Datasets&Dataloaders.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zmgy107/Pytorch-Learning-Notes/blob/main/Datasets%26Dataloaders.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "tOywlSUHkrRo"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading a Dataset"
      ],
      "metadata": {
        "id": "wSGwPujYkHv6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is an example of how to load the Fashion-MNIST dataset from TorchVision. Fashion-MNIST is a dataset of Zalando’s article images consisting of 60,000 training examples and 10,000 test examples. Each example comprises a 28×28 grayscale image and an associated label from one of 10 classes."
      ],
      "metadata": {
        "id": "y080-E2vkjLm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We load the FashionMNIST Dataset with the following parameters:\n",
        "\n",
        "\n",
        "> root:is the path where the train/test data is stored,\n",
        "\n",
        "\n",
        "> train:specifies training or testing dataset,\n",
        "\n",
        "> download=True:downloads the data from the internet if it's not available at root\n",
        "\n",
        "> transform and target_transform specify the feature and label transformations"
      ],
      "metadata": {
        "id": "NHUg5bgCklj_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_data=datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data=datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n"
      ],
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        "colab": {
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        "outputId": "a20db139-4f46-4271-cde4-40a8664cc786"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
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              "version_minor": 0,
              "model_id": "36252c0168e64ca7a254fb07d6c71e44"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4396b6440204f6e96fb14a7b32a29ff"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Iterating and Visualizing the Dataset"
      ],
      "metadata": {
        "id": "lsBRrBeVnPid"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can index Datasets manually like a list: training_data[index]. We use matplotlib to visualize some samples in our training data."
      ],
      "metadata": {
        "id": "Dk5Gsu14nVQG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 创建标签字典\n",
        "labels_map={\n",
        "    0:\"T-Shirt\",\n",
        "    1:\"Trouser\",\n",
        "    2:\"Pullover\",\n",
        "    3:\"Dress\",\n",
        "    4:\"Coat\",\n",
        "    5:\"Sandal\",\n",
        "    6:\"Shirt\",\n",
        "    7:\"Sneaker\",\n",
        "    8:\"Bag\",\n",
        "    9:\"Ankle Boot\",\n",
        "}\n",
        "figure=plt.figure(figsize=(8,8))\n",
        "cols,rows=3,3\n",
        "# 3*3 每个位置依次添加图像\n",
        "for i in range(1,cols*rows+1):\n",
        "  sample_idx=torch.randint(len(training_data),size=(1,)).item() # 转变为数值型\n",
        "  img,label=training_data[sample_idx]\n",
        "  figure.add_subplot(rows,cols,i)\n",
        "  plt.title(labels_map[label])\n",
        "  plt.axis(\"off\")\n",
        "  plt.imshow(img.squeeze(),cmap=\"gray\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "ghGsy_70nb19",
        "outputId": "8ac94fb5-c7a6-40cf-8778-d7c386f714e4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 9 Axes>"
            ],
            "image/png": 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RRx9dKfvjsyTNmTMnq5s7d26lHMUb/bEtytmoMzl/ZziTBACggEESAIACBkkAAAoYJAEAKOg0cSe6GdknnJx55plZmzorDfTv379SjlYeiBIYfDB548aNWRsfmPavFe1jJArC+2SIOgkcURufHOGD25I0bdq0rM7f4BsFpVetWlUpRwlQ69evj3cWId8X6iRx+FUxpPxm76jfR9+XT8iIkh+6MkFGnd+4VK9PtbM6EwVE33mdfjF27NhK+QMf+EDW5uqrr87q7rjjjko5StbyokQsf/yJ9vGWW27J6vxN/9Ex8/zzz6+Ur7322qzNF7/4xUp5Z2/m72mcSQIAUMAgCQBAAYMkAAAFDJIAABR0mrgTBed9Mk20msSiRYsq5UGDBmVt/Ez0UXJPNAvE9u3bK+VHHnkka/Mnf/InlXI0K5APcEevH62CEiU6tBLNJuT3++67787aRAlHDzzwQKUc7Xcd/rONEkH6qq6uDHLAAQdUylHilU+Yir7jKEHD99coKSeaKafVtqPfb7QyyZQpUyrlrva7dtKVJL3IxRdfXCn/y7/8S9bmN7/5TVbnjzXRscf3yzorN9VdzeQ73/lOpXzRRRdlbU499dRKOeqDdRJ1on3yfb7Oe6szS9rOrvjEmSQAAAUMkgAAFDBIAgBQsNMBNj/Tv48/Svk16P333z9r469BR9eSo+vU/pr36tWrszb+2v2CBQuyNnVWE+nqChF+26NGjcra+M8oum4fxYt8XXQzur/m7uO4kjRixIhK+YILLsja9FVd/d79TeJRLLrOTfl14n3RCvT+91In9rJixYqsLup3vp8tXbq05bbbXZ0YmBf91g8//PBK2U8SIMWTAHQl3lhH1L+j1/ev9+1vfztr41chmjlzZtbGT3ji8ypK+9SVCSuiz+iFfm6cSQIAUMAgCQBAAYMkAAAFDJIAABTsdOKOX43g0ksvzdrcfPPNlXKUQOATAaJkhWjWeX8zdJQcsWzZsko5Slzx+xQFrqNkIh9Mjm7w9UlA0XuLEi9abSfazyi5ye9TtJrKfffdVylHkxn0VV2dTMBPYhElxfh+FyVeRa+1bdu2lq/v+1nUf30SRfRa48aNy+r8776ryU3tpE5yoU8KiT67n/3sZy1fq05ySXSs8fsUHTP9MSN6H5GhQ4dWytHEF7/73e8q5Ve+8pVZm/vvv79SPumkk7I20QQaBx54YKUcvf86k7sMGzasUq4z6UZHnEkCAFDAIAkAQAGDJAAABZ1e0P3rv/7rrM7HPQ466KCsjb/pNLo52d9wH8UEfRxEkkaOHFkpz549O2vjJzT319alfBLnujFJv09RGx8XiN6/jw35m/tL/ETkUWzIxzeiGJv//I877rhar98X1Im3ff3rX8/qfF+IYh++b0RxZ//dRPsUPa9OfMbHNqOJ7aM6H9fv6mTfe6qJEydmdePHj6+Uo3hfVOf5+P/rXve6rI2Pm0n573hnJ+bewffLOhMHRKLv3G971qxZWRuft/IHf/AHWRs/4YKUH8ej9+9jmVHs3v8O77333qxNZziTBACggEESAIACBkkAAAoYJAEAKOg00h8l5fgEj+XLl2dt/A330c3sa9eubblz0SzwPgll8uTJWZsf//jHlXK0ovaGDRsq5WilkjqrxkeJO35FDx+AlqQlS5ZUytFNuNGEA/7m8+j1/fOiNn470Xttd12dFGD48OFZ3Re+8IVK+YwzzsjazJs3r1KeMGFC1sZ/7tFN1FEyiE+siBItfILClClTsjZf/vKXK+UTTjgha3PooYdmdf6zjPa7nS1cuLBWHfoeziQBAChgkAQAoIBBEgCAgk5jkoMGDcrqfCwtusF048aNlXIUP/E3tEZxy+jGUD8JwCmnnJK1ufLKKyvlm266KWszbdq0lq8VxQT9+43em588wO+zlK/o7SdJkPLYplRvMgMfdxszZkzWxk9o/pWvfCVr4+NX7aZO/PGSSy7J6i688MKszveFqG/473D9+vVZmzqTZkd8u+h35yf7jyYz8BN9zJgxI2sTTeLhY9ZRnB3ojTiTBACggEESAIACBkkAAAoYJAEAKOg0cedDH/pQVve1r32tUvY3UEvSypUrK+VoUgCf5OCfI9W7mX/ZsmVZG3+j92OPPZa18UkVfnUTqd4K1qNHj87qfALFokWLsjbHHHNMpRwlQtRZRcKvziBJo0aNqpSj5JDPfe5zWV1f8MADD1TK0eoLixcvzur8dxolmvn+GiX3+JUMopU7ooQj3z+j39QRRxxRKX/jG9/I2njRKvF+wgOp6wlHQLujpwMAUMAgCQBAAYMkAAAFDJIAABR0mrizYsWKrO5Nb3pTpXzwwQdnbR5//PFK2SepSNI111xTKUfJCj5ZQsoTFqKZR3yb6PX9rCLRygvRyiBe9Pp+hZPx48dnbYYOHVopR4kYI0aMyOr8LDx+dh8pT0Z561vfmrXxCSy90cyZM7O6I488slKeO3du1ib63OvMeOMTdaKkHF8XtYlWL6mTFOTr/O8wctZZZ2V13/zmN7M6P2vTtdde23LbQG/AmSQAAAUMkgAAFDBIAgBQ0GlMss7K7nXiHvfcc09Wd8ghh1TK0WoEY8eOzeomTZpUKfvVNKR8ZXm/4oiU3zAd3UAdxSn9tqJY6lNPPdVyOz4m6FdOkaQNGzZkdfPnz6+U77///qzNb37zm6zOqxNja3cPP/xwVudj0dH3Hq3s4r+f6PPy247izL4v1Im7S/kkGtEKI4ceemilXGcyjLvuuiurO/7447M6P3lCNIkF0BtxJgkAQAGDJAAABQySAAAUMEgCAFDQaeJOlJSyq8yZM2e3vRZ6Z6KOd+aZZ2Z1fjKGKKkqWuHCJ9hEyTV+YoBoO74uSo6LnueTuKJJJPwEFTfccEPWxosmM4jem0/UiZLhooQnoN1xJgkAQAGDJAAABQySAAAUdBqTBNpZdFO8nzxg9OjRWZso3ubj81Hcrs5EE77OT0BQqhs1alSl7OOPknTzzTdXyn6i/Uj0PurESYk/oq/gTBIAgAIGSQAAChgkAQAoYJAEAKDAOpswwMx232wC2OOklPIMjt1gV/a7N77xjZXyOeeck7U57LDDsroDDzywUu7Xr1/Wxtdt3rw5a+MnM4hW8xg2bFhW179//0r5sssuy9pcccUVWV076ol+x7Gub+usz3EmCQBAAYMkAAAFDJIAABQQk0RRb4xJoiqaTL2nJ78nJondjZgkAABdwCAJAEABgyQAAAUMkgAAFHSauAMAQF/GmSQAAAUMkgAAFDBIAgBQwCAJAEABgyQAAAUMkgAAFDBIAgBQwCAJAEABgyQAAAUMkgAAFPSKQdLMtnT497yZbe9QfltP7x/6NjN7q5nNbfbH5WY2x8xOfoHbvNXM3tVd+4jeycwWdDgerjez/zSzCT29X+2kVwySKaVBO/5JWiTp7A51V+5oZ2Yv6rm93HP2AbuPmX1I0lclfUHSAZImSvqWpNf34G6hbzm7eWw8UNJKSV/v4f1pK71ikCwxs9PMbImZfczMVkj6JzPb18y+ambLmv++amb7NtvPNLNfum0kMzu0+f+vNbMHzWyzmS01s//Tod3rzOweM9tgZr8ysxd3eGxBcx/ulbSVgbJvMLOhkj4r6S9TStemlLamlJ5JKc1OKX2kRV8cbmbXm9nq5hnA9WY2vvnYLEkvl/SN5hnCN3ruXaJdpJSelPRDSUdJkpmdZWZ3m9kmM1tsZpd0bG9mF5jZQjNba2afbB7HXtUDu96jevUg2TRG0v6SDpJ0oaS/lvRSScdIOlrSCZIurrmt70l6d0ppsKSpkn4uSWZ2rKR/lPRuSSMkfVfSdTsOeE3nSzpL0rCU0rMv7C2hTZwkqb+kHxUe76wv7iXpn9TotxMlbZf0DUlKKf21pNskvbd5teS9u2j/0YuY2UBJb5Z0R7Nqq6QLJA1T49h0kZmd22x7lBpXPN6mxhnoUEnjdu8e7xn6wiD5vKRPp5SeSiltV+NL/2xKaVVKabWkz0j6k5rbekbSUWY2JKW0PqX022b9hZK+m1K6M6X0XErpnyU9pcYBcIevpZQWN/cBfcMISWs6+aOo2BdTSmtTSteklLallDZLmiXp1N2y1+htfmxmGyRtlPRqSX8jSSmlW1NK96WUnk8p3SvpKv2+j/2xpNkppV+mlJ6W9ClJfXJdxb4wSK5uXmbYYaykhR3KC5t1dbxR0mslLTSzX5jZSc36gyR9uHmpdUOzQ05w213cpb1HO1sraWQnl9eLfdHMBprZd5uXuzZJ+m9Jw8xs7126x+iNzk0pDVPjqsZ7Jf3CzMaY2Ylmdkvzkv5GSe+RNLL5nLHqcMxKKW1Toz/3OX1hkPR//SxTY1DbYWKzTmpcfhi44wEzG1PZUEq/Tim9XtJoST+WdHXzocWSZqWUhnX4NzCldFUn+4He73Y1riicW3i8s774YUmTJZ2YUhoi6ZRmvTX/S3/CTmle5bpW0nOSTpb0A0nXSZqQUhoq6Tv6ff9aLmn8juea2QA1roz0OX1hkPSuknSxmY0ys5FqXEb41+Zjv5M0xcyOMbP+ki7Z8SQz62dmbzOzoSmlZyRtUuNSriRdLuk9zb/MzMz2awbFB++2d4U9Tkppoxr965tmdm7z7HAfM5thZl9S531xsBpxyA1mtr+kT7vNr5R08O55J+gNmsem10saLukhNfrYupTSk2Z2gqS3dmj+Q0lnm9kfmlk/NY6F5rfZF/TFQfLzkuZKulfSfZJ+26xTSmmeGtmIN0t6VNIv3XP/RNKC5uWv96gRU1JKaa6kP1cjsWK9pPmSZu7i94E2kFL6iqQPqZGQs1qNqw7vVeNKRLEvqnHbyABJa9RItPip2/TfS/rjZubr13bpm0C7m21mW9T4w36WpHeklB6Q9BeSPmtmm9X4A23HlTE1H3+fpH9T46xyi6RValwZ6VMsJa7aAADKzGyQpA2SDkspPdHDu7Nb9cUzSQBAC2Z2djNEsJ+kL6txtWNBz+7V7scgCQCIvF6NRLJlkg6T9JbUBy89crkVAIACziQBACjodA5RM9vjTjP32qs6rj///PNZm1NOOaVS/tGP8lnB7rrrrkp59OjRWZsnn3wyq3v88ccr5WOOOSZrc88991TK9913X9Zm1KhRlfJnP/vZrM3mzZuzut0ppdQjKd/t0O8iUV/0XvOa11TK5513XtZm27ZtWd3GjRsr5Z/85CdZm7lz57Z8fbPqV7onXknqiX63J/a5Oup8nzNmzKiUjz766KyNP65J0tVXX53V9Vad9TnOJAEAKGCQBACggEESAIACBkkAAAp65eK/Y8ZU5iUPEyGeeeaZStkHwKU4mccHuNeuzSfGf/GLX1wpz5s3L2vz3HPPVcoDBgzI2vR04k5fECXkRAk4dZJyvPHjx2d1P/2pn12uay6+OF8CNerDXp1EnbqfCXavvffOF4Dxx5Hp06dnbSZMmFApX3rppVmbOXPmZHVPP/10pfzjH/84a7PPPvtUyv642htwJgkAQAGDJAAABQySAAAUtF1Msk5sxF+Xj+KGPk556623Zm2mTZuW1a1cubJSXrZsWdbmyCOPrJSPOOKIrI2P+wwaNChrs2rVqqwO3aursbY//MM/zOre/OY3V8rvete7sja+L65bty5rs3379qzOTz7Rv3//rI2ftOL9739/1uaWW27J6jzij+3L90EpjkF6F110UVY3a9asSjmKSfbGGKTHmSQAAAUMkgAAFDBIAgBQwCAJAEBB2yXu1HH66adXykuXLs3aDBkypOV2opuqfcJPdIPv8uXLK2V/U64kjR07tlI+8MADszbRzPzY9a688sqs7qSTTqqUDzjggJbb2bJlS1bnV5bxN2NL0ogRI7K6rVu3dlqW8skLbrzxxqyN/y1Ebd7znvdkddj9/OQQfuKAqE2UbLh+/fpKuV+/flmbBQsWZHV+5Zk3vOENWRu/wlK07ej41xXRZBm7YxUbziQBAChgkAQAoIBBEgCAgraPSe63335ZnZ/gPLreXmeC8yhu6WNR0Y3fgwcPrpSjSQFWrFhRKUcTnGPXiyYcf81rXpPV+UkjVq9enbXxN+FHMW0/aUTUJupTPtYT3cTtJ8T3sSgpj4G++93vztpEMdHzzjsvq8Ou9aIXVQ/P0Xd+xhlnVMq33XZby+0+++yztV7f/zZOPfXUrI2PSdaZZL/dcCYJAEABgyQAAAUMkgAAFDBIAgBQ0PaJO694xSuyOp+oE00m4BNwDjvssKxNdMP2q171qkp58eLFWZuHH364Uo4mHPB10WQC6H5+lXY/SYAkLVq0KKvzSRT77rtv1sbfNB0lMURJOXX4pKA6K3X4fY4sWbIkq4t+U/734lfDQferkwQzadKkSnnDhg0tn1P3BvzrrruuUn7LW97S8jlPPfVUVuffR1cnANgdEwdEOJMEAKCAQRIAgAIGSQAACto+JnnyySdnddFEwJ6/GTyKW5544olZnZ/gPJqgeujQoZ2+lpSvSE9McveYOnVqpRxNyBzFPnwMMLohO5oYoNV2oudEdXWe1xVRbNP3XymP3Uar1KN71bnp338vF198ccvnRH0nil/7+OLChQuzNjNmzKiU58yZ03Lb0aQIezLOJAEAKGCQBACggEESAIACBkkAAAraPnFn9OjRWZ1ProkSMfyqG9FN3n6ld0lau3ZtpRwlPvgbr6NVQPwNtgMHDszaoPsdffTRlXKUuBNN/uCTD5588smsjf9Oo5vBfdJEdyXg1OX7a/T+o3069thjK2USd7pXV5O1/Iot0eQQrbZb17x587I6nwgXJe60+8ognEkCAFDAIAkAQAGDJAAABQySAAAUtH3iTpTw4ldjiGam93VRAtCPfvSjrO6OO+6olD/2sY9lbfxMGf3798/arFixolKOZu5B9/OJO9HsTFGfqjOLk28TJQDVSe6J+KSNOskXUcKa3060mkm07Wi1FHSfqK/47+H444/P2tRZ9aNO36kzu8+1116b1X3lK19p+bx2m2HH40wSAIACBkkAAAoYJAEAKGj7mKS/mVaSNm3aVClHcR9/XX7kyJFZm3e+851Znb95NppwwN90G92w7W9G7+oNvtg5fqKH6HOvs0pCFKOsE9epo84qJNE++n2KtuPfR7T6QzRRwvTp0+OdRbeoM6lEFJOcP39+y+f579jnbEj1jj8bN27M6vxxc7/99svabN26tVKO4q91Yv49hTNJAAAKGCQBAChgkAQAoIBBEgCAgrZP3PGJGJK0Zs2aSjm64faQQw6plH/961/Xer0nnniiUo4mIfCJO1FyhA+eR8FsdL/999+/ZZsoiaHO9+Pb7MpkhCgZrc5EBVFf9KLEjiFDhuzE3mFnRZOJ+AlPDj/88KzN3XffvdOvVafvRHXR78In5fzRH/1R1ub73/9+pRwllEWvH7XrCZxJAgBQwCAJAEABgyQAAAVtH5McPnx4Vrd9+/ZKedSoUVkbHwP43Oc+V+v1/DX4aPJ0f2NwFM/yN91GcSB0vzqxtejGbh8fiSYOqHNDeKvtvpDn+boo/ujb1I1PMQF/zP+263zmUd/ZsmVLy9eaOXNmVnfZZZe1fF6dY0udyfAj/v2/4x3vyNr4mGS7TZzCmSQAAAUMkgAAFDBIAgBQwCAJAEBB2yXu+ESdOgkF0cz0c+fO7Zb9eeyxx7K6OgFvf1P7Aw880C37g875/hOtmh4lX/i6KPmgzkoddZK6uprY4BNuoskM/OsNHDgwa7N+/fouvX5f5D/jXTmBxKpVq7K6pUuX7vR26t64X6cfXnHFFZXy7Nmzd3p/9nScSQIAUMAgCQBAAYMkAAAFbReT9BMDRDEdH2eKVtSOVl/vioceeiirO/jggzvdHymPBUWxTXS/YcOGVcpR/C2KYftJI+rehO/5OE8U/6yznTqi+FidSdD3lIml24FfYOGII47I2vgJSKJJ9jdt2pTVveENb6iUo0kIxo0bVynX6c833XRT1ub888/P6pYsWVIpR+9t2rRplfL999+ftbnmmmsq5TFjxmRt/O9Skh5++OFK+WUve1nW5vLLL6+UP/nJT2ZtXijOJAEAKGCQBACggEESAIACBkkAAAraLnFn9OjRlXKUnOCTcqKVH7Zt29bytaKbwX3ixV133ZW1mTx5cqUcJWf4/V64cGHL/UH3i1ZIGDx4cFbXv3//Srnu6updUSdxJrrRu87zBgwYUCl3dZ/33XffrC5aEae3+/SnP10pn3XWWVmbNWvWVMrRdzdhwoSsbtmyZZVylAD44IMPVsrr1q0r72zTo48+mtX9x3/8R1bnk2mi46jfx+j1p0yZUilHx+xoUpgoUciLPpPuxpkkAAAFDJIAABQwSAIAUMAgCQBAQdsl7vjZIx555JGdfo4kXXfddS2fVyepYcGCBVmdT2CIAvU+mWfDhg0tXws7p84KMVFyVpRo5ROrfCKPlCcR1PneI1G/q7OyjBe9vp/ZJVoFpI7x48dndX1x1qhbbrmlUn75y1+etYlm/PJ+97vfZXW+H0THsZ///OeVcr9+/bI2kyZNqpSjBJwnnngiq/N9I0py8/s4cuTIrI2fcajODGRRO78dSbrjjjuyuu7GmSQAAAUMkgAAFDBIAgBQ0HYxST+DfnRjqr8ZPLrx2d+862/KleI4kH+96AZqf8N2dJO3n8wgut6OF+bAAw9s2SZaRWbz5s1Z3ZVXXlkpf+hDH8rarFy5suXr+ThhFDeMYpJ+P6N+71eJiPq9v7E9inPViZtOnTo1q+uLMUnfV6LYmo9fR8eVaHITv8JI9J3740Z0rPGx+ajPR8/z7erErwcNGtTy9aMVT+r0eb+6klTvN/dCcSYJAEABgyQAAAUMkgAAFDBIAgBQ0HaJO9dff32l/PGPfzxrs3Tp0ko5So6Ibrr16kwmEAW8/baj14qC8OhefsWYuqLv1N+AHU0m4BNnou/dJ8XUXYWjzgofvk9FCRo+0WzFihVZm3HjxrV8LZ9A11f5zy9azcMnS0XfZZRA5Z/nVzeS8u84Sta65557KuUbbrgha3PhhRdmdX4SBJ+QKOV93u+zlCdS1p0Yw7+36Dh+//3319rWC8GZJAAABQySAAAUMEgCAFDQdjFJf/NuNOmuv3ZfZ0XtSJ04UHR93U/MG03oG8UX0L2i2JrvP1Hc8O67787qVq9e3fL1fH+J4o2+rk4fi9SJZUbvbfv27ZVydGP35MmTW27br1rfV82bN69lm65MTh89L5qw38ftomPNEUccUSkfeeSRWRsfW5TymGS0bf/6UZ+r8/6jHA3/fqM2uyO3gzNJAAAKGCQBAChgkAQAoIBBEgCAgrZL3PGi2fN9UkwUOPbJPKtWrcra1EmOiG4q98kYUcDbJ1Cg+0U3dvvvNEo0eOCBB7K66Dv0/M3OXZ2wos6KDHVWD4m241dyiFa/qWPo0KFdel5v448/PtlFyo8/0Xce9S+fuBIdx3w/iLazfPnySjmaQCJaYcO/XrTtOhOn1DmORgk4/ti6bt26ltvZFTiTBACggEESAIACBkkAAAraPiYZTajrJ/mNJhyYMmVKpTx//vysTZ0bvceMGZPV+ThBtB0/0TS6XzTZc5244fr167O6OjfP+21FMSQfe4naRBOT+3hQ9Dw/SbafTD2qe/TRR7M2Ed+HoxgW4nifjy1GeRRRjNmL+oXfto85R/sUTRwQxRu3bNlSKQ8aNChr43M7otf37y16/Sgm6fv44sWLsza7A2eSAAAUMEgCAFDAIAkAQAGDJAAABW2fuBOtzuBvdI4C5X4m/J/85CdZmyjA7E2cODGrq7MaRFdXf0B9I0aMyOrqrBqwcuXKrO4lL3lJy+f5m5/rvFbUD6KJJvy2o1Vk/OtFiTv7779/pVxndRMpT74YPnx4ref1NY899lhW51fhiCYuiZLMfKJOneNIlIDjj1HRKi9LlizJ6kaPHl0pR4k7PlEn6s++79RZOUnK+/y9996btdkdOJMEAKCAQRIAgAIGSQAAChgkAQAoaPvEnWXLlmV1PuAczR5fZ4WGOsk1UeKOT7yIEjiixAt0r2g2JP+dRslZdWZNiWZ68gliUf/xCRp1ZgWS6vUX/7xNmzZlbXzCTZSw4WdakfLEiigpCPHMXT5RJkpSGTBgQFYXJbh4PpnH91Mp7+PR93vAAQd06fV9H4+Odb5fRvtYZ/WQhx56qOX+7AqcSQIAUMAgCQBAAYMkAAAFbR9YuOeee7K6l770pZVydJ28zqoOdUyYMCGr86uORK9fZ6V7vDBHHXVUVufjhn7lDCmOYR933HGVsp+wQspjLXXijf6G6WgfI3ViOFFs0+9TdGN7xPfpUaNG1XpeXzN37tys7pxzzqmUo+88WuHDf5/RZAI+NhzFEX2/jPpFnYlTohi733adOGadSTak/L3dddddtZ7X3TiTBACggEESAIACBkkAAAoYJAEAKGj7xJ277747q6tzo3OdAHMd0Q3bdSYq2Lp1a7e8PsqiG7THjh1bKUf9wE9GIeU3hJ9xxhlZG59w45NdpDxR6PTTT8/abN68OavziR2LFy/O2vjEiui9+RVO7rvvvqxN9DyfqLNo0aKsDaTbb789q/NJelECTh1Rco8/1kVJOb6u7gpEdSYq8G3qTCYQHZ+jPueTiaJj/e7AmSQAAAUMkgAAFDBIAgBQ0PYxySg24mMA0fXuOnHDOqJr8P6a/+DBg7M2TzzxRLe8PspmzJiR1flJz/fff/+szc0339xy2z/96U+7vmMdPPjgg1ldNHn67px8IpqEwceHVq9evbt2p61EsWLfx6LvN4pT+uNWFG/0/aKrkwLUef06MdGuvo9o2z7fY+nSpVmb3YEzSQAAChgkAQAoYJAEAKCAQRIAgIK2T9yJ+OCxv4Fckh577LFuea1oNQgfTB8xYkTWpqeC0H3JsmXLatXVUWeCCt/vomQbvwLEiSeemLWJEhsWLFhQKd97771ZG5+MFiVo+Loo0WPhwoVZHeqJvju/UtGkSZOyNk899VRW55NZ6kwCEN3w79VdhcO/fpSU47cVJUn6zyTqc1ECXVcnQehunEkCAFDAIAkAQAGDJAAABb0yJnnnnXdWylFM8OKLL+6W17rllluyOh9nevTRR7M2y5cv75bXR1kUH/Fxlegm5q6u3F5n0vwnn3yyUo5iUXPmzMnq6kx+EU2o3kqd1e6jdtHnEX1ukL74xS9Wyl/96lezNlGMuU6fq3Mzvxf107p1rUQxUb/QQNRP/MT/kvThD394p19/V+BMEgCAAgZJAAAKGCQBAChgkAQAoMB66gZNAAD2dJxJAgBQwCAJAEABgyQAAAUMkgAAFDBIAgBQwCAJAEABgyQAAAUMkgAAFDBIAgBQwCDZBWa2wMxe1dP7gfZiZjPN7JcdysnMDu3JfULv0Vl/oq91XdsPkmZ2spn9ysw2mtk6M/sfMzu+p/cLvVvzD6XtZrbFzFaa2RVmNqin9wvtz8xuNbP1ZrbvHrAvM83suWY/32Jmj5vZRd207SvM7PPdsa1dqa0HSTMbIul6SV+XtL+kcZI+IylfyXYPY2a9csHrPubslNIgSdMlvURS96zkvYvQ5/Z8ZjZJ0sslJUnn9Oze/K/bU0qDmn39jZK+ZGbH9vRO7S5tPUhKOlySUkpXpZSeSyltTyndlFK6d8elLTP7cvOvsifMbMaOJ5rZUDP7npktN7OlZvZ5M9u7+dghZvZzM1trZmvM7EozGxbtgJkd2dz2+c3y68zsHjPb0DzDfXGHtgvM7GNmdq+krRy0eoeU0lJJcyRNbV7W+t/vtXlW8K5W22j2x38xs9VmttDMLjazvcxs32Zfmtqh7ajmWezoZpk+13tcIOkOSVdIekfHB5pnXt80s/80s81mdqeZHRJtpHmFbbGZnRY8tm/zuLioeRXkO2Y2oM7OpZTulvSQpCM7bO8cM3ug2f9uNbOOjx3ZrNvQbHNOs/5CSW+T9NHmGersOq/fE9p9kJwn6Tkz+2czm2Fmw93jJ0p6RNJISV+S9D0zs+ZjV0h6VtKhko6VdIakHQczk/RFSWPV6AwTJF3iX9zMpku6UdL7UkpXNf+6+kdJ75Y0QtJ3JV3nLpucL+ksScNSSs92/a1jT2FmEyS9VtL6F7CZr0saKulgSaeqcbD805TSU5KuVaPf7PAmSb9IKa2iz/U6F0i6svnvNWZ2gHv8LWpcLRsuab6kWX4DZnampKskvTGldGvwGpeqcYJxjBrHv3GSPlVn55qhrMMlzW2WD2++1gcljZJ0g6TZZtbPzPaRNFvSTZJGS3qfpCvNbHJK6R+a7/FLzbPUs+u8fo9IKbX1PzUGsSskLVFj0LtO0gGSZkqa36HdQDUuYYxpPv6UpAEdHj9f0i2F1zhX0t0dygvU6KhLJJ3Wof7bkj7nnvuIpFM7PO+dPf2Z8a9b+t0CSVskbZC0UNK3mn0xSXpRh3a3SnpX8/9nSvplh8eSGgepvSU9LemoDo+9W9Ktzf9/laTHOjz2P5IuSPS5XvVP0smSnpE0sll+WNJfdXj8Ckn/r0P5tZIedv3p483+ONVte0dfM0lbJR3S4bGTJD1R2KeZzePqBkmbm9v5un6/zOInJV3dof1ekpZKOk2Ny8YrJO3V4fGrJF3S4f18vqc/91b/2v1MUimlh1JKM1NK4yVNVePs76vNh1d0aLet+b+DJB0kaR9Jy5uXATao8Rf4jstXB5jZvzUvw26S9K9qnI129B5Jv0rVv9QOkvThHdtsbndCc592WPwC3zL2HOemlIallA5KKf2FpO1d3M5INfrjwg51C9X4C1+SbpE00MxObMasjpH0o+Zj9Lne4x2SbkoprWmWfyB3yVUdjmmStqlxPOvog2oMWvcXXmOUGicMv+nQX37arC+5o9nPB6txkjFF0heaj41Vh36bUnpejf42rvnY4mbdDh37dVto+0Gyo5TSw2r8dTK1RdPFapxJjmx++cNSSkNSSlOaj39Bjb+YpqWUhkh6uxp/gXX0HkkTzezv3HZnddjmsJTSwJTSVR13s2vvDm1ga/O/AzvUjanxvDVqnEEc1KFuohp/kSul9Jykq9W42nG+pOtTSpub7ehzvUAzJvgmSaea2QozWyHpryQdbWZH78SmzpN0rpl9oPD4GjX+mJvSob8MTY2knJZSSislXSNpx+XRZerQb5vhrAlq9N1lkiaYWcdx5n/7tdqkX7b1IGlmR5jZh81sfLM8QY2DyB2dPS+ltFyN6+RfMbMhzQSJQ8zs1GaTwWpcSttoZuMkfSTYzGZJZ0o6xcwubdZdLuk9zb/4zcz2M7OzzGzwC36z2OOllFarcQB4u5ntbWbvlBQmVrjn7RgEZ5nZYDM7SNKH1LiCscMPJL1ZjWSHH3Sop8/1DudKek7SUWpcKThGjcv3t6kRp6xrmaTTJX3Agls1mmd1l0v6uw6JX+PM7DV1Nm5mIyS9QdIDzaqrJZ1lZqc3Y5AfVuME5FeS7lTjbPejZrZPM4nobEn/1nzuSjVi8Hu0th4k1RioTpR0p5ltVWNwvF+NL6qVCyT1k/SgGgkXP5R0YPOxz6iR1r9R0n+qkTiRSSltkPRqSTPM7HMppbmS/lzSN5rbnK/GNX30HX+uxh9Va9W4LPWrms97nxpnoo9L+qUaA+E/7ngwpXRn8/GxamTS7qinz/UO75D0TymlRSmlFTv+qfG9vm1nspJTSovUGCj/r8WZ1R9To5/c0Qwn3SxpciebPKmZgbpFjczW1Wr0V6WUHlHjStvX1ThLPVuNW6OeTik93SzPaD72LTVi6Q83t/s9SUc1L/v+uO772912BF8BAIDT7meSAADsMgySAAAUMEgCAFDAIAkAQAGDJAAABZ2mFZsZqa99WErJT6CwW7Rrv5sxY0alfMopp2Rt7rijegvvkiVLsjZPPvlkVjd27NhK+cgjj8zaPPPMM5XyDTfckLVZuHBhVren6Yl+1659Dt2jsz7HmSQAAAUMkgAAFDBIAgBQwCAJAEBBp9PSEczu29o9cef362v/Xp1pGM8777yWdYcffnjWxifOPP3001mb4cOr64JPmDAhazNgQL5I/Pz58yvllStXZm369etXKQ8ZMiRrs3Xr1kr5v//7v7M2n/nMZ7K6zZs3Z3W7Cok72N1I3AEAoAsYJAEAKGCQBACggJgkito9JlnHl7/85azu1FNPzerWr19fKT/11FNZG/9bevbZZ7M269ata9kmiqXus88+lfKwYcOyNi96UXVukOi37dsMHpyvzbx06dKs7q1vfWvLbXcXYpLY3YhJAgDQBQySAAAUMEgCAFDAIAkAQAGJOyhqt8Qdn/AS9e3x48dXyldccUXWZtOmTS23vdde+d+XUcKN5xNlVqxYkbWJknn8KiDRSiHPP/98pVwnucZPgCBJI0aMyOr+67/+q1KeNWtWy213FYk72N1I3AEAoAsYJAEAKGCQBACg4EWtmwC9h58owN9cL8Uxwf79+7fcto9J7r333lmbbdu2VcrRJOh+4oDoeVG80ccX99tvv6yNj2VG+xjFO0844YSsDugLOJMEAKCAQRIAgAIGSQAAChgkAQAoIHEHvUadm+cnTJhQKT/33HNZmyFDhmR10aofrURJOd6gQYOyun79+rV8XpRc5Ou2bt2atfHJRdFqItF+Dx06tOU+Ab0RZ5IAABQwSAIAUMAgCQBAATFJ9Ck+JhnF36Kb8H2ccvny5VkbH0uMYqT+9aL4o5+oXIpjkK1ef/v27Vkb/96i+OvSpUuzuih2id7N95VoUvsbb7yxUp4zZ84u259XvOIVWd306dMr5TFjxmRt/H5v2LBhp16XM0kAAAoYJAEAKGCQBACggEESAIACEnd2gTe+8Y2V8nnnnZe1uf322yvlv//7v9+l+4SGQw89tFKuk9wiSdOmTauUN2/enLXxiTp77ZX/DepX3YiSdKLnDRgwoNPXkvJVQAYOHJi1OfbYYyvlaMWRxx9/PKvzq6VEq6JEq4eg5/kJJOpMuiFJ3/nOdypl/9uRpOOOO65Sfu1rX5u18f3poIMOytpEfd731X333TdrM3z48ErZv1dJ+tu//dtKmcQdAAC6CYMkAAAFDJIAABQwSAIAUNBnE3d8AkW0GkTkT//0Tyvlww47LGvz8Y9/vFK+7bbbsjbve9/7KuVPfepTWZsLLrggq3v44Ycr5QsvvDBrM3v27Er5nnvuydps2bIlq+sL/GoW0UoZ0Sw4F110UaX8sY99LGuzbNmyStknu0h5v/PJNqXneVGigzd69Ois7oc//GGlHK3uceCBB2Z1ixYtqpQnTpyYtZk3b17LfcLu5/tT1OdmzJiR1Y0fP75SjhJefP+JkhTrrGoTJRP5mZ+ilXh8At3KlSuzNtHsWDuDM0kAAAoYJAEAKGCQBACgoO1iknVujPXxmugG0zoxSB+HkvJYVHSd/JFHHqmU/Y3gkrRkyZJK2cezJOnyyy/P6vwN2yNGjMjavPOd76yUo1jRL37xi0r5E5/4RNam3UVxu1WrVlXKUf+J4nT33ntvpRzdhO9FK4z4fhdNJhDx+xn1af960WQCv/71ryvlE044IWsT3eztVyGJ4pbEJHte1C+jGKQX/f79RBtRf/ITb0STbPg+7+PyUvw79L+NKLbp+3z02/Ux2Tor6nTEmSQAAAUMkgAAFDBIAgBQwCAJAEBB2yXu1FE3GaKjz33uc1ndzJkzs7rVq1dXyoMHD2657TFjxrRss27duqzuvvvuy+p8oHrkyJFZGx9wjxJYzj777Eq5NybuRJ+7Tz6IEg2GDBmS1a1fv75SjpIh/CoF0aoYPkGhzsQBddv5pI0o0cH33yipLEqs8K8fTVSA3a/O5BTeFVdckdVFvwOfHBYlKdZZHcbvU9S/ooSjbdu2ZXVenUTG6dOnV8p33XVXy+12xJkkAAAFDJIAABQwSAIAULBHxySjG6brrqrd0ZlnnpnVXXLJJZVyFIeKJvT1MZxoUgK/j37iACm/oTV6X9FK3HUm6N60aVOlfMghh2Rt9t9//6yutzn44IOzOh9DiWJ9UQy3KxPCR7FxH4+J+nidm52jNr4PRXEeH3vysVYpjiv5WPiwYcNa7iO6V1cnCvjsZz9bKQ8aNChrE0184ftqdIz024ri8L6PR7+vOpPCRMdaH3ePfk9//Md/XCkTkwQAoJswSAIAUMAgCQBAAYMkAAAFuyVxJwqmRnVenUkBPvjBD2Z1f/mXf1kpR0Fpf2NsFBSOguJ+1vu1a9dmbXwySJQk41cKGTduXNZm+PDhWZ1/LwcccEDWxid1RO/f32g/YcKErE27O+qoo1q2iZKjoiQdn+ASJR/USbips4pNdLO1r4t+G74uem9r1qyplKN9rjNxQZQM1tvVOWZ1JbFQipNy/HdTJ0nnsssuy+p8co2fbESKk3L8cSNK6Ip+B63aRJ9RNPGFP0ZHSUFe1Oacc86plD/60Y+23E5HnEkCAFDAIAkAQAGDJAAABbskJunjJ3VuuK/LT8z9gQ98IGszf/78Sjm63u4nuo6uZUeTlz/++OOVcrSKu3/96Lr9pEmTKuVoMt9oEgK/31G80ccuohiT33ZvnLC6zoQJUT+MJnv2ohiSj1lFn3udeF+0T3VuyPYxySi26SfSj+JcUbzT/4ajGHpvVye3ok6Mrm7+gzdlypSs7tJLL62UH3zwwayNz5uIJv5fvnx5VucXT4gmIfBx0yjGXScmGcU7fUz9F7/4RdbGx8aj2KbfpwMPPDBr0xnOJAEAKGCQBACggEESAIACBkkAAAp2OnHHB1Oj1aqjwHQrr371q7O697///VndCSecUCnPmzcva+ODwNFKGf4m/OgG2+h5PuHmgQceyNr4hBE/AUH0elHgPppMwKuTeBElYvgEksMOO6zla7Wb6IZ331+jRBp/w30k+k59v4+Se+qok7gTTRTg+6tfsUbKfxuPPvpo1ibatn9v0Qrw7axOUk6dyU26cuyT4sSVT33qU5VylCw1a9asSjn6zt/0pjdVytHKL1F/HjVqVKXskwal/DOJ3of/jUXJPdGx9u67766UFy1alLWZOnVqpRwlq/nX29mkM84kAQAoYJAEAKCAQRIAgAIGSQAACnY6cSdK1PH8jAZ+FvaozifESPHMJ7/97W8r5Wg2HZ/MEs0UsWHDhko5mnEmSuDwiQ5jx47N2vjZe6JZcXxyRjRTRDQLkA+C15mpIgpmr1ixolKOZvdpdxMnTszqfGJFlIzw2GOPtdx2ne8rauO/9zorh0Si5Bq/7SiJxCfcRO812m8v+tzaWZQsVWdWMH+si1bl8f3w+OOPz9oceuihWd2cOXMq5U984hMt9yc61taZgSuancq3i479vk2UAOWTcqJVdqJtT548uVI++eSTsza+j69cuTJr41c4io6HneFMEgCAAgZJAAAKGCQBACjY6ZjkjBkzKuWPfOQjWRsfZ4muAftr0NGkAFFMyd9gH62e4W+ojW7q9teyFy9enLWJrt37GfTrzGhf5wbjKG4a3eDr40VR3NRf8589e3bW5rrrrquUFy5c2HIf200Ur162bFml7Fc6kKQbb7yx5bajFWL8jdXRihC+LopJRv21zgofvm9EMX0fn7nzzjuzNkcccURW52PxUSy8t3nd615XKb/iFa/I2qxevbpSfuSRR7I2/hj1s5/9LGsTHX98X3nve9+btfHfZ8QfD6KY87Bhw7I6P5lAlFuxadOmSjmKSfr4+dChQ7M20W/F9/HoWOvzAKLfhf8dRGNNZziTBACggEESAIACBkkAAAoYJAEAKOg0cefoo4/O6r71rW9VyqtWrcra+GBuFHD1CThRMDlKyvGJDtGs975NtB1/g22UJBPtt39edMO/fy9RoNq38UFyqV7CzUMPPZS18d9JnZn5/eQKvUHUN/x3Om3atKzNrbfemtX5m72jpC7f76KELZ+gECUsROpMFOCTv6J+f9JJJ1XK/oZ1KU7Q8AkZXV3hpJ188IMfrJSjY5RfzedlL3tZ1sYn10UTd0SfuU9cjPqc7xfR6iw+KSeaOCBK3PHJlVGf830+WpVo48aNnW5Xit+/T1arkxQUJe74sSaalKAznEkCAFDAIAkAQAGDJAAABZ3GJE844YSszl+Xj65v++vE/rq9lMfyojbRNWh/7TqKu/jnRZMx+32MJt2NbjT37z+KAfg4QbTt7373u5Xy9ddfn7VZt25dVlfnJm5/LX/p0qVZmyh20NtEN2j77z26mX/58uVZ3bnnnlspR/3Ox0OiGJaP60Rxpoj/TiM+BhtNJnDKKae03M7atWuzOr+f0Srx/v1GcaY91Utf+tKszvef6Lfn48DRseaoo46qlI899tisTXSsqzPxhG/jJ1yX8mOtn3BcyifZkPLjRhST9H2+zqQAUa5AFBOtMymLf70o3unfb5R/0hnOJAEAKGCQBACggEESAIACBkkAAAo6zRq45pprsjofTI1W6jjjjDMq5YMOOihr4xNeooBvlKxQZyV3n0AQBcV9olB0M320wsbjjz9eKd97771ZG7+ywm9+85usjU/8qHOjsBQH2LvCB9PrJIbs6XzwP7rh3fcFv4pDyZQpUyrlKEHAJwTUmXAg6ptRXZ0+7V8vStAYPXp0Vuf99re/zep8gkr0O/Qro0QJQHuq008/Pat7yUteUinXWXEnOo74Phb91up8n1F/9sl9UXKL7ztRAlKdZJpoUhT/XuokokXH+qg/1fmt+PcbfbZ+n3Z2BRvOJAEAKGCQBACggEESAICCTi8gRzez+1Wdb7/99qzNN77xjUrZxzOk/Eb9qE107borEy1HcQIfL4muZUev769v+8nco9eLbuqOJk/oiigG4N9LFNuMYhftzvepOrG9KKYcmTx5cqW8YMGCrI3/LqK4ZZ04S/Sd+hvzo3ij/w1Fkz1HN7t7PqYuSTNmzKiUo9+L33Y7xSRnzZqV1fmJuV/96ldnbepMHu6/h+h7GTt2bMt9jI5jflt+sgwp73NRH4iOv+vXr6+Uo1ieP0ZGx0O/eEMU2x0yZEhW538HDz74YNbG5wFEx1Vft2LFiqxNZziTBACggEESAIACBkkAAAoYJAEAKKi3BEEHN954Y6UcrZThg9BRIoIP3q5cuTLfuRo3pvoZ7qU8mB0lUPgV6cePH5+1iRIPfDA7SvzortUPoqQOn4RTZ3KFvsJPUFEnKWbu3Lm1tu1XM49WUfGvF71+ndXWo8QO/71HiVe+TbQKSTT5hzd//vyszn9u0X77zz9ahaWd+AREX474JBVJGjduXKU8YcKErI1PDJPyzzNKrvH9MDoe+H7w8MMPZ22iiQLuuuuuSjla+Wbq1KmV8g033JC1ee1rX1sp/+xnP2u5HSlPFPJJo1I+OUb0u/STuUSrMnWGM0kAAAoYJAEAKGCQBACggEESAICCnU7c8aLZE6K6PU00Y8qeJpopB2V+1o46iU9Lly6ttW2foBb1n658X9E+Rvy266yaEyWQ1Xm9KLHDJwpFSXV+9pO+KFpVxtfdc889WZvZs2fvql3apaLZmbzvfe97LdvsycdjziQBAChgkAQAoIBBEgCAghcckwT2FHVWwfAeffTRWtuOVinorZ544omszt+kHX220c3uQLvjTBIAgAIGSQAAChgkAQAoYJAEAKCAxB30GgMHDqyUo5v7/U3x0YoXfd3GjRuzOr+STpS4E606ArQ7ziQBAChgkAQAoIBBEgCAAmKS6DV8TCyaBHz79u2V8ooVK2pt28fg/GTiewI/eXkUk/WfiY/RlviY5IABA7I20aTnQLvjTBIAgAIGSQAAChgkAQAoYJAEAKCASDt6jVe+8pWV8rHHHpu1WblyZZe27ZNgoqSYnlZnn7qacDRp0qRKecyYMVmbs846q1L+0Y9+1KXXAvYknEkCAFDAIAkAQAGDJAAABcQk0Wv8+7//e6X85JNPZm3qTh6Aquuvv75SHjJkSNbm5ptv3l27A+w2nEkCAFDAIAkAQAGDJAAABQySAAAU2J54UzQAAHsCziQBAChgkAQAoIBBEgCAAgZJAAAKGCQBAChgkAQAoOD/A+Io/IoW0bzWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a Custom Dataset for your files"
      ],
      "metadata": {
        "id": "zwRx-10SwXNY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A custom Dataset class must implement three functions: __init__, __len__, and __getitem__. Take a look at this implementation; the FashionMNIST images are stored in a directory img_dir, and their labels are stored separately in a CSV file annotations_file."
      ],
      "metadata": {
        "id": "fZvcDqWuwzDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from torchvision.io import read_image\n",
        "\n",
        "class CustomImageDataset(Dataset):\n",
        "  def __init__(self,annotations_file,img_dir,transform=None,target_transform=None):\n",
        "    self.img_labels=pd.read_csv(annotations_file)\n",
        "    self.img_dir=img_dir\n",
        "    self.transform=transform\n",
        "    self.target_transform=target_transform\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.img_labels)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    img_path=os.path.join(self.img_dir,self.img_labels.iloc[idx,0])\n",
        "    image=read_image(img_path)\n",
        "    label=self.img_labels.iloc[idx,1]\n",
        "    if self.transform:\n",
        "      image=self.transform(image)\n",
        "    if self.target_transform:\n",
        "      label=self.target_transform(label)\n",
        "    return image,label\n"
      ],
      "metadata": {
        "id": "3HVm2Iulw2ck"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __init__"
      ],
      "metadata": {
        "id": "ntMzmW5YzXlf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The __init__ function is run once when instantiating the Dataset object. We initialize the directory containing the images, the annotations file, and both transforms (covered in more detail in the next section).\n",
        "\n",
        "The labels.csv file looks like:"
      ],
      "metadata": {
        "id": "sn_hAO6Yzemj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tshirt1.jpg, 0\n",
        "tshirt2.jpg, 0\n",
        "......\n",
        "ankleboot999.jpg, 9"
      ],
      "metadata": {
        "id": "Mwt-NuShzhIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):\n",
        "    self.img_labels = pd.read_csv(annotations_file)\n",
        "    self.img_dir = img_dir\n",
        "    self.transform = transform\n",
        "    self.target_transform = target_transform"
      ],
      "metadata": {
        "id": "E0Ml74N_zjcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __len__"
      ],
      "metadata": {
        "id": "DWtgsiUIzlqL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The __len__ function returns the number of samples in our dataset."
      ],
      "metadata": {
        "id": "IsY0y2dBznZb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example:\n",
        "def __len__(self):\n",
        "    return len(self.img_labels)"
      ],
      "metadata": {
        "id": "mZUJi5y_zqH_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __getitem__"
      ],
      "metadata": {
        "id": "AlNTg4qRztKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The __getitem__ function loads and returns a sample from the dataset at the given index idx. Based on the index, it identifies the image’s location on disk, converts that to a tensor using read_image, retrieves the corresponding label from the csv data in self.img_labels, calls the transform functions on them (if applicable), and returns the tensor image and corresponding label in a tuple."
      ],
      "metadata": {
        "id": "tWmHbpeozxpo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def __getitem__(self, idx):\n",
        "    img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])\n",
        "    # convert to a tensor\n",
        "    image = read_image(img_path)\n",
        "    # get the correponding label\n",
        "    label = self.img_labels.iloc[idx, 1]\n",
        "    # call the trasform unctions on them if apply\n",
        "    if self.transform:\n",
        "        image = self.transform(image)\n",
        "    if self.target_transform:\n",
        "        label = self.target_transform(label)\n",
        "    # return the tensor image and correponding label in a tuple\n",
        "    return image, label"
      ],
      "metadata": {
        "id": "2BOfXifq0AbT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing your data for training with DataLoaders"
      ],
      "metadata": {
        "id": "Mx3jSiHs0sHI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Dataset retrieves our dataset’s features and labels one sample at a time. While training a model, we typically want to pass samples in “minibatches”, reshuffle the data at every epoch to reduce model overfitting, and use Python’s multiprocessing to speed up data retrieval.\n",
        "\n",
        "DataLoader is an iterable that abstracts this complexity for us in an easy API."
      ],
      "metadata": {
        "id": "COO0JVc30xSe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)\n",
        "test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)"
      ],
      "metadata": {
        "id": "4LSEHJau1Oy7"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Iterate through the DataLoader"
      ],
      "metadata": {
        "id": "YfmcdxHt1W0d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have loaded that dataset into the DataLoader and can iterate through the dataset as needed. Each iteration below __returns a batch of train_features and train_labels__ (containing batch_size=64 features and labels respectively). Because we specified shuffle=True, after we iterate over all batches the data is shuffled (for finer-grained control over the data loading order, take a look at Samplers)."
      ],
      "metadata": {
        "id": "lInvMwVu1axV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display image and label\n",
        "train_features,train_labels=next(iter(train_dataloader))\n",
        "print(f\"Feature batch shape:{train_features.size()}\")\n",
        "print(f\"Label batch shape:{train_labels.size()}\")\n",
        "img=train_features[1].squeeze()\n",
        "label=train_labels[1]\n",
        "plt.imshow(img,cmap=\"gray\")\n",
        "plt.show()\n",
        "print(f\"Label:{label}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "GoaVAHR81xy1",
        "outputId": "96aab602-e9dd-4683-bf8f-19f575424422"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature batch shape:torch.Size([64, 1, 28, 28])\n",
            "Label batch shape:torch.Size([64])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label:4\n"
          ]
        }
      ]
    }
  ]
}