GAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L3tpmgDqgwB_"
   },
   "source": [
    "some useful commands, for loadinf and transfering dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3831,
     "status": "ok",
     "timestamp": 1583986864072,
     "user": {
      "displayName": "zeinab taghavi",
      "photoUrl": "",
      "userId": "07214630829740929140"
     },
     "user_tz": -210
    },
    "id": "_rHX0kaSEizQ",
    "outputId": "b771f85d-883b-45a6-adfd-782fed5c2741"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/my_git/GAN_pytorch_2\n"
     ]
    }
   ],
   "source": [
    "! pwd\n",
    "# ! ls ./data/CAT/\n",
    "# ! wget  -P ./data  \"https://storage.googleapis.com/kaggle-data-sets/13371/18106/bundle/archive.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1583154626&Signature=ki%2FEkWgeBWG0SQmkf5EhoZ2N%2BNwsq687TanAxfiRIzb4nFyJjrSnk94OYvQApexrJDJy2dv5GiiJU6Jd5DILjpl4LJPI9ssVx223h%2BkiDo5KrC%2FanebIZpt4NHtT43U2SFPYNF2sJMxuohXpOK%2Bu2vh8YZ10I7UhpBh5bquJgpzJzZueUwxmXIDvC9rorg%2BjZ4eWNoH1H6EacvvV14pIesfwbF%2BJjPJ0rBkLKqUP4bZlamy7qiOYXfIChDxcmJLI0O0D%2BrGRl6BA3KVVnx6sLP3Qx6B7Jr6ebAci%2Bp2WnknA5iyY40LEnckar%2FeSTcMs5wfawXANd%2FN%2BB5gppSjC9w%3D%3D&response-content-disposition=attachment%3B+filename%3Dcat-dataset.zip\"\n",
    "# rename .zip file\n",
    "#! unzip ./data/archive.zip\n",
    "#! mv ./data/CAT*/*.jpg ./data/cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QM1vOF0sEmkL"
   },
   "outputs": [],
   "source": [
    "import torch , torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets , transforms\n",
    "import torchvision.utils as vutils\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N-y_JLZ3Em1N"
   },
   "outputs": [],
   "source": [
    "batchSize = 16\n",
    "imageSize = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EyZRtKxcEoOg"
   },
   "outputs": [],
   "source": [
    "transform1 = transforms.Compose([transforms.Resize((imageSize,imageSize)), transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])\n",
    "dataset = datasets.ImageFolder(root='./data', transform=transform1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9qsZ_L2eFByk"
   },
   "outputs": [],
   "source": [
    "dataloader = DataLoader(dataset,batch_size = 16 , shuffle= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2492,
     "status": "ok",
     "timestamp": 1583986989863,
     "user": {
      "displayName": "zeinab taghavi",
      "photoUrl": "",
      "userId": "07214630829740929140"
     },
     "user_tz": -210
    },
    "id": "YlpqF1ACFPJq",
    "outputId": "a10cdcb0-bcfe-45a9-b287-7028e49c0a98"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f660da324e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XAPE60zwJjvjNXQcktwZfFncNly7ggW7TsV/65V8GYO7gQXbv2gVALpGiOm/E2aFvfZ2i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\nMIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "fig, axs = plt.subplots(2)\n",
    "axs[0].imshow(np.transpose(dataset[0][0].numpy(),(2,1,0)))\n",
    "axs[1].imshow(np.transpose(dataset[4][0].numpy(),(1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EEx68shSJxG4"
   },
   "outputs": [],
   "source": [
    "def weight_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        m.weight.data.normal_(0.0,0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        m.weight.data.normal_(1.0,0.02)\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8EbH2AaaJ2OS"
   },
   "outputs": [],
   "source": [
    "class G(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            nn.ConvTranspose2d(100, 512, 4, 1, 0, bias = False),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias = False),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias = False),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(128, 64, 4, 2, 1, bias = False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias = False),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "\n",
    "    def forward(self,x):\n",
    "        output = self.main(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hGjKJA5rKnYP"
   },
   "outputs": [],
   "source": [
    "class D(nn.Module):\n",
    "    def __init__(self):\n",
    "    super().__init__()\n",
    "    self.main = nn.Sequential(\n",
    "          nn.Conv2d(3, 64, 4, 2, 1, bias = False),\n",
    "          nn.LeakyReLU(0.2, inplace = True),\n",
    "          nn.Conv2d(64, 128, 4, 2, 1, bias = False),\n",
    "          nn.BatchNorm2d(128),\n",
    "          nn.LeakyReLU(0.2, inplace = True),\n",
    "          nn.Conv2d(128, 256, 4, 2, 1, bias = False),\n",
    "          nn.BatchNorm2d(256),\n",
    "          nn.LeakyReLU(0.2, inplace = True),\n",
    "          nn.Conv2d(256, 512, 4, 2, 1, bias = False),\n",
    "          nn.BatchNorm2d(512),\n",
    "          nn.LeakyReLU(0.2, inplace = True),\n",
    "          nn.Conv2d(512, 1, 4, 1, 0, bias = False),\n",
    "          nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self,x):\n",
    "    output = self.main(x)\n",
    "    return output.view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 326
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 812,
     "status": "ok",
     "timestamp": 1583986993522,
     "user": {
      "displayName": "zeinab taghavi",
      "photoUrl": "",
      "userId": "07214630829740929140"
     },
     "user_tz": -210
    },
    "id": "_7T3HoGzKrE9",
    "outputId": "60d6f1df-cfcf-4c85-b12d-a25130a18493"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D(\n",
       "  (main): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
       "    (12): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gen = G()\n",
    "model_gen.apply(weight_init)\n",
    "\n",
    "model_dis = D()\n",
    "model_dis.apply(weight_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZI200E8OKtRI"
   },
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "optimG = torch.optim.Adam(model_gen.parameters(), lr = 0.0002)\n",
    "optimD = torch.optim.Adam(model_dis.parameters(), lr = 0.0002)\n",
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3295,
     "status": "error",
     "timestamp": 1583997842053,
     "user": {
      "displayName": "zeinab taghavi",
      "photoUrl": "",
      "userId": "07214630829740929140"
     },
     "user_tz": -210
    },
    "id": "S33L2Rz703Xk",
    "outputId": "692c0a3a-9f03-4e4f-e634-e5577ae1f84a"
   },
   "outputs": [],
   "source": [
    "model_dis.load_state_dict(torch.load('model_dis.pth'))\n",
    "model_gen.load_state_dict(torch.load('model_gwn.pth'))\n",
    "model_dis.eval()\n",
    "model_gen.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6700735,
     "status": "ok",
     "timestamp": 1583483840409,
     "user": {
      "displayName": "zeinab taghavi",
      "photoUrl": "",
      "userId": "07214630829740929140"
     },
     "user_tz": -210
    },
    "id": "SXZIdUwNKvVE",
    "outputId": "d2be03c7-160f-4a77-c050-6b1f7b6c596a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    for i, data in enumerate(dataloader, 0):      \n",
    "        model_dis.zero_grad()\n",
    "        real, _ = data\n",
    "        input = real\n",
    "        target = torch.ones(input.size()[0])\n",
    "        output = model_dis(input)\n",
    "        err_dis_real = criterion(output, target)\n",
    "\n",
    "        noise = torch.randn(input.size()[0], 100, 1, 1)\n",
    "        fake = model_gen(noise)\n",
    "        target = torch.zeros(input.size()[0])\n",
    "        output = model_dis(fake.detach())\n",
    "\n",
    "        err_dis_fake = criterion(output , target)\n",
    "        err_dis = err_dis_fake + err_dis_real\n",
    "        err_dis.backward()\n",
    "        optimD.step()\n",
    "\n",
    "\n",
    "        model_gen.zero_grad()\n",
    "        target = torch.ones(input.size()[0])\n",
    "        output = model_dis(fake)\n",
    "        err_gen = criterion(output , target)\n",
    "        err_gen.backward()\n",
    "        optimG.step()\n",
    "\n",
    "        print(epoch , i)\n",
    "        if i % 100 == 0 :\n",
    "            vutils.save_image(real , f'real_image{i}.jpg',normalize=True)\n",
    "            vutils.save_image(fake.data , f'fake_image{i}.jpg',normalize=True)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m1FaW0cYK-T7"
   },
   "outputs": [],
   "source": [
    "torch.save(model_dis.state_dict(), 'model_dis.pth')\n",
    "torch.save(model_gen.state_dict(), 'model_gwn.pth')\n",
    "model_dis.load_state_dict(torch.load('model_dis.pth'))\n",
    "model_gen.load_state_dict(torch.load('model_gwn.pth'))\n",
    "model_dis.eval()\n",
    "model_gen.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BQt19FyyfIl0"
   },
   "outputs": [],
   "source": [
    "# Done!"
   ]
  },
  {
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