{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<h1 style=\"font-family: Verdana; font-size: 28px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\"><center><br>Project: Photo to Monet</center>\n<center><br>Understanding of CycleGAN</center></h1>\n\n<center><img src = \"https://img.wikioo.org/ADC/Art-ImgScreen-1.nsf/O/A-9GEHSH/$FILE/Claude-monet-le-bassin-aux-nympheas-reflets-verts.Jpg\"\nwidth = \"1000\" height = \"400\"/></center>   \n","metadata":{}},{"cell_type":"markdown","source":"<p id=\"toc\"></p>\n<h1 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><center><br>OVERVIEW</center></h1>","metadata":{}},{"cell_type":"markdown","source":"A GAN consists of at least two neural networks: a generator model and a discriminator model. The generator is a neural network that creates the images. For our competition, you should generate images in the style of Monet. This generator is trained using a discriminator.\n\nThe two models will work against each other, with the generator trying to trick the discriminator, and the discriminator trying to accurately classify the real vs. generated images.\n\nYour task is to build a GAN that generates 7,000 to 10,000 Monet-style images.","metadata":{"papermill":{"duration":0.012341,"end_time":"2022-09-22T09:21:28.500803","exception":false,"start_time":"2022-09-22T09:21:28.488462","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#\" data-toc-modified-id=\"-1\"></a></span></li><li><span><a href=\"#Basic-idea-of-CycleGAN\" data-toc-modified-id=\"Basic-idea-of-CycleGAN-2\"><br>Basic idea of CycleGAN</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#1.-CycleGAN-compnonets\" data-toc-modified-id=\"1.-CycleGAN-compnonets-2.0.1\">1. CycleGAN compnonets</a></span></li><li><span><a href=\"#2.-Basic-schema-of-CycleGAN-training\" data-toc-modified-id=\"2.-Basic-schema-of-CycleGAN-training-2.0.2\">2. Basic schema of CycleGAN training</a></span></li><li><span><a href=\"#3.-CycleGAN-Generators-Losses\" data-toc-modified-id=\"3.-CycleGAN-Generators-Losses-2.0.3\">3. CycleGAN Generators Losses</a></span></li><li><span><a href=\"#4.-CycleGAN-Discrminators-training\" data-toc-modified-id=\"4.-CycleGAN-Discrminators-training-2.0.4\">4. CycleGAN Discrminators training</a></span></li><li><span><a href=\"#5.-CycleGAN-Discrminators-Losses\" data-toc-modified-id=\"5.-CycleGAN-Discrminators-Losses-2.0.5\">5. CycleGAN Discrminators Losses</a></span></li></ul></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-3\"></a></span></li><li><span><a href=\"#Data-description\" data-toc-modified-id=\"Data-description-4\"><br>Data description</a></span></li><li><span><a href=\"#\" data-toc-modified-id=\"-5\"></a></span></li><li><span><a href=\"#Import-modules\" data-toc-modified-id=\"Import-modules-6\"><br>Import modules</a></span></li><li><span><a href=\"#\" data-toc-modified-id=\"-7\"></a></span></li><li><span><a href=\"#Constants-and-Variables\" data-toc-modified-id=\"Constants-and-Variables-8\"><br>Constants and Variables</a></span></li><li><span><a href=\"#\" data-toc-modified-id=\"-9\"></a></span></li><li><span><a href=\"#Constants-and-Variables\" data-toc-modified-id=\"Constants-and-Variables-10\"><br>Constants and Variables</a></span></li><li><span><a href=\"#\" data-toc-modified-id=\"-11\"></a></span></li><li><span><a href=\"#Auxilary-functions-and-classes\" data-toc-modified-id=\"Auxilary-functions-and-classes-12\"><br>Auxilary functions and classes</a></span><ul class=\"toc-item\"><li><span><a href=\"#Visual\" data-toc-modified-id=\"Visual-12.1\">Visual</a></span></li><li><span><a href=\"#Loading-data\" data-toc-modified-id=\"Loading-data-12.2\">Loading data</a></span></li><li><span><a href=\"#Neural-Netowork\" data-toc-modified-id=\"Neural-Netowork-12.3\">Neural Netowork</a></span></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-13\"></a></span></li><li><span><a href=\"#Load-Data\" data-toc-modified-id=\"Load-Data-14\"><br>Load Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-dataset\" data-toc-modified-id=\"Create-dataset-14.1\">Create dataset</a></span></li><li><span><a href=\"#Create-Dataloader\" data-toc-modified-id=\"Create-Dataloader-14.2\">Create Dataloader</a></span></li><li><span><a href=\"#Check-input-images\" data-toc-modified-id=\"Check-input-images-14.3\">Check input images</a></span></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-15\"></a></span></li><li><span><a href=\"#Build-CycleGAN-Model\" data-toc-modified-id=\"Build-CycleGAN-Model-16\"><br>Build CycleGAN Model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Generator\" data-toc-modified-id=\"Generator-16.1\">Generator</a></span><ul class=\"toc-item\"><li><span><a href=\"#Convolutional-Layer\" data-toc-modified-id=\"Convolutional-Layer-16.1.1\">Convolutional Layer</a></span></li><li><span><a href=\"#Transpose-Convolutional-Layer\" data-toc-modified-id=\"Transpose-Convolutional-Layer-16.1.2\">Transpose Convolutional Layer</a></span></li><li><span><a href=\"#Residual-block\" data-toc-modified-id=\"Residual-block-16.1.3\">Residual block</a></span></li><li><span><a href=\"#Generator\" data-toc-modified-id=\"Generator-16.1.4\">Generator</a></span></li></ul></li><li><span><a href=\"#Discriminator\" data-toc-modified-id=\"Discriminator-16.2\">Discriminator</a></span></li><li><span><a href=\"#CycleGAN\" data-toc-modified-id=\"CycleGAN-16.3\">CycleGAN</a></span></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-17\"></a></span></li><li><span><a href=\"#Train\" data-toc-modified-id=\"Train-18\"><br>Train</a></span><ul class=\"toc-item\"><li><span><a href=\"#Learning-rate\" data-toc-modified-id=\"Learning-rate-18.1\">Learning rate</a></span></li><li><span><a href=\"#Initialize-model\" data-toc-modified-id=\"Initialize-model-18.2\">Initialize model</a></span></li><li><span><a href=\"#Train-model\" data-toc-modified-id=\"Train-model-18.3\">Train model</a></span></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-19\"></a></span></li><li><span><a href=\"#Train-results\" data-toc-modified-id=\"Train-results-20\"><br>Train results</a></span><ul class=\"toc-item\"><li><span><a href=\"#Plot-losses\" data-toc-modified-id=\"Plot-losses-20.1\">Plot losses</a></span></li><li><span><a href=\"#Model-predictions\" data-toc-modified-id=\"Model-predictions-20.2\">Model predictions</a></span></li></ul></li><li><span><a href=\"#\" data-toc-modified-id=\"-21\"></a></span></li><li><span><a href=\"#Save-results\" data-toc-modified-id=\"Save-results-22\"><br>Save results</a></span><ul class=\"toc-item\"><li><span><a href=\"#Save-model\" data-toc-modified-id=\"Save-model-22.1\">Save model</a></span></li><li><span><a href=\"#Save-predictions\" data-toc-modified-id=\"Save-predictions-22.2\">Save predictions</a></span></li></ul></li></ul></div>","metadata":{"toc":true}},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Basic idea of CycleGAN</left></h2> \n","metadata":{}},{"cell_type":"markdown","source":"- With CycleGAN we want to get such Generator that will be able to generate Monet-style image from Photo input\n\n\n- In CycleGAN we dont have ground thruth output for generators. \n\n\n- Instead of that, couple of Generators and Dsicrimiantors will compete with each other in order to produce the most accurate Generator","metadata":{}},{"cell_type":"markdown","source":"#### 1. CycleGAN compnonets","metadata":{}},{"cell_type":"markdown","source":"- In CycleGAN we have:\n\n\n    1. Generator Photo - generates photo-like images from input \n\n    2. Generator Monet - generates Monet-like images from input \n    \n    3. Discriminator photo - it should predict if input photo image was generated - then output '0' \n    or it is real - in that case output '1'\n    \n    4. Discriminator Monet - it should predict if input Monet image was generated - then output '0' \n    or it is real - in that case output '1'\n    ","metadata":{}},{"cell_type":"markdown","source":"#### 2. Basic schema of CycleGAN training\n\n- There are two branches in trianing - for photo and Monet image, which basically mirror each other \n\n\n- Training iteraion of photo branch:\n\n\n    1. Starting with 'real photo' with 'Generator Monet' we get generated Monet image\n    \n    2. Given generated Monet image with 'Generator Photo' we can once again produce photo image and called it 'cycle photo'\n    \n    3. Ideally 'real photo' and 'cycle photo' should be the same and that brings as to the first loss function which estimates the difference between them - 'Cycle Loss'\n    \n    4. Next thing we need to consider is that if 'Generator Photo' will get photo image as an input it should generate the same photo. Such examine is called 'Identity check' and estimating the difference between those input and output from generator we can evaluate 'Identity Loss'\n    \n    5. Last but not least, Generator should produce such a good image that it would fool Discriminator into consider generated image as real. So if we take 'generated Monet' after 'Generator Monet' as an input to 'Monet Discriminator' we want to get the output value as close to '1' as possible, which will mean that Discriminator is fooled. Estimating the difference between output value from Discriminator and '1' we will calculate 'Adversarial Loss' \n    \n\n- The same steps with mirrored Generators and Discriminators are made for Monet image as an input\n\n\n\n![image.png](attachment:image.png)","metadata":{"heading_collapsed":true},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"#### 3. CycleGAN Generators Losses\n\n- Total generator loss is a sum of 'Identity', 'Adversarial' and 'Cycle'\n\n\n- It is also reasonable to add weights to each of 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"}}},{"cell_type":"markdown","source":"#### 4. CycleGAN Discrminators training\n\n- In order for Dicriminator not to be easily fooled by Generator, it should also be trained\n\n\n- The main purpose of discriminator is to correctly identify if input image was generated or it is real. With taht in mind training process is cinstructed pretty straignt forward:\n\n\n    1. Take a generated or real image as in input to discriminator\n    \n    2. The produced output should be '0' in case of generated input and '1' in the other case\n    \n    3. Estimate the difference between output and target value ('1' or '0') to alculate 'Disc Loss'\n\n- Monet Discriminator is trained in the same manner as Photo Discriminator\n\n![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"#### 5. CycleGAN Discrminators Losses\n\n\n- Total Discriminator loss is calculated as arithmetic mean between Loss with generated image in the input and real image in the input\n\n\n![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAs8AAADhCAYAAAA3S1jYAAAABHNCSVQICAgIfAhkiAAAABl0RVh0U29mdHdhcmUAZ25vbWUtc2NyZWVuc2hvdO8Dvz4AAAAuaVRYdENyZWF0aW9uIFRpbWUAAAAAANCS0YEgMDQg0YHQtdC9IDIwMjIgMTc6NDg6MzS7lYBsAAAgAElEQVR4nOydeXwTZf7H3zNJ2vROb1paylGOcrZQ7lsQEEEQ5RZXUHEVUZF1XVRA8YfIrlzqeuCxgusCKioCgihQDwSRG8oNLW2BAi2927TN8fsjSUnTpE3TNE1x3q9XYDLzHN88fWbmM9/5Ps8j6PV6PRISEhISEhISEhISNSI2tAESEhISEhISEhISjQVJPEtISEhISEhISEjYiSSeJSQkJCQkJCQkJOxEEs8SEhISEhISEhISdiKJZwkJCQkJCQkJCQk7kcSzhISEhISEhISEhJ1I4llCQkJCQkJCQkLCTiTxLCEhISEhISEhIWEnkniWkJCQkJCQkJCQsBNJPEtISEhISEhISEjYiSSeJSQkJCQkJCQkJOxEEs8SEhISEhISEhISdiJvaAMkJCQkJCQkJJzFmTNnyMnJoVmzZkRGRja0ORK3IYJer9c3tBESEhISEhISEs7g559/Ji8vDwBRFAkODqZ169YEBwc3sGUStwuS51lCQkJCQkLitkGtVlds63Q6srKyyM7OxtPTk06dOhEeHt6A1kncDkjiWUKiEVJcXMy1a9fIzMykuLiYsrIydDod3bt3JywsrKHNk5CQkGgwNBpNpe96vR69Xk9JSQkHDhzAy8uLXr164e3t3UAWSjR2JPEsIdFI0Ol0nDx5koyMDMrLyxEEAZ1OhyAIAKhUKgoKCiTxLCEh8adGp9NVe6yoqIhdu3YRFRVF586dEUVp7gSJ2iGJZwkJN6ewsJAjR46Qm5uL+RAFvV5fIZwBBEFAGsIgISHxZ0an09l1HdTr9aSnp5OdnU3v3r0lLzSQk5NT5cEjICAAuVySipZILSIh4abodDp+//13srOz7b4ZFBcXu8AyCQkJCfckJyenVo6EkpISkpKSSExM/FO/tUtNTeX48eOV9gmCgEKhYPjw4Q1klfsiiWcJCTckPT2d48ePo9Vq7c5jCuOQkJCQ+LOSn59fqzdwer0erVbLgQMHaNWqFW3btq1H69yX06dPV9kXERGBr69vA1jj/kjiWULCjdDpdPzxxx/cuHGj1nm1Wm2lUeYSEhISfzYUCgWiKOLt7U1xcbHdDgWtVsv58+fR6/W0a9eunq10Ly5dulQxjsb04BESEkJWVhZdunRpYOvcE0k8S0i4CRqNhp07d1JeXg5Q6/jlsrIyaeCLhITEn5rIyEjkcjmZmZmUlZVVvJEzXVerQ6fTcfHiRYA/lYA2eZ3N7zkymYzmzZtL8c42kFpFQsINKC4u5ueff66YYsnRgX+lpaXONEtCQkKiUSGKIk2aNKFJkyYVczyfP3+egoICNBpNjZ5orVbLxYsXUSqVNG/e3DVGNyCpqakVDxnmXuebN2/StWvXBrbOfZHEs4REA5Ofn8+vv/5aq/hmMNwkgoKCEAQBjUZDaWmpNNuGhISEhBFRFAkLCyMsLIyrV6+SnJyMTqer0cmg1Wo5ffo03t7et/0gwlOnTlUZYCmTyWjRokW9ep01Gg3p6elcu3aNrKwscnNzKSkpQavVVswkpdfrCQ0NpUmTJjRt2pSIiAiUSmW92VQbpOW5JSQaELVaze7du6tM6m8LURQJDw+nvLycgoKCCi+Lv78/vr6++Pv7S6/ZJCQkJKyg0+m4cOECZ8+etSsWWhRFBg8efNtOY5eSksKJEyeqeJ3z8vIYOnSo0+8lWVlZHDlyhHPnznH9+vVa5zeFknTr1o24uDin2lZbJPEsIdFA6HQ6fvjhB8rKyuxKHxkZiVarJTc3l5iYGJo2bSqNhJaQkJCoJTdv3uTgwYOUl5dX+8ZPEAS8vb0ZNGjQbTmeZNu2bRWeXhPh4eEEBAQ4bdaRrKwsTpw4wdGjR8nNza3Y7+PjQ1RUFFFRUQQEBKBSqfDz80OpVFJeXo5CoaCwsJCCggIuX75MZmYm58+fr3hrEBwczNixY4mKinKKnbVFEs8SEg3EL7/8Qm5ubo1zknp7e6NSqbh+/TpxcXE0a9bstryQS0hISLiK4uJi9u3bR2lpabVv/hQKBVFRUXTs2NGF1tU/9el11mg0nDp1iuTkZM6cOVOxPygoiISEBGJjY2nSpIlD5R4+fLhi/QOZTEbfvn0ZPHiww7Y6Sp3E87n0XK7lFjnTHol6pFVkIBHBt+frp8bGyZMnuXjxYo0xymFhYRQXF+Pn50d8fLwUkiEhISHhJHQ6HXv37iU3N9dmGIdpoZBu3boREhLiYgvrD1te56CgIGJjY+tU9uHDh/n2228Bg4e4Y8eOxMfHo1Kp6lSuCY1Gw5EjR9i6dSsAPXr04K677nJK2fZSpzvx8ys/JkcdgI/CclUzwWr6KvurJLOVry7Hqqu3lvnqUqdplwBQndewtnXqQV/NceOuolIZg9qVsHDWo9WUL+EKCgsLK6ZDqo6YmBgyMjKIj48nMjLSBZZJSEhI/HkQRZHevXvzyy+/UFBQYNWZodfrKS8v5/jx4wwcOPC2eOuXkpKCRqNBEG5phpCQEHJzc506w0bbtm2ZNGmS08ozIZfLSUxMJCoqig0bNrB//34AlwroOonnMq2Cz0a9SqRvNhXKUBDNtk1qUTTbtkhTkU60yGNvOouy7bXBarrGagM1lv3tUR8Op3k69oeWcCp79+5Fp9NVunCZI4oikZGRXLlyhX79+uHv7+9iCx3lBjt2fGDcrnhSNPuOxT5LqnvotnbM8iZmWV9ND7jW7LLMY60MR36LrfYwx9ZNuWpardaH3r2HOc2TI1ETGfz3v4uM2zX1kZr6oeV30cqxmvquZV+x1neqq8dWHY7YZut7Tf3dejuVl3szcuRUwsPDreRxPqIo0rdvX3bv3m1zkSmTgE5JSaFVq1Yusas+OX36dJVwQUEQaNmypVPfbtb39alJkyaMGzeOr776iv379xMSEkL37t3rtU4TTmglc1HoqPi0ls7VAtiZNlhJVy82UEsb7BETEvXJxYsXUavViKJoM2QjMjKSnJwc+vXr14gGBF7mvvvasWVLEeHhMrP9NfU5R8W0s8usS7l1saem49aP3bhRxIABg/n+++9rKFui7lzmvvvi2LmzmJgYOTWLR2v73C2NY/n0Nt9y1ubcsJ7X5ExISbnJ+vXfu7Rvy+VyevbsWe2UoeXl5Zw7d46YmJhGHT538eJFq17n/Px8EhMTG9Ayx4iOjuaee+5h7dq17Nq1i3bt2uHn51fv9daxBxhFm0m41aeXVvJWWzlWWxsk8dyQmAZRgO1FUKKjo7l8+TIDBw5sRML5Bo8/3pELF8q5dq0lKpWMij5Z6WO5z5E09VWuq9PUlKf6cqZP30hqqn2ztEjUhctMmRLHhQvlpKa2kPr2bdy3/f396dKlC0eOHEGv11e5Rut0Ojw8PDh79izt27d3uX3OwtpqgvXhdXYlLVq0oEePHuzfv58ff/yRe++9t97rrFvwjmAqQgRBZrZtEtQys22zY5XSySzyGPdVm8fKdp1ssLZtxQartrraBsfaQa839wZKNATHjx+vdm7RsLAwrl271sg8zvm8+GIH9u4tISkpGpVKTkXfq/SR2dhf2zSOfJxVtyt/Q03lCPb+gSQcJp/nn+/CyZNlUt/+k/Ttpk2bVrsoilqt5tKlS3bPy+9unD17topn3eR1buyrKQ4ePBhPT0+Sk5MrTYlXX4h1yy7Ug/i0zGM82ewSqfaUd8sGfU4GI3r+wsKzDWeD/Q8Mgo2y7bSh4glfoiHQaDRcvnzZ5nFvb29KSkpo3bo1AQEBLrSsLpSyZElHNm7MY9u2KDNx4e439Iayz9zLVte6JOqXUpYubc+mTQUkJTWT+vafqG8nJCRU64GVy+WcPXvWhRY5jwsXLlTZ5y5eZ7VaXaeHEqVSSa9evdBqtfzxxx9OtMw6zol5FmRg/gqmTvG88L+/n2Bqko0pvERPnno/gVVdRRtlQ+qeqxyNbsqYGFnNNhgKNXzMQyV0eSy470d+HDuSXx/yQ2w0cc3V2CCJ5wbj1KlT1U5Lp1Kp0Gq1tGzZ0oVW1Y2lS9uwatVVdu9uRkSEB5VvoO70GtkZaepSLvVgn3Qu1ydLl7ZhxYpr7N0bYxTO7tgnpb5dH8jlcuLi4jh+/LjVa3ZZWRmXLl2iTZs2DS44a8PZs2eriNOGjHXOzc3l8OHDXLp0iWvXrlUM1vT09CQqKoouXbrQqVOnWpUZHx/PTz/9RHJyMnfeeWd9mF2Bc2KeqwhEC0FXkU6oPp0xzT3/iOPcU4Z0+bvT6PtfBatXR9FbbkijCpdhM+5XX8rm/6Rw4C/RjGleg0gVjCe/edx2pXQYbbRDhLtNXLMNG6SY5wYlLS3N5rHw8HCys7O54447XGhR3XjvvdYsWZLB7t0xxMUpcf4N1N3T2CsC6ss+AbM5KiWcyKpVLVmyJIO9e5vTooUn7tHfpL7tSqKjozl//jzFxZbT8Bpinz09Pbly5QrNmjVrAOsc4/z581X2yeVyYmNjXfoQkJWVRVJSEsnJyZX2K5VKZDIZarWaCxcucOHCBX766SceeOABu2ftUKlUhIWFcf36dTIzMx1aiMVenNBipnADs45fx0F1viFexBr35wSLCDI5TaO8iVUY8pWkZvHMv9JYd0RNoVxBxz5RLH0+hkGqUt5+fB9zD2nRn9jN1qGdyXgtguIjKcx+4xLfnSmh2MOLrkPa8M4LLYg3F5gVAlY0sw8bthq+l1y8wLzXDrHuYAGFCi869u/E0pe6MihYBH0pe/7zE8+sSeX4DQ3KsFBGPjSYt6dHEySUsuejHTzzyUXjsXBGTh/G2zOaESRatomt9rJXhItmx+r+15aoPRkZGTZjnUVRpKysrFF5MTZu7MFrr11i9+7mJCR44b4ioCHTUM91S9QHn37aiYULDX07Lk7q23/Wvi2KIh07dqyYP9gSnU5HRkZGoxHPZ86cqRLrHB4eTk5ODt26dXOJDWq1mi1btlSIZplMRnx8PImJiZVErkaj4dy5c+zYsYPs7GzWrFnDX/7yF7sFdHR0NNevXyc9Pb1exXPdeqpg+sckPk1eWtO2WHUbK9vVxhQbTyrTsbJCXp57jl3NWrJv1wDyNndmUlE641+9zlXBhyf/Hc9DQQqmvHEnWUsiUZbnsHDeWc50T+Dkb/eQ81UXup04ziP/LURr+vlWbTBeJGzFFJfd4OXZv7Arpjf79s4kb+dIJhUcZfz8c1xFRJN8hEffLWb8e49QcPIpzrzVjtwPf2DZCdCc2M+j75Qw/v1ZFJx+njNvdyD3w60sO6G30V7OiK02v/BJuJLTp09XOy1deXl5oxmssWXLMB5//BAbNkSRkOBNRX/DeP5U+m7tc7unMRcB9Vm3dC47m61b72Tu3FPGh0Kpb//Z+3ZoaCheXl5Wj5WWlpKXl2dzXmh3w1qss1arJTY21iWLvqSkpPD++++TnJyMp6cnQ4YM4R//+AejRo2qInBNYTOzZs0iJiaG3NxcvvjiC7vriomJAeDatWtO/Q2WOKHVxBoEXR1n4TA/YQUZ5SdusP6aijmPhdHCS4ZcpWLWX8JR7rvG9nxTGdwqSxHMkvXD+HFWGBGeMrwjmzCxt5KzZwopreJ5trTH2u8z2F5+9ALrr0YyZ3ZLWnjLkQc0YdajsSj3nGN7roCuqJR8QYEqwBOFqCA8viebf32ExZ3l6ArLyBfkqFReKGQKwhN6s3nPEyzuLLfxYGHdBqvtZfNvIYnnhkCtVlNSUmL1AiWXyyksLKRt27YNYFnt2bbtfmbM2MXKlRH07u2Le97gGyqNpbCo79kVJJzJtm3jmD59N598EkVCgg/u39+kvl3fiKJYrVPDy8uLS5cuuc4gB7HldS4oKKh3p41Go2Hbtm2sXbuW3NxcOnTowKxZs+jXr1+Nb1rlcjkPPPAAwcHBXLlypWKa15oIDQ0FqPcZN5wc82x2ojltUF3legouq8lSeRPrL1aULY/0oZkul5RrArQynXyiUTzquPz7Bf7x6RUOXdcZnNf5ajQ9QVfhXRapGtdsLKeKfYbtgsv5ZAWqiPW/FX8tb6qimf4KKddEPBITWdTvK54a8T7/7t6CYQPaMHlsW7oGi3h078mi/ut5athK/t09lmED2zL53vZ0Da7v2Grni+eLFy9SWlrq9HKdjZ+fH1FRUS6vNyUlpcpKTiaCgoIoLi5uFEtvJyVNY/r0TSxd2oQpUwKpfFM19cmTZtvmN15r27X93ljzVleW3uKYCWsePvNX2u7xILx161abb1RkMhl9+vRpFDPH7No1kcmTv2XFiiaMHOmP9b7trJCExpbG2scV9rgHLVu25MyZM1bD7kpKSsjMzHR754e1WGdXeZ0PHjwIGK4HY8eOpWPHjrXKL5fLGTJkCJ9//jlJSUnExcXVmMcU3pGVlVV7g2tjW92LMIrUagVwdcLPWjpL4QcGb7V5PvFWOr0hjVBFyItozp5j4ktXiP+/fiQP98dXpmfP0m0Mv2x+korV2CBabAu2bTWWJyCCRwgzVj3K2PPpbN19gS1bdtDv/WOs/HwyM1uEMuPNJxl7/hJbd51jy+Zt9HvvCCu/fJCZLUTrZTttkRnncvLkyWpnkagP9Hq9zaWtq8PDw6PaOTzrg8zMTJvto9PpKl4xuTNHj87jr3/9giVLmjB9ehC2b3wys23nC1H99WzuanmNnnu68EoXsVZ5XS+ehXqqV29WdsNi6tfW+rdWq6W4uNjtxfOhQ7OYMeNrVqyIqKFv11YcupsIdkS8NqQ97oEoioSEhHD9+vUqxzQaDcXFxWg0Grcdr3LixIkqwj88PJzc3Fx69uzpEhvCw8OZNGmSw0t1x8XFERAQYPcgQNPAw5KSEofqs5e6P3bYnH/YYrvauGbLdObbcEv8ifhFeROWU8y5wlthCuVXikmV+RAbIeeW2DYcyzt5k3MhkTwyLABfmQi6co6eKTHGO1u5SVWyj6rHjNt+MSrCbuZyLl+oyFOenkuqqCI2Ugaacq7nQlDrlkybOZwNXzzIgshLfLwjB62mjOt5ENQ6lmmPjWTDxodZ0DSFj7/PNdjlcFyzWZtWyVN/FyRHhKyr6xNF0eayq/VJUVGR1f0BAQHcvHnT7QecXL78JtOmreSxx4KZPj2YWyLZUrya9jvw0ZSzoNNZBOE0gnASmcdJgqLOMmjqNT47okdvTCcEBvHPba2ZHit3vK4qdeexoNM6+iwrRueUMuvYFnaX7964+prgCBcvLmfSpA95/vkwO/q2ZT+vKY0jn4YOzzDdJwQX1WUrjXvRtm1bm/1ZqVRy5coVF1tkP9bCSlwZ69yhQwceeughh4WzidjYWADS09PtSi+Xyykrq99VKp3keTZ5nKrxfFr1kIoWeSzSWD6VCgKKzk2Z0vQPVq3OYuiccCKKclj24VX0g+IZ4S+CVoanQsv59BJyinxRhnvjn5PDrxd19Gmu4edPj/BNgRJKS8jUe5r9BrGyDcaTpTS/mNQrckSzYz5B/oR2iWNK9EZW/TuVoc+3IqLwKsveO4t+yAhGBIhcXPMlXdeH8tF7Qxnb0oPijEyScxRER3qT+uk6uq0P46P3RxiOpV8l+aaC6Ka+t+aTdprX3uxvIUhTW7mSK1eu2PQ6e3t74+Pj47YeC4CMjE8YOfJ5RozwZ86cMGr2IjnqeTbkS5zfko0zlQgayEotYcfHmczum8cv6zvw7mgPBIWCzv1rKqs29Zrqxmy7NnnNv1v+X1sb7c2LWbtLOEpGxkeMHfsikycH8vjjoVTftw/j+N+rrv3TnfM6Wpa1Y+ZzabtX3/b39wewGn4nCILbOkEa2us8cOBABg0a5JSyQkJCAMjLy3NKec5ArDlJNej1WPWQOnV1QXNRKIIikPkrOnNH6ikSB3yH6p7DfBfRjq9fjCJUFEERzP2jAzi2cicdX0wnp0cHlt+lY9WUrwgakcS/acsnL7eha+pRBs65QIaeamzQcejdb2g1eC0tBq+hxeBPaDF4LU/+rAePpsz/93DuuPgTid1Xohq2le8i+/P1ojhCRRktp45mdf9cFkxcgXfr14iaspei++5jxUh/Wj0wjtUDclgw/nW8W71M1ORfKLpvMitG+iLYbAdHvPa3vhsuR86/KFW33LQ74erQEoCrV6/aPKZWq2natKkLrakdeXmbGDduFvHx3vzzn02pLDZFG5+6eFNBEeBJVJSS6ObeJAwK5fm1Hdg8S2DNrAy+L5Chv36dEb4HWXhUBL2OPcuS6d5sN0qPH1DF7GPK8jxu6g1lqs9c4emhPxDi/TW+kTuZtOwm2XpbdQvV2ldy+hzPDP+EcN+V+AS+T8/J+0m6LoK+jD3LvqF7s6UoPRajivmAKcszDDboS9mzbAPdmy1E6TEPVcwqpiy/VGFf3T3PDS8wGuKccgY3bqxn5Mgn6dXLh1deiaTmvl1fbxEMH/31HEb4njL063quy7lvP+qjTPfo2+aIokhAQIDVe11RUVG9x9Y6ijWvs0ajcYnXuVOnTk4TzkBF+JetN7mW6PV6ZDKZ0+q3hnMGDDriUbYznjdwTDeKx1ROp4yJYsX70aywWraMgc/cSdYzt2yY/OpdTH7VPF0ke/YmVG+DPIxFW2axqBpblS3bs2JNB4Mdlr/PM4QJ86cxYb61dghlwvyHjcfsa4e6D8bUUx8TzzeG17PQMHbaGu3r6elJXl6ey+Ov7SUv7wf+8pe/0K6dkrVrm1PZIyRW811mts9ej5PpJgxVb6Ry+jwTSb+30/hqr8Dw+FtCV3MwlUcXl/PQj3fwW7yCm3+kMn3cMZb1H8riznm8ct8R9o7szYGNYSiOnmXq2D0802oMa8cqEGrjeVZf5eVxP7DrjtHs+7oN0eprvPXABsY/puLQC1k8uriIh36cy2/xntz843emj9vEsv5zeEX4hUcXF/LQj/P5LV7JzT9+Zfq4z1nW/0UWd7dSj91tpTNrb4nakpf3HRMnPkp8vDerV8dgX9928O+lKWdBQhqvnjBcd0WFSECYB50Hqnj0uXCmxMsQEI3hSH74x8rtqMfOfqLJY0HCdn58aAy/zg1ArFUfs/WdOuS1p1739DwDREVFWb2e63Q6ysvL3S7uuTqvsyumRXV2WyiVylql12q17i6e4ZZ3s47Cz2o6e0S4tXQNbQNuYINl2ToaetWmPxu25gD19/dHp9O5JOas9qTyyCMT0Gph7doWVC8uLPc5Kp7Nb8yVvatCqC/tQzWcStGjNxPPunwN+YIMVZAHClFOeM82bE5ri0wmo/zndNalhbP4hSiaB4gwoCPvrQvgXBPRho1Q1RNtOFb++xnWp0fz8sK2tPCWgXcUs55rz/LRyXw3OYB8wQNVkJdhOsqeA9mcNgiZTEbZrlLjMW/jsaFsTrvTeEGvi8DQ4o7iojFQWnqQBx6YQkiIvJZ929GHHcMYi8T5rdg408MYjqRmx8fXmN03l1/Wt+fd0QoEhZzO/b1rKMvRc8raG4va1mP5f32LZ/cjPDycEydOWD3m6elJVlZWvS7IUVuseZ3LyspcFuvsbAoLC+1Oq9Fo0Gq19T5Yue6t6KwBbXaFdcgs8hhvhnaFf7jCBnPR2lA22GoHc2Ev4QpshbQIgkBgYKCLrbGHq9x3X1cyMzV88UUrKt/grH0sX7fW5bUtNvaLaHUgVPR5QzqPfq1YNKyIZ9v/QJfhh3huxVWOZhvyFFws4kaILzE+pjI8aDe8NaO7eCNYfV1su+6ClDyyQoKJVZm8giLymGCa6fK43LwPi4Zl82z7pXQZ/gXPrTjJ0WzRaN9gFg27wbPtF9Bl+Kc8t+JYxbG6v942PQBI2M9VpkwZSm6uls8/j6V2fbsufy8BRYCHWThSCM+vbc/mWSJrZl02hiPdYITvYbNwpJN0b/YTSo+dqGJ+Z8ryfLNwpKs8PXQnId6b8I3cxaRlOTbCkSzfqFT9lJw+zzPD1xLu+yY+gavpOfkPi3CkfxnDkT6yEo70CkqPF1HFvMWU5WlODEdyz77t7e1tU3TK5fJ6n9mhNtjyOhcXFzeaxbgs0Wg0APj4+NSYNiUlBTBMT1ufOOERpLbCz5qotCb8rAlEe8qzU6w71QYL0dwgNtTUDu77VH87cuPGDZvHSktLCQ4OdqE19pDFjBmduXBBzebNrVEqb4lF60LZluBw9MaJ1WO6tCIOZ3nQpq0SAVM/loFHADPWDSPtYE/+NlRJ2vqj9Ou8h9XnjGl0QsUsHfYJd1v2m3shjfv0hn2CZxNmrPsbaQen8rehvqSt/5p+nT9m9TkBPCKYsW4haQcf5m9D/Uhbv55+nd8xHHOKyJDOZfu5ypQpncjIKGfz5tbU/BBY3/3agz7PRNAvK4ev9lb2cGsOpvPo4nLGfzOIAvVwznweSe6y4yw7IEKpMRypawIHro7lzPooMhf/xjObtFb6utm5Yu2jvmYIR2o9iH3X55J34X4m5f3B+MdOkn7gdx5dXMz4b+ZQoH6JM593InfZtyw7IKA5+CuPLi5i/DcvUqBewpnPu5K77HOWHXBWv3bfvu3h4WHzWEFBgQstsY1Op+PSpUtVxiM0Zq8zwOXLlwHsGidkWlmwvtd1qGNLGr2ZjdZT7Awb7BG2btAObnxRuh25efOmzWNlZWX4+vq60JqaKOS557pw6FAxSUlxqFQe2CeUnS0yLMrQ6/jhjXQORkUwsadpND6GY+U6rt8UCIqLYNpz3diwZwgLml3n46+L8W7pT2hWPmdv3hLAJzce563vS6xMR2dL4MgAOX6tggi7cZNzubdsK0+5SaosiNgmGqMN7Zj23Dg27HmKBc3O8/HXOWjLy7h+E4LiOjDtucls2PM8C5qd5uOvs9HWSWSY2yxRM4U8/XRXTp4s4fvv2zrYt+vyt8LqflM4UmqKHr1ZH9Tlaw2rzwZ5GMN92rA5bQiLu8sp//0y69LCePqFpjQPUNJ0QEfeW9eNCS2s2WjjnDL1YWM40pyFbQwr5AY1ZdZzcSh/OMl3KcbVcYO8UN82gZ0AACAASURBVIiehPccwOa0Z1ncXYEu3zwcyZPwnkPYnPYSi7sr6thOooXd7oevr6/VsTOmOc3dgeTk5CrrIDR2rzMYVkkE+wSxKTa9vgfkO6GnVicQrQlJe4SfDH1OOiN67GbhGcGGqLQmPmu2QZ99mhHxG1h4mhptMC9bf/MYIzp+wMJTGNMJNn6fc9vBed5qyfPsSmy9xvPy8qK0tBRvb28XW2SLMpYujWfz5pts29YWlUpBzeLCluCom8goz9OQkVFORlopx3+5wdIHDzBprYyZb7elr9LUpw3n3cV39tJmwDE2ntWhRaTgYi7JWTKim3nj2TOWic2usXz+JU5laUj7+ThPPX6MfUWeVsI2DHWX5hSRmlpIamqB4XOpgBvFIoqe8UxpkcaqV8+TphYpv57BsiUn0I/uRpt1n9BmwLdsPFuOFoGCixkkZymIbuZL6jvv0mbAl2w8W2Y8lkZylgfRzQIQ6ywyTO0gUT2FLFzYke+/zzU+FLq6b1cnYAWLcCSD59ajX0tjONJOugw/wnMrMmsIR4q1EY5kW7iDSEFKvlk4kmGfIRwpn8vNexvDkf5Fl+FfWoQjDTKGI71Ml+H/dXI4knv3bS8vL6szzJSWlrqFeNbpdKSlpVUK2RBFkbKyMtq0adNovc4pKSkUFhYSExNjVyhGcnIyAC1atKhXu5w7z7M9A9q0hSyYuI9XzxtHIMtlBAR50zmxCY8+FMuUOE8EBAT/CP75XiD+MSZRWdOgOssBctZtEFQt+edHTfCPkd9KWyVd1bKFgLb885NI/JuLdbYBBFJ37+Fo836MaSnabUOd2kHQYhg06FwUCgXl5eVOL9fZ6HS6eh99a45arba6EqKHhwcKhcJldtTE0qWdWbEijb17OxAR4UlF/7F7MJVlWIMjA4VEQM+BVw8S8yoIgoBPqBcd+key4qfWPJioREAw89DJafnXnqy+dJAFA75myk09yrAAhjwykDfv90aQ+/LyxgEUzfqDftG7UKuCGfXcSN6818cs9MPcDh2HFn9Gq8VmDSMomPDFi2y4rxnzN46jePY2EkP+R5GHPwljhvP18q709G7O6rQvWTBgPlNualGGRTDkkYd5834VUdrprL70GQsGzGXKTQ3KsCiGPPIEb94fYLShpvao6Zh7Cgx3YunSbqxZk8nevR2MwtnRvl35wa12fy+wJhB1acUczvKga1tD365I5xHAjHVDGXsqm61brrFl/RH6Lb3Eyl8Gcn+lcCR7+gncOi+t9R3TbzPmMa3U6xnOjHV/Y+ypFLZuOc2W9Zvot/QgK395lJmtI5ixbgFjT51n65YTbFn/Of2W7mPlL08xs7W9dlk7prdin3th622hTqejtLTUxdZUxZrXOSwsjPz8/Ebtdf79998BaNeuXY1pz507h1qtplWrVrWeoaO2OHF5bitC2ZqgMx5LnNmDjeN9ELRasi7ns+Ob88x+MJNf/jWAdwcrERTedO7mg3WBaFm2LZFaOZ1WKyJT+NG5u7/1fNWJVPN8dZ0BQ5/D5ne2cuCvRvFcK6EMWq2ATC46YEPd/9qWjBgxwvmF3gaUlZUhimIVT4Uoim4zpdHq1Z147bXzJCW1p0ULL2onlK2lcVA8y1UsOj7cMC1kNXmFsFi2F7YxflcxYfmdTFhuvWyvuHa8vas9b9d0I5dHsuj4iyyqtL9yvco2Caz4vhsrqhxrwoTls81sMP9NkUxY/nfrx5winiWqY9Wq9rz22gX27etYzUOhvX3bUfFseli3ELB6LT+8kcHBqCYs7SmHfLP05TquF8gIi4tgWlxTps1pw+t9tvPx18U82OtWOFL/cMO94OTGE+z0bc2s4f6Ileyw9DxXttOvVTBhN7I5lyvQP9Rw3BCOFGwMR/IkLK4t0+LimDanH6/3eYOPv87h4TkBZBd4EBbXnmlxHZk2ZzCv91nEx19n8/DfI5HVSTy7r3AGg+fZ2kIpDbFyrSXmXmeTeBZFkdLSUlq3bt3A1jlOZmYmZ86cwdfXl8TExBrTm4R2x44d69s0J83zbEsoVxF+ppMGFH5eRIV7IwoC0U0DSOgRSb+Qnxm6+CRjeyQyvDSFu4afo+dnd/FKGw171vzBM59e4XiWDmVoICMf7MHbfwknSBBQp6bw/KJDfHa4ALVfEKNm9OPf0yMJyjvN3XecInFeBD+sPIHnXx9g56h07h58mJ6fT+PlyFOMHnCUrotac+rTZPZfKsIjoQ+f/iOYjUt+ZduZXLJVrfnnv8fwgN9h7hrwOz03PsErcSL6/COM7r2f7m/Ek7LmIL9fyiXHvxUvLJ/MU509uPnHz8x+9Re+S86l2FNF17uG8c6r/UnwzuHtyf/H3H2l6A/NZeuoGWS8lYj+wn7mvfQt6/7IolDhT8fBQ1n6f6MYFCKiz/mJu7v/ROKitvyw5Ac8n17Grplht1YjtOdBQHDvC9PthkajsfqKTyaTuYXn+dNPe/DSS2dISupAQoIvtRfK1r7XxfNcFzFZ17w0Mpudfy5v3brVqYue6PV6Dhw4UKv51WUyGd27d69YTcxRPvusOwsWnCUpqQNxcUYHTJ36dt08z+V5WjIyNIg6PTmXCvlu9UVe/0pk5ldtDOFI+bf64MV3fqPrB/589FU8Y9vIKb6YS3KW3BiO1IqJzbazfP4l+rwWjc/JUzz1+AnC3+vIk1ZthNKcYlJTPW4Ja0HEJ1RFaM8uTGmxhlWvXmDoP9sRkZ/BsiXJ6EffS5t1a2jzYRM++uoexrZRUnzxsjEcyY/Ud96j2weRfPTV/Yxt40XxxXSLcCRH+7b5/OXueZ9SKpU2zxG5XN6gcz1X53V2x9UP7WXz5s0A9OzZs8a2PXfuHBcuXMDX17cxiGcwiLOaRJyZuDZ6T6vE4goy+kyLpd//kvnqGAxvd6sszcnTPPp+KQ99PI7f4jy4eeI002f/xrLEe1ncNotXZu9h76ChHHirCYrTx5n6xA6eiZnK2h4yZLo81u+M4s0N0+kV6o1YklFhtyCTIdNm8b/d8Xy3biatC08yYdgWRs+J54OPH+ZfwQW8N/1dXvi4KxOeMbvJCjIEUYZMe42PvtSy6eOnSfBW883flvPg8mNMej+YV5/axpl7/srJL2IJyD7F36d9xCMftWH/00158rO/cizxA0rfWMaaYR5QeoHnZ3zCrn6Ps++TBKJL03jriTcY/1wQx/4zgAiZApk2k/Xb2/Pm1rfpFe6P6MiS6G56UbodseWN0Ov1eHp6Wj3mKrZsGcPcuUfYtKkdCQl+OCaUbwfxbPl/Y7DZtO1c9Hp9rcWzNS+ceXnm/9uDVqslLy+vTuL5yy8HM2fOUZKSOtbwUFibvl0Xz7O1cKQIVvwUawxHEo3hSAKGcKQerL50iAUDNpmFI/W3CEc6SL/oJGM40l1m4Uiihc06Di3+n0U4kpwJX7xgDEe6l+LZ20kMWWcMRxrG18sT6Okdw+q0r1gwYKFZONIM3rw/gCjtQ6y+tI4FA/5mPNbUSjiSI22FWZu7J9Vdt+VyOWVlZQ0inm9Xr/Ovv/7KlStXCA8Pp1+/fjWm37NnDwB9+/Z1yd+hbjUIUNXzbL5dnYAz5jMTfkJwAO2DyjmVoUHf7pZY1RWVG0YgqzwNCyJ06czm3Z2RyUTKD1xk3dWmLH4shuZ+AvToynsrAjkXKoAoIurK6Tg6keEx3ggI6EtuiXVDvQoG39+ZNp4iKKLp3QoOdenM3WEyIIAe8QHkHM8lX29+MTXZLWPg5L4k+MkBX/r3jkT3bhZpsh4s+e4VNL7+qDwFiOrAxAF+rDmZSakQjbepDYyDRcoP7Wf9lXa8/LeutPCSgXcrZj3Zg+UP7GN7zkCmK0REXSkd7x3L8BZ+VKyQVquQkfq56UrUDlEUXRp7bcm2bVOZMWMbn3zSmt69A3BcKFv73pjEc22EqrvYbGl/w+Ls5bnrOqBp27ZJPPHEHtavb2vxUFjXvu2geJYHsOj4nRYhQVXTC2Gt2F5omkJPxYTlQ2yG+3jFteXtXe2M4UjVlCuPYNHxF8xCoSz/F1G2iWfF912N4Ujmx5owYfmTTFhu7TdFMmH5c9aP1alvm4dtNHzfbkzcjl7njIwMdu7ciUwmY+zYsTWmP3XqFJcuXUKlUtkV3uEMnCDPzYWbhaCzFHXmnmdEi2OG/YYRyJVPMo9u7VnUdydPjfqCf3dryrD+zZk8ujldQ0QK0gu4EagixsdUt5x2A+JohwAFAog+tIzyomKgkCAYz01j/aIXYUGmQYlylB4CwcE+xpAIUHrKQadHW+Exl92yW/QhMsyjwlYPDxlodWgQuLxnF/9YfZRD1zQIokBZXi6avnp0Fb/bZINAQfoNsoKaEOt/q2x5VBOa6c6QclWAZiKIKlrGGD0MtYkxr3KDkHAFtoSAK5YNrY45c7bTu7cfI0cGUzeh7CLxnJfKmKbHaLfrXpb2sPe1cHX1WgrnuojYhsorncvW+Pvfd9Krlx933BGIc/t2XcI2GrqP0Uhsdv+wDdObltqEItU3Go2GtLS0Svsau9dZrVbz2WefATB06NAaV27UaDTs2LEDgJEjR7rM++/kmGezC1C1Mz9gccyQT5eZy+FcT7q2UJp1UAEUgcxYfj9jL2SyNSmdLd/9Rr8Pz7Lys7u533je6auxQSYTqRxzbZEO43G9YV+FeK84R6zcdK2JaeNHe/pHJs45Qvzy2STf0wRfUceel19ieJp5vZi1naktLWctAUE0tpEgGn+HA+Ea9bS6oLPjJOsLmUzG4MGD6330rTnVXWDLyspcZoclq1d/zZgxd/K//2UxZUo4jokJW99rKZ41+SxI2M2rJ271IdFDQUT7cKa+kMjL4wPwqiRgHBHPAqnbDnG0VTfGtHEHkeBMUSRhzocfbmHYsP4sW3aFuXOjcV7fbozi2fJ/d7dZa7bfPSkpKXEr4Qxw/PjxKqsJNmavs0aj4ZNPPkGtVtOhQwd69epVY57t27eTm5tLq1atXPrA4ISeKlL7eYoxS2dKU84PH5/jYHhzJnYxTStkTKfVcD1PICg2mmmP9GXD+jEsiLjCxz8W4B2tIjQnl7N5Jhv0nPz+IG/9Wmj08goWNghmdgsWv0E0+25+wTQTn1VitS1vaCKFJ1I5F9aFR+6OwFcUQV/I0ZP5aK1dpAUZfs0jCMvO5FzBrfLK0zJJlYUTGymvWpcjc0ZX+r3OwTyu0Z0/Wq2WnJwcp/72mhAEweqFVqfT2Vy22xUMGDCAzz/fwlNPXeC773Iw9A3LeVatfexNU/u5XRPnD+VS+ljS0+/l4vE7eXucnv9O28VLv8Kt88rB+WN1hWxe+hNfnRUcL8OtPuaCQ8Kcnj17snnzjyxenM7//ncD5/Xthv6bO9JHGtoGR/u1+/btkpISm84ijUZT7QqE9YFGo+HKlSuV7jMKhYLS0lLi4uJcaouz2LJlC9euXSM8PJxRo0bVmD4lJYWDBw+iVCrtSu9M6i6ebQo6sxOiilCG8oJSMq6pycgs4vjBdJb+40cmbZIxc34X+nqahJ7hc3HdD7R5YC8bUzWGBQfSs0jOkRMd6Y1nfDsmRl5h+crznMotI+2Pgzy14CD7ipVGDzJW7ANz724VAVxpARSo/ERsTYRXFtRe4YH4Z1/i1/Pl6MoLSHrnS77J84OsPDJ1IgieeCrKSE+9SU5hOWLX/kxpdoZVy46QVipQnn2WZav2oR9+ByMCZWbCXYZdQtmquDb7vX8yGsJbUF38ZkOKZ4A777yT1157i4kTk/nuu5s4LpSdITJAEeBDVJQfUVF+xLQJY+wLvXi6SxFbNueiMZatOXuemb0/xV/5PmGdtvPWIV1FGSWnz/DM8I8J912GT+A79Jy8j6TrIujyeXvYu8z9uYj/jX+dkGmnUSOj5PRxnhm+jHDfefgE/h89J+82pLdyQ8/c8S13tX0ab5+n6Th2K9vff4eAuC/4Q2NIU556iHmj/0aE/xR8gh6j1wOb+TXLUJY+Zzej/OfyypdbeGjwLOKi76dJp1d482C5g21lLjSc26dlMhmCICCKot2f6vDw8KhVWaIootVq6xz3PGDAAD78cB1PP33eiX27HsRiXhpjfDfz/H5nPtQ1nNjXX/+REb5PsvAodbC9fvq2sygsLLR5L2mImTaOHTuGTqerJOjDw8MpLy8nMjLSpbY4g3379nH06FGUSiWTJk2q8U2xWq3m888/B+Duu+9GpVK5wswKnBTzLKOyh9YkPi1ejVWIOB0H3vuRmPdAEER8An3okBjDik8782AnLwwLItwSjC0nDWb15V9ZMHUtU3J1KIODGDJhOG/e5Y0g8+Hlfw+l6OU99Ou3DbV/KKNmjuXN4d4IhZbiWKgshivZahKZmIlUy0EMQlURbv60bCzPo+9Ilo9dzbOjn+F1/ybc+cg0PvlXNOMnf8PAmQH8/mFP7h8fzbr/m0fHvTP4/eMhzP/P4xTPW0ti2zco8ggi4a7JfP1qX0Kt1VWnRVMkXIGt6eh0Op1brEY1c+ZMCgoKGDfu7+ze3ZPevc3jRM0ftmrzmltmtm3uRbK2bdmvzfIKCjw8BXQ640wEumK+/Pgmb3w0lZWRxfxn2pfMW3CeSZs7E1p6lZfHfc+uO8aw7+u2RKuv89YD/2P8YwEc+yqBJ7+bzLFmGyj98EXWjPIAdTrPj9vIrjseYN/XnYhWX+GtB95l/GNBHPuqNxHm4V15R3n+LzvJn/ksKS+0onTXRh55PBm1XxRyZFB+icXj3mP3nXPY/0VXIkou8cGMRdz/RBjHNgwmTJQj01ziozXlbPp2NQk+BXwz/WEeXPgTkzaPIkyoTVtZHnMuPXr0oKioqFZ5jh49anPGDY1GQ0RERK1nznDGq+Zx48aRlfUmEyc+wYYNCYwcGUbd+ra5qLPj76UpYEFCkpVwpDCmvtCNl8erjOFIUFX41lTPLdtStx2xCEeyN68jae05l80fBNynbzsLW9dtT09Ply8UptFouHr1aqXzT6FQUFhYSNu2bV1qizPIyMjg+++/RyaTMXXqVLuE8JdffolaraZ9+/YumZrOEufFPNs784M8mEWbphpGAVczxZ0Q0p7tRzoYyw5kwgv3MOEF6wLRK7YDb/+3E29b2uDfmU0nupjZICAEJ7L9ZHdjugQ2nep2K4+o4on1i3nCrOz2T82l0Pj7tp/tdyutfy82netdqWy/cbMpvM+w3euNhUxeZv772rPn9L0V7RD14hKyXjSzNbY/K74YwAprAjhgCJvS73RAKJunMV2snYe7DZ5wJ7y9vcnOzq6yX61Wu8Wk+gBz586luDif4cNf4/ff+xEXZ5qhwNXi2eyGq9VwZuNRPjjgxbCFoSgwCLo+s/oyvr0S8GXC2Cb8fVkul7QyVL+fZn16M15e2I4W3iJ4N2XWc+1ZPjqZ7dndmO5vssnoKf79COvTW/Hyws608JaBd3NmPdeV5aMPsT27L9NDbv2Gsv3H2VHckZVz2xPuKcJdE5kz9Dd++l1AQEb5vp9YmxbPsgWJRCtFULbm0Xl3sOrOn9mRO4QHREObDHxknHFGnkD6D4pF968M0rQywuR1ERjOPe+Cg4MJDg6uVZ5jx45VezwiIoKIiIi6mOUwM2fOJDs7m3HjXmL37t707h2Ey8Sz8ZM4fyAbZ/oiIqItVnN4wzFmTUtCEzGWZZ0sxWYtRayugM1Lf+LA37oypo2sdnkt+pFWa5oBqK7iGZwjnt3znqJWq63u9/T0bDCvszlhYWHk5eU1Oq9zbm5uxQDBUaNGERUVVWOepKQkLly4gEqlYvTo0fVtolXqrqhsxjUbt83DNWoM6xCtpLMsT2YlT002CDbKdqUNDd0OJoEt4Qr8/Pys7tdqtTYvwg3B/PmvMGfOXPr02UNKihrHXmub76/tR8f+F7/B33cNvr6f4O29ho5/vUbnpSNZcofSUK7oRXSUpzG9HKVSjqABjV6kICWXrJBgYlW3ypTHhNBMl0dKhuVNXaQg5SZZIWHEquRm6cNopssxpr8lZgoycsgPC6OZl2mfF126RSE3/t6CC5lcyf6Zcb6jEYS7EYS7UPT8nDMlWVy6aqxbVBHZRFlhu4eHAjQ6NPq6vpqXqIl58+bx4osvMWrUH5w6VYTjfduRv1NtwpE+w1/5AWGdvrcIRzrLM8P/Q7jvCnwC36Xn5N+N4UgFZuFI/7QIR1pBuO+L+AS+Rs/JSVbDkfRZ+xjp/y8WfPgVvZvMZsjbueiQUZ56hHmjXyDCfzo+QU/R64HvKkKQQOTmns1M7TWTQOVYPFUz6D19O4cLzcWy4GBbNY6+bevNjEKhwNvb22V2mHudzW0oKipqdF5njUbD+vXrUavVdO3alfj4+BrzZGRk8NNPPyGTyZg4caJLJwIwxzmLpJjEmTXPZ609pFa81dWmq65s3MAGV7WDtXRWbJBwCbZeO2m1WhQKBcXFxS694FbHK6+8Tm5uHn36fMJvvw2iRQtHF5dwxPMso/OcEax/2B8RAVHhSWhTX/zklesQBPMbrGClPrObr97Qz4WKB1Oo7BETbKQXK6XR6wFBhmBWr1Bx3skNMcJNR7Hz4mzu8LDyG/OMdQuWdZvXY29buZ93rjG8eZo//xU0mlIGD17J3r2DHezbjnieqZq3UjiSYAxHyuGNjyazMrKE/0zbyLwFF5i0uSOhpZm8PG6HMRyptTEcaZ0xHCneGI70OaUfzjMLR/qKXXdMZd/XHY3hSO8z/rFAjn3VyxiOZOzDcjky7XXWb2rHm3veoFeEP2J5OovGfcjuO2ez/4t4IkrS+GDGEu5/IpRjGwYSVpbMwqmfcWbSIk7u7kTAjYP8/a7/45GVXdj/UitEq+dZbfu2e9+nbM2SJAiCSwXc7eR13rx5M9euXaNp06Z2eZAtvdQ1TWNXn9T9Ma/KbBYW2w3ipTUXjw1lg6vbwVo6ywuUe16UbkcCAwNtHvPy8iIvL8+F1tTMqlXv0r37YAYO/JmUlBKqilzTpzrvnWMeOmVIALGxQcTGBtEyxg8/uaU3SrDIc0sA+7UKJuxGNudyb9VfnnKTVFkwsdFyKotnOX6twgi7ccMi/Q1SZaHERivM6pDjF67C60Y2l0tN+7QcO5Ru9BrK8G0VRZObaSRn3ipLX5RLWpbOLtvr5p2TzmV7eeWV1xky5B769ElysG871q8rfdfqOPOlMRxpRCgKY7/sM6sP49v74K0KZcLYJmgvGsKRyo3hSHMWtqWFtxx5UFNmPdcB5Q/JbM827wPGPvz7Udant2TOwk7G9M2Z9VwCyh8OGdOb2SLIEHVldJw8iuGtAlF5Kyjf9ytr0zrz/IKuRCvlyANjeXTeIFTf/8qOXBE8OrLkwBp+fCWBCC8PvJv1YOKdgZw9mk6pzb7uSL92z76dnZ1tc6YNQRDw9/d3iR23k9d53759HDt2DJVKxQMPPFBjenMvdY8ePezyUtcnYs1JqsPsCdLumR8cEH62yrM6+0QN2wVHGRO3jOePUI82uEM7WLHBDS9KtyumlQSteeYEQSA/P78BrKqeb7/9jpYtu3Lffb+Rm6uheqFsTXA4csOklmlM/dhQn6JnAlNaXGLVq+dIU4uUX09n2ZJj6Ef3YESgHAQPPD00pJ/PJadAi9izN1NanGfVq8nG9BdZtuQA+tEDDOnN6vbo3oX+wlHee+8SeVodl3ds4M3dpsUcZCh63Mm0NmdYOW8XpwoEtHnprJv9DAkTt5KiMwlly99nSzzb237SuewIn332OfHx/Rg69CdSUoqpXd92pF/r2P/it/j7foqv7xq8vdfS8a/X6bx0RA3hSHor4UgGG+QxwTTT5VcTjhRuEY4UbiUcyVieGEDL5j4Vb1UKLlzjSvYexvnejyCMQxDGoui5kTMl2YYQJAEu7/yK6QNnENNsCs2bP8jE/2Sh0ejR2X0uN17xfP36dZvHNBqNy8TzsWPH0Gq1lYR8WFgYGo2mUXmdT506VTFA0N7QC9M0dk2bNuWuu+5ygZXV45ywjQoR50CYgtV0okUee8IUAM11Fox6n1fP3HqlISo8iWjdkqlPjuLlUaF4VTtfsz02QOquJI42H8yYVqKVdA3dDtWlk3AlSqXS6vRGarXa6mBCdyAp6RcGDerFsGFJ/PzzcJRKc8Fn0aeq7JOZ7TO/EVrbNt3QobKwtJbXVLZYtT6PGOZvHE/x7K0khvyXIo8AEsbcxdfLEwgVZKBozv0PRLJu3hI6/jyZ378cwPyN0yme/TmJIe9Q5KEiYcx9fL28lyG9Wb1C2EDeeOcsDz43jyaLfIi7ezxLZrfhpw9Fg+jwaMeLX72E+qmPGRz5Brn4027IPXy65j5aiNa8ceZtZvl7q2s3a+0onc+1Zdu2Hxg0qD8TJvzGDz8MRaXyxL6+bfnQVlPfNqTvPGc46x/2Q0SGqFCYhSPdylM1HMm8b1icU5XCkaydOxbniV4w1iFWrUMQkclv1W0IQbqLnRcftxqCpDm6gYnT9xD/8eskT4jBV9Sy59kpDE+xLLumc7m6djRve/fi6tWrVvfL5XLy8vJcMk2aRqPh8uXLVr3O7dq1q/f6nUVWVhbffvstYH/ohWkaO3u91K7AOVPVVQjoGgRivcc1G07exKceYeOUQEQEtCVFHN6yg1nPfIIm/FmWxZmekM0Ffy1s0N1k89vfcOCJwYxpJbNDADuzHaylq61Ydx7m88K6M66eRshEYGAghYWFVfaXlpai0WgaZG5Qe/jmm+0MGtSD4cN38v33w8wEtOUNzvJ7LW+a8nAWHZ/NoupuqgHxbCpMqHTMb+qDFE699V3Zpisrvu/GCqv1+jDwtRfJes3sWJverPi+r1l62za2vG8mO+9/CqXc0MfP/esnCFShMopjj5aDWLrlDpZasz9gNJsKx1Qq22/qOxROrY2gsPVdwhEMD4c9GDMmqRZ9u7bi2fDdEI6kQrR63FJsVq7PPBypf6ghfc3hSNeNacAMggAAIABJREFU6WXG9NctwpHM7aBS3b6tmtLk5gWSM0XuaGbYpy/KIb1ERbMQkbxDpznXpA9v39fcsPCXLpujR2+i9TcXz+ZlOiqe3bNv2xos6OvrS2lpqUuu40ePHq0yziA0NJSCgoIGm9WmtqjV6oop5uwNvThx4kStvdSuoO491d3imgGFvz9REYFERQYR06oZY58cy9Ptc9jyYyYaYx5Nyh/MHLMQ/5ZzCRvyEW+dKK8ou+TCQZ6ZvIDw2Cfwafc8PZ/YTlKWAPqbvD3pBebuK+B/j/yVkCcPoEak5Nxenpk4l/AWD+DT+nF6PraJpGyhHtrBWjrL8mz8Lerhib5ly5YEBQWhUqlc/gkMDKzysZU2PDyc0NBQp/52e4iOjrZ5TKlUkpWV5UJr7EelUpGUtJ+0NIF77tmFoX/Z+5rbmR9Hy3WSLcVHebzVFAYtOE5GsZ78s7/y+kepxA1PJEp09m+tbZu4n3eusfDNNzu4ft2L4cN/QK3WU3PfduRvRA15LcWmzPjdIJ4N4UhpFuFIx9GP7m4MR1IYw5HyjOFIvZjS4oJZOFIKy5YcRD+6X5VwpKrCXYaixx1Ma3OOlfOSOFUgos3LYN3s50iYuI0UnRyfyFD8b5zh19Nl6EpzSPrXe3yTGwjXssnUmt9fHD0fLcW0+3D+/Hmbx7y8vGo9l7kj2FpNMD8/v1HFOn/55Ze1Cr3IzMxk69atQMMPELTEiWEbtfS+1slLa23bZIfJJrMTWlTg4WEc5SwIoMvny/VXeeONF1gZUcB/Zq9k3r8OM2ltX0LLLvLyw2vZ1e8x9v0nnujSNN6atYLxfw/m2Mf9ePKzZzjW7W1KV7zLmjs9oOwcz89Yza5+T7NvbQ+i1Sm89fj/MX5uMMfWDCGiXttBtN4OttI5+aLUvn17p5Z3uxEUFFThlbc22CQrK8utLgbmqFQqDh8+RadOrZg0KYn164dSqV9V8dQ54Hmu1gNVW09sXby4NvJ6J/Lqp1OZ9ew/iXsjB01AJP2mPMd/n41FVp/1Vvvd1MYSjqJSqdi79zAdOrTmnnt+ZMeOu6m+b5sLTnv+fiYhbClWLUW5eRrzumXg0Yz5G++jePZ3JIZ8RpGHPwljRhjDkURjOFIE6+a9TsefJ/H7l/2Zv/Ehimd/QWLIe8ZwpHEW4UjmdljY59GWF796HvVTaxkcucoYgnQ3n64ZawhBGvIgyyct5NneI3ldFcOds5/jk/diGX/XagZODGHf25aC3NHzwv36dmpqqs2ZZUpKSoiJial3G24Hr/O2bdsq5ma2J/RCrVazYcMGtxkgaIkTFkkxuzi4Iq65prJNaUzptKWc+W4nHxzzY9izUSi4CUCfh+5hfFtvwJ8JdzXj7+/d4JJWRHX4D9ZfacvLcxNo4SWCd0tmzerO8mn72J7bn+m+5naIlB/cw/rL7Xn57z0N6b1aM2t2H5ZP2cP2m0OZHlLTg4Uj7WArXTUiXIp5djmiKOLj42M1dKO4uJi0tDTatWvnlqEbYBAZx49foFOnljz77D6WL++LbYEhAsFW9ln7jh1p7CnH3rrAsFqoHjAN+LP8br2c0IFj+fzgvRZpip1gc11/p0RdUKlUJCefo1OnWJ54Yg/vvDMA2+1eS/EsD2fR8Vlm4UhW8gZ0MQtHMhzzmzrNGNJj+G4IR0o0LJxVpSxTOJLZsTa9WfF9HxvhS2bbAXewqXBoFZs8Wg5g6ZZBxhAky9/UjMkfrGHyB+bHerAn668V37cXTqq5bRqZeFar1ZSUlFgVzp6enuTn59f7QL3qvM6NJdb5xIkT7N+/H6VSaXfoxZdffklubi4dOnRwiwGCljhpeW5z0VYPws9UT40CUQS07P/ncvyXG3LpyjWUe4dz/wuPsKSvEgoFEP2IjlRWlKX0VCBodGgQKUjLIiuoCbEBsgrb5dFNaKY7Q8oVoI3pqd2QtyDtOllBkcT6yyrskUdH0Ex3kpSrQIj5b3dUKNvzMGIlXaWy3e/C9GcgJCTEqnjW6/V4eHhw5coVpyxJXF8YBPRFWrZsio+Pgldf7Y3jwq+mPPUhMm+nNJbfJeqCoW+fp3375gQH76+mb9fW82zPdxzMW9t6Gktec++7+/TtQ4cO2TwWGBhIeXl5vTs/GrvX2Tz04p577rHrbavJSx0eHs6oUaPq20SHqNtfXYBK06A5U/hVOmarbIuyjCdh50cfZf2kIERBQJR7EtpEhZ+ictlCjR5z8Vb5esOPFUSRitAQ89k6BAs7zBddEEQrZde2HSzTOSDCK9JKuJJWrVqRmpqKIAhVQjc0Gg3p6eluLZ7BIDJ27dpD//69UCjkLFhgXJa+1uLQkTxSGtvfGxaZTIZOpzMu7VwZd1mCviYM4UlnaNeuBSDw6qv9qPp3cKZ4FmqRtrEJYGe1TcP3bTB4nW/evFltGsnrXD3moRcDBw4kLi6uxjxHjhyp8FJPmjTJbQYIWuLkmGcHBWJt4pqrK9sobJVBwcS2CEW0aYOl3ab8MvxiwgnLzuRcgUD/YMPTcHn6NVJlYcRGyivnR4Zfi0jCsi5zLl+gv9HLXJ52lVRZBLFRplHRdWkHB4Syzb+Fe1yU/kx4e3vj7e1NUVFRlVd/5eXlFBYWkpWV5ZJBJ3UhPj6erVt3cM89w2na1J+HH+6MfcIPG2ncXZi6e5qGJzExkZycnCr7TbPwNAavGEB4eDi7d/9G//698PT04KWXzB8ORSAS638HaNg+4EgauBWyBIawJdN3gcphTH/evg1w+PBhmwuj+Pv7c/36dRISEurdBkvCw8MpKipqFOeXKfSiffv2DBo0qMb0GRkZbNq0CYBx48a5ZApAR3FCzLPZ06Or4pptCkTxVv6KY1ZEpWW9pt+CiKJbP6Y0W8yq5ccYurArEQXnWLZqH/rhjxlGLWs88PQo53xKFjlFofh3G8yUmBdY9cYBhr7Sk4iC0yxb+Sv6EU8zQiWrQzvYSmdPO1Tzt5BwOZGRkTZHbOv1es6fP+/24hlgwIABfPHFt0yePJbAQG/GjWuL7ZsjVvbV9SbrzqKkIdI0LGFhYYSFhTW0GU7B9HA4ZswIWrQIZOrUDkh9+8/bt/Pz86udDcnX1xdfX996DdlQq9VkZmZW2ufp6Ulubi7/z955x0dR5n/8PbMlPVlCSCMQAiGhdwhSIogIFhRRQVBU7J5nO8/z53mA5dTDO0A89RQVu2ADEQEVARWRIr0EQkmnhfS6m22/P7IbNpvdZJPsbjb4vF+vSWZnnvLdmWdmPvPd7/M8/fv391i97sI29KK5U2+npqbSpUsXtFptq2zwpNfajTHPrRV+uJjOWdnWbdisO7MBGnierftUPZm77D6q/v4Rw5IXUanuwODJM1j1/KjaXs7qntx4U1eWP/8Y/bbdz473JzH3g0epevIdhiW+SKU6nMFX3caqFy6lk+zMBpsbhtdG4fCNm9IfkeTkZE6ePAk0HHVDr9dTUlLSLrzPABMnTmTp0o+4/faZ+PurueqqnjR88DX1sHTXQ7atH/BtmUZcy+4mNTWVzz9fXfdyKNr2H7dtb9++3ek+Pz8/ysvLGTBggEdtOHjwYINtnTp1orq62uefFcePH29W6IXt1NsAv/zyC7/88kur7bjttttISEhodTmOaL14rht/2K7xezKu2Zn4VHXluU2v8lxjNoSOZnVG/bi2kBv/SsWNF9L5J45l8eepLHZoQxCX/uMVCuba2JA4nsVfXVbby7kt45qdphNe57ZElmU6duzo1JNhNps5cuQIY8eO9bJlLWPatGlkZr7ItGlPsm7dLC67LIEL7cvXHsS+VLc704jr2RNMnDiRF19czIwZD/HZZzfYCGho+3Mu2rY32L9/PzqdzmE/FajtBF5ZWUl4eLjHbGjvXmfrs2706NEuhV5s2bIFrVbb6mnOraGRpaWlrSrHFdw/zrOn45odle1qOq+HSrjTBkfpmmODNa2gLejbt2/dm7SjjoPV1dXk5OT4fOdBK48//jhVVeVcddU/2bv3fnr37oRvPogvVsEh8BT33nsv5eXlTJv2JJs23c6oUV3xjXMu2ranOX/+PLm5uYDjsflVKhWlpaUe76zXnr3OtjjqTOyI8ePHM378eLfVu3r1avbt2+e28hzhvphnr8U1O0rnDfHpKRscpfOgDYI2ITQ0lNDQUKdvxHq9nrS0NCIiIggMDPSydS1j7txnABOjRv2bPXseICHBfpxnX3gQeyKNr9gn8BTWl8PJk//Fjh330rt3FL5xzkXb9hQVFRXs2LHDaSdBgOjoaI8PEdfevc5/FNwTtuFsnGdPxDX/YTzFbrahbhG0FQMGDODXX38FGno1TCYTsiyzd+9eRo8e3RbmtYi5c5+joKCQUaPe4bff7ichIRzfexB7Ok1b1S3wJHPnPoPBUMOoUYvZs+ch0bYv4rat1WrZsmVLo8I5MDCQwsJCBg4c6FFbLhav88WO7J4iLAJacrBeTwha9ykc5FHY5ZEdlK1wULbCRRvsy/O0DY7SucsGR9+vKRvEw7at0Wg0hIWFOb1BGwwGysvLOXz4sJctax1LlrzO8OFjSU1dSmZmCXXtsMGiaGRfe07TkqW1dQu8wbPPvshtt93JqFFvirZ9kbZtrVbL5s2bMRgMDYYTtaVDhw6EhoZ6VMA25nVOSkryWL2C5uPemGcR19xKGxylc6cNQkC3NUOHDmXTpk1OBbRerycnJ4ewsDDi4uK8bF3L+eabtUycOIHu3Rc0I7TePqFzr4/zPH9MzGYzN954Y1ub8YdgyZLXOXkym+7dF7S1KX8YvNW2CwoK2LlzJ0aj0WkHQagdW/ncuXNujct1hCOvc2RkJFVVVcLr7GO0Oua5vEZFuRYHwg+cCz9omUBsQnxib4ODPC6JT3v7XBHAF7I1uY71eJnt9kkg2V+8Np/r0pkc2GAzuL3U8DuUVRlohqoReIjAwEDi4+PJzs5u1AN98OBB1Gp1uxpHd8OGjZSUlLS1GX8YfHkCgYuNb7/9tq1NELiZtLQ0MjIy6j47ux8HBgZSVVVF3759PTpusDOvszdCRQTNp1XiOSbMwL0fd3OwpwmR1qSGa43IM9v9bymtscG1vGaHNjadt7GflhorZ8qoBBfyCTxN3759OXPmDDU1NY0K6N27dzN06NB2JaCFoBMIBL5MWVkZu3fvpqKiosm0siwTEhKCQqHw+EhIjmYT7NixIzqdTnidfZBWiee3593tLjsEgj8MsiwzdOhQtm/f3mgHFauA7tWrl8cGehcIBII/AmVlZRw4cICSkpJG77u2xMTEUF5e7vFO3FqtlsLCwnrbrLHOwuvsm3hubkmBQOCUjh070qNHD06ePInJZHKazmAwkJaWRnFxMYMGDUKWRWcxgUAgcAWDwUBGRgZ5eXlUVlY2K298fDynT58mNTXVo9NwQ63X2V7QC6+zbyPEs0DQRiQlJVFQUNCkJ8RkMnH69GnOnz/P0KFDxc1UIBAInHDs2DFyc3OpqanBYDC0qIyuXbty6tQpRo8e7fFx9y9mr3NeXp7DcBRPc+bMGY/XIcSzQNBGyLLMqFGj2LRpE9XV1Y2mNZvN1NTUsH37diIiIhgwYEC7mUxFIBAIvMXZs2epqqoCaHQEDWd069aNU6dOMWrUqFZPF+0KF7PXOS0tjbS0tLY2wyMI8SwQtCFWAb1lyxZqamqaTG82mzl//jybN29Go9GQmJhIVFSUFywVCAQC3ycgIKBuJtfmCGdZlunSpQt5eXmMGjWKsLAwT5lYx8XqdY6OjiYlJaWtzaBDhw4eK1syN/e1TCAQuJ2ysjJ+/fVXjEZjs/MqFAq6detGnz59PGCZQCAQtB8OHjxIVlZWs/J06NABhUJBTU0Nw4cP99qvetu2baOgoKDettjYWPR6PSNHjvSKDYKWIXofCQQ+QGhoKCNHjsTf39/FoQgvEBgYSFFRkYcsEwgEgvZDcHCwy2mt3madTkdQUBBjx471mnB25nUuLCwkOTnZKzYIWo4I2xAIfITw8HBGjx7N1q1bMRgMLnd28fPzIyAgwMPWCQQCge/japxybGwsNTU1FBYW0r9/f6+Pp3/w4EFkWUalUtVt02g0GI1Gj4YbCNyD8DwLBD5EYGAgEyZMIDQ0FLVa7VIeWZbx8/PzsGUCgUDg+4SEhDjdJ8syMTExREVFkZ+fT0xMDBMmTGiTiaj8/PxQKBRIklS3lJaWkpSU5HVbBM1HxDwLBD6IyWTi2LFjZGZmYjQaG+340rFjR+Lj4+ncubMXLRQIBALfZM2aNXXrarWa0NBQVCoVZWVlQO2IGt26dRPj5gtajAjbEAh8EFmW6dWrF3FxcWzfvh2TyYROp3OYVq/Xi7ANgUAgsKBSqQgJCcFkMlFRUUF1dTVhYWEkJSV5Zfg5wcWP8DwLBD6OyWQiJyeH48ePo1AoqK6urjcroVKpZPz48fj7+7ehlQKBQOAbnD59mpqaGoKDg9FoNB6fIVDwx0OIZ4GgnWAymcjIyCArKwuz2Vz3k6NWq+Xqq69uY+sEAoFAIPhjIMSzQNDOMJlMFBQUkJubS35+PgEBAYwbN66tzRIIBAKB4A+BEM8CQTvGZDJRVVXVrLFNBQKBQCAQtBwhngUCgUAgEAgEAhcR47QIBAKBQCAQCAQuIsSzQCAQCAQCgUDgIkI8CwQCgUAgEAgELiLEs0AgEAgEAoFA4CJCPAsEAoFAIBAIBC4ixLNAIBAIBAKBQOAiQjwLBAKBQCAQCAQuIsSzQCAQCAQCgUDgIkI8CwQCgUAgEAgELiLEs0AgEAgEAoFA4CJCPAsEAoFAIBAIBC4ixLNAIBAIBAKBQOAiQjwLBAKBQCAQCAQuIsSzQCAQCAQCgUDgIkI8CwQCgUAgEAgELiLEs0AgEAgEAoFA4CJCPAsEAoFAIBAIBC4ixLNAIBAIBAKBQOAiQjwLBAKBQCAQCAQuIsSzQCAQCAQCgUDgIkI8CwQCgUAgEAgELiLEs0AgEAgEAoFA4CJCPAsEAoFAIBAIBC4ixLNAIBAIBAKBQOAiQjwLBAKBQCAQCAQuIsSzQCAQCAQCgUDgIkI8CwQCgUAgEAgELiLEs0AgEAgEAoFA4CLKtjZAIBAIBAKBwF2kpaVx7tw5oqOjSUhIwN/fv61NElxkSGaz2dzWRggEAoFAIBC4g59//pmysjIAJEnC39+f7t2707179za2THCxIMSzQCAQCASCi4YffvgBnU7XYLtSqaRnz54kJia2gVWCiwkR8ywQtFNMJhOVlZXk5+ej1Wrb2hyBQCDwCQwGg9PtR44c4bvvvuPcuXNetkpwMSFingWCdoLJZCIzM5NTp05RVVWFXq8Han+W7NmzJ8nJyW1soUAgELQ9JpOp0f16vZ5du3YRGRnJ4MGDUSqFFBI0D9FiBAIf59SpUxw/fpyKigqsUVaSJNXt79ChA9XV1W1lnkAgEPgMJpMJV6JRTSYT586d48cff2TYsGFERER4wTrBxYIQzwKBj5Kbm8uhQ4cc/gRp+3AwGAx1XmiBQCD4I1NQUIAkSS4JaLPZjF6vZ+fOnSQnJ9OjRw8vWOgb5OTkcPbs2XrbgoOD6datG4GBgW1kVftBiGeBwMcoKytj586dVFdXYzabkWW50QeB9QEgEAgEf3TKy8tdEs62GI1G0tPTqaioYODAgR6yzHfQarXs378fuOCIUSgUnDt3jsjISCGeXUCIZ4HARzCZTBw5coTMzMy6ba54UPR6fZMxfgKBQPBHIDQ0FKj1oup0OpcdC0ajkby8PKqqqrjkkks8aWKbc/Dgwbp1awhgdHQ05eXlInzFRcRoGwKBD6DVatm4cSOZmZmYzeZmeU70er3wPAsEAgHQqVMnJkyYQJcuXQgKCkKtVqNWq+v1E3GGyWSiuLiYPXv2eMHStkGr1TYI1/Dz86OsrIx+/fq1kVXtD+F5FgjamOLiYn777TdMJpNLN3h7XO0gIxAIBH8EAgMDSUxMJDExkYqKCo4ePUp+fj4KhYKamppG8xqNRs6dO8fRo0fp1auXlyz2HrZeZyudOnWiurpaeJ2bgfA8CwRtSHFxMVu3bq0Lu2iuCJZlGZVKhclkEqEbAoFAYEdwcDDDhg0jNTWV0NBQl4alMxgMZGZmkpWV5XkDvYgzr3NJSQlJSUltZFX7RHieBYI2wiqcm4Msy3Ts2BFZltHpdFRUVGA0Guvi/AQCgUDQkODgYC655BKysrI4fvw4Op2uUWeFwWAgLS2N4ODgi8Yju3fv3gbbvOl1NhgM5ObmcurUKc6ePcv58+eprKyksrKyXrrAwEA6depEhw4diI2NJSEhwefOgZieWyBoA0pKSvj1118B17zNgYGBdOjQgeLiYiRJIjo6mvDwcCIiIsQA/wKBQNAMqqqq2LFjB1qt1ulshFDrrFCr1YwfP77d32e1Wi0//vhjveeNn58fKpWK/v37e0ycFhQUcPToUbKysjh58qTDNAqFAn9/f6BWYDuaWj0yMpIxY8bQu3dvnzgXrRLPn741hTOV1gMu2fy3rNeL35Tqp5Hs8zjbbp/Gms5B3gZlNJXHJp0zexp8p0bskhxtb+w72Zftyney325fls1/u/JGdtcx+rJHELQtBoOBDRs2NHrTtqJSqejUqRMlJSUEBQXRrVs3oqOjvWClQCAQXLyYTCZ+++03ysrKMBqNTtOpVCoiIyMZMmSIF61zP9u2baOgoKDetri4OKqrqxk1apRb6zIYDBw6dIhdu3Zx6tSpuu0KhYLExETi4uKIiooiOjqakJAQh/lLSko4c+YM2dnZdcMIAoSHh3PNNdeQkJDgVpubS6vE8zWP/pcrEn4n3L/MgXC0F3iNCT/rf9lmn4NynIpmW7HoYN2hPY7KByS5/meH4toujbO67LdLkuVTY/bY7q+t1+yyndDAVku6w3l++KvNzH/wHgRty+bNm+tuBI0RGRmJ0WjEZDLRq1cvn/vZSiAQCNozJpOJPXv2kJ+f36iAVqvVDB48mMjISC9a5z685XUuKSnh559/5uDBg3XHs2PHjiQnJ5OYmNhiwWuNQd+6dSvZ2dkoFApGjx7N+PHj3WJ3S2i17/vGpF+IDS7kgtiTbdatok+2WbdLU5dOtsvjajq7sl21wWG69moDTZb9jT/szfFr2UkWuI1Dhw41KZxlWaZz586cPXuWpKQkunfv7iXrWsePP87kf//7noav47Yvf9it23+2fxl0ZX9T+VxJ68jO5pTXWL6W2uc8fUGBnptuuo+HHnoIgedZu3Y6b7/9AzU11sbd1DlqbN3ZvtamcZbO3e21JWlc31dYaGD69Ht5/PHH8TSyLDNkyBC2b99OcXGx007XBoOBffv2cdlll/lEyEBz2bt3b4PwQE/EOm/fvp19+/ahUCgYNGgQSUlJ9O7du9XlKpVKevbsSUJCAvv27WPt2rX88ssvGAwGJk6c6AbLW2BT64uwFYUtFZ+O0nlbALvTBgfpPGIDzbTB/uYl8CYlJSVN9t62Cuf8/HwuueQSwsLCvGNcKzlw4E/cffeXTJ0aRJcuKstW2+sEB+uupHF13Vm5vpbGUZ7ml/Hhh7tZuXKlEM9eYNu2u7jrrpXcdlsIvXr54fxctuSz/Tm2/0VRtsvT2voaq79xe2q1V3PrarhNqntWOf5eb7yxnW+//dYr4hlq77kjR45ky5YtlJWVOUxjHUb02LFj9OnTxyt2uQutVkthYWG9bX5+fpSWlnpsXOfJkyczbNgwt5erVCoZNmwYYWFhrFu3jt9++w2gTQR0K8WzRbRZLwZPemmFt9rBvubaYHtDE3ib33//vdHOgUqlkqioKEpLS0lNTa3rQOHrZGS8zK23vsuTT3bggQc01LXbBg9p+20tSeOpcr2dpqk8jZdz6FAxF9koWj5JRsY85sz5iJde6sicOWGItu35tr1ly2mvt21ZlklJSeHnn392Og60VqslOzub7t27t5t7Mzj2Onfs2BGdTuexUMCgoCCPlGulZ8+eXHvttXz++efs2LGDbt260bNnT4/WaU/rxnmWrEXIICls1q2CWmGzbrOvXjqFXR7LtkbzOFhvlQ2O1h3Y4NBWb9vQsuNgNitadaoFrSM9PR2tVut0EhRZlomNjaW8vJzRo0e3m5tzXt4ypk6dy/XXB/PAAx2oa3cNFkUj+5qTpiWLu+r25ndoqhzH7UjgPvLy3mLq1AXcfnsoc+Zo8I12Idq2p/D392fQoEHIsuw0jUKh4MiRI160qnU48zpfDOM6JyQkcPXVV2M0Glm7di1ardar9TtvJS4heUB82uexXGwuiVRXyrtgg7k4j8kpW5h/rO1scP2FQXJStos21L3hC7yNwWDgxIkTgPNh6WJjYykqKmL06NHtJqbu/PlPuOqqBxg5MoBnn+1E+3mgt5V9tl621tYl8CS1bfthRo4M4KmnImj7tiPatjeIioqic+fOTgV0TU0NZ8+e9bpQaynOvM4BAQEXRQf0fv360aNHD0pLS9m+fbtX63ZDS5Xc7CmW+PRvh5BGHEAavh9p+D6k4XuQhu1GGrYLacQBHtnrqGxFXf6srWdYnSO5INatYtJ+nwJMZcy7/itGvV+FydXv5BFvtc1NqVU2CPHcVhw8eLDR2f+snQOHDh3aboRzaem3zJhxL4MG+bN0aQy++UB359Kaum2Fhbu+g7iWPUVp6Uquvvpu0bb/oG17wIABKBSOf6k1m82oVKo6Z4gvo9VqGwxNd7F4nW255pprAOrG7fYW7ol5tjZ4N8U1X/t/vTn+cG26ss05jP5YxdKlcVyirE2jiVLgNO7XrGPNe5nsur0L13WTG7dBki1fQ7bbZ3sByxfEZ7uIa3Zig4h5bhNMJhOnT592uj80NJSSkhIGDhzYjmYJPMpdd80iIkLBhx92pl4bxK5N+mRMpjvSuJLH0eIu+yRAzG/lbnS635k9+3Z69VKLtv0HbduyLDN48GCnfVSqq6tXeU8VAAAgAElEQVTJy8ujV69ePu3s2LNnT4Ntno51bg4Gg4GKigqKi4sJCAjA398fjUbT7HI0Gg19+/bl8OHDHDlyhMGDB3vA2oa44cxf8PjWLQ0EYvOEX3BEAImW7cUdZSSFks5xgSSqavNVZxXw6L9zWL5PS4VSRb9RcSx4Mp5xGh2vPbCdx/cYMR/azNrLB5D3YgxV+zJ56D/ZrEuvpkodwJAJSbzx9wQG2QrMOgEr29iHE1trP1dnnOSpF/ewfHc5FaoA+o3tz4J/DGFcRxnMOra+9zOPfpDFwfMG/CM7cdUd43ltThfCJR1b3/2BR9/PsOyL4qo5V/DanV0Jl+2PibPj5aoIl232tf5sC5pHWlpao50Eg4KCUCgUxMbGetGq1nCKG24YQX6+gS1bEvBtEdCWafBw3QL3k8GsWRMwm+HDD+Pwnbbka2nwcN1tT1RUFEFBQU6HFfXz8yM3N7fNJ+pwRmOxzgMHDmwjqy5MnnL06FFOnDjRYGzt8PBwxo0b1+xZBAcOHMjhw4dJT0/3mnhuXUuVrH+s4tPqpbWuyw3XcbDeaEyx5aKy7qup4JnHj7Opa3e2b0qldM0Abq7M5abn8zkjBfHn1wdxR7iKWf+ZSMFLsfjri5n/1DHShw8m7bdrKV45kKGHDnL3xxUYrV/foQ2Wm4SzmOKa8zzz0BY2xV/C9m33UrrxKm4u389Nc49zBhnD4X3c878qbnrzbsrTHib9v70oeWcDCw+B4dBO7nmjmpveepDyo0+S/lpfSt5Zy8JDZifHyx2x1bY3PoG3yM3NdSqeo6KiKCoqcss4mN7hFLNm9SUvz8CaNV1p+HOrr/2E3RZpbEWAJ+sW17J7OcUNNwwmL8/ARx91xjfakq+l+WO17f79+zvt4K3T6cjLy/OyRa7jzOvcVrHOBoOBXbt2sWTJElavXk16ejpGo5HOnTsTHx9Pjx49UKvVFBUVsXLlSl577TVKSkpcLj8hIQGFQsGJEydcmrnXHbjB8yxT3wtqe5HZfW7JEHC2F6wkoz90nhXnNDxzXyQJARIEaHjw9igWPXyO78rimBMkX7ALGVQdeWnFFRiC/NGoJYiNZsYl/nyQXoGugefZ/ns4/376/SdZcSaWZx7qTkKgDETz4D2JLLr3ON+V9OGWSh1lkgpNmB8qWUnUoBTW/DoShUKmZlsNZZISjSYAlUJJ1OBLWLN1FAqFfV1OjpdL4RoO0vnITemPwunTpzEYDEiS1EBAy7JMTU0NSUlJ7WRkjTIeeWQgaWk6fvqpGxqNCvd5mi6GNE3lcVddviMuMjMzncYYhoSEEBcX52WLWsp5HnhgACdP1oi2Ldp2HREREQQEBFBZWdlAROv1eiorKykrK/O5cLvGvM5tMcX4oUOH2LhxY50YTk5OJjk52aGHODc3l40bN5Kdnc27777Lgw8+6NLzUalUkpiYSHp6OgUFBURHR7v9ezSos3XZJerHPHtC+NWvp/yUlgJNIImhcl3ZytgguppKyDwnQQ870SuZOLXjJP/30Wn25JtqnddlWgwpYLKWb9uprp6gxoF9tevlp8oo6KAhMfRC/LWys4au5tNknpNRDxvGc2NW8vDkt3h9eAJXpCYxc2oyQzrKqIen8NzYFTx8xSu8PjyRKy5NZub1fRjS0dOx1e6/MX3//fdee9NrDX5+fowZM8arQjUjIwNwPMJGbGwsJSUldOvWzWv2tJwynn22L99/X8H27d3RaJQ4fvCl26zbepEcrTf3s4Q5v5Aru58jZetAnh3oyFvlmXpbltfkYJ/J7rjRSF3OyjXiqWu5uRw6dKjBNrPZXCc01Gp1O5jOuIx//nMg27ZV8dNPCY20bU8JU19P42jxhj2+QXJyMvv27XN4D/fz8yMvL8/nJk1x5HUODw/HYDDQoUMHr9lhMBjYsGEDO3fuBKBHjx5Mnjy5Uc93ly5duPnmm1mxYgXZ2dmsX7+e66+/3qX6rPHSubm5XhHPctNJXCiixcOvOVjHpqw6UQiNjj5hrk0jNRDyMoZjJ5nxj9OEzR7D4R+uIWvD1XwxLRBFvYvUkQ1y/XochnVI9fNYypOQQR3BnUvuIWf1tfx1dCA53/7AmCu/YGmWBOpO3Pnqn8lZcyN/HRNEzpr1jJn0KUuzaOQ4OLKhGce13suI+9Dr9ZhMJsxms9cWk8nU5GKfR6vVUlxc7Pbv3xjOfnaSZZny8nKSk5O9ak9LWbBgEO+9d47Nm7tbvHL2wtW6KFq2GPTM638MSTqKJKWhUKcRHneMcbec45N9ZsyWdFKHcF5e35M5icqW19Wg7lLm9V/OqIVVmNxSZiuPhcvlt73AkCSpgUfO+lmW5QbxjL7ISy8N5OOPi1i/PqGJtt3cxVdDL1xNYytovW1P27dtK7GxsU5DN6qqqjh79qyXLWocZ17noqIirz5vzp49y5tvvsnOnTvx9/dn1qxZ3HrrrS6FjPj7+3PzzTcTHBzM4cOHyc3NdanOqKgogAbf31PIrS7B6fjDjgSds7jmxoQf2IrUkLhAIourOF5xQazrT1eRpQgiMUZpI7Zr95WmFXE8Ipa7rwgjWCGDSc/+9GpLvLP1QnVkq3yhHAffKSReQ2RRCcfLpLo8+twSsmQNibG1giC/BMJ7dmf2vZP47IvbmBebzbIfijEaasgvhfCeicy+7yo+++ou5nXOZNn3JbV2tTiu2eaYOnwR8AzWsARvLa5gn8fZDdBT5OXlObU1MjISrVbbLjoJLlmSyEsv5bB+fXdiYtQ0/uBrjSiUGDa3B9m5fck61puNH8dypaKMh0an88AaU62AVgUwYGwY3YLcLUhxg/3uOAbNqUfQWhYs6M6SJXmsWtXNhbbdXHHYksUXxLStaG4re3wHWZbp3Lmzw30mkwmdTud0Su+2YNeuXQ2eO+Hh4YSGhnrV6/zLL79QWFhI586due+++5o9+5+/vz8pKSkYjUa2bt3qUh6r59lZJ09308qWau3g5kgAOxLKLRB+1Pc0qwZ0ZlbnEpYsLSCnRkZfVMLCd85gHteVyaG1+f1URnJzqymuBP+oQEKLi/k1w4SpRsdP7+3j63J/KKzmrNmJ51lS1IlwXVkVWacra5dTFWSdruS8VkY1sDezupxmyetZ5Ogk9IVnWPjmMcwT+jM5TCbj4y9JunkjX2UaMEpQnneWw8UqusQGkvXRcpKmf8dXmfrafblnOFykokvnYOTGBLBbxowWeIOsJuaXTUxM9I4hreCTTwYxf34Wmzf3oHfvAJp+8LVGEIIqzI+4OH+6dAtk8LhOPPlhX9Y8KPHBg3l8X67AnJ/P5ODdzN8vg9nE1oWHGd51M/7qDWjitzNrUSlF5toytemneeTyDUQEriI4diM3Lyyi0OysbqlR+6qPHufRSe8TFfwKQR3eImXmTn7Kl8Fcw9aFXzO86wL81S+giX+bWYvyam0w69i68DOGd52Pv/opNPFLmLUou86+1otncS23hjff7F33Uth02z4IHLIshy1LmmU5Ahy1+Z8OHLP5fxw4YbOcBDJslkzM+b8zOfhb5u/PBLItSw6Qa7PkAadsltPAGZvlLHDOZskHzluWAstSaFmKbJZioAQotfwvA8qBCpv/lUCVg6Xasmgtiw6osVn0gMFmMVIbumRdbEWe/TPfd+jevbtT50tAQABFRUVetsgx1mHfbG1tC6+zlVGjRnH33Xe3aPg5gJEjRwJNP0utBAQEAO1FPJtxEiLQUuFnn8dGPFv3qTowd/EALss6wrDUdWiu3cu6mF6sejqOTrIMqo7cOCWMA69spN/TuRSP6MuiK00smbWS8Mk/8TrJvP9MEkOy9nPpYyfJs/0ODWwwsed/X9Nj/IckjP+AhPHvkzD+Q/78ixnUnZn7+iQuy/iZYcNfQXPFWtbFjmXVc73pJCvofssUlo4tYd6MxQT2fJG4WduovOEGFl8VSo9bp7E0tZh5N/2LwB7PEDdzC5U3zGTxVcG1IR9u89pf+GwNKHE3rnqC/2iUlpY63O7n50dhYaHPe52//HIMjz12mM2bExk8OAjXvEit9f7ab1Mz6tFYxhQUsXKbbUykAsPuXO55Qc9NX19GufZK0j/vTMnCAyzcJYOulGdv2Me2IUPYdWYa6Su6cPaFrTy62lgXAuKy51l7jmembWBTz/Fsz3+C0pM3cnPpTm66L43cXdu554VKbvr6ccq1c0n/fAAlC1ezcJeEYfcW7nmhgpu+nku5dgHpnw+lZOHnLNzVuFBvT+K5vV77X311CS++eLIZbbuF58pgYF7/E0hSOpJ0FIX6KOFxJxh3Sz6f7MMuHKmHm8ORypjXf4Ubw5Fsj427ynNUtm+0bVtCQ0NRKpUOBbRer+f8+fNtYFVDHM0m2BZeZ4VCwfTp05k4cWKrylEqlcTHx6PT6VwKj/F2x3v3dBh0NlKGG8Yp7nDdUKquq5/OPz6OxW91YbHDshVc+uhECh69YMPM569k5vO26WLZum1w4zYoI3nu2wd5rhFb/bv3YfEHfWvtsP9+fhFMnzub6XMdHYdOTJ97l2Wfa8eh9Z0xzYhJFbyDVqvFZDI5HGUjJCQESZJ8eoSN9etv4E9/2sGKFd0s4sLVTj4Km222D0JH6/afoaGAkZA6BdOnk4EjmWbMgy6IZ1OZgTJJgSZcXTuaTUoSa3KSUSgU6H/JZXlOFC/8PY5uYTKk9uPN5WEcj5ad2Aj1H+IX9ut3pLMitwvPzE8mIVABgXE8+EQfFk05zLqZYZRJajThAahkFVEpl7ImZxwKhYKaTTrLvkDLvstZkzPRMnOZK8ejqX2+JTDaC999dx0PPLCL1au7N6NtN2wXrp2v2pjvYXN78NW9aiQDFGRp+WHZOR4aXcKWFX343xQVkkrJgLGBTZTV0mvK0UtXc+ux/99cG13Na3vsfYuQkBCHHmadTucTnueKigpKSkocep2HDx/uNTs0Gg1333232zrsRUREkJ2dzfnz55ss09sDF8hNJ3GlCIuAdoOHtH46hYM8iiby+IINkpOy2/I4+N4N6WIlMzMTcOyZk2XZKz2BW8qmTbOYOXM1r7wSx2WXhVH/IefMM2e7v6ULTrbLGE0g1V07tenUY3rw3BWV/KXPBgZO2sMTi8+wv7A2T3lGJecjgomvi41W02tST6YMDERy6PFyXnd5ZikFER1JrBuFQUYZ35GuplJOdRvFc1cU8pc+Cxg46QueWJzG/kLZYt94nrviPH/pM4+Bkz7iicUH6va5x0MnaC7r18/gttvW8corcVxySQiut+3WnC8JVZjaJhwpgic/7MOaB2U+ePCUJRzpPJOD99qEI6UxvOvP+Ks3oonfwaxFZTbhSGd45PKNRASuJjh2EzcvLHYSjmS9ppzbX330BI9O+pCo4FcJ6rCUlJm/24Uj/dsSjvSug3CkZ/FXP40m/r/MWpTjxnAk32zbzjq6WTuwe3NaaEc48jpHRESg0Wi86nUeOXKkW59vVieTK8K4/Ylndwm/VsfzNiUkvWGDrUe4rWxwdhysnmiBp3HmiVCpVJSWlvrs8F3btv2JWbO+YPHiOGbNiqBxoexIcLTmwYnDfaacSvYWqElK9q8NabJ6AtVh3Ln8CnJ2p/DXy/3JWbGfMQO2svS4JY1JchKi0VyRYeuFtGwz126T/KK5c/lfydl9C3+9PJicFasYM2AZS49LoI7hzuXzydl9F3+9PIScFSsYM+CN2n2tFhlWuwSu8tNPc5gzZyULFnRuQdt2d7tWM+rRGMYUFDcSjjSOcu0k0j+PpWThQbtwpMHsOjOV9BVxnH3hNyfhSDbXiqOlLhxpHNvzH7eEI/1uCUfawT0vVHHT149Rrv0H6Z/3p2ThN5ZwpF8toUpPU659ifTPh7g5HMk323aXLl2c7gsMDGzWhB7uxpHXOTAwkIKCgnYzopMzVCoVQLNeTrz1q64bXvOaK/wciUpHws+RQHSlPBfFulttsBPNPukx982fwy5GnHVYCAoKAmpvbL5GRsZL3HHHMubPj2XOnE40LZSdCY7WiAy7MswmNvwnl91xMcxIUXKh/SpAbyK/SCK8dwyznxjKZ1snMK9rPstWVRHYPZROBWUcK7oggNO+Osh/v692EP/pTOAoACUhPcKJPF/E8ZILtukzi8hShJMYbbDY0IvZT0zjs60PM6/rCZatKsaoryG/CMJ792X2EzP5bOuTzOt6lGWrCjG6RWSIa9lV0tLmc//9K3jppbgWtu3WnC8cbreGI2VlmjHbtEFTmbF2Aq1wtSXcJ4k1ORN4YbgS/Y5TLM+J5JG/d6ZbmD+dU/vx5vKhTE9wZKOTa8rahi3hSI/NTyIhUIkyvDMPPtEb/w1prMu0TPAVHoBK9iMqJZU1OX/hheEqTGW24Uh+RKVMYE3OP3hhuKqVx8n2ePte2w4MDHQa9yzLMpWVlW1gVS2OvM4ajQaNRkNYWFgbWeVeXBHE1thza8dBTyM3naQxLN5MD3iKzUW5TE75mfnHpAtlucFLay5KZ/KgL5mfjgti1saewoNM7r+M+UdpNJ27j4NLItyV4+CjN6WLEb1e7/Amq1KpfNLrnJe3jKlTn+eOOyJ44IEoXBfK7hUZ+lIDeXl68nJ0HNxyngW37eLmDxXc+1oyo/2t9dXeDzLe2EZS6gG+OmbCiEx5RgmHCxR06RqIX0oiM7qeY9HcbI4UGMj55SAPP3CA7ZV+DsI2auvWFVeSlVVBVlZ57ZJdzvkqGVXKIGYl5LDk+RPkaGX0+XksfOkQ5ilDSVr+Pkmp3/DVMT1GJMoz8jhcoKJL12Cy3vgfSalf8tWxGsu+HA4XqOnSNQy5VcfJVhgJmuLUqde5+eZ/c999nSzCuSVtu7XnyVEZkl04Uq3nVj2muyUcaSMDJ+3jicVnmwhHSnQSjuRcuINMeWaZTThS7bbacKQyTnW7xBKO9G8GTvrSLhxpnCUc6RkGTvrYzeFIvt22/f39HYbimUymNhPPzrzOhYWF7d7rDHDmzBkAl0bssHbSt4737GncMz13nfBzoeObqZJ5M3by/InaRigrFYSFBzBgWDT33NGDWb3USJKMFBbDy//rQGhXBa2bVa9+OiksgZffjSI0XmmTzr7shp3vJE1PXn4vhtB4q3ilxTYAZP20lf3xY7iuu+yyDa2fXdD94tk6UYJSWb8pNdUT39vjLnsrHso6QYsjJElq8bA9nuL8+c+46qo/M3JkME891Zm6NoV9u7Hf5uizwmbd9oXN0br9ZzO7nt9N/PO1xymoUwB9x8ay+Oee3DbMHwnJxkOnpPv9KSzN3s281FXMKjLjHxnGhLsv5dUbA5GUwTzzVSqVD/7OmC6b0Go6cs0TV/Hq9UE2oR+2dpjY88In9HjB5sBIKqZ/8TSf3dCVuV9No+qh9QyL+JRKdSiDr5vEqkVDSAnsxtKcL5mXOpdZRUb8I2OYcPddvHqjhjjjHJZmf8K81MeZVWTAPzKOCXf/iVdvDLPY0NTxaGqfeBFuiry8j7jqqieYPDmMxx6LoeVtu/6LW/POFzjyrppyqthboGZIcm3brkunDuPO5Zcz9Ugha789x7cr9jFmQTavbLmUG+uFI7nSTqDhy4Bt27F+N0se62RjflHcufyvTD2Sydpvj/LtitWMWbCbV7bcw709Y7hz+TymHjnB2m8P8e2KzxmzYDuvbHmYe3u6apejfTiwz7cICAhw+KuiwWCgqqqqDSyqnU3Qfh4DjUaD0Wi8KLzO1lE2GgubsZKXlwdAx44dPWqTFfeI5wZTWzcm/Gq3D7t3BF/dGIhkMlNwqowfvj7BQ7edY8u/x/C/cf5IykAGDLP2hm6hSLWzwWiSUKhCGDAstAlbHZStDGPACKvwabkNSBKYSlnz+np2PTCG63rIrtuADBIYjRIKhYN8jdkgWcfYdC8xMTGUl5e7vVx3I0mSVzpOFBQUON2n1Wp9KmSjtPQbpk69m4EDA1m6tDuuiwk3i2elhucOTqod2aaRvFJkIt9VJFk+a5i+aCLTFzkuO6B3L17b1IfXmnqQK2N57uDTPFdve/16/ZMGs/j7oSxusC+a6YsesrHB9jvFMn3R3xzvE+LZ45SWfsO0aQ8wcGAgL78cT+vadkvFs9X7aydgzUY2/CeP3XHRLEhRQplNer2J/HIFkb1jmN27M7MfS+Jfo75j2aoqbht5IRxpbFStOE776hAbg3vy4KRQ5Hp22Hue69sZ0qMjkecLOV4iMbZT7f7acKSOlnAkPyJ7JzO7d29mPzaGf436D8tWFXPXY2EUlquJ7N2H2b37Mfux8fxr1HMsW1XIXX+LRdHitm3G19u2s3AAvV7vtbGFbSktLaW0tNSh1zklJcXr9ribs2fPUlpaSnx8fAPnnCOs40EnJCR42LJa3CSenQllB4LO8lkVEkBcdBAyEl1iQxk8IoYxEVu4/IUjTB0xlEm6LK6cdIKUj6/g2WQDW9/fzaMfneZggRH/TuFcNXsYr90RRbgkoc3M5Mnn9/LJ3gq0IeFcc+coXp8TQ3jxMa6ecIRhT8Ww4ZVD+N13Cxun5HL1+P2kfH4Lz8QcYcqlBxnyXCJHPjzMzpwq1IMv4aMnw/nqX7+xPr2EQk1PXn59CreG7OXK1J2kfHk/z/aRMZfuZ8qo3xn+n4FkfrCbHdmlFId25++Lbubh/mqKdm3hoed/Zd3hEqr8NAy5ciJvPD+GwQHFvDbrRR7focO89wnWXjOHvP8OxXzid56au4blvxdQoQyl32UTWPDPqxkXIWMu+oWrR/zMsOeS2fDSj/g98m823Rt54Wbpsre69WfbnqFDh7q/0HZMeXm5wyHqoHZYo9DQ0DawqiE63R5uu+0OYmPVfPRRT5ovlB2laY3nuTVisrV5aWc2u/9Czs7Odqv3zGw2k5mZ2ayOVEFBQXTt2rXVdZeWbuT22+8gOTmAjz5KpPVtu3WeZ32pkbw8A7LJTHF2BeuWZvCvlTL3rkyqDUcqu9AGM974jSFvh/LuykFMTVJSlVHC4QKlJRypBzO6fseiudmMerELQWlHePiBQ0S92Y8/O7QRdMVVZGWpbZ4VMkGdNHRKGcishA9Y8vxJLn+5FzFleSx86TDmKdeTtPwDkt6J5t2V1zI1yZ+qjFOWcKQQst54k6Fvx/LuyhuZmhRAVUauXThSa8Sz7wpngODgYIfbTSYTer3ey9ZciHW+WL3Oe/fuBaBbt25Nps3MzESn07kstN2Be8Z5diaU64m4+jeUugtHunCDGjW7B2M+PcLK/TCpl6VBSAoMaWnc85aOO5ZN5bdeKooOH2POQ9tZOOw6Xkgu5NmHt7Ht0svY9Wo0qvRD3PKnH3m060w+TFGgMJWxYmMcr664nZGRQchVp+rqlxQKFMYCPt08gHUr7qFn+RGmT1rLlL8M5O137+TfHct58843+fuyQUx/xGqPbJP3HO9+aWT1uw8zOEjL148v5raFB7l5aTjPP/wd6dfeR9oXPQgrPMrfZi/j7ncS2floHH/++F4ODH8X3X/+zQcTVaDL5Mm7PmDTmPvY/t5gutTk8t8/LeSmJ8I5sGwsMUolCuM5Vqzvw6trX2VklMXL4LK32vazwJPodDqHwtnPzw+9Xu8j4zufYdasiVRVmVi9ug8tE8qOPrc38Wz/vz3YbHvs3cfBgwebDLVyNM29szxms5nCwkIKCwubVZ5er6dHjx4uWu2ILO6+ezpGI3Yvha1p263xPDsKR4ph8c+JlnAk2RKOJFEbjjSCpdl7mJe62iYcaaxdONJuxnT5yRKOdKVNOJJsZ7OJPS98aheOpGT6F3+3hCNdT9VD3zEsYrklHOkKVi0aTEpgPEtzVjIvdb5NONKdvHpjGHHGO1iavZx5qX+17OvsIBypJcfKZHfcfY+QkBCH241Go5ctqfU6Wx01VoKDgy8ar7NWq+XAgQOAaw66w4cPA9CvXz+P2mWLhzzP9iLORlxb44XtR4GQJKTwMPqE6zlyyoC594WyTJX62gkRwvxQKZREDejPmk39UShl9L9nsPxMLC/cH0+3UAmGD+bNxRqOd6rNK5v09JsyjEnxAUhImKslO7tVjL9hAElqGcLjuKQ77Bk4gKujFEAoIwaGUXyolDJb0W8NU0HBpTNHMThEAQQzdlQspv8VkCMP56W18zCEhKFRS9C5DzNSQ/ggLR8dXQmU6pel37uTFaeTeebxISQEyhDYnQcfHMGi2Tv4riSVOUoFsklHv2lTmdQtGKkpoezsXPjoTeliwlnHEaVS6bU34sY5ww039Ccvr4bvv+9DfbHQHKHc3sVzc4Sqr9hsu+593N1Pwb682glkWsMZbrhhGPn5ejZs6Iv72nYLxbMyjOcOTrQLCWqYXorswXcVPS2fNUxfNMFpuE9A72Re29TLEo7USLnKGJ47+HebUCj7/zL+SYNY/P0QSziS7b5opi/6M9MXOfpOsUxf9ITjfa1q20abY+6bWIdNc4QsyxgMBq/d4x15ncPCwjAYDBeF13nbtm1otVr69u3r9KXFilar5dChQ0B7Es8SNPQ82643JuAs+eyEX20PZGuZtenUQ/rw3OhNPDzlK14f2pkrxnZl5pRuDOkoU55XzvkOGuIDrfUq6DW2N72QoEICOZDunf0sZdraZHnQywFEdrR0AjSr8feT6BhRG04CMv7+SjCaMdpf9JIEchCxndR1ZalVSjCaMEhwautP/N/bB9hzzoAkQ01pKYbRZkx1dlCXrzznPAXh0SSGKerKVnaJoqspnczTQLwEsobuXQNtvofc+DFucC7a7qH7R8JsNjv0qCkUCjeIg9YzZ04KJ09q+emnfmg0KloulB19bg/i2V44e6ted+b1bZHRVtx114W27e9vHdrQHW27NWEbbd3GaEc222sE3yIgIMDhvR1ArVZTU1PjFfHszOucn5/PJZdc4vH6PU1BQQFbt25FoVBw+eWXN5l+y5Yt6HQ6hg4d6tVfdt1wpm2Fm0yjnqYPeEkAACAASURBVE9bzzOy3T4Z05kS9pb4MSTBdkIEGdQa7lx4PVMz8ln7Uy7frtvOmLdP8Mqnky09kMEsWeu2t0FGobS5+UmS5dqU69uCfEHT1rMT6k0wUjdUnOWzbHMDkGqPh/HIJmb8ZT+DFj3I4SnRBMsmtj47j0k5UsN6JVt7bY6JteezrKjbrlDKNDhuDoWyk3Ph5ptSXl5em/Uybg4hISHExMR4pS6j0ejw5qpQKHzC83z+fBKw3SIubNtRc8SEs22+LJ5bWpavCQzb7yOw5cyZZIzG33B83lvTttujeLb/315s9u227e1RohzhyOscEhJCUFDQReF1XrVqFUajkUsvvbTJ0anKy8vZvXs3AGPGjPGGeXW4OebZ5gJodOQH7PbJYNaz4b0T7I6KZ8EAJVRKF+owGMivUhHZI47ZPbowe05f/jXjK5b9WMFtg8LoVFzCsRKZsRESSGbSvt/LxsBePDjYWoZMPYFvb6t1v9nuwq5rmw4ewHUCWIG9mK04nMXxyAG8dlUMwQoJTKXsTyvDGGxbL1jHew6Jjyay8CzHy2XGdqw9Jvrcs2QpokjsrOTCzIAyF0JGmhDKDV5a3H/R79u3r8k4SV8hJSWlTcdYNpvNPuF5/vLLbxk7dgiTJh3i++8H4u/v7OHVEsHRDPFsKGPe4E38MzuGZcdTuSOq/v7iz3+mx8xsYv9+Hfuej0TZ4oe1vXB2lBay1m9nf4/RXJdk/x18TWBcsFlQn3XrNjBu3AiuuOIQv/wy2I1tu72JZ1fL9iWbbf/7Jjqdrq1NcOp1LigouCi8zuvXr+f06dNERUUxbty4JtP/9NNP6HQ6Ro0a5fVhYN3QUmWaP6se6CtqyMvXknemioN78ljw1EZuXq3g3n8MsEyIYBWYEhnLfyTplh18lW3AKEmU5xZwuFhJl9hA/AYmMyP2DIuWnORIiZ6c3/fw8Lw9bK/2Q0Km3iQu1s91dtuJa7tY5Ho3ozrPszWdrUi1faDJBER1ILQwm19P6jHVlPHT/1bxdWkIFJRy1iiDpMJPpSc3s5DiSj3y0DHM6prOkkX7yKmR0BccZ+GSHZgnjWOyRtGwriYnV3FwLup9X/fhC2/iTSHLcpt06vBF/P392bDhN06dCmDSpAPUto2WTBzhLE0zFklJhH8hn6zU1Z/5z6zn20/PoQxX4Xjyh+ZOvNDEYipnzYLvWHlMakVd3lpsBYd7kWW5WYs7y1MoFJhMrR9K8+uvf0Cni2TSpAPUzujrjrbd1ue8uW2jre1oje2eadvuoC1G1LBn7969DZ65ISEhhIeHt3uv86FDh9i5cyf+/v7cfPPNTabPzMxkz549+Pn5MXbsWC9YWJ/Wi+eWzKqHiV1vbiT+sm/oOnEtox7ZxypdLIs/mswrYwMuiF7LW3/3meNYOqacebd8TGDfd4i77QCV11/B4smBSH6xPPP6BMZn/8aYsW/R+5HjdLznWl69whofjAP7sCmfhgK4LvxDpp6XvO6QORPetWWqR1/FousMLJnyF8JHvsrrXMb7L49nyMnVXHrfVvIUidx4UxwHXniafg9t54yyJ3Pfu5/LTnzEsORb0IxawrrOM1i1YDSdZJu6WjzLoo3YF3gUZ95lk8nkMwJeo9Gwa9cRMjJkrrvOKqBbIpRbIzJq84ycGMH+T3LJNF3YZz6Ty6c7OnHZCKleudVHT/DopI+JCn6NoA7vkjJzFz/ly5iL07gm9COe/XIPd4x/l95d/kN0/y94dbexLq8+K52npiwhJvQfBIW/xMhbt/BrgQymEl674l88/ksFn970NyJm70frwNazP6ziyuQHCAx6gH5Tv+G7t5YQ1ns5vxus5e/iqSkPExN6I0HhdzLy1q/5tUDGXLyRa0If4dkvv+GO8ffTu8t1RPefx6u7a5p5vOyPnXuv5ZCQkLoJj5wtQIPPzpBlue6nZVfKs663VgBoNBp+/nkXOTlqrr3WXW27rYVl+xDN5vwfmBx8P/P308Lv4Jm27S6qq6ud/tJqMBhQq9Uerb+oqIjy8vJ6Nli9zu19NsGzZ8+ydu1aAKZNm9akF1mr1fLNN98AcPnll7fJKFaSuRW/u1/zl6UsnfYjsWHV1PfQuhhKYL1QmtXxzdWynaVrqmxv2OD94/DN72XszdAy/55rWnq6G/DNN9/UPSR9GUmSGDp0qFfinn///XfOnDnTQFwEBwejVCrb5A3ZGSUlJfTv340pU8J4441+XGhHdm2vwWdnac7brNt6kRysG0qZN2QDJx4dgf7/jjBo83U83bc2TfZraxm5uRfPqX5lSc9p7Hs+CqX2LE8OWcn6y65m9cs96aLN57+3fsmLfhM48J7M/bHr2DvhUlZ/OprBQTq+nvM6txVewYk1w4g0nOGZlHf4buItfPFsL2Kqz/D2nW/zrOp6Dnw2nEj9Ue7t+i66d/7DB9eoG9pcupfbe73JiXufZOXfe6Db9Dl3P7CZn0OuZPveWxhszuKZlLl8N/FvfPHsUGKqs3j7znk8q7qPA0tN3BP7Cnsn3MHqT2cyOKicr+fcwW2F93NizRQiJReOVb3Ptcd4zpx/kpVVwubNmz3RNFzi22+/BRwPVyfLMkOGDPFaXwN7att2d0aP9mfFisG0rm3n0Pg5sVk3lDNv8Gb+mR3NsuNjLeFIF/YXf77FEo50Lfue74SyyXIb+yw1aVPW+m2WcCSlc5tb9LnhPnP+d1zZfRUpW9/h2YHKZuWFirp1X2jbjjh69CjHjx9vsF2WZUwmE1OmTPFo/Zs2bWowmlNMTAwmk4kRI0Z4tG5PotVqeeuttygpKWHSpEmMHDmyyTxff/01+/fvJz4+njvuuMPzRjpAbl12q1iTaTJcwyUPqcIuj+VttMkwBVdtaGLdKza05XFwL+0hZAO8a6darXZYn8Fg8Imf/WzRaDQcPJjFqlVFzJ17nPoPtJYszfU0gRTelVsnV7NieQkGFGCs5IsVJVx2SwLhEnXl6nccY0VuFx6bn0RCoBJleGcefKIX/huO8F2hBCi49O4UBocoQQ5m7LgYTBmF5BgV6Lfv4cOcZJ6c14cu/kqUHbpyz1PD0Xy/hx9KbK8Nx7bW7NzPD1X9efjxvkT5BdD1ypk8dnmtOJBQoN++iQ9zhvDkvBGW8pO456nL0Xy/mR9KJEDJpXffYLEtnLHjemLKyCXH2BovY/u49tqK2radwdatVfzlL0fwaruWlET4FzUSjqREana5zjy1jSymctYs+N7FcCQZo9EdNtGKvL7dtp0NQ+rn5+fxzuBFRUUN6g8JCaGgoMCrw7O5G4PBwMcff0xJSQl9+/Z1STgfOnSI/fv3uxze4Slar6icxjU3JfwcCURHQtIV8dmUDZKTsr1pQ1sfB6snWuBJgoKCHG7X6XQYDAYvW9M0Go2GbdsO8tprOcydm07DB5kzMdFakaGg9iHpz8Rb4yn7/CQ7ahQY0zP4LLsbt0wKsDxCa8suzyyjICKcRI2yLr8yviNdTWVk5gFyILHRfpZ9StRqJRjMGMwy5ScLOV24h2nBjyJJDyNJD6FK2UB6dQnZZ6y248RGmfK8Ysoio+kaYN0WyMChXes6MZafPMPpwp+YFnwFkjQRSZqAKmU56dUFZJ+RQNYQGx1gY5sKDCYM5taIDEFTWF8O33//bCvbdvPPUW04Up5dOFIen+6I4LIR9cusPnqSRyd9QlTw6wR1WEbKzN024Ugf8+yXe7lj/DJ6d1lEdP8veXW3oS5vbTjSq8SEziMo/F824UillnCkSj696UkiZh9oEI5kLtjKVaEvMu+dz7kk+l4mvFaMCQX6rD08NeWvxITeQlD4/Yy8dU1tmcgUbV3NLSPn0MH/Gvw0t3HJnHXsrbC9nl0R6u2zbVdXVzvcrlar8fPz82jd+/bta7AtJCSE6OhoAgMDPVq3J1mzZg2nTp0iKiqKa65p+lfxkpKSul+8pk2b1qaTjrmhpboiEB0JSReEX6u9tLbhDW1lg7eOg6OybW2wHguBJwkNDXX4M7bZbEav17ulU5S76datG9999zP//W8Wr7+eiWtC2VGalnmpAi9N5npTBp9sMXJgxQkKr+3N+CBl3f4LD2a5fn7rcI7W9i3ZC3MZUCJJEnLnVDbq3sBsfhOz+S3M5rcx18zj6T4qG1scfwezGZBqvczWbRfGolciSTJy5+vYqPsJs/lnzOYtmM1bMde8x9N9LN/DoW2tERhtfy37ergW1AroTZt+45VXMnn++XRa1rabe37MBE+I59JjWaw4cqGenJUZ7BvVgwkhZsusggrQ5vPMtE1s6pnK9vxHKT05lZtLd3PTfUc5KylRGM7z7gdGHvnmXo5kP8SbQ/L4x/wj5JsVoD/HC9NWsbnPFHbmv0Tpybu4vXILN/7pAPlSJ/687l7uiAxk1heLKfhoGP52tkpKFQpjPitWK5m/9VW+vicSWZ/DC9PeZHOfOezM/5zSk//g9spvuPFPW8nXpjH/lg9JH/c30orXU3zgUYbu/B93v5KN0e6abnm79o227YjGJsDypIB15nU+f/48SUlJHqvX0+zbt48DBw7UeZCbEsIGg4EVK1ag0+kYMWIEPXv29JKljpGbTtIEDUazcEX4ORCILolPRyLVkZC0E83t0lPcXBuaEOtCPHuF4OBgp2EiarWaiooKL1vkGikpKXzzzQbmzj3Cp5/m0bRQbq3QsHnQ+nVm1k0q1n9+mI9XGbl+VlcCUFj21wrUkB7hRJ4v4njJBeGpzywiSxFOYhfFhbLqCdRaL1hwjyiii85w+OwFG82VFeQUmG1sceYxUxIS1YGA84Wc0lm3GTmwJwdDXfldiC7K5vBZyab8YnIKTE7KduShc/X4+bbA8EUGDRrE2rU/snDhSd59N5vmt+3mt2vXw5GOsyI3jsfm92wkHGmEXThSkU04UlKLw5GQLDPXzpzKpB7haAJV6Lf/zIc5g3hy3jBLmT2556nL0Hz/Cz9U9eelXZ/y47NDiAlQE9g1hRkTO3Bsfw66Jq+j9i+ea2pqnO7zpHi+GL3OeXl5rF69GoBbbrnFpWHm1qxZw7lz5+jcuTNXXnmlp01sErnpJI1hE/jvM15aV4Stp23whePgwAYfvSldTFhvZs5moSoqKvK2SS6TmprKO+98yoMP7mHdurO47plriciwFbwqhs7qRYeVu/jQnMTMFDUXRGZtWlXKQGYl5LDk+ZPkaGX0+adY+FIa5ilDmdyhflp7gaoaMZLZSdm88tROjpTLGEvPsfyhlxk8Y0vtz+qSGj+1ntwTxRSXGyxetAuLevggxkp7efPNLEqNJk798CmvbjbWfWfViEnMTjrKK09t4Ei5hLE0h+UPPcjgGd+QaXIkYC4Oz3N7IjU1lW+++YEnnzzIypWnaF7bbkm79mfirV0p+zzDEo6UyWfZ8dwyKdBy5mrLLs8sdRCOFN5IOJLCJhypiNOFe5kW/BckqTYkyfVwJMt3k8Po3i3I8quKTPnJs5wu/IVpwVOQpGuQpKtQpXxeG4J01sSpjV8w59LbiO86g27dZjHjvfMYDGZLbHdT9bVf8Xz27Fmn+2RZJjQ01CP1Xoxe55KSEj755BMArr76auLi4prMY/VS+/n5ceutt3raRJdopXi2FNHWXlpjPvMm/xO533LeL2zoCS/+djnhCU/Tb+EZDB73FMtkbfqR1Rl4/zg06Y0XeANnnUdkWfb5GRmnTZvG888vZMaMrWzalI9zoexMbDTnYUndZ0WfPsxMNBI3vR+DlbYixFK2Oo65X03lsvTvGRbxLJpeX7Cu60RWvT6QTpKjh7Y1rwLUCTy98h5uLP2e8bH3ERT3b14uSuGjDy4nQVaAKpEbb43jwFN/o9/tv3HGVN9WKXIc/3njUqqW/JXoDrcz5b0IHn6oFyrJEsqh7s3TK5/hxtJPGR87nqC4P/Ny0RV89MF0EmRntrXWQyeu5+aSmprK0qWfcPvtO1i37gyut+3mnp/ac1sbjpRpCUc6aQlHsmubjl6kzLUvfq6FI41lo+41zGabkKSauXbhSI3YKiksM/AquBCCdA0bdd9jNv+A2fwjZvMmzDVLeVL/JTPm/ErYo//mcNYqsrK+5Iu7IlHUO34Xp+f53LlzTvcZDAaCg4M9Uu/F5nW2hl5otVoGDBjAsGHDmsyTm5tb56WeMWNGm8Y52+Ke6bnrRJzF29PY7IL1hlWzGXKn0XSNlW2xQVIR4ZfHJ//f3rnHRXGee/w3szduwi6XhazIRRQV8UKMEBFQiEhOauIlxhqT9PZp+8mtTVObpsbkpI2naWzaRs9pUtue9NOmzUk0OZqoMVJOotEosRBBEjRBAUVczbLLLoKwC8vu+WNZGHZnL7CzN3i+n898dmfmed/3mXfemfeZZ55538M9+Mb9crB2OWsfDr57EWK5FMzwjUhoHTiLtRMH/ust1D68AqtnsPxy464HPjkeHTzWlzBIJJKQG0GCD4vFEtDZ/aKionD9+nWn7X19fTAYDAHTY7w8+uij6O7uxh13PIMjR27HkiVKjHTa3LbmuM7t3LkdIc9/cQqea3hkZF2UiCeqf4InOGnXvfEzrOOkjcheiJcqb8ZLTvnm492eglHlTLnvEfTcN7Iunb4Y2w8WYLtTWhZADJY9/yK0z/PtYwBIMP3uh/DB+h8jQmy7hs6/eBRQyCFnbR2/dHoZth+8bSh/btq78G7PmlHHP+W+Pw3p5qJuvFonxsO6devQ2vorrFv3JA4dWoGyMhU8t22uYeipbdvvMyJApsKmeyT4+p6ziDw5iLW7piFylEErxpSsBCg7dDhvYFGcZMvLFo6kwIxpItQPy4qcdLKFI11G4zUWZWm2/dYb13G5bwrSErkPqI7GrWM7GjHMY7KmIqWzeShPdihPPS73yRF9+hzOpxTh93dnIIZlAEsnzpzRYTCWm7+jvmNp2wxCtW1rNBre7TKZDF1dXYiPjxe8TK1W69LrXFJSInh5gYAberF27VqP8lwvdUVFBTIzM/2totf4bjwPe0gDYfg55sdNI8KtxWn45J1GtN5XjKyhWGyr5iz+py4VZQsvoZFzsfY1n8aWZw/jjZpO9IinILe0BNufq8AyySncmX8ci39XgNa/nsCpVh30cbPx1M7v44cLIgAwGLh8Gv/+1Fv4a7UG18WxmHfbv+E3v1yFIrkOv79nMzZXG2H99Ft4767H0P6HpYjwaz04ynkywoVj5cqVIfkBnCPezogmFHFxcbzGs8lkwsDAAMxms9+HNvKVLVu2wGzuxZ13vojjx1dhzpx4jDYufDSePXaiYzUmfTFEPaTtrcNDs3+O+vt/ibefno/Y9o/xwqstmPONxUhlx2MkCKUzMR42b96Mnp4e3HHHNtTVrfGibY/FeOYakBIs2jQLirJavJa8AO8XSAFYObIiSArmY1Pma9i5rRkrfj0LN123hyOtxe2KQbw9XL6z8SzJL8AD2R9jx5YarNhViGzLV9j9+O/xg0vlqK1aiUxGBpl0ABcu6KHvTkTslEiIeI9hxHiW5JfjgexD2LHlQ6zYVY5sy2Xsfvxp/ODSenz8EyViO87h4y/6UZh9A8d2vIx3DAqgT4trgyJkjaovX4zn0GrbRqMRRtt0lU5ER0fDZDL5pX/h8zrHxsZCLpeHpdf5k08+QUNDA+RyuVehF9wPBOfPn+/VMHaBRIAzziL4cc0MwFgRUzQPy1rO4M0LgN2gb3u/HvW3LMBt0RZY7Tr0X8LPv/cmPsxcg08adqKr+kFs7P4Q9zxZg2usCCLzFbz61gAe+9uzOFf7K+ya14KnX6yFxsoCA2345Xf+G0dmPYB/nX0NXf/6Kb7Zewjrf/oJNGwKHn3zp/hWYjQ2/eUf0P6hCBF+rQe+sA5XeQtvPLMsC7FYHPJLIA1nAG5juGQyWUjHPXN55plteOSRH6G09BBaW29gdIfH95qb71XseF7fjue1rx+XqMXY9vcHkFb5PObIVyJ56atoq3gK//hxNkRB0Smw7Xki8uyzz2Lr1i0oLDzoRdse6/nB8H9RzpyhcKQcnnAk0VA40mqUfflP3JK4bSgcaQX2vTwfScNvCrl5czzc0kxs3ftdrO/6J0pVDyE69TcO4UhZWH//NDRs+Rlyv1mNqxbHY2Gd85fOxta9T2N915soVVUgOvVx/LrzNvz9b3djTvm38LuNA9i5pBzxWT/Cy9iAv+7agJubdmHZ1w+gnTe+f6zXfei17ZaWFrcfgScmJgpeplqtdhoaLyEhARqNBrNnzxa8PH/T3t6OyspKiEQir0MvxuqlDjQ+ur8YjqEstIeUTw4u8rbdkJi4Obh/eSWe3n8NTz6RBrFFh7f2a1D2/bmIP/jOcH4Ddafxpnomfv7jhciMZIGoDDzy0M343TdrcVifD0CEZZtWIC9GBDCxKF6aBsvL19BmYaE4/TFea5+P3/4kD9MiGCBiOr73WAl23nMC/+wqwf1R3GPxVz24knPMm3MuKOY5YMTHxw/fbB2H8xKJRNBqtVAqlcFQbcz84hcv4MKFFhQWvoOTJ9chMzMOzp45+7qcZxvfOryQ8SYfxiE/wObZs/9aMOLps3iRj+uykpatxp5P1zjIdPmg83jShKZnLlx55plt0Gp1KCz8m4e2zenjPHmexcl4ruHhkXVRIp6o3ownOLK2cCQMr0dkL8BLlXlD4UjcfPPxbs+to8qZct9DnHAkFtLpt2D7wXxOuBBXpxgse377UDgSj/5xK/Buz0qnY5BOX47tB8uw3en40nHvn1/HvX/m5lWAE9qR4z3cs8l13Xi1Hnptu7293eU+k8nkl1CCxsZGp20ymQwqlSpkYn69hRt6sWrVKqSkpHhMM1YvdTAQ4DFvyGjz6+gTXEPQMW/OBcdEonxdDq4frMcpM4vBljPYfSUH9y2PGrIdbXl1X9ZCG6/EjLgRXcWpyUizdKL1KgB2ClTJ0qF9Q184D1phtjLovvgV1PoTWJexAYzyHjDK9ZDcvg9fGnW4pLEbsBjRfdyeYr568FRfjv8ddAjBG9NEhGVZREZG8u7r7e2FWq0OsEa+8frre5Cbm48VKw7g6tU+OHvm+BY+Ga4B6ErGm3wmgsx4Fm6+oXMt28OiuEs4jAENADt3voLi4goUFu5Fa2sP+M+d0G8NGIHznChL6LXtrq4umEwm3vYslUr9Eu+sVqudwkQUCkVYjrBhNBqHPxDMz8/HwoULPab5/PPPx+ylDgasZxE3MPCf4Tdk6I4ymt2OPmE7nKgli7DW0oDXTw2gYX8ddCtvQWmUmKMwTyfOjFQDw9oNce4+uzdXBIZlwN50Oz64shfWjn2wdrwDq3Y/rFd3YOssMUYu/PHWA5+cY726qwdXabheOsLfJCYm8t5w7THiWq020Cr5RFXVEUybNg+rV78Hg2EA/jcOScb9Evxr2d2Y5gzDhGyn58iePXuRm5uP8vJ3XbRtV4beeIzDYBuoobqEVtu209DQ4PJBUC6XIyEhQfCwQD6vc2RkZFh6nfft2zemsZmvXbuG9957D4D3XupgIcBXSyzHwOQaaV6EErgMP3D131GOuw7bujQLm1bJ8PWDJxBZa8baF2YikmNcgxFhSoYSSp0G57sZFCfY8hi4rMFFkRIzVCLUD3vTRaPzB4uYTBVS9C1o1DAoS7Udu7VXj8t9cUhLlIwY4qO84r7UA+tFPfDJ8Z2L0LkpTXSysrLQ1tbGu89isaClpcUvsXL+5PDhKpSXF2H16v2orFyPiAgJnB9I+cILxhqSQDKeZYJPaWlpsFUQjKqqIygvL0Z5+V4cP77RoW0nI7zatl03xxAmhvM/1HQOrbYNAD09PTAYDC7fpJjNZkydOlXQMt15nZcvXy5oWf7m6NGjaGpq8jr0wmg0Yvfu3WPyUgcTHx+ZGPjHQ+qQBpy8nPJzNN4lWLR2ERTvV+E15OHem2UjhzmURrKoEJvSzmPnS5+hzcRgQNeM3/5nLawri3G73F4lXA+w/VhFkOQtxwNZF7DjPz7CuRsMBq+3442fPYW871baJkRgpJBJ+3G5pQP6ngEMjrseeP67rAcvvNUh9kQ/0YmJiYFMJuPd19/fD51O5/IL7lAlIiICBw78HzQaMSoq/hdGo70Tdly4D518+8e7hJonOJgydC0LzYEDVQASqW0HXSY02vbp06cB8E9Dr1AocOPGDa8m+BgLE8Xr/Pnnn+Ojjz4aU+jF22+/DYPBgLlz54bEDIKeYH3PgnFjKLNjMPw4NyRvDEmneF7AfhGKshfj3gwzUu/MR57EnmYof0YESLLwzKvfQVnzbtyS8z3Il+7CodS7se+FAs4XzlwdOLrJZmLra5ux/vo+lM69G9HztuLX+hL8/ZVVyBSxgHQW1m/IQMNzDyL34aO2L5wDGdfs8lyE1lP9ZCA5OZl3u9VqhVgsRktLS4A18h25XI7q6jq0tBhx1117wd85OhoW/ghbCHYHH2wZupaFJiIiAlVVx9HWZkZFxR5Q2568bVur1aKrq8vlfqlUipSUFEGHHFWr1TCZTKO2KRQK6HS6sIp1Hk/oxfvvv4/m5mYkJydj1apV/lZREBirD192rNr8F/zp3rNQxQ1gXOEao/bZ/7M821meNI5yfHl7q4OjXDjrwCfHYn/1FdRd0OPZ7ywf7+kmxkhPTw+OHj0KwNl7wbIsGIbBypUrQ37MZz4MBgPmzZuBO+/MxCuv3IGRDs+h7QX19W+olS2czLe//UNcvHgFR44c8eJsEWPB1razkZenwP79G0Fte3K1bYvFgsrKSpjNZt799jeKBQUFiIuLE6zcqqoqp7eRKpUKMpkMubm5gpXjT4xGI/74xz/CYDAgPz/fKw9yfX398AyCGzZsgEKh8FmPQMRKCxzzzMCvcc2+Gqk+GJ/B14FPbiw62GWJQBETE4PIyEjeKbktFgsiIiLQ1NSEnJycIGjnG3K5HJ99dgE5OZlISDiCbdtuQyh2xBPX4CD8VJXFFAAACKdJREFUha1tN2HevBl4+OFDeOWVVQiNc05tOxDU1NS4NJwBQKlUwmg0Cmo4u/M6h9NsgpWVlTAYDMjKyvLKcOZOvQ0Ae/bsEUSPJ5980u9hLgKM8zz0qkWQ8ZrJU+x3HYiAkpubi5qaGt64OaPRiEuXLiEtLQ0xMTFB0M435HI5Tp6sQ15eDgAW27aVI/Q6Yn/IhIp+hL+wPxzOnJmGhIRoatuTpG23tra6nIobsHmddTodFixYIGi5jY2NTn2ETCaDXC4Pq1hn+8QuixYt8ihrNpvx6aefYtasWYKV/+WXXwqWlycEmp6ba8S5M/zgpRyPzLDcZPEUC6zD8EIEkqSkJMhkMrcfB9bX16OoqCiAWglHRkYGDh8+goqKUkRESLF1axlCryP2t0ywyib8iVwuR01NA+fhcCWCf86pbfuLzs5O3g/2uCQkJMBkMgk6UpJ9NkHusI/2ETbKysoEKyeQeGPwi8VirFmzRtByd+zY4TZWXUiECdsYZezxGYiO/8PZUxwKOvDJeaMDEWhYlsWMGTN4PQuA7em7t7cXzc3NyMrKCoKGvlNQUID9+w/jrrtuR2NjJ3JyuMM3cdsdCwAYqQZuu2R4tnG3sw7bGIcxhh3386dxXucry1EXvnR88nxp3B2fO/3dpQeqq0/hpptUIPwL9+Hw1KmryM21j67g7tzDhYynNN7kNZ403v46bvPlWDylcS330UcfIz09HYFEr9fj5MmTbmXsBu2SJUsELbuxsRGO46XLZDKkpaWFldc5FAjk5EzCxjxTXLOPOvDJCanD6AuUCAzp6ek4f/68U0ybHZPJhKamJiQnJ4dl+AYAlJSUYP/+w3jwwQdRX38+2OpMeFhWhI0bNwZbjUmB/eHwsccew5Ej4TW5UTgSFxcX0Lat1+tx4sQJj4ZXZGQkYmNjBY91nmhe58mCzzHPb9QmITZiEKOeJkeFaDj88u5jhlb5nky5+xzSMjzbnPJjMHqweLsOFjdphNaBL7/A1sMXbQbExkhBBB6WZZGbm4vTp0+7vEEPDg6iuroapaWlYTn6BmAzoM+ePRtsNQhCcEpKSlBXVxdsNQiBaW1tdflWkMu0adOg0WgEN2jJ6xy++NRL33brfLRcMQDOgwkQHrE6/PqfLJXvQ8AQ40OlUqGpqQnd3d28+61WK8xmM6qrq7F06VLBp3wlCIIgbFgsFtTU1KCjo8Oj4RwbGwudToeFCxcKPq4z32yCnZ2dWLZsmWDlEP7Bp5bw+D03C6UHQUx4Fi5ciOPHj7vcbzab0d3djerqaixZsoQMaIIgCIFRq9VoaGiA2Wz2aDhLJBKIxWIkJCRAqVQKqoer2QSTkpLI6xwGUO9MEAFCLpcjMzPT6TUdl8HBQXR1daG6uhoWiyWA2hEEQUxcNBoNPvjgA9TW1rodx9kOy7JQKpWwWq2CT1LS3t4Oo9E4qi+Ij4+HVqsN2w/HJxvhGVxJEGFKTk4ONBoNbty44VJmcHAQer0eR48exa233oqoqKgAakgQBDEx0Ol0aGlpgVarhdlsBsPYRunx5HFmWRapqano7OxEcXGx4HrZvw3h6hEREYHExMSw/eZlskFniSACCMuyyM/Px7FjxzA4OOhSzmq1ore3F8eOHUNeXh6Sk5MDqCVBEER48tlnn6G9vZ3Xu+zNUGYsy0KlUuGrr75CUVGR4MZse3s7TCbTKCPe7nUWevIVwn+Q8UwQASYmJgaLFi1CbW2t29AMq9WKgYEB1NbWIjExEfPmzSMvNEEQhBv0er1bx4Q7WJbF1KlTodPpUFhY6Jf77WTwOp86dSqgs/3ZcfdGV2gmxpkiiDAjOTkZ2dnZOH/+PCwWi1uPiMViQUdHBz788EOoVCrMnz9/wtxkCYIghCQ6OhoGg8HttyV8REVFQaFQQK/XY+nSpX75aG+yeJ2DYTgHGuqBCSJIzJw5Ez09Pbh69apHT4n9RnvlyhVcuXIFiYmJSE9Ph0pFs8wRBEHYiYqKGrPhrFQq0d/fj/7+fhQXF/vNOcHndZbJZBPG61xQUIBZs2YFW42A1GX4ny2CCGMWLFiA/v5+6HS6Mb1q1Ol00Ol0qKurw9e+9jU/akgQBBE+REdHey0bFRWF+Ph4dHR0YMaMGZg+fbrf9OLzOiclJQ2PIT0RyMzMDLYKAYOGqiOIIMKyLBYvXoz09HSIRCKv01mtVkRFRUEmk/lRO4IgiPBiypQpHmVYlkVKSgoAW5xsUVGRXw1ngN/rLBaLkZGRMSG8zpMNOmMEEWRYlsXcuXMhEolw8eJFDAwMeJVOJpON+fUkQRDERCY6OhpWq5X33hgdHY2YmBh0dXWhu7sbs2fPxtSpU/2uk16vR39/P+Ry+fDkVxKJBFqtdsJ4nScbZDwTRIgwe/ZsxMfH48yZMxgcHPRoREskEkil0gBpRxAEEfpIpdJhw5llWURGRiI2NhYDAwO4fv06RCIR5syZg9TU1IDpJJPJkJGR4bQ9LS2NvM5hCmP1ZuBDgiACRk9PD+rr69Hb2wuTyeRSTqlUQqFQIDs7O4DaEQRBhDZVVVUQi8Xo7e2FWCxGfHw8EhISkJKSQsN9EoJAxjNBhCAWiwVtbW344osvIBKJYDQanWQSEhKQnp4ekNeOBEEQ4YJWqwVgG1PfH0POEQQZzwQRwhiNRpw7dw5qtRqRkZGjBoGPi4tDbm4u4uPjg6ghQRAEQUwuyHgmiDCgt7cXarUazc3NEIvFYBgGfX19qKiooJg5giAIggggZDwTRBhhsVig1WqhVqvR29uLwsLCYKtEEARBEJMKMp4JgiAIgiAIwktokhSCIAiCIAiC8BIyngmCIAiCIAjCS8h4JgiCIAiCIAgvIeOZIAiCIAiCILyEjGeCIAiCIAiC8BIyngmCIAiCIAjCS8h4JgiCIAiCIAgvIeOZIAiCIAiCILyEjGeCIAiCIAiC8BIyngmCIAiCIAjCS/4f9s05SrYtTxwAAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Data description</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"The dataset contains four directories: monet_tfrec, photo_tfrec, monet_jpg, and photo_jpg. The monet_tfrec and monet_jpg directories contain the same painting images, and the photo_tfrec and photo_jpg directories contain the same photos.\n\nWe recommend using TFRecords as a Getting Started competition is a great way to become more familiar with a new data format, but JPEG images have also been provided.\n\nThe monet directories contain Monet paintings. Use these images to train your model.\n\nThe photo directories contain photos. Add Monet-style to these images and submit your generated jpeg images as a zip file. Other photos outside of this dataset can be transformed but keep your submission file limited to 10,000 images.\n\nNote: Monet-style art can be created from scratch using other GAN architectures like DCGAN. The submitted image files do not necessarily have to be transformed photos.\n\n\n- Files:\n\n    - monet_jpg - 300 Monet paintings sized 256x256 in JPEG format\n    - monet_tfrec - 300 Monet paintings sized 256x256 in TFRecord format\n    - photo_jpg - 7028 photos sized 256x256 in JPEG format\n    - photo_tfrec - 7028 photos sized 256x256 in TFRecord format\n","metadata":{}},{"cell_type":"markdown","source":"## \n\n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Import modules</left></h2> ","metadata":{}},{"cell_type":"code","source":"import os\nimport shutil\nimport time\n\n\nimport pandas as pd\nimport numpy as np\nimport itertools\nimport matplotlib.pyplot as plt\n\nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.init as init\nfrom torch.nn import Conv2d, LeakyReLU, InstanceNorm2d\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom torchvision import transforms as T\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.215600Z","start_time":"2022-09-04T09:58:02.682786Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:28.724375Z","iopub.status.busy":"2022-09-22T09:21:28.723064Z","iopub.status.idle":"2022-09-22T09:21:30.741102Z","shell.execute_reply":"2022-09-22T09:21:30.740138Z"},"papermill":{"duration":2.034497,"end_time":"2022-09-22T09:21:30.743725","exception":false,"start_time":"2022-09-22T09:21:28.709228","status":"completed"},"scrolled":true,"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Constants and Variables</left></h2> ","metadata":{}},{"cell_type":"code","source":"MONET_DIR = '../input/gan-getting-started/monet_jpg/'\n\nPHOTO_DIR = '../input/gan-getting-started/photo_jpg/'\n\nSAVE_PATH = '../images/'\n\n\n\nRADNOM_STATE = 12345\n\nIMAGE_SIZE = [256, 256]\n\nBATCH_SIZE = 1","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.220829Z","start_time":"2022-09-04T09:58:04.218018Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:30.795318Z","iopub.status.busy":"2022-09-22T09:21:30.794846Z","iopub.status.idle":"2022-09-22T09:21:30.800254Z","shell.execute_reply":"2022-09-22T09:21:30.799307Z"},"papermill":{"duration":0.020824,"end_time":"2022-09-22T09:21:30.802146","exception":false,"start_time":"2022-09-22T09:21:30.781322","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ndevice","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.279417Z","start_time":"2022-09-04T09:58:04.223734Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:30.828456Z","iopub.status.busy":"2022-09-22T09:21:30.828103Z","iopub.status.idle":"2022-09-22T09:21:30.900574Z","shell.execute_reply":"2022-09-22T09:21:30.899639Z"},"papermill":{"duration":0.088082,"end_time":"2022-09-22T09:21:30.902763","exception":false,"start_time":"2022-09-22T09:21:30.814681","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n\n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Auxilary functions and classes</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"### Visual","metadata":{"heading_collapsed":true,"papermill":{"duration":0.012426,"end_time":"2022-09-22T09:21:30.953475","exception":false,"start_time":"2022-09-22T09:21:30.941049","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Unnormalize image","metadata":{"hidden":true,"papermill":{"duration":0.012349,"end_time":"2022-09-22T09:21:30.978436","exception":false,"start_time":"2022-09-22T09:21:30.966087","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def unnormalize(img, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):\n    \n    # To normalize: X_norm = (X - mean) / std\n    # To unnormalize: X = (X_norm * std) + mean\n\n    for t, m, s in zip(img, mean, std):\n        t.mul_(s).add_(s)\n        \n    return img","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.332110Z","start_time":"2022-09-04T09:58:04.282551Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.005018Z","iopub.status.busy":"2022-09-22T09:21:31.004658Z","iopub.status.idle":"2022-09-22T09:21:31.010419Z","shell.execute_reply":"2022-09-22T09:21:31.009246Z"},"hidden":true,"papermill":{"duration":0.021826,"end_time":"2022-09-22T09:21:31.012962","exception":false,"start_time":"2022-09-22T09:21:30.991136","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Show grid of images from dataloader","metadata":{"hidden":true,"papermill":{"duration":0.013668,"end_time":"2022-09-22T09:21:31.039010","exception":false,"start_time":"2022-09-22T09:21:31.025342","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def show_images(data):\n\n    fig, ax = plt.subplots(2, 4, figsize=(10, 6))\n    \n    it = iter(data)\n    \n    for i in range(4):\n        \n        p, m = next(it)\n\n        ax[0][i].imshow(unnormalize(p[0]).permute(1,2,0))\n        ax[1][i].imshow(unnormalize(m[0]).permute(1,2,0))\n        ax[0][i].set_title('Photo')\n        ax[1][i].set_title('Monet')\n\n    plt.show()","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.338485Z","start_time":"2022-09-04T09:58:04.333386Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.066299Z","iopub.status.busy":"2022-09-22T09:21:31.065317Z","iopub.status.idle":"2022-09-22T09:21:31.073231Z","shell.execute_reply":"2022-09-22T09:21:31.072105Z"},"hidden":true,"papermill":{"duration":0.023498,"end_time":"2022-09-22T09:21:31.075373","exception":false,"start_time":"2022-09-22T09:21:31.051875","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Visualize training losses per epoch","metadata":{"hidden":true,"papermill":{"duration":0.012291,"end_time":"2022-09-22T09:21:31.100587","exception":false,"start_time":"2022-09-22T09:21:31.088296","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def plot_train_loss(log):\n    \n    df = pd.read_csv(log)\n\n    fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7,6))\n\n    df.plot(x='epoch', y='loss_gen_total_avg', \n            kind='line', ax=ax[0], color='b', label='Total')\n\n    df.plot(x='epoch', y='loss_gen_photo_avg', \n            kind='line', ax=ax[0], color='r', label='Photo Generator')\n\n    df.plot(x='epoch', y='loss_gen_monet_avg', \n            kind='line', ax=ax[0], color='g', label='Monet Generator')\n\n\n    df.plot(x='epoch', y='loss_disc_total_avg', \n            kind='line', ax=ax[1], color='b', label='Total')\n\n    df.plot(x='epoch', y='loss_disc_photo_avg', \n            kind='line', ax=ax[1], color='r', label='Photo Discriminator')\n\n    df.plot(x='epoch', y='loss_disc_monet_avg', \n            kind='line', ax=ax[1], color='g', label='Monet Discriminator')\n\n\n    ax[0].set_title('Generator Losses')\n    ax[1].set_title('Discriminator Losses')\n\n    ax[0].set_ylabel('Loss')\n    ax[1].set_ylabel('Loss')\n\n    plt.tight_layout()\n    plt.show()","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:21:08.920149Z","start_time":"2022-09-04T10:21:08.911574Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.127428Z","iopub.status.busy":"2022-09-22T09:21:31.126559Z","iopub.status.idle":"2022-09-22T09:21:31.136313Z","shell.execute_reply":"2022-09-22T09:21:31.135387Z"},"hidden":true,"papermill":{"duration":0.025202,"end_time":"2022-09-22T09:21:31.138383","exception":false,"start_time":"2022-09-22T09:21:31.113181","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Loading data","metadata":{"heading_collapsed":true,"papermill":{"duration":0.013845,"end_time":"2022-09-22T09:21:31.165942","exception":false,"start_time":"2022-09-22T09:21:31.152097","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Custom dataset class for training","metadata":{"hidden":true,"papermill":{"duration":0.01393,"end_time":"2022-09-22T09:21:31.193515","exception":false,"start_time":"2022-09-22T09:21:31.179585","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class ImageDatasetCV(Dataset):\n    \n    def __init__(self, monet_dir, photo_dir, size=(256, 256), normalize=True):\n        \n        super().__init__()\n        \n        self.monet_dir = None\n        \n        if monet_dir:\n            \n            self.monet_dir = monet_dir\n            self.monet_idx = dict()\n            \n            for i, filename in enumerate(os.listdir(self.monet_dir)):\n                self.monet_idx[i] = filename\n\n                \n        self.photo_dir = photo_dir\n        self.photo_idx = dict()\n        \n        for i, filename in enumerate(os.listdir(self.photo_dir)):\n            self.photo_idx[i] = filename\n        \n        \n        if normalize:\n            self.transforms = T.Compose([T.Resize(size), \n                                         T.ToTensor(), \n                                         T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n                                        ])\n            \n        else:\n            self.transforms = T.Compose([T.Resize(size), \n                                         T.ToTensor()\n                                        ])    \n            \n        \n    def __getitem__(self, idx):\n        \n        random_idx = idx\n        \n        if self.monet_dir: \n            \n            random_idx = int(np.random.uniform(0, len(self.monet_idx.keys())))\n            \n            monet_path = os.path.join(self.monet_dir, self.monet_idx[random_idx])\n            monet_img = self.transforms(Image.open(monet_path))\n        \n        \n        photo_path = os.path.join(self.photo_dir, self.photo_idx[random_idx])\n        photo_img = self.transforms(Image.open(photo_path))\n        \n        if self.monet_dir:\n            return photo_img, monet_img\n        else:\n            return photo_img\n        \n    \n    def __len__(self):\n        \n        if self.monet_dir:\n            return min(len(self.monet_idx.keys()), len(self.photo_idx.keys()))\n        else:\n            return len(self.photo_idx.keys())","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.401528Z","start_time":"2022-09-04T09:58:04.346509Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.222867Z","iopub.status.busy":"2022-09-22T09:21:31.221733Z","iopub.status.idle":"2022-09-22T09:21:31.237335Z","shell.execute_reply":"2022-09-22T09:21:31.236308Z"},"hidden":true,"papermill":{"duration":0.033407,"end_time":"2022-09-22T09:21:31.239660","exception":false,"start_time":"2022-09-22T09:21:31.206253","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Neural Netowork","metadata":{"heading_collapsed":true,"papermill":{"duration":0.0141,"end_time":"2022-09-22T09:21:31.268224","exception":false,"start_time":"2022-09-22T09:21:31.254124","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Save model state","metadata":{"hidden":true,"papermill":{"duration":0.012272,"end_time":"2022-09-22T09:21:31.293726","exception":false,"start_time":"2022-09-22T09:21:31.281454","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def save_checkpoint(state, save_path):\n    torch.save(state, save_path)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.410620Z","start_time":"2022-09-04T09:58:04.403150Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.320655Z","iopub.status.busy":"2022-09-22T09:21:31.319591Z","iopub.status.idle":"2022-09-22T09:21:31.324789Z","shell.execute_reply":"2022-09-22T09:21:31.323922Z"},"hidden":true,"papermill":{"duration":0.020855,"end_time":"2022-09-22T09:21:31.326855","exception":false,"start_time":"2022-09-22T09:21:31.306000","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Load model state","metadata":{"hidden":true,"papermill":{"duration":0.012207,"end_time":"2022-09-22T09:21:31.351540","exception":false,"start_time":"2022-09-22T09:21:31.339333","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def load_checkpoint(ckpt_path, map_location=None):\n    ckpt = torch.load(ckpt_path, map_location=map_location)\n    print(' [*] Loading checkpoint from %s succeed!' % ckpt_path)\n    return ckpt","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.419223Z","start_time":"2022-09-04T09:58:04.412591Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.379172Z","iopub.status.busy":"2022-09-22T09:21:31.378365Z","iopub.status.idle":"2022-09-22T09:21:31.383498Z","shell.execute_reply":"2022-09-22T09:21:31.382614Z"},"hidden":true,"papermill":{"duration":0.020911,"end_time":"2022-09-22T09:21:31.385478","exception":false,"start_time":"2022-09-22T09:21:31.364567","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Load Data</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"### Create dataset","metadata":{"papermill":{"duration":0.012508,"end_time":"2022-09-22T09:21:31.435287","exception":false,"start_time":"2022-09-22T09:21:31.422779","status":"completed"},"tags":[]}},{"cell_type":"code","source":"img_dataset = ImageDatasetCV(monet_dir=MONET_DIR, photo_dir=PHOTO_DIR)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.427721Z","start_time":"2022-09-04T09:58:04.421771Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.462941Z","iopub.status.busy":"2022-09-22T09:21:31.461380Z","iopub.status.idle":"2022-09-22T09:21:31.800550Z","shell.execute_reply":"2022-09-22T09:21:31.799589Z"},"papermill":{"duration":0.355493,"end_time":"2022-09-22T09:21:31.803033","exception":false,"start_time":"2022-09-22T09:21:31.447540","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create Dataloader ","metadata":{"papermill":{"duration":0.012104,"end_time":"2022-09-22T09:21:31.828070","exception":false,"start_time":"2022-09-22T09:21:31.815966","status":"completed"},"tags":[]}},{"cell_type":"code","source":"img_dataloader = DataLoader(img_dataset, \n                            batch_size=BATCH_SIZE, \n                            pin_memory=True)\n\n# Enable pin_memory to automatically put the fetched data Tensors \n# in pinned memory, and thus enables faster data transfer \n# to CUDA-enabled GPUs","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:04.431722Z","start_time":"2022-09-04T09:58:04.429428Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.855195Z","iopub.status.busy":"2022-09-22T09:21:31.854205Z","iopub.status.idle":"2022-09-22T09:21:31.859436Z","shell.execute_reply":"2022-09-22T09:21:31.858600Z"},"papermill":{"duration":0.020944,"end_time":"2022-09-22T09:21:31.861497","exception":false,"start_time":"2022-09-22T09:21:31.840553","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Check input images","metadata":{"papermill":{"duration":0.015594,"end_time":"2022-09-22T09:21:31.889855","exception":false,"start_time":"2022-09-22T09:21:31.874261","status":"completed"},"tags":[]}},{"cell_type":"code","source":"show_images(img_dataloader)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.909069Z","start_time":"2022-09-04T09:58:04.433033Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:31.916217Z","iopub.status.busy":"2022-09-22T09:21:31.915487Z","iopub.status.idle":"2022-09-22T09:21:35.801209Z","shell.execute_reply":"2022-09-22T09:21:35.800365Z"},"papermill":{"duration":3.90516,"end_time":"2022-09-22T09:21:35.807350","exception":false,"start_time":"2022-09-22T09:21:31.902190","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Build CycleGAN Model</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"### Generator","metadata":{"papermill":{"duration":0.01632,"end_time":"2022-09-22T09:21:35.873773","exception":false,"start_time":"2022-09-22T09:21:35.857453","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"#### Convolutional Layer","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016278,"end_time":"2022-09-22T09:21:35.906822","exception":false,"start_time":"2022-09-22T09:21:35.890544","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def Convlayer(in_ch, out_ch, kernel_size=3, stride=2, \n              use_leaky=True, use_inst_norm=True, use_pad=True):\n    \n    \n    # Convolution\n    if use_pad:\n        conv = nn.Conv2d(in_ch, out_ch, kernel_size, stride, 1, bias=True)\n    else:\n        conv = nn.Conv2d(in_ch, out_ch, kernel_size, stride, 0, bias=True)\n        \n        \n    # Activation Function\n    if use_leaky:\n        actv = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n    else:\n        actv = nn.GELU()\n        \n        \n    # Normalization\n    if use_inst_norm:\n        norm = nn.InstanceNorm2d(out_ch)\n    else:\n        norm = nn.BatchNorm2d(out_ch)\n        \n    \n    return nn.Sequential(conv, \n                         norm, \n                         actv)\n    ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.915001Z","start_time":"2022-09-04T09:58:06.910822Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:35.940995Z","iopub.status.busy":"2022-09-22T09:21:35.940641Z","iopub.status.idle":"2022-09-22T09:21:35.947923Z","shell.execute_reply":"2022-09-22T09:21:35.946968Z"},"hidden":true,"papermill":{"duration":0.026999,"end_time":"2022-09-22T09:21:35.950091","exception":false,"start_time":"2022-09-22T09:21:35.923092","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Transpose Convolutional Layer","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016246,"end_time":"2022-09-22T09:21:35.982706","exception":false,"start_time":"2022-09-22T09:21:35.966460","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def Upsample(in_ch, out_ch, use_dropout=False, dropout_ratio=0.5):\n    \n    # Transposed Convolution\n    convtrans = nn.ConvTranspose2d(in_ch, out_ch, 3, stride=2, padding=1, output_padding=1)\n    \n    # Normalization\n    norm = nn.InstanceNorm2d(out_ch)\n    \n    # Activatin Function\n    actv = nn.GELU()\n    \n    \n    if use_dropout:\n        \n        # Dropout layer\n        drop = nn.Dropout(dropout_ratio)\n        \n        return nn.Sequential(convtrans, \n                             norm, \n                             drop, \n                             actv)\n\n    else:       \n        return nn.Sequential(convtrans, \n                             norm, \n                             actv)\n         ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.921716Z","start_time":"2022-09-04T09:58:06.916634Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.018371Z","iopub.status.busy":"2022-09-22T09:21:36.017752Z","iopub.status.idle":"2022-09-22T09:21:36.024004Z","shell.execute_reply":"2022-09-22T09:21:36.023080Z"},"hidden":true,"papermill":{"duration":0.026379,"end_time":"2022-09-22T09:21:36.026300","exception":false,"start_time":"2022-09-22T09:21:35.999921","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Residual block","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016933,"end_time":"2022-09-22T09:21:36.059461","exception":false,"start_time":"2022-09-22T09:21:36.042528","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Resblock(nn.Module):\n    \n    def __init__(self, in_features, use_dropout=False, dropout_ratio=0.5):\n        super().__init__()\n        \n        layers = list()\n        \n        # Padding\n        layers.append(nn.ReflectionPad2d(1))\n        \n        # Convolution layer\n        layers.append(Convlayer(in_features, in_features, 3, 1, use_leaky=False, use_pad=False))\n        \n        # Dropout\n        if use_dropout:\n            layers.append(nn.Dropout(dropout_ratio))\n        \n        # Padding\n        layers.append(nn.ReflectionPad2d(1))\n        \n        # Convolution\n        layers.append(nn.Conv2d(in_features, in_features, 3, 1, padding=0, bias=True))\n        \n        # Normalization\n        layers.append(nn.InstanceNorm2d(in_features))\n        \n        \n        self.res = nn.Sequential(*layers)\n        \n        \n    def forward(self, x):\n        \n        return x + self.res(x)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.931592Z","start_time":"2022-09-04T09:58:06.923510Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.094570Z","iopub.status.busy":"2022-09-22T09:21:36.093572Z","iopub.status.idle":"2022-09-22T09:21:36.102206Z","shell.execute_reply":"2022-09-22T09:21:36.101389Z"},"hidden":true,"papermill":{"duration":0.028341,"end_time":"2022-09-22T09:21:36.104338","exception":false,"start_time":"2022-09-22T09:21:36.075997","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Generator","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016184,"end_time":"2022-09-22T09:21:36.136869","exception":false,"start_time":"2022-09-22T09:21:36.120685","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Generator(nn.Module):\n    \n    def __init__(self, in_ch, out_ch, num_res_blocks=6):\n        \n        super().__init__()\n        \n        model = list()\n        \n        # Padding layer\n        model.append(nn.ReflectionPad2d(3))\n        \n        \n        # Convolution input_channels -> 64\n        model.append(Convlayer(in_ch=in_ch, out_ch=64, \n                               kernel_size=7, stride=1, \n                               use_leaky=False, \n                               use_inst_norm=True, \n                               use_pad=False))\n        \n        # Convolution 64 -> 128\n        model.append(Convlayer(in_ch=64, out_ch=128, \n                               kernel_size=3, stride=2, \n                               use_leaky=False, \n                               use_inst_norm=True, \n                               use_pad=True))\n        \n        # Convolution 128 -> 256\n        model.append(Convlayer(in_ch=128, out_ch=256, \n                               kernel_size=3, stride=2, \n                               use_leaky=False, \n                               use_inst_norm=True, \n                               use_pad=True))\n        \n        # Residual Block\n        for _ in range(num_res_blocks):\n            model.append(Resblock(in_features=256))\n\n            \n        # Transposed convolution 256 -> 128\n        model.append(Upsample(in_ch=256, out_ch=128))  \n        \n        # Transposed convolution 128 -> 256\n        model.append(Upsample(in_ch=128, out_ch=64))    \n        \n        # Padding Layer\n        model.append(nn.ReflectionPad2d(3))\n        \n        # Convolutional layer\n        model.append(nn.Conv2d(in_channels=64, out_channels=out_ch, \n                               kernel_size=7, padding=0))\n        \n        # Activation function Tanh\n        model.append(nn.Tanh())\n        \n        \n        self.gen = nn.Sequential(*model)\n        \n        \n    def forward(self, x):\n        \n        return self.gen(x)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.939138Z","start_time":"2022-09-04T09:58:06.933163Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.171768Z","iopub.status.busy":"2022-09-22T09:21:36.171401Z","iopub.status.idle":"2022-09-22T09:21:36.181941Z","shell.execute_reply":"2022-09-22T09:21:36.180958Z"},"hidden":true,"papermill":{"duration":0.030768,"end_time":"2022-09-22T09:21:36.184000","exception":false,"start_time":"2022-09-22T09:21:36.153232","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Discriminator","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016517,"end_time":"2022-09-22T09:21:36.217472","exception":false,"start_time":"2022-09-22T09:21:36.200955","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Discriminator(nn.Module):\n    \n    def __init__(self, in_ch, num_layers=4):\n        \n        super().__init__()\n        \n        model = list()\n        \n        # Convolution in_channels -> 64\n        model.append(nn.Conv2d(in_channels=in_ch, out_channels=64, \n                               kernel_size=4, stride=2, padding=1))\n        \n        \n        # Convolutions i=1:  64 -> 64 \n        #              i=2:  64 -> 128\n        #              i=3: 128 -> 256 \n        #              i=4: 256 -> 512   \n        \n        for i in range(1, num_layers):\n            in_chs = 64 * 2**(i-1)\n            out_chs = in_chs * 2\n            \n            if i == num_layers - 1:\n                model.append(Convlayer(in_chs, out_chs, \n                                       kernel_size=4, stride=1))\n                \n            else:\n                model.append(Convlayer(in_chs, out_chs, \n                                       kernel_size=4, stride=2))\n        \n        # Convolution 512 -> 1\n        model.append(nn.Conv2d(in_channels=512, out_channels=1, \n                               kernel_size=4, stride=1, padding=1))\n        \n        self.disc = nn.Sequential(*model)\n        \n        \n    def forward(self, x):\n        \n        return self.disc(x)\n        ","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.946676Z","start_time":"2022-09-04T09:58:06.940743Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.253193Z","iopub.status.busy":"2022-09-22T09:21:36.252107Z","iopub.status.idle":"2022-09-22T09:21:36.261061Z","shell.execute_reply":"2022-09-22T09:21:36.260173Z"},"hidden":true,"papermill":{"duration":0.028749,"end_time":"2022-09-22T09:21:36.263043","exception":false,"start_time":"2022-09-22T09:21:36.234294","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### CycleGAN","metadata":{"heading_collapsed":true,"papermill":{"duration":0.016845,"end_time":"2022-09-22T09:21:36.296411","exception":false,"start_time":"2022-09-22T09:21:36.279566","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class CycleGAN(object):\n    \n    def __init__(self, in_ch, out_ch, epochs, device, start_lr=0.01, lmbda=10, idt_coef=0.5, decay_epoch=0):\n        \n\n        \n        # Regularization coefficients\n        self.lmbda = lmbda\n        self.idt_coef = idt_coef\n        \n        # Set device\n        self.device = device\n        \n        # Generator Monet -> Photo\n        self.gen_mtp = Generator(in_ch, out_ch)\n        \n        # Generator Photo -> Monet\n        self.gen_ptm = Generator(in_ch, out_ch)\n        \n        # discriminator for Monet-generated images\n        self.disc_m = Discriminator(in_ch)\n        \n        # discriminator for Photo-generated images\n        self.disc_p = Discriminator(in_ch)\n        \n        # Initialize model weights\n        self.init_models()\n        \n        # Optimizator for generators\n        self.adam_gen = torch.optim.Adam(itertools.chain(self.gen_mtp.parameters(), \n                                                         self.gen_ptm.parameters()), \n                                         lr=start_lr, betas=(0.5, 0.999))\n        \n        # Optimizator for discriminator\n        self.adam_disc = torch.optim.Adam(itertools.chain(self.disc_m.parameters(), \n                                                          self.disc_p.parameters()), \n                                         lr=start_lr, betas=(0.5, 0.999))\n        \n        # Set number of epochs and start of learning rate decay \n        self.epochs = epochs\n        \n        # Set decay epoch \n        self.decay_epoch = decay_epoch if decay_epoch > 0 else int(self.epochs/2)\n        \n        # Set rule for learning step decay\n        lambda_decay = lambda epoch: start_lr/(epoch-self.decay_epoch) if epoch > self.decay_epoch else start_lr\n\n        \n        # Define scheduler for generator and discriminator\n        self.scheduler_gen = torch.optim.lr_scheduler.LambdaLR(self.adam_gen, lr_lambda=lambda_decay)\n        self.scheduler_disc = torch.optim.lr_scheduler.LambdaLR(self.adam_disc, lr_lambda=lambda_decay)\n\n    \n    # Initialize weights\n    def init_weights(self, net, gain=0.02):\n\n        def init_func(m):\n\n            # Name of the class\n            classname = m.__class__.__name__\n\n            # If class has attribute \"weight\" (to initialize) and \n            # has either convolutional layer or linear \n            if hasattr(m, 'weight') and \\\n               (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n\n                # Initialize weights with values drawn from normal distribution N(mean, std)\n                init.normal_(m.weight.data, mean=0.0, std=gain)\n\n                # Set bias value with constant val\n                if hasattr(m, 'bias') and m.bias is not None:\n                    init.constant_(m.bias.data, val=0.0)\n\n            # Initialize BatchNorm weights\n            elif classname.find('BatchNorm2d') != -1:\n\n                init.normal_(m.weight.data, mean=1.0, std=gain)\n                init.constant_(m.bias.data, val=0.0)\n\n        # Apply weight initialization to every submodule of model\n        net.apply(init_func)\n            \n    \n    # Initialize models\n    def init_models(self):\n        \n        # Initialize weights\n        self.init_weights(self.gen_mtp)\n        self.init_weights(self.gen_ptm)\n        self.init_weights(self.disc_m)\n        self.init_weights(self.disc_p)\n        \n        # Set device for models\n        self.gen_mtp = self.gen_mtp.to(self.device)\n        self.gen_ptm = self.gen_ptm.to(self.device)\n        self.disc_m = self.disc_m.to(self.device)\n        self.disc_p = self.disc_p.to(self.device)        \n        \n        \n        \n    # Enable/ disable gradients for model parameters\n    def param_require_grad(self, models, requires_grad=True):\n        for model in models:\n            for param in model.parameters():\n                param.requires_grad = requires_grad\n\n        \n    # Cycle generation: x -> y_gen -> x_cycle\n    def cycle_gen(self, x, G_x_to_y, G_y_to_x):\n\n        y_gen = G_x_to_y(x)\n        x_cycle = G_y_to_x(y_gen)\n\n        return y_gen, x_cycle    \n    \n    \n    # Define BCE logistic loss\n    def mse_loss(self, x, target):\n        \n        if target == 1:\n            return nn.MSELoss()(x, torch.ones(x.size()).to(self.device))\n        else:\n            return nn.MSELoss()(x, torch.zeros(x.size()).to(self.device))\n    \n    \n    # Define Generator Loss\n    def loss_gen(self, idt, real, cycle, disc):\n        \n        \n        # Identity Losses:\n        loss_idt = nn.L1Loss()(idt, real) * self.lmbda * self.idt_coef\n        \n        # Cycle Losses:\n        loss_cycle = nn.L1Loss()(cycle, real) * self.lmbda\n        \n        # Adversarial Losses:                \n        loss_adv = self.mse_loss(disc, target=1)\n        \n        # Total Generator loss:\n        loss_gen = loss_cycle + loss_adv + loss_idt\n        \n        return loss_gen\n    \n    \n    # Discriminator Loss\n    # Ideal Discriminator will classify real image as 1 and fake as 0\n    def loss_disc(self, real, gen):\n\n        loss_real = self.mse_loss(real, target=1)\n        loss_gen = self.mse_loss(gen, target=0)\n\n        return (loss_real + loss_gen)/2\n    \n    \n    # Train\n    def train(self, img_dl):\n        \n        history = []\n        dataset_len = img_dl.__len__()\n        print_header = True\n        \n        for epoch in range(self.epochs):\n            \n            # Start measuring time for epoch\n            start_time = time.time()\n\n            \n            # Nulify average losses for an epoch\n            loss_gen_photo_avg = 0.0\n            loss_gen_monet_avg = 0.0\n            loss_gen_total_avg = 0.0\n            \n            loss_disc_photo_avg = 0.0\n            loss_disc_monet_avg = 0.0\n            loss_disc_total_avg = 0.0\n            \n            \n            # Iterate through dataloader with images\n            for i, (photo_real, monet_real) in enumerate(img_dl):\n\n                photo_real, monet_real = photo_real.to(device), monet_real.to(device)\n\n                \n                # Disable gradients for discriminators during generator training                \n                self.param_require_grad([self.disc_m, self.disc_p], requires_grad=False) \n\n                # Set gradients for generators to zero at the start of the training pass \n                self.adam_gen.zero_grad()\n\n\n                # =======================================\n\n                # FORWARD PASS THROUGH GENERATOR\n\n                # ----------- Cycle photo ---------------\n\n                monet_gen, photo_cycle = self.cycle_gen(photo_real, \n                                                        self.gen_ptm, \n                                                        self.gen_mtp)\n                \n                # ----------- Cycle Monet ---------------\n\n                photo_gen, monet_cycle = self.cycle_gen(monet_real, \n                                                        self.gen_mtp, \n                                                        self.gen_ptm)\n\n\n\n                # ----------- Generate itself ---------------\n\n                \n                # Real Monet -> Identical Monet\n                monet_idt = self.gen_ptm(monet_real)      \n                \n                # Real photo -> Identical photo\n                photo_idt = self.gen_mtp(photo_real)\n\n    \n                # =======================================\n\n                # DISCRIMINATOR PRECTION ON GENERATED IMAGES\n\n\n                # Discriminator M: Check generated Monet \n                monet_disc = self.disc_m(monet_gen)\n\n                # Discriminator P: Check generated photo\n                photo_disc = self.disc_p(photo_gen)\n\n\n                # =======================================\n\n                # CALCULATE LOSSES FOR GENERATORS\n\n                # Generator Losses\n                loss_gen_photo = self.loss_gen(photo_idt, photo_real, photo_cycle, photo_disc)\n                loss_gen_monet = self.loss_gen(monet_idt, monet_real, monet_cycle, monet_disc)\n                \n                \n                # Total Generator loss:\n                loss_gen_total = loss_gen_photo + loss_gen_monet\n            \n            \n                # Update average Generator loss:\n                loss_gen_photo_avg += loss_gen_photo.item()\n                loss_gen_monet_avg += loss_gen_monet.item()\n                loss_gen_total_avg += loss_gen_total.item()\n\n                \n                # =======================================\n\n                # GENERATOR BACKWARD PASS \n\n\n                # Propagate loss backward\n                loss_gen_total.backward()\n\n                # Make step with optimizer\n                self.adam_gen.step()\n\n                \n                # =======================================\n\n                # FORWARD PASS THROUGH DISCRIMINATORS\n\n                \n                # Enable gradients for discriminators during discriminator trainig\n                self.param_require_grad([self.disc_m, self.disc_p], requires_grad=True) \n                \n                # Set zero gradients\n                self.adam_disc.zero_grad()\n                \n                \n                # discriminator M: Predictions on real and generated Monet:\n                monet_disc_real = self.disc_m(monet_real)\n                monet_disc_gen = self.disc_m(monet_gen.detach())\n\n                # discriminator P: Predictions on real and generated photo:\n                photo_disc_real = self.disc_p(photo_real)\n                photo_disc_gen = self.disc_p(photo_gen.detach())\n\n       \n                # =======================================\n\n                # CALCULATE LOSSES FOR DISCRIMINATORS\n\n                # Discriminator losses\n                loss_disc_photo = self.loss_disc(photo_disc_real, photo_disc_gen)\n                loss_disc_monet = self.loss_disc(monet_disc_real, monet_disc_gen)\n\n\n                # Total discriminator loss:                \n                loss_disc_total = loss_disc_photo + loss_disc_monet\n\n                \n                # =======================================\n\n                # DISCRIMINATOR BACKWARD PASS \n\n\n                # Propagate losses backward\n                loss_disc_total.backward()\n                \n                # Make step with optimizer\n                self.adam_disc.step()\n                \n\n                # =======================================\n\n                # Update average Discriminator loss\n                loss_disc_photo_avg += loss_disc_photo.item()\n                loss_disc_monet_avg += loss_disc_monet.item()\n                loss_disc_total_avg += loss_disc_total.item()\n                \n                \n            # Calculate average losses per epoch \n            loss_gen_photo_avg /= dataset_len\n            loss_gen_monet_avg /= dataset_len\n            loss_gen_total_avg /= dataset_len\n            \n            loss_disc_photo_avg /= dataset_len\n            loss_disc_monet_avg /= dataset_len\n            loss_disc_total_avg /= dataset_len\n            \n            \n            # Estimate training time per epoch\n            time_req = time.time() - start_time\n            \n            \n            # Expand training history\n            history.append([epoch, loss_gen_photo_avg, \n                                   loss_gen_monet_avg, \n                                   loss_gen_total_avg,\n                                   loss_disc_photo_avg, \n                                   loss_disc_monet_avg, \n                                   loss_disc_total_avg ])\n            \n            \n            # Print statistics\n            if print_header:\n                print('EPOCH | LOSS: Gen photo | Gen Monet | Disc photo | Disc Monet | TIME MIN')\n                print_header = False\n                \n            print('------+-----------------+-----------+------------+------------+----------')\n            print('  {:3} |        {:>8.2f} |  {:>8.2f} |   {:>8.2f} |   {:>8.2f} |     {:4.0f}'.format(epoch+1, \n                                                                                        loss_gen_photo_avg, \n                                                                                        loss_gen_monet_avg, \n                                                                                        loss_disc_photo_avg, \n                                                                                        loss_disc_monet_avg, \n                                                                                        time_req//60))\n            # Step learning rate scheduler\n            self.scheduler_gen.step()\n            self.scheduler_disc.step()\n\n        # Save training history\n        history = pd.DataFrame(history, columns=['epoch',  \n                                                 'loss_gen_photo_avg', \n                                                 'loss_gen_monet_avg', \n                                                 'loss_gen_total_avg',\n                                                 'loss_disc_photo_avg', \n                                                 'loss_disc_monet_avg', \n                                                 'loss_disc_total_avg' ])\n        \n        history.to_csv('history.csv', index=False)\n        \n        \n    def predict(self, image):\n        \n        with torch.no_grad():\n            \n            self.gen_ptm.eval()\n                            \n            image = image.to(self.device)\n\n            monet_gen = self.gen_ptm(image)\n\n            \n        return monet_gen","metadata":{"ExecuteTime":{"end_time":"2022-09-04T09:58:06.965581Z","start_time":"2022-09-04T09:58:06.948177Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.331572Z","iopub.status.busy":"2022-09-22T09:21:36.331241Z","iopub.status.idle":"2022-09-22T09:21:36.365617Z","shell.execute_reply":"2022-09-22T09:21:36.364731Z"},"hidden":true,"papermill":{"duration":0.054903,"end_time":"2022-09-22T09:21:36.367823","exception":false,"start_time":"2022-09-22T09:21:36.312920","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Train</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"### Initialize model","metadata":{"papermill":{"duration":0.016367,"end_time":"2022-09-22T09:21:36.433808","exception":false,"start_time":"2022-09-22T09:21:36.417441","status":"completed"},"tags":[]}},{"cell_type":"code","source":"gan = CycleGAN(3, 3, epochs=60, device=device)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:01:21.534356Z","start_time":"2022-09-04T10:01:21.265346Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.469355Z","iopub.status.busy":"2022-09-22T09:21:36.468708Z","iopub.status.idle":"2022-09-22T09:21:36.871291Z","shell.execute_reply":"2022-09-22T09:21:36.870209Z"},"papermill":{"duration":0.42362,"end_time":"2022-09-22T09:21:36.874109","exception":false,"start_time":"2022-09-22T09:21:36.450489","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Train model","metadata":{"papermill":{"duration":0.016477,"end_time":"2022-09-22T09:21:36.907419","exception":false,"start_time":"2022-09-22T09:21:36.890942","status":"completed"},"tags":[]}},{"cell_type":"code","source":"gan.train(img_dataloader)","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:09:02.563358Z","start_time":"2022-09-04T10:01:22.788722Z"},"execution":{"iopub.execute_input":"2022-09-22T09:21:36.942402Z","iopub.status.busy":"2022-09-22T09:21:36.941488Z","iopub.status.idle":"2022-09-22T11:24:14.068289Z","shell.execute_reply":"2022-09-22T11:24:14.065089Z"},"papermill":{"duration":7357.170172,"end_time":"2022-09-22T11:24:14.093958","exception":false,"start_time":"2022-09-22T09:21:36.923786","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Train results</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"### Plot losses","metadata":{"papermill":{"duration":0.022408,"end_time":"2022-09-22T11:24:14.184496","exception":false,"start_time":"2022-09-22T11:24:14.162088","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plot_train_loss(log='history.csv')","metadata":{"ExecuteTime":{"end_time":"2022-09-04T10:21:30.115051Z","start_time":"2022-09-04T10:21:29.865353Z"},"execution":{"iopub.execute_input":"2022-09-22T11:24:14.285439Z","iopub.status.busy":"2022-09-22T11:24:14.285096Z","iopub.status.idle":"2022-09-22T11:24:14.801365Z","shell.execute_reply":"2022-09-22T11:24:14.800368Z"},"papermill":{"duration":0.542435,"end_time":"2022-09-22T11:24:14.803723","exception":false,"start_time":"2022-09-22T11:24:14.261288","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Model predictions","metadata":{"papermill":{"duration":0.0238,"end_time":"2022-09-22T11:24:14.851963","exception":false,"start_time":"2022-09-22T11:24:14.828163","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"- Define dataset and dataloader for photos only","metadata":{"papermill":{"duration":0.022865,"end_time":"2022-09-22T11:24:14.898851","exception":false,"start_time":"2022-09-22T11:24:14.875986","status":"completed"},"tags":[]}},{"cell_type":"code","source":"photo_dataset = ImageDatasetCV(monet_dir=None, photo_dir=PHOTO_DIR)\n\nphoto_dataloader = DataLoader(photo_dataset, batch_size=BATCH_SIZE)","metadata":{"ExecuteTime":{"end_time":"2022-08-05T10:34:01.158951Z","start_time":"2022-08-05T10:34:01.143398Z"},"execution":{"iopub.execute_input":"2022-09-22T11:24:14.949376Z","iopub.status.busy":"2022-09-22T11:24:14.948319Z","iopub.status.idle":"2022-09-22T11:24:14.959235Z","shell.execute_reply":"2022-09-22T11:24:14.958337Z"},"papermill":{"duration":0.038948,"end_time":"2022-09-22T11:24:14.961408","exception":false,"start_time":"2022-09-22T11:24:14.922460","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- View predicted results","metadata":{"ExecuteTime":{"end_time":"2022-08-05T10:34:05.699762Z","start_time":"2022-08-05T10:34:05.695726Z"},"papermill":{"duration":0.024769,"end_time":"2022-09-22T11:24:15.010100","exception":false,"start_time":"2022-09-22T11:24:14.985331","status":"completed"},"tags":[]}},{"cell_type":"code","source":"with torch.no_grad():\n    \n    j = 0\n    for photo in photo_dataloader:\n\n        photo = photo.to(device)\n        \n        monet_gen = gan.gen_ptm(photo)\n        \n        fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n        \n        ax[0].imshow(unnormalize(photo[0].cpu()).permute(1,2,0))\n        ax[1].imshow(unnormalize(monet_gen[0].cpu().detach()).permute(1,2,0))\n        \n        ax[0].set_title('Photo')\n        ax[1].set_title('Monetesque photo')\n        \n        plt.show()\n        j += 1\n        if j > 3:\n            break\n            \n","metadata":{"ExecuteTime":{"end_time":"2022-08-05T13:33:31.074983Z","start_time":"2022-08-05T13:33:28.082636Z"},"execution":{"iopub.execute_input":"2022-09-22T11:24:15.060560Z","iopub.status.busy":"2022-09-22T11:24:15.059506Z","iopub.status.idle":"2022-09-22T11:24:16.616378Z","shell.execute_reply":"2022-09-22T11:24:16.615418Z"},"papermill":{"duration":1.587583,"end_time":"2022-09-22T11:24:16.621904","exception":false,"start_time":"2022-09-22T11:24:15.034321","status":"completed"},"scrolled":true,"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Save results","metadata":{"papermill":{"duration":0.040083,"end_time":"2022-09-22T11:24:16.700762","exception":false,"start_time":"2022-09-22T11:24:16.660679","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Save model","metadata":{"papermill":{"duration":0.041414,"end_time":"2022-09-22T11:24:16.781299","exception":false,"start_time":"2022-09-22T11:24:16.739885","status":"completed"},"tags":[]}},{"cell_type":"code","source":"save_dict = {\n    'epoch': 0,\n    'gen_mtp': gan.gen_mtp.state_dict(),\n    'gen_ptm': gan.gen_ptm.state_dict(),\n    'desc_m': gan.disc_m.state_dict(),\n    'desc_p': gan.disc_p.state_dict(),\n    'optimizer_gen': gan.adam_gen.state_dict(),\n    'optimizer_desc': gan.adam_disc.state_dict()\n    }","metadata":{"execution":{"iopub.execute_input":"2022-09-22T11:24:16.860510Z","iopub.status.busy":"2022-09-22T11:24:16.859458Z","iopub.status.idle":"2022-09-22T11:24:16.867482Z","shell.execute_reply":"2022-09-22T11:24:16.866606Z"},"papermill":{"duration":0.050364,"end_time":"2022-09-22T11:24:16.869779","exception":false,"start_time":"2022-09-22T11:24:16.819415","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"save_checkpoint(save_dict, 'model.ckpt')","metadata":{"execution":{"iopub.execute_input":"2022-09-22T11:24:16.949600Z","iopub.status.busy":"2022-09-22T11:24:16.948402Z","iopub.status.idle":"2022-09-22T11:24:17.332847Z","shell.execute_reply":"2022-09-22T11:24:17.331867Z"},"papermill":{"duration":0.428035,"end_time":"2022-09-22T11:24:17.335421","exception":false,"start_time":"2022-09-22T11:24:16.907386","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## \n<p id=\"toc\"></p>\n<h2 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"font-family: Verdana; font-size: 24px; font-style: normal; font-weight: bold; text-decoration: none; text-transform: none; letter-spacing: 3px; background-color: #8DE7E3; color: black;\" role=\"tab\" aria-controls=\"home\"><left><br>Save results</left></h2> ","metadata":{}},{"cell_type":"markdown","source":"- Get prediction for all photos","metadata":{"papermill":{"duration":0.042631,"end_time":"2022-09-22T11:24:17.492999","exception":false,"start_time":"2022-09-22T11:24:17.450368","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!mkdir ../images","metadata":{"execution":{"iopub.execute_input":"2022-09-22T11:24:17.572977Z","iopub.status.busy":"2022-09-22T11:24:17.572582Z","iopub.status.idle":"2022-09-22T11:24:18.655451Z","shell.execute_reply":"2022-09-22T11:24:18.654068Z"},"papermill":{"duration":1.126057,"end_time":"2022-09-22T11:24:18.658335","exception":false,"start_time":"2022-09-22T11:24:17.532278","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nwith torch.no_grad():\n    \n    \n    for i, photo in enumerate(photo_dataloader):\n        \n        monet_gen = gan.predict(photo)\n\n        monet_gen = T.ToPILImage()(unnormalize(monet_gen[0].cpu().detach())).convert('RGB')\n        \n        monet_gen.save(SAVE_PATH + str(i+1) + \".jpg\")\n        \n        ","metadata":{"ExecuteTime":{"end_time":"2022-08-05T13:41:34.450823Z","start_time":"2022-08-05T13:41:34.111492Z"},"execution":{"iopub.execute_input":"2022-09-22T11:24:18.777449Z","iopub.status.busy":"2022-09-22T11:24:18.776991Z","iopub.status.idle":"2022-09-22T11:27:18.089562Z","shell.execute_reply":"2022-09-22T11:27:18.088551Z"},"papermill":{"duration":179.375339,"end_time":"2022-09-22T11:27:18.092107","exception":false,"start_time":"2022-09-22T11:24:18.716768","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Create ZIP archive","metadata":{"papermill":{"duration":0.036608,"end_time":"2022-09-22T11:27:18.166327","exception":false,"start_time":"2022-09-22T11:27:18.129719","status":"completed"},"tags":[]}},{"cell_type":"code","source":"shutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")","metadata":{"execution":{"iopub.execute_input":"2022-09-22T11:27:18.241942Z","iopub.status.busy":"2022-09-22T11:27:18.241297Z","iopub.status.idle":"2022-09-22T11:27:22.392517Z","shell.execute_reply":"2022-09-22T11:27:22.391519Z"},"papermill":{"duration":4.191949,"end_time":"2022-09-22T11:27:22.394669","exception":false,"start_time":"2022-09-22T11:27:18.202720","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]}]}