{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "iUebtXJXCDp2" }, "outputs": [], "source": [ "# !unzip -q '/content/drive/MyDrive/flowers/flowers.zip' -d '/content/flowers'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "_rWYs7_s61qi" }, "outputs": [], "source": [ "import os\n", "import torchvision.transforms as tt\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import ImageFolder\n", "import torch\n", "import torch.nn as nn\n", "from tqdm.notebook import tqdm\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "import statistics\n", "import random\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "DoypUEVoBh6d", "outputId": "10cc93bf-7812-4626-8f7c-b0d6b175521e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\shiva\\Desktop\\projects\\Generative Networks\n", "['flowers']\n", "cuda\n" ] } ], "source": [ "image_size = 32\n", "batch_size = 16\n", "stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)\n", "print(os.getcwd())\n", "DATA_DIR = 'jpg'\n", "\n", "print(os.listdir(DATA_DIR))\n", "\n", "train_ds = ImageFolder(DATA_DIR, transform=tt.Compose([ tt.Resize(image_size),\n", " tt.CenterCrop(image_size),\n", " tt.ToTensor()]))\n", "\n", "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=3, pin_memory=True)\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "print(device)\n", "z_dim = 20" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0dnaaXhIREV0BY9qAKio+eq9Kb2/vqO/39vaOXPdxHo8HgUBg1IWIiKa+MW1AdXV1qKiowO7du0e+F4lEcODAAdTX14/ljyIioklOfRbc8PAwjh8/PvJ1e3s7Dh06hKKiItTW1uLhhx/GP/3TP2H27Nmoq6vDd7/7XVRVVeGOO+4Yy3UTEdEkp25ABw8exBe/+MWRrzdu3AgAWL9+PbZt24ZvfvObiEajuP/++zE4OIjPf/7z2LVrF7xer+rn+EMhOJyyAzRPvnwMyokh+fgOAGj3yvPzXPKxMADgc8vHq+R5fara3pi8dqZfVRqDLVFVvn2oT5wtCOhG2pSW5ouzzrTuNhwelI8Qynh0f0xw2HVjm5xO+RghRzasqp3xyMfUvHXo/GezXog7dI04a7Pp3it4Jio/YzabUZWGXbl/kpZ8dE82p9s/vf3y18X7FWN7AGDg/d+Ks20DA+JsMpES5dQNaNmyZbAs64LX22w2PPHEE3jiiSe0pYmI6Cpi/Cw4IiK6OrEBERGREWxARERkBBsQEREZwQZERERGsAEREZERbEBERGQEGxARERnBBkREREawARERkRHqUTxXSt2smXC5ZfOY8gvkc5j642dU63h7+H1xdkawVlW70BYUZ9MZ3TArj0/+sRY1pbo5feg+/4cLXsiZ/ccvHvqjEz7dDK6aOUXirC0RV9Xuaj8pznr8flXtIuWssWlp+by24rhuHtiQS367DA3rZinGhuSDBr0FdaraUUs+Oy5YIJ9JBwA5y6HKZxLyx2cyKZuTdo7TI79vFfgqVbVdimOQsOIpKCXM8giIiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMoINiIiIjGADIiIiIybsKJ6S0kK4PbJxJTavfExJRjcxBScHj4mzr5zVjdi4u/bL4qzPJR+tAwAJn1uczTh9qtpef6Eqnz8YEWdtSd3IoViHfLRSHnQjarLZIXE24/aoard1tqvyrmSJOJuLy8ffAIBVXibO3lZzr6p2LCW/zZOZpKp21BoWZwcy8n0JAGejuseyyyZ/Dso50qrabru8dmG+fDQVADgd8mOQVEY+simdkW0jj4CIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjICDYgIiIygg2IiIiMmLCz4DKOYdidsllwfa094rrdJ7pU6wiH5TOkugNtqtqVhcXibH3ebara6axiNpnXpqo9BIcqn/PnyZeSks92A4Dj7zSLsw5EVbVTHvlcOm9KPk8NAFzuclX+1Gn5/LBIzquqXTjjGnE2mK+bA1heppjTqJ3XFpHPvDvd8VtV7VRMNwsONvljwq2YvwYAdq/8seyw6x6bmmMQu0M+SDMVl8314xEQEREZwQZERERGsAEREZERbEBERGQEGxARERnBBkREREawARERkRFsQEREZAQbEBERGcEGRERERkzYUTwfvNMKh0s2ViLcFxfXjUTOqtbhy5P36Ok3yEfOAEB/1XFxtrlXPgYDAKrPzhdnHRnZyKNzLK9u3Ed+mVuc/bD/A1XteKhTnLUXyMaDnOP0ycfInIl/qKrtzelG90SHS8XZjtMFqtozy4Pi7IxS3bodBYqnGHdWVTuakY9tOvLOIVXtYEmJKl9UWSPOJnPy+xUAwC5/DrKgG6tlQb4Wu01xvCJcBo+AiIjICDYgIiIyQt2A9u7di9tvvx1VVVWw2Wx48cUXR11/zz33wGazjbqsXr16rNZLRERThLoBRaNRLFy4EJs3b75gZvXq1eju7h65PPfcc5e1SCIimnrUJyGsWbMGa9as+dSMx+NBRUXFJS+KiIimvnF5DWjPnj0oKyvDnDlz8OCDD2JgYOCC2WQyiUgkMupCRERT35g3oNWrV+NnP/sZdu/ejX/5l39Bc3Mz1qxZg2z2/KdYNjU1IRgMjlxqauSnMxIR0eQ15u8Duvvuu0f+PX/+fCxYsAAzZ87Enj17sGLFik/kN23ahI0bN458HYlE2ISIiK4C434a9owZM1BSUoLjx8//pkuPx4NAIDDqQkREU9+4N6BTp05hYGAAlZWV4/2jiIhoElH/CW54eHjU0Ux7ezsOHTqEoqIiFBUV4fHHH8e6detQUVGBtrY2fPOb38SsWbOwatWqMV04ERFNbuoGdPDgQXzxi18c+frc6zfr16/Hli1bcPjwYfz7v/87BgcHUVVVhZUrV+If//Ef4fF4VD8nHgUcTkuUzWblm5GXl69ax2eWhsTZ/v6wqjbS8nwm+paqdGv4lDi70CmfGwcAfuWfSaOxmDjbF21T1fZWDIuzxdN0s/qiZ+V/IHB4Uqra8WS3Kt+fkM+a8+VmqGoPn5DfV/qzurmBqZD8NowXDKpq9ybk+aGw7nHf2npAlb/2hh5xdtacm1S1PS75/TaWTqhq5yz57LhMRl43Ixzrp25Ay5Ytg2VduDG8+uqr2pJERHQV4iw4IiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMoINiIiIjBjzzwMaK9Org3C5HaKsP08+Z276jHLVOgpC8pvo2BHdfK9sxi3OuouCqtol+fLtTCo/hNYH4aCnPzr5wdvi7FD+CVXtpV+UT1kPFOn2/XBOvn9OHOtT1c4v8aryqZJ+cTYZP6OqPaiY1ZfpkK8DAOagVJwNxH2q2rPiIXF2ReWNqtr70rqZdyeOvSvOxtOvq2rfcMNicTbPoZvTOJyQD3jL2tPybFY2G5FHQEREZAQbEBERGcEGRERERrABERGREWxARERkBBsQEREZwQZERERGsAEREZERbEBERGQEGxARERkxYUfxzL2mAB6PbHm2ItnIHgBwhWQjIs7pOSEfPVLoKFPVLim9UZzNs89U1U5E5KNEnCFLVdubr7vbWKfk2ZxN9ztRyiXf90cPfaCqvfim5fJ15KKq2oMR3fyjmE++nUmP7jb0l8jHt/j9xara3lCROFtk6W4TR0q+7qrQHFXtxYW6x1un7TZx9oSjVVU7nT0rztZUTlfV7osmxdnTivusU3h35REQEREZwQZERERGsAEREZERbEBERGQEGxARERnBBkREREawARERkRFsQEREZAQbEBERGcEGRERERrABERGRERN2Ftxv3vpvOJyy/jjvtmpx3bqKkGodRaU14uyNN69W1XZa8nUnEvJZYAAQi8TE2fSwbo4ZogOq+PTrPOJse1g+3wsAvF55ftGyKlXt7s7D4mzKp5tjlufW/e4X60yLs9HTCVXtIcewOOuxy2eHAbrZfva8oKq2PSW/3yZ7z6hq5+nGI+J6xdqvdX1OVdtW6BNny6ZVqGp/mP5QnN11+L/E2WRGluMREBERGcEGRERERrABERGREWxARERkBBsQEREZwQZERERGsAEREZERbEBERGQEGxARERnBBkREREZM2FE8w2k7HDlZf3TnucR1uzr7VetYs3S9OHtd7S2q2me6hPMqAJzpGVLVTibi4uzw6bOq2olBXT5QVi7O3vQZ+dgRALCl5TNTQtXTVLU7FeNbhHfVEckB+b4HgMFu+f3WnfaraucS8sXnIvL7FQBYcXnenlegql1YIr9fFfryVbXTSd12JuPyx2diSDf6KptMibOnz+jWfcYnH8P09m/3ibOZdFaU4xEQEREZoWpATU1NuPnmm+H3+1FWVoY77rgDra2tozKJRAKNjY0oLi5GQUEB1q1bh97e3jFdNBERTX6qBtTc3IzGxkbs378fr732GtLpNFauXIlo9E+HlI888ghefvllvPDCC2hubkZXVxfuvPPOMV84ERFNbqrXgHbt2jXq623btqGsrAwtLS249dZbEQ6H8eyzz2L79u1Yvnw5AGDr1q247rrrsH//ftxyi+41EiIimrou6zWgcDgMACgqKgIAtLS0IJ1Oo6GhYSQzd+5c1NbWYt++87+AlUwmEYlERl2IiGjqu+QGlMvl8PDDD2Pp0qWYN28eAKCnpwdutxuhUGhUtry8HD09Peet09TUhGAwOHKpqZF/ABwREU1el9yAGhsbceTIEezYseOyFrBp0yaEw+GRS2dn52XVIyKiyeGS3ge0YcMGvPLKK9i7dy+qq//0sdIVFRVIpVIYHBwcdRTU29uLiorzf1Ssx+OBxyP/yGYiIpoaVEdAlmVhw4YN2LlzJ9544w3U1dWNun7RokVwuVzYvXv3yPdaW1vR0dGB+vr6sVkxERFNCaojoMbGRmzfvh0vvfQS/H7/yOs6wWAQPp8PwWAQ9957LzZu3IiioiIEAgE89NBDqK+v5xlwREQ0iqoBbdmyBQCwbNmyUd/funUr7rnnHgDAD3/4Q9jtdqxbtw7JZBKrVq3CT3/60zFZLBERTR02y7Lkw7SugEgkgmAwiIXLroPD6RD9n9v+R/XFQ3/U2vq+aj1B92fE2fmzPquqjZT8L6B9HfKZTQDgycrnanlRpKqd6oqp8v6QfDtt3jZV7TOZ4/J1zNCdc1NUFBJnk8O6h1HboW5VPtIhrz+7Yqmqtqew7uKhP8oL6u4rcyrd4myx8qXg4oB8LZ60bvae26G7r7jc8sUnorq5jomE/LGfTOtmwZ3NJcTZ9zzyiTbxeAKNGx9FOBxGIBC4YI6z4IiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjICDYgIiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjLikj6O4UrIpaKw5WT9MWfJx2wM9Ll06yiQj5157v8+q6pde618fEcqlqeqjYHp4uhnqnSTyrNR3SiemEc+MsVtK1HVvuEm+b4/euy/VbWT0bA4O634/B83ciGeoVJVvrqwXJx1u3TjcqDIF1+r285ovvwTjt/54FVVbZyW7/uSPN26SzzFqnxVUF4/4Nc9lp0hnzhreXS1rVy/OHumq0OcTThSohyPgIiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjICDYgIiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIyYsLPgnO4MHE5ZfxwKR8V1b5yzTLsScbKj46iqcjh8Vpx1O3SzwzJJ+ZysWEw+DwoAiopnqfJxxey4RCqtqt1+RD7HLDtcpqqd7Jav+7hdPq8LAPLypqvy9qxNnB0aku97AMjPl2fdbr+qdqBSPu8wmNbdx985/Rtx9s2e/1LVHh7IqfJBb0ic9bhVpWHZsuJsKj2kqh1JnhFnhzKnxdlsyhLleARERERGsAEREZERbEBERGQEGxARERnBBkREREawARERkRFsQEREZAQbEBERGcEGRERERrABERGRERN2FI/bkw+nyyHKhpwLxXVLSnVjZI61vS2vHShQ1e4+OSCvXZhS1XZiUJwN5svHpQBAoUs3MsWeaBdnBxO627C/X752j192fzonk5KP10mGXaraXiuhynvs8vE6Drdu1ovbJ79dUqmwqrbDku8fW57u92FngXw7vX7dqCSPT7cWh1O+f4aG46raZwYGxdlopE9VG86kOOpW3IQ5G0fxEBHRBMYGRERERrABERGREWxARERkBBsQEREZwQZERERGsAEREZERbEBERGQEGxARERnBBkREREawARERkRETdhZcOulCLiubUdXTMyyua7edVq3D7Torzs6uK1fVPjPUK876nF5V7fKgfMaTwya//QCgxKmbZeUrypdnvSWq2hl/QJ7N6rbzLOQz0gLeoKp2MN2vyjvc8oeqLaebG+h2yffnwffeUtWuishvw3RZt6p2TjZuDABQVlipqu106p4aM7mcOOvx6vaPZVfMGXQMqWo7FNsZKsoTZzPJHI7i4s9vPAIiIiIjVA2oqakJN998M/x+P8rKynDHHXegtbV1VGbZsmWw2WyjLg888MCYLpqIiCY/VQNqbm5GY2Mj9u/fj9deew3pdBorV65ENBodlbvvvvvQ3d09cnnqqafGdNFERDT5qf7QuWvXrlFfb9u2DWVlZWhpacGtt9468v28vDxUVFSMzQqJiGhKuqzXgMLhjz6cqqioaNT3f/7zn6OkpATz5s3Dpk2bEIvFLlgjmUwiEomMuhAR0dR3yWfB5XI5PPzww1i6dCnmzZs38v2vfvWrmDZtGqqqqnD48GF861vfQmtrK375y1+et05TUxMef/zxS10GERFNUpfcgBobG3HkyBG89dbo0zLvv//+kX/Pnz8flZWVWLFiBdra2jBz5sxP1Nm0aRM2btw48nUkEkFNTc2lLouIiCaJS2pAGzZswCuvvIK9e/eiurr6U7NLliwBABw/fvy8Dcjj8cDjkX9uPBERTQ2qBmRZFh566CHs3LkTe/bsQV1d3UX/z6FDhwAAlZW6N4IREdHUpmpAjY2N2L59O1566SX4/X709PQAAILBIHw+H9ra2rB9+3b8xV/8BYqLi3H48GE88sgjuPXWW7FgwYJx2QAiIpqcVA1oy5YtAD56s+mf27p1K+655x643W68/vrrePrppxGNRlFTU4N169bhO9/5zpgtmIiIpgb1n+A+TU1NDZqbmy9rQecMJ8JwOGVniff0fyiuW1epmzVWW1sszg4M685qP/y+/OY/+v4JVe3BQIE4O+26uaraFYW67exPy+eB2abLb28AKCgtFGfPDCqGhwHoiv9BnC32yG9vACiKymeHAYDbJ3+dNB5Nq2qno6fE2b6h1ouH/szhE+3i7Ozb5PsSADyBkDjr8ulmKWazupfHw0NhRXHdvnfabeKsJz+jqu3Pkz8fOu1ueWFbFuAsOCIimqjYgIiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjICDYgIiIygg2IiIiMYAMiIiIj2ICIiMiIS/48oHFnt310EQj6XOKyaz+3VLWMQIF8DMb2X29T1S4tC4izp3ujqtqdfUlxNjJbN77DY+lGvbh88tEj/Zk2Ve1QYLY429P5vqp29+BxcdYZkN/eAFABnyqfZ8nHGWWSQ6rasYFucbZn+JCqNkqy4qgn/xpV6Txf0cVDfxRP6O6zw8PDqnwud+FPff44u0s3Eiqdle9Pb57uKb2yTP7Za+mEfJxR2pEBcPFRVjwCIiIiI9iAiIjICDYgIiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMmLCzoKbVlsHl1u2vP9xy23iuvaMbg7T0Fn5nLTphfNVtd+15DO4suk+Ve1ITD47bDATV9V2BeSz9wDAnpbPsdv9+/+tqt3WP12cfffoYVXtWFx+Xzk9cExVe851K1X5sox81lxeNqGqnXTL59KFBwZUtQsq/OJsXkFIVTudlc+Zy6bl8wgBIOD3qPI5lIizZwbDqtqwyefYedwFqtKhgHzdgbJp4mwylgTw+kVzPAIiIiIj2ICIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjIiAk7iudzN3wGXq9sHEZ7e4e47vb/87JqHdF4SpwNFMhHmgDA4JB8RI1l0+0qV548f6K3S1U7V6sbxeMYdouznV0nVbWPnWiVh22637eyOfn4loEB3W14bJpubNP119SJs16XboxMKnpGnHW45COeAMDr84qzvjzd4ycVlo+oycvXrdvt1t2GyaR8LFAmoxtn5PMExFmPU7edyMnzFcVl4mzcKxsHxSMgIiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIxgAyIiIiPYgIiIyAg2ICIiMoINiIiIjGADIiIiIybsLLg33twHp1O2vA9OyOdwhWPymU0AkM3ZxFmbPAoAcNgseW27bjaV5ZAv5g8nT6hq7zrcrMrftmypOFuYX6yq3RnrFmdtdt0OSmfks+DSCfnMQADYd7RFlW+Yt0ycnV48U1U7dlr+NGD9P/l9FgDskN9vvY4CVe2Ea0he26ebX5jJ6J4nUumkOGuD7n4Y8heKs8GCUlVttz0kzrps8ll9GeEm8giIiIiMUDWgLVu2YMGCBQgEAggEAqivr8evfvWrkesTiQQaGxtRXFyMgoICrFu3Dr29vWO+aCIimvxUDai6uhpPPvkkWlpacPDgQSxfvhxr167Fe++9BwB45JFH8PLLL+OFF15Ac3Mzurq6cOedd47LwomIaHJTvQZ0++23j/r6n//5n7Flyxbs378f1dXVePbZZ7F9+3YsX74cALB161Zcd9112L9/P2655ZaxWzUREU16l/waUDabxY4dOxCNRlFfX4+Wlhak02k0NDSMZObOnYva2lrs27fvgnWSySQikcioCxERTX3qBvTuu++ioKAAHo8HDzzwAHbu3Inrr78ePT09cLvdCIVCo/Ll5eXo6em5YL2mpiYEg8GRS01NjXojiIho8lE3oDlz5uDQoUM4cOAAHnzwQaxfvx5Hjx695AVs2rQJ4XB45NLZ2XnJtYiIaPJQvw/I7XZj1qxZAIBFixbh97//PX70ox/hrrvuQiqVwuDg4KijoN7eXlRUVFywnsfjgceje48LERFNfpf9PqBcLodkMolFixbB5XJh9+7dI9e1traio6MD9fX1l/tjiIhoilEdAW3atAlr1qxBbW0thoaGsH37duzZswevvvoqgsEg7r33XmzcuBFFRUUIBAJ46KGHUF9fzzPgiIjoE1QNqK+vD3/1V3+F7u5uBINBLFiwAK+++iq+9KUvAQB++MMfwm63Y926dUgmk1i1ahV++tOfXtLCDn5wCja78ABNMWLFUo7Lsdnl41gsZfG0vDSQVf61NCsv7rTHVaV/9vzzqvzeg3vF2b7ePlVtm0M+YiVn6carOBzyPw1bukkv6BrsUOWbOw+Is9ct/5+q2on4CXE2lk6oaqcS8tu8JF83Rsay5GOBksrROrmMW7eWrLy+2+FV1fYXyE/MKgwFVbVDvjJx1mXPE2czwudk1bPas88++6nXe71ebN68GZs3b9aUJSKiqxBnwRERkRFsQEREZAQbEBERGcEGRERERrABERGREWxARERkBBsQEREZwQZERERGsAEREZER6mnY4+3ceA0rp5lToxjFI5/eMWo9sqxuFI9qLcp1A4oRQsriOdW+ATKKMSg5xQghQLOVQM6mq625X+WyytvQpssnkylxdjgaVdWOJeSjmLLK/ZNJZcTZeFQ3EioRS4qz2lE86aTud/NkXL6WVEK+LwEg6XDIs275OgAgYclHK8Ut+f6Jxz7KXuz502ZpnmGvgFOnTvFD6YiIpoDOzk5UV1df8PoJ14ByuRy6urrg9/ths/3pN9BIJIKamhp0dnYiEAgYXOH44nZOHVfDNgLczqlmLLbTsiwMDQ2hqqoK9k8ZKj3h/gRnt9s/tWMGAoEpvfPP4XZOHVfDNgLczqnmcrczGLz4ZG6ehEBEREawARERkRGTpgF5PB489thj8HjkHxI2GXE7p46rYRsBbudUcyW3c8KdhEBERFeHSXMEREREUwsbEBERGcEGRERERrABERGREZOmAW3evBnTp0+H1+vFkiVL8Lvf/c70ksbU9773PdhstlGXuXPnml7WZdm7dy9uv/12VFVVwWaz4cUXXxx1vWVZePTRR1FZWQmfz4eGhgYcO3bMzGIvw8W285577vnEvl29erWZxV6ipqYm3HzzzfD7/SgrK8Mdd9yB1tbWUZlEIoHGxkYUFxejoKAA69atQ29vr6EVXxrJdi5btuwT+/OBBx4wtOJLs2XLFixYsGDkzab19fX41a9+NXL9ldqXk6IB/eIXv8DGjRvx2GOP4e2338bChQuxatUq9PX1mV7amLrhhhvQ3d09cnnrrbdML+myRKNRLFy4EJs3bz7v9U899RR+/OMf45lnnsGBAweQn5+PVatWIZGQD0icCC62nQCwevXqUfv2ueeeu4IrvHzNzc1obGzE/v378dprryGdTmPlypWI/tng00ceeQQvv/wyXnjhBTQ3N6Orqwt33nmnwVXrSbYTAO67775R+/Opp54ytOJLU11djSeffBItLS04ePAgli9fjrVr1+K9994DcAX3pTUJLF682GpsbBz5OpvNWlVVVVZTU5PBVY2txx57zFq4cKHpZYwbANbOnTtHvs7lclZFRYX1/e9/f+R7g4ODlsfjsZ577jkDKxwbH99Oy7Ks9evXW2vXrjWynvHS19dnAbCam5sty/po37lcLuuFF14Yybz//vsWAGvfvn2mlnnZPr6dlmVZt912m/W3f/u35hY1TgoLC61//dd/vaL7csIfAaVSKbS0tKChoWHke3a7HQ0NDdi3b5/BlY29Y8eOoaqqCjNmzMDXvvY1dHR0mF7SuGlvb0dPT8+o/RoMBrFkyZIpt18BYM+ePSgrK8OcOXPw4IMPYmBgwPSSLks4HAYAFBUVAQBaWlqQTqdH7c+5c+eitrZ2Uu/Pj2/nOT//+c9RUlKCefPmYdOmTYjFYiaWNyay2Sx27NiBaDSK+vr6K7ovJ9ww0o/r7+9HNptFeXn5qO+Xl5fjgw8+MLSqsbdkyRJs27YNc+bMQXd3Nx5//HF84QtfwJEjR+D3+00vb8z19PQAwHn367nrporVq1fjzjvvRF1dHdra2vAP//APWLNmDfbt2weH4rNeJopcLoeHH34YS5cuxbx58wB8tD/dbjdCodCo7GTen+fbTgD46le/imnTpqGqqgqHDx/Gt771LbS2tuKXv/ylwdXqvfvuu6ivr0cikUBBQQF27tyJ66+/HocOHbpi+3LCN6CrxZo1a0b+vWDBAixZsgTTpk3D888/j3vvvdfgyuhy3X333SP/nj9/PhYsWICZM2diz549WLFihcGVXZrGxkYcOXJk0r9GeTEX2s77779/5N/z589HZWUlVqxYgba2NsycOfNKL/OSzZkzB4cOHUI4HMZ//Md/YP369Whubr6ia5jwf4IrKSmBw+H4xBkYvb29qKioMLSq8RcKhXDttdfi+PHjppcyLs7tu6ttvwLAjBkzUFJSMin37YYNG/DKK6/gzTffHPWxKRUVFUilUhgcHByVn6z780LbeT5LliwBgEm3P91uN2bNmoVFixahqakJCxcuxI9+9KMrui8nfANyu91YtGgRdu/ePfK9XC6H3bt3o76+3uDKxtfw8DDa2tpQWVlpeinjoq6uDhUVFaP2ayQSwYEDB6b0fgU++tTfgYGBSbVvLcvChg0bsHPnTrzxxhuoq6sbdf2iRYvgcrlG7c/W1lZ0dHRMqv15se08n0OHDgHApNqf55PL5ZBMJq/svhzTUxrGyY4dOyyPx2Nt27bNOnr0qHX//fdboVDI6unpMb20MfN3f/d31p49e6z29nbrN7/5jdXQ0GCVlJRYfX19ppd2yYaGhqx33nnHeueddywA1g9+8APrnXfesU6ePGlZlmU9+eSTVigUsl566SXr8OHD1tq1a626ujorHo8bXrnOp23n0NCQ9Y1vfMPat2+f1d7ebr3++uvWTTfdZM2ePdtKJBKmly724IMPWsFg0NqzZ4/V3d09conFYiOZBx54wKqtrbXeeOMN6+DBg1Z9fb1VX19vcNV6F9vO48ePW0888YR18OBBq7293XrppZesGTNmWLfeeqvhlet8+9vftpqbm6329nbr8OHD1re//W3LZrNZv/71ry3LunL7clI0IMuyrJ/85CdWbW2t5Xa7rcWLF1v79+83vaQxddddd1mVlZWW2+22rrnmGuuuu+6yjh8/bnpZl+XNN9+0AHzisn79esuyPjoV+7vf/a5VXl5ueTwea8WKFVZra6vZRV+CT9vOWCxmrVy50iotLbVcLpc1bdo067777pt0vzydb/sAWFu3bh3JxONx62/+5m+swsJCKy8vz/ryl79sdXd3m1v0JbjYdnZ0dFi33nqrVVRUZHk8HmvWrFnW3//931vhcNjswpX++q//2po2bZrldrut0tJSa8WKFSPNx7Ku3L7kxzEQEZERE/41ICIimprYgIiIyAg2ICIiMoINiIiIjGADIiIiI9iAiIjICDYgIiIygg2IiIiMYAMiIiIj2ICIiMgINiAiIjKCDYiIiIz4/5XC5+pxNb5kAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = train_ds[101][0]\n", "plt.imshow(img.permute(1, 2, 0))" ] }, { "cell_type": "markdown", "metadata": { "id": "ru0yTqGm7Tze" }, "source": [ "# GENERATOR - DISCRIMINATOR" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "_hA7nXZJ7nTH" }, "outputs": [], "source": [ "class Encoder(nn.Module):\n", " def __init__(self, latent_size):\n", " super(Encoder, self).__init__()\n", "\n", " self.main = nn.Sequential(\n", " # 3 x 32 x 32\n", " nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1, bias=False),\n", "# nn.BatchNorm2d(32),\n", "# nn.LeakyReLU(0.2, inplace=True),\n", "# # 32 x 32 x 32\n", "# nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(64),\n", " nn.LeakyReLU(0.2, inplace=True),\n", " # 64 x 16 x 16\n", " nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(128),\n", " nn.LeakyReLU(0.2, inplace=True),\n", " # 128 x 8 x 8\n", " nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(256),\n", " nn.LeakyReLU(0.2, inplace=True),\n", " # 256 x 4 x 4\n", " nn.Conv2d(256, latent_size, kernel_size=4, stride=1, padding=0, bias=False),\n", " # latent_size x 1 x 1\n", " nn.Flatten(),\n", "# nn.Linear(3072, 1000),\n", "# nn.Linear(1000, latent_size),\n", "# nn.Sigmoid()\n", " )\n", "\n", " def forward(self, x):\n", " return self.main(x)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "5jEoA6Tz7cia" }, "outputs": [], "source": [ "class Decoder(nn.Module):\n", " def __init__(self, latent_size):\n", " super(Decoder, self).__init__()\n", "\n", " self.main = nn.Sequential(\n", " # latent_size x 1 x 1\n", " nn.ConvTranspose2d(latent_size, 256, kernel_size=4, stride=1, padding=0, bias=False),\n", " nn.BatchNorm2d(256),\n", " nn.ReLU(True),\n", " # 256 x 4 x 4\n", " nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(128),\n", " nn.ReLU(True),\n", " # 128 x 8 x 8\n", " nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(64),\n", " nn.ReLU(True),\n", " # 64 x 16 x 16\n", " nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1, bias=False),\n", "# nn.BatchNorm2d(32),\n", "# nn.ReLU(True),\n", "# # 32 x 32 x 32\n", "# nn.ConvTranspose2d(32, 3, kernel_size=2, stride=2, padding=1, bias=False),\n", "# nn.BatchNorm2d(3),\n", "# nn.ReLU(True),\n", " # 3 x 32 x 32\n", "# nn.Linear(latent_size, 1000),\n", "# nn.Linear(1000, 3072),\n", "# nn.Sigmoid(),\n", " )\n", "\n", " def forward(self, x):\n", " return self.main(x)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class VAE(nn.Module):\n", " def __init__(self, z_dim):\n", " super(VAE, self).__init__()\n", " self.z_dim = z_dim\n", " self.img_to_mu = Encoder(z_dim).to(device)\n", " self.img_to_sigma = Encoder(z_dim).to(device)\n", " \n", " self.decoder = Decoder(z_dim).to(device)\n", "\n", " def forward(self, x):\n", " mu = self.img_to_mu(x)\n", " sigma = self.img_to_sigma(x)\n", " \n", " epsilon = torch.rand_like(sigma)\n", " \n", " z_recons = mu + sigma * epsilon\n", " z_recons = z_recons[:, :, None, None]\n", " \n", " x_recons = self.decoder(z_recons)\n", " return x_recons, mu, sigma\n", " \n", "model = VAE(z_dim = z_dim).to(device)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LKbH2k0t716E", "outputId": "3b27872a-892c-471c-fc34-eaad5c7c727e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([16, 3, 32, 32]) torch.Size([16, 3, 32, 32])\n" ] } ], "source": [ "latent = torch.randn(batch_size, 3, 32, 32, device=device)\n", "fake_images = model(latent)\n", "print(latent.size(), fake_images[0].size())" ] }, { "cell_type": "markdown", "metadata": { "id": "WBj5F3W8A5US" }, "source": [ "#TRAINING" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "0s6dPdXWAm6Z" }, "outputs": [], "source": [ "def fit(epochs, lr):\n", " \n", " loss_criteria = nn.BCELoss()\n", " optimizer = torch.optim.Adam(model.parameters(), lr = lr)\n", "\n", " torch.cuda.empty_cache()\n", " \n", " for epoch in range(epochs):\n", " losses = []\n", " for real_images, _ in tqdm(train_dl):\n", " \n", " real_images = real_images.to(device)\n", " recons, mu, sigma = model(real_images)\n", " \n", " recons_loss = loss_criteria(recons, real_images)\n", " kl_div_loss = - torch.sum(1 + torch.log(sigma.pow(2)) - mu.pow(2) - sigma.pow(2))\n", "# break\n", " \n", " total_loss = recons_loss + kl_div_loss\n", " optimizer.zero_grad()\n", " total_loss.backward()\n", " optimizer.step()\n", " losses.append(total_loss.item())\n", " \n", " # Log losses & scores (last batch)\n", " print(\"Epoch [{}/{}], loss: {:6f}\".format(epoch+1, epochs, sum(losses)))\n", " i = random.randint(1, 1000)\n", " real_img = train_ds[i][0].to(device).flatten()\n", " real_img = real_img.unsqueeze(0)\n", " img, mu, sigma = model(real_img)\n", " image = img.squeeze().cpu().detach().numpy()\n", " image = np.reshape(image, (32, 32, 3))\n", " plt.imshow(image)\n", " plt.show()\n", " \n", " \n", " return losses" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 493, "referenced_widgets": [ "32d9091373b94dcb8da7993777c7f147", "bc3b45d764194fbd87c79effe73c3fb1", "d294f5e55c414d3485bb35d162cf92b1", "f786e92f3ae34bd581074d43bafabc2b", "a81e34bf2ba9449fb3dbf7398f5001cf", "0afecd52b90c4929ad0f57ce4b7f0a91", "eb845e53a44e468b9bb311dadc5ccdd4", "0ae0fc0c1f2e4f8cbceb9d5df9e80f3d", "effa64f229be4ae3a5c3d62ce9b39ca1", "46d6ce307d5a46ecaa2405c304675bac", "ae0d4745d52f4819bf4711ef17e3db63", "ea172668c450444eb13e9d364a8091f5", "fa9516e96c5548e08c920840dfd89d2c", "e9d5c51df4f8446f89806cb2f09619d5", "431787a23cc94d8a98432da0ab0128ac", 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