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{
"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 EmnistDataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"dataset = EmnistDataset(train=False, sample_to_balance=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dataset.load_or_generate_data()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import random_split, DataLoader"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"d1, d2 = random_split(dataset, [len(dataset)-10, 10])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"55898"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(d1)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"dl = DataLoader(d1, batch_size=16)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3494"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dl)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" 0, 0, 0, 0, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 3, 65, 109, 140, 204, 215, 217, 217, 201, 154, 22, 0, 0, 0],\n",
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" 244, 252, 247, 207, 100, 84, 223, 251, 254, 250, 127, 4, 0, 0],\n",
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" 252, 244, 220, 127, 39, 48, 218, 250, 255, 250, 127, 4, 0, 0],\n",
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" 221, 127, 79, 10, 5, 37, 217, 250, 254, 249, 125, 4, 0, 0],\n",
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" 35, 1, 0, 0, 22, 84, 234, 252, 250, 220, 50, 0, 0, 0],\n",
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" 9, 0, 0, 0, 46, 127, 246, 254, 245, 204, 34, 0, 0, 0],\n",
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" 0, 0, 0, 10, 128, 209, 254, 254, 220, 139, 9, 0, 0, 0],\n",
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" 0, 9, 35, 163, 246, 253, 249, 232, 122, 45, 0, 0, 0, 0],\n",
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" 1, 35, 91, 219, 253, 254, 234, 187, 67, 20, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 46, 207, 244, 247, 220, 80, 24, 1, 3, 8, 34,\n",
" 52, 164, 219, 253, 249, 234, 155, 79, 4, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 2, 81, 232, 251, 235, 179, 39, 12, 5, 22, 46, 115,\n",
" 139, 221, 246, 254, 234, 188, 79, 32, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 3, 112, 244, 254, 236, 193, 130, 127, 129, 173, 209, 245,\n",
" 250, 254, 253, 232, 154, 79, 4, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 46, 206, 242, 249, 248, 249, 250, 250, 250, 250, 250,\n",
" 250, 243, 219, 95, 22, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 22, 154, 201, 217, 222, 245, 249, 249, 233, 222, 217,\n",
" 217, 202, 158, 36, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 1, 20, 32, 39, 51, 114, 125, 125, 82, 51, 37,\n",
" 37, 32, 20, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 2, 4, 5, 9, 32, 37, 37, 21, 9, 4,\n",
" 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
" dtype=torch.uint8)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d1.dataset.data[0]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Tensor"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(dataset.data)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EMNIST Dataset\n",
"Num classes: 80\n",
"Input shape: [28, 28]\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: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
"\n"
]
}
],
"source": [
"print(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def display_images(dataset, shift=0):\n",
" fig = plt.figure(figsize=(9, 9))\n",
" for i in range(9):\n",
" x, y = dataset[i + shift]\n",
" ax = fig.add_subplot(3, 3, i + 1)\n",
" x = x.squeeze(0).numpy()\n",
" ax.imshow(x, cmap='gray')\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" ax.set_title(dataset.mapper(int(y)))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x648 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_images(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x648 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_images(dataset, 9)"
]
},
{
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
