CXR8_nb.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of COVIDNet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TJJOOlgfUS5"
      },
      "source": [
        "# DATASET EXTRACTION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_ZM9zRc_hfZ"
      },
      "source": [
        "## Import datasets and github\n",
        "* Clone chestxray dataset from the github link https://github.com/ieee8023/covid-chestxray-dataset.git\n",
        "\n",
        "* RSNA Pneumonia Detection Challenge dataset\n",
        "https://www.kaggle.com/c/rsna-pneumonia-detection-challenge and unzip rsna_dataset here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mupjv71A_BZ8",
        "outputId": "fe0a62bf-2eb3-4378-8657-b460eb82355d"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHYcG21mFwbp"
      },
      "source": [
        "! unzip -q '/content/drive/MyDrive/Medical_data_lias/data/data.zip' -d '/content/data/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5wJrNmlhvjT",
        "outputId": "29f92dcc-4036-45ef-f91a-d5fd56c4127f"
      },
      "source": [
        "! pip install pydicom einops\n",
        "import numpy as np\n",
        "import torch\n",
        "import pandas as pd\n",
        "import os\n",
        "import random \n",
        "from shutil import copyfile\n",
        "import pydicom as dicom\n",
        "import cv2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydicom\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/15/df16546bc59bfca390cf072d473fb2c8acd4231636f64356593a63137e55/pydicom-2.1.2-py3-none-any.whl (1.9MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9MB 12.8MB/s \n",
            "\u001b[?25hCollecting einops\n",
            "  Downloading https://files.pythonhosted.org/packages/5d/a0/9935e030634bf60ecd572c775f64ace82ceddf2f504a5fd3902438f07090/einops-0.3.0-py2.py3-none-any.whl\n",
            "Installing collected packages: pydicom, einops\n",
            "Successfully installed einops-0.3.0 pydicom-2.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1Z22FipJrgn"
      },
      "source": [
        "# Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6JccNtU9SPc",
        "outputId": "f72397ae-c61e-4655-91a2-453321e0c615"
      },
      "source": [
        "!git clone https://github.com/iliasprc/COVIDNet.git\n",
        "! pip install -r ./COVIDNet/requirements.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'COVIDNet'...\n",
            "remote: Enumerating objects: 321, done.\u001b[K\n",
            "remote: Counting objects: 100% (126/126), done.\u001b[K\n",
            "remote: Compressing objects: 100% (92/92), done.\u001b[K\n",
            "remote: Total 321 (delta 52), reused 88 (delta 31), pack-reused 195\u001b[K\n",
            "Receiving objects: 100% (321/321), 48.43 MiB | 7.75 MiB/s, done.\n",
            "Resolving deltas: 100% (141/141), done.\n",
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            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from -r ./COVIDNet/requirements.txt (line 8)) (2.4.1)\n",
            "Collecting omegaconf\n",
            "  Downloading https://files.pythonhosted.org/packages/d0/eb/9d63ce09dd8aa85767c65668d5414958ea29648a0eec80a4a7d311ec2684/omegaconf-2.0.6-py3-none-any.whl\n",
            "Collecting byol-pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/46/18/ab37291b46173cf7e0a8a0290058a01d697609d26816ac490dd9d144b24c/byol_pytorch-0.5.6-py3-none-any.whl\n",
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            "Collecting PyYAML>=5.1.*\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/a5/393c087efdc78091afa2af9f1378762f9821c9c1d7a22c5753fb5ac5f97a/PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636kB)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (4.7.2)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (3.10.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (1.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (3.0.4)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (3.4.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->-r ./COVIDNet/requirements.txt (line 8)) (3.1.0)\n",
            "Installing collected packages: PyYAML, omegaconf, byol-pytorch\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-5.4.1 byol-pytorch-0.5.6 omegaconf-2.0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6iIjYBxGsWg",
        "outputId": "71bbb5a2-64aa-4075-c87e-153e62c4e814"
      },
      "source": [
        "from COVIDNet.model import *\n",
        "model = CovidNet('large', n_classes=3).cuda()\n",
        "\n",
        "from byol_pytorch import BYOL\n",
        "from torchvision import models\n",
        "\n",
        "# resnet = models.resnet50(pretrained=True).cuda()\n",
        "\n",
        "# learner = BYOL(\n",
        "#     resnet,\n",
        "#     image_size = 224,\n",
        "#     hidden_layer = 'avgpool'\n",
        "# )\n",
        "\n",
        "learner = BYOL(\n",
        "    model,\n",
        "    image_size = 224,\n",
        "    hidden_layer = 'fc2'\n",
        ")\n",
        "\n",
        "\n",
        "opt = torch.optim.Adam(learner.parameters(), lr=1e-4)\n",
        "\n",
        "def sample_unlabelled_images():\n",
        "    return torch.randn(20, 3, 224, 224).cuda()\n",
        "\n",
        "for _ in range(100):\n",
        "    images = sample_unlabelled_images()\n",
        "    loss = learner(images)\n",
        "    opt.zero_grad()\n",
        "    loss.backward()\n",
        "    print(loss.item())\n",
        "    opt.step()\n",
        "    learner.update_moving_average() # update moving average of target encoder\n",
        "\n",
        "# save your improved network\n",
        "torch.save(model.state_dict(), '/content/drive/MyDrive/improved-covidnet_large.pt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: byol-pytorch in /usr/local/lib/python3.7/dist-packages (0.5.5)\n",
            "Collecting omegaconf\n",
            "  Downloading https://files.pythonhosted.org/packages/d0/eb/9d63ce09dd8aa85767c65668d5414958ea29648a0eec80a4a7d311ec2684/omegaconf-2.0.6-py3-none-any.whl\n",
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            "Collecting PyYAML>=5.1.*\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/a5/393c087efdc78091afa2af9f1378762f9821c9c1d7a22c5753fb5ac5f97a/PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636kB)\n",
            "\u001b[K     |████████████████████████████████| 645kB 10.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.8->byol-pytorch) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.8->byol-pytorch) (1.19.5)\n",
            "Installing collected packages: PyYAML, omegaconf\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-5.4.1 omegaconf-2.0.6\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZkurGVMKFPf"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9t--VlR7_F6",
        "outputId": "69a69fad-c6eb-4cba-ed35-f04e11170b4f"
      },
      "source": [
        "%cd COVIDNet\n",
        "! python main.py --help\n",
        "! python main.py #--dataset_name='COVIDx' --model='COVIDNet_small' --root_path='/content/data/data' --save='/content/drive/MyDrive/Medical_data_lias/saved_covid_cpkts'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'COVIDNet'\n",
            "/content/COVIDNet\n",
            "usage: main.py [-h] [--batch_size BATCH_SIZE] [--log_interval LOG_INTERVAL]\n",
            "               [--dataset_name DATASET_NAME] [--nEpochs NEPOCHS]\n",
            "               [--device DEVICE] [--seed SEED] [--classes CLASSES] [--lr LR]\n",
            "               [--weight_decay WEIGHT_DECAY] [--cuda] [--tensorboard]\n",
            "               [--resume PATH]\n",
            "               [--model {COVIDNet_small,resnet18,mobilenet_v2,densenet169,COVIDNet_large}]\n",
            "               [--opt {sgd,adam,rmsprop}] [--root_path ROOT_PATH]\n",
            "               [--save SAVE]\n",
            "\n",
            "optional arguments:\n",
            "  -h, --help            show this help message and exit\n",
            "  --batch_size BATCH_SIZE\n",
            "                        batch size for training\n",
            "  --log_interval LOG_INTERVAL\n",
            "                        steps to log.info metrics and loss\n",
            "  --dataset_name DATASET_NAME\n",
            "                        dataset name COVIDx or COVID_CT\n",
            "  --nEpochs NEPOCHS     total number of epochs\n",
            "  --device DEVICE       gpu device\n",
            "  --seed SEED           select seed number for reproducibility\n",
            "  --classes CLASSES     dataset classes\n",
            "  --lr LR               learning rate (default: 1e-3)\n",
            "  --weight_decay WEIGHT_DECAY\n",
            "                        weight decay (default: 1e-6)\n",
            "  --cuda                use gpu for speed-up\n",
            "  --tensorboard         use tensorboard for loggging and visualization\n",
            "  --resume PATH         path to latest checkpoint (default: none)\n",
            "  --model {COVIDNet_small,resnet18,mobilenet_v2,densenet169,COVIDNet_large}\n",
            "  --opt {sgd,adam,rmsprop}\n",
            "  --root_path ROOT_PATH\n",
            "                        path to dataset\n",
            "  --save SAVE           path to checkpoint save directory\n",
            "2021-04-16-17:24:34 | INFO | LOG | Checkpoint folder ./saved/COVIDNet\n",
            "2021-04-16-17:24:34 | INFO | LOG | date and time = 16_04_2021_17.24.34\n",
            "2021-04-16-17:24:34 | INFO | LOG | pyTorch VERSION:1.8.1+cu101\n",
            "2021-04-16-17:24:34 | INFO | LOG | CUDA VERSION\n",
            "2021-04-16-17:24:34 | INFO | LOG | CUDNN VERSION:7603\n",
            "2021-04-16-17:24:34 | INFO | LOG | Number CUDA Devices: 1\n",
            "2021-04-16 17:24:35.025712: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-04-16-17:24:36 | INFO | LOG | device: cuda:0\n",
            "train examples =  13467\n",
            "test examples =  1535\n",
            "2021-04-16-17:24:36 | INFO | LOG | CNN(\n",
            "  (cnn): MobileNetV2(\n",
            "    (features): Sequential(\n",
            "      (0): ConvBNActivation(\n",
            "        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "      (1): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
            "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (2): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (3): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (4): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (5): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (6): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (7): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (8): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (9): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (10): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (11): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (12): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (13): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (14): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (15): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (16): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (17): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (18): ConvBNActivation(\n",
            "        (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (classifier): Linear(in_features=1280, out_features=3, bias=True)\n",
            "  )\n",
            ")\n",
            " use optimizer SGD lr  0.01\n",
            "2021-04-16-17:24:39 | INFO | LOG | CNN(\n",
            "  (cnn): MobileNetV2(\n",
            "    (features): Sequential(\n",
            "      (0): ConvBNActivation(\n",
            "        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "      (1): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
            "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (2): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (3): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (4): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (5): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (6): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (7): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (8): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (9): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (10): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (11): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (12): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (13): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (14): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (15): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (16): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (17): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNActivation(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNActivation(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (18): ConvBNActivation(\n",
            "        (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (classifier): Linear(in_features=1280, out_features=3, bias=True)\n",
            "  )\n",
            ")\n",
            "2021-04-16-17:24:39 | INFO | LOG | Checkpoint Folder /content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34 \n",
            "2021-04-16-17:24:41 | INFO | LOG | train Epoch: [ 1/30]\t Sample [    0/13472]\t loss  0.2867\tacc  0.6875\t\n",
            "2021-04-16-17:25:19 | INFO | LOG | train Epoch: [ 1/30]\t Sample [ 2000/13472]\t loss  0.2784\tacc  0.7589\t\n",
            "2021-04-16-17:25:59 | INFO | LOG | train Epoch: [ 1/30]\t Sample [ 4000/13472]\t loss  0.1870\tacc  0.7836\t\n",
            "2021-04-16-17:26:38 | INFO | LOG | train Epoch: [ 1/30]\t Sample [ 6000/13472]\t loss  0.1396\tacc  0.8135\t\n",
            "2021-04-16-17:27:17 | INFO | LOG | train Epoch: [ 1/30]\t Sample [ 8000/13472]\t loss  0.1201\tacc  0.8244\t\n",
            "2021-04-16-17:27:57 | INFO | LOG | train Epoch: [ 1/30]\t Sample [10000/13472]\t loss  0.1065\tacc  0.8331\t\n",
            "2021-04-16-17:28:36 | INFO | LOG | train Epoch: [ 1/30]\t Sample [12000/13472]\t loss  0.0958\tacc  0.8406\t\n",
            "2021-04-16-17:29:03 | INFO | LOG | train summary  Epoch: [1/30]\t loss  0.0912\tacc  0.8436\t\n",
            "2021-04-16-17:29:03 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:29:41 | INFO | LOG | validation summary  Epoch: [1/30]\t loss  0.2183\tacc  0.8801\t\n",
            " s [0.8590832 0.9548023 0.       ] ,ppv [0.8576271 0.8941799 0.       ]\n",
            "2021-04-16-17:29:41 | INFO | LOG | Best val loss 0.21832263443251132 so far \n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_best_checkpoint.pth.tar\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-17:29:42 | INFO | LOG | train Epoch: [ 2/30]\t Sample [    0/13472]\t loss  0.0227\tacc  0.9375\t\n",
            "2021-04-16-17:30:22 | INFO | LOG | train Epoch: [ 2/30]\t Sample [ 2000/13472]\t loss  0.0300\tacc  0.8948\t\n",
            "2021-04-16-17:31:03 | INFO | LOG | train Epoch: [ 2/30]\t Sample [ 4000/13472]\t loss  0.0331\tacc  0.8840\t\n",
            "2021-04-16-17:31:42 | INFO | LOG | train Epoch: [ 2/30]\t Sample [ 6000/13472]\t loss  0.0291\tacc  0.8905\t\n",
            "2021-04-16-17:32:22 | INFO | LOG | train Epoch: [ 2/30]\t Sample [ 8000/13472]\t loss  0.0305\tacc  0.8901\t\n",
            "2021-04-16-17:33:01 | INFO | LOG | train Epoch: [ 2/30]\t Sample [10000/13472]\t loss  0.0297\tacc  0.8909\t\n",
            "2021-04-16-17:33:41 | INFO | LOG | train Epoch: [ 2/30]\t Sample [12000/13472]\t loss  0.0293\tacc  0.8924\t\n",
            "2021-04-16-17:34:09 | INFO | LOG | train summary  Epoch: [2/30]\t loss  0.0291\tacc  0.8934\t\n",
            "2021-04-16-17:34:09 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:34:47 | INFO | LOG | validation summary  Epoch: [2/30]\t loss  0.1962\tacc  0.8906\t\n",
            " s [0.92020375 0.9322034  0.        ] ,ppv [0.835131  0.9311513 0.       ]\n",
            "2021-04-16-17:34:47 | INFO | LOG | Best val loss 0.19620093979803904 so far \n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_best_checkpoint.pth.tar\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-17:34:48 | INFO | LOG | train Epoch: [ 3/30]\t Sample [    0/13472]\t loss  0.0019\tacc  1.0000\t\n",
            "2021-04-16-17:35:28 | INFO | LOG | train Epoch: [ 3/30]\t Sample [ 2000/13472]\t loss  0.0161\tacc  0.9137\t\n",
            "2021-04-16-17:36:07 | INFO | LOG | train Epoch: [ 3/30]\t Sample [ 4000/13472]\t loss  0.0178\tacc  0.9056\t\n",
            "2021-04-16-17:36:48 | INFO | LOG | train Epoch: [ 3/30]\t Sample [ 6000/13472]\t loss  0.0157\tacc  0.9142\t\n",
            "2021-04-16-17:37:28 | INFO | LOG | train Epoch: [ 3/30]\t Sample [ 8000/13472]\t loss  0.0164\tacc  0.9134\t\n",
            "2021-04-16-17:38:08 | INFO | LOG | train Epoch: [ 3/30]\t Sample [10000/13472]\t loss  0.0163\tacc  0.9131\t\n",
            "2021-04-16-17:38:47 | INFO | LOG | train Epoch: [ 3/30]\t Sample [12000/13472]\t loss  0.0162\tacc  0.9135\t\n",
            "2021-04-16-17:39:15 | INFO | LOG | train summary  Epoch: [3/30]\t loss  0.0159\tacc  0.9148\t\n",
            "2021-04-16-17:39:15 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:39:53 | INFO | LOG | validation summary  Epoch: [3/30]\t loss  0.2200\tacc  0.8906\t\n",
            " s [0.92020375 0.9322034  0.        ] ,ppv [0.8377125 0.9290541 0.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "Epoch     3: reducing learning rate of group 0 to 5.0000e-03.\n",
            "2021-04-16-17:39:54 | INFO | LOG | train Epoch: [ 4/30]\t Sample [    0/13472]\t loss  0.0008\tacc  1.0000\t\n",
            "2021-04-16-17:40:33 | INFO | LOG | train Epoch: [ 4/30]\t Sample [ 2000/13472]\t loss  0.0099\tacc  0.9281\t\n",
            "2021-04-16-17:41:14 | INFO | LOG | train Epoch: [ 4/30]\t Sample [ 4000/13472]\t loss  0.0099\tacc  0.9255\t\n",
            "2021-04-16-17:41:54 | INFO | LOG | train Epoch: [ 4/30]\t Sample [ 6000/13472]\t loss  0.0086\tacc  0.9332\t\n",
            "2021-04-16-17:42:35 | INFO | LOG | train Epoch: [ 4/30]\t Sample [ 8000/13472]\t loss  0.0086\tacc  0.9325\t\n",
            "2021-04-16-17:43:15 | INFO | LOG | train Epoch: [ 4/30]\t Sample [10000/13472]\t loss  0.0084\tacc  0.9325\t\n",
            "2021-04-16-17:43:54 | INFO | LOG | train Epoch: [ 4/30]\t Sample [12000/13472]\t loss  0.0079\tacc  0.9335\t\n",
            "2021-04-16-17:44:22 | INFO | LOG | train summary  Epoch: [4/30]\t loss  0.0075\tacc  0.9349\t\n",
            "2021-04-16-17:44:22 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:45:00 | INFO | LOG | validation summary  Epoch: [4/30]\t loss  0.2631\tacc  0.8932\t\n",
            " s [0.92020375 0.93559325 0.01639344] ,ppv [0.8416149 0.9303371 0.9999999]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "Epoch     4: reducing learning rate of group 0 to 2.5000e-03.\n",
            "2021-04-16-17:45:02 | INFO | LOG | train Epoch: [ 5/30]\t Sample [    0/13472]\t loss  0.0003\tacc  1.0000\t\n",
            "2021-04-16-17:45:41 | INFO | LOG | train Epoch: [ 5/30]\t Sample [ 2000/13472]\t loss  0.0060\tacc  0.9405\t\n",
            "2021-04-16-17:46:21 | INFO | LOG | train Epoch: [ 5/30]\t Sample [ 4000/13472]\t loss  0.0061\tacc  0.9387\t\n",
            "2021-04-16-17:47:01 | INFO | LOG | train Epoch: [ 5/30]\t Sample [ 6000/13472]\t loss  0.0054\tacc  0.9437\t\n",
            "2021-04-16-17:47:41 | INFO | LOG | train Epoch: [ 5/30]\t Sample [ 8000/13472]\t loss  0.0052\tacc  0.9435\t\n",
            "2021-04-16-17:48:21 | INFO | LOG | train Epoch: [ 5/30]\t Sample [10000/13472]\t loss  0.0051\tacc  0.9446\t\n",
            "2021-04-16-17:49:00 | INFO | LOG | train Epoch: [ 5/30]\t Sample [12000/13472]\t loss  0.0047\tacc  0.9453\t\n",
            "2021-04-16-17:49:28 | INFO | LOG | train summary  Epoch: [5/30]\t loss  0.0045\tacc  0.9465\t\n",
            "2021-04-16-17:49:28 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:50:05 | INFO | LOG | validation summary  Epoch: [5/30]\t loss  0.2838\tacc  0.8919\t\n",
            " s [0.9134126  0.9378531  0.01639344] ,ppv [0.8459119 0.9242762 0.9999999]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "Epoch     5: reducing learning rate of group 0 to 1.2500e-03.\n",
            "2021-04-16-17:50:07 | INFO | LOG | train Epoch: [ 6/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-17:50:45 | INFO | LOG | train Epoch: [ 6/30]\t Sample [ 2000/13472]\t loss  0.0038\tacc  0.9499\t\n",
            "2021-04-16-17:51:25 | INFO | LOG | train Epoch: [ 6/30]\t Sample [ 4000/13472]\t loss  0.0042\tacc  0.9467\t\n",
            "2021-04-16-17:52:06 | INFO | LOG | train Epoch: [ 6/30]\t Sample [ 6000/13472]\t loss  0.0038\tacc  0.9486\t\n",
            "2021-04-16-17:52:45 | INFO | LOG | train Epoch: [ 6/30]\t Sample [ 8000/13472]\t loss  0.0038\tacc  0.9481\t\n",
            "2021-04-16-17:53:25 | INFO | LOG | train Epoch: [ 6/30]\t Sample [10000/13472]\t loss  0.0037\tacc  0.9486\t\n",
            "2021-04-16-17:54:04 | INFO | LOG | train Epoch: [ 6/30]\t Sample [12000/13472]\t loss  0.0035\tacc  0.9492\t\n",
            "2021-04-16-17:54:32 | INFO | LOG | train summary  Epoch: [6/30]\t loss  0.0033\tacc  0.9502\t\n",
            "2021-04-16-17:54:32 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-17:55:09 | INFO | LOG | validation summary  Epoch: [6/30]\t loss  0.2838\tacc  0.8906\t\n",
            " s [0.91001695 0.9378531  0.01639344] ,ppv [0.84810126 0.9201774  0.9999999 ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "Epoch     6: reducing learning rate of group 0 to 1.0000e-03.\n",
            "2021-04-16-17:55:11 | INFO | LOG | train Epoch: [ 7/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-17:55:50 | INFO | LOG | train Epoch: [ 7/30]\t Sample [ 2000/13472]\t loss  0.0031\tacc  0.9529\t\n",
            "2021-04-16-17:56:30 | INFO | LOG | train Epoch: [ 7/30]\t Sample [ 4000/13472]\t loss  0.0033\tacc  0.9509\t\n",
            "2021-04-16-17:57:09 | INFO | LOG | train Epoch: [ 7/30]\t Sample [ 6000/13472]\t loss  0.0030\tacc  0.9528\t\n",
            "2021-04-16-17:57:49 | INFO | LOG | train Epoch: [ 7/30]\t Sample [ 8000/13472]\t loss  0.0031\tacc  0.9525\t\n",
            "2021-04-16-17:58:28 | INFO | LOG | train Epoch: [ 7/30]\t Sample [10000/13472]\t loss  0.0030\tacc  0.9528\t\n",
            "2021-04-16-17:59:08 | INFO | LOG | train Epoch: [ 7/30]\t Sample [12000/13472]\t loss  0.0028\tacc  0.9531\t\n",
            "2021-04-16-17:59:35 | INFO | LOG | train summary  Epoch: [7/30]\t loss  0.0027\tacc  0.9538\t\n",
            "2021-04-16-17:59:35 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:00:13 | INFO | LOG | validation summary  Epoch: [7/30]\t loss  0.2879\tacc  0.8912\t\n",
            " s [0.9066214  0.94124293 0.01639344] ,ppv [0.8544    0.9163916 0.9999999]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:00:14 | INFO | LOG | train Epoch: [ 8/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:00:53 | INFO | LOG | train Epoch: [ 8/30]\t Sample [ 2000/13472]\t loss  0.0026\tacc  0.9568\t\n",
            "2021-04-16-18:01:33 | INFO | LOG | train Epoch: [ 8/30]\t Sample [ 4000/13472]\t loss  0.0028\tacc  0.9542\t\n",
            "2021-04-16-18:02:13 | INFO | LOG | train Epoch: [ 8/30]\t Sample [ 6000/13472]\t loss  0.0026\tacc  0.9560\t\n",
            "2021-04-16-18:02:52 | INFO | LOG | train Epoch: [ 8/30]\t Sample [ 8000/13472]\t loss  0.0026\tacc  0.9560\t\n",
            "2021-04-16-18:03:31 | INFO | LOG | train Epoch: [ 8/30]\t Sample [10000/13472]\t loss  0.0026\tacc  0.9564\t\n",
            "2021-04-16-18:04:10 | INFO | LOG | train Epoch: [ 8/30]\t Sample [12000/13472]\t loss  0.0024\tacc  0.9566\t\n",
            "2021-04-16-18:04:38 | INFO | LOG | train summary  Epoch: [8/30]\t loss  0.0023\tacc  0.9571\t\n",
            "2021-04-16-18:04:38 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:05:16 | INFO | LOG | validation summary  Epoch: [8/30]\t loss  0.2938\tacc  0.8919\t\n",
            " s [0.9117148  0.9378531  0.03278688] ,ppv [0.85238093 0.91915834 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:05:17 | INFO | LOG | train Epoch: [ 9/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:05:57 | INFO | LOG | train Epoch: [ 9/30]\t Sample [ 2000/13472]\t loss  0.0022\tacc  0.9588\t\n",
            "2021-04-16-18:06:36 | INFO | LOG | train Epoch: [ 9/30]\t Sample [ 4000/13472]\t loss  0.0024\tacc  0.9574\t\n",
            "2021-04-16-18:07:16 | INFO | LOG | train Epoch: [ 9/30]\t Sample [ 6000/13472]\t loss  0.0022\tacc  0.9588\t\n",
            "2021-04-16-18:07:56 | INFO | LOG | train Epoch: [ 9/30]\t Sample [ 8000/13472]\t loss  0.0022\tacc  0.9586\t\n",
            "2021-04-16-18:08:35 | INFO | LOG | train Epoch: [ 9/30]\t Sample [10000/13472]\t loss  0.0022\tacc  0.9589\t\n",
            "2021-04-16-18:09:14 | INFO | LOG | train Epoch: [ 9/30]\t Sample [12000/13472]\t loss  0.0021\tacc  0.9589\t\n",
            "2021-04-16-18:09:42 | INFO | LOG | train summary  Epoch: [9/30]\t loss  0.0020\tacc  0.9594\t\n",
            "2021-04-16-18:09:42 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:10:20 | INFO | LOG | validation summary  Epoch: [9/30]\t loss  0.2997\tacc  0.8912\t\n",
            " s [0.9134126  0.93559325 0.03278688] ,ppv [0.849921 0.92     1.      ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:10:21 | INFO | LOG | train Epoch: [10/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:11:00 | INFO | LOG | train Epoch: [10/30]\t Sample [ 2000/13472]\t loss  0.0018\tacc  0.9608\t\n",
            "2021-04-16-18:11:40 | INFO | LOG | train Epoch: [10/30]\t Sample [ 4000/13472]\t loss  0.0020\tacc  0.9607\t\n",
            "2021-04-16-18:12:19 | INFO | LOG | train Epoch: [10/30]\t Sample [ 6000/13472]\t loss  0.0019\tacc  0.9613\t\n",
            "2021-04-16-18:12:59 | INFO | LOG | train Epoch: [10/30]\t Sample [ 8000/13472]\t loss  0.0019\tacc  0.9607\t\n",
            "2021-04-16-18:13:38 | INFO | LOG | train Epoch: [10/30]\t Sample [10000/13472]\t loss  0.0019\tacc  0.9608\t\n",
            "2021-04-16-18:14:16 | INFO | LOG | train Epoch: [10/30]\t Sample [12000/13472]\t loss  0.0018\tacc  0.9609\t\n",
            "2021-04-16-18:14:44 | INFO | LOG | train summary  Epoch: [10/30]\t loss  0.0017\tacc  0.9612\t\n",
            "2021-04-16-18:14:44 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:15:22 | INFO | LOG | validation summary  Epoch: [10/30]\t loss  0.3057\tacc  0.8912\t\n",
            " s [0.9117148  0.9367232  0.03278688] ,ppv [0.8510301  0.91906875 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:15:23 | INFO | LOG | train Epoch: [11/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:16:02 | INFO | LOG | train Epoch: [11/30]\t Sample [ 2000/13472]\t loss  0.0016\tacc  0.9638\t\n",
            "2021-04-16-18:16:42 | INFO | LOG | train Epoch: [11/30]\t Sample [ 4000/13472]\t loss  0.0017\tacc  0.9631\t\n",
            "2021-04-16-18:17:21 | INFO | LOG | train Epoch: [11/30]\t Sample [ 6000/13472]\t loss  0.0016\tacc  0.9634\t\n",
            "2021-04-16-18:18:01 | INFO | LOG | train Epoch: [11/30]\t Sample [ 8000/13472]\t loss  0.0017\tacc  0.9631\t\n",
            "2021-04-16-18:18:41 | INFO | LOG | train Epoch: [11/30]\t Sample [10000/13472]\t loss  0.0016\tacc  0.9633\t\n",
            "2021-04-16-18:19:19 | INFO | LOG | train Epoch: [11/30]\t Sample [12000/13472]\t loss  0.0016\tacc  0.9633\t\n",
            "2021-04-16-18:19:47 | INFO | LOG | train summary  Epoch: [11/30]\t loss  0.0015\tacc  0.9636\t\n",
            "2021-04-16-18:19:47 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:20:25 | INFO | LOG | validation summary  Epoch: [11/30]\t loss  0.3118\tacc  0.8906\t\n",
            " s [0.91001695 0.9367232  0.03278688] ,ppv [0.85079366 0.91805094 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:20:26 | INFO | LOG | train Epoch: [12/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:21:05 | INFO | LOG | train Epoch: [12/30]\t Sample [ 2000/13472]\t loss  0.0013\tacc  0.9653\t\n",
            "2021-04-16-18:21:45 | INFO | LOG | train Epoch: [12/30]\t Sample [ 4000/13472]\t loss  0.0015\tacc  0.9646\t\n",
            "2021-04-16-18:22:25 | INFO | LOG | train Epoch: [12/30]\t Sample [ 6000/13472]\t loss  0.0014\tacc  0.9646\t\n",
            "2021-04-16-18:23:05 | INFO | LOG | train Epoch: [12/30]\t Sample [ 8000/13472]\t loss  0.0014\tacc  0.9646\t\n",
            "2021-04-16-18:23:45 | INFO | LOG | train Epoch: [12/30]\t Sample [10000/13472]\t loss  0.0014\tacc  0.9647\t\n",
            "2021-04-16-18:24:23 | INFO | LOG | train Epoch: [12/30]\t Sample [12000/13472]\t loss  0.0013\tacc  0.9653\t\n",
            "2021-04-16-18:24:51 | INFO | LOG | train summary  Epoch: [12/30]\t loss  0.0013\tacc  0.9656\t\n",
            "2021-04-16-18:24:51 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:25:28 | INFO | LOG | validation summary  Epoch: [12/30]\t loss  0.3178\tacc  0.8906\t\n",
            " s [0.9083192  0.9378531  0.03278688] ,ppv [0.8519108 0.9171271 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:25:29 | INFO | LOG | train Epoch: [13/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:26:08 | INFO | LOG | train Epoch: [13/30]\t Sample [ 2000/13472]\t loss  0.0012\tacc  0.9678\t\n",
            "2021-04-16-18:26:47 | INFO | LOG | train Epoch: [13/30]\t Sample [ 4000/13472]\t loss  0.0013\tacc  0.9666\t\n",
            "2021-04-16-18:27:27 | INFO | LOG | train Epoch: [13/30]\t Sample [ 6000/13472]\t loss  0.0012\tacc  0.9669\t\n",
            "2021-04-16-18:28:07 | INFO | LOG | train Epoch: [13/30]\t Sample [ 8000/13472]\t loss  0.0012\tacc  0.9667\t\n",
            "2021-04-16-18:28:46 | INFO | LOG | train Epoch: [13/30]\t Sample [10000/13472]\t loss  0.0012\tacc  0.9670\t\n",
            "2021-04-16-18:29:25 | INFO | LOG | train Epoch: [13/30]\t Sample [12000/13472]\t loss  0.0012\tacc  0.9676\t\n",
            "2021-04-16-18:29:53 | INFO | LOG | train summary  Epoch: [13/30]\t loss  0.0011\tacc  0.9678\t\n",
            "2021-04-16-18:29:53 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:30:29 | INFO | LOG | validation summary  Epoch: [13/30]\t loss  0.3238\tacc  0.8893\t\n",
            " s [0.9066214  0.9367232  0.03278688] ,ppv [0.8516746  0.91501105 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:30:31 | INFO | LOG | train Epoch: [14/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:31:10 | INFO | LOG | train Epoch: [14/30]\t Sample [ 2000/13472]\t loss  0.0010\tacc  0.9697\t\n",
            "2021-04-16-18:31:50 | INFO | LOG | train Epoch: [14/30]\t Sample [ 4000/13472]\t loss  0.0011\tacc  0.9686\t\n",
            "2021-04-16-18:32:30 | INFO | LOG | train Epoch: [14/30]\t Sample [ 6000/13472]\t loss  0.0010\tacc  0.9684\t\n",
            "2021-04-16-18:33:10 | INFO | LOG | train Epoch: [14/30]\t Sample [ 8000/13472]\t loss  0.0011\tacc  0.9679\t\n",
            "2021-04-16-18:33:50 | INFO | LOG | train Epoch: [14/30]\t Sample [10000/13472]\t loss  0.0011\tacc  0.9686\t\n",
            "2021-04-16-18:34:29 | INFO | LOG | train Epoch: [14/30]\t Sample [12000/13472]\t loss  0.0010\tacc  0.9691\t\n",
            "2021-04-16-18:34:57 | INFO | LOG | train summary  Epoch: [14/30]\t loss  0.0010\tacc  0.9694\t\n",
            "2021-04-16-18:34:57 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:35:34 | INFO | LOG | validation summary  Epoch: [14/30]\t loss  0.3294\tacc  0.8873\t\n",
            " s [0.9049236  0.93446326 0.03278688] ,ppv [0.8487261  0.91381216 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:35:35 | INFO | LOG | train Epoch: [15/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:36:13 | INFO | LOG | train Epoch: [15/30]\t Sample [ 2000/13472]\t loss  0.0009\tacc  0.9722\t\n",
            "2021-04-16-18:36:53 | INFO | LOG | train Epoch: [15/30]\t Sample [ 4000/13472]\t loss  0.0009\tacc  0.9709\t\n",
            "2021-04-16-18:37:32 | INFO | LOG | train Epoch: [15/30]\t Sample [ 6000/13472]\t loss  0.0009\tacc  0.9701\t\n",
            "2021-04-16-18:38:12 | INFO | LOG | train Epoch: [15/30]\t Sample [ 8000/13472]\t loss  0.0009\tacc  0.9694\t\n",
            "2021-04-16-18:38:51 | INFO | LOG | train Epoch: [15/30]\t Sample [10000/13472]\t loss  0.0009\tacc  0.9700\t\n",
            "2021-04-16-18:39:29 | INFO | LOG | train Epoch: [15/30]\t Sample [12000/13472]\t loss  0.0009\tacc  0.9708\t\n",
            "2021-04-16-18:39:56 | INFO | LOG | train summary  Epoch: [15/30]\t loss  0.0009\tacc  0.9710\t\n",
            "2021-04-16-18:39:56 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:40:33 | INFO | LOG | validation summary  Epoch: [15/30]\t loss  0.3349\tacc  0.8866\t\n",
            " s [0.9066214  0.9322034  0.03278688] ,ppv [0.84627575 0.91463417 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:40:35 | INFO | LOG | train Epoch: [16/30]\t Sample [    0/13472]\t loss  0.0001\tacc  1.0000\t\n",
            "2021-04-16-18:41:13 | INFO | LOG | train Epoch: [16/30]\t Sample [ 2000/13472]\t loss  0.0008\tacc  0.9737\t\n",
            "2021-04-16-18:41:53 | INFO | LOG | train Epoch: [16/30]\t Sample [ 4000/13472]\t loss  0.0008\tacc  0.9724\t\n",
            "2021-04-16-18:42:32 | INFO | LOG | train Epoch: [16/30]\t Sample [ 6000/13472]\t loss  0.0008\tacc  0.9716\t\n",
            "2021-04-16-18:43:13 | INFO | LOG | train Epoch: [16/30]\t Sample [ 8000/13472]\t loss  0.0008\tacc  0.9709\t\n",
            "2021-04-16-18:43:52 | INFO | LOG | train Epoch: [16/30]\t Sample [10000/13472]\t loss  0.0008\tacc  0.9716\t\n",
            "2021-04-16-18:44:30 | INFO | LOG | train Epoch: [16/30]\t Sample [12000/13472]\t loss  0.0008\tacc  0.9725\t\n",
            "2021-04-16-18:44:58 | INFO | LOG | train summary  Epoch: [16/30]\t loss  0.0008\tacc  0.9727\t\n",
            "2021-04-16-18:44:58 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:45:35 | INFO | LOG | validation summary  Epoch: [16/30]\t loss  0.3401\tacc  0.8866\t\n",
            " s [0.9066214  0.9322034  0.03278688] ,ppv [0.84627575 0.91463417 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:45:37 | INFO | LOG | train Epoch: [17/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-18:46:15 | INFO | LOG | train Epoch: [17/30]\t Sample [ 2000/13472]\t loss  0.0007\tacc  0.9752\t\n",
            "2021-04-16-18:46:55 | INFO | LOG | train Epoch: [17/30]\t Sample [ 4000/13472]\t loss  0.0007\tacc  0.9734\t\n",
            "2021-04-16-18:47:35 | INFO | LOG | train Epoch: [17/30]\t Sample [ 6000/13472]\t loss  0.0007\tacc  0.9726\t\n",
            "2021-04-16-18:48:14 | INFO | LOG | train Epoch: [17/30]\t Sample [ 8000/13472]\t loss  0.0007\tacc  0.9722\t\n",
            "2021-04-16-18:48:53 | INFO | LOG | train Epoch: [17/30]\t Sample [10000/13472]\t loss  0.0007\tacc  0.9729\t\n",
            "2021-04-16-18:49:32 | INFO | LOG | train Epoch: [17/30]\t Sample [12000/13472]\t loss  0.0007\tacc  0.9739\t\n",
            "2021-04-16-18:49:59 | INFO | LOG | train summary  Epoch: [17/30]\t loss  0.0007\tacc  0.9741\t\n",
            "2021-04-16-18:49:59 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:50:36 | INFO | LOG | validation summary  Epoch: [17/30]\t loss  0.3453\tacc  0.8873\t\n",
            " s [0.9083192  0.9322034  0.03278688] ,ppv [0.8451817 0.9166667 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:50:38 | INFO | LOG | train Epoch: [18/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-18:51:16 | INFO | LOG | train Epoch: [18/30]\t Sample [ 2000/13472]\t loss  0.0006\tacc  0.9782\t\n",
            "2021-04-16-18:51:56 | INFO | LOG | train Epoch: [18/30]\t Sample [ 4000/13472]\t loss  0.0006\tacc  0.9753\t\n",
            "2021-04-16-18:52:36 | INFO | LOG | train Epoch: [18/30]\t Sample [ 6000/13472]\t loss  0.0006\tacc  0.9749\t\n",
            "2021-04-16-18:53:15 | INFO | LOG | train Epoch: [18/30]\t Sample [ 8000/13472]\t loss  0.0006\tacc  0.9742\t\n",
            "2021-04-16-18:53:53 | INFO | LOG | train Epoch: [18/30]\t Sample [10000/13472]\t loss  0.0006\tacc  0.9747\t\n",
            "2021-04-16-18:54:33 | INFO | LOG | train Epoch: [18/30]\t Sample [12000/13472]\t loss  0.0006\tacc  0.9755\t\n",
            "2021-04-16-18:55:00 | INFO | LOG | train summary  Epoch: [18/30]\t loss  0.0006\tacc  0.9757\t\n",
            "2021-04-16-18:55:00 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-18:55:37 | INFO | LOG | validation summary  Epoch: [18/30]\t loss  0.3503\tacc  0.8860\t\n",
            " s [0.9083192  0.9299435  0.03278688] ,ppv [0.8425197 0.9164811 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-18:55:39 | INFO | LOG | train Epoch: [19/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-18:56:17 | INFO | LOG | train Epoch: [19/30]\t Sample [ 2000/13472]\t loss  0.0005\tacc  0.9797\t\n",
            "2021-04-16-18:56:57 | INFO | LOG | train Epoch: [19/30]\t Sample [ 4000/13472]\t loss  0.0006\tacc  0.9771\t\n",
            "2021-04-16-18:57:36 | INFO | LOG | train Epoch: [19/30]\t Sample [ 6000/13472]\t loss  0.0005\tacc  0.9769\t\n",
            "2021-04-16-18:58:15 | INFO | LOG | train Epoch: [19/30]\t Sample [ 8000/13472]\t loss  0.0006\tacc  0.9765\t\n",
            "2021-04-16-18:58:54 | INFO | LOG | train Epoch: [19/30]\t Sample [10000/13472]\t loss  0.0006\tacc  0.9767\t\n",
            "2021-04-16-18:59:33 | INFO | LOG | train Epoch: [19/30]\t Sample [12000/13472]\t loss  0.0005\tacc  0.9774\t\n",
            "2021-04-16-19:00:01 | INFO | LOG | train summary  Epoch: [19/30]\t loss  0.0005\tacc  0.9774\t\n",
            "2021-04-16-19:00:01 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:00:37 | INFO | LOG | validation summary  Epoch: [19/30]\t loss  0.3551\tacc  0.8860\t\n",
            " s [0.9083192  0.9299435  0.03278688] ,ppv [0.8425197 0.9164811 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:00:39 | INFO | LOG | train Epoch: [20/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:01:18 | INFO | LOG | train Epoch: [20/30]\t Sample [ 2000/13472]\t loss  0.0005\tacc  0.9802\t\n",
            "2021-04-16-19:01:58 | INFO | LOG | train Epoch: [20/30]\t Sample [ 4000/13472]\t loss  0.0005\tacc  0.9783\t\n",
            "2021-04-16-19:02:37 | INFO | LOG | train Epoch: [20/30]\t Sample [ 6000/13472]\t loss  0.0005\tacc  0.9784\t\n",
            "2021-04-16-19:03:16 | INFO | LOG | train Epoch: [20/30]\t Sample [ 8000/13472]\t loss  0.0005\tacc  0.9778\t\n",
            "2021-04-16-19:03:55 | INFO | LOG | train Epoch: [20/30]\t Sample [10000/13472]\t loss  0.0005\tacc  0.9778\t\n",
            "2021-04-16-19:04:34 | INFO | LOG | train Epoch: [20/30]\t Sample [12000/13472]\t loss  0.0005\tacc  0.9784\t\n",
            "2021-04-16-19:05:01 | INFO | LOG | train summary  Epoch: [20/30]\t loss  0.0005\tacc  0.9785\t\n",
            "2021-04-16-19:05:01 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:05:38 | INFO | LOG | validation summary  Epoch: [20/30]\t loss  0.3596\tacc  0.8860\t\n",
            " s [0.9083192  0.9299435  0.03278688] ,ppv [0.8425197 0.9164811 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:05:40 | INFO | LOG | train Epoch: [21/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:06:18 | INFO | LOG | train Epoch: [21/30]\t Sample [ 2000/13472]\t loss  0.0004\tacc  0.9812\t\n",
            "2021-04-16-19:06:57 | INFO | LOG | train Epoch: [21/30]\t Sample [ 4000/13472]\t loss  0.0004\tacc  0.9798\t\n",
            "2021-04-16-19:07:37 | INFO | LOG | train Epoch: [21/30]\t Sample [ 6000/13472]\t loss  0.0004\tacc  0.9794\t\n",
            "2021-04-16-19:08:16 | INFO | LOG | train Epoch: [21/30]\t Sample [ 8000/13472]\t loss  0.0004\tacc  0.9787\t\n",
            "2021-04-16-19:08:55 | INFO | LOG | train Epoch: [21/30]\t Sample [10000/13472]\t loss  0.0004\tacc  0.9789\t\n",
            "2021-04-16-19:09:34 | INFO | LOG | train Epoch: [21/30]\t Sample [12000/13472]\t loss  0.0004\tacc  0.9795\t\n",
            "2021-04-16-19:10:02 | INFO | LOG | train summary  Epoch: [21/30]\t loss  0.0004\tacc  0.9797\t\n",
            "2021-04-16-19:10:02 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:10:39 | INFO | LOG | validation summary  Epoch: [21/30]\t loss  0.3639\tacc  0.8860\t\n",
            " s [0.9083192  0.9299435  0.03278688] ,ppv [0.8425197 0.9164811 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:10:41 | INFO | LOG | train Epoch: [22/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:11:19 | INFO | LOG | train Epoch: [22/30]\t Sample [ 2000/13472]\t loss  0.0004\tacc  0.9826\t\n",
            "2021-04-16-19:11:59 | INFO | LOG | train Epoch: [22/30]\t Sample [ 4000/13472]\t loss  0.0004\tacc  0.9816\t\n",
            "2021-04-16-19:12:38 | INFO | LOG | train Epoch: [22/30]\t Sample [ 6000/13472]\t loss  0.0004\tacc  0.9814\t\n",
            "2021-04-16-19:13:18 | INFO | LOG | train Epoch: [22/30]\t Sample [ 8000/13472]\t loss  0.0004\tacc  0.9803\t\n",
            "2021-04-16-19:13:57 | INFO | LOG | train Epoch: [22/30]\t Sample [10000/13472]\t loss  0.0004\tacc  0.9805\t\n",
            "2021-04-16-19:14:36 | INFO | LOG | train Epoch: [22/30]\t Sample [12000/13472]\t loss  0.0004\tacc  0.9812\t\n",
            "2021-04-16-19:15:03 | INFO | LOG | train summary  Epoch: [22/30]\t loss  0.0004\tacc  0.9813\t\n",
            "2021-04-16-19:15:03 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:15:41 | INFO | LOG | validation summary  Epoch: [22/30]\t loss  0.3680\tacc  0.8847\t\n",
            " s [0.9066214  0.9288136  0.03278688] ,ppv [0.8409449 0.9153675 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:15:42 | INFO | LOG | train Epoch: [23/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:16:21 | INFO | LOG | train Epoch: [23/30]\t Sample [ 2000/13472]\t loss  0.0003\tacc  0.9841\t\n",
            "2021-04-16-19:17:00 | INFO | LOG | train Epoch: [23/30]\t Sample [ 4000/13472]\t loss  0.0004\tacc  0.9831\t\n",
            "2021-04-16-19:17:39 | INFO | LOG | train Epoch: [23/30]\t Sample [ 6000/13472]\t loss  0.0003\tacc  0.9829\t\n",
            "2021-04-16-19:18:19 | INFO | LOG | train Epoch: [23/30]\t Sample [ 8000/13472]\t loss  0.0004\tacc  0.9815\t\n",
            "2021-04-16-19:18:58 | INFO | LOG | train Epoch: [23/30]\t Sample [10000/13472]\t loss  0.0004\tacc  0.9818\t\n",
            "2021-04-16-19:19:36 | INFO | LOG | train Epoch: [23/30]\t Sample [12000/13472]\t loss  0.0003\tacc  0.9824\t\n",
            "2021-04-16-19:20:03 | INFO | LOG | train summary  Epoch: [23/30]\t loss  0.0003\tacc  0.9826\t\n",
            "2021-04-16-19:20:03 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:20:40 | INFO | LOG | validation summary  Epoch: [23/30]\t loss  0.3719\tacc  0.8834\t\n",
            " s [0.9049236  0.9276836  0.03278688] ,ppv [0.8393701 0.9142539 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:20:42 | INFO | LOG | train Epoch: [24/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:21:21 | INFO | LOG | train Epoch: [24/30]\t Sample [ 2000/13472]\t loss  0.0003\tacc  0.9856\t\n",
            "2021-04-16-19:22:00 | INFO | LOG | train Epoch: [24/30]\t Sample [ 4000/13472]\t loss  0.0003\tacc  0.9846\t\n",
            "2021-04-16-19:22:39 | INFO | LOG | train Epoch: [24/30]\t Sample [ 6000/13472]\t loss  0.0003\tacc  0.9847\t\n",
            "2021-04-16-19:23:19 | INFO | LOG | train Epoch: [24/30]\t Sample [ 8000/13472]\t loss  0.0003\tacc  0.9835\t\n",
            "2021-04-16-19:23:58 | INFO | LOG | train Epoch: [24/30]\t Sample [10000/13472]\t loss  0.0003\tacc  0.9836\t\n",
            "2021-04-16-19:24:36 | INFO | LOG | train Epoch: [24/30]\t Sample [12000/13472]\t loss  0.0003\tacc  0.9840\t\n",
            "2021-04-16-19:25:03 | INFO | LOG | train summary  Epoch: [24/30]\t loss  0.0003\tacc  0.9843\t\n",
            "2021-04-16-19:25:03 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:25:40 | INFO | LOG | validation summary  Epoch: [24/30]\t loss  0.3757\tacc  0.8840\t\n",
            " s [0.9049236  0.9288136  0.03278688] ,ppv [0.8393701 0.9153675 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:25:41 | INFO | LOG | train Epoch: [25/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:26:20 | INFO | LOG | train Epoch: [25/30]\t Sample [ 2000/13472]\t loss  0.0003\tacc  0.9861\t\n",
            "2021-04-16-19:27:00 | INFO | LOG | train Epoch: [25/30]\t Sample [ 4000/13472]\t loss  0.0003\tacc  0.9856\t\n",
            "2021-04-16-19:27:39 | INFO | LOG | train Epoch: [25/30]\t Sample [ 6000/13472]\t loss  0.0003\tacc  0.9857\t\n",
            "2021-04-16-19:28:19 | INFO | LOG | train Epoch: [25/30]\t Sample [ 8000/13472]\t loss  0.0003\tacc  0.9847\t\n",
            "2021-04-16-19:28:57 | INFO | LOG | train Epoch: [25/30]\t Sample [10000/13472]\t loss  0.0003\tacc  0.9847\t\n",
            "2021-04-16-19:29:36 | INFO | LOG | train Epoch: [25/30]\t Sample [12000/13472]\t loss  0.0003\tacc  0.9850\t\n",
            "2021-04-16-19:30:04 | INFO | LOG | train summary  Epoch: [25/30]\t loss  0.0003\tacc  0.9851\t\n",
            "2021-04-16-19:30:04 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:30:41 | INFO | LOG | validation summary  Epoch: [25/30]\t loss  0.3792\tacc  0.8840\t\n",
            " s [0.9049236  0.9288136  0.03278688] ,ppv [0.8393701 0.9153675 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:30:42 | INFO | LOG | train Epoch: [26/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:31:22 | INFO | LOG | train Epoch: [26/30]\t Sample [ 2000/13472]\t loss  0.0003\tacc  0.9871\t\n",
            "2021-04-16-19:32:01 | INFO | LOG | train Epoch: [26/30]\t Sample [ 4000/13472]\t loss  0.0003\tacc  0.9866\t\n",
            "2021-04-16-19:32:40 | INFO | LOG | train Epoch: [26/30]\t Sample [ 6000/13472]\t loss  0.0003\tacc  0.9864\t\n",
            "2021-04-16-19:33:20 | INFO | LOG | train Epoch: [26/30]\t Sample [ 8000/13472]\t loss  0.0003\tacc  0.9855\t\n",
            "2021-04-16-19:33:59 | INFO | LOG | train Epoch: [26/30]\t Sample [10000/13472]\t loss  0.0003\tacc  0.9856\t\n",
            "2021-04-16-19:34:37 | INFO | LOG | train Epoch: [26/30]\t Sample [12000/13472]\t loss  0.0003\tacc  0.9859\t\n",
            "2021-04-16-19:35:05 | INFO | LOG | train summary  Epoch: [26/30]\t loss  0.0002\tacc  0.9860\t\n",
            "2021-04-16-19:35:05 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:35:42 | INFO | LOG | validation summary  Epoch: [26/30]\t loss  0.3827\tacc  0.8834\t\n",
            " s [0.9049236  0.9276836  0.03278688] ,ppv [0.8380503  0.91527313 1.        ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:35:44 | INFO | LOG | train Epoch: [27/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:36:22 | INFO | LOG | train Epoch: [27/30]\t Sample [ 2000/13472]\t loss  0.0002\tacc  0.9881\t\n",
            "2021-04-16-19:37:01 | INFO | LOG | train Epoch: [27/30]\t Sample [ 4000/13472]\t loss  0.0002\tacc  0.9873\t\n",
            "2021-04-16-19:37:41 | INFO | LOG | train Epoch: [27/30]\t Sample [ 6000/13472]\t loss  0.0002\tacc  0.9874\t\n",
            "2021-04-16-19:38:21 | INFO | LOG | train Epoch: [27/30]\t Sample [ 8000/13472]\t loss  0.0002\tacc  0.9863\t\n",
            "2021-04-16-19:38:59 | INFO | LOG | train Epoch: [27/30]\t Sample [10000/13472]\t loss  0.0002\tacc  0.9865\t\n",
            "2021-04-16-19:39:39 | INFO | LOG | train Epoch: [27/30]\t Sample [12000/13472]\t loss  0.0002\tacc  0.9868\t\n",
            "2021-04-16-19:40:06 | INFO | LOG | train summary  Epoch: [27/30]\t loss  0.0002\tacc  0.9869\t\n",
            "2021-04-16-19:40:06 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:40:43 | INFO | LOG | validation summary  Epoch: [27/30]\t loss  0.3861\tacc  0.8827\t\n",
            " s [0.9049236  0.92655367 0.03278688] ,ppv [0.8367347 0.9151786 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:40:44 | INFO | LOG | train Epoch: [28/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:41:23 | INFO | LOG | train Epoch: [28/30]\t Sample [ 2000/13472]\t loss  0.0002\tacc  0.9896\t\n",
            "2021-04-16-19:42:02 | INFO | LOG | train Epoch: [28/30]\t Sample [ 4000/13472]\t loss  0.0002\tacc  0.9890\t\n",
            "2021-04-16-19:42:42 | INFO | LOG | train Epoch: [28/30]\t Sample [ 6000/13472]\t loss  0.0002\tacc  0.9890\t\n",
            "2021-04-16-19:43:21 | INFO | LOG | train Epoch: [28/30]\t Sample [ 8000/13472]\t loss  0.0002\tacc  0.9880\t\n",
            "2021-04-16-19:44:00 | INFO | LOG | train Epoch: [28/30]\t Sample [10000/13472]\t loss  0.0002\tacc  0.9881\t\n",
            "2021-04-16-19:44:39 | INFO | LOG | train Epoch: [28/30]\t Sample [12000/13472]\t loss  0.0002\tacc  0.9883\t\n",
            "2021-04-16-19:45:06 | INFO | LOG | train summary  Epoch: [28/30]\t loss  0.0002\tacc  0.9883\t\n",
            "2021-04-16-19:45:06 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:45:43 | INFO | LOG | validation summary  Epoch: [28/30]\t loss  0.3892\tacc  0.8827\t\n",
            " s [0.9049236  0.92655367 0.03278688] ,ppv [0.8367347 0.9151786 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n",
            "2021-04-16-19:45:44 | INFO | LOG | train Epoch: [29/30]\t Sample [    0/13472]\t loss  0.0000\tacc  1.0000\t\n",
            "2021-04-16-19:46:24 | INFO | LOG | train Epoch: [29/30]\t Sample [ 2000/13472]\t loss  0.0002\tacc  0.9901\t\n",
            "2021-04-16-19:47:05 | INFO | LOG | train Epoch: [29/30]\t Sample [ 4000/13472]\t loss  0.0002\tacc  0.9895\t\n",
            "2021-04-16-19:47:45 | INFO | LOG | train Epoch: [29/30]\t Sample [ 6000/13472]\t loss  0.0002\tacc  0.9900\t\n",
            "2021-04-16-19:48:27 | INFO | LOG | train Epoch: [29/30]\t Sample [ 8000/13472]\t loss  0.0002\tacc  0.9890\t\n",
            "2021-04-16-19:49:09 | INFO | LOG | train Epoch: [29/30]\t Sample [10000/13472]\t loss  0.0002\tacc  0.9892\t\n",
            "2021-04-16-19:49:50 | INFO | LOG | train Epoch: [29/30]\t Sample [12000/13472]\t loss  0.0002\tacc  0.9894\t\n",
            "2021-04-16-19:50:19 | INFO | LOG | train summary  Epoch: [29/30]\t loss  0.0002\tacc  0.9895\t\n",
            "2021-04-16-19:50:19 | INFO | LOG | !!!!!!!!!!    VALIDATION   , !!!!!!!!!!\n",
            "2021-04-16-19:50:56 | INFO | LOG | validation summary  Epoch: [29/30]\t loss  0.3922\tacc  0.8827\t\n",
            " s [0.9049236  0.92655367 0.03278688] ,ppv [0.8367347 0.9151786 1.       ]\n",
            "/content/COVIDNet/checkpoints/model_mobilenet_v2/dataset_COVIDx/date_16_04_2021_17.24.34/_model_last_checkpoint.pth.tar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxMLkmfSJ9sh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EU01FumK-JqG"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-X8LkBXI7cI"
      },
      "source": [
        ""
      ]
    }
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}