From 22900c8c40beee5211924084c6deefb3b4ae9af0 Mon Sep 17 00:00:00 2001 From: Fafa-DL <516451964@qq.com> Date: Fri, 21 May 2021 10:05:33 +0800 Subject: [PATCH] update --- 10 RL/作业HW11/HW11.pdf | Bin 0 -> 406939 bytes 10 RL/作业HW11/hw11_domain_adaptation.ipynb | 5792 +++++++++++++++++++ 10 RL/作业HW11/hw11_domain_adaptation_(en).ipynb | 5900 ++++++++++++++++++++ 10 RL/课件/drl_v5.pdf | Bin 0 -> 2299964 bytes 范例/HW11/HW11.pdf | Bin 0 -> 406939 bytes 范例/HW11/HW11_EN.ipynb | 5900 ++++++++++++++++++++ 范例/HW11/HW11_ZH.ipynb | 5792 +++++++++++++++++++ 7 files changed, 23384 insertions(+) create mode 100644 10 RL/作业HW11/HW11.pdf create mode 100644 10 RL/作业HW11/hw11_domain_adaptation.ipynb create mode 100644 10 RL/作业HW11/hw11_domain_adaptation_(en).ipynb create mode 100644 10 RL/课件/drl_v5.pdf create mode 100644 范例/HW11/HW11.pdf create mode 100644 范例/HW11/HW11_EN.ipynb create mode 100644 范例/HW11/HW11_ZH.ipynb diff --git a/10 RL/作业HW11/HW11.pdf b/10 RL/作业HW11/HW11.pdf new file mode 100644 index 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+ "metadata": { + "id": "b5cFq_TgWlQ_" + }, + "source": [ + "# Homework 11 - Transfer Learning (Domain Adversarial Training)\n", + "\n", + "> Author: Arvin Liu (r09922071@ntu.edu.tw)\n", + "\n", + "若有任何問題，歡迎來信至助教信箱 ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vNiZCGrIYKdR" + }, + "source": [ + "# Readme\n", + "\n", + "\n", + "這份作業的任務是Transfer Learning中的Domain Adversarial Training。\n", + "\n", + "\n", + "\n", + "> 也就是左下角的那一塊。\n", + "\n", + "## Scenario and Why Domain Adversarial Training\n", + "你現在有Source Data + label，其中Source Data和Target Data可能有點關係，所以你想要訓練一個model做在Source Data上並Predict在Target Data上。\n", + "\n", + "但這樣有什麼樣的問題? 相信大家學過Anomaly Detection就會知道，如果有data是在Source Data沒有出現過的(或稱Abnormal的)，那麼model大部分都會因為不熟悉這個data而可能亂做一發。 \n", + "\n", + "以下我們將model拆成Feature Extractor(上半部)和Classifier(下半部)來作例子:\n", + "\n", + "\n", + "整個Model在學習Source Data的時候，Feature Extrator因為看過很多次Source Data，所以所抽取出來的Feature可能就頗具意義，例如像圖上的藍色Distribution，已經將圖片分成各個Cluster，所以這個時候Classifier就可以依照這個Cluster去預測結果。\n", + "\n", + "但是在做Target Data的時候，Feature Extractor會沒看過這樣的Data，導致輸出的Target Feature可能不屬於在Source Feature Distribution上，這樣的Feature給Classifier預測結果顯然就不會做得好。\n", + "\n", + "## Domain Adversarial Training of Nerural Networks (DaNN)\n", + "基於如此，是不是只要讓Soucre Data和Target Data經過Feature Extractor都在同個Distribution上，就會做得好了呢? 這就是DaNN的主要核心。\n", + "\n", + "\n", + "\n", + "我們追加一個Domain Classifier，在學習的過程中，讓Domain Classifier去判斷經過Feature Extractor後的Feature是源自於哪個domain，讓Feature Extractor學習如何產生Feature以**騙過**Domain Classifier。 持久下來，通常Feature Extractor都會打贏Domain Classifier。(因為Domain Classifier的Input來自於Feature Extractor，而且對Feature Extractor來說Domain&Classification的任務並沒有衝突。)\n", + "\n", + "如此一來，我們就可以確信不管是哪一個Domain，Feature Extractor都會把它產生在同一個Feature Distribution上。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3-qnUkspmap3" + }, + "source": [ + "# Data Introduce\n", + "\n", + "這次的任務是Source Data: 真實照片，Target Data: 手畫塗鴉。\n", + "\n", + "我們必須讓model看過真實照片以及標籤，嘗試去預測手畫塗鴉的標籤為何。\n", + "\n", + "資料位於[這裡](https://drive.google.com/open?id=12-07DSquGdzN3JBHBChN4nMo3i8BqTiL)，以下的code分別為下載和觀看這次的資料大概長甚麼樣子。\n", + "\n", + "特別注意一點: **這次的source和target data的圖片都是平衡的，你們可以使用這個資訊做其他事情。**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DF-i0sVlnUbq", + "outputId": "164a5e20-ca5f-4574-f385-154f79ac4834" + }, + "source": [ + "# Download dataset\n", + "!gdown --id '1P4fGNb9JhJj8W0DA_Qrp7mbrRHfF5U_f' --output real_or_drawing.zip\n", + "# Unzip the files\n", + "!unzip real_or_drawing.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[1;30;43m串流輸出內容已截斷至最後 5000 行。\u001b[0m\n", + " inflating: real_or_drawing/train_data/0/106.bmp \n", + " inflating: real_or_drawing/train_data/0/107.bmp \n", + " inflating: real_or_drawing/train_data/0/108.bmp \n", + " inflating: 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matplotlib.pyplot as plt\n", + "\n", + "def no_axis_show(img, title='', cmap=None):\n", + " # imshow, and set the interpolation mode to be \"nearest\"。\n", + " fig = plt.imshow(img, interpolation='nearest', cmap=cmap)\n", + " # do not show the axes in the images.\n", + " fig.axes.get_xaxis().set_visible(False)\n", + " fig.axes.get_yaxis().set_visible(False)\n", + " plt.title(title)\n", + "\n", + "titles = ['horse', 'bed', 'clock', 'apple', 'cat', 'plane', 'television', 'dog', 'dolphin', 'spider']\n", + "plt.figure(figsize=(18, 18))\n", + "for i in range(10):\n", + " plt.subplot(1, 10, i+1)\n", + " fig = no_axis_show(plt.imread(f'real_or_drawing/train_data/{i}/{500*i}.bmp'), title=titles[i])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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mPXBCcT97tZ/lGwnGmJ+HE1j8hllked7t+AcEBAQEBAQEBAQEbC7E5WNUKNH/Dk47YxDtOSAg4HmI8OEfEBAQEBAQEBAQEHA3nIvNHJzL05vWccMICAh4HiJQ/QMCAgICAgICAgICAgICtjDCjn9AQEBAQEBAQEBAQEBAwBZG+PAPCAgICAgICAgICAgICNjCyF7OyaMTE3bHrt0AABO5NQMLdRUw1ujJkrykJ4Gh3z/zUy88MiDvclwYzDO6f9/rrXN5KwdsrCFpfVkBABKXbzIZAMCp48exMD93qVtfFMYYa0z/JdiF48Jjm4VBRWD6StGuOW/wk6z9zZXjct51/XtZa2Gt3VDBTUxM2j179rqnkislVC/cN3w6STRE++LSuTTd7fQAACMjU2levpBfe4F1wO2hXl8BALTa9TRvfGw6TWekbdpkcM3F8oyNRo2ur+09ipxZmZ9fSPOyGTU12SyZHSmYbqeTZjWb6k6YSH/x/1ubbLheJicn7d69e/2TX/D/Bbii5jj4R2nu5bhXcd+9xM/01Iv3p/X741r7/Uz707FjxzE3tzE7Nj46bHdtd+3QN71ur5ce7/a0bxjj2mgmm0vzSuVSms7lXH671UrzOtTGIsshw30bo+vT0Ujsd0R1YQeUa0w2n+/Vi2P/0PT8nI4uerzvXrbvP5fuq9D+sWx+YQmrq/UN1cvIyLCdmZkBAHR67r0iQ2Na31jj0/T8AwbOZ9IFUpvV1yzN2uMMs/Z431g4sI0Phr/UIDu9LvrOJVsvB7gNnTh+Ys5aqwb9CjA5OWn37dvXd29uL/wMqS2ltj+wCCm9XjuLpU33qH/66/f/8pnbOh4X/Bh0WXOXvm6if/h+ydcvFotp+sI7HD16dMO2rFjI2aFKwV1f3sH22ZxB0FuyXcvn3XW8TQOAJFZbxeWu46Ue7+8na+ujv4jNunkX+dElss2AlP5h1uuIkt3tundZXq6j0WxtqF6i0qiNRrbLs/oHuPgl+9qgGbBXuk6HiaB1kDMy76d6i2mMSPzYEZH9ojmTjWROJuOeuxXPDeyaRzHcBuKefxk9ntX5o73E2Da4G7vMePkskubShuqlWinZiXEX9c6Pt3Yd4+P7URyr7el7Lzm3R/0tS/2JX8am7X3wuOLtUDbScu9rD36MoavH3B/XPh4s1Yv/HsxEVNd0LbbVg+yIHXDct4L5hRpW682B9XJZH/47du3GH/2Di7KTLbhJVkyNO5No4STpoDn4Q8dPAEzEkwY6btecijyVV18DH9BxL0e7wP/+UoNMQoejhI0ZTfjkfWytoeeWaJCpu/zCiGvk3/6qVzzj51wPxph0UBg42Rkwobyc44z+IcSfq7+PeJFDzk7oAzKK1pbxoMUC9yx+JsONW6/v79v/pIMndnoP7vTUoNJMd/1up7X22GViz569+PSn73P39x+4NFFq04dMVyKGNlsrad7/+PB/TdOnT50HALzx9T+W5unHKw0cAKw3PFSv3a6G4f3i/R8HADzx1BfSvO98y79M06MjE+43HQpjarXca6vLAIAHvvzpNK/X0/IqF8cAAO97r4ZxHqMFi8lpXWSIMu4Zz5w+meY9/NWvpen66ioAoN1212+1tF9dKfbu3YvPf97VSyKhWq3VejEDbA9j4IeY7T9jYNK6+rbxoPCwPBGhPkQDgu9HPBjwB1gmE/VfB4P7cd9iLQb33XSxkp+FJh16LYe7X/pNa45dLnZtn8ZfvedXAAD1tiurs/NL6fFzc4v6rMVhAMDI5I4074W3vTBNz8y49vbUU0+leSePH03Txa62IyNtt9HQvpenkbFSchOkAk2842TtuLYsbRUAjp04kaaXVlx/MfThkaVrZfNuLI1oIhbRuTyR8PftUX/n9Tkr7cVPJH7pl38TG8XMzAz+22/+KgDgzPxpAEAxR2Nam9pj1pVllNHjrRa1m2ws78GLOIPHXb94EtGki9N+4seLqVFGy7UjRpU/TnkBUps2L1pGa9I0V0enM/iDKiMXs1RXbD+70vcLNAF95798xzFsEPv27cMDDzzgnrPn7t3rG1f0GVoyptXJhsbx2nron9dqefgPMABYWnL9cm5uLs3rW7BNQ2DT5Jzgy47Le2JiIk2PjbkxhD92B/2e5xaW5mRc57WaW6CemtIx6OB1B9O0b1H+/e+8886B97wcDFUKeMNrnD0q5NwdOh0dgy21OW+CM0bb5vDY9jS99wXXAgC2bdO81RW1ha2Gjr2NhlvMr6+qLevG+oFppT4MDWy5DC04pIuc3Od4XMj4TH1+HiL873lcMeuk5aOq//o8yXb5Z8+5DYQ/eO/fYaOIRrZj+PvfCwDIiC0wWbJPA+bCvAgTySJM33OvM96WrG6MzGRduljT8Wz56Ok03ay5eotLOgZ0R8Y0XRoHAHRyw2leQnWUdF17ypFdzTX1Xr3lefeoebXLmQkdO7s5vW8sc6H+BU9etPK7WO5ei3/4w9goJsZH8O/f6aLpVSpV90ydAQsXADodZ7+WVubTvAyNCzl51tmujscTk9v0ZmQb4sjVLW+i8dx/dNh9p42WR9K8QqTn+gU2HoMXGmpfW8Ydz+X1+XoN3RCzTWcThgpqm8jkotnRa2XyRh5f7SzPh1NbaV27/s+//iGsh8v68I+iCEWZpOQLZQBAYrRBZHq0GrX2O2vwotEzX4BHpu+EtavafR+wfTsD/f9feLPU1lziAfiTNqJ+0OUDsWs0Tz32xTRr7w3XpenTjx0BAFx/10vddTa2eXnF4KLodmlwtmsH/77dL057u5fh1bC194rMeh9CMnjzbjIf90ZsnY8qb/f6NpD7Pub5JZ5pg9wo46D/Wn63wS9YdDq0SEQTqdQWWX2Za/bRh8yUa1fTUzNpXhzzpIcmcH5BhIx1n2HMDAEAvvrwl9O8pdVfTtN33no3AKCY0Q/05WUdRFptZ3haLZ3ItFs6wYyt+4ivDA2lecWiDphL8+fT9Cc/+Un3rFDM7BhP02MT7n1ntjvDff9992PjsIDxbUuemz/8yYoP+vDvW6ZK5OMj0d/3L3bSBaSO4p5OxDBgAS6ijxNQ2k/oeWc5x5MSSVvqpbwbNHhRUG/FH6M5PxHIsE3nFRG/sotNg4VBzy+8yUDfoY+nOn1YZ6WOFo9ou1xcOJOmb73ldgD0HgAK9DGasWtXkaOstucoS3UoH9E81ln6oFlYcs919LhO5Lo9rYOhIdePuokybPo+VsWQ+oUbAMjk6AOV+rlJ6552cnmmIO1l0CLQlWK1sYr7HnALZSfPHXfXN9pWCg19r9um3aTtWE3L8kSifd/Kx0+ro7/J5WnnicrFf7QWi+U0jz9UB4373Z7WUV0mYPzxy7u+fheI5wrlit7LP4pnWwFArTb4o9nXIS/AxjTB7El9Fdf5kL1SNBpNPPilrwAAjh516wgtYrnwB3BPbFyU1fZSKGif8LvgWWp7vBPFls9ft9tVW8Z2Sdsy7WT1LdBEF1yxv2xbTbKRgoE728xoiAcv4LTbrs3xLv+zDQugK/Y+IwsSeWrnXRoDPLOO7X51SD/wpmYcy3bXgZvSvA5oAYzG/pWFWQDAqaceTvMWz6td7MjYzQXfR+zzcwfeYKHD/oO+b+Pt0lzZvrPX3njwJtSgXeyNw1x8p5/y/G5vhhYGkgFje4Y+BoqR2rW9LZ3n3G5c2446+tH30FHd4Ficcx+xnRLZh6rOnzJFtyhWmdqT5nVmdqXp5aI7N5ZNGQDoHP1Kms7LIuqeW+9I85ojypCbo8VbPwftn8fwhpv7X8ti4zWUxAmaq9I2pR9HNB7G3M+lXgpFff5TZ06l6T3VUQC6iAUAq221LaM0njRb7p5s80YruuDSkw2n+abWZSbS8SyRj/F2Wz/Gaw0d+zIl+V6mMahM86i22OQTtUfTvCjL8zu9l+kIi5EWWHs9vW7eOvsSJTJfSQYvugLBxz8gICAgICAgICAgICAgYEvjsnb83V6SW2GI4OkgtOptkjW/MLRN37+aJ/89A0q+3xS36+1eDNqx79uWk50uWjHuo8NeclfEH1/H14BWlvJtt/sTnXg8zTt8+iE9teroNabkKVObvJ45yG1hgJ9fjlbT7jyoNJPt426FqpDT4zneFWSasYR2jWOmAJNvXbrbQOVOZ8bG70qRnyA9dyyrjC1a/G/0dEX05KL73bHZGv2GbnDJhWheaZb33cSlMGu1fXv/5BbRl3jH3u8exT3Nu+XmV6dpT72Lu7qbw/7LeVqx9O4UvBK/sqJltGu320F4/evfkuZ98jN/n6YPH3kCAHD7TTvTvOlpZhrEfe8EADG1i9173fVvuPH6NO8Ln7svTX/i459M0y97+T0AgJe/8uVp3hNHHkzTjz/2GADg5tvcbsfDD+lOxkZgvM+tLl/rwUv5YvathIsdJDrloF12d46wA7q8m7V255fdDkA7O35nKEMUP2ZB+XdhFxbD/mB+Z4h9/9inkKiyib9vv8MZ/RFd8P/GkSQW9bZ795bs9NdpdfsE0ecTee69+1+Q5s2dVXeRP3/yEABg/36l9BYLukPQbSoVtteVe1C5Dw/Rqr6R31FZN+van5467Fhc7Y7Wy46dB9K0L7WVOjMCiF3g6bE0jjBDp39HTbQuuGJ4syby9Fn5/Sbs+PfiHhZqjlq8KrogGZo6jGS0Pd82KloAtAPy+ArRjmU3pDSkdtxkuQ0SfV52dpIMuRPm1vpa8g43EaZgMzLWkW1khkySeOqk3rPW1J2hjuwUt5paV8zD7BH9NC+7yUMjuku3uqIMFU/zNsnm7rWcPnUK73rXzwEAlpfdbh+zGvpo1DLmZ/LE7Mkyy8f9rs8NZYDfPaczmbX14fKFvUT2pe9ZBrgVsN2MBzFbqG14xkHcRwHWdnjwoPb766+/fs39oz526ObDwiL2PtBig8sF3WlMaNfPMxW4bbebunN7dtbZjV55Ms3LTpN9Gaqk6XjGUZKH8po3SbuNidjTDrF0TE/bfCJuT50u0Ynb2o7bsnMa97hPsM+y301fZyLWlz9gR3+Am9362l5XhpQBvPbp+sZrX28Rj5f0MFlp+wfKenx3W10wKmTvd0243djMpLJODpW1PXbEHh7Yq+4chaL2wxMnzgIA5g/rzvO2kj55SzSajh7Sb43eiSNpeuetjj16zcFr9P4NYjK0tI79XHggvR99MxZsJvycbFW0o3hHv8/Fq+PaZp7qaqSs5dqEswPjQ0rPX0nI5YncUhOxHzmaZ1VLasOXhbnWJHbuE099VZ+r6H4/Map9k79XcvKsE6PaH9tks5brrr1kyB5Vod9jExV1UfCuKa1Ex9NGT9NRzx3PiTtK1qzPLgs7/gEBAQEBAQEBAQEBAQEBWxjhwz8gICAgICAgICAgICAgYAvjsqj+BgZZUUH0NIIMiYBkWRDkmfJz1gsLcpG/1mAAJahPHdSrz5u1lEk5IL9n+tfa548GhCsEgAwpKx7/7GcBAMuPP5bmjV+v9JpK1YkfwausXzLEyyaA3tvTykpDKjRzz61K4841HSUmQ+XHAlswSoH1+la19mA6n1cyZgpgLqeUmY5QdUZK5CpAlJ6KD8/FeSQYd77t2uDv/qVSw1eJ9jnYi2KwC8SzoFUGa1XArxu7G7CQYj+1MV6T1ycYZ9YqNp8/q1SyHLlmjI052hGLoyyQoN74uBOKecNrfjDNO3rkcJp+/ImnAQC33KSuBtUq066EUkTK3iykcuLYUQDAe/+/30/zHn3kEX2+SaVgQVTADx1ScZNOS9/xwH7Xd7yqv002qb9IRUepEOJ6p11cFd/T4Th8Ul8dcvgeT7lkYSe6rm+ZUZZp3krXymSELhhxHonAeTvHZTQgFpkZQLd0B+i90rBwF7eJ2ERx0oXFRfzpBz8MQIWw6kS9fvwxdZ+qSlSUG264Mc0bLlfTdLfpxH6+9hUVWV1eVlrcmbOzaXppydHtygUty/ExtTMz066/7NquopOgyAxJ4upjZvvuNG9sXKl/XRHgYap/Hy06FffTes/2icCRfUxdhch2cWQ9sZX+mpcW3ro0MpkMhkRo6vyyFylV2zQ2rM86bdy77qvo+JJfUFpxS9wpMiRalM0zXVzroNl0fT6hsZrbu7eZHRbLpMLIFd0zZKjJq9AAACAASURBVNeho7clcgS7KXXbWq+eOt4ngEm+NTH3DXFXsFRVrbbSoWurjr7a2WRxv26vh7NnHQ2YVfU9uE1F/t5URoOiGPSrrOu1+gR/vRDpAPq++6Pvv3WxrhDqJc7V8ZLtrrbJaYock4qaXuJZNhcmVV3viItChyi+PF76PhrT2M7uK7VFJwhXqKhI31hRx9AyTcmGJpww2dh+FYFDR9tcc8m5EKzO67VyHXUr8NOIbJ9bB7sMOhG6uVl1qzp3RinlsW/z7K7Gbg19440fY5g8vlZ8+XIidF0aNnV58x4KrI7Pk0Y/V07WiSp1jbjFftt+mged1PderKktuUbcH3m8nJ7WKBYFET/es13HjW3TKjK3S9Kff/CJNG+iq/VWzTsxyJWGuhoskWvb2F7nGlLP6rMuNZXe3+sr97XCmX1i6X6/eFBowyuFsUhk7GjIXK9OLipjpdE0Xco62nxMArHVArm7yHMXI3Wt6VPCJzc93zNsS8utWSOB2bor415M8+sR7XB5sanliFzeQa5n4jLDQcJYc2/EujaQo+LPRPou9Sb9UISoORRxLq913PRzJT8WXcTihR3/gICAgICAgICAgICAgIAtjPDhHxAQEBAQEBAQEBAQEBCwhXGZqv6q3J/xCv6k5J8lyswg8VpW7L0UFYxpY4NCsF8KfRS1AT8nhlhKx47oR3miN6W59PxZolYff0IpqPf//d8AAIYptuf0NlVprAu95sxT7jfdNlE5NgGDYhsPKjh+1x5R+U8fd7SyyCpNkZX2o0gpnFm5bptV/em6sfAeDVH1I6J9xUJ7ailjB4baUybnKXr6m8lRUsMelXwOKnAprEMbs2sSG0ec9LAsathNH0u4oPF5kwGq/v1x34lWKjSoWn0uzXv8yX9K0/m8UriuO/Bidy16lnxJKUHFkqMSLdXOpnk9osuOVKfkmZQa/bFP/m2anlt0bgMvufOeNK9aUHeR++//nLvOiFLV3vq2707TK22lPD/5tFNIPXJcXQFuvvlFafqNr34bAGBqykUY+NhffRqbgbScB8S2xwBKax8ldYCLRjwgxvSFad+OWVG671qSZuox0yw91Z/R59CUhideT0nb35Qp/fz7tVFX1lVnlmuk77cJdMyVlRr+7qMfB6BRKkZHldLaIipzRWj9nabaruFJpfpPjjtqYNxVV4GVZbW18wvzafrYcRctIEO2p1Qgld8h13f2792R5h08oKrau3a5fG7vOVJO70rEB477zpTRQQrpOaKEc9xgm/h+Sm2EbS48XXtAdJcrhDERinlHm+x13H07LS33IaI+mtiV8TZyV5mh96pF7ndxj91h1KazMrv2OYrHXNPBguO2e3CWr4MeRwqg/pAItZrHvw7R871qf4HcC3ioyRItuC1czlZHf9/nciPPEHfXj618JYiiCJWKs+delZ/bGVPGfZSdDCn5R2Bqc7zmGStDSpctFNX+lGU8yWTXuhq5P9ZGUmLqsLcbPaLzdvrSrh30elRefa6Ya12R1nUD83OiPurysw9v5707QovcSIYqWq4lGbsLec274drb0/SOnfsBACNj6r5gyL4MUQSSsrjNFGj+mxS1XFaERVxoU+SHWN2a1N2V3WP0YjMzbv5SLOhveJ5y8oRzce11tR9kyRZkDI1hPkLGOpVx4Ri9KZOzJAEkdnsa0YbdYfhZxVXS0nfNaF4f9pu2u/HmJdu1/I8t6K3y0zoe7XqB1F2svz9w7a40vbCwBAAokdtrTDHaKxK5YXxMxxj2kC2K69nevRrlBjTe9UruWQ6fX0rzVlZ1bOSAZb5v8pwn4YmC92eya8+7UiRJjHrbUfBzOVee3S7ZDmrPeXGX7jSov3d1XMjI87RX9V0NudlYapvFimvPhYjaM3QubYw713Z07nHdNTel6R5cGc7PHkvzooi+6URhv0OuBJWszsULRVefvRZHsyH3eZr/dZvOJnb7pmTaj8tDrlwSP5e/SLWEHf+AgICAgICAgICAgICAgC2MyxT3s6n4hl+I4EWFLC8KRWtFszhmtLng/zUX69uJuuSDrcGgRSgON1pf0VWZTndtPNGM0ZMrJbdKWSERoiJdbPmkrvbMnXe7oaVdGo/z9DFlBNRbrsj3X+Niy27ChgystX07iz7PY9CuD8f35nf1K0ydLq/Mrt2VBICe9eJIuprWpTjH+bx71yijq9MsYJOT/Jh2ZAoZeu5E6ogUkwztTMc+DmkyeGmrj/OQrN3Z7d/hlOObuBWwsDCLP3r/bwAAej134de99jvT46OjGqOzLTtNvEPLz9qVnY92W8t/28z1aXpyQoXHfHnzruHEuArJzM074bPf/v3/lOYdPabifq991T8HAOzcdm2at7JfhWCSp52Y4ulTKvCTM7qiOTXjdltvv0N3LSoVZTosLSlr4cV3ONbA4oqKD/Y994RjH5TKbsWby2czoGXM8e4H74ineX0x1MV2EHsjWk8UR3YO+gWOKJlZ2wZZ7MzIOm2GdiCi/j1/+V/7WLur/cWzfDiuOTNwwGXrd6tot8cMMMp2oCG/MlgQC0h2KvMFtaO33/WKNF2QNjJ3TkWqJse1jZVld7JEsZAnSLBv9w7dObEdt2rPY0aeGF052eUZHtHrj46p2FBe7pHlGPJWy7224vpGj3a5cwWKYy9MjogYHSx+lycRTS8yZCMSCaX1+3THztfbJgwwSZxgddntqLQb7r1y9KyVjD5fLP1olEQpt5MY5cminz9oXkK7MXGHxhe/c031wuKoficxXyB1sz7BKb/jOngn2DMl+BcsLuaPs2gx7/7zqNOWuOZss3M07hVzrrwS2sXbLKSPJ4wVC71Hqay24pp9jqXCrIkTJ9SGe5bIHbffkea99KV3pemREWXfjI+78SaX13YQUz3Wam63rUECYp229gnfv7u0y7+yqqJbCyJoNzenzJyzZ3WMWFpybLR2n6Ahzx3WptlWPvs7/ia1t10ZG9qk6lUh8Uu/o3/zTXemedfs07G9VJCdPN6BJRufLakYWFF2dstF3W0GxTBvy/yrtaD1Mr+k5eqZg0NVtXVVSvtnKFXU/u3Yo/OEZtPV4eyZo2leneq1MsTjiYyHNJ6ZAfOzTRVltAls27WZlIlKO6y2R8/nx0n6btk2pv3/lh2uf2+fUPuT2adzum5Px5htu1x/iYhJcevt16Xp06cc+zIh0dh2XfvL/KIrw5FRrdcijWfLIlA7XNa86YLef0l2/2djEp6j9pjlzXNpI/2MJU1aP8f33wVMn75CRCaDUt69WyTfIPksMw60XGbnzwEA4rYyFoo0p8nLLntCArQ9ooLleGyV8soPaRsvkpDg6MQ+AEA71rpYSVRUsd6QZyizuCB948jYnaV7dgtr51S9utoxv7MPAKWc2gkrYx99YqFLjMZywc3xu0mm79qDEHb8AwICAgICAgICAgICAgK2MMKHf0BAQEBAQEBAQEBAQEDAFsbliftZA9t1awU9s5aAE5OgXVSQOK998R8VKTut7zIc01VzB2hS9Yv/eQ2QvkuxcJpLN2pE11hUikRe4pJHRIlKjJ5bEVpTke9QV7pcpadiEQ2h0Z1vEf19VhU/bNtRN24QAQuzmbEwLwN94ofUCjpCr29RLPUS0VgMibp4HZQiU0+Ilp+FK4POqorEschcZchRBy2tPxXJXySROMu9hpa1JfGnWGKf2nVEewa5e2yG2NUzRS5Xwq7tLs742XNO0O78nMYPHxvdmaa7QslmKmuGqKIZKZehstIt20Pa7k6dPJemPWVzZEQpS+2mlmG95uqjnFX6/+59SjW65oCj7k1OqZjZzUQ52v+CmwEApYq2hQe//DlNf8ml8yQGdfeBm9P0GIkUVcpO0OiJp76S5n31kfvT9NKSo3p61tnSslI/N4SUo75WpLQvpK2k++wNXcbbnkxfW6M2TLk9b6dY7JKFXKSMMzmiPmaJSiuiNHWK1WtIyMsLB9Xq2t9OzyqNc3zStbedOzXePAvKGTvgxTlrUNeJNjk6thim4WHXzm+9Td1FrrnphWn6QRGQXGyqgM/OnSow6euTY0R3SEg1Q+Kl0xOOYtjtDBbYyeTkWtRGmGrri7BLsYJ7q9qPOx0/1rCrBBndaK1LlDEsMqX5haLQ19vcxlhQVX4v19yM8SVOEqw2RBBL3sELygFAnm4RiStCgfiI+4b13OOxe64mCRa2SIQqIvppRvpchvsLC6IKxTRhamWfv4X8hvput8duaSIwZ3Tc559nZSyj7giT6PjTbrKbjMx3YhqrKB7zkLi95SqbLe5nUCw6Omld4lNPTKhd/6Ef/oE0ffeL7wYA/On7P5DmfehDH0rTN93oxqoXvlBt9U033ZCmX/rSl6ZpP+ZmyC2LB9xzZ5w72ec+/9k0Lya3o/3X7AMAbN+urjx1GueXZc5Qqykl/cTxU2n6s5/9PADgscceS/Ny6zxLKniM5w4G6lrl/7ckgjcyovHa9+w+CAC49rrb0rx8Qe2+p16vkhjb/JzOKYsFdbWZnHSuccM0ZwO5chpxA1lY1HH0Ax/4cJqeFVfVXbtVeO6tb/2f0vTYuLPLJRrbR0b1Xa7ZfysAIKJPi5PH1dV1taY06ZLMBQv0/CwqmApQDxiLrhQWJrVhVuwCMfn7XN/82MFusTtGdP66Y8w9t3cFBoBrr9WxNaK5bKmUfvGkeTfcokJ8oxNuLlVbVreIpXM6zkdCZR/bpq4EnZzOv770VeduXB1Wu9ruqA0+tuDmh72Czh+zvO/LExUROwdR/Xl6ZOBFiMWWreNqe9kQe5uRsStHhpfdDs6LG1C7rm0pN6bv1RGXunxR+8DYuM6FCyUtt7zMPxNyFchktT1G0jazVm3LWKzlNiw23uTJxaNC3yjiupFQGzrT0TnZWUkbO1iEmPtWJe+e27S1P/daNOcYcvfIiu242LdO2PEPCAgICAgICAgICAgICNjCCB/+AQEBAQEBAQEBAQEBAQFbGJdF9U+SBK2Go0i2VkXRnfg31ZJSJMpCT2QqKSsr9uR3WTqeUIDCRp1iTHrKXx/lUdPZrI/pqmCdyTSaANF4LKnJFiS2cIGkLYdHNaZqRfJjUqjt1ZRimm0rRa3ZcOc88NDDad6rXqYU1X1C1YnPO7qK7W0u9W8wBrhQUGG1KdZvN+OoTNfeo7S+fQeV7lccIjVriSWbJYqdIUp4q+Hoeucp6sH23UpvqqZxr4luTJyjnsS9XTmv1JjTD2kc99lzSiHzsH3KvX3Ef8lcq+QvJ286qtUR3HvP6wEAZ845mmK7o+263lC6XqXi6Ma1Va2L1VWlMkVCqc7nlMqVo7IuE+2+OuwUSsvUHzn+9PFjLm75d73th9O8Qk77Q3nI0YuYnlQileDZs+5dmJY2RtTF173ORQXoJko5P3biCX2+ikYgyBecCfJKrACwSn1/uebKaGLctRW7CQqyg8DuIoOIawnR2RKi1yepexCpdQ+gmQKq2p8jmiS/d9bzjMlOtRK1OY2Wo+udX3wqzWvWlLIZtd3v6kQJrbe0DqvDrpF3u1qXvZ4+S9QmqqwMDVniPmdL+qyRqNX2+lrWxuFjXm/b4aimB2/QmLlfe1z7+xfuuw8AcN3uqTSvTjRSX4aNurbRFlGJu0Q7VttAVP8sUf3lFXvkhrOyrG17uOpsIlNWe12i4IkrmCFOaUQUfO+a0+d2QXaK3Q6KkbsHMd7TSAgA4L3tMtlL0/2eOSwSoXWm0XzIvY3jTUexjJtEv99R1XaVO+Fcns7G2q6SDtGaaY4QC7+0R8aZXQfjjrtHm1w4hipaViWhwFeHdcxqkgp8veWeO5fXZ2n3tL14z48OjdEZomtTMBtk8u6PalEzp8dVVXtalNezWXX1+iQ2DmNM6t7goxdcd1DVwr/lNd+SpkfFfWaUolPs27cnTQ+POLtw7NiRNO/lr/imNM2x2ZeXXV8rk8tHj+ZUp065MebRRx9N8w4f1sgxO3c6t6OXvESjBmzboa46KaWcXAPZdc2raR8+/HSax8M5R3LYjDjjlwtrLeKu7zOufoZH1QXj+ms1csJ+iQteJkX2LrmdnjrrFN+//FWdU37qU+pCsX/v3jR9153uupWStuk6zSN85IR8Rsvnmr3aBnwkpuqwlnucaL3nxEaxMnpMY3Kp6t5xYkZdBeoUT/2JR47rOw65fjtM0SIKJZ13W5nz+EhSm1WL3tXOSISOaICtdye4/Dz58c0MqY1vrriynIt0LNk9pX2+QHUQ+WtQW5ya1nOL0s7nZ5XeX6Cxd3q36y+lYf3N4TNar48ddpGS9h/cl+aN1dUWPfLFJ937cWS1jNZxQtG24COuUL32RVtIv6ekLDIb3z+OjEElJ+OgjMM5nsvTPWpy/6Wm9hE7SnMXiS7Saagtr1Z1ztMm98DVmivv6rRS9Vs0BnnP5SJFoWmRG19GWmVE/aE0xO7Qrg67FNli9pSOAU/PunqbGNJrNpd1zjeS0f5QT5zNyw6pzc0O671y4oZdLnrXwfXrJez4BwQEBAQEBAQEBAQEBARsYVzWjn8cd1FbcKuPccOtdHQ5nu8e3c21EkuwS7GLG6u62h733OpFFOtqYnNRV0VqS7rqUZaV3sKwrgwaWunID7tVkYjextAuiI+7Xc7QChbtcBbb7r6WlvALeRVei1vuGc88dUif9eyJNH3+mO5m5mTlP0c7Ybu3665USeKAri66+NObHdd30A5P/4q3SycUL7lDO/4HbnZCM9/0pm9P88YnVYAnk9WVr0jU/TIZvSfvEFoRG2vfooyB6gjFm5drWaorQ7HEUzYIiTudu0Hr5cO//+sAgF6swnaGGoFhhkefeonPe3ZhYFNBsE7XrSx+8C9+Jz2ekEjIzTc44aVtU9ekedWq7hD0JP7qsVnd7R0Z0eNT01pHvg1EJLx26EndefHsgCr1p6cf/VKantnl+vRS/Wiad+68tvcjRx4BABw/qqv3xbKuHt96q2OLzM6qGNOpM8r6eNHtuot0/IRbLT9+St9r25T2F79S3vF2YpPFGf3OkKVd/EEbRJZWgVvE/KmvuB13Fv0qUVlwnOucxBs3RRJuy7MJdv2Bd3ATq30zlvpsxLpifXz2q2m6seLyY7K5IyQg2Y5cG2l0tHwrRSprWgdu19071uhdeWdkaMztzuWq8vtN2FVLbIJ229Xzooh6PfaE2txPf/ozabou77owp/c9c0bbWyIicU16/kaD3oUF6SQ9MlKlPNpZlh2vHu38LtOOf7HoGEmtNscUVzQarg6LtKPX346FsbYOG4nzPcunSzuaXdr+T0SYya/2b0Z3MQAKUh6eyderKaskW6WdSrGzLdolHCHm0W3jroyXjp1N8853aKzO6bk+znaD2BllEo+tZNzOdT6rO9hjFHPci0OND+uYM9eZS9NRR3Y/s1rvZ4nJ0DKuvvO065KjnbG80X44MeXO2bttf5q3a1zHvRnp+wvzg9vIlcOkTBFf18O0u8XodNy7xbSbfPKU2vBez5Xzq7/51Wke958TJ9SGF6VOsyQyOTev7KMTJ914sWOHCsQuLWl5PfTQQwCAB774QJr3HW/WOcctt7ld8BoJA/Mu89TUhDyH2tc6seV4x5/TzxWSXoz6nHvfSRmb73yRjnvXHFg7J2oToygmtt38gps7HDuiTIwTR5U9MVrQOdn8GbdzefRpPX52TudHtabrtzEZhlvvvCVN35l/EQCgXNZ+OEpMi6rMuzMZYsl0tM8URHB5eER3pkfHp+i4ts267IJaYtNNEgPOI94s8TgPsa2eXRaT/WQmViK2rETqpTuICTF3Vuz+kpb/JLGLDImdF2TM5zkZFSGqVbeL22nRLja0Pfg2klAfOLuq5T466sp1127duR6jvrntkLO3ddq5TqgubJfEav3PYmYja9ozqK2RvhdtBuvPwghLzM9jchHPnbQ9DpWcjc9llcGXJMT0GnK2OI4HMxaKBa3DfFnKg+qdfwcZx2ISbZ1fUjtXGXLXqg4pWykGsSj99xBpbVYndU5WFOb38VPKXKoWia1DgsEGru9253QMK5WVaTual7EvcWWVXKTfhB3/gICAgICAgICAgICAgIAtjPDhHxAQEBAQEBAQEBAQEBCwhXF54n7dLlbOOnEe23TUjyzRWjtElcxZR7dYPnM6zTv11JNp+twxJ9S0dE6pZpbiE2aIsl0UsbKJ7UpjMXmlfnSFGhux8BnFEe4KFSnpKBWsTOyvnHHHS1NKfb61pJSY2oKjdjxxvwqqNM8p9Xl5Xt+xLJSeb7lX6XKNvF631XQ3nq76GMTYVFxKyMYfL1SVe7Ltttek6RtucVSvoao+s6cAAv30+KxQ6RMWbSQBoFjEfho1pabw73NCuTG0/mQG0IYSpumQaMz1974VALD3kfemeYeeOqo/3GRa+OXCguIdC026a9Wd5fhJpeMNi2ii7Wm773a1XGdmHD2oWtW6OHJYadBepAQApmccvfLsaW2XS4t6/EX7rwcA9Oj6588rHdCLhzzwtX9M85qrSkGbmnS0s+mdSsPMkvCm13q89xXfob9vKzW42dRnmT3j6HLn51TwZNsOdVuYrEwDAD7/gHsWFvLaDHg3IGtZDIqMg6fNd0isbelMml4WFwby2sD0Lm2jhaJS/yD9ocvigCWtTx8zNspQjPSM/j4TuYLdNq19bG5OhS/PLbr6aJMdLVlyfZG+lSfqYr5MsXI5Tr20005T6fPteaX9LsweBQBM7rodgPb1jSAyBnlpPMdEBO70ub9Ojy8vUxuvCtWYKPnnzyl9vC3tjWO8c70ODSl923jxOSor/p2nzccs7rei/TgnMYBXSPA1l2O/M/eMLFzV75Ll2yBR9tex4/53/Ht+r57QM6PEu1ENvMxlIYoyKAu9stN2ZZzpahsqUIxj/yRtooYWSdD0JTJHKL1A291XyDblx5QWHPfcwz/29FE9bpVGWco42vHUuNoL5s96UcSkroXQXFD7kRXhy2KOxJpqWq+FrMsv5rU/VnP63tWq3mu/UGwzJX3+lYzS3M+cdvOFVm1zB3xj1D3Ot4lCsdh/wgXJFokRz86qLZuZcbTRyUmlaX/4wx9K03ffrYK/r3/DGwAAqzW1CV/4wn1p+iMf+QsAwEte8pI0z7vxAMB5Eew9T8K9jz/+WJrescfZ/RUSkB0l1zbvxuEFhgGgYfT6l3Z5fHZhLdD17avg+s4ucoWtjKpdT6SdtsmG5gs6l73uWud+OTmh7//iO1UccHpM8/28+chJrde/+Nu/TdOHjrvxqlBWW/T6b/1Wve4dToh6106da1uifHu3KHaLZUFmX8aG5gPFirrfTExrnzh52H0DLC+q3Rwiob+ctGObzho3Xn/GGGRkjPE21vb1Ee3fPn+IhMQny1ovk0VX7qsNnRscOaFz3R0z2o+qMq8rlUjUlcTXfLlGOX2Wyoi6UA2NORtUb9MYVtVvr917xP6QO141q7Zy97R71iPzNMawnYjIxUBOiYheH1udp/T8eGOkLMzG94+ttejKt0NWbCz3VxbjHRKBwyESuSuRMHtFvklXSOS4U9e5ar6sv4tFGL5DY5SlefE5cW1vkyvnR/5G3ZN2iFvqG16rbjw5q9/B3ay7V0x13TNaLzu2H3DvQi6hUU7Hw+UVta/5URnP2B2aXEfigsvviDtP3zz2AoQd/4CAgICAgICAgICAgICALYzw4R8QEBAQEBAQEBAQEBAQsIVxeVT/JEZHYrPnvJJnpFSs+UMal7wuatxnDqni/cpppY3W5x21N5soZShPKo4x0UdyGXev4hypNNKaxaKoa9YbSvVK6HhH7hGTYm+RA7eLcvbQNlVINF2iJkt86JOPfCXNKxPFoktKv+PTM/K/Uv5qDX3HtsSf3b7D0Yc2J86yYiCdjW7h6Y933a3xc3def3eajsqOxr1S03fKMvuelEojTzFc514p7QtKa1teZrXqhvzPlCe6mVyLXQk4tvze65xbwpvfrNTF3/yt303TdYoH7+9h16GLPRteAdZadIS+NzXu1Prf8m3vTI/Pzqmby8yEi72ciyp8hTQVC91ueorUQykixOlTqri8KK4pxmhZvmC/xnb21KwuUZeXV5XKNNl09733rjelea2W0tmqVddGOtSfSkWlDvrYpRyhYWRY6Yizs5o/PeVoWd/11h+j59PnWhAF029/3W4AwGMPqrLxRpCWrB1E9Sfauij/9tpKuWrWlJ7aqruyzhJ1MkO2gbnWbYkjn+SIbkeK06blyi2OKR58j/qDFEsZSsO8Y48qYR+cfrl7Pmr3JK6OalcoaEQXbOSJyk/Kta26e8d2UxVsO+RC0G44+5VfnJFn1vZzpbBWYzb3RL19gRRsC1QUWZEenprUdlUh9fhE2mZfFBL6fYVimZfLjl5aIEp3l1yW2h2pN1bSp7LwCucdciUrEE3SDkhFfVR9cZmiNhitQ/v3MbNtn22I16RrQllPKBbzlaIXW8yuSLQFUU4fJop1jmi93jMmjpQSm6UoFXlRor4xp78ZntK6aBotw54oTe/bpuNHaUjps0+ddBThs6eVIl6o6hjek3GjSJTWbk3bs5X23qO2e92kPlde6pCjZJiylntpVGnJE9bRcncefGWa98Xzake+evbLAIC5s+pStTkwNIfwLila5/Pn1YWqXXG2dnRUy5Cp+GNjLr9ANPN77tH32UUuTMWixB0nJf+FBU1XJdJDPqdzOp7rGBnc9+zel+bx2LY47+aSOXLf7BIVPpF2nqXA6zyGRzT2+P7Vp9b9bMMYQJ69IuWdKyk1O5PT585KX8pRhKwSuQWVhZrM7kmjY9pnKkSlr60423/kjI5RC6T+vij9eCTDEUYokobQkDmCQpko1Y2m6zOdLrt/ap/oxa6O2GWzMqT9d/uOPWl6/uxJ90zkQrZCkR+GxbXBpmM0NgxjImQLovruL9jXbvSPXsb1+5EiRS6jSFHVkhuveVz8Hig4+QAAIABJREFU6uManWhhUcemfdtdulTUdlmhCAFDFfdM7PqbyfIY4u7Vo3LP5fT30/Lt0iGfwyxFD9o24+qgWNN5XJNscNLR68ZSIJbaI7vBRd71YxNdZ4wxKIhrQtxxbSihsbtYVlvrqfrs0nSE3Fqz3n7ReNrqar12yeVoReZk3N5b9eU03Y5cP/rUZ3V+/Rh9555fcNfauVvHoDtvPZiml8VVaZnmBudoLlmU6HelUe2PEc35lvsiDEjEOJo/tozO9c4uu8gAEz03z0sSjsrQj7DjHxAQEBAQEBAQEBAQEBCwhRE+/AMCAgICAgICAgICAgICtjAui+pfLBRw4Lp9AIBhocEYQxRPomIdf9JR/PN0h207VNFzqOioFavLSpuYP69UsdW2XndcVDVNUS9WIIrXkGe8EB25TcqMKT2GlEizpLJYKYgCckuprocf/nKa9tEACiC17AIpNA8p1eqmFzkK/e6bbtPjTFUXdchyxbkCMA3x2UJMrgj79+8HANx114vTPKaVRWm5allbVt0n2qZnkfZR+AaoCPfnEWUopfgzz97QcYnW0CeQvZaTf88r70nTTx56Ok1/5CN/qe+Q0pL4WZ5dlV9rLbq9frrN9Ph+Sh9I0z05L2HFd1LT9dQ6piB7aiYA5MgfY2HB9amp6Rk6dzxNd9uOfjRHSv5HT2q5La86ReAXv1jLdWJC+24vds/Q6yp9qUtq2r6OSnltV+fnlaL/hS99LE03m+4aN17/wjTvwAtuTdNxy12rOiL0rsIm9ZeU/un6JlOi+6n+Lt1sqUvT4rLSz5dXXHooq7S+HtVhp5WsSVtysWgtE71eaNSr81qurXlVo+2KWmsmITqyIcqmvFO7qfSv2oLS1roSmaA4RbbzBUrnK07pdRG5d2jX6VkaVC5CbR6z/lob95WxAOLYvUO54uqZoziYhO23qHpTuzf0DOWKqMdXtA2OT2h/GRtX+mlX2kChpJTWVkvLMGNd3Q4Nq1rvap3qrebqqLZEUWnItnSFv9kjHmdMz+qzY6ImGkp3Y1Jclr6VofeOMlou3mYcOez6GyupXymsTVK3HitU/G5X+0PNahmfabnnWiWzV6IIOwV5lU5Xy29kWMtihmiMpaprmxFRnHv03uN7KnJPvVedxvhFiXJRyhOldlyPL62649ftm07z9o5qHbek79SbOq84ZzXdWdZrxQuuvXz+vEb+mS3q8eacmw81F5R6vykwgLlgzC7S3KTX0fpfkDa9b5+qyx84oC5gfoysUkSfW2/VeUyZ1LA7Mj/j/jlB0QDuucepXGcySqE9dEhp0Csrrv5379bxcKSqY9RH/86NERHNFyYntZ4imRTEHeoz66iLe4p/r7c+9XWzYSKDXEXar8xbY6LEs5udn5/ls2xD2aVSojWQC8aIUfvFr10su3u94Jrdad7LXqZzvVFxT1ld1f4XN5RevzDr3A9HhojSvndfms50JKoIuZP0aN4fe6o/2b88zXGHx6fWpJeXiDp9WiOzFMSFSk3hxudrNopg5Xk8q98SzZ2p9llxa905rG1welz71vCY63dT23UMvn6/ttEGRQjpJq6f9Fa1suZmtdxjaZtLyzrel4e1P2SHXT9eJsr66Tm9/rK4cwwNa98t0LuUxc2nnNd3XabvgoT6WeLbHo07ETUy78ahtmWzoi24/tIRm5Ih128ui6Yo9Hc7+v6PH9d204tc/d5+7c40r0aRFzotLQM/p1hepAgAkbZHU3TveOqU1hW7FzWa7viRYxrl7eWvUHeWc+L+9PARcnPPaLlfO+zm6CuL+kyNjvbNIn2H9WoyV6WIc0X6Jl5puLloJG2pdxE3v7DjHxAQEBAQEBAQEBAQEBCwhXFZO/5RxqBadauOZVl4yq7ojpId0hXJYsFdulLRFag4phWcjluNmD+nogzLFIs8pjjKccutVrEwBu+aGVmZ65KSVbNBK5qyAmJpx95UdBVydMaJZQ2P665dk1ay66vu+u0ebS1kdJWrPKmrUdfc4VbIR4d1ZRPglRe/i+3uz3E3NxPriQZu2+7edYJEsXglOSuCH8yIyPQJ6Kx93vV2/CMRlzPR2jw+t+/3JMoTPcNYvCzy8T3f+31penFRVwQ/8Qm3g5AlUR2+1Fo+wMbhxP1c2/PvyOyLPkE5WWq2JCBmadXf0yeyJHiSoV32Ulmvu1N2ZvyuJ9DPJDhxzO0+femBf0rzVhe1Hzbrrp/+w9IH0rxbbr49Tc9sc0yFk7OH0rxd25W9sHvHjQCAoyeVNfO1Rz6fppdpVXtu6TAA4NS5r6V5J0+rkMp1+93uf+Oc68/dTYgXD2sBb1/kf95h7YuwLo2kR+Is7ZaKs/TEJmTJHkQsKrOq57ZX3LN3aAfR7xYDQH3Jtdf6HF1/Tleam6su3SMxobijT9tuulXjFu34d1v63J7VkCE7Wn6UBKP26C5SUeIG8850zEKE0qcnp0WIJ94c9oyV+2Wk7edo54j36zyTZpXKr0cMmRkRIitXlY01v6Tn7tqpQmU+XvL8ipZ1j9gFe3a63bMO7SbPLat41rgIbeVozDp6QoU7vUiRyWn5Fmj3MePvxYQKeu8m1XFBksWS7jJZ2s1oC+ttTkTdet2N73JGSFA2rv9NVtwYu2tcx4x6Qcfah2VX4vyitpVaXXcztm93uyG7x5SNdKCo4/ZoVq+VzfidJ30WZuPsH3HlunNc5xWthOIlT7sdaN7pzWSI+QSXToi5ZJo6n8lKhZQoxnKXdtzOzOqzNmtut+WxpSfTvPmCvtdyw81tbE/LbTNgYFKWnme85HLEjKSx3Qu0zsxo2U9P6w7lnAhpbt++Pc1LaAuU5wGeLba8rOVVKOTX/K62on1uYVGZUn7E3bVLd+WuvfbaNP2pT38CAPDlB+9P8/LE3Mjl3JjfNx72je1rxS+fU3E/6Djgd0ZZjLdDTIyOsHKyNJ5nBsR4T8i+wNAY09Txorbs2lm9pmW9Z6faneHK9e6ZyC5sn9G5ajbjnnF5Se3byqja0HSXnGxti+fant1Dg2iWRORKZZ2TTM+4ul9Z1B3Ww4dUJK1aE/FNYZVxW7xiRAaJCFP6d4loXEtIyNMPk7sntA91aHf7kVNid6lepyraBidH1dasLLqd3+NPKOvl5NO6S3z8mJvzzM0p63l61zVpevzgLe7+wypeWqfxoiqx6/NVbUNxm4TTRWiynNN2xVOpKEcCj8LuYoYdz48SmcOnfWwzRBeh9VEolNY8U4aYxlaYS6NFrZeb9qodO3zSlWWFRBknhmhcoPeKZV53bkHbYKaq73rykCuL+TkdI+IMlQXcPU6d1j74xJM69rdb7l4r1EctjQu5tnuu3oJec5FE6ttUyS88uA8AYMg2rDZ5fufKoyHzvOQi4othxz8gICAgICAgICAgICAgYAsjfPgHBAQEBAQEBAQEBAQEBGxhXBbVH9bCCD8klv+jmtJ8LCn5tcQFoNdQml83UepGXignHaLZcbpA18oIybPdWKU8issodA2ONR4TFckILaqfakXvJaIqFYqNmuvosxarjva6uqzUknMnT6bpme1KG82VHPWj3dZySYjD6dkXmUJH/iY69wYwiAI/CNWqo/ZWSNCPKXp5cdHIECWfKeVm0FpRnzafWZNm94BstJbqj2g9qr9L85vxrfzP+DfXHNiXpv/VO34iTR856mjphw8r1apAcVDjVKlw8wT/rNVY1D6ucEJCj1xnNqXxcYx3TTfqru2zwJJloUU6NxUfobxeV/vGqSfvAwCcOfqw/oYUFL1433xNqYVPED1qYsS19wP7XqTPQrGwl5YdDTSmd73x4N1p+jzFli6dcX2KWKSIY6Iudxyta2RkRM7bDGcMmypTppRFFvejcktEJLTTUvpVq0HCb/J/kWLe2iadu0S0/jmXbiyqHasvqztKc8Xlt8imdkj8b3XFndsm4VN2ffCU0TaLFRG91bfBbI/ogFRH8aqeWxhyz2KyJMZJ75gT15LkOnnXzaBhWnUn8O/F9FyuF/+OUUGfb4iE2bI5107yBW1YubzS4xfmtQ1aofCz15UhenhT+l6DxifuL9m8a8N5cq0pEqW11nC/MzQmWXIdSeRScUzDMdMRqY56ImbWJXePOrWXtlD/Upu7Cd2lmI9wcLcbL7J79gEARolGWaPY0Ofqrj08QX18ld4rd7OzHdfdekea1zjzuJ5bV/pryQhFmqiVBRYKFB+ATKL9sUg20Q9VSVZ/n6GxJoajIPfIPSAmanxHxEl7XT3eqWm9nVlReujivCuDhZpSdU+RTYwlVnumqHOMTYHpF5sC+l3nYrJrPp+nCrfdpuJ9NREjy5NrQ4vckj53331p+uMf/ygAoDqs84gbblShQCPj2NyctgOmr9/7z5xw7M03X5/mNYm2Oj42Jb/R+uj1iO7r3ZbIJg1y0QKAJNmcOdblIAJQlHoRU4S4j95P8dRTW0JjOAn9+ersUXuyVK/shmZ7Hbmn2o+d0+puu23C2UCed7IQnzFSrtBnrZMAt29DXRpjWkRNzog7hukT+6W5fE7rc3TCuZxMTKpbwakj6uZXW3BuIr5/Jr1NcNUwGaDo51DuvSPqDzG915QIHO6b0nHjyJzW26MnnN1dXVb7U4D+ftuots3tw64tnHhKRegeuu+Labq25Fw0mst6/ZOzmj447tzNDPRZCn2x7V260aE2RONGQfr0EI2HWRbGpPqyImjcJ+RN7S2dw/p6Xcet+HJgrU3dtzPyjZCnb5Q2uZPUFs+vOX79ARUsNbHrD48+djjNu/EGdZvYOaZlOCfuwFlynanqFBuNmhv7h0coM9JzsyJAyN9FcUvnZEPiUp7QGFamss5Gblxok21YWdR33b5N3XRGpd2OTqq7x1JT3eNPz3kXg0vPxcKOf0BAQEBAQEBAQEBAQEDAFkb48A8ICAgICAgICAgICAgI2MK4LKq/gdKWILTHiCiP1qh658I5Fwu8dlapbzuvuSlNt0Q1v0EUjoTojRlSa61IDMqEFH27HT23JyqUnY5SY5jq5alMlnShc3lSsZT8Lv2+XFFKnhVqrRLDgWGi81ZG9NysV81sEa0Ta+OERqJUajdDEnMAmN5WIHrVbbe6eOmlIaWQZFg9U6grEVPumUpIlBiNm72WlgYARjhUfK0stZGUkrhOVACTRgWgmNUD3AIMMx2JhnPDTUoj/Mmf/hkAwLt+9l1p3vwcKXl6t4TsZlL9baoonVL9WRm979y1dET2OvCuNUmP6KMcAYAo0VmhZydEDWTl32FpyOWcPkGL3GCyUgZRogW7neLXFoQO2CBF9TbFHq2U3bWu2XuzPitd/2uPPJCmjx1/GgBw8Dq1DRPjqtB6dtbFP/3cAx8HACyT8vpG4O2DL3eOtsDllnTcc/epo/fF+nX9xbY1sz6r5dKaI/XjWUcJrFO0icaK1kun7o43KRLAalPLtSEuBh2iIzIV3tPjWcWc7aB3nUns2j4GAMaqTYsb7lqs5J3JkiL5mPS9nhzfFKa/TZ/XuzPw+7HLkI9nnyfXl0JF22hH2lujqWW9mxTEy/QuC7OOjjw8otRJ9gU7dthRBnfu1fi8UxOqhD1/3o1xLYoPPLlN42gPeQozRxGh/hB3xKWJ+nO3S23QaDqT82ltI3GPFMylDeQzEklgE7j++XwGe3e5MW72nHPhe/SIUgxnl7Td2JZ7l3qd+lOe5gVCJ58nF5XyhKrM17sU2WfVncvRFJi+6mnJLavlU8pre8j49sKK0FRWva4rwzypjrPPUSaRMYHs5CjNmHYMKZX3wXl3rbbVvCrFW56XPpktbLw+GMZEyEn8az92JoPC1UDHoC7FYB8d1XnA7t2uzZ4kN8bxMaWVfvzjH0/TH/gfLuLLjTceTPO279B6zIrfzMlTSt0eG9P+df317ndtoqkvEaV8fMJFZBga0j69Qm5PNnVboHGT3ECuNtU/iRPUxTWrtuT6DLuIFQprXfa8Ox8A0JQHeXHF5Plxu8nuZvp+eRn7y+yywvNiaQNs163hSENSbjTPa67qeOZtcKej/bdJkWMKspdI07w+1xKmlOeFujwyohGmZmY0osTRp12EjEWJduNd2TYEAyTyzZJGwzDaZ0s5HS+umxR3NnIxfvCIpp9ccHanyXMDsuv5szpX2T3h3jUbqwvYbIvmbPI7dl2LCzovH5txc6L8hEYKMEbr2EfVaRIlf6ik7+U/p4qk6p+JOYwM2Wv/P0+saTLq3S0j+Ptv3KYliUVdxsny0LA8s153cUm/IzsydtQbGolqpKq2ZUjafpTVennkyTNpurtDqf7jJfdeHVLPX5zXdx2VyfLYiJZFjyIYRXmXX61QtARyOT+75NrLaluvX8nq/R8+5NpbY0nHsJ07tY0M0ZTEu1tkyf2pEmkbKchcNCc28WK7+mHHPyAgICAgICAgICAgICBgC+PyxP0A9LygjuyS52m3u0e76D0Rhakvnkvz6iu6InzyhFsJrq3qqpihVUYWNxkT0b12S1c52w0SR/KxuGlVqtMjwRGvc0THR0ZICErWuExbrxlxnGRZOGrRLr7J66rL+LTu/vT8jizHa6flFc+QSHezsbnwG+YsCHXgOt19OiixuiMSUbJarEhEGIN3nxJSP2H2gpFyj0gIpj82vRdOY3ElvVfkHzZhdga9jKwOZ2nHKEM7OpGsfEUZZhFovZiS1vE/u9XtIPzUD74+zfvLv/27NP30SbcyN79KhbFhWI29e8EOszuqMIMy6VzPemnQzj2vgJeovcLXB4lWterazx5+yq3gz65qf+3bBW66+2apsh45pDtxS10nSlMaVQ7Mjql9abpacWV9/KTGtC7T812zV0WgxsZde4xpV2+5pjs/X37QsQNmzzlBl2Zzs+qnvz64XjgmfZyKSGn55IiN1BGb0VzS5+p0aVdxXt+rI7FgWwtah+267mK1JB57nXZ+VmO9bre3dkefd7PSeNXJeiJI3jjQznJHz81w25NuzsKeUUHbg5F4x7HsmthNEPgB9H38/8wWYnRk13KBhJVAQl/bZtwuEovusLBmlCOhQNnR6WZJ+IjK9fySa4/Fsrb3oTHdeelJ3+mRiNXqqvZNIwNARG2sTeWeyI5bQnlt2hGPecdM7tHr27lmpkBP3nWtWOiVotHs4MFH3Xh9/IQbN9oJMcZo3M+LiFFlRPt7QmJxSwtu5+VLD/xjmne4p+PuPqP1uT2SnRPasUSLxnhJ1ogJWC1puRcyfmePxMWyJFDpRX1prtEnJinX5/JNSORzz+hYmj5Zd2VQaGn89JFJ3a1eElZJIbe5ey1RZFAqFSXtrt2oaxn2Mx/X9tFiUeuuJTu3hw+rKFb+ej2+a5fOI6pVN7ZOTOhuLd9LWQOaN7NN50n1utvp6tE8qUdstulp175e9epXpHmLi7rzvLLs+uyp0yqW1iKWDD8L1+lzhSgToTLkyqggc0UvzgoAZRL/9M/apd3iGo3NQ6Yq1ySGHokD5ig/K22gUtXr59o6XuXExpXKOk/isdePLV3axe61me3mnjVJ9Fn5WTI+BjvZbcv7i8xAk7GjWNVviJndyqqaPe3YfvOzbrfXbpKArJGxIxG7YPP6fGMtnXvUH30EAPCRB/T9TyXa3ptF179bWR5j1D5EVJ/nJHsop/anPbojTZfFxpVmtN62XasCqFNT7txMRe3qEgmNtsUuFYs6RhlmdXgWJ4mmg0RPY9rdtyI4FxMLgAW4owHziI0isQlWZS41OuGYeasrOuds1lUAMicMrWyGxXzV1k5vd+mb8zq3On5K7fLiitqRIbHHo0ViuBDRoSPCrkvEzDx3fjlNlyuuHw0f0HYRk2jitMyZMuM6Vjx5VK+1eNbV++279btmbEjT55Z03l6P3H3rDWU/xPTNnPdCgavuvIsxncKOf0BAQEBAQEBAQEBAQEDAFkb48A8ICAgICAgICAgICAgI2MK4LKq/BZDEIuzgRa9IGCcpkACYCDR0iQY0e04FFo6fcuke07CGlLqx94670nRHBEuOPvlYmpcjknyn7u7RJmZDk8W6hJaUJypngwRJKkI7a1Bc92Wi7rbkGRebdE2iD45vV3qSSVhpziFKSFxIqJq2tzam7kZwobDPcEWpYm84qDcpn/oYAGDlhNZLxPTG2AshKnq0PhQRJbzny5soTUx1skIF4vi1IBcMT/U3RClidwzvIlEmWlpEsbizItCVI/oUx+3O5rU+82XXNl+5W39/19tfnKb/6p8cvfG3/sbFlO5ukg+GUsilvvsUvjTpxbx4Ja6PZi0uIjmiqubpvVmsstt2VKY6UaVOn1bBJptzZTAxo7+PSPiyJcKZRRKXaZAwUU3o1RzbNEd24OkjDwEAvvTw59K8kSEV7bnt5pel6T3bbwEAjI0pdfjU2UNpevaUozVtm9oLADjyuFI7rxQWFonQG1UwiYwHich5QZWYqI0srldfcWXBdizXVioXVvS6nUXX9ltLerxOVP+6iMI0ibLaIRqzF3NkGmtf2vj/yd6AIH3LYK2QDwB0krXUv0yfsJqem02kvWb8NTcOYwyy0o48rZNFqFi0rC7iqmfPK322VFElnI504KlJpdgZkJAh3bck40vtvNIBCyVt+8NCvRsiqr/tEcVO4gnXOlqvCalblf24yHYuyzRMd38W6+TjGXYPEpsaURvN9FF93bXGx50oG9uIK0WCDBqJK9tu5NqDIfp+jmiS+UIi9yV6PZeb/K5WV0rtyqr2gWUaP5Yq7r1unCQxI60W+C6XIUE9Fj9tyxxgZVXH+g65P1WKro6GiZqZIyqsj5XeJtpzTDHLR3s638lJuXfo+dGiepH6ykVr5wcbQWSilN6bCB23Tu5QfZ5z8gxFmmcx1f/LDz4IAOiRcFu5RC6NE9q/br/diQRXqxQTe077z7JQ8Q31g5Mndf534w1OzJUp5efnlMK6Y8c2AMBN11+rL0BzjwURfPvoJz6T5jWp/zK86OlzSfnPZrOYmnZ9cGTUzYUTEtHrkl33c0Z2r+zRGNOVdI76FIt2xuQLZKQN5EiwmedHRRE+q5AoapbcVn088Sa5m9VX1cZ6G8bjXUTjTUov5jkljUJJnxuaa48FoqdXyX1mdMKV3+x5ZyuiaONufjaxSERoLRIh6Gj5eHp89fFPp+mHlt38qb3zNr1AhdxKRTjWUvlZcsNjgdiOxHav58mujyvVPyq5ut2+Xd2hd1x7Y5ouiAuFpXrv9on4iqgii1/TuNDzOqc0rkTkdhTTXNOLTbJqNoszJzIe6dxg4x8xmUwWY2POFag85GzK2SfV5YjdhQvimmHJlhZK2p59G6q3VVg0t1Nt1/kV/fZ5esG1qRnqDxNDWm+1gqvjF16vdfUJcjnaM+Vs6fX7tL8VSSC8WhCXw7aOcbWK1tuB690YsneCBHBJJNiPtwDQjFwdLLbUTlqq72LOlUfbj4EX+bgMO/4BAQEBAQEBAQEBAQEBAVsY4cM/ICAgICAgICAgICAgIGAL4/JU/RMgFindzpKjO5RY0Zeo/jtfcAAAMP/0E2lenej1VmIOrnaUqvDCu1+Spu/99u9M054OtvM6jct+6LHH0/TZ447GHFNMw3xVqcNtcTdoUHzFIwtKX2oLZaQa6fFu/f9v78uaLLmO87L2u/bePdOzzwAgAGIhBBKmKMqUSFkOOUJ6UfjFcoTf/Zv8akkO+0VhhaSQLCvsEG1xEcVFJEFinbVn6b3vXqsfTmblV+jboGbmMgJu5fcwfabqVtWps9fJL79UOke342gYnVWNA/3i21+p00vberyUuO1AK0VynxCdhKq1iDjLiJLdCl5/5Xp97Oa60ky+980fERHR4VifGwP96iiXmNKn70lEFAKt5yh35dIGOjAqV0esODlDijAogQbMHYZwnZSh8jD/tNuGmLWF0vkKcs+PQEF2VQViKQHl7orpdKhUf+260ns2OVauRghYQMzYCuPFN/8SNWmYdTx5qIss0zyI2i5SM1E5dwZ966c/dlT7n/1QqfYSC5eIaMZ10IJYv1Okh3K0hGqmfSAMULHc5fGjD/WZe09A9XT/YyIi2h0oLf/6Fe2762uq7jxkBdIHD5Ruh9SxL779m0RE1Fl2efqbv/xbem5AvQj1sARqYlEgrU1ooqDsXWmPHnB0kcMTVXrtZUD7miiFqxg5ChbGc8YoBRNWtZ3C8zPC5zKtvkHlx4gbzb+fRH0eFa/PiABQ1eeB/g9U9VjiDtchU8546FPAI6KEqWsSbzoEFxKvoRTt6IA+0CxzCJ8y5XnqZKhlGUX626Wetuebr7q2+fCOUpHRBeL1W24sFfcCIqLH+6q2e3js0rNCC+HylRt1en3ZUVZLVDAvgD4u5Yrb8EjZhPjdxPWF0Qo8oGTGTKUP2SUogT7+7PDI91ybTrOAs6FzZbsL7gQ8l2IklgJKc8yUxwlEvvCgXnJf62XEczCEmqd/sa1x5XuF608pzD+DXGnDA3bNywaa1ylEWzjg4ydDpVZG0WkXign0lwxib5+M9b4fPHbRiwaZzrV0qOcTVi5vLaQ+FL7vU7vr1l0yTQ/HmocSqPTSNnpAi330SNv8t//uW0REdPmyrmdm4Kr5yisv1um9PUd//tu/1fF4CuurTsf1z/v3dQ7Y31eK6sa6ozSvQ1zyVkvztfvE0axvQH2H0CdEvb44Q+kdFa2F6v9pKteLRhBGtMLz3CWOhtAFRXYf+m/O+UMXi9lE51bpwxG4PaELD7pDictd3HAL0DIK2a3IB5dBz0dXAHc+wbES3DmGA9cnU3BHwzFayhjdFjBUUzpD1zVO+Hp9GOs3RG/FtQ1xTfECbT/PjLIkmrj+Ee67tp+9+780fxNdJ0Vvfp2IiKoXfqU+5o3VRSn/+Mcu+7nO4VGibjSNuZUjkvmFvmuvp+W6fdm96/a6DnZrfa2Dduzq8BDKbwruZuIGgp5EHkTjyngMy2Ht4oMiPHgFUEXjZAnoAAAgAElEQVTiTqbHyoaJmNeyC+xPvh9St+1o8bt7Oy6v4F4Q+RCFgos1SbQscZ7zOPLXBKZNr9T2utzWMugk7rfLoT7r+EgvvHrJuQ0MP9B6b8FaeLnr7rUJPmhhpXm5N3Dnp7m2i9VLOF+7d5iAi3jggcvgRN11hym72E70/pJ/IqKlZTfeRFxZYajfyJ+EWfwNBoPBYDAYDAaDwWA4x3g6i7+n4hFidcL9Vow33F/bIiKiqKs7WNWx7qbFbbcz/+INFVj77d/9fT0POxmyufnCG1+sj9145c06nfLuKO6SVmBlyFiwZDrRnXCJ3Uyk4iQ+xGF+8INv62+Hbqdx7bruhF95XUVn0CoXzBFUwPisRcFxHXnPpVqAMIa7kYhhuR2kd97+Un0qqlQA56OPXB08SnVndQ12u6acvwqEaKakO2tLGIuaLVVoWZ/A7q7sos0ysE7prUiMEbBhTUPYxezzvQYjzUsLrDtjznYClkBhpBARdSFOKbE1DsVlWrG+d36R2ykLBpK3mHjxtVahWCYa7UPLKhGxrEY8ec3rPMsdhlGdpdr2D564WNvV+El9rI/KbCw6M9gFVoOHeXEVkiI7A+Ops4XxeAZCWmD1W73ohFBu3PpafewrX/5NPb9ysU4fHrm+VZanLddERAUzHaYD/9Tvnguf6KcVljsIL2UcXzeFd/XBQlGxhfLxvlrOnhzoby/6Ov6F3KTGIy33yVTbWcZWYLQG49giOZ4Xj5tIrfNlNX8nvmYZ4eUoPOpDGcxhAgTwW7HseGEkmZr7zKeB73nUicVywZartlpwS+gvIe/2d5Z0HBuApTM+cf0laalVJWppnS/DmHbz+stERJTHW/WxvT3d4d++5QQo795XVsrsQOcPYX1UMH9U0EZmbGVJoa5zZPOwIJwH5e+BNaZCoVoeE5DtE4CZZ8zzc8mTMT7nmVFVVPL40ord/ZMWCO6BANyM6V05tJ/xVOtlwufHKYg2AieuB+Ncytaxf9zRse1mT9tAm6liQxAfO0T2GLehZFnvuQzj1P4eC5mNtV0cAiNA+ssJvMvugTJ7qn3N1/v77vgUhJ1C6BPCjCkWpeYr9/V96rC4pIwLwwGwu0BgbGONretQRt/8P9+s0x9++CEREb3w4gv1sRwYA6urKry2ve3EWsWaTkQ0HIKoKa9vDg/VYoW/PThw/avTAQvvkgpw3bvj5rDH0CfWV/W8jJE4TuG4iEJ+8tzqjLKv5jCpnhdxktDVm58jIqK1dWeJy2BIzWb5qWsKEGvLIV1w/0cLawjMmHYLRCbZgojzWUPgkccKz0ObHzLA3HMrmIMCeFbA430QRHAeBVhP3bKxxkUruIjQyj2JmkKDLWZIRCz+t4j68YoZRYeOkZi979p+Ceze9udUVJw2XT8oChCMTnQ+94SK9ETnhRyXSSjkV8pf7Y8xrFW9lMVBU2TPomijS4+BaaG9iShhJgeK+6EYrrADptDscP1Ygrizz/WFqwhc/lXctxe1FCNy68tez7Xjxw/dOJTDvOeH2gZl7QFEE0phbKGJW7MNRzo+A1mPdve0vlvMXBu1YO5OgAETOkv9tevax35vWYWqOzwfHYEY5p072h7GlWOCtVZ1bbG6ou0i5bVD2NZ1SssDAe+pFnLqu5dorIOQWMNp35f+crZd3yz+BoPBYDAYDAaDwWAwnGPYh7/BYDAYDAaDwWAwGAznGE9H9SeqKTwiKOKDWJoPlJ2SKdNVohSGPFP65OqGo8n8y9/73fpYZ0NpNEitDYUWDzQVpB+1u/3GX6JmfEOf0wGQV3wUzWJKRAX0q8ldjSX+k3suHmQH2CRIoyghr0LhB52nZnxWKUCmop1FP3taCK2n33cuEjdeVHG/6j19/jIzZ4/huYmn6S5TimaVlm9eQB0H+ttVz5UXiu6MQWBL3q0H18TwrGMRoYBYlV34bZvb2BHQdNCtQHRSIqBXTpGeDvfiEJc0BtpWCvl+4XOvEhHR9nedsMidMdIWF4j5TP+aeY5ij34j5jNTd7EtgQvJ3Y9VRPPBXdde0xmUe0vvtb7uaEvtNtD1QGFR4ppjnNgYRK+EZpyCiF23q5TsKddxmSvVajrRPvLd2+p6cn/H0bpe/twb9bEL6zfrtDA27927TUTNd352VFSWfB8W0vNzoNUBJ/OQY1Pfvf1hfSwoNQ/iJnQwUDrzwUMVIRoFenwl5Vi7MDhMIe74hOmVaYXjxTx6Iwi/YQzl2ptk/pgCv6xTFfTHCtsj9w0/1IMRiEB5LRaRY2r2ImiYQRBQf8mN4QGLS+XgPzaagEgbU9hHEB+3Ajeh3cfOzWVworTnhw/VfWznkYqKxYmj7nU7SmVu9/Rd7++4seD2nQf1sTt3PqjTPov9HBzqmHEANPDr11/k99OBzPe0DUno+NAHaiPUkQfKSsKULaG8M6hvmWtypuwW5WlK8VPDIwpiiQHPrhjwzHQCtGqmZ6Iw6QxEyzxupB6IxqFLUwl9gx9JGawhPgQXjFmHhQZbIKjV0zEtZVe0FNpFDvPHlGNyj6Esh9B1DvddfY6BfrsDIqazgb7jlN08POjP4ZLSlsXbrfAWM98L/MCnPouvyZrsAOj1I6DfT7j//OCH36mPDQd6/s03nfskCrleuaLuje/+/N06vbPj5skVUF70YT4V0T4UBj4+VkHMO3fcHHXt2rW57zVi6uy7j+/Vx65e1ljaVeTmG6TEe/PiyVPTxWAeFknxF4RhROsXtjjtxpIS5pW5NGmY771gjk0OrsG1QT7VdlixWyuO2x76UvK6DuPBR/AZkGXiIqR5DXzMrMtXDC45bXDXyEqhgYN4IbxsCS8hddQQzgV3iILbTlG3oQXUUzqi6s53XXrixpL2LXUhLjd07SHr+jDXMacC17OAxdSqoc73+VDbOH4PiToerqkGIIY55XnqYFevr2ZaLjGLZQ6As17RaYFHdKvAteIodQU7ybEutN6xDojHTfzGCcB1TaqzrN3Rnr9esiKlJ/uOIl9V7h0TcPdeXlE3HxG2xD5+cgwuRXuOXr/WgXEd+ku1rOvWsnTp5b4eC0JYk7H4cgfWBr1En3uwz0KEUHxJX+cjEd0u8936WDjW9/K5jT3Zg/NQ7i1YlPVY+DKD957AfNMqZO5lgc1PmWrM4m8wGAwGg8FgMBgMBsM5hn34GwwGg8FgMBgMBoPBcI7xVFR/jzQOeslUoulIqZZroMiZMqVFqOdERMdtpQS99EWnOr91WRW+x6C6HyAliOkpEagdojqusIo8pDfloPDK1AhU1/eATluw20DsYXHonkjGD0D1YVRTzIAWVXFATIx7ibTIgMtPY1IvhmZW0+o5pu/aitIjJ0B16wUuryGoRieBpkNW88c4s6OGW4D+tsVUet9T+lJVAB2YaY1tD6me4ArAaWQ/dqHcAmJ3CCijAPLaCVjZF+gwBaQroC9NmJ9UgNvCBFRDr1xxdN9XX3E0ncePMK784oCswnk136BpQ7oU+hW2y0zpxE92lIo+PHL0dIwLnLSVytTvi3K6tudZpfU2Y8XzWaH3R4VYYhpvDN1lAEFT944czXp2/+P62NrqhTp9MlF6qbhjrC6t18cePtip0z/7uYtFurbh4jk3KGnPiKqqqCyE6u/KtZhqWd0HVdZv/51zS3i8c7s+duu6qromgRvzfJCYjS5o7Gm/pxSxyYGj86X3lRqYpkrdy1hBG6lcGTQYoaQiNRVj23s8Pp1F8ZLLAujDpY/jKPQdiXQSahtJVkAd/Yaj3XZWXXxybEvPCj/wa6r/YOzKZQYqxkvLSvfLmBM6ADXdCcRVl3q9X2pdrsL1h0A7Jh73X7ilccpbLe0vP/iRoxt/+P4P62MR0AFX1lzbTYDa+fEH6iq2/9hRRV9+XdXSL2+rym8n4XorQFG+xLlKs1qqT5Aeg/Yi6YTnykY0jmdERURTfsTB2JWbn4PyMAZ8riTaA0Rn6Sg9VkIo+11QCoc2uN7T46usxl9BBAF/qJTOezNXXnsQG9sbQWFJ34Z5P4R2OmDF+MfgQvIEFJ8P993xEqLKUKrvOgLXF8EGxGpvXdIxbYddgYI4OnXN88D3/Drii9C/0b3lH77//Tr983cdVf8ff/zT+pjQ+4mI3njDuVsFIfZlfd+HDzVyyf3794moSdXHePJvvfU2ERF99NHt+pi4BxARjXjduLS0VB/LYWxPEtf/Pv6ZuteUUI/dVUezns3mR63AebSOQPJLoPSfBd/3qZW4tZiMjQUouuO7VqVEZJo/hkqUFlTEj8EdQyLPEBFNi5SfqddHoFIesAtuAGNGBdTigvOCay4PnlWJWw6EZ5J7EhHlTDNGl0WPIPIC1EvB810K9PUMvifSIadlnbYIt9iioGrkInB0bnFEsQs6LlcYLYXdpALSeisn4ObHc3+xpuuBYKp9j451LPFCjryBEQ6Apj1k19JdiMIxPQSXwak7n6zrvNGDNZWo+WO9ZbBUEheBMbgle6FS0j1wqcv4N0EA624Y42U+CjyJELGAflVWVPC7r7L7kBfpHIztTXLahjl6MtJyL3Iud/juCGE+3erDwpXX1aOJ1tVxqvfqLLu13NbWlfrYzp336vRgz/02BLfZpKXpZXZHS2KdA1ch0lDQdeeDE40Wgy5R6ELb87m9gdJ/3tb2GrOr+5LHffxT1mRm8TcYDAaDwWAwGAwGg+Ec46ks/hURVbzrKNb1HHaoULHE41jYwxPdtVq5erVO33zjLfc7EJuIQBAE47DKLlaB8lQYp1TExGAX06tOW+8xtjKKYMj9cRc2gN2kzrLbofEC3HEs5qelDGAHzUOmgbxCJdcsRuxHdrjbvNs0hdik07FabrschzkApcQYrDMxWwNbkK+jTMu9C+JIMVtMQrDCxyBI0uPf+milhd3BmGOHtkIQoMq03nzOC4oGhWjhzKVdQBtqiDrCLia3rTDU62fY9pg9cP2G29lL4tPWnKdHVVvAassJxmZtiPvNCYCLP5CNdng/jLGcglhWp+N2/MKOtvc+iJdIfFxqq0Vqc1lFnDod9+7TFJkc2sYHR4/csemj+th0qm1suc3iIj5Y+kJl/rz05jt1epY58ZR0ou/dgrjgly67fL340kvuPiCu86yoqopyfp8Bx7z+3v/9Vn3+299U8cFHDxxrod/W3dNLa7pTHvddfaws6/v1QKT0wmUV2cz4Wfd8tRwf3FUrGDEzx4OyLmBsEVHFxg47iKF59Q489JFGI3N/kI2EDBKC6/zI9cNkVS1yl958uU5//hu/RkRE7Qurjd8/L0TH6fjEjV9dEMpJIbb04ye7/DvdnY+B8bW2zEwEZBDB+X5f29je7m0iItrfU4ZKB3baj49cXlIQ/Oy1dXyY8G792oay165sA4PlkbNSv/sjtbSmI7Vyvfn660REdOGSCksRzG8VRGzOq5RPY4xnZH04iOBgnDx/fymLisYnLr/TE9dfu13tDyjwK9axrVUdb65eVKafHEcBI2yQs5GOY8cnrrwPjsf1scegvjceu+v2hyjGpOUqlv4ULKJoHZU5IYPg276v7WIWurx6MTDmujqmzoBJV3H7b62DQOSKjgnBWESeFiC2iPA8CliMVeZJFEa+e1fF8X7r679JREQXt3Ws39jcrNMRi2Z1ulpfKKaKIsXjsauTWze1zaKI3s9/5phaP/mpCgJub6s43+vc5jHe/P7BAaTduhGFCpFY0hq7d0SLvweCz/NYMGdB5t5FMgJ8P6Bux41dwp5KUx3LG3HHmaWIFroS1kzCBMsjEAeENVfVEPdkZiMKuALzL+ABNgDRRfJx7ObjKF4Nv5UlRw7HmiJx6Zl5cmkUAnV9Mc3BWlsAg3jNjeG3XrhFRETf/dkdel54nWUKvvA7Lt9rlzn/yBRG1hjPxz6OddDGmBnjtXTc94Ed4KWQX26nBbR3AqYRDVzbL6e6bn840edmM1dGr33pX9bHNjd0DvL5GyiHsTiFcXV/yPHqJ2pZ9iOdG6JQ71XmwhyEesNvGK5DretFsDALKmZuvO8wu7MKNX8H+2oFf/LQlatfAaMV2NrHzHglYCLjdJMCKzhPhSmhZd1egTXPhRvu+SdP4PnKQvJ5/ZUDM/Ek1bVwznPLCsyXaazl1Wba7Pq6rhn7fX0+jl1hzIL6sGaLW8DUnbkxeXDs6hi/oT8Js/gbDAaDwWAwGAwGg8FwjmEf/gaDwWAwGAwGg8FgMJxjPB0/s6pq8Y9QhBeQGjMFEQwWURsCff/1116v0xGLjRVAVz6LmiAUrAnQgGIULpixCBzQo2a+0kAk5nEUgkgJ0FiEyVMBrbYDYmgVU8qzqeY1qDCveq+Q0ynET0ZxFKFzCatsUeQyoQFPJo5m8hd/8df1uS/6SocLWbAjAho8hoxtMYUM2XEBihNCGSWhCAEiVQp+23Lng+AMyknpygLljnKkwrMrAca8DbzTtLEcqK4ZuFiEqIrF982q+RTZdOCohdscxzUKF7Mn9k915BDRl7N+P5+FCG0Y61NcX4B+NJnC+xSOSpX0VZRma1Pjmi93Hd318a666fRXVEhmdtFRNt9/98f1sXSs4oI5U7IHJ9AHIa/bl9Xlx2N3jsePlUp1+45Srg+Z/nl85CifxQKosmVZ0pjjV//3P/lTIiL6qz/7i/p8BeIuVy66ckkzHed2Hj3WmzF9vNVVinAAFDVgZJIQxdN1EN480XEmZ/ebYAZ1CWOaz50yhP7mN9yf5AXgGv807b8RQxrHQWjz3VVHU77+eRW8+/yXv1SnN647l5hKhMAWou9T0ZTFDsdTR1urYHAaTZRCd3TsStP30TUI7+XK4OJFbePra0qnO95TyuPRoWt7baDjdWO9bmPJlcUJBOtNgZIeh66d5zOlpF/YUsp3h4V/joC2vHPnZ3V6qefay6WrKpS2tKyuAgm4vhQ8rzSp/vPil7tjYaTXPis8Ioq5nNdYjC2IgN4Pc7HEBM9gLfDoPojCfezqcAjLhjzD9gxCfkw/3RtouRXg2lIwJXUEVP8MRH1FpG4ZRB2DWPteIrGvgSaKccT9mOn5Lb1/CMJ3a+AOts/U9xGMw6sddVNJ4ha/32Lh+x61WPyw4raRtHT8ub2j4+p3fuho9zeAnp8sg0tlzGu2I3VpKTJdc/XABeBLb79dP1/w8EjpuA9YCDAGoas2jJHHXKcvXtDxJUx0LLx7342xJ6DdNzsA8eeB63MzoM+3E4g1DuOurCs974zZ9Zeg+ef7PnW7rq9Inx0Dtdub6Vgmbqc4VjdFftl1Dl4qR1dYoHSXFQsJwjRZgYixzy6NUQw08kLHrdrdwcM+qUjZtRfdADMYF2fsVtBw1YX5COeeTt/1yxbU+xRcbULP9Z9p5sbfIHx+98sqblN1zbkZpyzOGcGaNgCB1SrlcoHxrQKRtoLX0h6UZbiqc0wQqKtP9eQjIiLKB9pHchAKJS6DOILvGk/zNebrShADxn5eMFUdha5H4H65x+uoCdRbXul58vVZ8m2ANHNse+KGsgiKv8DzQ4q7bq014zF8f0eFeff31K1UxqTBQN0iVlfAbfWSm7t3H+k1k5G2cQIR3snMtdflNRVNvHZJx6RddgN8MlE3JGyHGbl1xGym5TeFOSrn76HU0+cPwS0y9l0b76/qvNSHcdL3tD1MU/feY3BvCkjrc3TCdTx05VIWZ6+VzeJvMBgMBoPBYDAYDAbDOYZ9+BsMBoPBYDAYDAaDwXCO8XRUf8+r5REDprxUQF/Kp5pe3nR04Nd+9Wv1sa1LqiY7GzmayVmU6oaipNCgGyLnqG7OVCnYx0D6ulBXKoibiTSVtHTFkKcQ+xUoRyccu3MC9NCffKR03xLcFar8dCxuVOkumW7W50gBU4yK8BwQmtjjPUeh+9Z3lJryhXdQ4VZiooKCLNLz+W8B9LEACj4ACliLqU4+uHN0gY0Vc912QE0b47imTBPsQSzKqoXxZd35Pty/1waqZ+R+OwB6Z5Zrk24BDTFldc29MbwrtL2fvOuUQvcrRxPKUXX3meGdVgo+LbLeOFE1jkBc9Tl0wCJTys9srLSnmGm4qLzrg8pvNxHXF6CBA2V6Ju48QGGLY/3t8NBRjloQj7TqAYU2d+d9ePeg0rxG6KbD/WxjQ10JngDtX2iA9++5uNFptoD+UpWUM2Vuf9cpwGbQR/tAaU25vY7BzYcOIW4yuX4msbSJiDY3IIJCrvWSTZwLASp7h6Aen3AkkRxo0ulYKWLlxB0PgVqJXjT+HCaruFIQaTzpIAaqWU/z3d1QavLaZaf23d9WmnReaJ8YHbhxptVjitwCYix75JHPtGtRGB8DRW8yBQVvfh6EGqaVFaVZvvaqiwJx5YoqLk/HShV+dFvH74IjkWxeUXq/V2h7Pzl29TYZKTWyBRTmJY484ANl9ORQ7y9RFtaWNbOrEI+5KN39nxwoJb4L6vBlgBRXl66gXrHkxV2jZghiQO9nhB/41OV2MmQ3lOUVbSuhr+39yY7rT2OYP4qevsuM1aFzoMymELVgaUnbW4up2y2gaCcxUIHZdS/LtNyPjrWO5PjGhQ29HlSQpzyWTEEFH++V8pg1AX7yBNXSwZ0vilxeVyH/u090HJOoJ12IFrQI+L5PnY4brzoc8UT+T0Q0mmkev/W97xMR0Q9+okr72GdWV109xeDCkARY9jAP83if5kgd1nFFGMW4jvAh3nvccnn8+XvqIub5+tztK87t5WWO/kTUdCk5ZuVqmReIiHKMpEQKWetNYX06mag7l6jWJzzXVIsay+rY6i7fSGnPgX4vbknoRhJhpAyRJIdspbDmRNp/wTTsAsqi4ULL40aA8xm6QNTBh+ZfL+kc6x3SEmECixAf3+2oYrlE6MF49kewFj1iPw/xfFsEsdwrKwrG7r4BuwP74CKCqv3EfRqp/hGsDUKmbJeNGPfgIrymUSyodP0f15yFfwxpXpPBWL8KEUQm91xEmDG4PWXoQStR1iCq1hMYfyQ6RhUpJd7DEs103Cy58prfYNij3DvgWnQRKPg74/5916ePdnUOHQ/V/ajdZbeIRF0pDiAiSJGKG5C2yxn0hxzWzb0VNzdcvnajPvbo0R1Iu/GpSrTd+jm4fniujiIY+5bBdznlNUE6hegoPS330dCNQ1UJEQoSbWNYxsORKwOMDOHBt1GLy0Mi+XifUj9m8TcYDAaDwWAwGAwGg+Ec46ks/mVV1bFrQ96RTECoAGOJJ7yTfCn8XH2sAEWfinfbC0KrIwiOgFVLLD1PIJbj4z3d4Tk6cZYc3AE7GaqlaMZW9TBEER+IJ8q7pFPYVfrciuZld+Tu/9O/+/v62P9+V2M5okVftjcvgfXo8oZaPH70YxfD+/XXXiEiosFQrVDPCo90hztjQQfcoYPNLiq5vC/qRj+tw87iMluyUFRrHeqom2i5JGxiRAmJZdiNClnkosrm5+Uy7/BFuDsM94rYnNfe0Ot7yB7g81sYqxvEURKob3nuYKAPW1nTvH7/A2ep+qvb3yQioqOBtoXngVj85e9ZtgSpr6qx+Q6iXac1dyjHeKHQ98Qyknrz9/XyoROaQWGQx481vbrqdoXbbRX8Gwx1R/Lo2PW9INcdZR9i8ebMHphC3POjvd06XYDlWKyhPvT9Aixs73z5K0REVLFV4r/88X+e+05PA8/zqcWWv29846tERNQG1sndDz+o0yICGIMoGFW6K3+w78o9SbT8lpZAkcrTOooCdxytaD0Qcun23I5tCX1oAO1Q8pKD5TuDXe2Ax0wfhTmhH4fMSkiWdEe5u6aW2x4IzCRLbld9CvV6uKfWtbjnLJtr2y+4AwuwklVlRenUjQDLPTdATYBRFoEIW9Vmy/Oy7sS/8srLdXqNrZf9rp4PfR3nNmAATBK3678C8XsxJrnPwmz9FWi3hBYvtuZ4ev/Vnt5LmBwV0BO2tq/U6Unh2v6H0O7Wt9RalMRaXxK3HOOAJ2ChFWutsN+8f7K86NnwPKKY2T8zHmdGIxhbM+j7bP0LAs3zuALhy47LT6ejZRXMdA5M+mANGTsrVW9VrVQpjHlp5eb1i1eV1UGxWtHEep/0tHywmUq5hZgXaCMeW7hPjrUNjsdqnZwCA8TjpRQy2kYjfa/qF4i3Pis8z6vZBiLGhc9FiPUILbhjYBTt7OycuiYAK7QPY4nMZ2K1/eR9pyPXDmKYgx/uqLWy1eYY7iBUFUI77vTcuPTSSy+dyj+RvmMGjMmTQ10flmit5OQHH2j/KkDFsd1y4+6VK050dgZ5emZ4RJ4wrDjfQQSWZVyL5rJmg/kaxrqCJ/8ZsDdKiGGeQYx1aQO4lvYgRnkYuDx4Hs63+L6nmYXt1mmBUOxHJczXssaegrAclieySeq2BfcKWzpulBxnXkT4qkWoMFYFlYUbI3Jm8tJE79tgXfD61gOWEWUR/JZFNYGlUKbAFAUGi7fsvgea/R/YMMx8bIO1t9vWZ83u/SMRER3uax/dO1LxZS8RRpb2h7v3db5msiCFXWUkINMXV/GerD8aQtgwhnLbEn3zRWhjprMp3fvoPSIiOtx3onyTibab0VjTbX6ZFViveCDYd8zCvyEwNUTEj4iou6LfY59/4wtERHT3jgoJvvtTFd4V0exeD9RoCfuDe0YI3yU5sL1nUh+wcJexjYjI99x5FCkdT4AlCvN8p+vGqSQBBiA8K2aWpDAEzeJvMBgMBoPBYDAYDAbDP1PYh7/BYDAYDAaDwWAwGAznGE8n7ldVVHCsaaGMZ0AnrvaVzhuyOEsMNMU8VDqDxwGu00KpKfcfqJjDT99X6sVH99zxnX2lQBwdA72QKWYoeILiJkJ/ioBm1BCmYHGRKVDkrn3ji3X6Mscd/8meCtE8OVBa2Y1NpY689pKLAfnV3/hyfWwbYnv2Oo4a0us7mkocYRT754cQSirgzFdQFuvXXJV3QLBvDJSfu6nL3wQYS9MMqMfH4ILBFKnhDMT14Fkp01dSODIFOzYAAByzSURBVD/DfDGx/yyWcMic5TDQevWhvYWhuzCOQHAw0fP9vpbtGpf7Zk+PbXWUPjS97174yZ5rAyi+88zwgOrP4i0+CO5VUAef1AB0P8C0vBcKFmr+Twbadn1290DRn9mJUjpFhNMLlXoZJO/X6WMWMvFgeABmImUcR7VM9ZklCATxEFGLDBIRTUB8cDhUOm5v1dF0ywrqGJU5OX35iqNGx/HzxyUnqqhkd4N1FrR75fMa23oJ4rkf7Tu3hhzolEghKwuhRuqxfl9FZ1CQqs0UrSVQwGy19HxnyeXFg2tWVpUWJvTJKVAnU6BUEo9/PlAvMV8xP7/VU3puC8QF20AtTNilJwLFwDzVMXcycnXoSX9fAIe5oopybrsyZrfbIJTYwvi2QjXWY8PB4FR6fV1p4qswDr/wkrqg5Tz+HwJVOAdXM5nD8hTicAN9NmFxKGzv129er9MXLjjhyscwZ3ihlvtay9Vxuqv94v7de3W6yLRwRbAJKYBXr6rbQJfpgDJONoS9nhGBH1CP3VBEfG8IFPEY3P267CJSgtjRrNQ53ucJYDBQmmoM7mNeR9MSu7rTUSHEEmwVubRzEMxaXlH654ip+Eh5RMFR0Z3rdbQukE4dM322uwRiTr6+SwljecpiaUOk2cOzRPyzDbG3FwFH9WchzLG0z/mdUcS6cExC90dJ4/m8odIG9cTvNsu1HeCaKmbxRg/m6wnQWdNieupZJfhqZized0oc9xPP39oEkczZaco7kfaV/X1tcxUImy31nUvb9rZzr1mEuB95HvlMG6/vhyJwUFZCy8e2PYOymIp7C2TLBzHeKAb6OaexXKvGfMBuB0BpD6GOxDUMr8d6FVeeFFwsMD1kd1mcL88qz4pV+9DdYwZU+dpd44w28EzwKvK5D4ci7ge3j6FcZR1SVvp+BdCwKWP3Hg/XcVpveF+v7cYlz1cRY3SRLSu3zsCyyrCNcB/PC52DMsjXbOTSHz9QgVj8RmpvOGF1H1yZCrjeB/dDWWqiyyXBe1XcXiqP32AB1VOWJU0nPHZyHfihPn9pSddM4ik6OAE3IfgGqNdk0IZXN9X1+qXX3qzTh/tuDfzxe7r+LUG8Ly1mfE91uYwibaMieh3AegAFTeUNej1dx2B7mYzdvYoC1r8+fuNAe+QGlbT1m7q/rOubkr9ZBkeHnOez536z+BsMBoPBYDAYDAaDwXCOYR/+BoPBYDAYDAaDwWAwnGM8HdUf5ONFMdcLlYIh9CwiovZ0yJcoFTMHavNHjx215fs/ea8+9vf/8OM6/XBXaZFjpge2QQG739bn3rzi6F5boJ7fivS3Ra00rK+LFFJhXsyAsvRrb71Qp48f3iYiontjpXhMfKXz/tt/89t1+sZll5cAwvVGQO341//qa+480yOFQrko1FT/EhQtgSr6N3fcXs/PHinV7WSiNJMx12veoKTr/TOgpFT1b5Dr84s460+DeftSzUj3p6F1FGAcelYzRarnfwRl3Cm7OIgypneGIv6zQhX6UckVy3iO6jNSyMqmywARUQ4uGEi3i5iWhaqoQ4jb+2Ai1EG9ptvR9MGxoxmv9kFFHZTihf6JtHtUXY1CKUullcV76lawu/uoTpeeq5d7dzV2aq+v9PaNTRdPvmDqZrUATnlZljRjtfgJU3Nb8C7bQJ3e2nb9OcRYx9AHZhNHdZ1hjHmk+AL9O2SKf7GufR5dkqKaHqvtNoCY3PU1oEiNqtYkccWhsiqgvAr1LUogFnqsdRwArUzqMAIadwC/reM91+P78/eXqqpqyptQdWOI9460N+kpqG6PcYslljn2i91ddUXDcVfo18Oh/haV0Ucj116PjrQNv3DzWp1+/TXnJtIBt43Ni6o0v33ZUYhnpP3hMcxvpefe4SqrihMRVUDJHBzrcyccKePkRCmnESgKSyzy2cz18Rm6gjwjgjCoY7z3D527xAh9wRo0R1dvY4iTHoA3W8mRXoqpHmx3l+t0NtF2VM5c2zoCF5N2B10/HH328BBomEDVFZecyRgiAQAdfMptZwlcQIYDpa6fsDo0RhgqGiFygELNfR7VzDugeL+ywZFSFuKqhPDq8ebitmtzFy8AnThH90c3btwHte8RuGxIn0OqaZVihBJtZ/IbdCXC+SytzwNdmU5f33wThT+H3j0vtjzS2BMY15DqP8/VMwQV8Js3bxAR0a1bt07d55lRVXVfkDkbnx+3gNrL87gHSv5YVuJ2hKr2YQT9oK3uLaqaj2sDpYcX7FqbA70+gDqUCBHoSoBlJeMtUpPRTa/keOQV+JpihIUpRB+SiDQ4Rk0n2pd9Xp/4leR1ERFKKopb7r4eU8mjQOslSbTPytwJ3Z9SoNqXUm/gmujH6O6i5dbiyC4BULOnIM4+5bLIcKzHRVfbjZEB5HUNItrkfPy92+oija5xW2tujDuET74KY2hBf5NSxj5U5NifXH0UvNb2oK09K8qioOGxm9MSdr1Kwf0gAtp+wVF0RjCui7sdEZHH35mTkbbL3/h1db2W64mIfviDfyCiZrvEIT7hiB+t9nw38SAQV0+9JoTv3KXQ9c2ti1twDbpMufRZ3hJYhxI1z5/ou/bWNJ3znJ+nbkyvqrPrxSz+BoPBYDAYDAaDwWAwnGPYh7/BYDAYDAaDwWAwGAznGE+p6k8kjNfAY/XQFvBVhqDyzVT/AOhuH97bqdN/9Kd/TkREd3b26mNLPaX4Xr2kdNsXX3BUyq1NpWduLGv6xWtOsbEHqtQeUNWF4tpUSNQ9D6GvF0C1antK/egl7qV///qrcE+lWl1aU6qgzxSnBsUOqCOR0L5CUQSlhcLjCsorpPIrteSHd9zx+8cQYQGUu1WVdD5FLwAaMgkV5ywVXF+uh/vDabkM6dt4q5ra1qDdY16rU/lD+qUHVPY4YBp1pO1mkoEqfibPWmyFyLvNU7Zt0Bk53fgd0q/YdQOjGkRAMQshXbC6ewTKveDZUj83AEqSD8rYqyuOQtaKMVoD0JvYRQLVvCe5/lbo4Z22Huu3tT16EI3g9u2PXJ4huscrt16r00nLUbaFduYtQELWI6U39jocXQMouDNQJK5DFOSgoDsG9fgTd58hIQ0VlMGB+idUT99XamZVIW1M8gD0WaCFieJys12AijC8n56HcufrsK7Pcp2Q5yJ910O3BS6vivO6AB1sCsOQ1ngslbFaqOvuGIw9/JZN2t1ptXIc8/F8CqrZRycyb0EbTtTFImXK9xgU3+/c1wg0nb7rL9euKMV690Apq9PMzXvH4EowAdeb44Fze7v/QF0R/ACVumGOrfOv93r/vYM6nTMVV+afyWRMz4ssy2hnx7k7PHrk8phBJJgw0L4znbp8IaV+o6dlmTB1Me1oWa9vKf318EjfJeD3noEbUQ5hYXyOHJBN9VkzDyjMPFcNT7AMsG+5Mt5/rC6KGM2hYnXnEurdB5efFtRLyarSRaHXtxrUc8n3InqKwve92m0x5zaxt6ftCGnrdYQZGD/COZR7pIji3I3zlc/HPfitD2rW4ipX4ToCnlXSacVpD+1QfK8GvR8i7ci41XAynLe4IKW/t1uwZmMFfyKi119/w53nteo8N4SnRUVEubgrsTsszjEpqNcXPLegCncA9PqIr/OAku8BFT8t4F4puzU0xniFUPixrHDJIeNiUYLrGqwthLaPgu8wBVHJ9Ybr3yxD/XrFjKnLM3RFKMFVk6n4YSiuEIug+hOFobihuGO4dgrC02tRH/p0AFr804wp48CoxnoJoL4lcoLvgVtBB1z+lt28N5hCdCZwl/BDWQfBegD7I/fzFsyX3WX9nlracHPUYKyZRZdKdFGQfopU/3SC7o0uPeW5ZRFusVVVUcbrrjBz79iB/ooRVAKfyx2+W8YTpe+H7B74q1/7Rn0sArfT733zb+r0jF1evFiftbSk6R7XEY4JM4yqxK6Eva5e0wHV/YjrowXfyeJmSqRjGs4b3ZZePxzobw8O3dzoR+rmNyu0vfXZDa4ekj+lu5jF32AwGAwGg8FgMBgMhnOMp7P4k+6wiiBGAHGUO0u6gzXinavM112lDz7W2MT37z8gIqIvvvb5+tjXv/prdXoF7ntx08VZpQCtV5inqvGXiAg2QqgsZZcSd7IB9dYeCOKBpk285Cw5HRAqDEvYxYQtUzGEQMhaCsCqF3P8Zp/3XBZhwaxIN3fkSRm86yQHq6MnAj6a5wDFfDg/YAxuWJlRzEceWp4hIjHPeH7Wrryex5io8lzYn4JdX8m2DywED8oaRdAkVmwXmrwHFoRBye1ZrNiLsPxXc0T7vMZp+uRhr9mwa4i1Ek8XsNO/f6RWrdHQpZf7aGEAAUcW0ylhJz6OtMEX/OApMEUapAqmsAQ+7M6jZZp3qo+Hev+ToQqv3fgVzeuFy04kDS3PXWD+iDBSveO6iHrxvPp+Ce8qt3zdsc0b4ngctxhEhzwUM+Jd6hwsODMQxEKRpIAtDWjFDkDEzWMWFQHzqAqxPZx+94YgFn26UEwtL+mdfj+i5g5/zS5ANg6MqSJcJFlaBE+m1Uro5Vdecs/ncSZOwNqNIqPcd8NG3GVkRzDLC4+B9R9ZWB98+CERaQxqouYOf58tJzEI/KBVdP/IWYy7PWVyJIne6/DYtYcABGeDEMS5AnkmWMmgv5YgYCishw5YmzMYn1MebxJ+V38B1pgsy+nRI9d/T/hde0v6riUKvnK5VA0zoCaFtBG1wOocgzBpotf1fRbDRHE9GLMmnE5QxApEFY8OOZ4xtOu1tbU63euz4NWxWlD29jTWe8hjT9WwCGq609X1TofHrMFA6z0H1sPhgWM2+ivKDlwEfM+jVuQKdcbCj/tPlI3SiOfObSOA95lnrWvEH4f5lAq0+M8R9oW5WZhGHiyTggjZFr5kCp58Wri2ITAGFnFh8gQBsNLg+SiCK6yHjQ0V2Hr77Xfq9Pq6Mk4WB69el1S8mEI2SQaMI7GIYzv3cG5mC2AC4weyJ1DANGVGTNZgOiEzUJhcwHiF8UXYaikIB89A+EzaENZFMUdAMs/x/fT+uFacshgZspdQmNaP3Fo5iET09vnHsjiO6QqziUu2sk7AAovvms+YiQHvh/NlwczFxtoHnlUBC3LKQnMVsixxkS3x4Ps6puQTtf4Tr0tzaO9TyEvKFu9OVwX/Ll3SsTDjuauc6trLB/HBAAUtee71oG+GHjCEE5eHkC3MfoOJ94zwiCqe/4YDN26j6GsYqxVcRDMjYMUEwEb86m/9DhERLa+o2Puf/bc/rtODI2V4ydy+tKLzGTKMJ9w2BwOdI3CNvsRzSAvKfWlZ8y394eREn+lDuUrfxW8VGun5J0+UATdihsXmhY36WAgMlNHQ5fHoaMDPNnE/g8FgMBgMBoPBYDAY/lnCPvwNBoPBYDAYDAaDwWA4x3gqqr/nexQxPSRgrmQJlKoSYimmh44yN54g7UypB7/y2otERPR7v/2V+tjNayroV+VI/3F0hhzoRUiZ8YRqAhRZjB3qMV02Q8oO5Dvg2O1II/QgXubd27eJiOjeQ40//sW33tD8wW+FPo3U1xyEmiKmWHn1sYWr+7lnAstjkgGNmgXbkBbr+3PoxHgIxW48vI7fpTyL6i/0dKTvN+R4+JZ4f/htLeCFcS+RpuiOB5D/0J9fntJ2vELpU7szvddg5ihWMT9rITtiHkF5zolLPKdcsHwa9cJiSCjYlwKVB6vo5ZuOCrS+ou3yg4+Vwnp8IjRKiJMa6bPGLKaFNGmMIZwyja+aQ70mIrpycZXzqs9fWVXaVRQqrezRQ+f+0wdBlapQoZZ2e4Xz6u5/RvU+FSqqKBVxKS5PESslIgoqpeqXTHELkBaHYmvcdtEDBumnKJwpVPMIRH8CH4dgcWeA9o6x6+dS/ZF+K8fmoxbnAjplWZymArt3kL4LbgFI9Zd4x3L5AuoliiLavuSEWoXi2xQn1EbeFgGdCvMPv+UOEUH54TjmQT9KOq4+hiBOOw9BOIeqTFTHDcYY7avL0N6Zpuk12gXWm7h9QSeez4CuXWIa9FLIl/RpuaSNqp7PCN/3qdt1lMjLl5wo2uYFpcyjQKXPc3A6g9jcU6XPpkw1vQDjQQeEFHsXOnCdowiv9LQAHj9WlyGhEC/3lWbpAdk2Ymq0xLcnIup2ldLZ5vjnSEsuG7RkV5YtEJlCej+O1XXMdqBsbl2/XqfHY/fegxMVBl0EwjCkzQ033kvdvP/++/V5dMMQSjZIlzYo35LGtuV7SBnXMbBisa0ulAeWrTTgKcRoR8p3KPdFFzJUjKskRjiKemGfazzGHQOqPwqBLjP19o033qyPXblyBa5b8BqM7xny/CflWcD6F90TxR3HB1cIFPILg9OCfOgCgep6IvCIop4oKihCh1jHGbimiThoiS5gIDIXBqfXdLgQkHyhuB+2gfFYx4KpiKTBvVodFQMP2R0qarEAm/fUnsmn4Ps+LbGonbT3k6HeN98Htwl+hxTo8SkIE9f3xPU/+DXl0PdkuVEV6AJ12jUG6ff5FOY2Lnek+o9hjK247VzYUhr41oa6FT0Z8W+hD6bwvVOc6Nwna73G+Ab9zOf2JGvthXUfnssr7scZlFULxPkqFkjMc33/L/361+r0zVu3iIjoT/7rH9bHjg+Vqh+BW4OswdMp9AGol7jl2mO/B/R96G8ZF8xwpHnptnVMFMHCGI5NwW2g7iewthmhOwa4MPjSHtAtANbK4poxGg1O5fOTMIu/wWAwGAwGg8FgMBgM5xj24W8wGAwGg8FgMBgMBsM5xlNxZ6qKKGO1T6GsVBDfMWwp/a9IHd3g5GivPrYFSv2X3nS0qyubGvsY+ekVUDhFfbNJhUSeMR8C5cmqQKrS6XjvPtxLYohHqF4P6ReZFnbzksZ+DQJwK4A4wjFTcjCOsR+BC0EpeZXEImKTejV1q1ZdBfrN+0f6LsccdgDp9fiu4kGBuUKaN7J6pFxRubwZhv40VX8evR3dDpoUMs4fPHVemPtmnjQdAz1IKK9FobSs7zwBxeCaIip1tZh4y/LuNXUaqYuoFi00S6QAQzoLmeqPQRWgjUfQHrtMg0Zq9KUtpcP2u64fTqCNdmK98ZhDUiDLPE21PCYc1xpjkWfoWiPPBerlUk8pfOnosE63Vh0d7eWXb9bHVpYgUggrx0pM5EXEWPb8gMIO0/2YWudDGBAPYhhXzNHzE+jDodIVKeA4rzGo8YJKMbb9gNsjxmUP4LwqJWMbQCr7nJdBCt6pq5vtTfoLKiujOjM+QKoQY3cT9NOo5ahvOo48P98vjGLa3Hb06OMj10b295TanYN69Qufe5WImmq6M6A+Uu0mBK5oJVJSIQoD0x9PQJF9HtClaWlJqX/St4cDpXHf3bl/6nosd3SrqHOITGdMYyx1bv9LS/reV64qbXmZ8yVP8qDOnhXra+v07//df3D35b7R6YK7C+FcwrRmaFdHx6po/OjRDhER9fo6HsTQH5BWL5ENMHb1CNwxRA0c5wykKEu9dDrqPoAK6AFHDZhM9Bg2d/ktuohcBop4ApTRjKN6IMV5CVwQJF7zaKT5/8P/9Ef0vIjjmG7cuEFERH/wB39ARETf+ta36vMPHjyo0zs7ruyPjo7qY0jDlvjU+A5Ntii62bmCWp6jYC35IlIXh08+q8WRGJpufI2QP+5fdN/Bemb3T2ChN86329q+vvCFLxAR0auvvlofmxftYJGU/6qqwIXE3RdVtqcQC1zKpenmh2sqdxzdV9E9pQC32JypxfisDCnbUkdNvwF4lrgBnnYfIFKqOc4LFYyrUt8YIWU00vkS3W1DVutvJTp3ttvaZ4Tq3+64axoRpZ4RaZrSx7fvEhFRzP0X6fs4x8h8Tel8l8faFRLGBxDyp3YHYsPLWABl5eMnDNfbDMptDN9Asz03hkWJHoshMkybXbECLT5ahnXUAVPSQ3D9wjbkQ714dX/Qe5VQ9kI/l6l1Eb3G8wKKEpf53pIbOyJwe+iBS5HPrqLXrr9YH9u4cLlO/48//3MiIhoe6zi3vqmuZTPwWzhitf040W/XBL5T9w7cOgQjchC4tq2tujx3Ybx5+EjXLCFHgdnc1MghJ7BOGI9d3w8j7e/jiY6Tk7G2TXFbxGgG6EZXu/94v/jbxSz+BoPBYDAYDAaDwWAwnGM8lcW/pIqGYlXnnb9ipju6kwp2mDg+9QC2g7Zv6m55t++EYKZg9cxg76iAXciULTIYr3eeZRjFNHIfrTsiRoZbbGCR5+eihRhvH/LOUwZiGEOwlqJVTcT7fNjBSQu0IDK7oHTvUi7Isux9wvyN5fPhQMtYdoUaljzcaZb6+Cfsfktxeg1LwOk8oShPiQI93mlrYUNYiO/fFPg6bTFvZhWtPyC0IkwHENVJS3hvsYayxWgRteJ7PrV4VzmKJO457hhDfF0ulzDE89qGROgk8PWdIogpjfktuT6yAlkAutMrwkfVUHe329Beg8g9qwOxtvcOQfCOh42kjYwFzUHCO8Iz2CW992C3Tt96VfvRN37jG0REdP3mVX0X3BXncaQiid36/Lv+vh9Sq+NiOnuViHKhNWlO7cOx9pLulPfWWAwpB1EwsLZUTfUpzsD8vqXtFa/BLFSNv3yV/rQSYVFkX8y7HkVSUZizOvVbr7Gfr20kTPqc51M5fmaEUURrzACbsBWsgLYQAAVFxCJjsPijdVHy34ghDekI+l4cieCV3h+t0NJ30cK7saEiSvKs2QyEoWCuqO8JO/VovRIrPtZrBu2pyE9bUtGSPwLBrB5blqSsFmGN6XR69KW3v+zyUoiVHa2EyCqRMVmPnZxADOSKhXTBOoxMiHym13XbbE0GC01QQBl2ecyEskABYBFVRBYBCjh2Om4NMhpqu8F8jUbO+oaMtJeuv1SnL2wpW1Fy2LBZN2lzp3+wADgROfeeX//614mI6CtfUcHkAViXpB6OgYFxgmn+LV4zANEvLDvpa9hm0zQ7dR5jpCPboY5fjfHewXItFv0GS6Y8Pa4hW7HPom1ERG+99Vadfuedd4ioyQJA/DLE/cqyrNuPjOsYC3z/QMV2xeKPa7JOB0QTeyKaqPlHVga22VokDsd9sGjPuLzR2htE2j8Knk+KAsZNED7LRLwP1tpZOk8wDtYGkb5LlKBAqzuegMU/BCu2MG286vQ68VlRFGXNRgjqOQat3SBkyO0NWUQRfCNETL8MYO0UL6nA5frmZp3us/DlFEQXcyhXYQd40IaHUBaPdu64+wNzspWA6CgLq0cRzhUwt/N0lvR0DiNoN8VY0ylboVHANgTx45Dro55PG2uIZ0NZlTRN3fggzO3BUPtLCXPA9lXHDm0vqZX+f/71X9bp3UeObRfC+vjCRWDooTYer9ViKMsBzAfDoRu/uj1to1eBYbfNlnwc2+7vPKzT3bYrt4M97SM4jtaCqrG2oaPd02KbRETbSy4P/b7mpYQ6PD5243aWMiv/U9jkZvE3GAwGg8FgMBgMBoPhHMM+/A0Gg8FgMBgMBoPBYDjH8D6NDnDqx563S0R3fnnZ+WeJ61VVbf7in50Nq5dfCqxePpuwevlswurlswmrl88urG4+m7B6+WzC6uWzCauXzybOrJen+vA3GAwGg8FgMBgMBoPB8P8XjOpvMBgMBoPBYDAYDAbDOYZ9+BsMBoPBYDAYDAaDwXCOYR/+BoPBYDAYDAaDwWAwnGPYh7/BYDAYDAaDwWAwGAznGPbhbzAYDAaDwWAwGAwGwzmGffgbDAaDwWAwGAwGg8FwjmEf/gaDwWAwGAwGg8FgMJxj2Ie/wWAwGAwGg8FgMBgM5xj24W8wGAwGg8FgMBgMBsM5xv8Di5ncn1yWphAAAAAASUVORK5CYII=\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 120 + }, + "id": "3eMs7DbVt4Ee", + "outputId": "fa42d3e0-2bbd-4c37-ee12-1d48e99d681a" + }, + "source": [ + "plt.figure(figsize=(18, 18))\n", + "for i in range(10):\n", + " plt.subplot(1, 10, i+1)\n", + " fig = no_axis_show(plt.imread(f'real_or_drawing/test_data/0/' + str(i).rjust(5, '0') + '.bmp'))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "moXQw9To5TqZ" + }, + "source": [ + "# Special Domain Knowledge\n", + "\n", + "因為大家塗鴉的時候通常只會畫輪廓，我們可以根據這點將source data做點邊緣偵測處理，讓source data更像target data一點。\n", + "\n", + "## Canny Edge Detection\n", + "算法這邊不贅述，只教大家怎麼用。若有興趣歡迎參考wiki或[這裡](https://medium.com/@pomelyu5199/canny-edge-detector-%E5%AF%A6%E4%BD%9C-opencv-f7d1a0a57d19)。\n", + "\n", + "cv2.Canny使用非常方便，只需要兩個參數: low_threshold, high_threshold。\n", + "\n", + "```cv2.Canny(image, low_threshold, high_threshold)```\n", + "\n", + "簡單來說就是當邊緣值超過high_threshold，我們就確定它是edge。如果只有超過low_threshold，那就先判斷一下再決定是不是edge。\n", + "\n", + "以下我們直接拿source data做做看。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "mn2MkDLV7E2-", + "outputId": "edbe96e1-4689-4a72-f9c1-bc0bfe4d7fae" + }, + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "titles = ['horse', 'bed', 'clock', 'apple', 'cat', 'plane', 'television', 'dog', 'dolphin', 'spider']\n", + "plt.figure(figsize=(18, 18))\n", + "\n", + "original_img = plt.imread(f'real_or_drawing/train_data/0/0.bmp')\n", + "plt.subplot(1, 5, 1)\n", + "no_axis_show(original_img, title='original')\n", + "\n", + "gray_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)\n", + "plt.subplot(1, 5, 2)\n", + "no_axis_show(gray_img, title='gray scale', cmap='gray')\n", + "\n", + "gray_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)\n", + "plt.subplot(1, 5, 2)\n", + "no_axis_show(gray_img, title='gray scale', cmap='gray')\n", + "\n", + "canny_50100 = cv2.Canny(gray_img, 50, 100)\n", + "plt.subplot(1, 5, 3)\n", + "no_axis_show(canny_50100, title='Canny(50, 100)', cmap='gray')\n", + "\n", + "canny_150200 = cv2.Canny(gray_img, 150, 200)\n", + "plt.subplot(1, 5, 4)\n", + "no_axis_show(canny_150200, title='Canny(150, 200)', cmap='gray')\n", + "\n", + "canny_250300 = cv2.Canny(gray_img, 250, 300)\n", + "plt.subplot(1, 5, 5)\n", + "no_axis_show(canny_250300, title='Canny(250, 300)', cmap='gray')\n", + " " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " from ipykernel import kernelapp as app\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8THSdt_hmwYh" + }, + "source": [ + "# Data Process\n", + "\n", + "在這裡我故意將data用成可以使用torchvision.ImageFolder的形式，所以只要使用該函式便可以做出一個datasets。\n", + "\n", + "transform的部分請參考以下註解。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WZHIBGknmi8Z" + }, + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.autograd import Function\n", + "\n", + "import torch.optim as optim\n", + "import torchvision.transforms as transforms\n", + "from torchvision.datasets import ImageFolder\n", + "from torch.utils.data import DataLoader\n", + "\n", + "source_transform = transforms.Compose([\n", + " # Turn RGB to grayscale. (Bacause Canny do not support RGB images.)\n", + " transforms.Grayscale(),\n", + " # cv2 do not support skimage.Image, so we transform it to np.array, \n", + " # and then adopt cv2.Canny algorithm.\n", + " transforms.Lambda(lambda x: cv2.Canny(np.array(x), 170, 300)),\n", + " # Transform np.array back to the skimage.Image.\n", + " transforms.ToPILImage(),\n", + " # 50% Horizontal Flip. (For Augmentation)\n", + " transforms.RandomHorizontalFlip(),\n", + " # Rotate +- 15 degrees. (For Augmentation), and filled with zero \n", + " # if there's empty pixel after rotation.\n", + " transforms.RandomRotation(15, fill=(0,)),\n", + " # Transform to tensor for model inputs.\n", + " transforms.ToTensor(),\n", + "])\n", + "target_transform = transforms.Compose([\n", + " # Turn RGB to grayscale.\n", + " transforms.Grayscale(),\n", + " # Resize: size of source data is 32x32, thus we need to \n", + " # enlarge the size of target data from 28x28 to 32x32。\n", + " transforms.Resize((32, 32)),\n", + " # 50% Horizontal Flip. (For Augmentation)\n", + " transforms.RandomHorizontalFlip(),\n", + " # Rotate +- 15 degrees. (For Augmentation), and filled with zero \n", + " # if there's empty pixel after rotation.\n", + " transforms.RandomRotation(15, fill=(0,)),\n", + " # Transform to tensor for model inputs.\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "source_dataset = ImageFolder('real_or_drawing/train_data', transform=source_transform)\n", + "target_dataset = ImageFolder('real_or_drawing/test_data', transform=target_transform)\n", + "\n", + "source_dataloader = DataLoader(source_dataset, batch_size=32, shuffle=True)\n", + "target_dataloader = DataLoader(target_dataset, batch_size=32, shuffle=True)\n", + "test_dataloader = DataLoader(target_dataset, batch_size=128, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hdwDEMrOycs5" + }, + "source": [ + "# Model\n", + "\n", + "Feature Extractor: 典型的VGG-like疊法。\n", + "\n", + "Label Predictor / Domain Classifier: MLP到尾。\n", + "\n", + "相信作業寫到這邊大家對以下的Layer都很熟悉，因此不再贅述。" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3uw2eP09z-pD" + }, + "source": [ + "class FeatureExtractor(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(FeatureExtractor, self).__init__()\n", + "\n", + " self.conv = nn.Sequential(\n", + " nn.Conv2d(1, 64, 3, 1, 1),\n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(64, 128, 3, 1, 1),\n", + " nn.BatchNorm2d(128),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(128, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(256, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(256, 512, 3, 1, 1),\n", + " nn.BatchNorm2d(512),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2)\n", + " )\n", + " \n", + " def forward(self, x):\n", + " x = self.conv(x).squeeze()\n", + " return x\n", + "\n", + "class LabelPredictor(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(LabelPredictor, self).__init__()\n", + "\n", + " self.layer = nn.Sequential(\n", + " nn.Linear(512, 512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 10),\n", + " )\n", + "\n", + " def forward(self, h):\n", + " c = self.layer(h)\n", + " return c\n", + "\n", + "class DomainClassifier(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(DomainClassifier, self).__init__()\n", + "\n", + " self.layer = nn.Sequential(\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 1),\n", + " )\n", + "\n", + " def forward(self, h):\n", + " y = self.layer(h)\n", + " return y" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lxdBIPhF0Icb" + }, + "source": [ + "# Pre-processing\n", + "\n", + "這裡我們選用Adam來當Optimizer。" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hrxKelBy0PJ7" + }, + "source": [ + "feature_extractor = FeatureExtractor().cuda()\n", + "label_predictor = LabelPredictor().cuda()\n", + "domain_classifier = DomainClassifier().cuda()\n", + "\n", + "class_criterion = nn.CrossEntropyLoss()\n", + "domain_criterion = nn.BCEWithLogitsLoss()\n", + "\n", + "optimizer_F = optim.Adam(feature_extractor.parameters())\n", + "optimizer_C = optim.Adam(label_predictor.parameters())\n", + "optimizer_D = optim.Adam(domain_classifier.parameters())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xuAE4cqJ0itR" + }, + "source": [ + "# Start Training\n", + "\n", + "\n", + "## 如何實作DaNN?\n", + "\n", + "理論上，在原始paper中是加上Gradient Reversal Layer，並將Feature Extractor / Label Predictor / Domain Classifier 一起train，但其實我們也可以交換的train Domain Classfier & Feature Extractor(就像在train GAN的Generator & Discriminator一樣)，這也是可行的。\n", + "\n", + "在code實現中，我們採取後者的方式，畢竟GAN是之前的作業，應該會比較熟悉:)。\n", + "\n", + "## 小提醒\n", + "* 原文中的lambda(控制Domain Adversarial Loss的係數)是有Adaptive的版本，如果有興趣可以參考[原文](https://arxiv.org/pdf/1505.07818.pdf)。\n", + "* 因為我們完全沒有target的label，所以結果如何，只好丟kaggle看看囉:)?" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lRAFFKvX0p9y", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c8e9d016-0954-4f86-f428-91c1482d901e" + }, + "source": [ + "def train_epoch(source_dataloader, target_dataloader, lamb):\n", + " '''\n", + " Args:\n", + " source_dataloader: source data的dataloader\n", + " target_dataloader: target data的dataloader\n", + " lamb: control the balance of domain adaptatoin and classification.\n", + " '''\n", + "\n", + " # D loss: Domain Classifier的loss\n", + " # F loss: Feature Extrator & Label Predictor的loss\n", + " running_D_loss, running_F_loss = 0.0, 0.0\n", + " total_hit, total_num = 0.0, 0.0\n", + "\n", + " for i, ((source_data, source_label), (target_data, _)) in enumerate(zip(source_dataloader, target_dataloader)):\n", + "\n", + " source_data = source_data.cuda()\n", + " source_label = source_label.cuda()\n", + " target_data = target_data.cuda()\n", + " \n", + " # Mixed the source data and target data, or it'll mislead the running params\n", + " # of batch_norm. (runnning mean/var of soucre and target data are different.)\n", + " mixed_data = torch.cat([source_data, target_data], dim=0)\n", + " domain_label = torch.zeros([source_data.shape[0] + target_data.shape[0], 1]).cuda()\n", + " # set domain label of source data to be 1.\n", + " domain_label[:source_data.shape[0]] = 1\n", + "\n", + " # Step 1 : train domain classifier\n", + " feature = feature_extractor(mixed_data)\n", + " # We don't need to train feature extractor in step 1.\n", + " # Thus we detach the feature neuron to avoid backpropgation.\n", + " domain_logits = domain_classifier(feature.detach())\n", + " loss = domain_criterion(domain_logits, domain_label)\n", + " running_D_loss+= loss.item()\n", + " loss.backward()\n", + " optimizer_D.step()\n", + "\n", + " # Step 2 : train feature extractor and label classifier\n", + " class_logits = label_predictor(feature[:source_data.shape[0]])\n", + " domain_logits = domain_classifier(feature)\n", + " # loss = cross entropy of classification - lamb * domain binary cross entropy.\n", + " # The reason why using subtraction is similar to generator loss in disciminator of GAN\n", + " loss = class_criterion(class_logits, source_label) - lamb * domain_criterion(domain_logits, domain_label)\n", + " running_F_loss+= loss.item()\n", + " loss.backward()\n", + " optimizer_F.step()\n", + " optimizer_C.step()\n", + "\n", + " optimizer_D.zero_grad()\n", + " optimizer_F.zero_grad()\n", + " optimizer_C.zero_grad()\n", + "\n", + " total_hit += torch.sum(torch.argmax(class_logits, dim=1) == source_label).item()\n", + " total_num += source_data.shape[0]\n", + " print(i, end='\\r')\n", + "\n", + " return running_D_loss / (i+1), running_F_loss / (i+1), total_hit / total_num\n", + "\n", + "# train 200 epochs\n", + "for epoch in range(200):\n", + " # You should chooose lamnda cleverly.\n", + " train_D_loss, train_F_loss, train_acc = train_epoch(source_dataloader, target_dataloader, lamb=0)\n", + "\n", + " torch.save(feature_extractor.state_dict(), f'extractor_model.bin')\n", + " torch.save(label_predictor.state_dict(), f'predictor_model.bin')\n", + "\n", + " print('epoch {:>3d}: train D loss: {:6.4f}, train F loss: {:6.4f}, acc {:6.4f}'.format(epoch, train_D_loss, train_F_loss, train_acc))\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "epoch 0: train D loss: 0.1824, train F loss: 1.9425, acc 0.2798\n", + "epoch 1: train D loss: 0.1747, train F loss: 1.6179, acc 0.4222\n", + "epoch 2: train D loss: 0.1471, train F loss: 1.4900, acc 0.4728\n", + "epoch 3: train D loss: 0.1009, train F loss: 1.3943, acc 0.5086\n", + "epoch 4: train D loss: 0.1259, train F loss: 1.3428, acc 0.5334\n", + "epoch 5: train D loss: 0.1062, train F loss: 1.2801, acc 0.5546\n", + "epoch 6: train D loss: 0.0911, train F loss: 1.2487, acc 0.5668\n", + "epoch 7: train D loss: 0.0983, train F loss: 1.1978, acc 0.5854\n", + "epoch 8: train D loss: 0.1133, train F loss: 1.1724, acc 0.5958\n", + "epoch 9: train D loss: 0.1073, train F loss: 1.1322, acc 0.6034\n", + "epoch 10: train D loss: 0.0904, train F loss: 1.0863, acc 0.6180\n", + "epoch 11: train D loss: 0.0935, train F loss: 1.0693, acc 0.6302\n", + "epoch 12: train D loss: 0.0883, train F loss: 1.0300, acc 0.6444\n", + "epoch 13: train D loss: 0.0689, train F loss: 0.9681, acc 0.6624\n", + "epoch 14: train D loss: 0.0722, train F loss: 0.9682, acc 0.6612\n", + "epoch 15: train D loss: 0.0685, train F loss: 0.9147, acc 0.6828\n", + "epoch 16: train D loss: 0.0567, train F loss: 0.8989, acc 0.6874\n", + "epoch 17: train D loss: 0.0544, train F loss: 0.8864, acc 0.6948\n", + "epoch 18: train D loss: 0.0469, train F loss: 0.8289, acc 0.7090\n", + "epoch 19: train D loss: 0.0386, train F loss: 0.7910, acc 0.7208\n", + "epoch 20: train D loss: 0.0344, train F loss: 0.7653, acc 0.7408\n", + "epoch 21: train D loss: 0.0323, train F loss: 0.7361, acc 0.7430\n", + "epoch 22: train D loss: 0.0279, train F loss: 0.7048, acc 0.7476\n", + "epoch 23: train D loss: 0.0235, train F loss: 0.6578, acc 0.7764\n", + "epoch 24: train D loss: 0.0304, train F loss: 0.6403, acc 0.7744\n", + "epoch 25: train D loss: 0.0205, train F loss: 0.6064, acc 0.7922\n", + "epoch 26: train D loss: 0.0163, train F loss: 0.5798, acc 0.8000\n", + "epoch 27: train D loss: 0.0210, train F loss: 0.5692, acc 0.8038\n", + "epoch 28: train D loss: 0.0160, train F loss: 0.5047, acc 0.8256\n", + "epoch 29: train D loss: 0.0162, train F loss: 0.5004, acc 0.8234\n", + "epoch 30: train D loss: 0.0167, train F loss: 0.4775, acc 0.8354\n", + "epoch 31: train D loss: 0.0121, train F loss: 0.4674, acc 0.8344\n", + "epoch 32: train D loss: 0.0153, train F loss: 0.4146, acc 0.8572\n", + "epoch 33: train D loss: 0.0106, train F loss: 0.4228, acc 0.8514\n", + "epoch 34: train D loss: 0.0120, train F loss: 0.3631, acc 0.8798\n", + "epoch 35: train D loss: 0.0106, train F loss: 0.3788, acc 0.8696\n", + "epoch 36: train D loss: 0.0099, train F loss: 0.3357, acc 0.8862\n", + "epoch 37: train D loss: 0.0080, train F loss: 0.3365, acc 0.8874\n", + "epoch 38: train D loss: 0.0107, train F loss: 0.3356, acc 0.8836\n", + "epoch 39: train D loss: 0.0100, train F loss: 0.2909, acc 0.9022\n", + "epoch 40: train D loss: 0.0089, train F loss: 0.2918, acc 0.9008\n", + "epoch 41: train D loss: 0.0083, train F loss: 0.2711, acc 0.9028\n", + "epoch 42: train D loss: 0.0064, train F loss: 0.2483, acc 0.9182\n", + "epoch 43: train D loss: 0.0046, train F loss: 0.2543, acc 0.9100\n", + "epoch 44: train D loss: 0.0084, train F loss: 0.2540, acc 0.9152\n", + "epoch 45: train D loss: 0.0041, train F loss: 0.2339, acc 0.9202\n", + "epoch 46: train D loss: 0.0074, train F loss: 0.2190, acc 0.9282\n", + "epoch 47: train D loss: 0.0048, train F loss: 0.2370, acc 0.9198\n", + "epoch 48: train D loss: 0.0071, train F loss: 0.2318, acc 0.9220\n", + "epoch 49: train D loss: 0.0034, train F loss: 0.1876, acc 0.9350\n", + "epoch 50: train D loss: 0.0045, train F loss: 0.1872, acc 0.9374\n", + "epoch 51: train D loss: 0.0084, train F loss: 0.1950, acc 0.9346\n", + "epoch 52: train D loss: 0.0070, train F loss: 0.1916, acc 0.9318\n", + "epoch 53: train D loss: 0.0049, train F loss: 0.1941, acc 0.9334\n", + "epoch 54: train D loss: 0.0099, train F loss: 0.1793, acc 0.9382\n", + "epoch 55: train D loss: 0.0033, train F loss: 0.1637, acc 0.9434\n", + "epoch 56: train D loss: 0.0023, train F loss: 0.1728, acc 0.9420\n", + "epoch 57: train D loss: 0.0043, train F loss: 0.1708, acc 0.9412\n", + "epoch 58: train D loss: 0.0028, train F loss: 0.1847, acc 0.9378\n", + "epoch 59: train D loss: 0.0069, train F loss: 0.1737, acc 0.9412\n", + "epoch 60: train D loss: 0.0036, train F loss: 0.1593, acc 0.9468\n", + "epoch 61: train D loss: 0.0038, train F loss: 0.1557, acc 0.9474\n", + "epoch 62: train D loss: 0.0011, train F loss: 0.1480, acc 0.9530\n", + "epoch 63: train D loss: 0.0032, train F loss: 0.1301, acc 0.9556\n", + "epoch 64: train D loss: 0.0040, train F loss: 0.1401, acc 0.9536\n", + "epoch 65: train D loss: 0.0023, train F loss: 0.1346, acc 0.9554\n", + "epoch 66: train D loss: 0.0057, train F loss: 0.1721, acc 0.9456\n", + "epoch 67: train D loss: 0.0029, train F loss: 0.1329, acc 0.9550\n", + "epoch 68: train D loss: 0.0018, train F loss: 0.1368, acc 0.9542\n", + "epoch 69: train D loss: 0.0018, train F loss: 0.1217, acc 0.9564\n", + "epoch 70: train D loss: 0.0025, train F loss: 0.1293, acc 0.9590\n", + "epoch 71: train D loss: 0.0031, train F loss: 0.1214, acc 0.9608\n", + "epoch 72: train D loss: 0.0041, train F loss: 0.1260, acc 0.9572\n", + "epoch 73: train D loss: 0.0009, train F loss: 0.1003, acc 0.9636\n", + "epoch 74: train D loss: 0.0035, train F loss: 0.1338, acc 0.9582\n", + "epoch 75: train D loss: 0.0057, train F loss: 0.1070, acc 0.9622\n", + "epoch 76: train D loss: 0.0058, train F loss: 0.1066, acc 0.9662\n", + "epoch 77: train D loss: 0.0028, train F loss: 0.1194, acc 0.9594\n", + "epoch 78: train D loss: 0.0050, train F loss: 0.1160, acc 0.9614\n", + "epoch 79: train D loss: 0.0025, train F loss: 0.1187, acc 0.9590\n", + "epoch 80: train D loss: 0.0025, train F loss: 0.1405, acc 0.9544\n", + "epoch 81: train D loss: 0.0021, train F loss: 0.1090, acc 0.9662\n", + "epoch 82: train D loss: 0.0040, train F loss: 0.1000, acc 0.9630\n", + "epoch 83: train D loss: 0.0015, train F loss: 0.0984, acc 0.9702\n", + "epoch 84: train D loss: 0.0069, train F loss: 0.0954, acc 0.9678\n", + "epoch 85: train D loss: 0.0035, train F loss: 0.1349, acc 0.9592\n", + "epoch 86: train D loss: 0.0021, train F loss: 0.0909, acc 0.9684\n", + "epoch 87: train D loss: 0.0015, train F loss: 0.0911, acc 0.9710\n", + "epoch 88: train D loss: 0.0012, train F loss: 0.1186, acc 0.9578\n", + "epoch 89: train D loss: 0.0018, train F loss: 0.0885, acc 0.9690\n", + "epoch 90: train D loss: 0.0031, train F loss: 0.0822, acc 0.9704\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o8_-0iSSje4w" + }, + "source": [ + "# Inference\n", + "\n", + "就跟前幾次作業一樣。這裡我使用pd來生產csv，因為看起來比較潮(?)\n", + "\n", + "此外，200 epochs的Accuracy可能會不太穩定，可以多丟幾次或train久一點。" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wly5AgH2jePv" + }, + "source": [ + "result = []\n", + "label_predictor.eval()\n", + "feature_extractor.eval()\n", + "for i, (test_data, _) in enumerate(test_dataloader):\n", + " test_data = test_data.cuda()\n", + "\n", + " class_logits = label_predictor(feature_extractor(test_data))\n", + "\n", + " x = torch.argmax(class_logits, dim=1).cpu().detach().numpy()\n", + " result.append(x)\n", + "\n", + "import pandas as pd\n", + "result = np.concatenate(result)\n", + "\n", + "# Generate your submission\n", + "df = pd.DataFrame({'id': np.arange(0,len(result)), 'label': result})\n", + "df.to_csv('DaNN_submission.csv',index=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZtXnEMUNCE78" + }, + "source": [ + "# Training Statistics\n", + "\n", + "- Number of parameters:\n", + " - Feature Extractor: 2, 142, 336\n", + " - Label Predictor: 530, 442\n", + " - Domain Classifier: 1, 055, 233\n", + "\n", + "- Simple\n", + " - Training time on colab: ~ 1 hr\n", + "- Medium\n", + " - Training time on colab: 2 ~ 4 hr\n", + "- Strong\n", + " - Training time on colab: 5 ~ 6 hrs\n", + "- Boss\n", + " - **Unmeasurable**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "duk7k43Am9xH" + }, + "source": [ + "# Learning Curve (Strong Baseline)\n", + "* This method is slightly different from colab.\n", + "\n", + "![Loss Curve](https://i.imgur.com/vIujQyo.png)\n", + "\n", + "# Accuracy Curve (Strong Baseline)\n", + "* Note that you cannot access testing accuracy. But this plot tells you that even though the model overfits the training data, the testing accuracy is still improving, and that's why you need to train more epochs.\n", + "\n", + "![Acc Curve](https://i.imgur.com/4W1otXG.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s6UfXzef-wNl" + }, + "source": [ + "# Q&A\n", + "\n", + "有任何問題 Domain Adaptation 的問題可以寄信到ntu-ml-2021spring-ta@googlegroups.com。\n", + "\n", + "時間允許的話我會更新在這裡。\n", + "\n", + "# Special Thanks\n", + "這次的作業其實是我出在 2019FALL 的 ML Final Project，以下是我認為在 Final Report 不錯的幾組，有興趣的話歡迎大家參考看看。\n", + "\n", + "[NTU_r08942071_太神啦 / 組長: 劉正仁同學](https://drive.google.com/open?id=11uNDcz7_eMS8dMQxvnWsbrdguu9k4c-c)\n", + "\n", + "[NTU_r08921a08_CAT / 組長: 廖子毅同學](https://drive.google.com/open?id=1xIkSs8HAShdcfV1E0NEnf4JDbL7POZTf)\n" + ] + } + ] +} \ No newline at end of file diff --git a/10 RL/作业HW11/hw11_domain_adaptation_(en).ipynb b/10 RL/作业HW11/hw11_domain_adaptation_(en).ipynb new file mode 100644 index 0000000..15c4ccf --- /dev/null +++ b/10 RL/作业HW11/hw11_domain_adaptation_(en).ipynb @@ -0,0 +1,5900 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "hw11_domain_adaptation (en).ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true, + "machine_shape": "hm" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "b5cFq_TgWlQ_" + }, + "source": [ + "# Homework 11 - Transfer Learning (Domain Adversarial Training)\n", + "\n", + "> Author: Arvin Liu (r09922071@ntu.edu.tw)\n", + "\n", + "If there are any questions, please contact ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vNiZCGrIYKdR" + }, + "source": [ + "# Readme\n", + "\n", + "In homework 11, you will need to implement Domain Adversarial Training in Transfer Learning. As shown in the bottom left part of the figure.\n", + "\n", + "\n", + "\n", + "> \n", + "\n", + "## Scenario and Why Domain Adversarial Training\n", + "Now we have labeled source data and unlabeled target data, where source data might be relavent to the target data. We now want to train a model with source data only and test it on target data.\n", + "\n", + "What problem might occur if we do so? After we have learned Anomaly Detection, we now know that if we test the model with an abnormal data that have never appeared in source data, our trained model is likely to result in poor performance since it is not familiar with the abnormal data.\n", + "\n", + "For example, we have a model that contains Feature Extractor and Classifier:\n", + "\n", + "\n", + "When the model is trained with source data, the feature extractor \n", + "will extract meaningful features since it is familiar with the distribution of it.It could be seen in the following figure that the blue dots, which is the distribution of source data, has already been clustered into different clusters. Therefore, the Classifier can predict the label based on these clusters.\n", + "\n", + "However, when test on the target data, the Feature Extractor will not be able to extract meaningful features that follow the distribution of the source feature distribution, which result in the classifier learned for the source domain will not be able to apply to the target domain.\n", + "\n", + "\n", + "## Domain Adversarial Training of Nerural Networks (DaNN)\n", + "\n", + "Based on the above problems, DaNN approaches build mappings between the source (training-time) and the target (test-time) domains, so that the classifier learned for the source domain can also be applied to the target domain, when composed with the learned mapping between domains.\n", + "\n", + "\n", + "\n", + "In DaNN, the authors added a Domain Classifier, which is a deep discriminatively-trained classifeir in the training framework to distinguish the data from different domain by the features extracted by the feature extractor. As the training progresses, the approach promotes a domain classifier that discriminates between the source and the target domains and a feature extractor that can extractor features that are discriminative for the main learning task on the source domain and indiscriminate with respect to the shift between the domains. \n", + "\n", + "\n", + "The feature extractor are likely to outperform the domain classifier as its input are generated by the feature extractor and that the task of domain classification and label classification are not conflict.\n", + "\n", + "This method leads to the emergence of features that are domain-invariant and on the same feature distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3-qnUkspmap3" + }, + "source": [ + "# Data Introduce\n", + "\n", + "Our task contains source data: real photos, and target data: hand-drawn graffiti.\n", + "\n", + "We are going to train the model with the photos and the labels, and try to predict what the labels are for hand-drawn graffiti.\n", + "\n", + "The data could be downloaded [here](https://drive.google.com/open?id=12-07DSquGdzN3JBHBChN4nMo3i8BqTiL). The code below is for data downloading and visualization.\n", + "\n", + "Note that: **The source and target data are all balanced data, you can make use of this information.**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DF-i0sVlnUbq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d1f5d9b1-abcb-4afe-bb3a-eee6bf94ceb6" + }, + "source": [ + "# Download dataset\n", + "!gdown --id '1P4fGNb9JhJj8W0DA_Qrp7mbrRHfF5U_f' --output real_or_drawing.zip\n", + "# Unzip the files\n", + "!unzip real_or_drawing.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: real_or_drawing/train_data/0/107.bmp \n", + " inflating: real_or_drawing/train_data/0/108.bmp \n", + " inflating: real_or_drawing/train_data/0/109.bmp \n", + " inflating: real_or_drawing/train_data/0/11.bmp \n", + " inflating: 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real_or_drawing/train_data/9/4990.bmp \n", + " inflating: real_or_drawing/train_data/9/4991.bmp \n", + " inflating: real_or_drawing/train_data/9/4992.bmp \n", + " inflating: real_or_drawing/train_data/9/4993.bmp \n", + " inflating: real_or_drawing/train_data/9/4994.bmp \n", + " inflating: real_or_drawing/train_data/9/4995.bmp \n", + " inflating: real_or_drawing/train_data/9/4996.bmp \n", + " inflating: real_or_drawing/train_data/9/4997.bmp \n", + " inflating: real_or_drawing/train_data/9/4998.bmp \n", + " inflating: real_or_drawing/train_data/9/4999.bmp \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0_uO-ZSDoR6i", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "18f1a1f9-5a11-4437-a94f-5b98114c7421" + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def no_axis_show(img, title='', cmap=None):\n", + " # imshow, and set the interpolation mode to be \"nearest\"。\n", + " fig = plt.imshow(img, interpolation='nearest', cmap=cmap)\n", + " # do not show the axes in the images.\n", + " fig.axes.get_xaxis().set_visible(False)\n", + " fig.axes.get_yaxis().set_visible(False)\n", + " plt.title(title)\n", + "\n", + "titles = ['horse', 'bed', 'clock', 'apple', 'cat', 'plane', 'television', 'dog', 'dolphin', 'spider']\n", + "plt.figure(figsize=(18, 18))\n", + "for i in range(10):\n", + " plt.subplot(1, 10, i+1)\n", + " fig = no_axis_show(plt.imread(f'real_or_drawing/train_data/{i}/{500*i}.bmp'), title=titles[i])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Ut5paV8zD7BH9NC+7yUMjuku3uqIMFU/zNsnm7rWcPnUK73rXzwEAlpfdbh+z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9WwCGq609X1TofHrMFA6z0H1sPhgWM2+ivKDlwEfM+jVuQKdcbCj/tPlI3SiOfObSOA95lnrWvE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\nBsM5xv8Di5ncn1yWphAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3eMs7DbVt4Ee", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 120 + }, + "outputId": "1a9814f9-5956-4254-c65c-f7deb4241247" + }, + "source": [ + "plt.figure(figsize=(18, 18))\n", + "for i in range(10):\n", + " plt.subplot(1, 10, i+1)\n", + " fig = no_axis_show(plt.imread(f'real_or_drawing/test_data/0/' + str(i).rjust(5, '0') + '.bmp'))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "moXQw9To5TqZ" + }, + "source": [ + "# Special Domain Knowledge\n", + "\n", + "When we graffiti, we usually draw the outline only, therefore we can perform edge detection processing on the source data to make it more similar to the target data.\n", + "\n", + "\n", + "## Canny Edge Detection\n", + "The implementation of Canny Edge Detection is as follow.\n", + "The algorithm will not be describe thoroughly here. If you are interested, please refer to the wiki or [here](https://medium.com/@pomelyu5199/canny-edge-detector-%E5%AF%A6%E4%BD%9C-opencv-f7d1a0a57d19).\n", + "\n", + "We only need two parameters to implement Canny Edge Detection with CV2: `low_threshold` and `high_threshold`.\n", + "\n", + "```cv2.Canny(image, low_threshold, high_threshold)```\n", + "\n", + "Simply put, when the edge value exceeds the high_threshold, we determine it as an edge. If the edge value is only above low_threshold, we will then determine whether it is an edge or not.\n", + "\n", + "Let's implement it on the source data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mn2MkDLV7E2-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "outputId": "99414b46-b70a-4d39-81d3-666e28835596" + }, + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "titles = ['horse', 'bed', 'clock', 'apple', 'cat', 'plane', 'television', 'dog', 'dolphin', 'spider']\n", + "plt.figure(figsize=(18, 18))\n", + "\n", + "original_img = plt.imread(f'real_or_drawing/train_data/0/0.bmp')\n", + "plt.subplot(1, 5, 1)\n", + "no_axis_show(original_img, title='original')\n", + "\n", + "gray_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)\n", + "plt.subplot(1, 5, 2)\n", + "no_axis_show(gray_img, title='gray scale', cmap='gray')\n", + "\n", + "gray_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)\n", + "plt.subplot(1, 5, 2)\n", + "no_axis_show(gray_img, title='gray scale', cmap='gray')\n", + "\n", + "canny_50100 = cv2.Canny(gray_img, 50, 100)\n", + "plt.subplot(1, 5, 3)\n", + "no_axis_show(canny_50100, title='Canny(50, 100)', cmap='gray')\n", + "\n", + "canny_150200 = cv2.Canny(gray_img, 150, 200)\n", + "plt.subplot(1, 5, 4)\n", + "no_axis_show(canny_150200, title='Canny(150, 200)', cmap='gray')\n", + "\n", + "canny_250300 = cv2.Canny(gray_img, 250, 300)\n", + "plt.subplot(1, 5, 5)\n", + "no_axis_show(canny_250300, title='Canny(250, 300)', cmap='gray')\n", + " " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " from ipykernel import kernelapp as app\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8THSdt_hmwYh" + }, + "source": [ + "# Data Process\n", + " \n", + " \n", + "The data is suitible for `torchvision.ImageFolder`. You can create a dataset with `torchvision.ImageFolder`. Details for image augmentation please refer to the comments in the following codes." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WZHIBGknmi8Z" + }, + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.autograd import Function\n", + " \n", + "import torch.optim as optim\n", + "import torchvision.transforms as transforms\n", + "from torchvision.datasets import ImageFolder\n", + "from torch.utils.data import DataLoader\n", + " \n", + "source_transform = transforms.Compose([\n", + " # Turn RGB to grayscale. (Bacause Canny do not support RGB images.)\n", + " transforms.Grayscale(),\n", + " # cv2 do not support skimage.Image, so we transform it to np.array, \n", + " # and then adopt cv2.Canny algorithm.\n", + " transforms.Lambda(lambda x: cv2.Canny(np.array(x), 170, 300)),\n", + " # Transform np.array back to the skimage.Image.\n", + " transforms.ToPILImage(),\n", + " # 50% Horizontal Flip. (For Augmentation)\n", + " transforms.RandomHorizontalFlip(),\n", + " # Rotate +- 15 degrees. (For Augmentation), and filled with zero \n", + " # if there's empty pixel after rotation.\n", + " transforms.RandomRotation(15, fill=(0,)),\n", + " # Transform to tensor for model inputs.\n", + " transforms.ToTensor(),\n", + "])\n", + "target_transform = transforms.Compose([\n", + " # Turn RGB to grayscale.\n", + " transforms.Grayscale(),\n", + " # Resize: size of source data is 32x32, thus we need to \n", + " # enlarge the size of target data from 28x28 to 32x32。\n", + " transforms.Resize((32, 32)),\n", + " # 50% Horizontal Flip. (For Augmentation)\n", + " transforms.RandomHorizontalFlip(),\n", + " # Rotate +- 15 degrees. (For Augmentation), and filled with zero \n", + " # if there's empty pixel after rotation.\n", + " transforms.RandomRotation(15, fill=(0,)),\n", + " # Transform to tensor for model inputs.\n", + " transforms.ToTensor(),\n", + "])\n", + " \n", + "source_dataset = ImageFolder('real_or_drawing/train_data', transform=source_transform)\n", + "target_dataset = ImageFolder('real_or_drawing/test_data', transform=target_transform)\n", + " \n", + "source_dataloader = DataLoader(source_dataset, batch_size=32, shuffle=True)\n", + "target_dataloader = DataLoader(target_dataset, batch_size=32, shuffle=True)\n", + "test_dataloader = DataLoader(target_dataset, batch_size=128, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hdwDEMrOycs5" + }, + "source": [ + "# Model\n", + "\n", + "Feature Extractor: Classic VGG-like architecture\n", + "\n", + "Label Predictor / Domain Classifier: Linear models." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3uw2eP09z-pD" + }, + "source": [ + "class FeatureExtractor(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(FeatureExtractor, self).__init__()\n", + "\n", + " self.conv = nn.Sequential(\n", + " nn.Conv2d(1, 64, 3, 1, 1),\n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(64, 128, 3, 1, 1),\n", + " nn.BatchNorm2d(128),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(128, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(256, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2),\n", + "\n", + " nn.Conv2d(256, 512, 3, 1, 1),\n", + " nn.BatchNorm2d(512),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2)\n", + " )\n", + " \n", + " def forward(self, x):\n", + " x = self.conv(x).squeeze()\n", + " return x\n", + "\n", + "class LabelPredictor(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(LabelPredictor, self).__init__()\n", + "\n", + " self.layer = nn.Sequential(\n", + " nn.Linear(512, 512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 10),\n", + " )\n", + "\n", + " def forward(self, h):\n", + " c = self.layer(h)\n", + " return c\n", + "\n", + "class DomainClassifier(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(DomainClassifier, self).__init__()\n", + "\n", + " self.layer = nn.Sequential(\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(),\n", + "\n", + " nn.Linear(512, 1),\n", + " )\n", + "\n", + " def forward(self, h):\n", + " y = self.layer(h)\n", + " return y" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lxdBIPhF0Icb" + }, + "source": [ + "# Pre-processing\n", + "\n", + "Here we use Adam as our optimizor." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hrxKelBy0PJ7" + }, + "source": [ + "feature_extractor = FeatureExtractor().cuda()\n", + "label_predictor = LabelPredictor().cuda()\n", + "domain_classifier = DomainClassifier().cuda()\n", + "\n", + "class_criterion = nn.CrossEntropyLoss()\n", + "domain_criterion = nn.BCEWithLogitsLoss()\n", + "\n", + "optimizer_F = optim.Adam(feature_extractor.parameters())\n", + "optimizer_C = optim.Adam(label_predictor.parameters())\n", + "optimizer_D = optim.Adam(domain_classifier.parameters())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xuAE4cqJ0itR" + }, + "source": [ + "# Start Training\n", + "\n", + "\n", + "## DaNN Implementation\n", + "\n", + "In the original paper, Gradient Reversal Layer is used.\n", + "Feature Extractor, Label Predictor, and Domain Classifier are all trained at the same time. In this code, we train Domain Classifier first, and then train our Feature Extractor (same concept as Generator and Discriminator training process in GAN).\n", + "\n", + "## Reminder\n", + "* Lambda, which controls the domain adversarial loss, is adaptive in the original paper. You can refer to [the original work](https://arxiv.org/pdf/1505.07818.pdf) . Here lambda is set to 0.1.\n", + "* We do not have the label to target data, you can only evaluate your model by uploading your result to kaggle.:)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lRAFFKvX0p9y", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "eb1a0cd7-e2fd-49c2-e6c2-28d90b87a813" + }, + "source": [ + "def train_epoch(source_dataloader, target_dataloader, lamb):\n", + " '''\n", + " Args:\n", + " source_dataloader: source data的dataloader\n", + " target_dataloader: target data的dataloader\n", + " lamb: control the balance of domain adaptatoin and classification.\n", + " '''\n", + "\n", + " # D loss: Domain Classifier的loss\n", + " # F loss: Feature Extrator & Label Predictor的loss\n", + " running_D_loss, running_F_loss = 0.0, 0.0\n", + " total_hit, total_num = 0.0, 0.0\n", + "\n", + " for i, ((source_data, source_label), (target_data, _)) in enumerate(zip(source_dataloader, target_dataloader)):\n", + "\n", + " source_data = source_data.cuda()\n", + " source_label = source_label.cuda()\n", + " target_data = target_data.cuda()\n", + " \n", + " # Mixed the source data and target data, or it'll mislead the running params\n", + " # of batch_norm. (runnning mean/var of soucre and target data are different.)\n", + " mixed_data = torch.cat([source_data, target_data], dim=0)\n", + " domain_label = torch.zeros([source_data.shape[0] + target_data.shape[0], 1]).cuda()\n", + " # set domain label of source data to be 1.\n", + " domain_label[:source_data.shape[0]] = 1\n", + "\n", + " # Step 1 : train domain classifier\n", + " feature = feature_extractor(mixed_data)\n", + " # We don't need to train feature extractor in step 1.\n", + " # Thus we detach the feature neuron to avoid backpropgation.\n", + " domain_logits = domain_classifier(feature.detach())\n", + " loss = domain_criterion(domain_logits, domain_label)\n", + " running_D_loss+= loss.item()\n", + " loss.backward()\n", + " optimizer_D.step()\n", + "\n", + " # Step 2 : train feature extractor and label classifier\n", + " class_logits = label_predictor(feature[:source_data.shape[0]])\n", + " domain_logits = domain_classifier(feature)\n", + " # loss = cross entropy of classification - lamb * domain binary cross entropy.\n", + " # The reason why using subtraction is similar to generator loss in disciminator of GAN\n", + " loss = class_criterion(class_logits, source_label) - lamb * domain_criterion(domain_logits, domain_label)\n", + " running_F_loss+= loss.item()\n", + " loss.backward()\n", + " optimizer_F.step()\n", + " optimizer_C.step()\n", + "\n", + " optimizer_D.zero_grad()\n", + " optimizer_F.zero_grad()\n", + " optimizer_C.zero_grad()\n", + "\n", + " total_hit += torch.sum(torch.argmax(class_logits, dim=1) == source_label).item()\n", + " total_num += source_data.shape[0]\n", + " print(i, end='\\r')\n", + "\n", + " return running_D_loss / (i+1), running_F_loss / (i+1), total_hit / total_num\n", + "\n", + "# train 200 epochs\n", + "for epoch in range(200):\n", + " train_D_loss, train_F_loss, train_acc = train_epoch(source_dataloader, target_dataloader, lamb=0.1)\n", + "\n", + " torch.save(feature_extractor.state_dict(), f'extractor_model.bin')\n", + " torch.save(label_predictor.state_dict(), f'predictor_model.bin')\n", + "\n", + " print('epoch {:>3d}: train D loss: {:6.4f}, train F loss: {:6.4f}, acc {:6.4f}'.format(epoch, train_D_loss, train_F_loss, train_acc))\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "epoch 0: train D loss: 0.6985, train F loss: 1.9766, acc 0.2498\n", + "epoch 1: train D loss: 0.6149, train F loss: 1.6372, acc 0.3940\n", + "epoch 2: train D loss: 0.5808, train F loss: 1.4832, acc 0.4580\n", + "epoch 3: train D loss: 0.5382, train F loss: 1.4014, acc 0.4950\n", + "epoch 4: train D loss: 0.5447, train F loss: 1.3219, acc 0.5188\n", + "epoch 5: train D loss: 0.5326, train F loss: 1.2951, acc 0.5390\n", + "epoch 6: train D loss: 0.5226, train F loss: 1.2403, acc 0.5532\n", + "epoch 7: train D loss: 0.4900, train F loss: 1.2035, acc 0.5592\n", + "epoch 8: train D loss: 0.5101, train F loss: 1.1856, acc 0.5698\n", + "epoch 9: train D loss: 0.5225, train F loss: 1.1246, acc 0.5964\n", + "epoch 10: train D loss: 0.5220, train F loss: 1.1093, acc 0.5912\n", + "epoch 11: train D loss: 0.5066, train F loss: 1.0706, acc 0.6052\n", + "epoch 12: train D loss: 0.5185, train F loss: 1.0377, acc 0.6286\n", + "epoch 13: train D loss: 0.5209, train F loss: 0.9984, acc 0.6326\n", + "epoch 14: train D loss: 0.5053, train F loss: 0.9694, acc 0.6466\n", + "epoch 15: train D loss: 0.5004, train F loss: 0.9520, acc 0.6536\n", + "epoch 16: train D loss: 0.5029, train F loss: 0.9117, acc 0.6698\n", + "epoch 17: train D loss: 0.5030, train F loss: 0.8761, acc 0.6820\n", + "epoch 18: train D loss: 0.5070, train F loss: 0.8350, acc 0.6956\n", + "epoch 19: train D loss: 0.5002, train F loss: 0.8015, acc 0.7050\n", + "epoch 20: train D loss: 0.5012, train F loss: 0.7576, acc 0.7180\n", + "epoch 21: train D loss: 0.4745, train F loss: 0.7245, acc 0.7338\n", + "epoch 22: train D loss: 0.4758, train F loss: 0.7112, acc 0.7422\n", + "epoch 23: train D loss: 0.4809, train F loss: 0.6570, acc 0.7594\n", + "epoch 24: train D loss: 0.4655, train F loss: 0.6605, acc 0.7594\n", + "epoch 25: train D loss: 0.4652, train F loss: 0.6085, acc 0.7730\n", + "epoch 26: train D loss: 0.4646, train F loss: 0.5878, acc 0.7836\n", + "epoch 27: train D loss: 0.4659, train F loss: 0.5475, acc 0.7946\n", + "epoch 28: train D loss: 0.4590, train F loss: 0.5473, acc 0.7996\n", + "epoch 29: train D loss: 0.4525, train F loss: 0.4873, acc 0.8144\n", + "epoch 30: train D loss: 0.4700, train F loss: 0.4803, acc 0.8260\n", + "epoch 31: train D loss: 0.4266, train F loss: 0.4585, acc 0.8284\n", + "epoch 32: train D loss: 0.4336, train F loss: 0.4383, acc 0.8366\n", + "epoch 33: train D loss: 0.4315, train F loss: 0.4000, acc 0.8538\n", + "epoch 34: train D loss: 0.4428, train F loss: 0.3905, acc 0.8470\n", + "epoch 35: train D loss: 0.4312, train F loss: 0.3500, acc 0.8614\n", + "epoch 36: train D loss: 0.4079, train F loss: 0.3686, acc 0.8604\n", + "epoch 37: train D loss: 0.4254, train F loss: 0.3233, acc 0.8814\n", + "epoch 38: train D loss: 0.4171, train F loss: 0.3061, acc 0.8840\n", + "epoch 39: train D loss: 0.4090, train F loss: 0.3154, acc 0.8782\n", + "epoch 40: train D loss: 0.4138, train F loss: 0.2796, acc 0.8910\n", + "epoch 41: train D loss: 0.4126, train F loss: 0.2800, acc 0.8922\n", + "epoch 42: train D loss: 0.4138, train F loss: 0.2514, acc 0.8984\n", + "epoch 43: train D loss: 0.4031, train F loss: 0.2489, acc 0.9040\n", + "epoch 44: train D loss: 0.4026, train F loss: 0.2448, acc 0.9042\n", + "epoch 45: train D loss: 0.3953, train F loss: 0.2452, acc 0.9068\n", + "epoch 46: train D loss: 0.4011, train F loss: 0.2073, acc 0.9196\n", + "epoch 47: train D loss: 0.3797, train F loss: 0.2056, acc 0.9196\n", + "epoch 48: train D loss: 0.4075, train F loss: 0.1796, acc 0.9260\n", + "epoch 49: train D loss: 0.3665, train F loss: 0.2022, acc 0.9210\n", + "epoch 50: train D loss: 0.3878, train F loss: 0.1768, acc 0.9274\n", + "epoch 51: train D loss: 0.3766, train F loss: 0.1872, acc 0.9292\n", + "epoch 52: train D loss: 0.3856, train F loss: 0.1672, acc 0.9280\n", + "epoch 53: train D loss: 0.3860, train F loss: 0.1810, acc 0.9284\n", + "epoch 54: train D loss: 0.3835, train F loss: 0.1606, acc 0.9382\n", + "epoch 55: train D loss: 0.3757, train F loss: 0.1518, acc 0.9376\n", + "epoch 56: train D loss: 0.3606, train F loss: 0.1551, acc 0.9420\n", + "epoch 57: train D loss: 0.3900, train F loss: 0.1513, acc 0.9360\n", + "epoch 58: train D loss: 0.3733, train F loss: 0.1433, acc 0.9418\n", + "epoch 59: train D loss: 0.3781, train F loss: 0.1549, acc 0.9374\n", + "epoch 60: train D loss: 0.3671, train F loss: 0.1418, acc 0.9410\n", + "epoch 61: train D loss: 0.3714, train F loss: 0.1424, acc 0.9438\n", + "epoch 62: train D loss: 0.3735, train F loss: 0.1149, acc 0.9486\n", + "epoch 63: train D loss: 0.3480, train F loss: 0.1226, acc 0.9478\n", + "epoch 64: train D loss: 0.3759, train F loss: 0.1272, acc 0.9476\n", + "epoch 65: train D loss: 0.3868, train F loss: 0.1163, acc 0.9488\n", + "epoch 66: train D loss: 0.3570, train F loss: 0.1249, acc 0.9494\n", + "epoch 67: train D loss: 0.3590, train F loss: 0.1204, acc 0.9528\n", + "epoch 68: train D loss: 0.3621, train F loss: 0.1064, acc 0.9514\n", + "epoch 69: train D loss: 0.3715, train F loss: 0.1300, acc 0.9452\n", + "epoch 70: train D loss: 0.3666, train F loss: 0.1163, acc 0.9488\n", + "epoch 71: train D loss: 0.3579, train F loss: 0.1032, acc 0.9572\n", + "epoch 72: train D loss: 0.3571, train F loss: 0.1044, acc 0.9554\n", + "epoch 73: train D loss: 0.3607, train F loss: 0.1103, acc 0.9522\n", + "epoch 74: train D loss: 0.3675, train F loss: 0.0941, acc 0.9572\n", + "epoch 75: train D loss: 0.3549, train F loss: 0.1085, acc 0.9538\n", + "epoch 76: train D loss: 0.3855, train F loss: 0.0926, acc 0.9554\n", + "epoch 77: train D loss: 0.3695, train F loss: 0.0804, acc 0.9634\n", + "epoch 78: train D loss: 0.3608, train F loss: 0.1004, acc 0.9562\n", + "epoch 79: train D loss: 0.3665, train F loss: 0.0821, acc 0.9614\n", + "epoch 80: train D loss: 0.3634, train F loss: 0.0922, acc 0.9612\n", + "epoch 81: train D loss: 0.3746, train F loss: 0.0880, acc 0.9592\n", + "epoch 82: train D loss: 0.3659, train F loss: 0.0944, acc 0.9572\n", + "epoch 83: train D loss: 0.3665, train F loss: 0.0999, acc 0.9542\n", + "epoch 84: train D loss: 0.3551, train F loss: 0.1000, acc 0.9528\n", + "epoch 85: train D loss: 0.3457, train F loss: 0.0735, acc 0.9662\n", + "epoch 86: train D loss: 0.3575, train F loss: 0.0822, acc 0.9638\n", + "epoch 87: train D loss: 0.3600, train F loss: 0.0789, acc 0.9630\n", + "epoch 88: train D loss: 0.3516, train F loss: 0.0861, acc 0.9632\n", + "epoch 89: train D loss: 0.3534, train F loss: 0.0663, acc 0.9652\n", + "epoch 90: train D loss: 0.3590, train F loss: 0.0766, acc 0.9626\n", + "epoch 91: train D loss: 0.3585, train F loss: 0.0721, acc 0.9664\n", + "epoch 92: train D loss: 0.3643, train F loss: 0.0839, acc 0.9612\n", + "epoch 93: train D loss: 0.3680, train F loss: 0.0639, acc 0.9674\n", + "epoch 94: train D loss: 0.3507, train F loss: 0.0689, acc 0.9664\n", + "epoch 95: train D loss: 0.3654, train F loss: 0.0658, acc 0.9638\n", + "epoch 96: train D loss: 0.3551, train F loss: 0.0645, acc 0.9694\n", + "epoch 97: train D loss: 0.3816, train F loss: 0.0695, acc 0.9676\n", + "epoch 98: train D loss: 0.3600, train F loss: 0.0687, acc 0.9640\n", + "epoch 99: train D loss: 0.3623, train F loss: 0.0954, acc 0.9560\n", + "epoch 100: train D loss: 0.3498, train F loss: 0.0613, acc 0.9686\n", + "epoch 101: train D loss: 0.3638, train F loss: 0.0550, acc 0.9728\n", + "epoch 102: train D loss: 0.3671, train F loss: 0.0917, acc 0.9582\n", + "epoch 103: train D loss: 0.3597, train F loss: 0.0653, acc 0.9676\n", + "epoch 104: train D loss: 0.3465, train F loss: 0.0599, acc 0.9708\n", + "epoch 105: train D loss: 0.3656, train F loss: 0.0502, acc 0.9720\n", + "epoch 106: train D loss: 0.3554, train F loss: 0.0603, acc 0.9720\n", + "epoch 107: train D loss: 0.3583, train F loss: 0.0462, acc 0.9724\n", + "epoch 108: train D loss: 0.3674, train F loss: 0.0629, acc 0.9652\n", + "epoch 109: train D loss: 0.3669, train F loss: 0.0615, acc 0.9680\n", + "epoch 110: train D loss: 0.3623, train F loss: 0.0734, acc 0.9628\n", + "epoch 111: train D loss: 0.3548, train F loss: 0.0614, acc 0.9688\n", + "epoch 112: train D loss: 0.3677, train F loss: 0.0535, acc 0.9700\n", + "epoch 113: train D loss: 0.3502, train F loss: 0.0537, acc 0.9714\n", + "epoch 114: train D loss: 0.3513, train F loss: 0.0556, acc 0.9692\n", + "epoch 115: train D loss: 0.3518, train F loss: 0.0447, acc 0.9718\n", + "epoch 116: train D loss: 0.3491, train F loss: 0.0554, acc 0.9704\n", + "epoch 117: train D loss: 0.3554, train F loss: 0.0605, acc 0.9694\n", + "epoch 118: train D loss: 0.3543, train F loss: 0.0542, acc 0.9716\n", + "epoch 119: train D loss: 0.3615, train F loss: 0.0480, acc 0.9740\n", + "epoch 120: train D loss: 0.3532, train F loss: 0.0529, acc 0.9740\n", + "epoch 121: train D loss: 0.3449, train F loss: 0.0579, acc 0.9714\n", + "epoch 122: train D loss: 0.3479, train F loss: 0.0395, acc 0.9746\n", + "epoch 123: train D loss: 0.3475, train F loss: 0.0421, acc 0.9734\n", + "epoch 124: train D loss: 0.3581, train F loss: 0.0538, acc 0.9734\n", + "epoch 125: train D loss: 0.3534, train F loss: 0.0581, acc 0.9712\n", + "epoch 126: train D loss: 0.3562, train F loss: 0.0524, acc 0.9708\n", + "epoch 127: train D loss: 0.3454, train F loss: 0.0422, acc 0.9776\n", + "epoch 128: train D loss: 0.3405, train F loss: 0.0408, acc 0.9742\n", + "epoch 129: train D loss: 0.3396, train F loss: 0.0437, acc 0.9774\n", + "epoch 130: train D loss: 0.3548, train F loss: 0.0527, acc 0.9710\n", + "epoch 131: train D loss: 0.3605, train F loss: 0.0493, acc 0.9744\n", + "epoch 132: train D loss: 0.3554, train F loss: 0.0291, acc 0.9798\n", + "epoch 133: train D loss: 0.3692, train F loss: 0.0351, acc 0.9780\n", + "epoch 134: train D loss: 0.3574, train F loss: 0.0448, acc 0.9776\n", + "epoch 135: train D loss: 0.3637, train F loss: 0.0505, acc 0.9738\n", + "epoch 136: train D loss: 0.3533, train F loss: 0.0303, acc 0.9800\n", + "epoch 137: train D loss: 0.3575, train F loss: 0.0407, acc 0.9766\n", + "epoch 138: train D loss: 0.3654, train F loss: 0.0473, acc 0.9698\n", + "epoch 139: train D loss: 0.3564, train F loss: 0.0477, acc 0.9736\n", + "epoch 140: train D loss: 0.3594, train F loss: 0.0439, acc 0.9756\n", + "epoch 141: train D loss: 0.3615, train F loss: 0.0534, acc 0.9716\n", + "epoch 142: train D loss: 0.3626, train F loss: 0.0447, acc 0.9724\n", + "epoch 143: train D loss: 0.3552, train F loss: 0.0477, acc 0.9736\n", + "epoch 144: train D loss: 0.3594, train F loss: 0.0401, acc 0.9748\n", + "epoch 145: train D loss: 0.3519, train F loss: 0.0512, acc 0.9722\n", + "epoch 146: train D loss: 0.3644, train F loss: 0.0423, acc 0.9738\n", + "epoch 147: train D loss: 0.3492, train F loss: 0.0314, acc 0.9786\n", + "epoch 148: train D loss: 0.3645, train F loss: 0.0343, acc 0.9774\n", + "epoch 149: train D loss: 0.3485, train F loss: 0.0623, acc 0.9710\n", + "epoch 150: train D loss: 0.3539, train F loss: 0.0359, acc 0.9770\n", + "epoch 151: train D loss: 0.3383, train F loss: 0.0357, acc 0.9772\n", + "epoch 152: train D loss: 0.3428, train F loss: 0.0140, acc 0.9856\n", + "epoch 153: train D loss: 0.3711, train F loss: 0.0338, acc 0.9784\n", + "epoch 154: train D loss: 0.3526, train F loss: 0.0218, acc 0.9812\n", + "epoch 155: train D loss: 0.3636, train F loss: 0.0238, acc 0.9802\n", + "epoch 156: train D loss: 0.3655, train F loss: 0.0326, acc 0.9764\n", + "epoch 157: train D loss: 0.3746, train F loss: 0.0398, acc 0.9764\n", + "epoch 158: train D loss: 0.3770, train F loss: 0.0177, acc 0.9832\n", + "epoch 159: train D loss: 0.3667, train F loss: 0.0347, acc 0.9766\n", + "epoch 160: train D loss: 0.3615, train F loss: 0.0455, acc 0.9730\n", + "epoch 161: train D loss: 0.3699, train F loss: 0.0316, acc 0.9796\n", + "epoch 162: train D loss: 0.3600, train F loss: 0.0281, acc 0.9800\n", + "epoch 163: train D loss: 0.3743, train F loss: 0.0353, acc 0.9778\n", + "epoch 164: train D loss: 0.3729, train F loss: 0.0337, acc 0.9778\n", + "epoch 165: train D loss: 0.3600, train F loss: 0.0285, acc 0.9800\n", + "epoch 166: train D loss: 0.3825, train F loss: 0.0279, acc 0.9772\n", + "epoch 167: train D loss: 0.3800, train F loss: 0.0271, acc 0.9802\n", + "epoch 168: train D loss: 0.3631, train F loss: 0.0196, acc 0.9792\n", + "epoch 169: train D loss: 0.3825, train F loss: 0.0466, acc 0.9738\n", + "epoch 170: train D loss: 0.3749, train F loss: 0.0351, acc 0.9780\n", + "epoch 171: train D loss: 0.3659, train F loss: 0.0300, acc 0.9792\n", + "epoch 172: train D loss: 0.3611, train F loss: 0.0315, acc 0.9792\n", + "epoch 173: train D loss: 0.3542, train F loss: 0.0258, acc 0.9808\n", + "epoch 174: train D loss: 0.3645, train F loss: 0.0245, acc 0.9790\n", + "epoch 175: train D loss: 0.3623, train F loss: 0.0401, acc 0.9798\n", + "epoch 176: train D loss: 0.3489, train F loss: 0.0208, acc 0.9798\n", + "epoch 177: train D loss: 0.3673, train F loss: 0.0254, acc 0.9816\n", + "epoch 178: train D loss: 0.3676, train F loss: 0.0266, acc 0.9802\n", + "epoch 179: train D loss: 0.3599, train F loss: 0.0147, acc 0.9826\n", + "epoch 180: train D loss: 0.3705, train F loss: 0.0163, acc 0.9846\n", + "epoch 181: train D loss: 0.3721, train F loss: 0.0212, acc 0.9818\n", + "epoch 182: train D loss: 0.3705, train F loss: 0.0374, acc 0.9772\n", + "epoch 183: train D loss: 0.3504, train F loss: 0.0094, acc 0.9868\n", + "epoch 184: train D loss: 0.3629, train F loss: 0.0174, acc 0.9844\n", + "epoch 185: train D loss: 0.3746, train F loss: 0.0151, acc 0.9838\n", + "epoch 186: train D loss: 0.3766, train F loss: 0.0217, acc 0.9814\n", + "epoch 187: train D loss: 0.3674, train F loss: 0.0159, acc 0.9828\n", + "epoch 188: train D loss: 0.3672, train F loss: 0.0394, acc 0.9772\n", + "epoch 189: train D loss: 0.3692, train F loss: 0.0283, acc 0.9794\n", + "epoch 190: train D loss: 0.3707, train F loss: 0.0163, acc 0.9814\n", + "epoch 191: train D loss: 0.3647, train F loss: 0.0093, acc 0.9862\n", + "epoch 192: train D loss: 0.3716, train F loss: 0.0436, acc 0.9774\n", + "epoch 193: train D loss: 0.3701, train F loss: 0.0438, acc 0.9768\n", + "epoch 194: train D loss: 0.3543, train F loss: 0.0208, acc 0.9828\n", + "epoch 195: train D loss: 0.3705, train F loss: 0.0153, acc 0.9832\n", + "epoch 196: train D loss: 0.3621, train F loss: 0.0349, acc 0.9800\n", + "epoch 197: train D loss: 0.3755, train F loss: 0.0227, acc 0.9804\n", + "epoch 198: train D loss: 0.3640, train F loss: 0.0187, acc 0.9832\n", + "epoch 199: train D loss: 0.3559, train F loss: 0.0100, acc 0.9862\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o8_-0iSSje4w" + }, + "source": [ + "# Inference\n", + "\n", + "We use pandas to generate our csv file.\n", + "\n", + "BTW, the performance of the model trained for 200 epoches might be unstable. You can train for more epoches for a more stable performance." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wly5AgH2jePv" + }, + "source": [ + "result = []\n", + "label_predictor.eval()\n", + "feature_extractor.eval()\n", + "for i, (test_data, _) in enumerate(test_dataloader):\n", + " test_data = test_data.cuda()\n", + "\n", + " class_logits = label_predictor(feature_extractor(test_data))\n", + "\n", + " x = torch.argmax(class_logits, dim=1).cpu().detach().numpy()\n", + " result.append(x)\n", + "\n", + "import pandas as pd\n", + "result = np.concatenate(result)\n", + "\n", + "# Generate your submission\n", + "df = pd.DataFrame({'id': np.arange(0,len(result)), 'label': result})\n", + "df.to_csv('DaNN_submission.csv',index=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CpheoH_rvFbO" + }, + "source": [ + "# Training Statistics\n", + "\n", + "- Number of parameters:\n", + " - Feature Extractor: 2, 142, 336\n", + " - Label Predictor: 530, 442\n", + " - Domain Classifier: 1, 055, 233\n", + "\n", + "- Simple\n", + " - Training time on colab: ~ 1 hr\n", + "- Medium\n", + " - Training time on colab: 2 ~ 4 hr\n", + "- Strong\n", + " - Training time on colab: 5 ~ 6 hrs\n", + "- Boss\n", + " - **Unmeasurable**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GYO8InxavGsy" + }, + "source": [ + "# Learning Curve (Strong Baseline)\n", + "* This method is slightly different from colab.\n", + "\n", + "![Loss Curve](https://i.imgur.com/vIujQyo.png)\n", + "\n", + "# Accuracy Curve (Strong Baseline)\n", + "* Note that you cannot access testing accuracy. But this plot tells you that even though the model overfits the training data, the testing accuracy is still improving, and that's why you need to train more epochs.\n", + "\n", + "![Acc Curve](https://i.imgur.com/4W1otXG.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s6UfXzef-wNl" + }, + "source": [ + "# Q&A\n", + "\n", + "If there are any problem related to Domain Adaptation, please email to b05902127@ntu.edu.tw / ntu-ml-2020spring-ta@googlegroups.com。\n", + "\n", + "The Q&A section might be updated to here if I have time.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "C4TMXG_YCqVb" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/10 RL/课件/drl_v5.pdf b/10 RL/课件/drl_v5.pdf new file mode 100644 index 0000000000000000000000000000000000000000..138c20889f8bfce368873f618c0ff1386fc7de01 GIT binary patch literal 2299964 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