Load_resnet_trained_elsewhere.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os, os.path, time\n",
    "import argparse\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import *\n",
    "from torch.optim.lr_scheduler import *\n",
    "from torch.autograd import Variable\n",
    "from TALNetModel import *\n",
    "from pslaModels import *\n",
    "from util_in_multi_h5_unnorm_AST import *\n",
    "from util_out import *\n",
    "from util_f1 import *\n",
    "# from AudioResNet import resnet50\n",
    "# from AST import ASTModel\n",
    "from ast_models_original import ASTModel\n",
    "%matplotlib inline\n",
    "import subprocess\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7793654\n"
     ]
    }
   ],
   "source": [
    "curr_node = socket.gethostname().split('.')[0]\n",
    "batcmd=f\"squeue -u billyli | grep {curr_node}\"\n",
    "curr_slurm = subprocess.check_output(batcmd, shell=True, text=True)\n",
    "slurm_id = curr_slurm.strip().split(' ')[0]\n",
    "print(slurm_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()\n",
    "torch.cuda.device_count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VICTIM MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_StoreAction(option_strings=['--suffix'], dest='suffix', nargs=None, const=None, default='', type=<class 'str'>, choices=None, help=None, metavar=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n",
    "\n",
    "parser.add_argument(\"--model_type\", type=str, default='ast', help=\"the model used\")\n",
    "parser.add_argument(\"--dataset\", type=str, default=\"audioset_s\", help=\"the dataset used\", choices=[\"audioset\",\"audioset_s\", \"esc50\", \"speechcommands\"])\n",
    "parser.add_argument(\"--n_mels\", type=int, default=128, help=\"number of mel bins\")\n",
    "parser.add_argument(\"--dataset-portion\", type=str, default='full', help=\"balance set or full set\")\n",
    "parser.add_argument('--mean', default=-12.7508, type=float, help='normalizing mean')\n",
    "parser.add_argument('--std', default=11.7584, type=float, help='normalizing std')\n",
    "\n",
    "parser.add_argument(\"--exp-dir\", type=str, default=\"\", help=\"directory to dump experiments\")\n",
    "parser.add_argument('--lr', '--learning-rate', default=\"0.001\", type=str, metavar='LR', help='initial learning rate')\n",
    "parser.add_argument(\"--optim\", type=str, default=\"adam\", help=\"training optimizer\", choices=[\"sgd\", \"adam\"])\n",
    "parser.add_argument('-b', '--batch-size', default=12, type=int, metavar='N', help='mini-batch size')\n",
    "parser.add_argument('-w', '--num-workers', default=32, type=int, metavar='NW', help='# of workers for dataloading (default: 32)')\n",
    "parser.add_argument(\"--n-epochs\", type=int, default=1, help=\"number of maximum training epochs\")\n",
    "# not used in the formal experiments\n",
    "parser.add_argument(\"--lr_patience\", type=int, default=2, help=\"how many epoch to wait to reduce lr if mAP doesn't improve\")\n",
    "\n",
    "parser.add_argument(\"--n-print-steps\", type=int, default=100, help=\"number of steps to print statistics\")\n",
    "\n",
    "parser.add_argument('--freqm', help='frequency mask max length', type=int, default=0)\n",
    "parser.add_argument('--timem', help='time mask max length', type=int, default=0)\n",
    "parser.add_argument(\"--mixup\", type=float, default=0, help=\"how many (0-1) samples need to be mixup during training\")\n",
    "parser.add_argument(\"--bal\", type=str, default='bal', help=\"use balanced sampling or not\")\n",
    "# the stride used in patch spliting, e.g., for patch size 16*16, a stride of 16 means no overlapping, a stride of 10 means overlap of 6.\n",
    "parser.add_argument(\"--fstride\", type=int, default=8, help=\"soft split freq stride, overlap=patch_size-stride\")\n",
    "parser.add_argument(\"--tstride\", type=int, default=8, help=\"soft split time stride, overlap=patch_size-stride\")\n",
    "parser.add_argument('--imagenet_pretrain', help='if use ImageNet pretrained audio spectrogram transformer model', type=bool, default='True')\n",
    "parser.add_argument('--audioset_pretrain', help='if use ImageNet and audioset pretrained audio spectrogram transformer model', type=bool, default='False')\n",
    "\n",
    "parser.add_argument('--embedding_size', type = int, default = 1024) # this is the embedding size after a pooling layer\n",
    "                                                                    # after a non-pooling layer, the embeddings size will be twice this much\n",
    "parser.add_argument('--n_conv_layers', type = int, default = 10)\n",
    "parser.add_argument('--n_trans_layers', type = int, default = 2)\n",
    "parser.add_argument('--kernel_size', type = str, default = '3')     # 'n' or 'nxm'\n",
    "parser.add_argument('--n_pool_layers', type = int, default = 5)     # the pooling layers will be inserted uniformly into the conv layers\n",
    "                                                                    # the should be at least 2 and at most 6 pooling layers\n",
    "                                                                    # the first two pooling layers will have stride (2,2); later ones will have stride (1,2)\n",
    "parser.add_argument('--batch_norm', type = bool, default = True)\n",
    "parser.add_argument('--dropout', type = float, default = 0.0)\n",
    "parser.add_argument('--pooling', type = str, default = 'att', choices = ['max', 'ave', 'lin', 'exp', 'att', 'h-att', 'all'])\n",
    "parser.add_argument('--continue_from_ckpt', type = str, default = None)\n",
    "parser.add_argument('--addpos', type = bool, default = True)\n",
    "parser.add_argument('--transformer_dropout', type = float, default = 0.75)\n",
    "\n",
    "parser.add_argument(\"--eff_b\", type=int, default=0, help=\"which efficientnet to use, the larger number, the more complex\")\n",
    "parser.add_argument(\"--att_head\", type=int, default=4, help=\"number of attention heads\")\n",
    "parser.add_argument(\"--att_activation\", type = str, default = 'sigmoid', choices = ['sigmoid','softmax','relu','linear'])\n",
    "parser.add_argument('--suffix', type=str, default='')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TALtrans 64x400 best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resnet-audioset_s-full-pTrue-b188-lr1.6e-4-fm12-tm75-mix0.3-m-29.072655-std40.71728-epoch10-resnet_pretrain-small-feat2-38.2\n"
     ]
    }
   ],
   "source": [
    "args = parser.parse_args(args=['--model_type=resnet','--dataset=audioset_s','--n_mels=64', '--mean=-29.072655','--std=40.71728',\n",
    "                               '--lr=1.6e-4','--batch-size=188','--n-epochs=10',\n",
    "                               '--freqm=12','--timem=75','--mixup=0.3',\n",
    "                               '--transformer_dropout=0.25','--addpos=True','--pooling=att',\n",
    "                               '--embedding_size=1024', '--att_head=4',\n",
    "                               '--imagenet_pretrain=True','--suffix=resnet_pretrain-small-feat2-38.2'])\n",
    "if 'x' not in args.kernel_size:\n",
    "    args.kernel_size = args.kernel_size + 'x' + args.kernel_size\n",
    "args.kernel_size = tuple(int(x) for x in args.kernel_size.split('x'))\n",
    "args.target_length=400\n",
    "exp_id = f\"{args.model_type}-{args.dataset}-{args.dataset_portion}-p{args.imagenet_pretrain}-b{args.batch_size}-lr{args.lr}\\\n",
    "-fm{args.freqm}-tm{args.timem}-mix{args.mixup}-m{args.mean}-std{args.std}-epoch{args.n_epochs}\\\n",
    "-{args.suffix}\"\n",
    "print(exp_id)\n",
    "WORKSPACE = '/jet/home/billyli/data_folder/AllaboutAudioSet_hack/egs/audioset/exp'\n",
    "MODEL_FILE = os.path.join(WORKSPACE, exp_id, 'models', 'audio_model.4.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AST 128x1024 best model"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "args = parser.parse_args(args=['--model_type=AST','--dataset=audioset','--n_mels=128', '--mean=-3.6788461','--std=4.3739743',\n",
    "                               '--lr=1e-5','--batch-size=20','--n-epochs=10','--fstride=10','--tstride=10',\n",
    "                               '--freqm=36','--timem=192','--mixup=0.3',\n",
    "                               '--imagenet_pretrain=True','--suffix=ast_challenge-43.0'])\n",
    "args.target_length=1024\n",
    "exp_id = f\"{args.dataset}-{args.dataset_portion}-f{args.fstride}-t{args.tstride}-p{args.imagenet_pretrain}-b{args.batch_size}-lr{args.lr}\\\n",
    "-fm{args.freqm}-tm{args.timem}-mix{args.mixup}-m{args.mean}-std{args.std}-epoch{args.n_epochs}-{args.suffix}\"\n",
    "print(exp_id)\n",
    "WORKSPACE = '/jet/home/billyli/data_folder/AllaboutAudioSet_hack/egs/audioset/exp'\n",
    "MODEL_FILE = os.path.join(WORKSPACE, exp_id, 'models', 'audio_model.3.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/jet/home/billyli/data_folder/AllaboutAudioSet_hack/egs/audioset/exp/resnet-audioset_s-full-pTrue-b188-lr1.6e-4-fm12-tm75-mix0.3-m-29.072655-std40.71728-epoch10-resnet_pretrain-small-feat2-38.2/models/audio_model.4.pth\n"
     ]
    }
   ],
   "source": [
    "print(MODEL_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(addpos=True, att_activation='sigmoid', att_head=4, audioset_pretrain=True, bal='bal', batch_norm=True, batch_size=188, continue_from_ckpt=None, dataset='audioset_s', dataset_portion='full', dropout=0.0, eff_b=0, embedding_size=1024, exp_dir='', freqm=12, fstride=8, imagenet_pretrain=True, kernel_size=(3, 3), lr='1.6e-4', lr_patience=2, mean=-29.072655, mixup=0.3, model_type='resnet', n_conv_layers=10, n_epochs=10, n_mels=64, n_pool_layers=5, n_print_steps=100, n_trans_layers=2, num_workers=32, optim='adam', pooling='att', std=40.71728, suffix='resnet_pretrain-small-feat2-38.2', target_length=400, timem=75, transformer_dropout=0.25, tstride=8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now Use ImageNet Pretrained ResNet50 Model.\n"
     ]
    }
   ],
   "source": [
    "# Load model\n",
    "#model = Net(args).cuda()\n",
    "if args.model_type == 'TAL':\n",
    "    model = TALNet(args)\n",
    "elif args.model_type == 'TALtrans':\n",
    "    model = TransformerEncoder(args)\n",
    "elif args.model_type == 'TAL-new':\n",
    "    model = NewNet(args).cuda()\n",
    "elif args.model_type == 'Trans':\n",
    "    model = Transformer(args).cuda()\n",
    "elif args.model_type == 'resnet':\n",
    "    args.n_class=527\n",
    "    model = ResNetAttention(args)\n",
    "elif args.model_type == 'MMT':\n",
    "    model = MMTEncoder(args).cuda()\n",
    "elif args.model_type == 'VM':\n",
    "    model = videoModel(args).cuda()\n",
    "elif args.model_type == 'MMTLF':\n",
    "    model = LateFusion(args).cuda()\n",
    "elif args.model_type == 'AST':\n",
    "    model = ASTModel(label_dim=527, fstride=args.fstride, tstride=args.tstride, input_fdim=args.n_mels, input_tdim=args.target_length, imagenet_pretrain=args.imagenet_pretrain, audioset_pretrain=False).cuda()\n",
    "else:\n",
    "    print ('model type not recognized')\n",
    "    exit(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataParallel(\n",
       "  (module): ResNetAttention(\n",
       "    (model): ResNet(\n",
       "      (conv1): Conv2d(1, 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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\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=True)\n",
       "        )\n",
       "      )\n",
       "      (avgpool): Identity()\n",
       "      (fc): Identity()\n",
       "    )\n",
       "    (attention): Attention(\n",
       "      (att): Conv2d(832, 527, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (cla): Conv2d(832, 527, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "    (avgpool): AvgPool2d(kernel_size=(4, 1), stride=(4, 1), padding=0)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_ckpt = torch.load(MODEL_FILE)\n",
    "# print(prev_ckpt.keys())\n",
    "# start_ckpt = prev_ckpt['epoch']\n",
    "# # scheduler.load_state_dict(prev_ckpt['scheduler'])\n",
    "# temp = {}\n",
    "# for key, val in prev_ckpt.items():\n",
    "#     key = key.replace('module.','')\n",
    "#     temp[key] = val\n",
    "model = nn.DataParallel(model)\n",
    "# # print(model.keys)\n",
    "model.load_state_dict(prev_ckpt)\n",
    "\n",
    "# # optimizer.load_state_dict(prev_ckpt['optimizer'])\n",
    "# # write_log('Loading model from %s' % args.continue_from_ckpt)\n",
    "model.eval()\n",
    "model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_CLASSES = 527\n",
    "N_WORKERS = 6\n",
    "local = f\"/local/slurm-{slurm_id}/local/audio\"\n",
    "if args.dataset == 'audioset_s':\n",
    "    hf_val_eval_path = os.path.join(local, 'data_64_400_again.h5')\n",
    "elif args.dataset == 'audioset':\n",
    "    hf_val_eval_path = os.path.join(local, 'data_128_1024_again.h5')\n",
    "# hf_val_eval_path = os.path.join(local, 'data_128_1024_strong_cleaned.h5')\n",
    "mu_val=args.mean\n",
    "sigma_val=args.std\n",
    "                            \n",
    "def bulk_load2(file_path, mu, sigma):\n",
    "    hf_val_eval = h5py.File(file_path, 'r')\n",
    "    feat_a = (hf_val_eval['feat_a'][:]*2-mu)/(2 * sigma)\n",
    "    labels = hf_val_eval['labels'][:]\n",
    "    hf_val_eval.close()\n",
    "    return feat_a.astype('float32'), labels.astype('bool')\n",
    "\n",
    "gas_eval_x1, gas_eval_y = bulk_load2(hf_val_eval_path, mu_val, sigma_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20123, 400, 64), (20123, 527))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gas_eval_x1.shape, gas_eval_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total num of batches during testing 202\n"
     ]
    }
   ],
   "source": [
    "result = []\n",
    "batch_size =100\n",
    "x = gas_eval_x1 \n",
    "# x = new_gas_eval\n",
    "# x = gas_eval_x1_origin\n",
    "for i in range(0, len(x), batch_size):\n",
    "    with torch.no_grad():\n",
    "        input = Variable(torch.from_numpy(x[i : i + batch_size])).cuda()\n",
    "        output = model.forward(input)\n",
    "#                 print(output)\n",
    "        output = torch.sigmoid(output)\n",
    "#                 print(f'output shape: {output.shape}')\n",
    "#                 print(output)\n",
    "        result.append([output.data.cpu().numpy()])\n",
    "print('total num of batches during testing', len(result))\n",
    "result = [numpy.concatenate(items) for items in zip(*result)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20123, 527)\n"
     ]
    }
   ],
   "source": [
    "print(result[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20123, 527)\n"
     ]
    }
   ],
   "source": [
    "gas_eval_global_prob_multi = result[0]\n",
    "print(gas_eval_global_prob_multi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on Google Audio Set:\n",
      "   CLASS |    AP |   AUC |    d' \n",
      "---------+-------+-------+-------\n"
     ]
    }
   ],
   "source": [
    "print('Performance on Google Audio Set:')\n",
    "print(\"   CLASS |    AP |   AUC |    d' \")\n",
    "FORMAT  = ' %00007s | %5.3f | %5.3f |%6.03f '\n",
    "SEP     = ''.join('+' if c == '|' else '-' for c in FORMAT)\n",
    "print(SEP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3823157875602847 0.9656049219689555 2.573579451081439\n",
      " Average | 0.382 | 0.966 | 2.574 \n",
      "---------+-------+-------+-------\n",
      "       0 | 0.778 | 0.927 | 2.061 \n",
      "       1 | 0.079 | 0.829 | 1.346 \n",
      "       2 | 0.122 | 0.933 | 2.123 \n",
      "       3 | 0.393 | 0.968 | 2.613 \n",
      "       4 | 0.232 | 0.973 | 2.734 \n",
      "       5 | 0.060 | 0.940 | 2.196 \n",
      "       6 | 0.488 | 0.997 | 3.814 \n",
      "       7 | 0.432 | 0.983 | 3.009 \n",
      "       8 | 0.140 | 0.945 | 2.259 \n",
      "       9 | 0.117 | 0.947 | 2.282 \n",
      "      10 | 0.142 | 0.970 | 2.659 \n",
      "      11 | 0.186 | 0.978 | 2.846 \n",
      "      12 | 0.845 | 0.999 | 4.613 \n",
      "      13 | 0.510 | 0.995 | 3.692 \n",
      "      14 | 0.257 | 0.983 | 3.007 \n",
      "      15 | 0.800 | 0.996 | 3.776 \n",
      "      16 | 0.420 | 0.978 | 2.862 \n",
      "      17 | 0.612 | 0.995 | 3.612 \n",
      "      18 | 0.219 | 0.980 | 2.907 \n",
      "      19 | 0.231 | 0.974 | 2.745 \n",
      "      20 | 0.402 | 0.992 | 3.402 \n",
      "      21 | 0.213 | 0.971 | 2.675 \n",
      "      22 | 0.425 | 0.987 | 3.130 \n",
      "      23 | 0.467 | 0.981 | 2.929 \n",
      "      24 | 0.266 | 0.973 | 2.731 \n",
      "      25 | 0.156 | 0.894 | 1.762 \n",
      "      26 | 0.213 | 0.960 | 2.469 \n",
      "      27 | 0.303 | 0.963 | 2.526 \n",
      "      28 | 0.570 | 0.990 | 3.282 \n",
      "      29 | 0.296 | 0.986 | 3.104 \n",
      "      30 | 0.501 | 0.994 | 3.516 \n",
      "      31 | 0.339 | 0.986 | 3.117 \n",
      "      32 | 0.143 | 0.968 | 2.627 \n",
      "      33 | 0.171 | 0.974 | 2.746 \n",
      "      34 | 0.464 | 0.986 | 3.107 \n",
      "      35 | 0.425 | 0.978 | 2.853 \n",
      "      36 | 0.465 | 0.992 | 3.421 \n",
      "      37 | 0.425 | 0.969 | 2.648 \n",
      "      38 | 0.059 | 0.856 | 1.505 \n",
      "      39 | 0.296 | 0.976 | 2.794 \n",
      "      40 | 0.710 | 0.999 | 4.211 \n",
      "      41 | 0.150 | 0.930 | 2.084 \n",
      "      42 | 0.132 | 0.897 | 1.790 \n",
      "      43 | 0.767 | 0.996 | 3.703 \n",
      "      44 | 0.119 | 0.871 | 1.600 \n",
      "      45 | 0.342 | 0.958 | 2.442 \n",
      "      46 | 0.113 | 0.902 | 1.827 \n",
      "      47 | 0.377 | 0.977 | 2.824 \n",
      "      48 | 0.295 | 0.958 | 2.449 \n",
      "      49 | 0.421 | 0.973 | 2.732 \n",
      "      50 | 0.277 | 0.894 | 1.762 \n",
      "      51 | 0.107 | 0.941 | 2.205 \n",
      "      52 | 0.166 | 0.953 | 2.366 \n",
      "      53 | 0.299 | 0.952 | 2.358 \n",
      "      54 | 0.473 | 0.986 | 3.095 \n",
      "      55 | 0.126 | 0.911 | 1.908 \n",
      "      56 | 0.482 | 0.948 | 2.301 \n",
      "      57 | 0.576 | 0.965 | 2.556 \n",
      "      58 | 0.574 | 0.971 | 2.688 \n",
      "      59 | 0.552 | 0.966 | 2.577 \n",
      "      60 | 0.680 | 0.956 | 2.413 \n",
      "      61 | 0.428 | 0.961 | 2.496 \n",
      "      62 | 0.450 | 0.965 | 2.564 \n",
      "      63 | 0.349 | 0.979 | 2.888 \n",
      "      64 | 0.636 | 0.990 | 3.309 \n",
      "      65 | 0.296 | 0.966 | 2.577 \n",
      "      66 | 0.456 | 0.996 | 3.763 \n",
      "      67 | 0.554 | 0.981 | 2.935 \n",
      "      68 | 0.635 | 0.997 | 3.877 \n",
      "      69 | 0.663 | 0.994 | 3.580 \n",
      "      70 | 0.349 | 0.933 | 2.122 \n",
      "      71 | 0.497 | 0.990 | 3.291 \n",
      "      72 | 0.537 | 0.944 | 2.249 \n",
      "      73 | 0.550 | 0.975 | 2.761 \n",
      "      74 | 0.624 | 0.979 | 2.878 \n",
      "      75 | 0.408 | 0.979 | 2.876 \n",
      "      76 | 0.267 | 0.956 | 2.407 \n",
      "      77 | 0.704 | 0.995 | 3.672 \n",
      "      78 | 0.429 | 0.983 | 3.001 \n",
      "      79 | 0.318 | 0.969 | 2.641 \n",
      "      80 | 0.176 | 0.983 | 2.995 \n",
      "      81 | 0.633 | 0.978 | 2.848 \n",
      "      82 | 0.748 | 0.989 | 3.232 \n",
      "      83 | 0.573 | 0.962 | 2.510 \n",
      "      84 | 0.423 | 0.958 | 2.439 \n",
      "      85 | 0.515 | 0.970 | 2.653 \n",
      "      86 | 0.525 | 0.977 | 2.824 \n",
      "      87 | 0.394 | 0.971 | 2.678 \n",
      "      88 | 0.330 | 0.964 | 2.553 \n",
      "      89 | 0.697 | 0.978 | 2.849 \n",
      "      90 | 0.691 | 0.981 | 2.931 \n",
      "      91 | 0.692 | 0.978 | 2.860 \n",
      "      92 | 0.493 | 0.988 | 3.208 \n",
      "      93 | 0.427 | 0.964 | 2.545 \n",
      "      94 | 0.217 | 0.951 | 2.341 \n",
      "      95 | 0.516 | 0.986 | 3.110 \n",
      "      96 | 0.451 | 0.976 | 2.791 \n",
      "      97 | 0.516 | 0.985 | 3.073 \n",
      "      98 | 0.755 | 0.981 | 2.933 \n",
      "      99 | 0.793 | 0.987 | 3.146 \n",
      "     100 | 0.628 | 0.990 | 3.266 \n",
      "     101 | 0.912 | 0.993 | 3.447 \n",
      "     102 | 0.728 | 0.994 | 3.519 \n",
      "     103 | 0.716 | 0.992 | 3.431 \n",
      "     104 | 0.370 | 0.948 | 2.294 \n",
      "     105 | 0.425 | 0.940 | 2.205 \n",
      "     106 | 0.586 | 0.973 | 2.729 \n",
      "     107 | 0.556 | 0.969 | 2.637 \n",
      "     108 | 0.251 | 0.954 | 2.377 \n",
      "     109 | 0.422 | 0.970 | 2.657 \n",
      "     110 | 0.534 | 0.969 | 2.639 \n",
      "     111 | 0.388 | 0.951 | 2.339 \n",
      "     112 | 0.280 | 0.960 | 2.481 \n",
      "     113 | 0.158 | 0.975 | 2.765 \n",
      "     114 | 0.195 | 0.897 | 1.788 \n",
      "     115 | 0.692 | 0.997 | 3.940 \n",
      "     116 | 0.652 | 0.998 | 3.991 \n",
      "     117 | 0.525 | 0.975 | 2.761 \n",
      "     118 | 0.605 | 0.972 | 2.706 \n",
      "     119 | 0.645 | 0.981 | 2.943 \n",
      "     120 | 0.772 | 0.985 | 3.086 \n",
      "     121 | 0.576 | 0.991 | 3.353 \n",
      "     122 | 0.312 | 0.985 | 3.063 \n",
      "     123 | 0.309 | 0.945 | 2.265 \n",
      "     124 | 0.101 | 0.834 | 1.371 \n",
      "     125 | 0.174 | 0.927 | 2.054 \n",
      "     126 | 0.625 | 0.963 | 2.526 \n",
      "     127 | 0.416 | 0.969 | 2.644 \n",
      "     128 | 0.536 | 0.956 | 2.408 \n",
      "     129 | 0.694 | 0.965 | 2.564 \n",
      "     130 | 0.044 | 0.888 | 1.721 \n",
      "     131 | 0.650 | 0.992 | 3.431 \n",
      "     132 | 0.774 | 0.989 | 3.262 \n",
      "     133 | 0.784 | 0.992 | 3.439 \n",
      "     134 | 0.475 | 0.950 | 2.330 \n",
      "     135 | 0.051 | 0.875 | 1.625 \n",
      "     136 | 0.455 | 0.961 | 2.498 \n",
      "     137 | 0.814 | 0.930 | 2.085 \n",
      "     138 | 0.339 | 0.954 | 2.390 \n",
      "     139 | 0.497 | 0.987 | 3.160 \n",
      "     140 | 0.667 | 0.989 | 3.246 \n",
      "     141 | 0.529 | 0.994 | 3.534 \n",
      "     142 | 0.468 | 0.986 | 3.117 \n",
      "     143 | 0.393 | 0.991 | 3.343 \n",
      "     144 | 0.341 | 0.982 | 2.970 \n",
      "     145 | 0.436 | 0.993 | 3.510 \n",
      "     146 | 0.310 | 0.990 | 3.289 \n",
      "     147 | 0.674 | 0.995 | 3.650 \n",
      "     148 | 0.510 | 0.996 | 3.767 \n",
      "     149 | 0.390 | 0.983 | 3.009 \n",
      "     150 | 0.519 | 0.980 | 2.903 \n",
      "     151 | 0.439 | 0.974 | 2.738 \n",
      "     152 | 0.423 | 0.987 | 3.158 \n",
      "     153 | 0.279 | 0.980 | 2.896 \n",
      "     154 | 0.257 | 0.981 | 2.925 \n",
      "     155 | 0.611 | 0.985 | 3.062 \n",
      "     156 | 0.314 | 0.973 | 2.729 \n",
      "     157 | 0.420 | 0.995 | 3.609 \n",
      "     158 | 0.120 | 0.963 | 2.519 \n",
      "     159 | 0.180 | 0.971 | 2.680 \n",
      "     160 | 0.730 | 0.989 | 3.246 \n",
      "     161 | 0.529 | 0.977 | 2.815 \n",
      "     162 | 0.579 | 0.989 | 3.229 \n",
      "     163 | 0.251 | 0.975 | 2.777 \n",
      "     164 | 0.508 | 0.984 | 3.048 \n",
      "     165 | 0.589 | 0.991 | 3.357 \n",
      "     166 | 0.552 | 0.992 | 3.416 \n",
      "     167 | 0.506 | 0.988 | 3.204 \n",
      "     168 | 0.530 | 0.987 | 3.153 \n",
      "     169 | 0.684 | 0.997 | 3.931 \n",
      "     170 | 0.540 | 0.995 | 3.625 \n",
      "     171 | 0.566 | 0.991 | 3.353 \n",
      "     172 | 0.595 | 0.997 | 3.831 \n",
      "     173 | 0.471 | 0.988 | 3.199 \n",
      "     174 | 0.501 | 0.993 | 3.486 \n",
      "     175 | 0.275 | 0.955 | 2.395 \n",
      "     176 | 0.272 | 0.966 | 2.586 \n",
      "     177 | 0.645 | 0.995 | 3.609 \n",
      "     178 | 0.195 | 0.971 | 2.686 \n",
      "     179 | 0.343 | 0.986 | 3.113 \n",
      "     180 | 0.403 | 0.988 | 3.212 \n",
      "     181 | 0.387 | 0.984 | 3.036 \n",
      "     182 | 0.354 | 0.991 | 3.329 \n",
      "     183 | 0.685 | 0.986 | 3.095 \n",
      "     184 | 0.293 | 0.981 | 2.932 \n",
      "     185 | 0.772 | 0.996 | 3.736 \n",
      "     186 | 0.654 | 0.998 | 4.164 \n",
      "     187 | 0.514 | 0.995 | 3.683 \n",
      "     188 | 0.521 | 0.997 | 3.942 \n",
      "     189 | 0.485 | 0.980 | 2.914 \n",
      "     190 | 0.429 | 0.990 | 3.266 \n",
      "     191 | 0.352 | 0.990 | 3.287 \n",
      "     192 | 0.618 | 0.979 | 2.879 \n",
      "     193 | 0.618 | 0.988 | 3.191 \n",
      "     194 | 0.612 | 0.990 | 3.301 \n",
      "     195 | 0.729 | 0.986 | 3.108 \n",
      "     196 | 0.581 | 0.993 | 3.462 \n",
      "     197 | 0.578 | 0.997 | 3.832 \n",
      "     198 | 0.414 | 0.990 | 3.310 \n",
      "     199 | 0.637 | 0.992 | 3.437 \n",
      "     200 | 0.164 | 0.957 | 2.432 \n",
      "     201 | 0.642 | 0.987 | 3.156 \n",
      "     202 | 0.574 | 0.987 | 3.169 \n",
      "     203 | 0.542 | 0.996 | 3.765 \n",
      "     204 | 0.520 | 0.962 | 2.517 \n",
      "     205 | 0.665 | 0.994 | 3.588 \n",
      "     206 | 0.729 | 0.987 | 3.151 \n",
      "     207 | 0.874 | 1.000 | 4.749 \n",
      "     208 | 0.751 | 0.992 | 3.423 \n",
      "     209 | 0.707 | 0.995 | 3.648 \n",
      "     210 | 0.915 | 0.992 | 3.380 \n",
      "     211 | 0.827 | 0.998 | 4.033 \n",
      "     212 | 0.815 | 0.993 | 3.452 \n",
      "     213 | 0.362 | 0.960 | 2.484 \n",
      "     214 | 0.551 | 0.996 | 3.797 \n",
      "     215 | 0.516 | 0.973 | 2.728 \n",
      "     216 | 0.158 | 0.961 | 2.500 \n",
      "     217 | 0.259 | 0.992 | 3.410 \n",
      "     218 | 0.537 | 0.984 | 3.026 \n",
      "     219 | 0.181 | 0.981 | 2.932 \n",
      "     220 | 0.277 | 0.982 | 2.976 \n",
      "     221 | 0.195 | 0.987 | 3.131 \n",
      "     222 | 0.273 | 0.991 | 3.324 \n",
      "     223 | 0.314 | 0.988 | 3.193 \n",
      "     224 | 0.247 | 0.983 | 3.014 \n",
      "     225 | 0.161 | 0.988 | 3.207 \n",
      "     226 | 0.155 | 0.975 | 2.780 \n",
      "     227 | 0.190 | 0.977 | 2.816 \n",
      "     228 | 0.345 | 0.977 | 2.815 \n",
      "     229 | 0.210 | 0.976 | 2.801 \n",
      "     230 | 0.231 | 0.977 | 2.813 \n",
      "     231 | 0.404 | 0.990 | 3.277 \n",
      "     232 | 0.172 | 0.970 | 2.669 \n",
      "     233 | 0.158 | 0.969 | 2.645 \n",
      "     234 | 0.231 | 0.965 | 2.565 \n",
      "     235 | 0.090 | 0.947 | 2.290 \n",
      "     236 | 0.221 | 0.983 | 2.988 \n",
      "     237 | 0.089 | 0.957 | 2.423 \n",
      "     238 | 0.617 | 0.977 | 2.830 \n",
      "     239 | 0.180 | 0.959 | 2.460 \n",
      "     240 | 0.207 | 0.987 | 3.143 \n",
      "     241 | 0.397 | 0.988 | 3.214 \n",
      "     242 | 0.434 | 0.991 | 3.351 \n",
      "     243 | 0.294 | 0.977 | 2.826 \n",
      "     244 | 0.255 | 0.985 | 3.081 \n",
      "     245 | 0.467 | 0.990 | 3.303 \n",
      "     246 | 0.292 | 0.986 | 3.110 \n",
      "     247 | 0.284 | 0.989 | 3.248 \n",
      "     248 | 0.202 | 0.989 | 3.216 \n",
      "     249 | 0.570 | 0.988 | 3.203 \n",
      "     250 | 0.377 | 0.987 | 3.133 \n",
      "     251 | 0.184 | 0.974 | 2.755 \n",
      "     252 | 0.169 | 0.977 | 2.831 \n",
      "     253 | 0.254 | 0.987 | 3.139 \n",
      "     254 | 0.184 | 0.984 | 3.019 \n",
      "     255 | 0.554 | 0.992 | 3.420 \n",
      "     256 | 0.350 | 0.984 | 3.023 \n",
      "     257 | 0.347 | 0.987 | 3.146 \n",
      "     258 | 0.146 | 0.949 | 2.318 \n",
      "     259 | 0.338 | 0.978 | 2.847 \n",
      "     260 | 0.122 | 0.965 | 2.563 \n",
      "     261 | 0.427 | 0.982 | 2.980 \n",
      "     262 | 0.389 | 0.980 | 2.916 \n",
      "     263 | 0.257 | 0.985 | 3.054 \n",
      "     264 | 0.100 | 0.970 | 2.653 \n",
      "     265 | 0.201 | 0.983 | 2.994 \n",
      "     266 | 0.111 | 0.956 | 2.412 \n",
      "     267 | 0.249 | 0.966 | 2.588 \n",
      "     268 | 0.231 | 0.963 | 2.535 \n",
      "     269 | 0.160 | 0.971 | 2.681 \n",
      "     270 | 0.125 | 0.953 | 2.369 \n",
      "     271 | 0.420 | 0.981 | 2.948 \n",
      "     272 | 0.360 | 0.978 | 2.854 \n",
      "     273 | 0.101 | 0.966 | 2.574 \n",
      "     274 | 0.257 | 0.980 | 2.918 \n",
      "     275 | 0.097 | 0.961 | 2.500 \n",
      "     276 | 0.187 | 0.970 | 2.660 \n",
      "     277 | 0.110 | 0.950 | 2.324 \n",
      "     278 | 0.251 | 0.963 | 2.530 \n",
      "     279 | 0.093 | 0.944 | 2.252 \n",
      "     280 | 0.063 | 0.953 | 2.363 \n",
      "     281 | 0.636 | 0.998 | 3.971 \n",
      "     282 | 0.374 | 0.950 | 2.331 \n",
      "     283 | 0.366 | 0.967 | 2.594 \n",
      "     284 | 0.133 | 0.880 | 1.665 \n",
      "     285 | 0.396 | 0.972 | 2.695 \n",
      "     286 | 0.746 | 0.997 | 3.895 \n",
      "     287 | 0.763 | 0.997 | 3.892 \n",
      "     288 | 0.563 | 0.973 | 2.717 \n",
      "     289 | 0.629 | 0.988 | 3.179 \n",
      "     290 | 0.475 | 0.984 | 3.045 \n",
      "     291 | 0.432 | 0.989 | 3.219 \n",
      "     292 | 0.306 | 0.991 | 3.351 \n",
      "     293 | 0.452 | 0.995 | 3.658 \n",
      "     294 | 0.339 | 0.976 | 2.792 \n",
      "     295 | 0.345 | 0.983 | 2.993 \n",
      "     296 | 0.359 | 0.982 | 2.957 \n",
      "     297 | 0.057 | 0.918 | 1.966 \n",
      "     298 | 0.125 | 0.933 | 2.120 \n",
      "     299 | 0.049 | 0.896 | 1.783 \n",
      "     300 | 0.480 | 0.956 | 2.407 \n",
      "     301 | 0.440 | 0.968 | 2.617 \n",
      "     302 | 0.403 | 0.991 | 3.334 \n",
      "     303 | 0.335 | 0.982 | 2.966 \n",
      "     304 | 0.201 | 0.981 | 2.938 \n",
      "     305 | 0.299 | 0.976 | 2.801 \n",
      "     306 | 0.101 | 0.961 | 2.490 \n",
      "     307 | 0.350 | 0.972 | 2.702 \n",
      "     308 | 0.423 | 0.985 | 3.084 \n",
      "     309 | 0.504 | 0.984 | 3.047 \n",
      "     310 | 0.419 | 0.970 | 2.661 \n",
      "     311 | 0.354 | 0.972 | 2.694 \n",
      "     312 | 0.533 | 0.987 | 3.156 \n",
      "     313 | 0.229 | 0.979 | 2.877 \n",
      "     314 | 0.246 | 0.984 | 3.034 \n",
      "     315 | 0.394 | 0.993 | 3.488 \n",
      "     316 | 0.274 | 0.974 | 2.750 \n",
      "     317 | 0.197 | 0.972 | 2.706 \n",
      "     318 | 0.565 | 0.994 | 3.545 \n",
      "     319 | 0.449 | 0.983 | 3.004 \n",
      "     320 | 0.689 | 0.982 | 2.972 \n",
      "     321 | 0.228 | 0.987 | 3.150 \n",
      "     322 | 0.879 | 0.998 | 4.093 \n",
      "     323 | 0.640 | 0.998 | 4.080 \n",
      "     324 | 0.660 | 0.998 | 4.012 \n",
      "     325 | 0.806 | 0.982 | 2.974 \n",
      "     326 | 0.222 | 0.983 | 3.014 \n",
      "     327 | 0.129 | 0.965 | 2.570 \n",
      "     328 | 0.657 | 0.985 | 3.079 \n",
      "     329 | 0.668 | 0.986 | 3.122 \n",
      "     330 | 0.318 | 0.982 | 2.967 \n",
      "     331 | 0.600 | 0.995 | 3.614 \n",
      "     332 | 0.663 | 0.986 | 3.102 \n",
      "     333 | 0.739 | 0.983 | 3.007 \n",
      "     334 | 0.492 | 0.994 | 3.526 \n",
      "     335 | 0.519 | 0.991 | 3.343 \n",
      "     336 | 0.535 | 0.995 | 3.638 \n",
      "     337 | 0.552 | 0.992 | 3.387 \n",
      "     338 | 0.173 | 0.975 | 2.782 \n",
      "     339 | 0.396 | 0.990 | 3.292 \n",
      "     340 | 0.484 | 0.993 | 3.444 \n",
      "     341 | 0.057 | 0.912 | 1.916 \n",
      "     342 | 0.528 | 0.966 | 2.572 \n",
      "     343 | 0.328 | 0.973 | 2.736 \n",
      "     344 | 0.365 | 0.989 | 3.248 \n",
      "     345 | 0.177 | 0.971 | 2.683 \n",
      "     346 | 0.444 | 0.986 | 3.099 \n",
      "     347 | 0.573 | 0.983 | 2.996 \n",
      "     348 | 0.149 | 0.976 | 2.796 \n",
      "     349 | 0.074 | 0.959 | 2.461 \n",
      "     350 | 0.221 | 0.989 | 3.245 \n",
      "     351 | 0.372 | 0.964 | 2.540 \n",
      "     352 | 0.189 | 0.981 | 2.923 \n",
      "     353 | 0.282 | 0.980 | 2.908 \n",
      "     354 | 0.180 | 0.951 | 2.340 \n",
      "     355 | 0.389 | 0.959 | 2.459 \n",
      "     356 | 0.184 | 0.927 | 2.053 \n",
      "     357 | 0.172 | 0.939 | 2.184 \n",
      "     358 | 0.199 | 0.943 | 2.239 \n",
      "     359 | 0.494 | 0.937 | 2.162 \n",
      "     360 | 0.281 | 0.911 | 1.903 \n",
      "     361 | 0.370 | 0.933 | 2.117 \n",
      "     362 | 0.232 | 0.953 | 2.363 \n",
      "     363 | 0.270 | 0.970 | 2.661 \n",
      "     364 | 0.233 | 0.978 | 2.850 \n",
      "     365 | 0.333 | 0.976 | 2.785 \n",
      "     366 | 0.437 | 0.933 | 2.123 \n",
      "     367 | 0.562 | 0.997 | 3.911 \n",
      "     368 | 0.212 | 0.948 | 2.295 \n",
      "     369 | 0.575 | 0.991 | 3.349 \n",
      "     370 | 0.448 | 0.994 | 3.582 \n",
      "     371 | 0.497 | 0.994 | 3.532 \n",
      "     372 | 0.526 | 0.995 | 3.678 \n",
      "     373 | 0.749 | 0.991 | 3.334 \n",
      "     374 | 0.761 | 0.997 | 3.904 \n",
      "     375 | 0.189 | 0.919 | 1.977 \n",
      "     376 | 0.286 | 0.979 | 2.890 \n",
      "     377 | 0.633 | 0.988 | 3.182 \n",
      "     378 | 0.292 | 0.944 | 2.244 \n",
      "     379 | 0.195 | 0.921 | 1.995 \n",
      "     380 | 0.258 | 0.934 | 2.130 \n",
      "     381 | 0.139 | 0.927 | 2.058 \n",
      "     382 | 0.479 | 0.977 | 2.811 \n",
      "     383 | 0.286 | 0.959 | 2.465 \n",
      "     384 | 0.392 | 0.967 | 2.598 \n",
      "     385 | 0.488 | 0.977 | 2.829 \n",
      "     386 | 0.431 | 0.978 | 2.841 \n",
      "     387 | 0.262 | 0.933 | 2.115 \n",
      "     388 | 0.178 | 0.944 | 2.251 \n",
      "     389 | 0.223 | 0.983 | 2.989 \n",
      "     390 | 0.605 | 0.979 | 2.881 \n",
      "     391 | 0.406 | 0.938 | 2.178 \n",
      "     392 | 0.498 | 0.978 | 2.843 \n",
      "     393 | 0.565 | 0.988 | 3.186 \n",
      "     394 | 0.610 | 0.965 | 2.557 \n",
      "     395 | 0.407 | 0.958 | 2.446 \n",
      "     396 | 0.890 | 0.996 | 3.750 \n",
      "     397 | 0.901 | 0.998 | 4.065 \n",
      "     398 | 0.190 | 0.950 | 2.329 \n",
      "     399 | 0.682 | 0.994 | 3.532 \n",
      "     400 | 0.632 | 0.994 | 3.595 \n",
      "     401 | 0.178 | 0.963 | 2.526 \n",
      "     402 | 0.381 | 0.990 | 3.278 \n",
      "     403 | 0.651 | 0.998 | 4.008 \n",
      "     404 | 0.085 | 0.851 | 1.474 \n",
      "     405 | 0.070 | 0.845 | 1.433 \n",
      "     406 | 0.394 | 0.971 | 2.678 \n",
      "     407 | 0.109 | 0.865 | 1.561 \n",
      "     408 | 0.296 | 0.936 | 2.147 \n",
      "     409 | 0.107 | 0.875 | 1.626 \n",
      "     410 | 0.023 | 0.739 | 0.906 \n",
      "     411 | 0.568 | 0.993 | 3.477 \n",
      "     412 | 0.193 | 0.942 | 2.217 \n",
      "     413 | 0.222 | 0.961 | 2.500 \n",
      "     414 | 0.558 | 0.961 | 2.499 \n",
      "     415 | 0.518 | 0.974 | 2.760 \n",
      "     416 | 0.286 | 0.951 | 2.341 \n",
      "     417 | 0.312 | 0.937 | 2.167 \n",
      "     418 | 0.255 | 0.962 | 2.513 \n",
      "     419 | 0.380 | 0.965 | 2.560 \n",
      "     420 | 0.439 | 0.975 | 2.766 \n",
      "     421 | 0.390 | 0.980 | 2.902 \n",
      "     422 | 0.355 | 0.978 | 2.844 \n",
      "     423 | 0.602 | 0.984 | 3.036 \n",
      "     424 | 0.250 | 0.976 | 2.793 \n",
      "     425 | 0.412 | 0.982 | 2.971 \n",
      "     426 | 0.173 | 0.978 | 2.860 \n",
      "     427 | 0.621 | 0.984 | 3.020 \n",
      "     428 | 0.633 | 0.990 | 3.299 \n",
      "     429 | 0.464 | 0.994 | 3.588 \n",
      "     430 | 0.566 | 0.989 | 3.261 \n",
      "     431 | 0.667 | 0.996 | 3.699 \n",
      "     432 | 0.627 | 0.983 | 3.002 \n",
      "     433 | 0.468 | 0.989 | 3.237 \n",
      "     434 | 0.042 | 0.910 | 1.895 \n",
      "     435 | 0.196 | 0.972 | 2.710 \n",
      "     436 | 0.423 | 0.994 | 3.562 \n",
      "     437 | 0.316 | 0.953 | 2.365 \n",
      "     438 | 0.283 | 0.966 | 2.580 \n",
      "     439 | 0.214 | 0.918 | 1.964 \n",
      "     440 | 0.106 | 0.861 | 1.534 \n",
      "     441 | 0.261 | 0.932 | 2.112 \n",
      "     442 | 0.129 | 0.923 | 2.018 \n",
      "     443 | 0.316 | 0.968 | 2.629 \n",
      "     444 | 0.061 | 0.889 | 1.728 \n",
      "     445 | 0.476 | 0.994 | 3.517 \n",
      "     446 | 0.203 | 0.977 | 2.825 \n",
      "     447 | 0.027 | 0.855 | 1.496 \n",
      "     448 | 0.168 | 0.933 | 2.115 \n",
      "     449 | 0.413 | 0.982 | 2.956 \n",
      "     450 | 0.075 | 0.914 | 1.930 \n",
      "     451 | 0.241 | 0.949 | 2.319 \n",
      "     452 | 0.070 | 0.916 | 1.953 \n",
      "     453 | 0.120 | 0.929 | 2.077 \n",
      "     454 | 0.175 | 0.974 | 2.746 \n",
      "     455 | 0.656 | 0.996 | 3.771 \n",
      "     456 | 0.162 | 0.940 | 2.197 \n",
      "     457 | 0.409 | 0.952 | 2.359 \n",
      "     458 | 0.537 | 0.951 | 2.346 \n",
      "     459 | 0.321 | 0.941 | 2.211 \n",
      "     460 | 0.190 | 0.956 | 2.419 \n",
      "     461 | 0.202 | 0.968 | 2.627 \n",
      "     462 | 0.569 | 0.993 | 3.493 \n",
      "     463 | 0.564 | 0.990 | 3.291 \n",
      "     464 | 0.404 | 0.995 | 3.660 \n",
      "     465 | 0.511 | 0.989 | 3.216 \n",
      "     466 | 0.062 | 0.933 | 2.124 \n",
      "     467 | 0.545 | 0.976 | 2.792 \n",
      "     468 | 0.163 | 0.946 | 2.269 \n",
      "     469 | 0.339 | 0.975 | 2.779 \n",
      "     470 | 0.267 | 0.974 | 2.742 \n",
      "     471 | 0.145 | 0.902 | 1.830 \n",
      "     472 | 0.640 | 0.981 | 2.933 \n",
      "     473 | 0.061 | 0.905 | 1.856 \n",
      "     474 | 0.364 | 0.922 | 2.007 \n",
      "     475 | 0.018 | 0.844 | 1.433 \n",
      "     476 | 0.549 | 0.979 | 2.877 \n",
      "     477 | 0.184 | 0.977 | 2.809 \n",
      "     478 | 0.107 | 0.918 | 1.971 \n",
      "     479 | 0.467 | 0.975 | 2.767 \n",
      "     480 | 0.260 | 0.933 | 2.125 \n",
      "     481 | 0.273 | 0.945 | 2.259 \n",
      "     482 | 0.265 | 0.896 | 1.780 \n",
      "     483 | 0.258 | 0.950 | 2.329 \n",
      "     484 | 0.293 | 0.941 | 2.207 \n",
      "     485 | 0.053 | 0.897 | 1.792 \n",
      "     486 | 0.018 | 0.829 | 1.345 \n",
      "     487 | 0.115 | 0.934 | 2.132 \n",
      "     488 | 0.112 | 0.932 | 2.105 \n",
      "     489 | 0.117 | 0.925 | 2.039 \n",
      "     490 | 0.552 | 0.996 | 3.752 \n",
      "     491 | 0.598 | 0.996 | 3.771 \n",
      "     492 | 0.409 | 0.992 | 3.403 \n",
      "     493 | 0.285 | 0.950 | 2.320 \n",
      "     494 | 0.791 | 0.999 | 4.220 \n",
      "     495 | 0.191 | 0.973 | 2.714 \n",
      "     496 | 0.213 | 0.967 | 2.608 \n",
      "     497 | 0.176 | 0.964 | 2.539 \n",
      "     498 | 0.741 | 0.997 | 3.895 \n",
      "     499 | 0.346 | 0.969 | 2.635 \n",
      "     500 | 0.108 | 0.935 | 2.142 \n",
      "     501 | 0.405 | 0.960 | 2.475 \n",
      "     502 | 0.036 | 0.904 | 1.842 \n",
      "     503 | 0.160 | 0.960 | 2.476 \n",
      "     504 | 0.055 | 0.843 | 1.424 \n",
      "     505 | 0.071 | 0.836 | 1.386 \n",
      "     506 | 0.157 | 0.837 | 1.388 \n",
      "     507 | 0.049 | 0.794 | 1.163 \n",
      "     508 | 0.082 | 0.916 | 1.947 \n",
      "     509 | 0.105 | 0.855 | 1.497 \n",
      "     510 | 0.059 | 0.831 | 1.357 \n",
      "     511 | 0.256 | 0.961 | 2.495 \n",
      "     512 | 0.215 | 0.957 | 2.430 \n",
      "     513 | 0.034 | 0.874 | 1.618 \n",
      "     514 | 0.459 | 0.963 | 2.519 \n",
      "     515 | 0.482 | 0.948 | 2.303 \n",
      "     516 | 0.338 | 0.977 | 2.810 \n",
      "     517 | 0.524 | 0.994 | 3.533 \n",
      "     518 | 0.129 | 0.933 | 2.125 \n",
      "     519 | 0.160 | 0.943 | 2.239 \n",
      "     520 | 0.061 | 0.924 | 2.021 \n",
      "     521 | 0.430 | 0.992 | 3.382 \n",
      "     522 | 0.299 | 0.984 | 3.025 \n",
      "     523 | 0.103 | 0.942 | 2.228 \n",
      "     524 | 0.130 | 0.928 | 2.065 \n",
      "     525 | 0.328 | 0.895 | 1.770 \n",
      "     526 | 0.168 | 0.935 | 2.143 \n"
     ]
    }
   ],
   "source": [
    "classwise = []\n",
    "classwise_dict = []\n",
    "N_CLASSES = gas_eval_global_prob_multi[0].shape[-1]\n",
    "for i in range(N_CLASSES):\n",
    "    a, b, c = gas_eval(gas_eval_global_prob_multi[:,i], gas_eval_y[:,i])     # AP, AUC, dprime\n",
    "    classwise_dict.append({'mAP':a, 'AUC':b, 'dprime':c, 'id':i})\n",
    "    classwise.append((a, b, c))\n",
    "map, mauc = numpy.array(classwise).mean(axis = 0)[:2]\n",
    "print (map, mauc, dprime(mauc))\n",
    "# for c in classwise_dict:\n",
    "#     print(c)\n",
    "print(FORMAT % ('Average', map, mauc, dprime(mauc)))\n",
    "print(SEP)\n",
    "for i in range(N_CLASSES):\n",
    "    print(FORMAT % ((str(i),) + classwise[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occlusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "gas_eval_x1_origin = gas_eval_x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.2922494 1.4197426 -0.158013 0.62080556\n"
     ]
    }
   ],
   "source": [
    "min_val = numpy.min(gas_eval_x1)\n",
    "max_val = numpy.max(gas_eval_x1)\n",
    "mean_val = numpy.mean(gas_eval_x1)\n",
    "std_val = numpy.std(gas_eval_x1)\n",
    "print(min_val, max_val, mean_val, std_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20123, 400, 64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gas_eval_x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 1020 is out of bounds for axis 0 with size 1020",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-bcafacdec4e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             \u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mmask_record\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompressed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhalf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_mel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mfill_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhalf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_mel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilence_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 1020 is out of bounds for axis 0 with size 1020"
     ]
    }
   ],
   "source": [
    "# new_gas_eval = []\n",
    "# time_frame = 1024#400\n",
    "# step = 16#40\n",
    "# half = 512#200\n",
    "# n_mel = 128#64\n",
    "\n",
    "# for record in gas_eval_x1[:,:1020,:]:\n",
    "#     record=numpy.ma.array(record,mask=False)\n",
    "#     silence_val = numpy.min(record)\n",
    "#     count = 0\n",
    "#     for i in range(time_frame):\n",
    "#         if i % step == 0:\n",
    "#             count +=1\n",
    "#         if count % 2 == 0:\n",
    "#             record.mask[i] = True\n",
    "#     mask_record = record.compressed().reshape(half,n_mel)\n",
    "#     fill_matrix = numpy.full((half,n_mel), silence_val)\n",
    "# #     print('shape', fill_matrix.shape)\n",
    "#     mask_record = numpy.vstack((mask_record, fill_matrix))\n",
    "# #     record.mask[100:300]=True\n",
    "# #     mask_record = record[~record.mask]\n",
    "# #     print(mask_record.shape)\n",
    "# #     mask_record = record.filled(fill_value=silence_val)\n",
    "#     new_gas_eval.append(mask_record)\n",
    "# new_gas_eval = numpy.array(new_gas_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_gas_eval.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-85-bec25885c8c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_gas_eval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lower'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.imshow(new_gas_eval[1].T, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gas_eval = []\n",
    "time_frame = 1024#400\n",
    "step = 16#40\n",
    "half = 512#200\n",
    "n_mel = 128#64\n",
    "\n",
    "for record in gas_eval_x1:\n",
    "    record=numpy.ma.array(record,mask=False)\n",
    "    silence_val = numpy.min(record)\n",
    "    count = 0\n",
    "    for i in range(time_frame):\n",
    "        if i % step == 0:\n",
    "            count +=1\n",
    "        if count % 2 == 0:\n",
    "            record.mask[i] = True\n",
    "    mask_record = record.filled(fill_value=silence_val)\n",
    "    new_gas_eval.append(mask_record)\n",
    "new_gas_eval = numpy.array(new_gas_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# plt.figure(figsize = (20,10))\n",
    "# plt.imshow(new_gas_eval[2].T, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_gas_eval = []\n",
    "\n",
    "# for record in gas_eval_x1:\n",
    "#     record=numpy.ma.array(record,mask=False)\n",
    "#     silence_val = numpy.min(record)\n",
    "#     record.mask[255:765]=True\n",
    "#     mask_record = record.filled(fill_value=silence_val)\n",
    "#     new_gas_eval.append(mask_record)\n",
    "# new_gas_eval = numpy.array(new_gas_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# plt.figure(figsize = (20,10))\n",
    "# plt.imshow(new_gas_eval[2].T, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# new_gas_eval = []\n",
    "# for record in gas_eval_x1:\n",
    "#     record += np.random.normal(0, 0.1, (1024,128))\n",
    "#     new_gas_eval.append(record)\n",
    "# new_gas_eval = numpy.array(new_gas_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# plt.figure(figsize = (20,10))\n",
    "# plt.imshow(new_gas_eval[2].T, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.cuda.amp import autocast,GradScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.2922494 1.4197426\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique(gas_eval_y.astype('float16'))\n",
    "print(np.min(gas_eval_x1), np.max(gas_eval_x1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "pgd_dataset = torch.utils.data.TensorDataset(torch.from_numpy(gas_eval_x1[:]), torch.from_numpy(gas_eval_y[:].astype('float32')))\n",
    "pgd_dataloader = torch.utils.data.DataLoader(pgd_dataset, batch_size=100, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pgd(model, dataloader, delta_global, num_steps, step_size, step_norm, eps, eps_norm, x_min, x_max, y_target=None):\n",
    "    \"\"\"Performs the projected gradient descent attack on a batch of images.\"\"\"\n",
    "    model.cuda().eval()\n",
    "    targeted = y_target is not None\n",
    "    criterion = nn.BCEWithLogitsLoss().cuda()\n",
    "    loss_global = 0\n",
    "    for epoch in range(num_steps):\n",
    "        torch.cuda.empty_cache()\n",
    "        loss_global = 0\n",
    "        dataiter = iter(dataloader)\n",
    "        for step, (feat_a, y) in enumerate(dataiter):\n",
    "            feat_a = feat_a.cuda()\n",
    "            \n",
    "            x_adv_1 = feat_a + delta_global #.to(x.device)\n",
    "            x_adv_1 = x_adv_1.clamp(x_min, x_max)\n",
    "            # only video\n",
    "           \n",
    "            N_1, T_1, V_1 = x_adv_1.shape\n",
    "#             print(N_1,T_1,V_1)\n",
    "\n",
    "            #######   data loaded ###################\n",
    "            _x_adv_1 = x_adv_1.clone().detach().requires_grad_(True).cuda()\n",
    "\n",
    "            #######   get gradient #################\n",
    "#             with torch.no_grad():\n",
    "#             model.train()# double check\n",
    "            with autocast():\n",
    "                out = model(_x_adv_1)\n",
    "#                 print(out)\n",
    "#                 out = torch.sigmoid(out)\n",
    "#                 epsilon = 1e-7\n",
    "#                 out = torch.clamp(out, epsilon, 1. - epsilon)\n",
    "#                 temp_out = out.clamp(0,1).cuda()\n",
    "#                 print(temp_out.shape, y.shape, temp_out.max(), temp_out.min())\n",
    "#                 print(temp_out.shape, y.shape)\n",
    "\n",
    "                loss = criterion(out, y.to('cuda', non_blocking=True))\n",
    "                loss_global += loss.item()\n",
    "#                 print(loss.item(), loss_global)\n",
    "                loss.backward()\n",
    "#             break\n",
    "            ########   updated _x_adv ###############\n",
    "            with torch.no_grad():\n",
    "            # Force the gradient step to be a fixed size in a certain norm\n",
    "                if step_norm == 'inf':\n",
    "                    gradients = _x_adv_1.grad.sign() * step_size\n",
    "                else:\n",
    "                    # The grad is normalized here.\n",
    "                    # gradients = _x_adv.grad \n",
    "                    # Only normalize w.r.t. to feats at a timestep\n",
    "                    gradients = _x_adv_1.grad/_x_adv_1.grad.norm(step_norm, dim=-1).view(-1, T_1, 1)\n",
    "                    gradients *= step_size\n",
    "                if targeted:\n",
    "                    # Targeted: Gradient descent with on the loss of the (incorrect) target label\n",
    "                    # w.r.t. the image data\n",
    "                    x_adv_1 -= gradients\n",
    "                else:\n",
    "                    # Untargeted: Gradient ascent on the loss of the correct label w.r.t.\n",
    "                    # the model parameters\n",
    "                    x_adv_1 += gradients\n",
    "            # Project back into l_norm ball and correct range\n",
    "            if eps_norm == 'inf':\n",
    "                # Workaround as PyTorch doesn't have elementwise clip\n",
    "                x_adv_1 = torch.max(torch.min(x_adv_1, feat_a + eps), feat_a - eps)\n",
    "                delta = x_adv_1 - feat_a\n",
    "            else:\n",
    "                delta = x_adv_1 - feat_a\n",
    "            # Assume x and x_adv are batched tensors where the first dimension is\n",
    "            # a batch dimension\n",
    "            # 100 10 2048\n",
    "#             print(delta)\n",
    "            mask = delta.view(delta.shape[0], -1).norm(float(eps_norm), dim=1) <= eps #100*(20480) => 100\n",
    "            scaling_factor = delta.view(delta.shape[0], T_1, -1).norm(float(eps_norm), dim=-1) #100 10 2048 => 100 40\n",
    "            scaling_factor[mask] = eps\n",
    "#             print(scaling_factor)\n",
    "            # .view() assumes batched images as a 4D Tensor\n",
    "            delta *= eps / (scaling_factor+1e-7).view(-1, T_1, 1)\n",
    "#             print(delta)\n",
    "#             return delta\n",
    "            delta = delta.float()\n",
    "            delta = torch.mean(delta,0)\n",
    "#             print(delta)\n",
    "            delta_global += delta * N_1 / 20123\n",
    "#             print(delta_global)\n",
    "            for name, param in model.named_parameters():\n",
    "                if param.requires_grad:\n",
    "                    try:\n",
    "                        param.grad.zero_()\n",
    "                    except:\n",
    "                        pass\n",
    "        print(f'global loss:{loss_global}')\n",
    "    return delta_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "global loss:2.654737657867372\n",
      "global loss:2.892211507074535\n",
      "global loss:3.645229876972735\n"
     ]
    }
   ],
   "source": [
    "torch.cuda.empty_cache()\n",
    "perturb = pgd(model, pgd_dataloader, torch.zeros((400,64)).cuda(), num_steps=20, step_size=0.03, step_norm='inf', eps= 0.5, eps_norm='2', x_min=-2.29, x_max=1.42, y_target=None )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gas_eval = perturb.cpu().numpy() + gas_eval_x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f769922e070>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.imshow(new_gas_eval[2].T, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'classwise' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-14422395dd77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasswise\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'classwise' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(classwise)\n",
    "plt.figure(figsize=(400,100)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial Routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for batch in range(1, args.ckpt_size + 1):\n",
    "        x, y = next(train_gen)\n",
    "        global_prob = model(x)[0]\n",
    "        global_prob.clamp_(min = 1e-7, max = 1 - 1e-7)\n",
    "        loss = criterion(global_prob, y)\n",
    "        if args.gradient_accumulation > 1:\n",
    "            loss = loss / args.gradient_accumulation\n",
    "        #if n_gpu > 1:\n",
    "        #    loss = loss.mean()\n",
    "        train_loss += loss.item()\n",
    "        if numpy.isnan(train_loss) or numpy.isinf(train_loss): break\n",
    "        loss.backward()\n",
    "        global_step += 1\n",
    "        if global_step % args.gradient_accumulation == 0:\n",
    "            optimizer.step()\n",
    "            if args.scheduler == 'warmup-decay':\n",
    "                scheduler.step() \n",
    "            optimizer.zero_grad()\n",
    "        if batch % 500 == 0:\n",
    "            sys.stderr.write('Checkpoint %d, Batch %d / %d, avg train loss = %f\\r' % \\\n",
    "                            (checkpoint, batch, args.ckpt_size, train_loss / batch))\n",
    "            tb_writer.add_scalar('lr', optimizer.param_groups[0]['lr'], global_step)\n",
    "            tb_writer.add_scalar('loss', train_loss / batch, global_step)\n",
    "            \n",
    "        del x, y, global_prob, loss         # This line and next line: to save GPU memory\n",
    "        torch.cuda.empty_cache()            # I don't know if they're useful or not\n",
    "    train_loss /= args.ckpt_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(opt, model, adv_jam, trainloader, max_batches = 100):\n",
    "    model.eval()\n",
    "        \n",
    "    batches = 0\n",
    "    total = 0\n",
    "    error = 0\n",
    "    loss_ = 0.\n",
    "    torch.manual_seed(0)\n",
    "\n",
    "    for X_batch, y_batch in trainloader:\n",
    "        # forward pass\n",
    "        X_batch, y_batch = X_batch.cuda(), y_batch.cuda()\n",
    "        # plot(X_batch[0], y_batch[0])\n",
    "        output = model(adv_jam(X))\n",
    "        opt.zero_grad()\n",
    "        neg_loss = -nn.CrossEntropyLoss()(output, y)\n",
    "\n",
    "        neg_loss.backward()\n",
    "        opt.step()\n",
    "\n",
    "\n",
    "\n",
    "        # Evaluate performance\n",
    "        adv_out = model(X_adv)\n",
    "        adv_prediction = sigmoid(adv_out.squeeze().cpu().detach())>0.5\n",
    "        adv_accuracy = (adv_prediction.numpy()==y_batch.cpu().numpy()).mean()\n",
    "        print(\"Accuracy = {0}\".format(adv_accuracy))\n",
    "                # project into feasible region\n",
    "        adv.jam.param1.clamp_(0,1)\n",
    "        adv.jam.param2.clamp_(1, 40)\n",
    "        adv.jam.param3.clamp_(0,224)\n",
    "        total += X.shape[0]\n",
    "        error += (output.max(1)[1] != y).sum().item()\n",
    "        loss_ += nn.CrossEntropyLoss(size_average=False)(output, y).item()\n",
    "            batches += 1\n",
    "            if batches > max_batches:\n",
    "                break\n",
    "\n",
    "        return error / total, loss_ / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def projected_gradient_descent(model, x, y, num_steps = 20, step_size = 0.1, step_norm=2, eps = 10, eps_norm=2,\n",
    "                               clamp=(x_min, x_max), y_target=None):\n",
    "    \"\"\"Performs the projected gradient descent attack on a batch of images.\"\"\"\n",
    "    x_adv = x.clone().detach().requires_grad_(True)#.to(x.device)\n",
    "    N, T, V = x.shape\n",
    "    targeted = y_target is not None\n",
    "    num_channels = x.shape[1]\n",
    "    criterion = nn.BCELoss()\n",
    "\n",
    "    for i in range(num_steps):\n",
    "        _x_adv = x_adv.clone().detach().requires_grad_(True)\n",
    "        \n",
    "        out = model(_x_adv) # N x T x 1\n",
    "        out_flatten = out.reshape(-1) \n",
    "        y_flatten = y.reshape(-1)\n",
    "        loss = criterion(out_flatten, y_flatten)\n",
    "        \n",
    "        #prediction = model(_x_adv)\n",
    "        #loss = criterion(prediction, y_target if targeted else y)\n",
    "        loss.backward()\n",
    "\n",
    "        with torch.no_grad():\n",
    "            # Force the gradient step to be a fixed size in a certain norm\n",
    "            if step_norm == 'inf':\n",
    "                gradients = _x_adv.grad.sign() * step_size\n",
    "            else:\n",
    "                # The grad is normalized here.\n",
    "                #gradients = _x_adv.grad \n",
    "                # Only normalize w.r.t. to feats at a timestep\n",
    "                gradients = _x_adv.grad/_x_adv.grad.norm(step_norm, dim=-1).view(-1, T, 1)\n",
    "                gradients *= step_size\n",
    "                \n",
    "            if targeted:\n",
    "                # Targeted: Gradient descent with on the loss of the (incorrect) target label\n",
    "                # w.r.t. the image data\n",
    "                x_adv -= gradients\n",
    "            else:\n",
    "                # Untargeted: Gradient ascent on the loss of the correct label w.r.t.\n",
    "                # the model parameters\n",
    "                x_adv += gradients\n",
    "\n",
    "        # Project back into l_norm ball and correct range\n",
    "        if eps_norm == 'inf':\n",
    "            # Workaround as PyTorch doesn't have elementwise clip\n",
    "            x_adv = torch.max(torch.min(x_adv, x + eps), x - eps)\n",
    "        else:\n",
    "            delta = x_adv - x\n",
    "\n",
    "            # Assume x and x_adv are batched tensors where the first dimension is\n",
    "            # a batch dimension\n",
    "            mask = delta.view(delta.shape[0], -1).norm(eps_norm, dim=1) <= eps\n",
    "\n",
    "            scaling_factor = delta.view(delta.shape[0], T, -1).norm(eps_norm, dim=-1)\n",
    "            scaling_factor[mask] = eps\n",
    "            #print(scaling_factor.shape)\n",
    "            # .view() assumes batched images as a 4D Tensor\n",
    "            delta *= eps / scaling_factor.view(-1, T, 1)\n",
    "\n",
    "            x_adv = x + delta\n",
    "            \n",
    "        x_adv = x_adv.clamp(*clamp)\n",
    "\n",
    "    return x_adv.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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