vin_CXR_512_classifier.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAge7kp8lxrB",
        "outputId": "8be629ec-adfd-485d-ccd1-cf9a60913a21"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roE1QRYFl703"
      },
      "source": [
        "!unzip -uq gdrive/MyDrive/cs492i_project/Vin_CXR_512.zip -d Vin_CXR_512/\n",
        "!unzip -uq gdrive/MyDrive/cs492i_project/train.csv.zip train.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rc5uecXQGAaq",
        "outputId": "410da5a3-1162-4d03-dfa3-725c915f39ac"
      },
      "source": [
        "! pip install tensorboardX tensorboard pyyaml webcolors \n",
        "! pip install -U git+https://github.com/albumentations-team/albumentations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorboardX\n",
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            "\u001b[?25hRequirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (2.7.0)\n",
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            "Collecting webcolors\n",
            "  Downloading webcolors-1.11.1-py3-none-any.whl (9.9 kB)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.10.0.2)\n",
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            "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) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard) (2021.10.8)\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) (3.1.1)\n",
            "Installing collected packages: webcolors, tensorboardX\n",
            "Successfully installed tensorboardX-2.4.1 webcolors-1.11.1\n",
            "Collecting git+https://github.com/albumentations-team/albumentations\n",
            "  Cloning https://github.com/albumentations-team/albumentations to /tmp/pip-req-build-1aa4ts0l\n",
            "  Running command git clone -q https://github.com/albumentations-team/albumentations /tmp/pip-req-build-1aa4ts0l\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.1.0) (1.19.5)\n",
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            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==1.1.0) (3.13)\n",
            "Collecting qudida>=0.0.4\n",
            "  Downloading qudida-0.0.4-py3-none-any.whl (3.5 kB)\n",
            "Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.1.0) (4.1.2.30)\n",
            "Collecting opencv-python-headless>=4.0.1\n",
            "  Downloading opencv_python_headless-4.5.4.60-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.6 MB)\n",
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            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.1.0) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.1.0) (1.3.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.1.0) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.1.0) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.1.0) (1.1.0)\n",
            "Building wheels for collected packages: albumentations\n",
            "  Building wheel for albumentations (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for albumentations: filename=albumentations-1.1.0-py3-none-any.whl size=105152 sha256=73a80e6b7b06af556b07ad628e7cd514922f67f6e6eb1672f6397f851312add6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-j271lkzz/wheels/3a/25/ed/ec3b518e7a332d7f0a3bb37c280e1b784cf2f79b94b3c7d00b\n",
            "Successfully built albumentations\n",
            "Installing collected packages: opencv-python-headless, qudida, albumentations\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 0.1.12\n",
            "    Uninstalling albumentations-0.1.12:\n",
            "      Successfully uninstalled albumentations-0.1.12\n",
            "Successfully installed albumentations-1.1.0 opencv-python-headless-4.5.4.60 qudida-0.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qA4sm8JnKLPe",
        "outputId": "12a628b9-67a0-43c7-d1b2-b71bdee9525e"
      },
      "source": [
        "!pip install torchvision"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (0.11.1+cu111)\n",
            "Requirement already satisfied: torch==1.10.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.10.0+cu111)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.10.0->torchvision) (3.10.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDvEUbFll-PJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from pathlib import Path \n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import json \n",
        "import cv2 \n",
        "import albumentations\n",
        "from albumentations import *\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "import torchvision \n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Ucy5d_-EmApI",
        "outputId": "0693f228-43aa-488c-84fc-3bb962c3b3ab"
      },
      "source": [
        "traindata= pd.read_csv('train.csv')\n",
        "traindata.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>class_name</th>\n",
              "      <th>class_id</th>\n",
              "      <th>rad_id</th>\n",
              "      <th>x_min</th>\n",
              "      <th>y_min</th>\n",
              "      <th>x_max</th>\n",
              "      <th>y_max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50a418190bc3fb1ef1633bf9678929b3</td>\n",
              "      <td>No finding</td>\n",
              "      <td>14</td>\n",
              "      <td>R11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21a10246a5ec7af151081d0cd6d65dc9</td>\n",
              "      <td>No finding</td>\n",
              "      <td>14</td>\n",
              "      <td>R7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9a5094b2563a1ef3ff50dc5c7ff71345</td>\n",
              "      <td>Cardiomegaly</td>\n",
              "      <td>3</td>\n",
              "      <td>R10</td>\n",
              "      <td>691.0</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>1653.0</td>\n",
              "      <td>1831.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>051132a778e61a86eb147c7c6f564dfe</td>\n",
              "      <td>Aortic enlargement</td>\n",
              "      <td>0</td>\n",
              "      <td>R10</td>\n",
              "      <td>1264.0</td>\n",
              "      <td>743.0</td>\n",
              "      <td>1611.0</td>\n",
              "      <td>1019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>063319de25ce7edb9b1c6b8881290140</td>\n",
              "      <td>No finding</td>\n",
              "      <td>14</td>\n",
              "      <td>R10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1c32170b4af4ce1a3030eb8167753b06</td>\n",
              "      <td>Pleural thickening</td>\n",
              "      <td>11</td>\n",
              "      <td>R9</td>\n",
              "      <td>627.0</td>\n",
              "      <td>357.0</td>\n",
              "      <td>947.0</td>\n",
              "      <td>433.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0c7a38f293d5f5e4846aa4ca6db4daf1</td>\n",
              "      <td>ILD</td>\n",
              "      <td>5</td>\n",
              "      <td>R17</td>\n",
              "      <td>1347.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>2188.0</td>\n",
              "      <td>2169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>47ed17dcb2cbeec15182ed335a8b5a9e</td>\n",
              "      <td>Nodule/Mass</td>\n",
              "      <td>8</td>\n",
              "      <td>R9</td>\n",
              "      <td>557.0</td>\n",
              "      <td>2352.0</td>\n",
              "      <td>675.0</td>\n",
              "      <td>2484.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>d3637a1935a905b3c326af31389cb846</td>\n",
              "      <td>Aortic enlargement</td>\n",
              "      <td>0</td>\n",
              "      <td>R10</td>\n",
              "      <td>1329.0</td>\n",
              "      <td>743.0</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>958.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>afb6230703512afc370f236e8fe98806</td>\n",
              "      <td>Pulmonary fibrosis</td>\n",
              "      <td>13</td>\n",
              "      <td>R9</td>\n",
              "      <td>1857.0</td>\n",
              "      <td>1607.0</td>\n",
              "      <td>2126.0</td>\n",
              "      <td>2036.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           image_id          class_name  ...   x_max   y_max\n",
              "0  50a418190bc3fb1ef1633bf9678929b3          No finding  ...     NaN     NaN\n",
              "1  21a10246a5ec7af151081d0cd6d65dc9          No finding  ...     NaN     NaN\n",
              "2  9a5094b2563a1ef3ff50dc5c7ff71345        Cardiomegaly  ...  1653.0  1831.0\n",
              "3  051132a778e61a86eb147c7c6f564dfe  Aortic enlargement  ...  1611.0  1019.0\n",
              "4  063319de25ce7edb9b1c6b8881290140          No finding  ...     NaN     NaN\n",
              "5  1c32170b4af4ce1a3030eb8167753b06  Pleural thickening  ...   947.0   433.0\n",
              "6  0c7a38f293d5f5e4846aa4ca6db4daf1                 ILD  ...  2188.0  2169.0\n",
              "7  47ed17dcb2cbeec15182ed335a8b5a9e         Nodule/Mass  ...   675.0  2484.0\n",
              "8  d3637a1935a905b3c326af31389cb846  Aortic enlargement  ...  1521.0   958.0\n",
              "9  afb6230703512afc370f236e8fe98806  Pulmonary fibrosis  ...  2126.0  2036.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddCY5NdbvVde",
        "outputId": "894bbeb0-1a4f-4ecd-eac0-f62b0ff27173"
      },
      "source": [
        "obj_list = [\"Aortic_enlargement\",\n",
        "            \"Atelectasis\",\n",
        "            \"Calcification\",\n",
        "            \"Cardiomegaly\",\n",
        "            \"Consolidation\",\n",
        "            \"ILD\",\n",
        "            \"Infiltration\",\n",
        "            \"Lung_Opacity\",\n",
        "            \"Nodule/Mass\",\n",
        "            \"Other_lesion\",\n",
        "            \"Pleural_effusion\",\n",
        "            \"Pleural_thickening\",\n",
        "            \"Pneumothorax\",\n",
        "            \"Pulmonary_fibrosis\"]\n",
        "len(obj_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvJKFpcCqw0d",
        "outputId": "5ec5d4b7-3a11-4c8c-b5bb-54e545b80d4e"
      },
      "source": [
        "pic_id = os.listdir('./Vin_CXR_512/Vin_CXR_512/train')[0][:-4]\n",
        "smalldata = traindata[traindata['image_id'] == pic_id]\n",
        "lol = set(smalldata['class_id'])\n",
        "result = np.zeros(14)\n",
        "for i in lol:\n",
        "  result[i] = 1\n",
        "lol.discard(14)\n",
        "lol"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 6, 11, 13}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVsHIhKqFWHO"
      },
      "source": [
        "data_transform= {\n",
        "        'train': Compose([\n",
        "            Resize(512,512),\n",
        "            HorizontalFlip(),\n",
        "            RandomResizedCrop(512,512,p=0.5),\n",
        "            ShiftScaleRotate(rotate_limit=10),  \n",
        "            RandomBrightnessContrast(),\n",
        "            Normalize(max_pixel_value=1),\n",
        "            ToTensorV2()\n",
        "        ]),\n",
        "        'val': Compose([\n",
        "            Resize(512,512),\n",
        "            Normalize(max_pixel_value=1),\n",
        "            ToTensorV2()\n",
        "        ]),\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tGVjsrpyA7h"
      },
      "source": [
        "class CXR_DataLoader(torch.utils.data.Dataset):\n",
        "  def __init__(self, mode, transform=data_transform):\n",
        "    super(CXR_DataLoader, self).__init__()\n",
        "    self.mode = mode\n",
        "    self.transform = transform[self.mode]\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(os.listdir('/content/Vin_CXR_512/Vin_CXR_512/' + self.mode))\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "\n",
        "    indexlist = os.listdir('/content/Vin_CXR_512/Vin_CXR_512/' + self.mode)\n",
        "    pic_id = indexlist[index][:-4]               # Removing 4 characters relating to .png\n",
        "\n",
        "    X = plt.imread('/content/Vin_CXR_512/Vin_CXR_512/' + self.mode + '/' + indexlist[index])\n",
        "    \n",
        "    #dev Hoang\n",
        "    #print(X.max())\n",
        "\n",
        "    X = self.transform(image=X)['image']\n",
        "\n",
        "    smalldata = traindata[traindata['image_id'] == pic_id]\n",
        "    lol = set(smalldata['class_id'])\n",
        "    result = np.zeros(14)\n",
        "\n",
        "    for i in lol:\n",
        "      if i != 14:\n",
        "        result[i] = 1\n",
        "    y = (pic_id, result)\n",
        "\n",
        "    return X, y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8nghm3h3Uv-"
      },
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(CXR_DataLoader(mode='train'), batch_size=16,shuffle=True, num_workers=2)\n",
        "test_dataloader = torch.utils.data.DataLoader(CXR_DataLoader(mode='val'), batch_size=1,shuffle=False, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDXT4jsnQD3k",
        "outputId": "dcf05994-fa66-456f-9e6a-c30f0dc46492"
      },
      "source": [
        "! git clone https://github.com/levi3001/Yet-Another-EfficientDet-Pytorch.git\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Yet-Another-EfficientDet-Pytorch'...\n",
            "remote: Enumerating objects: 844, done.\u001b[K\n",
            "remote: Counting objects: 100% (79/79), done.\u001b[K\n",
            "remote: Compressing objects: 100% (38/38), done.\u001b[K\n",
            "remote: Total 844 (delta 45), reused 70 (delta 41), pack-reused 765\u001b[K\n",
            "Receiving objects: 100% (844/844), 121.22 MiB | 32.56 MiB/s, done.\n",
            "Resolving deltas: 100% (482/482), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pR_qL-ieXyev",
        "outputId": "42ad37f5-245e-48e6-a360-fce701bb5379"
      },
      "source": [
        "cd Yet-Another-EfficientDet-Pytorch/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Yet-Another-EfficientDet-Pytorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8QnSxpyI2Zj"
      },
      "source": [
        "# resnet 50\n",
        "\n",
        "model=torchvision.models.resnet50(pretrained=True)\n",
        "model.fc= nn.Linear(2048,14)\n",
        "model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NvCf6jbOmfN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09ff62f3-ef1e-4ae7-fdfc-8196f2b1530c"
      },
      "source": [
        "#efficientnet b0\n",
        "\n",
        "from efficientnet import EfficientNet\n",
        "from backbone import EfficientDetBackbone\n",
        "model = EfficientNet.from_pretrained('efficientnet-b3', load_weights=False, num_classes=14)\n",
        "model.parameters\n",
        "#model = EfficientDetBackbone( )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Module.parameters of EfficientNet(\n",
              "  (_conv_stem): Conv2dStaticSamePadding(\n",
              "    (conv): Conv2d(3, 40, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "  )\n",
              "  (_bn0): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "  (_blocks): ModuleList(\n",
              "    (0): MBConvBlock(\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(40, 40, kernel_size=(3, 3), stride=(1,), groups=40, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(40, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(10, 40, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(40, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (1): MBConvBlock(\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), groups=24, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(6, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (2): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(144, 144, kernel_size=(3, 3), stride=(2,), groups=144, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (3): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (4): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (5): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 192, kernel_size=(5, 5), stride=(2,), groups=192, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (6): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), groups=288, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (7): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), groups=288, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (8): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 288, kernel_size=(3, 3), stride=(2,), groups=288, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(288, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 12, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(12, 288, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (9): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (10): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (11): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (12): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (13): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 576, kernel_size=(5, 5), stride=(1,), groups=576, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 24, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(24, 576, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(576, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (14): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (15): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (16): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (17): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (18): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 816, kernel_size=(5, 5), stride=(2,), groups=816, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 34, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(34, 816, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(816, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (19): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (20): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (21): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (22): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (23): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (24): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 1392, kernel_size=(3, 3), stride=(1,), groups=1392, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(1392, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (25): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(384, 2304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(2304, 2304, kernel_size=(3, 3), stride=(1, 1), groups=2304, bias=False)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(2304, 96, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(96, 2304, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        (conv): Conv2d(2304, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "  )\n",
              "  (_conv_head): Conv2dStaticSamePadding(\n",
              "    (conv): Conv2d(384, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  )\n",
              "  (_bn1): BatchNorm2d(1536, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "  (_avg_pooling): AdaptiveAvgPool2d(output_size=1)\n",
              "  (_dropout): Dropout(p=0.3, inplace=False)\n",
              "  (_fc): Linear(in_features=1536, out_features=14, bias=True)\n",
              "  (_swish): MemoryEfficientSwish()\n",
              ")>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLmi0vqUbpoI"
      },
      "source": [
        "# model_backbone=model.backbone_net\n",
        "# model_backbone"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33vS-ZBo1o7b"
      },
      "source": [
        "device='cuda'\n",
        "def train_model(model, epoches, optimizer):\n",
        "  # model = Model()\n",
        "\n",
        "  loss_function = nn.BCEWithLogitsLoss()\n",
        "  model.to(device)\n",
        "\n",
        "  bestf1 = 0\n",
        "  for epoch in range(epoches):\n",
        "    # Train Models\n",
        "    model.train()\n",
        "    loss0 = 0\n",
        "    train_number_data = 0\n",
        "    for batch_id, (X, y) in tqdm(enumerate(train_dataloader)):\n",
        "      pic_id, labels = y\n",
        "      X = X.to(device=device)\n",
        "      labels = labels.to(device=device)\n",
        "\n",
        "      #Feed Forward\n",
        "      output = model(X)\n",
        "\n",
        "      #Loss Calculate\n",
        "      loss = loss_function(output, labels)\n",
        "      loss0 += loss.item()\n",
        "      #Back propagate\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "      train_number_data += X.shape[0]\n",
        "    print('Epoch', epoch, ': Train Loss', loss0/train_number_data)\n",
        "    \n",
        "    # Evaluate models\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "      test_loss = 0\n",
        "      test_accuracy = 0\n",
        "      test_num_data = 0\n",
        "      tp = torch.zeros(14).to(device)\n",
        "      fp = torch.zeros(14).to(device)\n",
        "      fn = torch.zeros(14).to(device)\n",
        "\n",
        "      for batch_idx, (X, y) in tqdm(enumerate(test_dataloader)):\n",
        "        pic_id, labels = y\n",
        "        X = X.to(device=device)\n",
        "        labels = labels.to(device=device)\n",
        "        output = model(X)\n",
        "\n",
        "        loss = loss_function(output, labels)\n",
        "        test_loss += loss.item()*X.shape[0]\n",
        "\n",
        "        # Calculate Accuracy\n",
        "        output = torch.sigmoid(output)\n",
        "        lol2 = torch.zeros(size=output.shape).to(device)\n",
        "        lol2[output > 0.5] = 1\n",
        "        accuracy = torch.sum(lol2 == labels, dim=0)/(X.shape[0])\n",
        "        test_accuracy += accuracy*X.shape[0]\n",
        "\n",
        "        # Calculate Mean F1\n",
        "\n",
        "        tp += torch.sum((lol2 == labels) & (labels == 1), dim=0)\n",
        "        fp += torch.sum((lol2 != labels) & (labels == 1), dim=0)\n",
        "        fn += torch.sum((lol2 != labels) & (labels == 0), dim=0)\n",
        "\n",
        "        test_num_data +=X.shape[0]\n",
        "      \n",
        "      tp /= test_num_data\n",
        "      fp /= test_num_data\n",
        "      fn /= test_num_data\n",
        "      test_loss /= test_num_data\n",
        "      test_accuracy /= test_num_data\n",
        "      f1 = tp/(tp + 0.5*(fn+fp))\n",
        "      mean_f1 = torch.mean(f1)\n",
        "\n",
        "      if mean_f1.item() > bestf1:\n",
        "        print('Save best:')\n",
        "        PATH = 'b3_efficient_best_2.pth'\n",
        "        torch.save(model.state_dict(), PATH)\n",
        "        bestf1 = mean_f1.item()\n",
        "        \n",
        "    print('Test Accuracy', torch.mean(test_accuracy).item(), '. Test Loss', loss.item(), '. F1:', mean_f1.item())\n",
        "    #print(loss)\n",
        "    #print(test_accuracy)\n",
        "\n",
        "  print(mean_f1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvdMklZ-VZvZ"
      },
      "source": [
        "# import torchvision\n",
        "# model = torchvision.models.resnet50(pretrained= True, progress= True)\n",
        "# model_ft = models.resnet18(pretrained=True)\n",
        "# num_ftrs = model_ft.fc.in_features\n",
        "# # Here the size of each output sample is set to 2.\n",
        "# # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
        "# model_ft.fc = nn.Linear(num_ftrs, 2)\n",
        "\n",
        "# model_ft = model_ft.to(device)\n",
        "\n",
        "# criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# # Observe that all parameters are being optimized\n",
        "# optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# # Decay LR by a factor of 0.1 every 7 epochs\n",
        "# exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "PYq1dZNGzKjC",
        "outputId": "c4de9d9b-f39c-4458-a07c-46f871f6946b"
      },
      "source": [
        "#train efficienet b0\n",
        "\n",
        "optimizer = torch.optim.AdamW(model.parameters(),lr=1e-4)\n",
        "#scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=1e-4, last_epoch=- 1, verbose=False)\n",
        "train_model(model, 100, optimizer)"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "Epoch 0 : Train Loss 0.03167624215041939\n"
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.7879083752632141 . Test Loss 0.46154833904334475 . F1: 0.10905508697032928\n"
          ]
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          "text": [
            "Epoch 1 : Train Loss 0.028643509622202426\n"
          ]
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          "text": [
            "Test Accuracy 0.7879083752632141 . Test Loss 0.30675103874611004 . F1: 0.10905508697032928\n"
          ]
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          "text": [
            "Epoch 2 : Train Loss 0.02822105727277937\n"
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8067609667778015 . Test Loss 0.3581786172664059 . F1: 0.16619962453842163\n"
          ]
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        {
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          "text": [
            "Epoch 3 : Train Loss 0.027989391753498596\n"
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8077360987663269 . Test Loss 0.42415346724114245 . F1: 0.1777174025774002\n"
          ]
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        {
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          "text": [
            "Epoch 4 : Train Loss 0.027991560899231865\n"
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          "output_type": "stream",
          "text": [
            "Test Accuracy 0.8100113272666931 . Test Loss 0.34628243411758114 . F1: 0.1755174696445465\n"
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          "text": [
            "Epoch 5 : Train Loss 0.027720611651314578\n"
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          "text": [
            "Save best:\n",
            "Test Accuracy 0.8112303018569946 . Test Loss 0.39026056369766593 . F1: 0.19556443393230438\n"
          ]
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          "text": [
            "Epoch 6 : Train Loss 0.027651244135319088\n"
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        {
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          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8133431077003479 . Test Loss 0.363894397392869 . F1: 0.19855996966362\n"
          ]
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        {
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          "text": [
            "Epoch 7 : Train Loss 0.027595894050186077\n"
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          "text": [
            "Save best:\n",
            "Test Accuracy 0.8161872029304504 . Test Loss 0.34452342807448333 . F1: 0.2127074897289276\n"
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          "text": [
            "Epoch 8 : Train Loss 0.027437879070803903\n"
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          "text": [
            "Test Accuracy 0.8079798817634583 . Test Loss 0.5111948820995167 . F1: 0.17974556982517242\n"
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          "text": [
            "Epoch 9 : Train Loss 0.027379426452287203\n"
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              "version_major": 2
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            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8161060214042664 . Test Loss 0.4103353419673762 . F1: 0.1943947672843933\n"
          ]
        },
        {
          "output_type": "display_data",
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              "0it [00:00, ?it/s]"
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        {
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          "text": [
            "Epoch 10 : Train Loss 0.02732864965178609\n"
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        {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8169998526573181 . Test Loss 0.3801432854961604 . F1: 0.22115060687065125\n"
          ]
        },
        {
          "output_type": "display_data",
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              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11 : Train Loss 0.02718971801403087\n"
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        },
        {
          "output_type": "display_data",
          "data": {
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              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8178937435150146 . Test Loss 0.40028984209389556 . F1: 0.22220008075237274\n"
          ]
        },
        {
          "output_type": "display_data",
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              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 12 : Train Loss 0.027087950207386805\n"
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        {
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          "data": {
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              "0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8201690912246704 . Test Loss 0.3257743440169309 . F1: 0.21689841151237488\n"
          ]
        },
        {
          "output_type": "display_data",
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              "0it [00:00, ?it/s]"
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        {
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          "text": [
            "Epoch 13 : Train Loss 0.026954589342627094\n"
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        {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8186250925064087 . Test Loss 0.45178882145721994 . F1: 0.21496498584747314\n"
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        {
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        {
          "output_type": "stream",
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          "text": [
            "Epoch 14 : Train Loss 0.02693561305386203\n"
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        {
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              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8235007524490356 . Test Loss 0.4452906262761514 . F1: 0.22421769797801971\n"
          ]
        },
        {
          "output_type": "display_data",
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              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 15 : Train Loss 0.02664042842469668\n"
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        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8078986406326294 . Test Loss 0.3459978031792811 . F1: 0.25777456164360046\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          "text": [
            "Epoch 16 : Train Loss 0.02650893825329822\n"
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        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8295140862464905 . Test Loss 0.4507298141335403 . F1: 0.2589479386806488\n"
          ]
        },
        {
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        {
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          "text": [
            "Epoch 17 : Train Loss 0.02629475834566751\n"
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        {
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          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8133431077003479 . Test Loss 0.36851066457373755 . F1: 0.3026757836341858\n"
          ]
        },
        {
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        {
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          "text": [
            "Epoch 18 : Train Loss 0.026256460626442123\n"
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        {
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          "data": {
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              "0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8313018679618835 . Test Loss 0.38651188123705127 . F1: 0.2451554834842682\n"
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        {
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          "name": "stdout",
          "text": [
            "Epoch 19 : Train Loss 0.02607718772744645\n"
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        {
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          "data": {
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              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8328458070755005 . Test Loss 0.4279331306378091 . F1: 0.2568258047103882\n"
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        {
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        {
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          "name": "stdout",
          "text": [
            "Epoch 20 : Train Loss 0.025979542796894423\n"
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        {
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          "data": {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8313018679618835 . Test Loss 0.43145202383831405 . F1: 0.2972215414047241\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "name": "stdout",
          "text": [
            "Epoch 21 : Train Loss 0.025822009354442718\n"
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        {
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          "data": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save best:\n",
            "Test Accuracy 0.8113115429878235 . Test Loss 0.328612423834524 . F1: 0.34183695912361145\n"
          ]
        },
        {
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        {
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          "name": "stdout",
          "text": [
            "Epoch 22 : Train Loss 0.025717450481274762\n"
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        {
          "output_type": "display_data",
          "data": {
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              "0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.831139326095581 . Test Loss 0.355541757840131 . F1: 0.31351667642593384\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_major": 2
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            "text/plain": [
              "0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 23 : Train Loss 0.025560595879351822\n"
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        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.824069619178772 . Test Loss 0.3037565534801355 . F1: 0.31436505913734436\n"
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        },
        {
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              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 24 : Train Loss 0.02532928329177202\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy 0.8337395787239075 . Test Loss 0.3442843434874833 . F1: 0.2791060507297516\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "0it [00:00, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 25 : Train Loss 0.025379374911306273\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "0it [00:00, ?it/s]"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYPmVsyhKwmi"
      },
      "source": [
        "#train resnet\n",
        "\n",
        "optimizer = torch.optim.AdamW(model.parameters(),lr=1e-4)\n",
        "#scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=1e-4, last_epoch=- 1, verbose=False)\n",
        "train_model(model, 100, optimizer)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "9caE-GJr23ha",
        "outputId": "abafa8ee-ef87-44ab-ba5a-5ef05f389611"
      },
      "source": [
        "f1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-aece6dfba588>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'f1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAgn1jMmbMwJ"
      },
      "source": [
        "PATH = '/content/gdrive/MyDrive/cs492i_project/efficientnet.pth'\n",
        "torch.save(model.state_dict(), PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCsFbMLbrBqU"
      },
      "source": [
        "!cp b3_efficient_best_2.pth.pth /content/gdrive/MyDrive/cs492i_project/efficientnet_b3_best_2.pth "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X72_0B_v3QqN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HF5ifILXPylD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "4d5806b4049e42a899ead06f22b83016",
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            "6413324f7c1e4a9baa5b0f910a0ce21d",
            "0c3d60be597c44b9acd3ca0b5751eada",
            "668c30120b014c9bad46c567bedf180d",
            "bb4026a11c3a4f1d9168749e7ecd156b",
            "2096faddd989422f979c69b29269007f"
          ]
        },
        "outputId": "21ec5d93-9509-4408-af11-f45ac00a6f57"
      },
      "source": [
        "from efficientnet_pytorch import EfficientNet\n",
        "efficientnet = torch.load('./gdrive/MyDrive/cs492i_project/efficientnet')\n",
        "\n",
        "model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=14)\n",
        "model.load_state_dict(efficientnet)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b0-355c32eb.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d5806b4049e42a899ead06f22b83016",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/20.4M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded pretrained weights for efficientnet-b0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLFEhGEH3dkp",
        "outputId": "e09056b7-7408-4ce7-fe8b-adcbafdaa69c"
      },
      "source": [
        "model=torchvision.models.resnet50(pretrained=True)\n",
        "model.fc= nn.Linear(2048,14)\n",
        "model.load_state_dict(torch.load( 'resnet50_best.pth'))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c7845e45654d4cdb8e5f2a59b4136c3a",
            "ea4696e80d4e46ff9f5351b755b2f343",
            "9a10b5ca6cec480d87bfc6a0d7af4874",
            "d8e019f5f95a4559849ebc5becf3ea23",
            "d997fb08919b4da68a65ef4dd0a9c5bd",
            "f4cb1ceed15a419baed27f4d756a4856",
            "701487dcfd2347309a0bf793804f8f87",
            "dc4c3cc4ed654078b3fb4684369caf0d",
            "18435020a1b1452ebf435257654f207a",
            "5f9fc1a70c1c4ecda5f586aadee8e987",
            "f1a1ce3a1d1d4ffba66b2d0c53b8c14f"
          ]
        },
        "id": "-aH8G4hyeHFz",
        "outputId": "d1612796-e597-4489-8f40-0e5487837ebe"
      },
      "source": [
        "model = model.cuda()\n",
        "model.eval()\n",
        "loss_function = nn.BCEWithLogitsLoss()\n",
        "with torch.no_grad():\n",
        "  test_loss = 0\n",
        "  test_accuracy = 0\n",
        "  test_num_data = 0\n",
        "  tp = torch.zeros(14).to(device)\n",
        "  fp = torch.zeros(14).to(device)\n",
        "  fn = torch.zeros(14).to(device)\n",
        "\n",
        "\n",
        "\n",
        "  for batch_idx, (X, y) in tqdm(enumerate(test_dataloader)):\n",
        "    pic_id, labels = y\n",
        "    X = X.to(device=device)\n",
        "    labels = labels.to(device=device)\n",
        "    output = model(X)\n",
        "\n",
        "    loss = loss_function(output, labels)\n",
        "    test_loss += loss.item()*X.shape[0]\n",
        "\n",
        "    # Calculate Accuracy\n",
        "    output = torch.sigmoid(output)\n",
        "    lol2 = torch.zeros(size=output.shape).to(device)\n",
        "    lol2[output > 0.5] = 1\n",
        "    accuracy = torch.sum(lol2 == labels, dim=0)/(X.shape[0])\n",
        "    test_accuracy += accuracy*X.shape[0]\n",
        "\n",
        "    # Calculate Mean F1\n",
        "\n",
        "    tp += torch.sum((lol2 == labels) & (labels == 1), dim=0)\n",
        "    fp += torch.sum((lol2 != labels) & (labels == 1), dim=0)\n",
        "    fn += torch.sum((lol2 != labels) & (labels == 0), dim=0)\n",
        "\n",
        "    test_num_data +=X.shape[0]\n",
        "\n",
        "  tp /= test_num_data\n",
        "  fp /= test_num_data\n",
        "  fn /= test_num_data\n",
        "  test_loss /= test_num_data\n",
        "  test_accuracy /= test_num_data\n",
        "  f1 = tp/(tp + 0.5*(fn+fp))\n",
        "  mean_f1 = torch.mean(f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c7845e45654d4cdb8e5f2a59b4136c3a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiyRvTQ6eOLt",
        "outputId": "a9ed9e8f-8202-4438-d59d-bac015f53cdc"
      },
      "source": [
        "torch.sigmoid(torch.Tensor([[1, 5, 6], [2, 3, 1]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.7311, 0.9933, 0.9975],\n",
              "        [0.8808, 0.9526, 0.7311]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwZXMAoZfJ3W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dff61693-0d9e-4d2f-89d9-e290287a080a"
      },
      "source": [
        "mean_f1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.6062, device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyRRBYKa4s9R",
        "outputId": "8ade4674-5a4b-4c7e-8ca1-f254042bf571"
      },
      "source": [
        "f1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.8954, 0.3956, 0.4319, 0.8745, 0.5641, 0.5442, 0.4577, 0.6180, 0.5788,\n",
              "        0.4899, 0.7855, 0.6180, 0.5333, 0.7001], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAcS_Sat4tRH",
        "outputId": "c4211e33-0c16-453f-9d33-9d82e5823124"
      },
      "source": [
        "test_accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.8532, 0.9374, 0.8623, 0.8635, 0.9420, 0.9238, 0.8760, 0.7440, 0.8510,\n",
              "        0.6257, 0.9056, 0.7201, 0.9841, 0.7691], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8NHzmFX5sH2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}