conv_encoders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torchvision.utils import save_image\n",
    "import PIL.Image as Image\n",
    "import numpy as np\n",
    "import random\n",
    "import shutil\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"./data/autoencoder_db/\"\n",
    "\n",
    "\n",
    "loader = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "train_data = datasets.ImageFolder(data_dir + 'train', transform=loader)\n",
    "valid_data = datasets.ImageFolder(data_dir + 'valid', transform=loader)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "validloader = torch.utils.data.DataLoader(valid_data, batch_size=32)\n",
    "\n",
    "unloader = transforms.ToPILImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_im(tensor, title):\n",
    "    image = tensor.cpu().clone()\n",
    "    x = image.clamp(0, 255)\n",
    "    x = x.view(x.size(0), 3, 96, 96)\n",
    "    save_image(x, \"./generated_data/image_{}.png\".format(title))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rIWqF2xZmZ8J1sJ5+kvX3OzfJ6+yToUNxjdq71lwNGU4CnxrkU/YjE7R1jJI2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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# img_path = random.choice(os.listdir(data_dir + 'train/anime/'))\n",
    "# pil_img = Image.open(data_dir + 'train/anime/' + img_path)\n",
    "img = unloader(iter(trainloader).next()[0][0])\n",
    "_ = plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 27648])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1, _ = iter(trainloader).next()\n",
    "img1.view(img1.size(0), 3*96*96).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class autoencoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(autoencoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Conv2d(3, 32, 5, stride=3, padding=1), \n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, stride=2),\n",
    "            nn.Conv2d(32, 64, 5, stride=1, padding=1),\n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, stride=2),\n",
    "            nn.Conv2d(64, 64, 3, stride=1, padding=1),\n",
    "            nn.ReLU(True)\n",
    "        )\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.ConvTranspose2d(64, 32, 3, stride=2, output_padding=1),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(32, 16, 5, stride=3, padding=1),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(16, 3, 2, stride=2),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(model, testloader, criterion, criterion2=None, mode=1):\n",
    "    test_loss = 0\n",
    "    for data in testloader:\n",
    "        images, _ = data\n",
    "        images = images.to('cuda')\n",
    "        output = model.forward(images)\n",
    "        if mode == 2:\n",
    "            output = torch.sigmoid(output.view(output.size(0), 3*96*96))\n",
    "            images = torch.sigmoid(images.view(images.size(0), 3*96*96))\n",
    "            test_loss += criterion(output, images).item()\n",
    "        \n",
    "        elif mode == 3:\n",
    "            loss1 = criterion(output, images).item()\n",
    "            \n",
    "            output = torch.sigmoid(output.view(output.size(0), 3*96*96))\n",
    "            images = torch.sigmoid(images.view(images.size(0), 3*96*96))\n",
    "            \n",
    "            loss2 = 0.1 * criterion2(output, images).item()\n",
    "            test_loss += loss1 + loss2\n",
    "        else:\n",
    "            test_loss += criterion(output, images).item()\n",
    "    return test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150..  Training Loss: 0.4933..  valid Loss: 0.9247.. \n",
      "Epoch: 1/150..  Training Loss: 0.2317..  valid Loss: 0.7528.. \n",
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      "Epoch: 126/150..  Training Loss: 0.0865..  valid Loss: 0.6580.. \n",
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      "Epoch: 127/150..  Training Loss: 0.0868..  valid Loss: 0.6580.. \n",
      "Epoch: 127/150..  Training Loss: 0.0874..  valid Loss: 0.6602.. \n",
      "Epoch: 127/150..  Training Loss: 0.0881..  valid Loss: 0.6580.. \n",
      "Epoch: 128/150..  Training Loss: 0.0865..  valid Loss: 0.6576.. \n",
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      "Epoch: 146/150..  Training Loss: 0.0863..  valid Loss: 0.6576.. \n",
      "Epoch: 146/150..  Training Loss: 0.0866..  valid Loss: 0.6575.. \n",
      "Epoch: 146/150..  Training Loss: 0.0863..  valid Loss: 0.6575.. \n",
      "Epoch: 147/150..  Training Loss: 0.0866..  valid Loss: 0.6579.. \n",
      "Epoch: 147/150..  Training Loss: 0.0873..  valid Loss: 0.6730.. \n",
      "Epoch: 148/150..  Training Loss: 0.0909..  valid Loss: 0.6587.. \n",
      "Epoch: 148/150..  Training Loss: 0.0868..  valid Loss: 0.6581.. \n",
      "Epoch: 148/150..  Training Loss: 0.0866..  valid Loss: 0.6576.. \n",
      "Epoch: 149/150..  Training Loss: 0.0863..  valid Loss: 0.6576.. \n",
      "Epoch: 149/150..  Training Loss: 0.0865..  valid Loss: 0.6575.. \n",
      "Epoch: 150/150..  Training Loss: 0.0866..  valid Loss: 0.6575.. \n",
      "Epoch: 150/150..  Training Loss: 0.0866..  valid Loss: 0.6577.. \n",
      "Epoch: 150/150..  Training Loss: 0.0861..  valid Loss: 0.6575.. \n"
     ]
    }
   ],
   "source": [
    "model = autoencoder().to('cuda')\n",
    "criterion = nn.MSELoss()\n",
    "criterion2 =nn.BCELoss()\n",
    "\n",
    "learning_rate = 1e-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,\n",
    "                             weight_decay=1e-4)\n",
    "\n",
    "num_epochs = 150\n",
    "\n",
    "running_loss = 0\n",
    "steps = 0\n",
    "print_every = 35\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    for data in trainloader:\n",
    "        steps += 1\n",
    "        img, _ = data\n",
    "        img = img.to('cuda')\n",
    "        output = model(img)\n",
    "        loss1 = criterion(output, img)\n",
    "        \n",
    "        img = torch.sigmoid(img.view(img.size(0), 3*96*96))\n",
    "        output = torch.sigmoid(output.view(output.size(0), 3*96*96))\n",
    "        loss2 = 0.1*criterion2(output, img)\n",
    "        \n",
    "        loss = loss1 + loss2\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "        if steps % print_every == 0:\n",
    "            model.eval()\n",
    "\n",
    "            with torch.no_grad():\n",
    "                valid_loss = validation(model, validloader, criterion, criterion2, mode=3)\n",
    "            \n",
    "            model.train()\n",
    "            \n",
    "            print(\"Epoch: {}/{}.. \".format(epoch+1, num_epochs),\n",
    "                  \"Training Loss: {:.4f}.. \".format(running_loss/print_every),\n",
    "                  \"valid Loss: {:.4f}.. \".format(valid_loss/len(validloader)))\n",
    "            running_loss = 0\n",
    "    if epoch % 10 == 0:\n",
    "        save_im(output, 'epoch '+str(epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  0,  0,  ...,  1,  1,  1],\n",
       "        [-1, -1, -1,  ..., -1, -1, -1],\n",
       "        [ 1,  1,  1,  ...,  1,  1,  1],\n",
       "        ...,\n",
       "        [ 1,  1,  1,  ...,  1,  1,  1],\n",
       "        [ 1,  1,  0,  ...,  1,  1,  1],\n",
       "        [-2, -2, -2,  ..., -1, -1, -1]], device='cuda:0')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth.type(torch.cuda.LongTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.2157,  0.1855,  0.1704,  ...,  1.3333,  1.3333,  1.3333],\n",
       "        [-1.7753, -1.7602, -1.7451,  ..., -1.4408, -1.8475, -1.6296],\n",
       "        [ 1.0000,  1.0000,  1.0000,  ...,  1.3043,  1.3333,  1.3333],\n",
       "        ...,\n",
       "        [ 1.0000,  1.0000,  1.0000,  ...,  1.3333,  1.3333,  1.3333],\n",
       "        [ 1.0000,  1.0000,  0.9095,  ...,  1.3333,  1.3333,  1.3333],\n",
       "        [-2.0015, -2.0015, -2.0015,  ..., -1.6296, -1.7749, -1.8765]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_list = ['blonde_hair','blue_eyes','bow']\n",
    "for folder in folder_list:\n",
    "    full_path = './data/anime-faces/' + folder\n",
    "    for img in os.listdir(full_path):\n",
    "        shutil.copy(full_path + '/' + img, './data/autoencoder_db/all_data/anime/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def split_folder(path_to_data, path_to_create_split, split_ratio=0.1):    \n",
    "    data_size = len(os.listdir(path_to_data))\n",
    "    \n",
    "    split_size = math.floor(data_size*0.1)\n",
    "    train_size = data_size - split_size*2\n",
    "    \n",
    "    for sizes, folder in zip([train_size, split_size, split_size], ['train', 'test', 'valid']):\n",
    "        count = 0\n",
    "        while count < sizes:\n",
    "            img = random.choice(os.listdir(path_to_data))\n",
    "            os.rename(path_to_data + img, path_to_create_split + folder +'/anime/' + img)\n",
    "            count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.floor(493*0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_data = './data/autoencoder_db/all_data/anime/'\n",
    "path_to_create_split = './data/autoencoder_db/'\n",
    "split_folder(path_to_data, path_to_create_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.isdir(path_to_create_split + 'train/anime')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PT",
   "language": "python",
   "name": "pt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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 "nbformat": 4,
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