Capnet.ipynb

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      "name": "Capnet.ipynb",
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      "mount_file_id": "1K2iaWOU1HrQ3L1Dr7vf-EceySSg747fk",
      "authorship_tag": "ABX9TyP0yplEVs+gq9ZM6hwdzm9w",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KHarsh98/Capsule-Forensics-v2/blob/master/Capnet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lKtwj9YHyNP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import torch\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gL6NYYfgKatW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "Copyright (c) 2019, National Institute of Informatics\n",
        "All rights reserved.\n",
        "Author: Huy H. Nguyen\n",
        "-----------------------------------------------------\n",
        "Script for Capsule-Forensics-v2 model\n",
        "\"\"\"\n",
        "\n",
        "import sys\n",
        "sys.setrecursionlimit(15000)\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "import torch.backends.cudnn as cudnn\n",
        "from torch.autograd import Variable\n",
        "import torchvision.models as models\n",
        "\n",
        "NO_CAPS= 10 #Changed from 10 to 15 capsules\n",
        "\n",
        "class StatsNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(StatsNet, self).__init__()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.data.shape[0], x.data.shape[1], x.data.shape[2]*x.data.shape[3])\n",
        "\n",
        "        mean = torch.mean(x, 2)\n",
        "        std = torch.std(x, 2)\n",
        "\n",
        "        return torch.stack((mean, std), dim=1)\n",
        "\n",
        "class View(nn.Module):\n",
        "    def __init__(self, *shape):\n",
        "        super(View, self).__init__()\n",
        "        self.shape = shape\n",
        "\n",
        "    def forward(self, input):\n",
        "        return input.view(self.shape)\n",
        "\n",
        "\n",
        "class VggExtractor(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VggExtractor, self).__init__()\n",
        "\n",
        "        self.vgg_1 = self.Vgg(models.vgg19(pretrained=True), 0, 18)\n",
        "        self.vgg_1.eval()\n",
        "\n",
        "    def Vgg(self, vgg, begin, end):\n",
        "        features = nn.Sequential(*list(vgg.features.children())[begin:(end+1)])\n",
        "        return features\n",
        "\n",
        "    def forward(self, input):\n",
        "        return self.vgg_1(input)\n",
        "\n",
        "class FeatureExtractor(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(FeatureExtractor, self).__init__()\n",
        "\n",
        "        self.capsules = nn.ModuleList([\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(96, 64, kernel_size=3, stride=1, padding=1), #changed from 256 to 96 filters\n",
        "                nn.BatchNorm2d(64), #changed from 64 to 16 - REVERSED\n",
        "                nn.ReLU(),\n",
        "                nn.Conv2d(64, 16, kernel_size=3, stride=1, padding=1), #changed from 64 to 16 neurons - REVERSED\n",
        "                nn.BatchNorm2d(16),\n",
        "                nn.ReLU(),\n",
        "                StatsNet(),\n",
        "\n",
        "                nn.Conv1d(2, 8, kernel_size=5, stride=2, padding=2),#changed from 8 to 4 neurons - EEVERSED\n",
        "                nn.BatchNorm1d(8), #changed from 8 to 4\n",
        "                nn.Conv1d(8, 1, kernel_size=3, stride=1, padding=1), #changed from 8 to 4 - REVERSED\n",
        "                nn.BatchNorm1d(1),\n",
        "                View(-1, 8),\n",
        "                )\n",
        "                for _ in range(NO_CAPS)]\n",
        "        )\n",
        "\n",
        "    def squash(self, tensor, dim):\n",
        "        squared_norm = (tensor ** 2).sum(dim=dim, keepdim=True)\n",
        "        scale = squared_norm / (1 + squared_norm)\n",
        "        return scale * tensor / (torch.sqrt(squared_norm))\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = [capsule(x.detach()) for capsule in self.capsules]\n",
        "        output = torch.stack(outputs, dim=-1)\n",
        "\n",
        "        return self.squash(output, dim=-1)\n",
        "\n",
        "class RoutingLayer(nn.Module):\n",
        "    def __init__(self, gpu_id, num_input_capsules, num_output_capsules, data_in, data_out, num_iterations):\n",
        "        super(RoutingLayer, self).__init__()\n",
        "\n",
        "        self.gpu_id = gpu_id\n",
        "        self.num_iterations = num_iterations\n",
        "        self.route_weights = nn.Parameter(torch.randn(num_output_capsules, num_input_capsules, data_out, data_in))\n",
        "\n",
        "\n",
        "    def squash(self, tensor, dim):\n",
        "        squared_norm = (tensor ** 2).sum(dim=dim, keepdim=True)\n",
        "        scale = squared_norm / (1 + squared_norm)\n",
        "        return scale * tensor / (torch.sqrt(squared_norm))\n",
        "\n",
        "    def forward(self, x, random, dropout):\n",
        "        # x[b, data, in_caps]\n",
        "\n",
        "        x = x.transpose(2, 1)\n",
        "        # x[b, in_caps, data]\n",
        "\n",
        "        if random:\n",
        "            noise = Variable(0.01*torch.randn(*self.route_weights.size()))\n",
        "            if self.gpu_id >= 0:\n",
        "                noise = noise.cuda(self.gpu_id)\n",
        "            route_weights = self.route_weights + noise\n",
        "        else:\n",
        "            route_weights = self.route_weights\n",
        "\n",
        "        priors = route_weights[:, None, :, :, :] @ x[None, :, :, :, None]\n",
        "\n",
        "        # route_weights [out_caps , 1 , in_caps , data_out , data_in]\n",
        "        # x             [   1     , b , in_caps , data_in ,    1    ]\n",
        "        # priors        [out_caps , b , in_caps , data_out,    1    ]\n",
        "\n",
        "        priors = priors.transpose(1, 0)\n",
        "        # priors[b, out_caps, in_caps, data_out, 1]\n",
        "\n",
        "        if dropout > 0.0:\n",
        "            drop = Variable(torch.FloatTensor(*priors.size()).bernoulli(1.0- dropout))\n",
        "            if self.gpu_id >= 0:\n",
        "                drop = drop.cuda(self.gpu_id)\n",
        "            priors = priors * drop\n",
        "            \n",
        "\n",
        "        logits = Variable(torch.zeros(*priors.size()))\n",
        "        # logits[b, out_caps, in_caps, data_out, 1]\n",
        "\n",
        "        if self.gpu_id >= 0:\n",
        "            logits = logits.cuda(self.gpu_id)\n",
        "\n",
        "        num_iterations = self.num_iterations\n",
        "\n",
        "        for i in range(num_iterations):\n",
        "            probs = F.softmax(logits, dim=2)\n",
        "            outputs = self.squash((probs * priors).sum(dim=2, keepdim=True), dim=3)\n",
        "\n",
        "            if i != self.num_iterations - 1:\n",
        "                delta_logits = priors * outputs\n",
        "                logits = logits + delta_logits\n",
        "\n",
        "        # outputs[b, out_caps, 1, data_out, 1]\n",
        "        outputs = outputs.squeeze()\n",
        "\n",
        "        if len(outputs.shape) == 3:\n",
        "            outputs = outputs.transpose(2, 1).contiguous() \n",
        "        else:\n",
        "            outputs = outputs.unsqueeze_(dim=0).transpose(2, 1).contiguous()\n",
        "        # outputs[b, data_out, out_caps]\n",
        "\n",
        "        return outputs\n",
        "\n",
        "\n",
        "class CapsuleNet(nn.Module):\n",
        "    def __init__(self, num_class, gpu_id):\n",
        "        super(CapsuleNet, self).__init__()\n",
        "\n",
        "        self.num_class = num_class\n",
        "        self.fea_ext = FeatureExtractor()\n",
        "        self.fea_ext.apply(self.weights_init)\n",
        "\n",
        "        self.routing_stats = RoutingLayer(gpu_id=gpu_id, num_input_capsules=NO_CAPS, num_output_capsules=num_class, data_in=8, data_out=4, num_iterations=2)\n",
        "\n",
        "    def weights_init(self, 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)\n",
        "\n",
        "    def forward(self, x, random=False, dropout=0.0):\n",
        "\n",
        "        z = self.fea_ext(x)\n",
        "        z = self.routing_stats(z, random, dropout=dropout)\n",
        "        # z[b, data, out_caps]\n",
        "\n",
        "        classes = F.softmax(z, dim=-1)\n",
        "\n",
        "        class_ = classes.detach()\n",
        "        class_ = class_.mean(dim=1)\n",
        "\n",
        "        return classes, class_\n",
        "\n",
        "class CapsuleLoss(nn.Module):\n",
        "    def __init__(self, gpu_id):\n",
        "        super(CapsuleLoss, self).__init__()\n",
        "        self.cross_entropy_loss = nn.CrossEntropyLoss()\n",
        "\n",
        "        if gpu_id >= 0:\n",
        "            self.cross_entropy_loss.cuda(gpu_id)\n",
        "\n",
        "    def forward(self, classes, labels):\n",
        "        loss_t = self.cross_entropy_loss(classes[:,0,:], labels)\n",
        "\n",
        "        for i in range(classes.size(1) - 1):\n",
        "            loss_t = loss_t + self.cross_entropy_loss(classes[:,i+1,:], labels)\n",
        "\n",
        "        return loss_t"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVGeYDTGF8hX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "import torchvision.transforms as transforms\n",
        "transform = transforms.Compose([\n",
        "        transforms.Resize((300, 300)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n",
        "    ])\n",
        "\n",
        "def image_load(image_path):\n",
        "  if image_path.split('/')[5] == 'Fake':\n",
        "    image = Image.open(image_path).convert('RGB')\n",
        "  else:\n",
        "    image = Image.open(image_path)\n",
        "  image = transform(image).float()\n",
        "  #image = Variable(image, requires_grad=True)\n",
        "  image = image.unsqueeze(0)  #this is for VGG, may not be needed for ResNet\n",
        "  return image.cuda()  #\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0iEwDR_LEdQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code for extracting model\n",
        "class Conv2dAuto(nn.Conv2d):\n",
        "  def __init__(self, *args, **kwargs):\n",
        "    super().__init__(*args, **kwargs)\n",
        "    self.padding = (self.kernel_size[0]//2, self.kernel_size[1]//2)\n",
        "\n",
        "\n",
        "def activation_func(activation):\n",
        "    return  nn.ModuleDict([\n",
        "        ['relu', nn.ReLU(inplace=False)],\n",
        "        ['leaky_relu', nn.LeakyReLU(negative_slope=0.01, inplace=False)],\n",
        "        ['selu', nn.SELU(inplace=False)],\n",
        "        ['none', nn.Identity()]\n",
        "    ])[activation]\n",
        "\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, activation='relu'):\n",
        "        super().__init__()\n",
        "        self.in_channels, self.out_channels, self.activation = in_channels, out_channels, activation\n",
        "        self.blocks = nn.Identity()\n",
        "        self.activate = activation_func(activation)\n",
        "        self.shortcut = nn.Identity()   \n",
        "    \n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "        if self.should_apply_shortcut: residual = self.shortcut(x)\n",
        "        x = self.blocks(x)\n",
        "        x += residual\n",
        "        x = self.activate(x)\n",
        "        return x\n",
        "    \n",
        "    @property\n",
        "    def should_apply_shortcut(self):\n",
        "        return self.in_channels != self.out_channels\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cp37bpfYSvKO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FeatExtractor(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(FeatExtractor, self).__init__()\n",
        "    IN_CHANNELS=3 #CHANGE NUMBER OF CHANNELS HERE\n",
        "    self.conv1 = torch.nn.Conv2d(in_channels=IN_CHANNELS, out_channels= 96, kernel_size = 7, stride = 4)\n",
        "    self.resBLOCK1 = ResidualBlock(2, 96)\n",
        "    self.resBLOCK2 = ResidualBlock(2, 128)\n",
        "    self.resBLOCK3 = ResidualBlock(2, 256)\n",
        "\n",
        "    #NOTE : How is the loss working to learn the correct characteristics of the generated images?\n",
        "\n",
        "  def forward(self, x0, x1):\n",
        "    out0 = self.conv1(x0)\n",
        "    out01 = self.resBLOCK1(out0)\n",
        "    out02 = self.resBLOCK2(out01)\n",
        "    out03= self.resBLOCK3(out02)\n",
        "\n",
        "    out1 = self.conv1(x1)\n",
        "    out11 = self.resBLOCK1(out1)\n",
        "    out12 = self.resBLOCK2(out11)\n",
        "    out13 = self.resBLOCK3(out12)\n",
        "\n",
        "    return out03, out13\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIOK3wBCJa9C",
        "colab_type": "code",
        "outputId": "3ee70786-ddff-4f9c-d3b1-ccad70518cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# FOR TRAINING\n",
        "\n",
        "import sys\n",
        "\n",
        "sys.setrecursionlimit(15000)\n",
        "import os\n",
        "import torch\n",
        "import torch.backends.cudnn as cudnn\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "from torch.optim import Adam\n",
        "\n",
        "from tqdm import tqdm\n",
        "import argparse\n",
        "from sklearn import metrics\n",
        "from scipy.optimize import brentq\n",
        "from scipy.interpolate import interp1d\n",
        "from sklearn.metrics import roc_curve\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "\n",
        "\n",
        "transform_fwd = transforms.Compose([\n",
        "        transforms.Resize((300, 300)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n",
        "    ])\n",
        "\n",
        "validation_split = .2\n",
        "shuffle_dataset = True\n",
        "random_seed= 42\n",
        "\n",
        "dataset= dset.ImageFolder(\"/content/drive/My Drive/FFHQ Dataset\", transform=transform_fwd)\n",
        "assert dataset\n",
        "\n",
        "\n",
        "# Creating data indices for training and validation splits:\n",
        "dataset_size = len(dataset)\n",
        "indices = list(range(dataset_size))\n",
        "split = int(np.floor(validation_split * dataset_size))\n",
        "if shuffle_dataset :\n",
        "    np.random.seed(random_seed)\n",
        "    np.random.shuffle(indices)\n",
        "split = 1000\n",
        "train_indices, val_indices = indices[split:split+2000], indices[:split]\n",
        "\n",
        "# Creating PT data samplers and loaders:\n",
        "train_sampler = SubsetRandomSampler(train_indices)\n",
        "valid_sampler = SubsetRandomSampler(val_indices)\n",
        "\n",
        "#dataset_train = dset.ImageFolder(\"/kaggle/input/sids-handcrafted-dataset/Sid's Handcrafted Dataset/\", transform=transform_fwd)\n",
        "#assert dataset_train\n",
        "dataloader_train = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=False,\n",
        "                                                  num_workers=0, sampler = train_sampler)\n",
        "\n",
        "dataloader_valid = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=False,\n",
        "                                                  num_workers=0, sampler = valid_sampler)\n",
        "\n",
        "#vgg_ext = VggExtractor()\n",
        "capnet = CapsuleNet(2, 0)\n",
        "featExt = torch.load(\"/content/drive/My Drive/model_checkpoints/featext.pt\")\n",
        "featExt.eval()\n",
        "capsule_loss = CapsuleLoss(0)\n",
        "optimizer = Adam(capnet.parameters(), lr =0.0005, betas=(0.9,0.999))\n",
        "\n",
        "#capnet.load_state_dict(torch.load(\"/kaggle/input/capsuleforensicbinarymodel/capsule_21.pt\"))\n",
        "#capnet.eval()\n",
        "\n",
        "capnet.cuda(0)\n",
        "featExt.cuda(0)\n",
        "\n",
        "    ##################################################################################\n",
        "\n",
        "#tol_label = np.array([], dtype=np.float)\n",
        "#tol_pred = np.array([], dtype=np.float)\n",
        "#tol_pred_prob = np.array([], dtype=np.float)\n",
        "\n",
        "\n",
        "count = 0\n",
        "loss_test = 0\n",
        "\n",
        "for epoch in range (0, 25):\n",
        "    count = 0\n",
        "    loss_train =0\n",
        "    loss_test = 0\n",
        "    tol_label = np.array([], dtype=np.float)\n",
        "    tol_pred = np.array([], dtype=np.float)\n",
        "    \n",
        "    for img_data, labels_data in tqdm(dataloader_train):\n",
        "        labels_data[labels_data > 0] = 5\n",
        "        labels_data[labels_data == 0] = 1\n",
        "        labels_data[labels_data == 5] = 0\n",
        "\n",
        "        img_label = labels_data.numpy().astype(np.float)\n",
        "        img_data = img_data.cuda(0)\n",
        "        labels_data = labels_data.cuda(0)\n",
        "\n",
        "        input_v = Variable(img_data)\n",
        "        \n",
        "        random_real_image = image_load(\"/content/drive/My Drive/FFHQ Dataset/Real/01089.png\")\n",
        "\n",
        "        x, _= featExt(input_v, random_real_image)\n",
        "        classes, class_ = capnet(x, random=False)\n",
        "        \n",
        "        loss_dis = capsule_loss(classes, Variable(labels_data, requires_grad=False))\n",
        "        loss_dis_data = loss_dis.item()\n",
        "        \n",
        "        loss_dis.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        output_dis = class_.data.cpu()\n",
        "        output_pred = np.zeros((output_dis.shape[0]), dtype=np.float)\n",
        "\n",
        "        for i in range(output_dis.shape[0]):\n",
        "            if output_dis[i, 1] >= output_dis[i, 0]:\n",
        "                output_pred[i] = 1.0\n",
        "            else:\n",
        "                output_pred[i] = 0.0\n",
        "\n",
        "        tol_label = np.concatenate((tol_label, img_label))\n",
        "        tol_pred = np.concatenate((tol_pred, output_pred))\n",
        "        \n",
        "    #pred_prob = torch.softmax(output_dis, dim=1)\n",
        "    #tol_pred_prob = np.concatenate((tol_pred_prob, pred_prob[:,1].data.numpy()))\n",
        "        loss_train+=loss_dis_data\n",
        "        count+= 1\n",
        "\n",
        "    acc_test = metrics.accuracy_score(tol_label, tol_pred)\n",
        "\n",
        "    loss_test /= count\n",
        "    \n",
        "    print('[Epoch %d] Train loss: %.4f   acc: %.2f'\n",
        "        % (epoch, loss_train, acc_test))\n",
        "\n",
        "\n",
        "#fpr, tpr, thresholds = roc_curve(tol_label, tol_pred_prob, pos_label=1)\n",
        "#eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)\n",
        "\n",
        "#fnr = 1 - tpr\n",
        "#hter = (fpr + fnr)/2\n",
        "#print('[Epoch %d] Test acc: %.2f   EER: %.2f' % (0, acc_test * 100, eer * 100))\n",
        "    #text_writer.write('%d,%.2f,%.2f\\n' % (opt.id, acc_test * 100, eer * 100))\n",
        "    \n",
        "\n",
        "\n",
        "print(acc_test * 100)\n",
        "print(tol_pred)  \n",
        "print(np.count_nonzero(tol_pred > 0.5))\n",
        "\n",
        "##### EVAL\n",
        "capnet.eval()\n",
        "\n",
        "capnet.cuda(0)\n",
        "\n",
        "    ##################################################################################\n",
        "\n",
        "tol_label = np.array([], dtype=np.float)\n",
        "tol_pred = np.array([], dtype=np.float)\n",
        "tol_pred_prob = np.array([], dtype=np.float)\n",
        "\n",
        "\n",
        "count = 0\n",
        "loss_test = 0\n",
        "\n",
        "for img_data, labels_data in tqdm(dataloader_valid):\n",
        "        \n",
        "    labels_data[labels_data > 0] = 5\n",
        "    labels_data[labels_data == 0] = 1\n",
        "    labels_data[labels_data == 5] = 0\n",
        "\n",
        "    img_label = labels_data.numpy().astype(np.float)\n",
        "    img_data = img_data.cuda(0)\n",
        "    labels_data = labels_data.cuda(0)\n",
        "\n",
        "    input_v = Variable(img_data)\n",
        "    \n",
        "    random_real_image = image_load(\"/content/drive/My Drive/FFHQ Dataset/Real/01089.png\")\n",
        "    x, _ = featExt(input_v, random_real_image)\n",
        "    classes, class_ = capnet(x, random=False)\n",
        "\n",
        "    output_dis = class_.data.cpu()\n",
        "    output_pred = np.zeros((output_dis.shape[0]), dtype=np.float)\n",
        "\n",
        "    for i in range(output_dis.shape[0]):\n",
        "        if output_dis[i, 1] >= output_dis[i, 0]:\n",
        "            output_pred[i] = 1.0\n",
        "        else:\n",
        "            output_pred[i] = 0.0\n",
        "\n",
        "    tol_label = np.concatenate((tol_label, img_label))\n",
        "    tol_pred = np.concatenate((tol_pred, output_pred))\n",
        "        \n",
        "    #pred_prob = torch.softmax(output_dis, dim=1)\n",
        "    #tol_pred_prob = np.concatenate((tol_pred_prob, pred_prob[:,1].data.numpy()))\n",
        "    count+= 1\n",
        "\n",
        "acc_test = metrics.accuracy_score(tol_label, tol_pred)\n",
        "\n",
        "loss_test /= count\n",
        "    \n",
        "    #print('[Epoch %d] Train loss: %.4f   acc: %.2f'\n",
        "     #   % (epoch, loss_train))\n",
        "\n",
        "#fpr, tpr, thresholds = roc_curve(tol_label, tol_pred_prob, pos_label=1)\n",
        "#eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)\n",
        "\n",
        "#fnr = 1 - tpr\n",
        "#hter = (fpr + fnr)/2\n",
        "#print('[Epoch %d] Test acc: %.2f   EER: %.2f' % (0, acc_test * 100, eer * 100))\n",
        "    #text_writer.write('%d,%.2f,%.2f\\n' % (opt.id, acc_test * 100, eer * 100))\n",
        "\n",
        "print(acc_test * 100)\n",
        "print(tol_pred)  \n",
        "print(np.count_nonzero(tol_pred > 0.5))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [27:29<00:00, 26.18s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 0] Train loss: 138.1879   acc: 0.98\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.47s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 1] Train loss: 132.5056   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:36<00:00,  2.48s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 2] Train loss: 130.2943   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.47s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 3] Train loss: 128.8053   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.47s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 4] Train loss: 127.8345   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 5] Train loss: 127.0569   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 6] Train loss: 126.4765   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.47s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 7] Train loss: 126.0510   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:36<00:00,  2.48s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 8] Train loss: 125.7161   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.46s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 9] Train loss: 125.4352   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.46s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 10] Train loss: 125.1944   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 11] Train loss: 124.9907   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 12] Train loss: 124.8161   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:33<00:00,  2.44s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 13] Train loss: 124.6631   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:33<00:00,  2.44s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 14] Train loss: 124.5294   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 15] Train loss: 124.4133   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 16] Train loss: 124.3118   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:32<00:00,  2.43s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 17] Train loss: 124.2234   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:33<00:00,  2.43s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 18] Train loss: 124.1474   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:32<00:00,  2.41s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 19] Train loss: 124.0798   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:35<00:00,  2.46s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 20] Train loss: 124.0200   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:33<00:00,  2.43s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 21] Train loss: 123.9657   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 22] Train loss: 123.9154   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:34<00:00,  2.45s/it]\n",
            "  0%|          | 0/63 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 23] Train loss: 123.8690   acc: 1.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63/63 [02:33<00:00,  2.44s/it]\n",
            "  0%|          | 0/32 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Epoch 24] Train loss: 123.8267   acc: 1.00\n",
            "100.0\n",
            "[0. 0. 0. ... 0. 0. 0.]\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 32/32 [18:21<00:00, 34.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "100.0\n",
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            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbezHeLZKgYH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#FOR VALIDATION\n",
        "import sys\n",
        "\n",
        "sys.setrecursionlimit(15000)\n",
        "import os\n",
        "import torch\n",
        "import torch.backends.cudnn as cudnn\n",
        "import numpy as np\n",
        "from torch.autograd import Variable\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "from tqdm import tqdm\n",
        "import argparse\n",
        "from sklearn import metrics\n",
        "from scipy.optimize import brentq\n",
        "from scipy.interpolate import interp1d\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "\n",
        "transform_fwd = transforms.Compose([\n",
        "        transforms.Resize((300, 300)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n",
        "    ])\n",
        "\n",
        "dataset_test = dset.ImageFolder(\"/content/drive/My Drive/FFHQ Dataset\", transform=transform_fwd)\n",
        "assert dataset_test\n",
        "dataloader_test = torch.utils.data.DataLoader(dataset_test, batch_size=32, shuffle=False,\n",
        "                                                  num_workers=0)\n",
        "\n",
        "#vgg_ext = VggExtractor()\n",
        "capnet = CapsuleNet(2, 0)\n",
        "\n",
        "\n",
        "capnet.load_state_dict(torch.load(\"/content/capsule_21.pt\"))\n",
        "capnet.eval()\n",
        "\n",
        "featExt = torch.load(\"/content/drive/My Drive/model_checkpoints/featext.pt\")\n",
        "featExt.eval()\n",
        "\n",
        "vgg_ext.cuda(0)\n",
        "capnet.cuda(0)\n",
        "featExt.cuda(0)\n",
        "    ##################################################################################\n",
        "\n",
        "tol_label = np.array([], dtype=np.float)\n",
        "tol_pred = np.array([], dtype=np.float)\n",
        "tol_pred_prob = np.array([], dtype=np.float)\n",
        "\n",
        "\n",
        "count = 0\n",
        "loss_test = 0\n",
        "\n",
        "for img_data, labels_data in tqdm(dataloader_test):\n",
        "        \n",
        "    labels_data[labels_data > 0] = 5\n",
        "    labels_data[labels_data == 0] = 1\n",
        "    labels_data[labels_data == 5] = 0\n",
        "\n",
        "    img_label = labels_data.numpy().astype(np.float)\n",
        "    img_data = img_data.cuda(0)\n",
        "    labels_data = labels_data.cuda(0)\n",
        "\n",
        "    input_v = Variable(img_data)\n",
        "    random_real_image = image_load(\"/content/drive/My Drive/FFHQ Dataset/Real/01089.png\")\n",
        "\n",
        "   #x = vgg_ext(input_v)\n",
        "    \n",
        "    #Change from vgg extractor to D1 Feat Extractor\n",
        "    x, _ = featExt(input_v, random_real_image) \n",
        "    classes, class_ = capnet(x, random=False)\n",
        "\n",
        "    output_dis = class_.data.cpu()\n",
        "    output_pred = np.zeros((output_dis.shape[0]), dtype=np.float)\n",
        "\n",
        "    for i in range(output_dis.shape[0]):\n",
        "        if output_dis[i, 1] >= output_dis[i, 0]:\n",
        "            output_pred[i] = 1.0\n",
        "        else:\n",
        "            output_pred[i] = 0.0\n",
        "\n",
        "    tol_label = np.concatenate((tol_label, img_label))\n",
        "    tol_pred = np.concatenate((tol_pred, output_pred))\n",
        "        \n",
        "    #pred_prob = torch.softmax(output_dis, dim=1)\n",
        "    #tol_pred_prob = np.concatenate((tol_pred_prob, pred_prob[:,1].data.numpy()))\n",
        "    count+= 1\n",
        "\n",
        "acc_test = metrics.accuracy_score(tol_label, tol_pred)\n",
        "\n",
        "loss_test /= count\n",
        "\n",
        "#fpr, tpr, thresholds = roc_curve(tol_label, tol_pred_prob, pos_label=1)\n",
        "#eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)\n",
        "\n",
        "#fnr = 1 - tpr\n",
        "#hter = (fpr + fnr)/2\n",
        "#print('[Epoch %d] Test acc: %.2f   EER: %.2f' % (0, acc_test * 100, eer * 100))\n",
        "    #text_writer.write('%d,%.2f,%.2f\\n' % (opt.id, acc_test * 100, eer * 100))\n",
        "\n",
        "print(acc_test * 100)\n",
        "print(tol_pred)  \n",
        "print(np.count_nonzero(tol_pred > 0.5))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}