analysis_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "002ed4c3",
   "metadata": {},
   "source": [
    "# Set-Up for Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d05940ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\benja\\anaconda3\\envs\\drlnd\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import dill\n",
    "import copy\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from utils.load_data import *\n",
    "from utils.analyse import *\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# cpu for latency measurements\n",
    "device = torch.device('cpu')\n",
    "\n",
    "# make the output deterministic\n",
    "SEED = 7\n",
    "torch.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c275be60",
   "metadata": {},
   "source": [
    "#### Initialize the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2415dd75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def are_lists_of_tensors_equal(list1, list2):\n",
    "    # Check if the lists have the same length\n",
    "    if len(list1) != len(list2):\n",
    "        return False\n",
    "\n",
    "    # Check if each pair of tensors is equal\n",
    "    for tensor1, tensor2 in zip(list1, list2):\n",
    "        # Use torch.equal to compare tensors element-wise\n",
    "        if not torch.equal(tensor1, tensor2):\n",
    "            print(\"unequal tensors\")\n",
    "            return False\n",
    "\n",
    "    # If all pairs of tensors are equal, the lists are equal\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928159d8",
   "metadata": {},
   "source": [
    "#### Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "363c44df",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('MAKE')\n",
    "_, test_data = dataset()\n",
    "g1, g1_names, num_g1_classes = g1_classes()\n",
    "_test_loader = DataLoader(dataset=test_data, batch_size=512, shuffle=False)\n",
    "_test_loader_td = DataLoader(dataset=test_data, batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e210eb49",
   "metadata": {},
   "source": [
    "## Run Analysis\n",
    "The following $\\text{Threshold}$ hyperparameter values are tuned such that the outputs of the Super-HBN network match the Sem-HBN network from prior papers (13 exits at branch 1 and 2, and 14  exits at branch 3). The fine-tolerance values will are varied for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe5bb6c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running AlexNet Model\n",
      "Testing Batch Size: = 512\n",
      "===========================================================================\n",
      "Branch: 1\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "AlexNet_results:\n",
      "{\n",
      "  \"test_accuracy\": [48.71],\n",
      "  \"hierarchical_accuracy\": [61.95],\n",
      "  \"specificity\": [1.0],\n",
      "  \"flops\": [96452608.0],\n",
      "  \"memory\": [33.39561367034912],\n",
      "  \"time_taken\": [18.2937542],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [20],\n",
      "  \"coarse_exits\": [0],\n",
      "}\n",
      "===========================================================================\n",
      "\n",
      "Running BranchyAlexNet Model\n",
      "Testing Batch Size: = 512\n",
      "===========================================================================\n",
      "Branch: 1\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "BranchyAlexNet_results_branch1:\n",
      "{\n",
      "  \"test_accuracy\": [51.43],\n",
      "  \"hierarchical_accuracy\": [63.94],\n",
      "  \"specificity\": [1.0],\n",
      "  \"flops\": [31703040.0],\n",
      "  \"memory\": [46.04724597930908],\n",
      "  \"time_taken\": [14.828854799999998],\n",
      "  \"branch1_exits\": [20],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [20],\n",
      "  \"coarse_exits\": [0],\n",
      "}\n",
      "Branch: 2\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "BranchyAlexNet_results_branch2:\n",
      "{\n",
      "  \"test_accuracy\": [53.03],\n",
      "  \"hierarchical_accuracy\": [65.68],\n",
      "  \"specificity\": [1.0],\n",
      "  \"flops\": [75628544.0],\n",
      "  \"memory\": [46.04724597930908],\n",
      "  \"time_taken\": [15.2168378],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [20],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [20],\n",
      "  \"coarse_exits\": [0],\n",
      "}\n",
      "Branch: 3\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "BranchyAlexNet_results_branch3:\n",
      "{\n",
      "  \"test_accuracy\": [51.24],\n",
      "  \"hierarchical_accuracy\": [63.95],\n",
      "  \"specificity\": [1.0],\n",
      "  \"flops\": [135806976.0],\n",
      "  \"memory\": [46.04724597930908],\n",
      "  \"time_taken\": [16.749331199999993],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [20],\n",
      "  \"fine_exits\": [20],\n",
      "  \"coarse_exits\": [0],\n",
      "}\n",
      "===========================================================================\n",
      "\n",
      "Running Sem_HBN Model\n",
      "Testing Batch Size: = 512\n",
      "===========================================================================\n",
      "Branch: 1\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "Sem_HBN_results_branch1:\n",
      "{\n",
      "  \"test_accuracy\": [64.59],\n",
      "  \"hierarchical_accuracy\": [64.59],\n",
      "  \"specificity\": [0.0],\n",
      "  \"flops\": [31375360.0],\n",
      "  \"memory\": [44.79663562774658],\n",
      "  \"time_taken\": [14.2615287],\n",
      "  \"branch1_exits\": [20],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [0],\n",
      "  \"coarse_exits\": [20],\n",
      "}\n",
      "Branch: 2\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "Sem_HBN_results_branch2:\n",
      "{\n",
      "  \"test_accuracy\": [65.92],\n",
      "  \"hierarchical_accuracy\": [65.92],\n",
      "  \"specificity\": [0.0],\n",
      "  \"flops\": [74973184.0],\n",
      "  \"memory\": [44.79663562774658],\n",
      "  \"time_taken\": [15.078986900000004],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [20],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [0],\n",
      "  \"coarse_exits\": [20],\n",
      "}\n",
      "Branch: 3\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "Sem_HBN_results_branch3:\n",
      "{\n",
      "  \"test_accuracy\": [51.43],\n",
      "  \"hierarchical_accuracy\": [65.55],\n",
      "  \"specificity\": [1.0],\n",
      "  \"flops\": [135151616.0],\n",
      "  \"memory\": [44.79663562774658],\n",
      "  \"time_taken\": [16.8666762],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [20],\n",
      "  \"fine_exits\": [20],\n",
      "  \"coarse_exits\": [0],\n",
      "}\n",
      "===========================================================================\n",
      "\n",
      "Running TD_HBN Model\n",
      "Testing Batch Size: = 1\n",
      "===========================================================================\n",
      "Branch: 1\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 10000\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TD_HBN_results_branch1:\n",
      "{\n",
      "  \"test_accuracy\": [54.87],\n",
      "  \"hierarchical_accuracy\": [64.48],\n",
      "  \"specificity\": [0.7486],\n",
      "  \"flops\": [31793152.0],\n",
      "  \"memory\": [46.53574848175049],\n",
      "  \"time_taken\": [148.77754149999998],\n",
      "  \"branch1_exits\": [10000],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [7486],\n",
      "  \"coarse_exits\": [2514],\n",
      "}\n",
      "Branch: 2\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 10000\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "TD_HBN_results_branch2:\n",
      "{\n",
      "  \"test_accuracy\": [55.78],\n",
      "  \"hierarchical_accuracy\": [66.37],\n",
      "  \"specificity\": [0.7478],\n",
      "  \"flops\": [75800576.0],\n",
      "  \"memory\": [46.53574848175049],\n",
      "  \"time_taken\": [235.7382174],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [10000],\n",
      "  \"branch3_exits\": [0],\n",
      "  \"fine_exits\": [7478],\n",
      "  \"coarse_exits\": [2522],\n",
      "}\n",
      "Branch: 3\t\tFine-Tolerance: None\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 10000\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "TD_HBN_results_branch3:\n",
      "{\n",
      "  \"test_accuracy\": [53.91],\n",
      "  \"hierarchical_accuracy\": [64.8],\n",
      "  \"specificity\": [0.7474],\n",
      "  \"flops\": [136060928.0],\n",
      "  \"memory\": [46.53574848175049],\n",
      "  \"time_taken\": [340.807956],\n",
      "  \"branch1_exits\": [0],\n",
      "  \"branch2_exits\": [0],\n",
      "  \"branch3_exits\": [10000],\n",
      "  \"fine_exits\": [7474],\n",
      "  \"coarse_exits\": [2526],\n",
      "}\n",
      "===========================================================================\n",
      "\n",
      "Running SuperHBN Model\n",
      "Testing Batch Size: = 512\n",
      "===========================================================================\n",
      "Branch: 1\t\tFine-Tolerance: 0.0\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.02\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.04\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.06\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.08\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.1\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.12\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.14\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.16\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 1\t\tFine-Tolerance: 0.18\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.2\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.22\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.24\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.26\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.28\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.3\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.32\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.34\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.36\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.38\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.4\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.42\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 1\t\tFine-Tolerance: 0.44\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.46\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.48\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.5\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.52\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.54\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.56\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.58\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.6\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.62\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.64\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.66\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.68\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 1\t\tFine-Tolerance: 0.7000000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.72\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.74\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.76\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.78\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.8\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.8200000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.84\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.86\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.88\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.9\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.92\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.9400000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 1\t\tFine-Tolerance: 0.96\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 1\t\tFine-Tolerance: 0.98\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "SuperHBN_results_branch1:\n",
      "{\n",
      "  \"test_accuracy\": [64.51, 64.27, 63.92, 64.04, 63.73, 64.02, 63.76, 63.93, 64.52, 64.07, 64.21, 64.21, 63.97, 63.59, 63.63, 63.92, 64.43, 64.16, 64.25, 64.26, 64.1, 63.73, 63.55, 63.74, 61.86, 58.07, 54.83, 51.79, 51.44, 52.05, 51.62, 52.12, 52.07, 52.07, 52.16, 52.4, 51.77, 51.84, 52.01, 51.89, 52.1, 51.71, 52.0, 51.69, 51.72, 52.04, 51.73, 51.86, 52.15, 51.67],\n",
      "  \"hierarchical_accuracy\": [64.51, 64.27, 63.92, 64.04, 63.73, 64.02, 63.76, 63.93, 64.52, 64.07, 64.21, 64.21, 63.97, 63.59, 63.63, 63.92, 64.43, 64.16, 64.25, 64.26, 64.1, 63.73, 63.55, 63.74, 63.74, 64.44, 64.9, 64.19, 64.63, 64.88, 64.15, 64.88, 64.63, 64.84, 64.99, 64.93, 65.02, 64.77, 65.21, 64.62, 65.15, 64.71, 64.75, 64.83, 64.6, 65.05, 64.85, 64.69, 64.96, 64.6],\n",
      "  \"specificity\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15, 0.5, 0.75, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
      "  \"flops\": [31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0, 31784960.0],\n",
      "  \"memory\": [46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889],\n",
      "  \"time_taken\": [14.678177100000084, 14.480928500000005, 15.296411299999932, 14.507644400000004, 14.371964699999921, 14.369630499999971, 14.568410700000072, 16.156224400000042, 15.327221899999927, 15.912941599999954, 17.64043580000009, 19.09391800000003, 15.227903100000049, 14.66696049999996, 14.735630299999912, 14.553087799999957, 14.553775099999939, 14.58009960000004, 14.817939299999807, 14.677081600000065, 14.557097999999996, 14.442212599999948, 14.576878000000079, 14.570015499999954, 14.55700569999999, 14.883208999999852, 15.184046999999964, 14.852702900000168, 14.796169799999916, 14.976531199999954, 14.990770699999985, 14.88909650000005, 14.928251100000125, 14.859749799999918, 14.970453099999986, 14.992646499999864, 14.818506099999922, 15.360773699999982, 15.141254599999911, 14.691844299999957, 14.597486799999842, 14.583936199999926, 14.729051900000059, 14.51454909999984, 15.65881719999993, 14.541060700000116, 14.844445499999892, 14.598340000000007, 14.822618700000021, 14.800871499999857],\n",
      "  \"branch1_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"branch2_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"branch3_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"fine_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 10, 15, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"coarse_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 17, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "}\n",
      "Branch: 2\t\tFine-Tolerance: 0.0\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.02\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.04\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.06\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 2\t\tFine-Tolerance: 0.08\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.1\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.12\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.14\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.16\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.18\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.2\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.22\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.24\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.26\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.28\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.3\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.32\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 2\t\tFine-Tolerance: 0.34\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.36\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.38\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.4\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.42\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.44\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.46\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.48\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.5\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.52\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.54\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.56\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.58\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 2\t\tFine-Tolerance: 0.6\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.62\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.64\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.66\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.68\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.7000000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.72\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.74\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.76\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.78\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.8\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.8200000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.84\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 2\t\tFine-Tolerance: 0.86\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.88\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.9\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.92\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.9400000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.96\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 2\t\tFine-Tolerance: 0.98\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "SuperHBN_results_branch2:\n",
      "{\n",
      "  \"test_accuracy\": [66.59, 66.64, 66.61, 66.92, 66.73, 66.11, 66.49, 67.04, 66.31, 67.1, 66.81, 66.81, 66.8, 66.58, 66.56, 66.68, 66.61, 66.42, 66.33, 66.99, 66.79, 66.88, 67.29, 66.48, 66.77, 66.96, 65.97, 64.73, 64.07, 56.31, 54.95, 53.93, 54.56, 53.74, 53.96, 53.94, 54.11, 53.66, 53.75, 53.48, 53.89, 54.07, 53.61, 53.8, 53.74, 53.82, 53.62, 53.7, 53.15, 53.31],\n",
      "  \"hierarchical_accuracy\": [66.59, 66.64, 66.61, 66.92, 66.73, 66.11, 66.49, 67.04, 66.31, 67.1, 66.81, 66.81, 66.8, 66.58, 66.56, 66.68, 66.61, 66.42, 66.33, 66.99, 66.79, 66.88, 67.29, 66.48, 66.77, 66.96, 66.6, 66.52, 67.58, 66.74, 66.71, 67.01, 67.4, 67.1, 67.06, 67.18, 67.31, 67.17, 67.02, 66.22, 66.89, 67.47, 66.42, 67.05, 67.07, 67.34, 66.7, 66.86, 66.45, 66.73],\n",
      "  \"specificity\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05, 0.15, 0.25, 0.75, 0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
      "  \"flops\": [75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0, 75792384.0],\n",
      "  \"memory\": [46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889],\n",
      "  \"time_taken\": [15.43726270000002, 14.85305040000003, 15.071279100000083, 16.335579400000142, 14.924398099999962, 15.025225300000102, 15.389241200000015, 15.487374600000066, 15.277600699999994, 15.13880979999999, 15.099074900000005, 15.414625099999967, 15.043619700000136, 15.967666000000008, 16.71000600000002, 15.696382800000038, 15.168654899999865, 15.136570899999924, 14.9232121, 15.13942330000009, 14.845088900000064, 15.249516399999948, 14.897458499999857, 15.00583030000007, 14.846005800000057, 15.02968420000002, 16.333131299999877, 16.440216899999996, 16.618624300000192, 19.835685099999864, 20.44732780000004, 20.636065900000176, 20.719370499999968, 20.60753160000013, 20.3786513, 20.28341730000011, 20.208019799999875, 19.358946800000012, 16.063041399999747, 15.542070500000136, 15.89899000000014, 15.693731000000298, 15.768071500000133, 15.349979299999632, 15.507258699999966, 15.351682399999845, 15.66531890000033, 15.202568300000166, 15.38410359999989, 15.6328867000002],\n",
      "  \"branch1_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"branch2_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"branch3_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"fine_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 15, 18, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"coarse_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 19, 17, 15, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "}\n",
      "Branch: 3\t\tFine-Tolerance: 0.0\n",
      "---------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.02\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.04\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.06\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.08\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.1\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.12\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.14\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.16\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.18\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.2\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.22\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.24\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 3\t\tFine-Tolerance: 0.26\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.28\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.3\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.32\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.34\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.36\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.38\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.4\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.42\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.44\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.46\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.48\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.5\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 3\t\tFine-Tolerance: 0.52\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.54\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.56\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.58\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.6\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.62\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.64\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.66\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.68\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.7000000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.72\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.74\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.76\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Branch: 3\t\tFine-Tolerance: 0.78\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.8\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.8200000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.84\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.86\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.88\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.9\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.92\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.9400000000000001\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.96\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "Branch: 3\t\tFine-Tolerance: 0.98\n",
      "---------------------------------------------------------------------------\n",
      "Timer Active\n",
      "# Testing Iterations: 20\n",
      "Timer Ended\n",
      "Getting FLOPs\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "Getting memory size\n",
      "\n",
      "SuperHBN_results_branch3:\n",
      "{\n",
      "  \"test_accuracy\": [65.08, 65.35, 65.99, 65.27, 65.68, 65.19, 64.95, 65.48, 65.71, 65.67, 65.02, 64.8, 65.31, 65.42, 65.44, 65.45, 65.37, 65.05, 65.89, 65.24, 64.98, 65.0, 63.93, 62.27, 57.99, 55.79, 51.79, 51.86, 51.68, 51.91, 52.18, 51.76, 52.07, 51.8, 51.91, 51.66, 51.95, 51.42, 51.48, 51.88, 51.53, 51.26, 51.66, 52.07, 51.88, 51.54, 51.53, 51.44, 51.56, 51.15],\n",
      "  \"hierarchical_accuracy\": [65.08, 65.35, 65.99, 65.27, 65.68, 65.19, 64.95, 65.48, 65.71, 65.67, 65.02, 64.8, 65.31, 65.42, 65.44, 65.45, 65.37, 65.05, 65.89, 65.24, 64.98, 65.0, 65.31, 65.34, 65.41, 65.89, 64.7, 65.66, 65.15, 65.26, 65.64, 65.25, 65.33, 65.28, 65.33, 65.21, 65.17, 64.97, 65.45, 65.18, 65.05, 64.63, 65.37, 65.34, 65.34, 64.79, 65.36, 64.95, 65.2, 64.65],\n",
      "  \"specificity\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.25, 0.55, 0.75, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
      "  \"flops\": [136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0, 136052736.0],\n",
      "  \"memory\": [46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889, 46.51927471160889],\n",
      "  \"time_taken\": [16.954802799999925, 16.9670372999999, 16.73878079999986, 16.982980899999802, 16.72829150000007, 16.93025099999977, 16.81718180000007, 17.149338100000023, 16.96083529999987, 16.833323400000154, 17.06500589999996, 16.740085200000067, 16.770159700000022, 17.903929900000094, 17.78608099999974, 19.04438140000002, 19.4101955000001, 21.899544800000058, 18.781275899999855, 16.82987409999987, 17.202066699999705, 16.878117100000054, 16.674032500000067, 16.835073600000214, 17.274859600000127, 17.24565070000017, 17.628648299999895, 17.39208460000009, 17.400065000000268, 17.274453999999878, 17.43992560000015, 17.45518940000011, 17.28065110000034, 17.444726100000025, 17.64864320000015, 17.321500399999877, 17.265389199999845, 17.29092770000034, 17.449806999999964, 18.39354850000018, 18.15243099999998, 18.537816399999883, 18.495973200000208, 18.798082899999827, 17.460497799999757, 17.079849999999624, 17.16391109999995, 17.4190951999999, 17.54445100000021, 17.171746600000006],\n",
      "  \"branch1_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"branch2_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "  \"branch3_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"fine_exits\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 11, 15, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20],\n",
      "  \"coarse_exits\": [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 18, 15, 9, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "}\n",
      "===========================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# models dictionary\n",
    "fine_tolerance = np.arange(0.0, 1.0, 0.02)\n",
    "models = {\n",
    "    'AlexNet': {'model': AlexNet, 'filepath': '../results/models/AlexNet.pth', 'analyser': AnalyseAlexNet, 'threshold': [[None]], 'fine_tolerance': [None]},\n",
    "    'BranchyAlexNet':\n",
    "        {'model': BranchyAlexNet, 'filepath': '../results/models/Branchy-AlexNet.pth', 'analyser': AnalyseBranchyNet, 'threshold': [[1e6, -1e6], [-1e6, 1e6], [-1e-6, -1e-6]], 'fine_tolerance': [None]},\n",
    "    'Sem_HBN':\n",
    "        {'model': SemHBN, 'filepath': '../results/models/Sem-HBN.pth', 'analyser': AnalyseSemHBN, 'threshold': [[1e6, -1e6], [-1e6, 1e6], [-1e-6, -1e-6]], 'fine_tolerance': [None]},\n",
    "    'TD_HBN':\n",
    "        {'model': TD_HBN, 'filepath': '../results/models/TD-HBN.pth', 'analyser': AnalyseTDHBN, 'threshold': [[1e6, -1e6], [-1e6, 1e6], [-1e-6, -1e-6]], 'fine_tolerance': [None]},\n",
    "    'SuperHBN':\n",
    "        {'model': SuperHBN, 'filepath': '../results/models/Super-HBN.pth', 'analyser': AnalyseHBN, 'threshold': [[1e6, -1e6], [-1e6, 1e6], [-1e-6, -1e-6]], 'fine_tolerance': fine_tolerance},\n",
    "}\n",
    "\n",
    "for model_name, model_contents in models.items():\n",
    "    test_loader = _test_loader_td if model_name == 'TD_HBN' else _test_loader\n",
    "\n",
    "    print(f\"Running {model_name} Model\\nTesting Batch Size: = {test_loader.batch_size}\\n\" + \"=\"*75)\n",
    "    state_dict, imagi = None, None\n",
    "    for idx, th in enumerate(model_contents['threshold']):\n",
    "        # structure for results dictionary\n",
    "        results = {\n",
    "            'test_accuracy': [],\n",
    "            'hierarchical_accuracy': [],\n",
    "            'specificity': [],\n",
    "            'flops': [],\n",
    "            'memory': [],\n",
    "            'time_taken': [],\n",
    "            'branch1_exits': [],\n",
    "            'branch2_exits': [],\n",
    "            'branch3_exits': [],\n",
    "            'fine_exits': [],\n",
    "            'coarse_exits': []\n",
    "        }\n",
    "        for i, ft in enumerate(model_contents['fine_tolerance']):\n",
    "            print(f\"Branch: {idx+1}\\t\\tFine-Tolerance: {ft}\\n\" + \"-\"*75)\n",
    "\n",
    "            # perform analysis and add to results dictionary\n",
    "            result_key = model_name + (f'_results_branch{idx+1}' if th[0] is not None else '_results')\n",
    "            globals()[result_key] = results.copy()\n",
    "            analysis = model_contents['analyser'](model_class=model_contents['model'], filepath=model_contents['filepath'], device=device, coarse_converter=g1, threshold=th, fine_tolerance=ft,\n",
    "                                                  test_loader=copy.deepcopy(test_loader))\n",
    "            analysis_results = analysis.perform_analysis()\n",
    "\n",
    "            # update results dictionary values\n",
    "            for key, value in zip(globals()[result_key], analysis_results):\n",
    "                globals()[result_key][key].append(value)\n",
    "\n",
    "        # debugging\n",
    "        formatted_results = \"{\\n\"\n",
    "        for key, value in globals()[result_key].items():\n",
    "            formatted_results += f'  \"{key}\": {value},\\n'\n",
    "        formatted_results += \"}\"\n",
    "        print(f\"\\n{result_key}:\\n{formatted_results}\")\n",
    "    print(\"=\"*75 + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ddb0600",
   "metadata": {},
   "source": [
    "***\n",
    "# Save Results\n",
    "#### Save the Experimentation Results for Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60a02052",
   "metadata": {},
   "outputs": [],
   "source": [
    "dill.dump_session('../results/experiment_results.db')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drlnd",
   "language": "python",
   "name": "drlnd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}