Created an abstract Dataset class for common methods.

1 files changed, 0 insertions, 148 deletions

diff --git a/src/notebooks/01b-dataset_normalization.ipynb b/src/notebooks/01b-dataset_normalization.ipynb
deleted file mode 100644
index 9421816..0000000
--- a/src/notebooks/01b-dataset_normalization.ipynb
+++ /dev/null
@@ -1,148 +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": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.datasets import EmnistDataLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_loaders = EmnistDataLoader(splits=[\"train\"], sample_to_balance=True,\n",
-    "        subsample_fraction = None,\n",
-    "        transform = None,\n",
-    "        target_transform = None,\n",
-    "        batch_size = 512,\n",
-    "        shuffle = True,\n",
-    "        num_workers  = 0,\n",
-    "        cuda = False,\n",
-    "        seed = 4711)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = data_loaders(\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean = 0.\n",
-    "std = 0.\n",
-    "nb_samples = 0.\n",
-    "for data in loader:\n",
-    "    data, _ = data\n",
-    "    batch_samples = data.size(0)\n",
-    "    data = data.view(batch_samples, data.size(1), -1)\n",
-    "    mean += data.mean(2).sum(0)\n",
-    "    std += data.std(2).sum(0)\n",
-    "    nb_samples += batch_samples\n",
-    "\n",
-    "mean /= nb_samples\n",
-    "std /= nb_samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.1731])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.3247])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "std"
-   ]
-  },
-  {
-   "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
-}