AUM_exp.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AUM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuG-mvwArzRJ",
        "colab_type": "text"
      },
      "source": [
        "Do not forget to setup GPU accelerator in `Runtime -> Change runtime type -> Hardware accelerator : GPU`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CR7R4X5mkhuW",
        "colab_type": "code",
        "outputId": "c1976f43-288f-42e9-8cae-3707aa315c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "! nvidia-smi\n",
        "! lscpu\n",
        "! pip install tensorboardX\n",
        "! pip install loguru"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Feb  6 10:41:32 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P0    28W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "Architecture:        x86_64\n",
            "CPU op-mode(s):      32-bit, 64-bit\n",
            "Byte Order:          Little Endian\n",
            "CPU(s):              2\n",
            "On-line CPU(s) list: 0,1\n",
            "Thread(s) per core:  2\n",
            "Core(s) per socket:  1\n",
            "Socket(s):           1\n",
            "NUMA node(s):        1\n",
            "Vendor ID:           GenuineIntel\n",
            "CPU family:          6\n",
            "Model:               63\n",
            "Model name:          Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "Stepping:            0\n",
            "CPU MHz:             2300.000\n",
            "BogoMIPS:            4600.00\n",
            "Hypervisor vendor:   KVM\n",
            "Virtualization type: full\n",
            "L1d cache:           32K\n",
            "L1i cache:           32K\n",
            "L2 cache:            256K\n",
            "L3 cache:            46080K\n",
            "NUMA node0 CPU(s):   0,1\n",
            "Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities\n",
            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.6/dist-packages (2.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.17.5)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (3.10.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorboardX) (45.1.0)\n",
            "Requirement already satisfied: loguru in /usr/local/lib/python3.6/dist-packages (0.4.1)\n",
            "Requirement already satisfied: aiocontextvars>=0.2.0; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from loguru) (0.2.2)\n",
            "Requirement already satisfied: contextvars==2.4; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from aiocontextvars>=0.2.0; python_version < \"3.7\"->loguru) (2.4)\n",
            "Requirement already satisfied: immutables>=0.9 in /usr/local/lib/python3.6/dist-packages (from contextvars==2.4; python_version < \"3.7\"->aiocontextvars>=0.2.0; python_version < \"3.7\"->loguru) (0.11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBOPbIC8hGp2",
        "colab_type": "text"
      },
      "source": [
        "## Define model & hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GGPhckn6b4KL",
        "colab": {}
      },
      "source": [
        "import tqdm\n",
        "import time\n",
        "from loguru import logger\n",
        "import pprint\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.optim.lr_scheduler as lrs\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import torchvision\n",
        "from torchvision.datasets.cifar import CIFAR10\n",
        "\n",
        "from torchvision.models.mobilenet import mobilenet_v2\n",
        "from torchvision.models.resnet import resnet18\n",
        "from torchvision.transforms import Compose, ToTensor, RandomCrop, RandomHorizontalFlip, Pad, Normalize, ColorJitter, Resize\n",
        "\n",
        "torch.__version__, torchvision.__version__ \n",
        "\n",
        "from tensorboardX import SummaryWriter\n",
        "from datetime import datetime\n",
        "\n",
        "import random\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhoeDNYts7qT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "\n",
        "class RandomErasingPIL(object):\n",
        "    '''\n",
        "    Class that performs Random Erasing in Random Erasing Data Augmentation by Zhong et al.\n",
        "    -------------------------------------------------------------------------------------\n",
        "    probability: The probability that the operation will be performed.\n",
        "    sl: min erasing area\n",
        "    sh: max erasing area\n",
        "    r1: min aspect ratio\n",
        "    mean: erasing value\n",
        "    -------------------------------------------------------------------------------------\n",
        "    '''\n",
        "\n",
        "    def __init__(self, probability=1, sl=0.05, sh=0.20, r1=1):\n",
        "        self.probability = probability\n",
        "        self.sl = sl\n",
        "        self.sh = sh\n",
        "        self.r1 = r1\n",
        "\n",
        "    def __call__(self, img):\n",
        "\n",
        "        if random.uniform(0, 1) > self.probability:\n",
        "            return img\n",
        "\n",
        "        for attempt in range(100):\n",
        "            area = img.size[0] * img.size[1]\n",
        "\n",
        "            target_area = random.uniform(self.sl, self.sh) * area\n",
        "            aspect_ratio = random.uniform(self.r1, 1 / self.r1)\n",
        "\n",
        "            h = int(round(math.sqrt(target_area * aspect_ratio)))\n",
        "            w = int(round(math.sqrt(target_area / aspect_ratio)))\n",
        "\n",
        "            if w < img.size[1] and h < img.size[0]:\n",
        "                img_return = img.copy()\n",
        "                pixels = img_return.load()\n",
        "                x1 = random.randint(0, img.size[0] - h)\n",
        "                y1 = random.randint(0, img.size[1] - w)\n",
        "\n",
        "                if img_return.mode == \"L\" or img_return.mode == \"I\" :\n",
        "                    for i in range(x1, x1 + h):  # for every col:\n",
        "                        for j in range(y1, y1 + w):  # For every row\n",
        "                            pixels[i, j] = (0)\n",
        "                else :\n",
        "                    for i in range(x1, x1 + h):  # for every col:\n",
        "                        for j in range(y1, y1 + w):  # For every row\n",
        "                            pixels[i, j] = (0, 0, 0)\n",
        "                return img_return\n",
        "\n",
        "        return img\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKV_gulz8C0i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "\n",
        "class ConstantTargetDataset(Dataset):\n",
        "    \"\"\"\n",
        "    Dataset returning a given target for all or only the choosen indexes of the given dataset\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dataset, target=False, idx_filter=[]):\n",
        "        self.dataset = dataset\n",
        "        self.target = target\n",
        "        self.counter = 0\n",
        "        self.idx_filter = idx_filter\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        tile, target = self.dataset[item]\n",
        "        if item in self.idx_filter:\n",
        "            return tile, self.target\n",
        "        else:\n",
        "            return tile, target\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataset)\n",
        "\n",
        "\n",
        "class AreaUnderTheMarginRanking():\n",
        "    \"\"\"\n",
        "    Implementation of the paper Identifying Mislabeled Data using the Area Under the Margin Ranking: https://arxiv.org/pdf/2001.10528v2.pdf\n",
        "\n",
        "    Currently the used dataset must not shuffle between epochs !\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO try to use matrix operations\n",
        "    # TODO manage the case of dataset with shuffling between epochs\n",
        "\n",
        "    def __init__(self):\n",
        "        # hist_delta_AUM_current_epoch dimensions: [n_sample, 2 (from in_logit & max(out_logits))]\n",
        "        self.hist_delta_AUM_current_epoch = torch.zeros(size=(0, 2))\n",
        "        # hist_delta_AUM dimensions: [n_epoch, n_sample, in_logit & max(out_logits)]\n",
        "        self.hist_delta_AUM = torch.zeros(size=(0, 0, 2))\n",
        "        self.reference_sample_idx = []\n",
        "\n",
        "    def accumulate(self, batch_logits, batch_ids, batch_targets):\n",
        "        \"\"\" To be called after batch prediction\"\"\"\n",
        "        for img_logit, img_id, img_target in zip(batch_logits.split([1] * len(batch_logits), dim=0),\n",
        "                                                 batch_ids.split([1] * len(batch_logits), dim=0),\n",
        "                                                 batch_targets.split([1] * len(batch_logits), dim=0)):\n",
        "            img_logit = img_logit.squeeze(dim=0)\n",
        "            target_logit = img_logit[img_target]\n",
        "            if img_target < len(img_logit) - 1:\n",
        "                notarget_logits = torch.cat([img_logit[:img_target], img_logit[img_target + 1:]], dim=0)\n",
        "            else:\n",
        "                notarget_logits = img_logit[:img_target]\n",
        "            notarget_logits = notarget_logits.max()\n",
        "            self.hist_delta_AUM_current_epoch = torch.cat(\n",
        "                [self.hist_delta_AUM_current_epoch, torch.tensor([[target_logit, notarget_logits]])], dim=0)\n",
        "\n",
        "    def accumulate_epoch(self):\n",
        "        \"\"\" To be called at the end of each epoch\"\"\"\n",
        "        if len(self.hist_delta_AUM) == 0:\n",
        "            self.hist_delta_AUM = self.hist_delta_AUM_current_epoch.unsqueeze(dim=0)\n",
        "        else:\n",
        "            self.hist_delta_AUM = torch.cat([self.hist_delta_AUM, self.hist_delta_AUM_current_epoch.unsqueeze(dim=0)],\n",
        "                                            dim=0)\n",
        "        self.hist_delta_AUM_current_epoch = torch.zeros(size=(0, 2))\n",
        "\n",
        "    def get_reference_aum_threshold(self, percentile=0.99):\n",
        "        reference_aum = self.hist_delta_AUM[:, self.reference_sample_idx, :]  # => [n_epoch, n_sample, in_logit & max(\n",
        "        # out_logits)]\n",
        "        reference_aum = torch.tensor(reference_aum)\n",
        "        reference_aum = reference_aum[..., 0] - reference_aum[..., 1]  # => [n_epoch, n_sample]\n",
        "        reference_aum = reference_aum.mean(dim=0)\n",
        "        reference_aum, _ = reference_aum.sort(dim=0, descending=False)  # => [n_sample]\n",
        "        aum_threshold_at_percentile = reference_aum[int(len(reference_aum) * percentile)]\n",
        "        return aum_threshold_at_percentile\n",
        "\n",
        "    def add_reference_class_to_ds(self, dataset, n_class, exclusion_idx=None):\n",
        "        \"\"\"\n",
        "        Will modify a given dataset by adding the reference class with only mislabeled data.\n",
        "        Original targets of the dataset must be [0...n_class-1].\n",
        "        Currently dataset must not shuffle between epochs !\n",
        "\n",
        "        :param dataset: Dataset to be modified\n",
        "        :param n_class: Original number of class in Dataset (original targets must be [0...n_class-1])\n",
        "        :param exclusion_idx: Do not add these idx in the reference class\n",
        "        :return: new dataset with the added reference class\n",
        "        \"\"\"\n",
        "        if exclusion_idx is None:\n",
        "            exclusion_idx = self.reference_sample_idx\n",
        "        n_reference_sample = int(len(dataset) / (n_class + 1))\n",
        "        print(\"select n_reference_sample\", n_reference_sample)\n",
        "        self.reference_sample_idx = random.sample(population=range(len(dataset)), k=n_reference_sample)\n",
        "\n",
        "        new = []\n",
        "        for el in self.reference_sample_idx:\n",
        "            if el in exclusion_idx or el in new:\n",
        "                done = False\n",
        "                new_el = el\n",
        "                while not done:\n",
        "                    new_el += 1\n",
        "                    if new_el not in new:\n",
        "                        if new_el < len(dataset):\n",
        "                            done = True\n",
        "                            new.append(el)\n",
        "                        else:\n",
        "                            new_el = 0\n",
        "            else:\n",
        "                new.append(el)\n",
        "        assert len(set(new)) == len(new)\n",
        "\n",
        "        dataset = ConstantTargetDataset(dataset,\n",
        "                                        target=(n_class - 1) + 1,\n",
        "                                        idx_filter=self.reference_sample_idx)\n",
        "        return dataset, self.reference_sample_idx\n",
        "\n",
        "    def get_mislabeled(self):\n",
        "        \"\"\"\n",
        "\n",
        "        :return: list(mislabel indexes of image in dataset)\n",
        "        \"\"\"\n",
        "        threshold = self.get_reference_aum_threshold()\n",
        "\n",
        "        # reference_aum = self.hist_delta_AUM[:,self.reference_sample_idx,:]\n",
        "        # reference_aum = torch.tensor(reference_aum)\n",
        "        reference_aum = torch.tensor(self.hist_delta_AUM)\n",
        "        reference_aum = reference_aum[..., 0] - reference_aum[..., 1]\n",
        "        reference_aum = torch.tensor(reference_aum).mean(dim=0)\n",
        "        # print(\"reference_aum.shape\",reference_aum.shape)\n",
        "        mislabeled = (reference_aum < threshold).nonzero().tolist()\n",
        "        # print(\"mislabeled\",mislabeled)\n",
        "        mislabeled = [el for el in mislabeled if el not in self.reference_sample_idx]\n",
        "        # print(\"mislabeled\",mislabeled)\n",
        "        print(\"percentage of mislabeled\", len(mislabeled) / (len(reference_aum) - len(self.reference_sample_idx)),\n",
        "              len(mislabeled), len(reference_aum), len(self.reference_sample_idx))\n",
        "        return mislabeled\n",
        "\n",
        "    def get_hist(self):\n",
        "        \"\"\"\n",
        "        To be use to visualize the results. After the training or after each epoch\n",
        "        \"\"\"\n",
        "        sample_1 = random.randint(0, self.hist_delta_AUM.shape[1])\n",
        "        sample_2 = random.randint(0, self.hist_delta_AUM.shape[1])\n",
        "        sample_3 = random.randint(0, self.hist_delta_AUM.shape[1])\n",
        "        sample_4 = random.randint(0, self.hist_delta_AUM.shape[1])\n",
        "        sample_5 = random.randint(0, self.hist_delta_AUM.shape[1])\n",
        "\n",
        "        sample_1 = self.hist_delta_AUM[:, sample_1, :]\n",
        "        sample_2 = self.hist_delta_AUM[:, sample_2, :]\n",
        "        sample_3 = self.hist_delta_AUM[:, sample_3, :]\n",
        "        sample_4 = self.hist_delta_AUM[:, sample_4, :]\n",
        "        sample_5 = self.hist_delta_AUM[:, sample_5, :]\n",
        "\n",
        "        plt.figure()\n",
        "        plt.subplot(511)\n",
        "        plt.plot(sample_1)\n",
        "        plt.subplot(512)\n",
        "        plt.plot(sample_2)\n",
        "        plt.subplot(513)\n",
        "        plt.plot(sample_3)\n",
        "        plt.subplot(514)\n",
        "        plt.plot(sample_4)\n",
        "        plt.subplot(515)\n",
        "        plt.plot(sample_5)\n",
        "        plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yF54m6SetY8J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "\n",
        "# global num_epochs, ds\n",
        "\n",
        "\n",
        "def train(model, optimizer, criterion, train_loader, device ='cuda', **kwargs):\n",
        "    \"\"\" On one epoch \"\"\"\n",
        "\n",
        "    global global_step\n",
        "    model.train()\n",
        "    time_spent = 0\n",
        "    with tqdm.tqdm(total=len(train_loader), leave=False, desc=\"Iterations\") as pbar:\n",
        "      for idx, (x, y) in enumerate(train_loader):\n",
        "            optimizer.zero_grad()    \n",
        "            x, y = x.to(device), y.to(device)\n",
        "            loss = None\n",
        "            \n",
        "            # torch.cuda.synchronize()\n",
        "            epoch_start_time = time.perf_counter()\n",
        "\n",
        "            loss=0\n",
        "            y_pred = model(x)\n",
        "\n",
        "            img_id = range(idx,idx+len(x))\n",
        "            l = []\n",
        "            for i in img_id:\n",
        "              l.append(i)\n",
        "            img_id = torch.tensor(img_id).to(device)\n",
        "            aum.accumulate(y_pred, img_id, y)\n",
        "\n",
        "            not_reduced_loss = criterion(y_pred, y)\n",
        "            loss += not_reduced_loss.mean()\n",
        "            loss.backward()        \n",
        "\n",
        "            optimizer.step()\n",
        "            # torch.cuda.synchronize()\n",
        "            epoch_end_time = time.perf_counter()\n",
        "            time_spent += epoch_end_time-epoch_start_time\n",
        "\n",
        "            if loss is not None:\n",
        "                pbar.set_postfix(loss=loss.item(), lr=optimizer.param_groups[0]['lr'])\n",
        "                writer.add_scalar(\"Train/batch loss\", loss.item(), global_step)\n",
        "            \n",
        "            pbar.update()\n",
        "            global_step += 1\n",
        "    logger.info(\"Time spent {:.2f}s\".format(time_spent))\n",
        "    aum.accumulate_epoch()\n",
        "    aum.get_hist()\n",
        "    print(\"aum.get_mislabeled() first 10 are:\",aum.get_mislabeled()[0:10])\n",
        "    print(\"aum.get_reference_aum_threshold()\", aum.get_reference_aum_threshold())\n",
        "\n",
        "    return time_spent\n",
        "  \n",
        "def test(model, test_loader, prefix=\"Test\", global_step=None, device='cuda'):\n",
        "    \"\"\" On one epoch \"\"\"\n",
        "\n",
        "    num_correct = 0\n",
        "    num_samples = 0\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for x, y in test_loader:\n",
        "            x, y = x.to(device), y.to(device)\n",
        "            y_pred = model(x)            \n",
        "            y_pred = torch.argmax(y_pred, dim=1)\n",
        "            num_correct += torch.eq(y_pred, y).sum().item()\n",
        "            num_samples += y.shape[0]\n",
        "        \n",
        "        accuracy = num_correct * 1.0 / num_samples\n",
        "        print(f\"{prefix} accuracy: {accuracy}\")\n",
        "        \n",
        "        writer.add_scalar(f\"{prefix}/accuracy\", accuracy, global_step)\n",
        "    return accuracy\n",
        "    \n",
        "def init_exp(device='cuda', img_size=32, batch_size=512, lr_policy=\"ReduceLROnPlateau\", keep_ds_frac=1.0):\n",
        "  \"\"\" Init one experiment \"\"\"\n",
        "\n",
        "  now = datetime.now()\n",
        "  global writer\n",
        "  writer = SummaryWriter(logdir=f\"/tmp/tb_logs/{now.strftime('%Y%m%d-%H%M%S')}\")\n",
        "  global global_step\n",
        "  global_step = 0\n",
        "\n",
        "  SEED=2019\n",
        "  random.seed(SEED)\n",
        "  torch.manual_seed(SEED)\n",
        "  np.random.seed(SEED)\n",
        "\n",
        "  global num_epochs, ds\n",
        "  if ds == 'CIFAR':\n",
        "    num_workers = 0\n",
        "    mean_std = ([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]) # CIFAR-10\n",
        "    num_epochs = 25\n",
        "\n",
        "    # Setup dataflow\n",
        "    train_transform = Compose([\n",
        "        Pad(4),\n",
        "        RandomCrop(32),\n",
        "        RandomHorizontalFlip(),\n",
        "        RandomErasingPIL(),\n",
        "        Resize(img_size),\n",
        "        ToTensor(),\n",
        "        Normalize(*mean_std)\n",
        "    ])\n",
        "\n",
        "\n",
        "    test_transform = Compose([Resize(img_size),\n",
        "                              ToTensor(), \n",
        "                              Normalize(*mean_std) ])\n",
        "\n",
        "    class ShortenCIFAR(CIFAR10):\n",
        "      def __len__(self):\n",
        "        return int(len(self.data) * keep_ds_frac)\n",
        "\n",
        "    train_ds = ShortenCIFAR(root=\"/tmp/cifar10\", train=True, download=True, transform=train_transform)\n",
        "\n",
        "    global aum, n_classes\n",
        "    aum = AreaUnderTheMarginRanking()\n",
        "    train_ds, reference_class_sample_idx = aum.add_reference_class_to_ds(dataset=train_ds, n_class=n_classes)\n",
        "    n_classes += 1 \n",
        "\n",
        "    train_loader = DataLoader(train_ds, \n",
        "                            batch_size=batch_size, num_workers=num_workers, \n",
        "                            pin_memory=False, shuffle=False, drop_last=False)\n",
        "    \n",
        "    val_ds = ShortenCIFAR(root=\"/tmp/cifar10\", train=False, download=False, transform=test_transform)\n",
        "    test_loader = DataLoader(val_ds, \n",
        "                          batch_size=batch_size, num_workers=num_workers, \n",
        "                          pin_memory=False, shuffle=False, drop_last=False)\n",
        "\n",
        "\n",
        "  model = resnet18(pretrained=True).to(device)\n",
        "  model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1,bias=False)\n",
        "  model.maxpool = nn.MaxPool2d(kernel_size=1, stride=1, padding=0) # Identity\n",
        "  model.fc = torch.nn.Linear(512, n_classes)\n",
        "  model = model.to(device)\n",
        "  \n",
        "  lr = 0.01\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, nesterov=True)\n",
        "  if lr_policy == 'ReduceLROnPlateau':\n",
        "    lr_scheduler = lrs.ReduceLROnPlateau(optimizer, mode='max')\n",
        "  elif lr_policy == 'OneCycleLR' :\n",
        "    lr_scheduler = lrs.OneCycleLR(optimizer, max_lr=0.1, total_steps=num_epochs, pct_start=6/num_epochs)\n",
        "  criterion = nn.CrossEntropyLoss(reduction='none').to(device)\n",
        "\n",
        "  return model, optimizer, lr_scheduler, criterion, train_loader, test_loader\n",
        "\n",
        "\n",
        "def run_one_exp(n_epochs, img_size, batch_size, lr_policy, keep_ds_frac, device, **kwargs):\n",
        "  \"\"\" Run a grid of experiments \"\"\"\n",
        "\n",
        "  results = {}\n",
        "  # The grid\n",
        "\n",
        "\n",
        "  # One experiment\n",
        "  # Init\n",
        "  print('\\n')\n",
        "  model, optimizer, lr_scheduler, criterion, train_loader, test_loader = init_exp(img_size=img_size, \n",
        "                                                                                  batch_size=batch_size, lr_policy=lr_policy, keep_ds_frac=keep_ds_frac,\n",
        "                                                                                  device=device) \n",
        "  # Train\n",
        "  time_spent = 0\n",
        "  for i in range(n_epochs):\n",
        "    time_spent += train(model, optimizer, criterion, train_loader, device=device, **kwargs)\n",
        "    acc_train = test(model, train_loader, prefix=\"Train\", global_step=0, device=device) \n",
        "    acc_test = test(model, test_loader, prefix=\"Test\", global_step=0, device=device) \n",
        "    lr_scheduler.step(acc_test)\n",
        "  aum.get_hist()\n",
        "\n",
        "  # Save results & display all performances from start\n",
        "  results['only result'] = \"time_spent: {:.2f}s acc_train: {:.2f} acc_test: {:.2f}\".format(time_spent, acc_train, acc_test)\n",
        "  pprint.pprint(results, width=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIH_Usu2AfhH",
        "colab_type": "text"
      },
      "source": [
        "# Benchmarking Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezumhzkX0g5h",
        "colab_type": "code",
        "outputId": "9594723a-fe74-4104-c1b9-0924b84d35ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# n_steps = 20\n",
        "n_classes = 10\n",
        "n_input_ch = 3\n",
        "\n",
        "img_size = 32 \n",
        "batch_size = 256\n",
        "\n",
        "ds = 'CIFAR'\n",
        "accuracy_test = True\n",
        "\n",
        "# Exp on CIFAR10\n",
        "# n Epochs => % of data to remove\n",
        "# 20E => 26% ?\n",
        "# 30E => 19%\n",
        "# 60E => 17% ?\n",
        "run_one_exp(n_epochs=60, img_size=img_size, batch_size=batch_size, lr_policy=\"OneCycleLR\", keep_ds_frac=1.0, device='cuda')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Files already downloaded and verified\n",
            "select n_reference_sample 4545\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:43:32.509 | INFO     | __main__:train:46 - Time spent 25.92s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:74: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:131: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.6266417335826642 28484 50000 4545\n",
            "aum.get_mislabeled() [[0], [2], [3], [4], [5], [6], [7], [8], [9], [10]]\n",
            "aum.get_reference_aum_threshold() tensor(0.3553)\n",
            "Train accuracy: 0.63174\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7473\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:45:04.862 | INFO     | __main__:train:46 - Time spent 25.45s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AOawYrgn+22+bZYt6r4OkbSnbd2Vx9o22RU1JEzLbUOh9cFCpNVMabjG7ZL1f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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.35551644483555167 16160 50000 4545\n",
            "aum.get_mislabeled() [[2], [9], [13], [15], [17], [19], [25], [27], [29], [35]]\n",
            "aum.get_reference_aum_threshold() tensor(-0.2157)\n",
            "Train accuracy: 0.68534\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7751\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:46:36.103 | INFO     | __main__:train:46 - Time spent 25.47s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KN69forBwkejGVUbIkJQNEmyQlA2SrJOQTSZZr5bHZJuHeUCGKOsSY9MXY8MX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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.2627213727862721 11942 50000 4545\n",
            "aum.get_mislabeled() [[9], [13], [17], [19], [22], [25], [27], [35], [38], [39]]\n",
            "aum.get_reference_aum_threshold() tensor(-0.4070)\n",
            "Train accuracy: 0.74154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7929\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:48:05.282 | INFO     | __main__:train:46 - Time spent 25.07s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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duTV+5OQgP3l2k8Vwt0Epten8nYYIulLqU8C81vqitXpRTbTWr2ENBpw9e7b9\nAuCFCrL5An91Y5HXL83yrXdmKWg4OhDiSH+QI/0h6zjEaG8Ap+TRETqc9Uyemwtxrs+vcX0uzvX5\nONfn1ri3nCyl5HY6FON9AT54JLordWiUhf488Bkrda4PCCul/l+t9T9s0P2FFiGdy/NXNxY5f2mW\nN96ZI7aeJeR18dGTA4S8Lm4uxPmzqwv8mwsPSt/xOB0cigbLYm8J/eH+IAGP5JQX2otEOseNeUuw\n59e4YYn3/YfJ0kIfLofiUDTIqQNhPvPECMcGQ0wOdDER3d3Fbxo+U9Sy0L+0XZSL5HJpH1LZPN+5\nvsj5SzP8yTtzrKVzdPlcfOzUIOdOD/OhySg+d+UfaSyZ5eZinBvzcW4uxLk5H+fmQoK7S4mKBURG\nuv0cGQhxtD/EkYGgtQ/RF5SUxkJzWUtljXDPWVa3dTy1sl4q43E6ONxvjJXJgS4mB0McGwwx3hfc\ntUSB7zkfurD/SGXz/Odr85y/NMufXpkjkckT8bv55Okhzr1vmOePRPG4Nv+DjQTcvH+sh/eP9VSc\nT+fy3F1KGqGfj3NjwQj+799ershf3x1wWyv2BEuum6MDIQ72iPtGaCyxZJYbC8ZN8q4l3jfm48zE\nyktOelwOjvaHODvRw08PjHLUEu/x3kBLrREs2RaFEslMjm9fXeD85Rm+fXWeZCZPb9DDJx4b5KXT\nwzx3pG/XrI5CQTOzmjIib1n1Nyyr3r52psfl4HDU+OiPDJQF/3A0hN8j67gKm/MwkSm5SUpW91yc\n+bXy35fP7eDoQIhjA10ctdwkkwOtNQ4kFrqwKfF0jj+7Os/rl2b49rV5UtkC0ZCHH39yhHNnhnnm\nUO+eWCAOh2Kk289It58PH6uMyDIWVNFtY7a3p2O8fnlmg/vGbs0Xxb5X3Df7isV4mutzcW7Y3CTX\n59dYjGdKZQIeJ5MDIX5ost/4ty3xHun2t/VCOCLo+5DVVJY/uzLP+Usz/Pm7C6RzBfq7vPzU2VHO\nnRnmAxO9LWONgHHfPDXew1PjG903dxaTNmve7L9Xw31ztN8m9APGwhf3TfuitWbBEu7rc5ZwW293\ny4mycHd5XRwdDPHDJwaYtKzuY4NdHbuCWfu5XNJrkFwCXzd4w+BoHf9VKxNbz/In78xx/tIM37m+\nSCZfYCjs46UzQ5w7M8xTYz0d8wdeKGimY+vcXEiU/fSW4NuttJL7psqqF/dN61Bc7P36/BrvFq1u\nK6oktp4tlQv7XBwbNH7to5abZHIwxFDY13FvZy25wMUjC/rb/w7+v58xx8phRN3fbQS+5j5S+5ov\nAo7O/k+7kszwrbfnOH95hoezDgYAACAASURBVL+6sUg2rxnp9vPS6SFeOjPMk6PdHSPi9bKSzFhR\nN4mS0N9YiHPfFiuslBV9Y4ulL4p9X8jb3AZ0KFprpmMprs+VJ99cn49zYy7OWrq8bnB3wM0xa0DS\niLYR7/4ub8cJ92Z0lqA/vAt3vgPrK5BasfYx27Ftn89sfS9vBPy1BL/WuZ7KzsDZmt6q5USG//T2\nLOcvzfA3N5fIFTQHe/z86JlhXjozzOMHI/vmD38npLK26BubC+fmQpxUtlAq12NF3xSF3oRadjHS\n4xf3TR0UCpqplfUNk29uzMdJZMpusmjIUwoFPFa0ugclnBU6TdDrRWvIrtcW+q06geI+l9r6/p6u\n+t4Eau2d7oY2dWEtzX96e5bXL8/w5q1l8gXNeF+Ac2eGOXd6mNMj4X3/n+BRKbpvihE3RaG/VeW+\n8brM5KlyTL3ZH+4PbojR3w/kC5oHD5PlMMC5so/bPr4x0OUtDUgeHTD+7eJAtlCb/Sno75VsqlLg\nU7HNxb96n01ufW93cOedQPHNwO0DYH41xTctS/x7t5cpaDgcDRoRPzPMyeEuEfFd5mEiw63F+Aax\nr3bfHOzxWzH1lS6cXRetQh50oeEGhJ1cvsC95WTJ0i5GldxciJPOld9shiO+isk3k9ZxJLB7detU\nRND3mlxm+zeA6n2xw8isbX1rh5dVgizm/MQIkvNEiPT0Mzw4RHdfP8rfs7kLye03CiM0jkIB8mnI\npSGfhXyadDrF1OIKDxZXmF5aY245xtzDNZZiq5DP4CGHhxwRr2Yk5GAw6GAwqIj6FL0+6HIXcBSy\n1j0zZrPdn3zxmv3YupazyufTRswBXD7rbyBixpx8EfCFa5yL1C7nCZEtaO4uJUpuknctN8mthQSZ\nfFm4R7r9FYJ9dNB0XmGfCHejkDj0vcblgVC/2XZKPlfRGSwsznLp+l2u331A7OECEZVgPJDhaE+e\nx3xpAvk1SF2G638Fl2Nb39vp2cEbQdXbgyfYvM6gUCiL1JZiZhe9jE3cMpXiWP3ZLoL5LYS0Vjmd\n31BdL3DY2ipwWlsRDaxZm0VGO1nHRd7hQTvc4PLidHtxub14vH4cLg+4vNY4jsf8rTk94PTajq3N\n5QUUpFfN31Rxn1qBlXvlc9u4F/M4iOsAHu1nlCDdOsATni4c/m68B7sJRvqI9PTRF+3HF+oFrxN8\nHvBlwafBLUbEXiGC3mo4XdxL+Xn98grnL6f4wX2AcR47cIZzPzLMx04Pcbg/VPu7hfw2bwZV1+Lz\nsPhu+RpbvK053EZEtnIHOZw2gaxHVG0CaS9XXbaQ27xej4LDVSl6FSJoiaPTA94uCPTVLrudkFYc\nu8v3LN3fXVHuYVpxcznNzcVkhQvHnvDJ7r452mP56S0Xzk7dN/aUrrdnl5iamWN+cZ7VlSWCOkmY\nBBGVZCyYY9SfZdiXod+dYsyRJKiTONOrkJ6GlRjMrW7/QHdwizeD6nPdG8/J22VdiMulRbizmOD8\n5RnOX5rh8pT5D/K+gxFeOj3MuTNDjPcFd7cChYJlwT3KuEFso6WqnHWL2QYhdW0ijtUCaRfV0j3t\nx8VyVfVoo7kLqWye24uJilQIxtVR6aPuDXoq8t4UB2V7gx5uLyYqQwHn4xVJ0oopXSdtPu7ifeoa\n0C3kzfyQireAGKRqvBlsOGdt23XaDvcO3ES2TsJr27fR774V4kNvUW4uxHn90gz/8dIsV2aMiD8x\n2s2Pnhnmk6eHGO0NNLmGdaK1+Q+tC2UB7vAY/2ZTDP+rSIlgxdbbZ0racTkUE9FgOQzQmnxzKBrc\n1ZSu21KMSNvQGaxUib/tevW5bGKbhyjzxrWd8Feci1Sec7XGHAQR9Bbi+twa//HSDK9fmuXanHGe\nnh3v4SVLxEe6/U2uodDuLCcypZTFS4kMh6JBJgdMStetMmS2Nfms9ZawsrXw13o7KJ7Tha2f4fLV\n0Rls8bbgCTXEbbQng6JKqVHgq8Agxhn7mtb61Ubdv13RWnN1do3XL81w/vIsN+bjKAUfmOjlf/r0\nKT55epihiK/Z1RQ6iN6gh95gLx+Y6G12VfYOpxsCvWZ7FLSGTLw+4bd3ECv3y+e2m7tSnNnui8AH\nfg6e/2ePVtctaOSgaA74Fa31W0qpLuCiUuoNrfU7DXxGW6C15u3pVV6/bCzxW4sJHAqeOdTHzzw3\nzidODzHQJSIuCC2Dslwy3i6IjDzaPXJp2xhBbGs3UfhAY+tv0TBB11rPADPW8ZpS6gowAuwLQdda\nc2kqxvlLZsbm3aUkTofiucN9/NwPHebjjw0SlTwggtC5uLyPHq7cqCrsxk2VUhPAk8B3q86/ArwC\nMDY2thuP3lO01nz//gqvXzYzNh88XMflUDx/NMo/eeEIHzs1JFOYBUHYMxou6EqpEPBHwC9rrSsC\nVLXWrwGvgRkUbfSz94JCQfPWvYecvzTLNy/PMB1L4XYqfmiyn1/66CQfPzUk05kFQWgKDRV0pZQb\nI+a/q7X+WiPv3UzyBc2FO8u8ftm4U+ZW03hcDj482c+XPnGcj54cJOIXERcEobk0MspFAb8DXNFa\n/0aj7tsscvkC37uzzOuXZvnm27MsrKXxuhy8cLyfc2eG+eETA3RJfgpBEFqIRlrozwOfBy4ppb5v\nnftVrfX5Bj5jV8nlC7x5a5n/eGmGb709y1Iig9/t5IdPDPDSmSFePD5A0CvZEgRBaE0aGeXyl0Db\nJVvI5gv89c0lzv/9DN96Z5aHySwBj5OPnhzk3OkhPnK8n4BHRFwQhNZnXypVOpfnr24scv7SLG+8\nM0dsPUvI6+JHTg7w0plhPnKsf18uSiAIQnuzbwQ9lc3zneuLvH5phjeuzLGWytHlc/GxU4P86Jlh\nPjQZbW4+C0EQhPdIRwt6KpvnP19b4PXLM/zplXni6RzdAXdpkeTnj0Q7N7eFIAj7jo4T9GQmx7ev\nLnD+8gzfvjpPMpOnN+jh048P89LpYZ470ofbKSIuCELn0RGCnkjn+NOr87x+aYZvX5snlS0QDXn4\n8SdHOHdmmGcO9eISERcEocNpW0FfS2X50yvznL80w5+/u0A6V6C/y8tPnR3l3JlhPjDRi9PRdkE3\ngiAIj0zbCfrf3XvI//HtG/zFu4tk8gWGwj7+y2fGOHdmmKfGenCIiAuCsE9pO0FP5wpcmVnjHz03\nzktnhnlytFtEXBAEgTYU9KcnevnL/+FFlCwYKwiCUEHTlqBTSi0Adx/x61FgsYHVaSbSltakU9rS\nKe0AaUuRca11zaTrTRP094JS6sJma+q1G9KW1qRT2tIp7QBpSz1ILJ8gCEKHIIIuCILQIbSroL/W\n7Ao0EGlLa9IpbemUdoC0ZVva0ocuCIIgbKRdLXRBEAShiqbFoUejUT0xMdGsxwuCILQlFy9eXNws\nbLFpgj4xMcGFCxea9XhB6Dhi61nuLiW4u5Qs7RfiaXwuJwGPE7+nuHcRKB67nQSsz8XrpTJuc87r\ncshEvhZCKbXp/J22mykqCPsVrTWL8Qx3lxLcWUpyz9rfXTYCvpLMVpQfDHsZ6PKRzuVJZvKsZ6x9\nNr+j5zoUBDyucofgLgq/q7ITcG/eMZTPV5bxuZySuqOBiKALQguRL2hmYuvcW0paYp3g7mJZtJOZ\nshg7FIz0+BnvDfKjZ4aZ6Asy1hcw+94Afk/tFbgKBU2qSuSTmVz5OJtnPZOzzts7gupzORbjadaz\nlecKO4yz8LtrvEFUnSt1Hu7aHYO9Q7F3Hvst46oIuiDsMZlcgQcPkyXXyJ2lJPeWk9xZSvBgeZ1M\nvlAq63E6GO31M9EX5NnDvRWiPdLtf6QVtxwOZQli4//7a61J5wpG3DfpGJKZXKkTMOdzldet782u\nZjd8J5vfWW/hcTmMyNs6gq07ge3fKgJuc49WXO1s219UKXUc+EPbqcPAv9Ba/0tbmReAPwZuW6e+\nprX+tQbWUxDaimQmZwm25c9eLvu1p1fWK6zYoMfJeF+Q44NdfPzUEON9AWsLMhT2tZWVqZTC53bi\nczvp2YX7Z/OFireBogvJ3jFUdg7lNw/7d5YTGR48rDyXzhW2r4ANl0NVvkFs8VZhH7vwe5ycGg5z\neiTS8H+fbQVda30NeAJAKeUEpoCv1yj6Ha31pxpbPUFoXVaSGe4uGcu66CK5t2ws7oW1dEXZ3qCH\nsd4AZ8d7GHv/QSZsot0X9MigY524nQ4ifgcRv7vh984XtNU52NxPto6g1ltF6brtbWQtlWN+NV3Z\nmWTz2Kf8fPGFI80R9Co+CtzUWj9qlkRBaBu01iyspbm7nOTOYsJyi5QHI2PrlYOQQ2EfY30BXjze\nz3hf0Ah2r3GR7IYACY3F6VCEvC5C3t1xRaWyhdLbQGCT8Y33yk5r/jng9ze59pxS6gfANPAlrfXb\n1QWUUq8ArwCMjY3t8NGC0HjyBc30yrpxjSxXhvzdXUpWRIQ4HYqRbj/jfQE+/fhwafBxIhpktGfz\nQUhBUMq4Z/weJ327+Zx6p/4rpTwYsX5Maz1XdS0MFLTWcaXUOeBVrfXkVvc7e/asljh0YS9I5/Lc\nX1437pDF8gDkvaUk9x8mKwbaPC6HEem+AGO9QSaixi0y3htgpMePWxYbF5qMUuriZql3d2KhvwS8\nVS3mAFrrVdvxeaXUbyqlolrrTklGL7Q4ibQZhCz6sO1W9nRsvcJ/GfK6GO8LcGK4i0+cHmK8N1By\nkQyFfRIXLbQtOxH0n2YTd4tSagiY01prpdTTmBwxSw2onyAAxge5ksxWRIvYByMX45WDkH1BD2N9\nAZ4+1Gu5RSyLuy9ArwxCCh1KXYKulAoCHwP+se3czwNorb8CfBb4olIqB6wDn9OSxlHYIVpr5tfS\n3FmsDPMr+rVXU7mK8sMRH2O9AT56YqAUm10M+evyySCksP+oS9C11gmo9OVbQl48/jLw5cZWTehE\ncvkC0ysp7i5XTl+/Zw1KprLlWGCnQ3Gwx894X5AnRrtLYX4TfQFGewP43DIIKQh2ZKao0HBS2Xxp\nJmSlPzvBg4fr5GyzarwuB+PWAOQPTUZLoj3eF+BAtwxCCsJOEEEXHpl0Ls/1uTiXpmJcnopxayHB\n3aUEM6upikHILq+L8WiAxw5EOHdm2GZpBxno8sog5G5QyEN8HpKLoBzgcIFygsN+7AKHs3zd4aw6\nL79LuyGCLtRFKpvn6uwaly3xvjwd49rsWinkr8vnYnIgxLOH+8oJoqx9T8Atg5CNpCjWq1Nmi02V\nj1enzee1GdA7y6q4AeWwBN5p6wScVcLvqLrm2qSD2IVy9s6nVM5e190oV9UB2jvKFqDeQdE7wBqQ\nB3LVMZDK/G99FTgHJIEvaK3famxVhb1iPZPnnZnVknhfmopxfT5O3nKVdAfcnD4Q4b/50GFOj4Q5\nMxJhtCcglnYjKIn1NKw+sATa2hcFe20GCpUDxLh8EB6B8AE49ENmHx6BYBS0NuJeKJjv6bzZF/Kg\nC+Xj0rVdKJfLgF7f4f0Klfcu5IAWjrWo683HOvfUF+CD/7ThVdiJhf7iFnHlLwGT1vYM8FvWXmhx\n4ukc70yvVljeN+bjpeRRfUEPp0ci/MjJQU6PmIRCI91+sbgfhUIBEvNVFnXRwrYEe1OxtgR6/HmI\nWMIdPmj2kYPg79kfLhKtqzqB4rGtg7B3CA0tl7d9rq7DDsuFhnbln6dRLpeXga9aoYpvKqW6lVLD\nWuuZBt1faACrqSyXp2K8PbVq/N7TMW4vJkr+7oEuL2dGInzy9DBnRiKcHgkzFPaJeNdDUawrBPpB\n2QWyOg1r0xvF2um1BNoS6/CB8ufiFujdH2JdD0qB02U2YQP1/qto4FtKKQ38ttb6tarrI8B92+cH\n1rkKQZdcLnvHSjLDZZtwX56KcXcpWbp+IOLjsZEIP/bEiLG8D0QYCPuaWOMW5r2IddGCHn+u7BKJ\nHCxb2CLWQgOpV9A/pLWeUkoNAG8opa5qrf9ipw+zOoLXwORy2en3hdosxdNcmorx9vQqlx4YAX/w\ncL10/WCPnzMjEX7q7CinRyI8diBMNORtYo1biEIBEgtVAl09wLiFWIdHLLE+ULaoixZ2oE/EWthT\n6p1YNGXt55VSXweeBuyCPgWM2j4ftM4JDWZ+NcXl6RiXHqyWLO+ZWKp0faIvwBOj3fzDZ8c5fcC4\nTboDnibWuImUxNou0PYBxilYnYFCZRpcnJ6yBT32bJULxLKwRayFFqSeFYuCgENrvWYdfxyoXo3o\nG8AvKqX+ADMYGhP/+XtDa83sasqyuMuDlvPWwglKweFokKcP9XJmJMJjByI8NhImvF+mvBcKJsa6\nWqArXCJbifUIjD5b5QIZKUeGiFgLbUg9Fvog8HVrYMwF/J7W+ptVuVzOY0IWb2DCFn92d6rbmWit\nefBwnbenY9YkHSPgS4kMYBYDPjoQ4kOTUU4fiHDmYISTw+FdScTfEmwQ62qftRUNks9Ufs/hLgv0\n6DNVLpCiz7qvZWKGBaHR1LME3S3g8Rrn7blcNPALja1aZ6K15u5S0rhNrIiTy9MxVpLGknQ5FJOD\nXfzwiQHOHDSW96nhcOcsnlAU600HGLcR6/AIjD5dFbZX9FlHRayFfU2HmnitQaGgub2UqJig8/b0\nKmtW1kC3U3FiKMxLp4c4PRLh9IEIx4e62jfpVKEAyaUtBhgf7FysSz5rEWtB2A4R9AaRL2huLsTL\nwj21ytvTMRIZM/3a43JwcjjMy08csAYrIxwb7MLjamGR0hpSMSPSySVILBrrOrFY49wSxGc3Eeth\nI9AHP7DJAKOItSA0gnoGRUeBr2J86Rp4TWv9alWZF4A/Bm5bp76mta4eOO0YsvkCN+bjlnAbAb8y\ns1Zaf9LvdnLqQJjPPnXQWN4jEY4OhJqfOTCfg/XlGuK8bDu2xLko2NWDikVcfjN4GOg1ghw9BqHB\nGgOM/SLWgrBH1GOh54Bf0Vq/pZTqAi4qpd7QWr9TVe47WutPNb6KzSWTK/Du3FrJ8r48vcqVmVUy\nOZO3O+hx8tiBCD/99Fgpr8nh/hDOvchrkl3faCUnlyqt6JI1vQjrK2yaC8MXMQOGgSj0jMPI+83n\nYNScC0at69Y5T3D32ycIwo6oZ1B0BmvGpxW6eAUzC7Ra0NueVDbPtdk1y9dtBLw6o+DpAxG+8MEJ\nHjtg8poc6gs2JimV1pBerS3Em7k4sona91LOsuUcjMLgY1Xi3FcW76JQO/dJuKMgdDA78qErpSaA\nJ4Hv1rj8nFLqB8A08CWt9ds1vt8yU/+rMwpenl7l+txaafGF7oCbMyMmo2Axr8lYb6D+vCaFfJUr\nY8lmRVeJdnG/qXvDZxPiKPQdLQtxyXK2ibOvW9wcgrAPUfUu/amUCgF/Dvy61vprVdfCQEFrHVdK\nnQNe1VpPbnW/s2fP6gsXLjxitXdGvRkFi8JdM6NgNrWJEG8izusP2dS94Y2UxTnQVz4uWtDV59wB\nmegiCAIASqmL1SnMi9SbD90N/BHwu9ViDqC1XrUdn1dK/aZSKrpFut1dYzWVNbHdlnBfmqqRUfBA\nmM+c6OKJvjwnwmn6WEUlrxkhnl6E6zVcHZl47QcqR9lCDvTB4KnaPufiOX8vuPbpVHxBEHaVeqJc\nFPA7wBWt9W9sUmYImNNaa6XU04ADWGpoTWtQzCj49tQyt+49YHbmAamVeXrVKr1qjcd8ST4TTHPw\nYIJ+R5yuQgx3ahkeLMHdTO2bFt0bgV4jwCX3Rm+lFS3uDUEQWox6LPTngc8Dl5RS37fO/SowBqUZ\no58FvqiUygHrwOd0vb6cHXL1e2+w/uevotaXCOVWOKVW+SAJHMp6nD2JYB5IR8BpiXNgDIJPVvmc\nqwYJPUFxbwiC0JbUE+Xyl8CWCqe1/jLw5UZVaityqTjdyTvkfL2ovlOkuwfIRIfxdQ9uHCQM9Il7\nQxCEfUPbzRQ9/eEfhw//eLOrIQiC0HLUHeXS8AcrtQDcfcSvR4E9H3DdJaQtrUmntKVT2gHSliLj\nWuv+WheaJujvBaXUhc3CdtoNaUtr0ilt6ZR2gLSlHiQ8QxAEoUMQQRcEQegQ2lXQX2t2BRqItKU1\n6ZS2dEo7QNqyLW3pQxcEQRA20q4WuiAIglBF0+LQo9GonpiYaNbjBUEQ2pKLFy8ubha22DRBn5iY\nYK+yLQrCviCfheXbsPguLF2HxesQnzP5hkqzqHsrZ1IH+sw5yYffNiilNp2/03YzRQVh35NYqhTt\nxevmePk26Hy5XGgQuoZh6abJzZ+ObX5P+4pVpSyhfTbRj5bFPxgFb1hyHrUgIuiC0Irks/DwjhFu\nu2gvvmvl2rdwekxG0IFTcOrHIDpptr6jRqTt5DJVa8oubdwSixB7ADM/MCmkqxf9LuJw2YS+tyqP\nUtVWPO/y1r6X0DBE0AWhmSSXbaL9LizdMPuHd6CQK5cLDULfpE20jxnR7h4Dh7O+Z7k80DVktnrQ\n2qwDkFyqXDg8aV8S0drPvW2tW/tw8/t5QrWFvpiauvq8pKbeMSLogrDblKxtm5W9aAn3+nK5nNMD\nvUcsa/tlI+DRYxCtYW3vBUqBt8tsPRP1fSefg9RK1RtA8XjZtrLXAixcNcfZ5CbPd5gFYbZ0AVWd\n3+eLl4ugC0KjSC7XFu2Htyut7eCAsbJPfaZStLvH67e2WxWny1jYwWj938kka7t/KhZGXzb/nsk3\nzbF9rMCOy1/p66/oAGq4hvy9ps4dQue0RBD2gnzOWNsl0b5edpMkbYt0OT3QexgGTsDJT1uibfm2\n/d1Nq35L4gmYrXu0vvKFghng3c4NlFyC5VvWgPDq5veriALawgVUPO/tatkB4XrXFL0DrGHWAMpV\nZwmzlql7FTgHJIEvaK3famxVBWEPSS5bQl3l216+DYVsuVyw34j1iU9Vinb3eEdZfi2FwwH+HrNx\ntL7v5DJVlr9l9VcPEK/cg+m/M+fsv3PF891bh4FucA317tmA8E7+4l7cYtHnl4BJa3sG+C1rLwit\nSz4HK3dtom0LA0za/tQdbug7Ygn3j1oDkpPGTeLvaV79hfpxeSA8bLZ60BrSa2Xff8UbwGLl+dlL\n5vOWA8JdlUJ/+ifg8c81pm02GmVCvAx81VpH9E2lVLdSalhrPdOg+wvCo7P+sOzPtov28q1KKywQ\ntUT7nE20J8Xa3o8oBb6w2XoP1fedfM78rW3lAkoumsleyeXt7/cI1PtXqoFvKaU08Nta6+pMYSPA\nfdvnB9Y5EXRhbyha20XXiD12O7FQLudwG992dNIIt31QUqxt4T2Q0Q4SKkLcFSThO0BC5Yi78iS8\nORLBHIl0jkQmTzyd4+neXl7chTrUK+gf0lpPKaUGgDeUUle11n+x04cppV4BXgEYGxvb6dcFAdZX\nqkTb8m8v36qcBBOIGtE+/lLZ0o4eE2tbKJHLF0ik88Qzltimc+Zz8TiTKx/bzsfTOZKZfOm4eD2T\nL9T1XLdT4XIoXjw+0PA21fWXrbWesvbzSqmvA08DdkGfAuxD1Aetc9X3eQ0rD/DZs2clb69Qm0Le\n5tu+XukmScyXyzlcxtrum4Rjn6yccBPobV79hV0hX9AkM1WiWxTVTI54Ol9xLlkl1tXX07n6BNjp\nUAQ9TkJeF8HS5qQvGKg4F/I6bcdV5zzl73lduxeauq2gK6WCgENrvWYdfxz4tapi3wB+USn1B5jB\n0Jj4z4VtScWqfNtW7PbyzSpru88S7U/YRHsSesYlqVQLo7WusmQ3t34TVaJcKdjm3Hp2k9jzKpTC\nElBnWVw9Lka6PSWBrRbigMd+zmkTZBdelwPVomGK1dRjoQ8CX7ca5AJ+T2v9TaXUzwNorb8CnMeE\nLN7AhC3+7O5UV2g7CnkTClY94WbJygRYxOGCnkNGrI99vHJQUqztPUFrTSpbqHAtFIU2ka4U5kpB\ntp23l8/kqHf9nIDHLrROgh4XQ2FfSWCLFu5moms/53c720aAG822gq61vgU8XuP8V2zHGviFxlZN\naBu0NtO9l2+VXSNF3/bSTciny2X9vUasJz9mG5CcNFPLxdp+zxQKmsVEmumVFFMP15lfSxFP5Wyu\nh7zlA67hosjkyRfqU2Cf21ESUmPdOukLeRjrCxDybO6C2CjO5rPDsT8FuNHI6JCwPfksrM2YLHyx\nBxC7Dyv3bZ8fQGatXF45TahX9Bgc/RHbhJtJE4srPDLrmTzTsXWmV9aZemjtV1JMr6wzHVtnZiVV\nc3DO43RUuiC8LiJ+NyPdvgqBDRQt3yrRrfAfe5y4nJI0qxURQReMLzv2wBLpKqGO3TdirqtEItAH\nkVEz4ebwCxAZKQ9Q9kyYiRzCjrBb10XBnlpZL4n19EqK5URlOluHgsGwjwPdft53sJtPnvYx0u3n\nQMTPSI+fwbCPkNeFxyUCvB8QQe908rmN1nXF/sHGPBdOD4RHIHIQDn3E7LtHzT4yaq55As1pTxtj\nt66LlnXRyt7Mug56nIz0+EuCPdLt50C3j5HuAAe6fQyGfbjFWhYsRNDbndRqlVhXWdir0xsz0/l7\njTj3HoZDH7aE2hLryKjJTyJ5qHdEtXVtBNtyizyidX2g20/Y59q3A3zCzhFBb2UKeVibrW1VF33Y\n1cuKOdzG/REZhYkPbRTryMi+zxn9KNSyru3CvRPr2u4OEetaaCQi6M0kvbbRFWIfbFydqmFd9xiB\n7hmHiectkT5Y3ocG2j+n9h6zmXVtjlNMrazvyLo+0G0EW6xrYa8RQd8tCnkTZx17YOKwKwYaH0Ds\nnhmMtONwQfgARMZg/IOV1nW35bv2hprTnjYmlc3bBLrSujb+6xSZ3ObW9ZmDkQrr+kC3n6GIWNdC\n61HPTNFR4KuYCUYaeE1r/WpVmReAPwZuW6e+prWunk3aWaTjNQYabX7s1enKVWrAJNIvWtJjz9oG\nG4vW9aBY1zukUNAsJTJVgl22rqdX1lnawro+c7CbT1Rb191+wn6xroX2ox4LPQf8itb6LaVUF3BR\nKfWG1vqdqnLf0Vp/9fshmgAAB2lJREFUqvFVbAKFQtm63jDQaB1X5z5WznJkyOizlVEhkYPmmi/c\nnPa0MalsvkKkxboWhM2pZ6boDFYaXCufyxVMatxqQW8fMgmITRm3R7UrZOWeZV1XrVbijZRFevSZ\nSrGOjJqV1MW63hFF67pSsMW6FoRHZUc+dKXUBPAk8N0al59TSv0AmAa+pLV+u8b3dz99bqFgMvLV\nHGgsWtdVyeWV0/JdH4TRp6uiQg6ayJBmrLre5hSt66I4P7Bb1mJdC0LDUbrO7DlKqRDw58Cva62/\nVnUtDBS01nGl1DngVa315Fb3O3v2rL5w4cLOa5xNld0gKzVcIatTlZn6wCz/VO0CKe0PQtew5Miu\nA6018XSOlWSW2HqW1XWzj61nWVnPMrea2tK6VgoGu3wlwTYTZMS6FoSdoJS6WL2uc5F6F4l2A38E\n/G61mANorVdtx+eVUr+plIpusQbpo3Pl38PXfs5WOQd0Wdb1yFNw6uXKyJDIQbGubRRTmpaEuEqc\nV9Yz1rVcqUwsac6tpnJbJm8KepylkD2xrgVh76knykUBvwNc0Vr/xiZlhoA5rbVWSj0NOIClhta0\nyNiz8A/+zyrren9l6SumOY3ZLeRkpnS8arOa7WVilnjnthBlp0MR8buJ+N2Erf1Yb4Bu67i0Bao+\n+90EPPs3bakgtAL1WOjPA58HLimlvm+d+1VgDEppdD8LfFEplQPWgc/pen05O6Xbsrw7gHQuXxLZ\nSnHOVojzBmFez27wPdtRCsI+I7LdlvAe6Pabz9XCXCXOIa+4PAShXaknyuUvgS3/h2utvwx8uVGV\naiey+UKFEK9uK85lSzqV3XoJrC6fq0J4jw2GKiznbr+n4np3wFzr8kp+aUHYj8hIIGax2NVUboML\nY3UTYbZbzsnM1stiBT1OugMeS4RdHIoGjRAH3FXiXGk1h/1unCLKgiDsgI4R9EJBs5bKVVjAtQb9\nalnNa+nclvf2u50V7onR3kDZKra5LKrFOex3yyCgIAh7RtsJ+sW7D/lXf3nbFi6XIZY0oryV197j\nclRYwcMRHyeGuza4LKqt5IjfvaurdAuCIDSKthP0eDrH1dlVIn430ZCHI/1By3Le6E+2f/a5RZQF\nQehs2k7QP3Ksnz/9lReaXQ1BEISWo+6Zog1/sFILwN1H/HoUaPykpeYgbWlNOqUtndIOkLYUGdda\n99e60DRBfy8opS5sNvW13ZC2tCad0pZOaQdIW+pBQjAEQRA6BBF0QRCEDqFdBf21ZleggUhbWpNO\naUuntAOkLdvSlj50QRAEYSPtaqELgiAIVYigC4IgdAgtLehKqU8qpa4ppW4opf55jetepdQfWte/\nay2R15LU0ZYvKKUWlFLft7afq3WfZqOU+ldKqXml1OVNriul1P9mtfPvlVLv3+s61ksdbXlBKRWz\n/Sb/Yq/rWA9KqVGl1LeVUu8opd5WSv1SjTJt8bvU2ZZ2+V18SqnvKaV+YLXlf65RprEaprVuyQ1w\nAjeBw4AH+AFwqqrMPwG+Yh1/DvjDZtf7PbTlC8CXm13XOtryYeD9wOVNrp8DXsekXH4W+G6z6/we\n2vIC8B+aXc862jEMvN867gLerfH31Ra/S51taZffRQEh69iNWYv52aoyDdWwVrbQnwZuaK1vaa0z\nwB8AL1eVeRn419bxvwU+qlpzdYZ62tIWaK3/AljeosjLwFe14U2gWyk1vDe12xl1tKUt0FrPaK3f\nso7XgCvASFWxtvhd6mxLW2D9W8etj25rq45CaaiGtbKgjwD3bZ8fsPGHLZXRWueAGNC3J7XbGfW0\nBeAnrNfhf6uUatdlmepta7vwnPXK/LpS6rFmV2Y7rFf2JzHWoJ22+122aAu0ye+ilHJaK73NA29o\nrTf9XRqhYa0s6PuNfw9MaK3fB7xBudcWmsdbmLwZjwP/O/DvmlyfLVFKhTCLuf+yti3c3o5s05a2\n+V201nmt9RPAQeBppdTp3XxeKwv6FGC3Ug9a52qWUUq5gAi7tTj1e2Pbtmitl7TWaevj/wU8tUd1\nazT1/G5tgdZ6tfjKrLU+D7iVUtEmV6smSik3RgB/V2v9tRpF2uZ32a4t7fS7FNFarwDfBj5Zdamh\nGtbKgv63wKRS6pBSyoMZMPhGVZlvAD9jHX8W+DNtjS60GNu2pcqf+RmM77Ad+Qbwj6yoimeBmNZ6\nptmVehSUUkNFf6ZS6mnM/5eWMxisOv4OcEVr/RubFGuL36WetrTR79KvlOq2jv3Ax4CrVcUaqmEt\nmw9da51TSv0i8J8wUSL/Smv9tlLq14ALWutvYH74/0cpdQMzuPW55tV4c+psyz9TSn0GyGHa8oWm\nVXgLlFK/j4kyiCqlHgD/I2awB631V4DzmIiKG0AS+Nnm1HR76mjLZ4EvKqVywDrwuRY1GJ4HPg9c\nsvy1AL8KjEHb/S71tKVdfpdh4F8rpZyYTuffaK3/w25qmEz9FwRB6BBa2eUiCIIg7AARdEEQhA5B\nBF0QBKFDEEEXBEHoEETQBUEQOgQRdEEQhA5BBF0QBKFD+P8B8dQdF/CKbU0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.21865581344186558 9939 50000 4545\n",
            "aum.get_mislabeled() [[9], [17], [25], [35], [39], [46], [47], [50], [51], [56]]\n",
            "aum.get_reference_aum_threshold() tensor(-0.5059)\n",
            "Train accuracy: 0.76356\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.8221\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:49:35.231 | INFO     | __main__:train:46 - Time spent 25.14s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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htY5ayi8opX5fKRXWWrfiLGeCUJN0Ns9KOmuItCnGJe+5INjpYqw6mqzuRXuc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\n",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.1959960400395996 8909 50000 4545\n",
            "aum.get_mislabeled() [[9], [17], [25], [35], [39], [46], [47], [51], [56], [64]]\n",
            "aum.get_reference_aum_threshold() tensor(-0.5456)\n",
            "Train accuracy: 0.79396\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.8532\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-02-06 10:51:03.920 | INFO     | __main__:train:46 - Time spent 25.07s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SaNZAD5+7lbgaYd/b1QKGFX+1NKPm2v+kaMUfLs2oaR6sbXpdxXGlo2VDmZpE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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "percentage of mislabeled 0.17918820811791883 8145 50000 4545\n",
            "aum.get_mislabeled() [[9], [17], [25], [35], [39], [46], [47], [51], [56], [64]]\n",
            "aum.get_reference_aum_threshold() tensor(-0.5961)\n",
            "Train accuracy: 0.7814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rIterations:   0%|          | 0/196 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.8218\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Iterations:  44%|████▍     | 86/196 [00:25<00:33,  3.26it/s, loss=0.53, lr=0.0103]"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}