List[int]] = (32, 64), + depths: Union[int, List[int]] = (2, 2), + activation: str = "relu", + block: Type[nn.Module] = BasicBlock, + levels: int = 1, + *args, + **kwargs + ) -> None: + super().__init__() + self.block_sizes = ( + block_sizes if isinstance(block_sizes, list) else [block_sizes] * levels + ) + self.depths = depths if isinstance(depths, list) else [depths] * levels + self.activation = activation + self.gate = nn.Sequential( + nn.Conv2d( + in_channels=in_channels, + out_channels=self.block_sizes[0], + kernel_size=7, + stride=2, + padding=1, + bias=False, + ), + nn.BatchNorm2d(self.block_sizes[0]), + activation_function(self.activation), + # nn.MaxPool2d(kernel_size=2, stride=2, padding=1), + ) + + self.blocks = self._configure_blocks(block) + + def _configure_blocks( + self, block: Type[nn.Module], *args, **kwargs + ) -> nn.Sequential: + channels = [self.block_sizes[0]] + list( + zip(self.block_sizes, self.block_sizes[1:]) + ) + blocks = [ + ResidualLayer( + in_channels=channels[0], + out_channels=channels[0], + num_blocks=self.depths[0], + block=block, + activation=self.activation, + *args, + **kwargs + ) + ] + blocks += [ + ResidualLayer( + in_channels=in_channels * block.expansion, + out_channels=out_channels, + num_blocks=num_blocks, + block=block, + activation=self.activation, + *args, + **kwargs + ) + for (in_channels, out_channels), num_blocks in zip( + channels[1:], self.depths[1:] + ) + ] + + return nn.Sequential(*blocks) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + # If batch dimenstion is missing, it needs to be added. + if len(x.shape) == 3: + x = x.unsqueeze(0) + x = self.gate(x) + x = self.blocks(x) + return x + + +class ResidualNetworkDecoder(nn.Module): + """Classification head.""" + + def __init__(self, in_features: int, num_classes: int = 80) -> None: + super().__init__() + self.decoder = nn.Sequential( + Reduce("b c h w -> b c", "mean"), + nn.Linear(in_features=in_features, out_features=num_classes), + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + return self.decoder(x) + + +class ResidualNetwork(nn.Module): + """Full residual network.""" + + def __init__(self, in_channels: int, num_classes: int, *args, **kwargs) -> None: + super().__init__() + self.encoder = ResidualNetworkEncoder(in_channels, *args, **kwargs) + self.decoder = ResidualNetworkDecoder( + in_features=self.encoder.blocks[-1].blocks[-1].expanded_channels, + num_classes=num_classes, + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + x = self.encoder(x) + x = self.decoder(x) + return x diff --git a/text_recognizer/networks/encoders/wide_resnet.py b/text_recognizer/networks/encoders/wide_resnet.py new file mode 100644 index 0000000..b767778 --- /dev/null +++ b/text_recognizer/networks/encoders/wide_resnet.py @@ -0,0 +1,221 @@ +"""Wide Residual CNN.""" +from functools import partial +from typing import Callable, Dict, List, Optional, Type, Union + +from einops.layers.torch import Reduce +import numpy as np +import torch +from torch import nn +from torch import Tensor + +from text_recognizer.networks.util import activation_function + + +def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: + """Helper function for a 3x3 2d convolution.""" + return nn.Conv2d( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + ) + + +def conv_init(module: Type[nn.Module]) -> None: + """Initializes the weights for convolution and batchnorms.""" + classname = module.__class__.__name__ + if classname.find("Conv") != -1: + nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2)) + nn.init.constant_(module.bias, 0) + elif classname.find("BatchNorm") != -1: + nn.init.constant_(module.weight, 1) + nn.init.constant_(module.bias, 0) + + +class WideBlock(nn.Module): + """Block used in WideResNet.""" + + def __init__( + self, + in_planes: int, + out_planes: int, + dropout_rate: float, + stride: int = 1, + activation: str = "relu", + ) -> None: + super().__init__() + self.in_planes = in_planes + self.out_planes = out_planes + self.dropout_rate = dropout_rate + self.stride = stride + self.activation = activation_function(activation) + + # Build blocks. + self.blocks = nn.Sequential( + nn.BatchNorm2d(self.in_planes), + self.activation, + conv3x3(in_planes=self.in_planes, out_planes=self.out_planes), + nn.Dropout(p=self.dropout_rate), + nn.BatchNorm2d(self.out_planes), + self.activation, + conv3x3( + in_planes=self.out_planes, + out_planes=self.out_planes, + stride=self.stride, + ), + ) + + self.shortcut = ( + nn.Sequential( + nn.Conv2d( + in_channels=self.in_planes, + out_channels=self.out_planes, + kernel_size=1, + stride=self.stride, + bias=False, + ), + ) + if self._apply_shortcut + else None + ) + + @property + def _apply_shortcut(self) -> bool: + """If shortcut should be applied or not.""" + return self.stride != 1 or self.in_planes != self.out_planes + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + residual = x + if self._apply_shortcut: + residual = self.shortcut(x) + x = self.blocks(x) + x += residual + return x + + +class WideResidualNetwork(nn.Module): + """WideResNet for character predictions. + + Can be used for classification or encoding of images to a latent vector. + + """ + + def __init__( + self, + in_channels: int = 1, + in_planes: int = 16, + num_classes: int = 80, + depth: int = 16, + width_factor: int = 10, + dropout_rate: float = 0.0, + num_layers: int = 3, + block: Type[nn.Module] = WideBlock, + num_stages: Optional[List[int]] = None, + activation: str = "relu", + use_decoder: bool = True, + ) -> None: + """The initialization of the WideResNet. + + Args: + in_channels (int): Number of input channels. Defaults to 1. + in_planes (int): Number of channels to use in the first output kernel. Defaults to 16. + num_classes (int): Number of classes. Defaults to 80. + depth (int): Set the number of blocks to use. Defaults to 16. + width_factor (int): Factor for scaling the number of channels in the network. Defaults to 10. + dropout_rate (float): The dropout rate. Defaults to 0.0. + num_layers (int): Number of layers of blocks. Defaults to 3. + block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock. + num_stages (List[int]): If given, will use these channel values. Defaults to None. + activation (str): Name of the activation to use. Defaults to "relu". + use_decoder (bool): If True, the network output character predictions, if False, the network outputs a + latent vector. Defaults to True. + + Raises: + RuntimeError: If the depth is not of the size `6n+4`. + + """ + + super().__init__() + if (depth - 4) % 6 != 0: + raise RuntimeError("Wide-resnet depth should be 6n+4") + self.in_channels = in_channels + self.in_planes = in_planes + self.num_classes = num_classes + self.num_blocks = (depth - 4) // 6 + self.width_factor = width_factor + self.num_layers = num_layers + self.block = block + self.dropout_rate = dropout_rate + self.activation = activation_function(activation) + + if num_stages is None: + self.num_stages = [self.in_planes] + [ + self.in_planes * 2 ** n * self.width_factor + for n in range(self.num_layers) + ] + else: + self.num_stages = [self.in_planes] + num_stages + + self.num_stages = list(zip(self.num_stages, self.num_stages[1:])) + self.strides = [1] + [2] * (self.num_layers - 1) + + self.encoder = nn.Sequential( + conv3x3(in_planes=self.in_channels, out_planes=self.in_planes), + *[ + self._configure_wide_layer( + in_planes=in_planes, + out_planes=out_planes, + stride=stride, + activation=activation, + ) + for (in_planes, out_planes), stride in zip( + self.num_stages, self.strides + ) + ], + ) + + self.decoder = ( + nn.Sequential( + nn.BatchNorm2d(self.num_stages[-1][-1], momentum=0.8), + self.activation, + Reduce("b c h w -> b c", "mean"), + nn.Linear( + in_features=self.num_stages[-1][-1], out_features=self.num_classes + ), + ) + if use_decoder + else None + ) + + # self.apply(conv_init) + + def _configure_wide_layer( + self, in_planes: int, out_planes: int, stride: int, activation: str + ) -> List: + strides = [stride] + [1] * (self.num_blocks - 1) + planes = [out_planes] * len(strides) + planes = [(in_planes, out_planes)] + list(zip(planes, planes[1:])) + return nn.Sequential( + *[ + self.block( + in_planes=in_planes, + out_planes=out_planes, + dropout_rate=self.dropout_rate, + stride=stride, + activation=activation, + ) + for (in_planes, out_planes), stride in zip(planes, strides) + ] + ) + + def forward(self, x: Tensor) -> Tensor: + """Feedforward pass.""" + if len(x.shape) < 4: + x = x[(None,) * int(4 - len(x.shape))] + x = self.encoder(x) + if self.decoder is not None: + x = self.decoder(x) + return x diff --git a/text_recognizer/networks/residual_network.py b/text_recognizer/networks/residual_network.py deleted file mode 100644 index c33f419..0000000 --- a/text_recognizer/networks/residual_network.py +++ /dev/null @@ -1,310 +0,0 @@ -"""Residual CNN.""" -from functools import partial -from typing import Callable, Dict, List, Optional, Type, Union - -from einops.layers.torch import Rearrange, Reduce -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -class Conv2dAuto(nn.Conv2d): - """Convolution with auto padding based on kernel size.""" - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) - - -def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential: - """3x3 convolution with batch norm.""" - conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,) - return nn.Sequential( - conv3x3(in_channels, out_channels, *args, **kwargs), - nn.BatchNorm2d(out_channels), - ) - - -class IdentityBlock(nn.Module): - """Residual with identity block.""" - - def __init__( - self, in_channels: int, out_channels: int, activation: str = "relu" - ) -> None: - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.blocks = nn.Identity() - self.activation_fn = activation_function(activation) - self.shortcut = nn.Identity() - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - residual = x - if self.apply_shortcut: - residual = self.shortcut(x) - x = self.blocks(x) - x += residual - x = self.activation_fn(x) - return x - - @property - def apply_shortcut(self) -> bool: - """Check if shortcut should be applied.""" - return self.in_channels != self.out_channels - - -class ResidualBlock(IdentityBlock): - """Residual with nonlinear shortcut.""" - - def __init__( - self, - in_channels: int, - out_channels: int, - expansion: int = 1, - downsampling: int = 1, - *args, - **kwargs - ) -> None: - """Short summary. - - Args: - in_channels (int): Number of in channels. - out_channels (int): umber of out channels. - expansion (int): Expansion factor of the out channels. Defaults to 1. - downsampling (int): Downsampling factor used in stride. Defaults to 1. - *args (type): Extra arguments. - **kwargs (type): Extra key value arguments. - - """ - super().__init__(in_channels, out_channels, *args, **kwargs) - self.expansion = expansion - self.downsampling = downsampling - - self.shortcut = ( - nn.Sequential( - nn.Conv2d( - in_channels=self.in_channels, - out_channels=self.expanded_channels, - kernel_size=1, - stride=self.downsampling, - bias=False, - ), - nn.BatchNorm2d(self.expanded_channels), - ) - if self.apply_shortcut - else None - ) - - @property - def expanded_channels(self) -> int: - """Computes the expanded output channels.""" - return self.out_channels * self.expansion - - @property - def apply_shortcut(self) -> bool: - """Check if shortcut should be applied.""" - return self.in_channels != self.expanded_channels - - -class BasicBlock(ResidualBlock): - """Basic ResNet block.""" - - expansion = 1 - - def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: - super().__init__(in_channels, out_channels, *args, **kwargs) - self.blocks = nn.Sequential( - conv_bn( - in_channels=self.in_channels, - out_channels=self.out_channels, - bias=False, - stride=self.downsampling, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.expanded_channels, - bias=False, - ), - ) - - -class BottleNeckBlock(ResidualBlock): - """Bottleneck block to increase depth while minimizing parameter size.""" - - expansion = 4 - - def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: - super().__init__(in_channels, out_channels, *args, **kwargs) - self.blocks = nn.Sequential( - conv_bn( - in_channels=self.in_channels, - out_channels=self.out_channels, - kernel_size=1, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.out_channels, - kernel_size=3, - stride=self.downsampling, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.expanded_channels, - kernel_size=1, - ), - ) - - -class ResidualLayer(nn.Module): - """ResNet layer.""" - - def __init__( - self, - in_channels: int, - out_channels: int, - block: BasicBlock = BasicBlock, - num_blocks: int = 1, - *args, - **kwargs - ) -> None: - super().__init__() - downsampling = 2 if in_channels != out_channels else 1 - self.blocks = nn.Sequential( - block( - in_channels, out_channels, *args, **kwargs, downsampling=downsampling - ), - *[ - block( - out_channels * block.expansion, - out_channels, - downsampling=1, - *args, - **kwargs - ) - for _ in range(num_blocks - 1) - ] - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - x = self.blocks(x) - return x - - -class ResidualNetworkEncoder(nn.Module): - """Encoder network.""" - - def __init__( - self, - in_channels: int = 1, - block_sizes: Union[int, List[int]] = (32, 64), - depths: Union[int, List[int]] = (2, 2), - activation: str = "relu", - block: Type[nn.Module] = BasicBlock, - levels: int = 1, - *args, - **kwargs - ) -> None: - super().__init__() - self.block_sizes = ( - block_sizes if isinstance(block_sizes, list) else [block_sizes] * levels - ) - self.depths = depths if isinstance(depths, list) else [depths] * levels - self.activation = activation - self.gate = nn.Sequential( - nn.Conv2d( - in_channels=in_channels, - out_channels=self.block_sizes[0], - kernel_size=7, - stride=2, - padding=1, - bias=False, - ), - nn.BatchNorm2d(self.block_sizes[0]), - activation_function(self.activation), - # nn.MaxPool2d(kernel_size=2, stride=2, padding=1), - ) - - self.blocks = self._configure_blocks(block) - - def _configure_blocks( - self, block: Type[nn.Module], *args, **kwargs - ) -> nn.Sequential: - channels = [self.block_sizes[0]] + list( - zip(self.block_sizes, self.block_sizes[1:]) - ) - blocks = [ - ResidualLayer( - in_channels=channels[0], - out_channels=channels[0], - num_blocks=self.depths[0], - block=block, - activation=self.activation, - *args, - **kwargs - ) - ] - blocks += [ - ResidualLayer( - in_channels=in_channels * block.expansion, - out_channels=out_channels, - num_blocks=num_blocks, - block=block, - activation=self.activation, - *args, - **kwargs - ) - for (in_channels, out_channels), num_blocks in zip( - channels[1:], self.depths[1:] - ) - ] - - return nn.Sequential(*blocks) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - # If batch dimenstion is missing, it needs to be added. - if len(x.shape) == 3: - x = x.unsqueeze(0) - x = self.gate(x) - x = self.blocks(x) - return x - - -class ResidualNetworkDecoder(nn.Module): - """Classification head.""" - - def __init__(self, in_features: int, num_classes: int = 80) -> None: - super().__init__() - self.decoder = nn.Sequential( - Reduce("b c h w -> b c", "mean"), - nn.Linear(in_features=in_features, out_features=num_classes), - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - return self.decoder(x) - - -class ResidualNetwork(nn.Module): - """Full residual network.""" - - def __init__(self, in_channels: int, num_classes: int, *args, **kwargs) -> None: - super().__init__() - self.encoder = ResidualNetworkEncoder(in_channels, *args, **kwargs) - self.decoder = ResidualNetworkDecoder( - in_features=self.encoder.blocks[-1].blocks[-1].expanded_channels, - num_classes=num_classes, - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - x = self.encoder(x) - x = self.decoder(x) - return x diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py index ac75d2f..e1324af 100644 --- a/text_recognizer/networks/transformer/attention.py +++ b/text_recognizer/networks/transformer/attention.py @@ -1,94 +1,73 @@ """Implementes the attention module for the transformer.""" from typing import Optional, Tuple -from einops import rearrange +from einops.layers.torch import Rearrange import numpy as np import torch from torch import nn from torch import Tensor +import torch.nn.functional as F +from text_recognizer.networks.transformer.rotary_embedding import apply_rotary_pos_emb -class MultiHeadAttention(nn.Module): - """Implementation of multihead attention.""" +class Attention(nn.Module): def __init__( - self, hidden_dim: int, num_heads: int = 8, dropout_rate: float = 0.0 + self, + dim: int, + num_heads: int, + dim_head: int = 64, + dropout_rate: float = 0.0, + causal: bool = False, ) -> None: - super().__init__() - self.hidden_dim = hidden_dim + self.scale = dim ** -0.5 self.num_heads = num_heads - self.fc_q = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_k = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_v = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_out = nn.Linear(in_features=hidden_dim, out_features=hidden_dim) - - self._init_weights() + self.causal = causal + inner_dim = dim * dim_head - self.dropout = nn.Dropout(p=dropout_rate) - - def _init_weights(self) -> None: - nn.init.normal_( - self.fc_q.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), - ) - nn.init.normal_( - self.fc_k.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), + # Attnetion + self.qkv_fn = nn.Sequential( + nn.Linear(dim, 3 * inner_dim, bias=False), + Rearrange("b n (qkv h d) -> qkv b h n d", qkv=3, h=self.num_heads), ) - nn.init.normal_( - self.fc_v.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), - ) - nn.init.xavier_normal_(self.fc_out.weight) + self.dropout = nn.Dropout(dropout_rate) + self.attn_fn = F.softmax - @staticmethod - def scaled_dot_product_attention( - query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None - ) -> Tensor: - """Calculates the scaled dot product attention.""" + # Feedforward + self.proj = nn.Linear(inner_dim, dim) - # Compute the energy. - energy = torch.einsum("bhlk,bhtk->bhlt", [query, key]) / np.sqrt( - query.shape[-1] - ) - - # If we have a mask for padding some inputs. - if mask is not None: - energy = energy.masked_fill(mask == 0, -np.inf) - - # Compute the attention from the energy. - attention = torch.softmax(energy, dim=3) + @staticmethod + def _apply_rotary_emb( + q: Tensor, k: Tensor, rotary_pos_emb: Tensor + ) -> Tuple[Tensor, Tensor]: + l = rotary_pos_emb.shape[-1] + (ql, qr), (kl, kr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k)) + ql, kl = apply_rotary_pos_emb(ql, kl, rotary_pos_emb) + q = torch.cat((ql, qr), dim=-1) + k = torch.cat((kl, kr), dim=-1) + return q, k - out = torch.einsum("bhlt,bhtv->bhlv", [attention, value]) - out = rearrange(out, "b head l v -> b l (head v)") - return out, attention + def _cross_attention(self) -> Tensor: + pass def forward( - self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None + self, + x: Tensor, + context: Optional[Tensor], + mask: Optional[Tensor], + context_mask: Optional[Tensor], + rotary_pos_emb: Optional[Tensor] = None, ) -> Tuple[Tensor, Tensor]: - """Forward pass for computing the multihead attention.""" - # Get the query, key, and value tensor. - query = rearrange( - self.fc_q(query), "b l (head k) -> b head l k", head=self.num_heads - ) - key = rearrange( - self.fc_k(key), "b t (head k) -> b head t k", head=self.num_heads - ) - value = rearrange( - self.fc_v(value), "b t (head v) -> b head t v", head=self.num_heads + q, k, v = self.qkv_fn(x) + q, k = ( + self._apply_rotary_emb(q, k, rotary_pos_emb) + if rotary_pos_emb is not None + else q, + k, ) - out, attention = self.scaled_dot_product_attention(query, key, value, mask) + if any(x is not None for x in (mask, context_mask)): + pass - out = self.fc_out(out) - out = self.dropout(out) - return out, attention + # Compute the attention + energy = (q @ k.transpose(-2, -1)) * self.scale diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py index 99a5291..9160876 100644 --- a/text_recognizer/networks/transformer/norm.py +++ b/text_recognizer/networks/transformer/norm.py @@ -20,3 +20,16 @@ class Rezero(nn.Module): def forward(self, x: Tensor, **kwargs: Dict) -> Tensor: x, *rest = self.fn(x, **kwargs) return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1.0e-5) -> None: + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x: Tensor) -> Tensor: + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) self.g + diff --git a/text_recognizer/networks/wide_resnet.py b/text_recognizer/networks/wide_resnet.py deleted file mode 100644 index b767778..0000000 --- a/text_recognizer/networks/wide_resnet.py +++ /dev/null @@ -1,221 +0,0 @@ -"""Wide Residual CNN.""" -from functools import partial -from typing import Callable, Dict, List, Optional, Type, Union - -from einops.layers.torch import Reduce -import numpy as np -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: - """Helper function for a 3x3 2d convolution.""" - return nn.Conv2d( - in_channels=in_planes, - out_channels=out_planes, - kernel_size=3, - stride=stride, - padding=1, - bias=False, - ) - - -def conv_init(module: Type[nn.Module]) -> None: - """Initializes the weights for convolution and batchnorms.""" - classname = module.__class__.__name__ - if classname.find("Conv") != -1: - nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2)) - nn.init.constant_(module.bias, 0) - elif classname.find("BatchNorm") != -1: - nn.init.constant_(module.weight, 1) - nn.init.constant_(module.bias, 0) - - -class WideBlock(nn.Module): - """Block used in WideResNet.""" - - def __init__( - self, - in_planes: int, - out_planes: int, - dropout_rate: float, - stride: int = 1, - activation: str = "relu", - ) -> None: - super().__init__() - self.in_planes = in_planes - self.out_planes = out_planes - self.dropout_rate = dropout_rate - self.stride = stride - self.activation = activation_function(activation) - - # Build blocks. - self.blocks = nn.Sequential( - nn.BatchNorm2d(self.in_planes), - self.activation, - conv3x3(in_planes=self.in_planes, out_planes=self.out_planes), - nn.Dropout(p=self.dropout_rate), - nn.BatchNorm2d(self.out_planes), - self.activation, - conv3x3( - in_planes=self.out_planes, - out_planes=self.out_planes, - stride=self.stride, - ), - ) - - self.shortcut = ( - nn.Sequential( - nn.Conv2d( - in_channels=self.in_planes, - out_channels=self.out_planes, - kernel_size=1, - stride=self.stride, - bias=False, - ), - ) - if self._apply_shortcut - else None - ) - - @property - def _apply_shortcut(self) -> bool: - """If shortcut should be applied or not.""" - return self.stride != 1 or self.in_planes != self.out_planes - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - residual = x - if self._apply_shortcut: - residual = self.shortcut(x) - x = self.blocks(x) - x += residual - return x - - -class WideResidualNetwork(nn.Module): - """WideResNet for character predictions. - - Can be used for classification or encoding of images to a latent vector. - - """ - - def __init__( - self, - in_channels: int = 1, - in_planes: int = 16, - num_classes: int = 80, - depth: int = 16, - width_factor: int = 10, - dropout_rate: float = 0.0, - num_layers: int = 3, - block: Type[nn.Module] = WideBlock, - num_stages: Optional[List[int]] = None, - activation: str = "relu", - use_decoder: bool = True, - ) -> None: - """The initialization of the WideResNet. - - Args: - in_channels (int): Number of input channels. Defaults to 1. - in_planes (int): Number of channels to use in the first output kernel. Defaults to 16. - num_classes (int): Number of classes. Defaults to 80. - depth (int): Set the number of blocks to use. Defaults to 16. - width_factor (int): Factor for scaling the number of channels in the network. Defaults to 10. - dropout_rate (float): The dropout rate. Defaults to 0.0. - num_layers (int): Number of layers of blocks. Defaults to 3. - block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock. - num_stages (List[int]): If given, will use these channel values. Defaults to None. - activation (str): Name of the activation to use. Defaults to "relu". - use_decoder (bool): If True, the network output character predictions, if False, the network outputs a - latent vector. Defaults to True. - - Raises: - RuntimeError: If the depth is not of the size `6n+4`. - - """ - - super().__init__() - if (depth - 4) % 6 != 0: - raise RuntimeError("Wide-resnet depth should be 6n+4") - self.in_channels = in_channels - self.in_planes = in_planes - self.num_classes = num_classes - self.num_blocks = (depth - 4) // 6 - self.width_factor = width_factor - self.num_layers = num_layers - self.block = block - self.dropout_rate = dropout_rate - self.activation = activation_function(activation) - - if num_stages is None: - self.num_stages = [self.in_planes] + [ - self.in_planes * 2 ** n * self.width_factor - for n in range(self.num_layers) - ] - else: - self.num_stages = [self.in_planes] + num_stages - - self.num_stages = list(zip(self.num_stages, self.num_stages[1:])) - self.strides = [1] + [2] * (self.num_layers - 1) - - self.encoder = nn.Sequential( - conv3x3(in_planes=self.in_channels, out_planes=self.in_planes), - *[ - self._configure_wide_layer( - in_planes=in_planes, - out_planes=out_planes, - stride=stride, - activation=activation, - ) - for (in_planes, out_planes), stride in zip( - self.num_stages, self.strides - ) - ], - ) - - self.decoder = ( - nn.Sequential( - nn.BatchNorm2d(self.num_stages[-1][-1], momentum=0.8), - self.activation, - Reduce("b c h w -> b c", "mean"), - nn.Linear( - in_features=self.num_stages[-1][-1], out_features=self.num_classes - ), - ) - if use_decoder - else None - ) - - # self.apply(conv_init) - - def _configure_wide_layer( - self, in_planes: int, out_planes: int, stride: int, activation: str - ) -> List: - strides = [stride] + [1] * (self.num_blocks - 1) - planes = [out_planes] * len(strides) - planes = [(in_planes, out_planes)] + list(zip(planes, planes[1:])) - return nn.Sequential( - *[ - self.block( - in_planes=in_planes, - out_planes=out_planes, - dropout_rate=self.dropout_rate, - stride=stride, - activation=activation, - ) - for (in_planes, out_planes), stride in zip(planes, strides) - ] - ) - - def forward(self, x: Tensor) -> Tensor: - """Feedforward pass.""" - if len(x.shape) < 4: - x = x[(None,) * int(4 - len(x.shape))] - x = self.encoder(x) - if self.decoder is not None: - x = self.decoder(x) - return x -- cgit v1.2.3-70-g09d2