cam_gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "#import foolbox\n",
    "import tqdm\n",
    "import imageio\n",
    "from torchvision import transforms\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import os\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import skimage.transform\n",
    "import skimage\n",
    "from skimage import io\n",
    "import cv2\n",
    "from torch.nn import functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.set_device(3)\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calcyulating the mean and the standard deviation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torchvision.models.resnet50(pretrained=True, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n",
    "model.to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ResNet, self).__init__()\n",
    "        \n",
    "        # define the resnet152\n",
    "        self.resnet = model\n",
    "        \n",
    "        # isolate the feature blocks\n",
    "        self.features = nn.Sequential(self.resnet.conv1,\n",
    "                                      self.resnet.bn1,\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False),\n",
    "                                      self.resnet.layer1, \n",
    "                                      self.resnet.layer2, \n",
    "                                      self.resnet.layer3, \n",
    "                                      self.resnet.layer4)\n",
    "        \n",
    "        # average pooling layer\n",
    "        self.avgpool = self.resnet.avgpool\n",
    "        \n",
    "        # classifier\n",
    "        self.classifier1 = self.resnet.fc\n",
    "       \n",
    "        \n",
    "        # gradient placeholder\n",
    "        self.gradient = None\n",
    "    \n",
    "    # hook for the gradients\n",
    "    def activations_hook(self, grad):\n",
    "        self.gradient = grad\n",
    "    \n",
    "    def get_gradient(self):\n",
    "        return self.gradient\n",
    "    \n",
    "    def get_activations(self, x):\n",
    "        return self.features(x)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \n",
    "        # extract the features\n",
    "        out_x = self.features(x)\n",
    "        \n",
    "        # register the hook\n",
    "        h = out_x.register_hook(self.activations_hook)\n",
    "        \n",
    "        # complete the forward pass\n",
    "        x = self.avgpool(out_x)\n",
    "        x = x.view((1, -1))\n",
    "        x = self.classifier1(x)\n",
    "       \n",
    "        \n",
    "        return x, out_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))\n",
    "std = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class cam_dataset(Dataset):\n",
    "    def __init__(self,img_transform1):\n",
    "        #/workspace/data_fine_grain/cub200/raw/CUB_200_2011/(base) ironman@bigbox:~$ CUB_200_2011\n",
    "        self.dir_path = './imagenet/fgsm/'\n",
    "        self.files_list = []\n",
    "        self.original_label = []\n",
    "        self.misclassified_label = []\n",
    "        self.img_transform1 = img_transform1\n",
    "        \n",
    "        for r, d, f in os.walk(self.dir_path):\n",
    "            for file in f:\n",
    "                if '.png' in file and file != \"fgsm_16-checkpoint.png\":\n",
    "                    #print(\"here\")\n",
    "                    #print(file)\n",
    "                    #print(file.split('.')[-2].split('_')[-2])\n",
    "                    #print(file.split('.')[-2].split('_')[-1])\n",
    "                    \n",
    "                    self.files_list.append(os.path.join(r,file))\n",
    "                    self.original_label.append(int(file.split('.')[-2].split('_')[-2]))\n",
    "                    self.misclassified_label.append(int(file.split('.')[-2].split('_')[-1]))\n",
    "\n",
    "        \n",
    "            \n",
    "    def __getitem__(self,index):\n",
    "        #if(self.train):\n",
    "           \n",
    "            img1_path = self.files_list[index]\n",
    "            img1_path = img1_path.rstrip()\n",
    "            img1 = io.imread(img1_path)\n",
    "            \n",
    "            #print(img1.shape)\n",
    "            if(img1.shape.__len__() == 2):\n",
    "                img1 = np.repeat(img1[:, :, np.newaxis], 3, axis=2) \n",
    "            #print(img1_path)\n",
    "            #plt.imshow(img1)\n",
    "            img1 = img1.astype(np.float)\n",
    "            img1 = cv2.resize(img1,(128,128),interpolation = cv2.INTER_AREA)\n",
    "           \n",
    "            img1 = self.img_transform1(img1)\n",
    "            label = self.original_label[index]\n",
    "            label = int(label)\n",
    "            mislabel = self.misclassified_label[index]\n",
    "            mislabel = int(mislabel)\n",
    "            return {\"img\": img1, \"label\": label,\"mislabel\":mislabel,\"path\": img1_path}\n",
    "   \n",
    "        #return img1,img2,label\n",
    "    def __len__(self):\n",
    "        return len(self.original_label)\n",
    "\n",
    "\n",
    "test_set = cam_dataset(transform)\n",
    "\n",
    "testloader = DataLoader(test_set,batch_size =1,shuffle = True, num_workers =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = test_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_10_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10817_431_452.png\n",
      "431\n",
      "check the name\n",
      "fgsm_10817_431_452\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17012_673_882.png\n",
      "673\n",
      "check the name\n",
      "fgsm_17012_673_882\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15974_633_487.png\n",
      "633\n",
      "check the name\n",
      "fgsm_15974_633_487\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_28_1_391.png\n",
      "1\n",
      "check the name\n",
      "fgsm_28_1_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_38_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_38_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_12_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_12_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_3_0_793.png\n",
      "0\n",
      "check the name\n",
      "fgsm_3_0_793\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11253_448_669.png\n",
      "448\n",
      "check the name\n",
      "fgsm_11253_448_669\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_39_1_845.png\n",
      "1\n",
      "check the name\n",
      "fgsm_39_1_845\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_21_0_395.png\n",
      "0\n",
      "check the name\n",
      "fgsm_21_0_395\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_4262_172_203.png\n",
      "172\n",
      "check the name\n",
      "fgsm_4262_172_203\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_17_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_20_0_758.png\n",
      "0\n",
      "check the name\n",
      "fgsm_20_0_758\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([920], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_46_1_31.png\n",
      "1\n",
      "check the name\n",
      "fgsm_46_1_31\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_44_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_44_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17188_680_889.png\n",
      "680\n",
      "check the name\n",
      "fgsm_17188_680_889\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_2_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_2_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_50_1_33.png\n",
      "1\n",
      "check the name\n",
      "fgsm_50_1_33\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_32_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_32_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_25_1_669.png\n",
      "1\n",
      "check the name\n",
      "fgsm_25_1_669\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_42_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_42_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_35_1_32.png\n",
      "1\n",
      "check the name\n",
      "fgsm_35_1_32\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11_0_4.png\n",
      "0\n",
      "check the name\n",
      "fgsm_11_0_4\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_53_1_728.png\n",
      "1\n",
      "check the name\n",
      "fgsm_53_1_728\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_51_1_0.png\n",
      "1\n",
      "check the name\n",
      "fgsm_51_1_0\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_47_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_47_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_24_0_257.png\n",
      "0\n",
      "check the name\n",
      "fgsm_24_0_257\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_41_1_680.png\n",
      "1\n",
      "check the name\n",
      "fgsm_41_1_680\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_14718_586_803.png\n",
      "586\n",
      "check the name\n",
      "fgsm_14718_586_803\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_30_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_30_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_37_1_112.png\n",
      "1\n",
      "check the name\n",
      "fgsm_37_1_112\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_5_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_5_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_49_1_680.png\n",
      "1\n",
      "check the name\n",
      "fgsm_49_1_680\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_23_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_23_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_7_0_391.png\n",
      "0\n",
      "check the name\n",
      "fgsm_7_0_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_43_1_26.png\n",
      "1\n",
      "check the name\n",
      "fgsm_43_1_26\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_27_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_27_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_16_0_29.png\n",
      "0\n",
      "check the name\n",
      "fgsm_16_0_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_52_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_52_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_4_0_735.png\n",
      "0\n",
      "check the name\n",
      "fgsm_4_0_735\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11699_465_419.png\n",
      "465\n",
      "check the name\n",
      "fgsm_11699_465_419\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_16995_673_606.png\n",
      "673\n",
      "check the name\n",
      "fgsm_16995_673_606\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_22_0_391.png\n",
      "0\n",
      "check the name\n",
      "fgsm_22_0_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_0_0_465.png\n",
      "0\n",
      "check the name\n",
      "fgsm_0_0_465\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_78_3_2.png\n",
      "3\n",
      "check the name\n",
      "fgsm_78_3_2\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15967_633_680.png\n",
      "633\n",
      "check the name\n",
      "fgsm_15967_633_680\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_48_1_327.png\n",
      "1\n",
      "check the name\n",
      "fgsm_48_1_327\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_14_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_14_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_33_1_46.png\n",
      "1\n",
      "check the name\n",
      "fgsm_33_1_46\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11718_465_636.png\n",
      "465\n",
      "check the name\n",
      "fgsm_11718_465_636\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_19_0_961.png\n",
      "0\n",
      "check the name\n",
      "fgsm_19_0_961\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([920], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_45_1_32.png\n",
      "1\n",
      "check the name\n",
      "fgsm_45_1_32\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11543_459_604.png\n",
      "459\n",
      "check the name\n",
      "fgsm_11543_459_604\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_34_1_327.png\n",
      "1\n",
      "check the name\n",
      "fgsm_34_1_327\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_101_3_4.png\n",
      "3\n",
      "check the name\n",
      "fgsm_101_3_4\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_2200_86_83.png\n",
      "86\n",
      "check the name\n",
      "fgsm_2200_86_83\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15_0_395.png\n",
      "0\n",
      "check the name\n",
      "fgsm_15_0_395\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_727_28_71.png\n",
      "28\n",
      "check the name\n",
      "fgsm_727_28_71\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_26_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_26_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_18157_720_509.png\n",
      "720\n",
      "check the name\n",
      "fgsm_18157_720_509\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_8_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_8_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_31_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_31_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15283_606_802.png\n",
      "606\n",
      "check the name\n",
      "fgsm_15283_606_802\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_1_0_876.png\n",
      "0\n",
      "check the name\n",
      "fgsm_1_0_876\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10482_417_572.png\n",
      "417\n",
      "check the name\n",
      "fgsm_10482_417_572\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_102_3_4.png\n",
      "3\n",
      "check the name\n",
      "fgsm_102_3_4\n"
     ]
    }
   ],
   "source": [
    "#plt.imshow(data[\"img\"])\n",
    "for data in testloader:\n",
    "    #net1 = net1.to(DEVICE)\n",
    "    #net2 = net2.to(DEVICE)\n",
    "    img1 = data['img'].to(DEVICE)\n",
    "    print(\"check the image\")\n",
    "    print(img1.shape)\n",
    "    path = data['path']\n",
    "\n",
    "    #img1 = img1.float()\n",
    "    img1 = img1.view(-1,img1.shape[-3],img1.shape[-2],img1.shape[-1])\n",
    "    print(img1.shape)\n",
    "    img1 = img1.float()\n",
    "    #print(img1.shape)\n",
    "    label = data['label'].to(DEVICE)\n",
    "    mislabel = data['mislabel'].to(DEVICE)\n",
    "   \n",
    "    resnet = ResNet()\n",
    "    resnet = resnet.to(DEVICE)\n",
    "    resnet.eval()\n",
    "    print(\"check the image sizes\")\n",
    "    print(img1.shape)\n",
    "    img1 = Variable(img1.cuda(), requires_grad=True)\n",
    "    pred, acts = resnet(img1)\n",
    "    max_index = pred.argmax(dim=1) \n",
    "    print(\"check the max index\")\n",
    "    print(max_index)\n",
    "    ##############################################################for max_index\n",
    "    pred[:,(max_index.item()-1)].backward()\n",
    "    gradients = resnet.get_gradient()\n",
    "    pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])\n",
    "    activations = resnet.get_activations(img1).detach()\n",
    "    for i in range(activations.shape[1]):\n",
    "        activations[:, i, :, :] *= pooled_gradients[i]\n",
    "    heatmap = F.relu(torch.sum(activations, dim=1)).squeeze()\n",
    "    heatmap = np.maximum(heatmap.cpu(), 0)\n",
    "    heatmap /= torch.max(heatmap)\n",
    "    #plt.matshow(heatmap.squeeze())\n",
    "    heatmap = heatmap.numpy()\n",
    "    #print(np.unique(heatmap))\n",
    "    p = np.percentile(heatmap, 97)\n",
    "    heatmap[heatmap>=p]=1\n",
    "    heatmap[heatmap<p]=0\n",
    "    print(np.unique(heatmap))\n",
    "    print(\"CHECK THE PATH\")\n",
    "    print(path[0])\n",
    "    print(label.item())\n",
    "    img = io.imread(path[0])\n",
    "    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "    superimposed_img = heatmap * 0.4 + img\n",
    "    name = path[0].split('/')[-1].split('.')[-2]\n",
    "    print(\"check the name\")\n",
    "    print(name)\n",
    "    \n",
    "    cv2.imwrite('./maxlabel_camfiles/'+str(name)+'_'+str(max_index.item()+1)+'.jpg', superimposed_img)\n",
    "    #########################################################################for label\n",
    "   \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for data in testloader:\n",
    "    #net1 = net1.to(DEVICE)\n",
    "    #net2 = net2.to(DEVICE)\n",
    "    img1 = data['img'].to(DEVICE)\n",
    "    print(\"check the image\")\n",
    "    print(img1.shape)\n",
    "    path = data['path']\n",
    "\n",
    "    #img1 = img1.float()\n",
    "    img1 = img1.view(-1,img1.shape[-3],img1.shape[-2],img1.shape[-1])\n",
    "    print(img1.shape)\n",
    "    img1 = img1.float()\n",
    "    #print(img1.shape)\n",
    "    label = data['label'].to(DEVICE)\n",
    "    print(\"CHECK THE PATH\")\n",
    "    print(path[0])   \n",
    "    resnet = ResNet()\n",
    "    resnet = resnet.to(DEVICE)\n",
    "    resnet.eval()\n",
    "    print(\"check the image sizes\")\n",
    "    print(img1.shape)\n",
    "    img1 = Variable(img1.cuda(), requires_grad=True)\n",
    "    pred, acts = resnet(img1)\n",
    "    max_index = pred.argmax(dim=1) \n",
    "    print(\"check the max index\")\n",
    "    print(max_index)\n",
    "    pred[:,(max_index.item())].backward()\n",
    "    gradients = resnet.get_gradient()\n",
    "    pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])\n",
    "    activations = resnet.get_activations(img1).detach()\n",
    "    for i in range(activations.shape[1]):\n",
    "        activations[:, i, :, :] *= pooled_gradients[i]\n",
    "    heatmap = F.relu(torch.sum(activations, dim=1)).squeeze()\n",
    "    heatmap = np.maximum(heatmap.cpu(), 0)\n",
    "    heatmap /= torch.max(heatmap)\n",
    "    #plt.matshow(heatmap.squeeze())\n",
    "    heatmap = heatmap.numpy()\n",
    "    #print(np.unique(heatmap))\n",
    "    \n",
    "    print(np.unique(heatmap))\n",
    "    \n",
    "    print(label.item())\n",
    "    img = io.imread(path[0])\n",
    "    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
    "    p = np.percentile(heatmap, 70)\n",
    "    heatmap[heatmap>=p]=1\n",
    "    heatmap[heatmap<p]=0\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "    superimposed_img = heatmap * 0.4 + img\n",
    "    name = path[0].split('/')[-1].split('.')[-2]\n",
    "    cv2.imwrite('./originallabel_camfiles/'+str(name)+'_'+str(max_index.item()+1)+'.jpg', superimposed_img)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_33_1_46.png\n",
      "1\n",
      "check the name\n",
      "fgsm_33_1_46\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11699_465_419.png\n",
      "465\n",
      "check the name\n",
      "fgsm_11699_465_419\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_30_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_30_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10817_431_452.png\n",
      "431\n",
      "check the name\n",
      "fgsm_10817_431_452\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_50_1_33.png\n",
      "1\n",
      "check the name\n",
      "fgsm_50_1_33\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:42: RuntimeWarning: invalid value encountered in greater_equal\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:43: RuntimeWarning: invalid value encountered in less\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_3_0_793.png\n",
      "0\n",
      "check the name\n",
      "fgsm_3_0_793\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17012_673_882.png\n",
      "673\n",
      "check the name\n",
      "fgsm_17012_673_882\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_4262_172_203.png\n",
      "172\n",
      "check the name\n",
      "fgsm_4262_172_203\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_44_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_44_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_16_0_29.png\n",
      "0\n",
      "check the name\n",
      "fgsm_16_0_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_2_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_2_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11543_459_604.png\n",
      "459\n",
      "check the name\n",
      "fgsm_11543_459_604\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_14_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_14_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_727_28_71.png\n",
      "28\n",
      "check the name\n",
      "fgsm_727_28_71\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_49_1_680.png\n",
      "1\n",
      "check the name\n",
      "fgsm_49_1_680\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_101_3_4.png\n",
      "3\n",
      "check the name\n",
      "fgsm_101_3_4\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([920], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_45_1_32.png\n",
      "1\n",
      "check the name\n",
      "fgsm_45_1_32\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_78_3_2.png\n",
      "3\n",
      "check the name\n",
      "fgsm_78_3_2\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_32_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_32_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_26_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_26_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11253_448_669.png\n",
      "448\n",
      "check the name\n",
      "fgsm_11253_448_669\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_53_1_728.png\n",
      "1\n",
      "check the name\n",
      "fgsm_53_1_728\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10482_417_572.png\n",
      "417\n",
      "check the name\n",
      "fgsm_10482_417_572\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_23_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_23_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_42_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_42_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_4_0_735.png\n",
      "0\n",
      "check the name\n",
      "fgsm_4_0_735\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11_0_4.png\n",
      "0\n",
      "check the name\n",
      "fgsm_11_0_4\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_48_1_327.png\n",
      "1\n",
      "check the name\n",
      "fgsm_48_1_327\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_7_0_391.png\n",
      "0\n",
      "check the name\n",
      "fgsm_7_0_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_31_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_31_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15974_633_487.png\n",
      "633\n",
      "check the name\n",
      "fgsm_15974_633_487\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_52_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_52_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_24_0_257.png\n",
      "0\n",
      "check the name\n",
      "fgsm_24_0_257\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_17_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_11718_465_636.png\n",
      "465\n",
      "check the name\n",
      "fgsm_11718_465_636\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([920], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_46_1_31.png\n",
      "1\n",
      "check the name\n",
      "fgsm_46_1_31\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_18157_720_509.png\n",
      "720\n",
      "check the name\n",
      "fgsm_18157_720_509\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_43_1_26.png\n",
      "1\n",
      "check the name\n",
      "fgsm_43_1_26\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15_0_395.png\n",
      "0\n",
      "check the name\n",
      "fgsm_15_0_395\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_17188_680_889.png\n",
      "680\n",
      "check the name\n",
      "fgsm_17188_680_889\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_47_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_47_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15283_606_802.png\n",
      "606\n",
      "check the name\n",
      "fgsm_15283_606_802\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_14718_586_803.png\n",
      "586\n",
      "check the name\n",
      "fgsm_14718_586_803\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_35_1_32.png\n",
      "1\n",
      "check the name\n",
      "fgsm_35_1_32\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_38_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_38_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_37_1_112.png\n",
      "1\n",
      "check the name\n",
      "fgsm_37_1_112\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_1_0_876.png\n",
      "0\n",
      "check the name\n",
      "fgsm_1_0_876\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_51_1_0.png\n",
      "1\n",
      "check the name\n",
      "fgsm_51_1_0\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_39_1_845.png\n",
      "1\n",
      "check the name\n",
      "fgsm_39_1_845\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_12_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_12_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_34_1_327.png\n",
      "1\n",
      "check the name\n",
      "fgsm_34_1_327\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_0_0_465.png\n",
      "0\n",
      "check the name\n",
      "fgsm_0_0_465\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_10_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_10_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_15967_633_680.png\n",
      "633\n",
      "check the name\n",
      "fgsm_15967_633_680\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_22_0_391.png\n",
      "0\n",
      "check the name\n",
      "fgsm_22_0_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_102_3_4.png\n",
      "3\n",
      "check the name\n",
      "fgsm_102_3_4\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_21_0_395.png\n",
      "0\n",
      "check the name\n",
      "fgsm_21_0_395\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_27_1_29.png\n",
      "1\n",
      "check the name\n",
      "fgsm_27_1_29\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_19_0_961.png\n",
      "0\n",
      "check the name\n",
      "fgsm_19_0_961\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_2200_86_83.png\n",
      "86\n",
      "check the name\n",
      "fgsm_2200_86_83\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_28_1_391.png\n",
      "1\n",
      "check the name\n",
      "fgsm_28_1_391\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_25_1_669.png\n",
      "1\n",
      "check the name\n",
      "fgsm_25_1_669\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_8_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_8_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_20_0_758.png\n",
      "0\n",
      "check the name\n",
      "fgsm_20_0_758\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_16995_673_606.png\n",
      "673\n",
      "check the name\n",
      "fgsm_16995_673_606\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_5_0_389.png\n",
      "0\n",
      "check the name\n",
      "fgsm_5_0_389\n",
      "check the image\n",
      "torch.Size([1, 3, 128, 128])\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the image sizes\n",
      "torch.Size([1, 3, 128, 128])\n",
      "check the max index\n",
      "tensor([818], device='cuda:2')\n",
      "[0. 1.]\n",
      "CHECK THE PATH\n",
      "./imagenet/fgsm/fgsm_41_1_680.png\n",
      "1\n",
      "check the name\n",
      "fgsm_41_1_680\n"
     ]
    }
   ],
   "source": [
    "#plt.imshow(data[\"img\"])\n",
    "for data in testloader:\n",
    "    #net1 = net1.to(DEVICE)\n",
    "    #net2 = net2.to(DEVICE)\n",
    "    img1 = data['img'].to(DEVICE)\n",
    "    print(\"check the image\")\n",
    "    print(img1.shape)\n",
    "    path = data['path']\n",
    "\n",
    "    #img1 = img1.float()\n",
    "    img1 = img1.view(-1,img1.shape[-3],img1.shape[-2],img1.shape[-1])\n",
    "    print(img1.shape)\n",
    "    img1 = img1.float()\n",
    "    #print(img1.shape)\n",
    "    label = data['label'].to(DEVICE)\n",
    "    mislabel = data['mislabel'].to(DEVICE)\n",
    "   \n",
    "    resnet = ResNet()\n",
    "    resnet = resnet.to(DEVICE)\n",
    "    resnet.eval()\n",
    "    print(\"check the image sizes\")\n",
    "    print(img1.shape)\n",
    "    img1 = Variable(img1.cuda(), requires_grad=True)\n",
    "    pred, acts = resnet(img1)\n",
    "    max_index = pred.argmax(dim=1) \n",
    "    print(\"check the max index\")\n",
    "    print(max_index)\n",
    "    ##############################################################for actual label\n",
    "    pred[:,(label.item())].backward()\n",
    "    gradients = resnet.get_gradient()\n",
    "    pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])\n",
    "    activations = resnet.get_activations(img1).detach()\n",
    "    for i in range(activations.shape[1]):\n",
    "        activations[:, i, :, :] *= pooled_gradients[i]\n",
    "    heatmap = F.relu(torch.sum(activations, dim=1)).squeeze()\n",
    "    heatmap = np.maximum(heatmap.cpu(), 0)\n",
    "    heatmap /= torch.max(heatmap)\n",
    "    #plt.matshow(heatmap.squeeze())\n",
    "    heatmap = heatmap.numpy()\n",
    "    #print(np.unique(heatmap))\n",
    "    p = np.percentile(heatmap, 97)\n",
    "    heatmap[heatmap>=p]=1\n",
    "    heatmap[heatmap<p]=0\n",
    "    print(np.unique(heatmap))\n",
    "    print(\"CHECK THE PATH\")\n",
    "    print(path[0])\n",
    "    print(label.item())\n",
    "    img = io.imread(path[0])\n",
    "    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "    superimposed_img = heatmap * 0.4 + img\n",
    "    name = path[0].split('/')[-1].split('.')[-2]\n",
    "    print(\"check the name\")\n",
    "    print(name)\n",
    "    \n",
    "    cv2.imwrite('./originallabel_camfiles/'+str(name)+'_'+str(label.item())+'.jpg', superimposed_img)\n",
    "    #########################################################################for label\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "|"
   ]
  }
 ],
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