DigitsRecognization-02.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MHX1203/DataWhale--/blob/master/DigitsRecognization-02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5Joz1YtIg4z",
        "colab_type": "text"
      },
      "source": [
        "# 增加内容\n",
        "\n",
        "## 数据增强\n",
        "\n",
        "- Random Noise Augmentation\n",
        "\n",
        "## 模型\n",
        "- resnet18\n",
        "\n",
        "## 损失函数\n",
        "- LabelSmooth\n",
        "\n",
        "\n",
        "## 模型集成\n",
        "- TODO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvygCSGWAfKs",
        "colab_type": "text"
      },
      "source": [
        "# 载入数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSJtADas584y",
        "colab_type": "code",
        "outputId": "dc95c3bc-bcda-4a0f-e22a-52782687c40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# 连接google drive\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQgtNlGd7Qlf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 解压数据\n",
        "\n",
        "!mkdir data\n",
        "!unzip drive/My\\ Drive/Data/Datawhale-DigitsRecognition/mchar_test_a.zip -d data/ > /dev/null 2>&1\n",
        "!unzip drive/My\\ Drive/Data/Datawhale-DigitsRecognition/mchar_train.zip -d data/ > /dev/null 2>&1\n",
        "!unzip drive/My\\ Drive/Data/Datawhale-DigitsRecognition/mchar_val.zip -d data/ > /dev/null 2>&1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6i7maQ8GAze3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir = {\n",
        "    'train_data': '/content/data/mchar_train/',\n",
        "    'val_data': '/content/data/mchar_val/',\n",
        "    'test_data': '/content/data/mchar_test_a/',\n",
        "    'train_label': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_train.json',\n",
        "    'val_label': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_val.json',\n",
        "    'submit_file': '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/mchar_sample_submit_A.csv'\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziiRRPnlCh9R",
        "colab_type": "text"
      },
      "source": [
        "# 导入相关库"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_P18kxkChaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from glob import glob\n",
        "import torch as t\n",
        "t.random.manual_seed(0)\n",
        "t.cuda.manual_seed_all(0)\n",
        "t.backends.cudnn.benchmark = True\n",
        "t.backends.cudnn.deterministic = True\n",
        "from PIL import Image\n",
        "import torch.nn as nn\n",
        "from tqdm.auto import tqdm\n",
        "from torchvision import transforms\n",
        "from torchvision.utils import save_image, make_grid\n",
        "from torch.optim import SGD\n",
        "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, MultiStepLR, CosineAnnealingLR\n",
        "from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patch \n",
        "import torch.nn.functional as F\n",
        "import json\n",
        "from torchvision.models.mobilenet import MobileNetV2\n",
        "from torchvision.models.resnet import resnet18, resnet34\n",
        "from torchsummary import summary\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_spj-S6AmmT",
        "colab_type": "text"
      },
      "source": [
        "# 查看数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFbUNRpX9q5G",
        "colab_type": "code",
        "outputId": "c8802468-de6b-438a-d73f-11cde3c45390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "def data_summary():\n",
        "  train_list = glob(data_dir['train_data']+'*.png')\n",
        "  test_list = glob(data_dir['test_data']+'*.png')\n",
        "  val_list = glob(data_dir['val_data']+'*.png')\n",
        "  print('train image counts: %d'%len(train_list))\n",
        "  print('val image counts: %d'%len(val_list))\n",
        "  print('test image counts: %d'%len(test_list))\n",
        "\n",
        "data_summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train image counts: 30000\n",
            "val image counts: 10000\n",
            "test image counts: 40000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VMYXTkpGy0D",
        "colab_type": "code",
        "outputId": "864814cf-42de-417a-e9e2-45f6ec2662b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def look_train_json():\n",
        "  with open(data_dir['train_label'], 'r', encoding='utf-8') as f:\n",
        "    content = f.read()\n",
        "\n",
        "  content = json.loads(content)\n",
        "\n",
        "  print(content['000000.png'])\n",
        "\n",
        "look_train_json()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'height': [219, 219], 'label': [1, 9], 'left': [246, 323], 'top': [77, 81], 'width': [81, 96]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Alt5F_c9H3pi",
        "colab_type": "code",
        "outputId": "f83cd640-1e04-4208-dd78-35a9fa00e418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "def look_submit():\n",
        "  df = pd.read_csv(data_dir['submit_file'], sep=',')\n",
        "  print(df.head(5))\n",
        "\n",
        "look_submit()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    file_name  file_code\n",
            "0  000000.png          0\n",
            "1  000001.png          0\n",
            "2  000002.png          0\n",
            "3  000003.png          0\n",
            "4  000004.png          0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDDFZoCWIjh_",
        "colab_type": "code",
        "outputId": "59b04183-18d8-4a18-f0ae-8295628cb4b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "def plot_samples():\n",
        "  imgs = glob(data_dir['train_data']+'*.png')\n",
        "  fig, ax = plt.subplots(figsize=(12, 8), ncols=2, nrows=2)\n",
        "  marks = json.loads(open(data_dir['train_label'], 'r').read())\n",
        "\n",
        "\n",
        "  for i in range(4):\n",
        "    \n",
        "    img_name = os.path.split(imgs[i])[-1]\n",
        "    mark = marks[img_name]\n",
        "\n",
        "\n",
        "    img = Image.open(imgs[i])\n",
        "    img = np.array(img)\n",
        "\n",
        "    bboxes = np.array(\n",
        "        [mark['left'],\n",
        "        mark['top'],\n",
        "        mark['width'],\n",
        "        mark['height']]\n",
        "    )\n",
        "    ax[i//2, i%2].imshow(img)\n",
        "    for j in range(len(mark['label'])):\n",
        "      rect = patch.Rectangle(bboxes[:, j][:2], bboxes[:, j][2], bboxes[:, j][3], facecolor='none', edgecolor='r')\n",
        "      ax[i//2, i%2].text(bboxes[:, j][0], bboxes[:, j][1], mark['label'][j])\n",
        "      ax[i//2, i%2].add_patch(rect)\n",
        "  plt.show()\n",
        "\n",
        "plot_samples()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_Yl-adlooZQ",
        "colab_type": "code",
        "outputId": "3d2da681-004e-40db-8e10-90e96af36b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "def img_size_summary():\n",
        "  sizes = []\n",
        "\n",
        "  for img in glob(data_dir['train_data']+'*.png'):\n",
        "    img = Image.open(img)\n",
        "\n",
        "    sizes.append(img.size)\n",
        "\n",
        "  sizes = np.array(sizes)\n",
        "\n",
        "  plt.figure(figsize=(10, 8))\n",
        "  plt.scatter(sizes[:, 0], sizes[:, 1])\n",
        "  plt.xlabel('Width')\n",
        "  plt.ylabel('Height')\n",
        "\n",
        "  plt.title('image width-height summary')\n",
        "  plt.show()\n",
        "\n",
        "img_size_summary()\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0ypIGBjrCRl",
        "colab_type": "code",
        "outputId": "16ff7be8-b678-40c9-cdd6-4beb16ca364b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "def bbox_summary():\n",
        "  marks = json.loads(open(data_dir['train_label'], 'r').read())\n",
        "  bboxes = []\n",
        "\n",
        "  for img, mark in marks.items():\n",
        "    for i in range(len(mark['label'])):\n",
        "      bboxes.append([mark['left'][i], mark['top'][i], mark['width'][i], mark['height'][i]])\n",
        "\n",
        "  bboxes = np.array(bboxes)\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(12, 8))\n",
        "  ax.scatter(bboxes[:, 2], bboxes[:, 3])\n",
        "  ax.set_title('bbox width-height summary')\n",
        "  ax.set_xlabel('width')\n",
        "  ax.set_ylabel('height')\n",
        "  plt.show()\n",
        "\n",
        "bbox_summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjP2XN1AXIt5",
        "colab_type": "code",
        "outputId": "abc0b446-e75f-40e3-9236-2c453276266e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "def label_summary():\n",
        "  marks = json.load(open(data_dir['train_label'], 'r'))\n",
        "  \n",
        "  dicts = {}\n",
        "  for img, mark in marks.items():\n",
        "    if len(mark['label']) not in dicts:\n",
        "      dicts[len(mark['label'])] = 0\n",
        "    dicts[len(mark['label'])] += 1\n",
        "\n",
        "\n",
        "  dicts = sorted(dicts.items(), key=lambda x: x[0])\n",
        "  for k, v in dicts:\n",
        "    print('%d个数字的图片数目: %d'%(k, v))\n",
        "\n",
        "label_summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1个数字的图片数目: 4636\n",
            "2个数字的图片数目: 16262\n",
            "3个数字的图片数目: 7813\n",
            "4个数字的图片数目: 1280\n",
            "5个数字的图片数目: 8\n",
            "6个数字的图片数目: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQecTS1hXYnj",
        "colab_type": "text"
      },
      "source": [
        "# 构建分类Baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWLfntE1SiwU",
        "colab_type": "text"
      },
      "source": [
        "## 网络配置信息"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jgH1oAgSi9Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Config:\n",
        "\n",
        "  batch_size = 16\n",
        "\n",
        "  lr = 1e-2\n",
        "\n",
        "  momentum = 0.9\n",
        "\n",
        "  weights_decay = 1e-5\n",
        "\n",
        "  class_num = 11\n",
        "\n",
        "  eval_interval = 1\n",
        "\n",
        "  checkpoint_interval = 1\n",
        "\n",
        "  print_interval = 50\n",
        "\n",
        "  checkpoints = 'drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/'\n",
        "\n",
        "  pretrained = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-30_acc-61.57.pth'\n",
        "\n",
        "  start_epoch = 0\n",
        "\n",
        "  epoches = 50\n",
        "\n",
        "  smooth = 0.1\n",
        "\n",
        "  erase_prob = 0.5\n",
        "\n",
        "config = Config()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IwUo2Emm_V9z",
        "colab_type": "text"
      },
      "source": [
        "## 构建数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gXYWz5f_QYR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "class DigitsDataset(Dataset):\n",
        "  \"\"\"\n",
        "  \n",
        "  DigitsDataset\n",
        "\n",
        "  Params:\n",
        "    data_dir(string): data directory\n",
        "\n",
        "    label_path(string): label path\n",
        "\n",
        "    aug(bool): wheather do image augmentation, default: True\n",
        "  \"\"\"\n",
        "  def __init__(self, data_dir, label_path, size=(128, 256), aug=True):\n",
        "    super(DigitsDataset, self).__init__()\n",
        "    self.imgs = glob(data_dir+'*.png')\n",
        "\n",
        "    self.aug = aug\n",
        "\n",
        "    self.size = size\n",
        "    if label_path == None:\n",
        "      self.labels = None\n",
        "    else:\n",
        "      self.labels = json.load(open(label_path, 'r'))\n",
        "      self.imgs = [(img, self.labels[os.path.split(img)[-1]]) for img in self.imgs if os.path.split(img)[-1] in self.labels]\n",
        "    \n",
        "  def __getitem__(self, idx):\n",
        "    if self.labels:\n",
        "      img, label = self.imgs[idx]\n",
        "    else:\n",
        "      img = self.imgs[idx]\n",
        "      label = None\n",
        "\n",
        "    img = Image.open(img)\n",
        "\n",
        "    trans0 = [                \n",
        "          transforms.ToTensor(),\n",
        "          transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "    ]\n",
        "    \n",
        "    min_size = self.size[0] if (img.size[1] / self.size[0]) < ((img.size[0] / self.size[1])) else self.size[1]\n",
        "    trans1 = [\n",
        "          transforms.Resize(min_size),    \n",
        "          transforms.CenterCrop(self.size)\n",
        "        ]\n",
        "\n",
        "    if self.aug:\n",
        "      trans1.extend([\n",
        "            transforms.ColorJitter(0.1, 0.1, 0.1),\n",
        "            transforms.RandomGrayscale(0.1),\n",
        "            transforms.RandomAffine(10,translate=(0.05, 0.1), shear=5)\n",
        "      ])\n",
        "\n",
        "    trans1.extend(trans0)\n",
        "    \n",
        "    img = transforms.Compose(trans1)(img)\n",
        "\n",
        "    if self.labels:\n",
        "      return img, t.tensor(label['label'][:5] + (5 - len(label['label']))*[10]).long()\n",
        "    else:\n",
        "      return img, self.imgs[idx]\n",
        "\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.imgs)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juc0VDqhLoeQ",
        "colab_type": "code",
        "outputId": "19ea3da7-bdcc-4481-95d4-2c2dfa184ed4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# test dataset\n",
        "dataset = DigitsDataset(data_dir['train_data'], data_dir['train_label'])\n",
        "print(len(dataset))\n",
        "img, label = dataset[0]\n",
        "\n",
        "print(img.shape, label.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30000\n",
            "torch.Size([3, 128, 256]) torch.Size([5])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxlBdmnhMu_H",
        "colab_type": "code",
        "outputId": "3a218df2-43d8-4457-efd4-7779eec2525a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 12), nrows=4, ncols=2)\n",
        "for i in range(8):\n",
        "  img, label = dataset[i]\n",
        "  img = img * t.tensor([0.229, 0.224, 0.225]).view(3, 1, 1) + t.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n",
        "  ax[i//2, i%2].imshow(img.permute(1, 2, 0).numpy())\n",
        "\n",
        "  ax[i//2, i%2].set_xticks([])\n",
        "  ax[i//2, i%2].set_yticks([])\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Gs5nXHr_b6p",
        "colab_type": "text"
      },
      "source": [
        "## 构建分类模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RAr_wQx_eh5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DigitsMobilenet(nn.Module):\n",
        "\n",
        "  def __init__(self, class_num=11):\n",
        "    super(DigitsMobilenet, self).__init__()\n",
        "\n",
        "    self.net = nn.Sequential(\n",
        "        MobileNetV2(num_classes=class_num).features,\n",
        "        nn.AdaptiveAvgPool2d((1, 1))\n",
        "    )\n",
        "    self.fc1 = nn.Linear(1280, class_num)\n",
        "    self.fc2 = nn.Linear(1280, class_num)\n",
        "    self.fc3 = nn.Linear(1280, class_num)\n",
        "    self.fc4 = nn.Linear(1280, class_num)\n",
        "    self.fc5 = nn.Linear(1280, class_num)\n",
        "\n",
        "  def forward(self, img):\n",
        "    features = self.net(img).view(-1, 1280)\n",
        "\n",
        "    fc1 = self.fc1(features)\n",
        "    fc2 = self.fc2(features)\n",
        "    fc3 = self.fc3(features)\n",
        "    fc4 = self.fc4(features)\n",
        "    fc5 = self.fc5(features)\n",
        "\n",
        "    return fc1, fc2, fc3, fc4, fc5\n",
        "\n",
        "\n",
        "class DigitsResnet18(nn.Module):\n",
        "\n",
        "  def __init__(self, class_num=11):\n",
        "    super(DigitsMobilenet, self).__init__()\n",
        "    self.net = resnet18(pretrained=True)\n",
        "    self.net.fc = nn.Identity()\n",
        "    # self.net = nn.Sequential(\n",
        "    #     MobileNetV2(num_classes=class_num).features,\n",
        "    #     nn.AdaptiveAvgPool2d((1, 1))\n",
        "    # )\n",
        "    self.fc1 = nn.Linear(512, class_num)\n",
        "    self.fc2 = nn.Linear(512, class_num)\n",
        "    self.fc3 = nn.Linear(512, class_num)\n",
        "    self.fc4 = nn.Linear(512, class_num)\n",
        "    self.fc5 = nn.Linear(512, class_num)\n",
        "\n",
        "  def forward(self, img):\n",
        "    features = self.net(img).squeeze()\n",
        "\n",
        "    fc1 = self.fc1(features)\n",
        "    fc2 = self.fc2(features)\n",
        "    fc3 = self.fc3(features)\n",
        "    fc4 = self.fc4(features)\n",
        "    fc5 = self.fc5(features)\n",
        "\n",
        "    return fc1, fc2, fc3, fc4, fc5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ua1rt4S5QqS1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ff75166d-7381-4009-cf96-db86e002ca93"
      },
      "source": [
        "# 查看网络结构\n",
        "net = resnet18(pretrained=True)\n",
        "net.to(t.device('cuda'))\n",
        "summary(net, input_size=(3, 128, 256), batch_size=config.batch_size)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1          [16, 64, 64, 128]           9,408\n",
            "       BatchNorm2d-2          [16, 64, 64, 128]             128\n",
            "              ReLU-3          [16, 64, 64, 128]               0\n",
            "         MaxPool2d-4           [16, 64, 32, 64]               0\n",
            "            Conv2d-5           [16, 64, 32, 64]          36,864\n",
            "       BatchNorm2d-6           [16, 64, 32, 64]             128\n",
            "              ReLU-7           [16, 64, 32, 64]               0\n",
            "            Conv2d-8           [16, 64, 32, 64]          36,864\n",
            "       BatchNorm2d-9           [16, 64, 32, 64]             128\n",
            "             ReLU-10           [16, 64, 32, 64]               0\n",
            "       BasicBlock-11           [16, 64, 32, 64]               0\n",
            "           Conv2d-12           [16, 64, 32, 64]          36,864\n",
            "      BatchNorm2d-13           [16, 64, 32, 64]             128\n",
            "             ReLU-14           [16, 64, 32, 64]               0\n",
            "           Conv2d-15           [16, 64, 32, 64]          36,864\n",
            "      BatchNorm2d-16           [16, 64, 32, 64]             128\n",
            "             ReLU-17           [16, 64, 32, 64]               0\n",
            "       BasicBlock-18           [16, 64, 32, 64]               0\n",
            "           Conv2d-19          [16, 128, 16, 32]          73,728\n",
            "      BatchNorm2d-20          [16, 128, 16, 32]             256\n",
            "             ReLU-21          [16, 128, 16, 32]               0\n",
            "           Conv2d-22          [16, 128, 16, 32]         147,456\n",
            "      BatchNorm2d-23          [16, 128, 16, 32]             256\n",
            "           Conv2d-24          [16, 128, 16, 32]           8,192\n",
            "      BatchNorm2d-25          [16, 128, 16, 32]             256\n",
            "             ReLU-26          [16, 128, 16, 32]               0\n",
            "       BasicBlock-27          [16, 128, 16, 32]               0\n",
            "           Conv2d-28          [16, 128, 16, 32]         147,456\n",
            "      BatchNorm2d-29          [16, 128, 16, 32]             256\n",
            "             ReLU-30          [16, 128, 16, 32]               0\n",
            "           Conv2d-31          [16, 128, 16, 32]         147,456\n",
            "      BatchNorm2d-32          [16, 128, 16, 32]             256\n",
            "             ReLU-33          [16, 128, 16, 32]               0\n",
            "       BasicBlock-34          [16, 128, 16, 32]               0\n",
            "           Conv2d-35           [16, 256, 8, 16]         294,912\n",
            "      BatchNorm2d-36           [16, 256, 8, 16]             512\n",
            "             ReLU-37           [16, 256, 8, 16]               0\n",
            "           Conv2d-38           [16, 256, 8, 16]         589,824\n",
            "      BatchNorm2d-39           [16, 256, 8, 16]             512\n",
            "           Conv2d-40           [16, 256, 8, 16]          32,768\n",
            "      BatchNorm2d-41           [16, 256, 8, 16]             512\n",
            "             ReLU-42           [16, 256, 8, 16]               0\n",
            "       BasicBlock-43           [16, 256, 8, 16]               0\n",
            "           Conv2d-44           [16, 256, 8, 16]         589,824\n",
            "      BatchNorm2d-45           [16, 256, 8, 16]             512\n",
            "             ReLU-46           [16, 256, 8, 16]               0\n",
            "           Conv2d-47           [16, 256, 8, 16]         589,824\n",
            "      BatchNorm2d-48           [16, 256, 8, 16]             512\n",
            "             ReLU-49           [16, 256, 8, 16]               0\n",
            "       BasicBlock-50           [16, 256, 8, 16]               0\n",
            "           Conv2d-51            [16, 512, 4, 8]       1,179,648\n",
            "      BatchNorm2d-52            [16, 512, 4, 8]           1,024\n",
            "             ReLU-53            [16, 512, 4, 8]               0\n",
            "           Conv2d-54            [16, 512, 4, 8]       2,359,296\n",
            "      BatchNorm2d-55            [16, 512, 4, 8]           1,024\n",
            "           Conv2d-56            [16, 512, 4, 8]         131,072\n",
            "      BatchNorm2d-57            [16, 512, 4, 8]           1,024\n",
            "             ReLU-58            [16, 512, 4, 8]               0\n",
            "       BasicBlock-59            [16, 512, 4, 8]               0\n",
            "           Conv2d-60            [16, 512, 4, 8]       2,359,296\n",
            "      BatchNorm2d-61            [16, 512, 4, 8]           1,024\n",
            "             ReLU-62            [16, 512, 4, 8]               0\n",
            "           Conv2d-63            [16, 512, 4, 8]       2,359,296\n",
            "      BatchNorm2d-64            [16, 512, 4, 8]           1,024\n",
            "             ReLU-65            [16, 512, 4, 8]               0\n",
            "       BasicBlock-66            [16, 512, 4, 8]               0\n",
            "AdaptiveAvgPool2d-67            [16, 512, 1, 1]               0\n",
            "           Linear-68                 [16, 1000]         513,000\n",
            "================================================================\n",
            "Total params: 11,689,512\n",
            "Trainable params: 11,689,512\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 6.00\n",
            "Forward/backward pass size (MB): 656.18\n",
            "Params size (MB): 44.59\n",
            "Estimated Total Size (MB): 706.78\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0_k9GnqPovK",
        "colab_type": "code",
        "outputId": "9ae95b58-c684-425f-ece2-10bff5f9ee50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "net = DigitsMobilenet()\n",
        "print(net)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DigitsMobilenet(\n",
            "  (net): Sequential(\n",
            "    (0): Sequential(\n",
            "      (0): ConvBNReLU(\n",
            "        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "      (1): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
            "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (2): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
            "            (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (3): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (4): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
            "            (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (5): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (6): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (7): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
            "            (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (8): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (9): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (10): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (11): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "            (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (12): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (13): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (14): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
            "            (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (15): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (16): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (17): InvertedResidual(\n",
            "        (conv): Sequential(\n",
            "          (0): ConvBNReLU(\n",
            "            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (1): ConvBNReLU(\n",
            "            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "            (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU6(inplace=True)\n",
            "          )\n",
            "          (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (18): ConvBNReLU(\n",
            "        (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU6(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (1): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  )\n",
            "  (fc1): Linear(in_features=1280, out_features=11, bias=True)\n",
            "  (fc2): Linear(in_features=1280, out_features=11, bias=True)\n",
            "  (fc3): Linear(in_features=1280, out_features=11, bias=True)\n",
            "  (fc4): Linear(in_features=1280, out_features=11, bias=True)\n",
            "  (fc5): Linear(in_features=1280, out_features=11, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJ5VJIB4SeUj",
        "colab_type": "code",
        "outputId": "083e7285-a6e7-4a86-ae62-f9534e5fe650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# 查看网络结构\n",
        "net.to(t.device('cuda'))\n",
        "summary(net, input_size=(3, 128, 256), batch_size=config.batch_size)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1          [16, 32, 64, 128]             864\n",
            "       BatchNorm2d-2          [16, 32, 64, 128]              64\n",
            "             ReLU6-3          [16, 32, 64, 128]               0\n",
            "            Conv2d-4          [16, 32, 64, 128]             288\n",
            "       BatchNorm2d-5          [16, 32, 64, 128]              64\n",
            "             ReLU6-6          [16, 32, 64, 128]               0\n",
            "            Conv2d-7          [16, 16, 64, 128]             512\n",
            "       BatchNorm2d-8          [16, 16, 64, 128]              32\n",
            "  InvertedResidual-9          [16, 16, 64, 128]               0\n",
            "           Conv2d-10          [16, 96, 64, 128]           1,536\n",
            "      BatchNorm2d-11          [16, 96, 64, 128]             192\n",
            "            ReLU6-12          [16, 96, 64, 128]               0\n",
            "           Conv2d-13           [16, 96, 32, 64]             864\n",
            "      BatchNorm2d-14           [16, 96, 32, 64]             192\n",
            "            ReLU6-15           [16, 96, 32, 64]               0\n",
            "           Conv2d-16           [16, 24, 32, 64]           2,304\n",
            "      BatchNorm2d-17           [16, 24, 32, 64]              48\n",
            " InvertedResidual-18           [16, 24, 32, 64]               0\n",
            "           Conv2d-19          [16, 144, 32, 64]           3,456\n",
            "      BatchNorm2d-20          [16, 144, 32, 64]             288\n",
            "            ReLU6-21          [16, 144, 32, 64]               0\n",
            "           Conv2d-22          [16, 144, 32, 64]           1,296\n",
            "      BatchNorm2d-23          [16, 144, 32, 64]             288\n",
            "            ReLU6-24          [16, 144, 32, 64]               0\n",
            "           Conv2d-25           [16, 24, 32, 64]           3,456\n",
            "      BatchNorm2d-26           [16, 24, 32, 64]              48\n",
            " InvertedResidual-27           [16, 24, 32, 64]               0\n",
            "           Conv2d-28          [16, 144, 32, 64]           3,456\n",
            "      BatchNorm2d-29          [16, 144, 32, 64]             288\n",
            "            ReLU6-30          [16, 144, 32, 64]               0\n",
            "           Conv2d-31          [16, 144, 16, 32]           1,296\n",
            "      BatchNorm2d-32          [16, 144, 16, 32]             288\n",
            "            ReLU6-33          [16, 144, 16, 32]               0\n",
            "           Conv2d-34           [16, 32, 16, 32]           4,608\n",
            "      BatchNorm2d-35           [16, 32, 16, 32]              64\n",
            " InvertedResidual-36           [16, 32, 16, 32]               0\n",
            "           Conv2d-37          [16, 192, 16, 32]           6,144\n",
            "      BatchNorm2d-38          [16, 192, 16, 32]             384\n",
            "            ReLU6-39          [16, 192, 16, 32]               0\n",
            "           Conv2d-40          [16, 192, 16, 32]           1,728\n",
            "      BatchNorm2d-41          [16, 192, 16, 32]             384\n",
            "            ReLU6-42          [16, 192, 16, 32]               0\n",
            "           Conv2d-43           [16, 32, 16, 32]           6,144\n",
            "      BatchNorm2d-44           [16, 32, 16, 32]              64\n",
            " InvertedResidual-45           [16, 32, 16, 32]               0\n",
            "           Conv2d-46          [16, 192, 16, 32]           6,144\n",
            "      BatchNorm2d-47          [16, 192, 16, 32]             384\n",
            "            ReLU6-48          [16, 192, 16, 32]               0\n",
            "           Conv2d-49          [16, 192, 16, 32]           1,728\n",
            "      BatchNorm2d-50          [16, 192, 16, 32]             384\n",
            "            ReLU6-51          [16, 192, 16, 32]               0\n",
            "           Conv2d-52           [16, 32, 16, 32]           6,144\n",
            "      BatchNorm2d-53           [16, 32, 16, 32]              64\n",
            " InvertedResidual-54           [16, 32, 16, 32]               0\n",
            "           Conv2d-55          [16, 192, 16, 32]           6,144\n",
            "      BatchNorm2d-56          [16, 192, 16, 32]             384\n",
            "            ReLU6-57          [16, 192, 16, 32]               0\n",
            "           Conv2d-58           [16, 192, 8, 16]           1,728\n",
            "      BatchNorm2d-59           [16, 192, 8, 16]             384\n",
            "            ReLU6-60           [16, 192, 8, 16]               0\n",
            "           Conv2d-61            [16, 64, 8, 16]          12,288\n",
            "      BatchNorm2d-62            [16, 64, 8, 16]             128\n",
            " InvertedResidual-63            [16, 64, 8, 16]               0\n",
            "           Conv2d-64           [16, 384, 8, 16]          24,576\n",
            "      BatchNorm2d-65           [16, 384, 8, 16]             768\n",
            "            ReLU6-66           [16, 384, 8, 16]               0\n",
            "           Conv2d-67           [16, 384, 8, 16]           3,456\n",
            "      BatchNorm2d-68           [16, 384, 8, 16]             768\n",
            "            ReLU6-69           [16, 384, 8, 16]               0\n",
            "           Conv2d-70            [16, 64, 8, 16]          24,576\n",
            "      BatchNorm2d-71            [16, 64, 8, 16]             128\n",
            " InvertedResidual-72            [16, 64, 8, 16]               0\n",
            "           Conv2d-73           [16, 384, 8, 16]          24,576\n",
            "      BatchNorm2d-74           [16, 384, 8, 16]             768\n",
            "            ReLU6-75           [16, 384, 8, 16]               0\n",
            "           Conv2d-76           [16, 384, 8, 16]           3,456\n",
            "      BatchNorm2d-77           [16, 384, 8, 16]             768\n",
            "            ReLU6-78           [16, 384, 8, 16]               0\n",
            "           Conv2d-79            [16, 64, 8, 16]          24,576\n",
            "      BatchNorm2d-80            [16, 64, 8, 16]             128\n",
            " InvertedResidual-81            [16, 64, 8, 16]               0\n",
            "           Conv2d-82           [16, 384, 8, 16]          24,576\n",
            "      BatchNorm2d-83           [16, 384, 8, 16]             768\n",
            "            ReLU6-84           [16, 384, 8, 16]               0\n",
            "           Conv2d-85           [16, 384, 8, 16]           3,456\n",
            "      BatchNorm2d-86           [16, 384, 8, 16]             768\n",
            "            ReLU6-87           [16, 384, 8, 16]               0\n",
            "           Conv2d-88            [16, 64, 8, 16]          24,576\n",
            "      BatchNorm2d-89            [16, 64, 8, 16]             128\n",
            " InvertedResidual-90            [16, 64, 8, 16]               0\n",
            "           Conv2d-91           [16, 384, 8, 16]          24,576\n",
            "      BatchNorm2d-92           [16, 384, 8, 16]             768\n",
            "            ReLU6-93           [16, 384, 8, 16]               0\n",
            "           Conv2d-94           [16, 384, 8, 16]           3,456\n",
            "      BatchNorm2d-95           [16, 384, 8, 16]             768\n",
            "            ReLU6-96           [16, 384, 8, 16]               0\n",
            "           Conv2d-97            [16, 96, 8, 16]          36,864\n",
            "      BatchNorm2d-98            [16, 96, 8, 16]             192\n",
            " InvertedResidual-99            [16, 96, 8, 16]               0\n",
            "          Conv2d-100           [16, 576, 8, 16]          55,296\n",
            "     BatchNorm2d-101           [16, 576, 8, 16]           1,152\n",
            "           ReLU6-102           [16, 576, 8, 16]               0\n",
            "          Conv2d-103           [16, 576, 8, 16]           5,184\n",
            "     BatchNorm2d-104           [16, 576, 8, 16]           1,152\n",
            "           ReLU6-105           [16, 576, 8, 16]               0\n",
            "          Conv2d-106            [16, 96, 8, 16]          55,296\n",
            "     BatchNorm2d-107            [16, 96, 8, 16]             192\n",
            "InvertedResidual-108            [16, 96, 8, 16]               0\n",
            "          Conv2d-109           [16, 576, 8, 16]          55,296\n",
            "     BatchNorm2d-110           [16, 576, 8, 16]           1,152\n",
            "           ReLU6-111           [16, 576, 8, 16]               0\n",
            "          Conv2d-112           [16, 576, 8, 16]           5,184\n",
            "     BatchNorm2d-113           [16, 576, 8, 16]           1,152\n",
            "           ReLU6-114           [16, 576, 8, 16]               0\n",
            "          Conv2d-115            [16, 96, 8, 16]          55,296\n",
            "     BatchNorm2d-116            [16, 96, 8, 16]             192\n",
            "InvertedResidual-117            [16, 96, 8, 16]               0\n",
            "          Conv2d-118           [16, 576, 8, 16]          55,296\n",
            "     BatchNorm2d-119           [16, 576, 8, 16]           1,152\n",
            "           ReLU6-120           [16, 576, 8, 16]               0\n",
            "          Conv2d-121            [16, 576, 4, 8]           5,184\n",
            "     BatchNorm2d-122            [16, 576, 4, 8]           1,152\n",
            "           ReLU6-123            [16, 576, 4, 8]               0\n",
            "          Conv2d-124            [16, 160, 4, 8]          92,160\n",
            "     BatchNorm2d-125            [16, 160, 4, 8]             320\n",
            "InvertedResidual-126            [16, 160, 4, 8]               0\n",
            "          Conv2d-127            [16, 960, 4, 8]         153,600\n",
            "     BatchNorm2d-128            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-129            [16, 960, 4, 8]               0\n",
            "          Conv2d-130            [16, 960, 4, 8]           8,640\n",
            "     BatchNorm2d-131            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-132            [16, 960, 4, 8]               0\n",
            "          Conv2d-133            [16, 160, 4, 8]         153,600\n",
            "     BatchNorm2d-134            [16, 160, 4, 8]             320\n",
            "InvertedResidual-135            [16, 160, 4, 8]               0\n",
            "          Conv2d-136            [16, 960, 4, 8]         153,600\n",
            "     BatchNorm2d-137            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-138            [16, 960, 4, 8]               0\n",
            "          Conv2d-139            [16, 960, 4, 8]           8,640\n",
            "     BatchNorm2d-140            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-141            [16, 960, 4, 8]               0\n",
            "          Conv2d-142            [16, 160, 4, 8]         153,600\n",
            "     BatchNorm2d-143            [16, 160, 4, 8]             320\n",
            "InvertedResidual-144            [16, 160, 4, 8]               0\n",
            "          Conv2d-145            [16, 960, 4, 8]         153,600\n",
            "     BatchNorm2d-146            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-147            [16, 960, 4, 8]               0\n",
            "          Conv2d-148            [16, 960, 4, 8]           8,640\n",
            "     BatchNorm2d-149            [16, 960, 4, 8]           1,920\n",
            "           ReLU6-150            [16, 960, 4, 8]               0\n",
            "          Conv2d-151            [16, 320, 4, 8]         307,200\n",
            "     BatchNorm2d-152            [16, 320, 4, 8]             640\n",
            "InvertedResidual-153            [16, 320, 4, 8]               0\n",
            "          Conv2d-154           [16, 1280, 4, 8]         409,600\n",
            "     BatchNorm2d-155           [16, 1280, 4, 8]           2,560\n",
            "           ReLU6-156           [16, 1280, 4, 8]               0\n",
            "AdaptiveAvgPool2d-157           [16, 1280, 1, 1]               0\n",
            "          Linear-158                   [16, 11]          14,091\n",
            "          Linear-159                   [16, 11]          14,091\n",
            "          Linear-160                   [16, 11]          14,091\n",
            "          Linear-161                   [16, 11]          14,091\n",
            "          Linear-162                   [16, 11]          14,091\n",
            "================================================================\n",
            "Total params: 2,294,327\n",
            "Trainable params: 2,294,327\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 6.00\n",
            "Forward/backward pass size (MB): 1597.29\n",
            "Params size (MB): 8.75\n",
            "Estimated Total Size (MB): 1612.04\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lk5WapGI_e8F",
        "colab_type": "text"
      },
      "source": [
        "## 训练模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPfxsksDLIub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ----------------------------------- LabelSmoothEntropy ----------------------------------- #\n",
        "class LabelSmoothEntropy(nn.Module):\n",
        "  def __init__(self, smooth=0.1, class_weights=None, size_average='mean'):\n",
        "    super(LabelSmoothEntropy, self).__init__()\n",
        "    self.size_average = size_average\n",
        "    self.smooth = smooth\n",
        "\n",
        "    self.class_weights = class_weights\n",
        "    \n",
        "\n",
        "  def forward(self, preds, targets):\n",
        "\n",
        "    lb_pos, lb_neg = 1 - self.smooth, self.smooth / (preds.shape[0] - 1)\n",
        "\n",
        "    smoothed_lb = t.zeros_like(preds).fill_(lb_neg).scatter_(1, targets[:, None], lb_pos)\n",
        "\n",
        "    log_soft = F.log_softmax(preds)\n",
        "\n",
        "    if self.class_weights is not None:\n",
        "      loss = -log_soft * smoothed_lb * self.class_weights[None, :]\n",
        "\n",
        "    else:\n",
        "      loss = -log_soft * smoothed_lb\n",
        "\n",
        "    loss = loss.sum(1)\n",
        "    if self.size_average == 'mean':\n",
        "      return loss.mean()\n",
        "\n",
        "    elif self.size_average == 'sum':\n",
        "      return loss.sum()\n",
        "    else:\n",
        "      raise NotImplementedError\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ7whAw7QDsG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJQmxS86ZRy-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ----------------------------------- SnapShot ----------------------------------- #\n",
        "class SnapShotBuilder():\n",
        "\n",
        "  \"\"\"\n",
        "  \n",
        "  Params:\n",
        "    n_epoch(integer): total training epoches\n",
        "\n",
        "    n_snap(integer): parameters of model saved times\n",
        "\n",
        "  \"\"\"\n",
        "  def __init__(self, n_epoch, n_snap):\n",
        "\n",
        "    self.n_epoch = n_epoch\n",
        "\n",
        "    self.n_snap = n_snap\n",
        "\n",
        "    pass\n",
        "\n",
        "  def __call__(self):\n",
        "\n",
        "    pass\n",
        "\n",
        "  def _scheduler(self):\n",
        "\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJ9gBN9v_ty8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Trainer:\n",
        "\n",
        "  def __init__(self):\n",
        "\n",
        "    self.device = t.device('cuda') if t.cuda.is_available() else t.device('cpu')\n",
        "\n",
        "    self.train_loader = DataLoader(DigitsDataset(data_dir['train_data'], data_dir['train_label']), batch_size=config.batch_size, \\\n",
        "                    num_workers=8, pin_memory=True, drop_last=True)\n",
        "    \n",
        "\n",
        "    self.val_loader = DataLoader(DigitsDataset(data_dir['val_data'], data_dir['val_label'], aug=False), batch_size=config.batch_size,\\\n",
        "                    num_workers=8, pin_memory=True, drop_last=True)\n",
        "\n",
        "\n",
        "\n",
        "    self.model = DigitsMobilenet(config.class_num).to(self.device)\n",
        "\n",
        "\n",
        "    self.criterion = LabelSmoothEntropy().to(self.device)\n",
        "\n",
        "    self.optimizer = SGD(self.model.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weights_decay, nesterov=True)\n",
        "\n",
        "    self.lr_scheduler = CosineAnnealingWarmRestarts(self.optimizer, 10, 2)\n",
        "    # self.lr_scheduler = (self.optimizer, [10, 20, 30], 0.5)\n",
        "    self.best_acc = 0\n",
        "\n",
        "    if config.pretrained is not None:\n",
        "      self.load_model(config.pretrained)\n",
        "      # print('Load model from %s'%config.pretrained)\n",
        "      acc = self.eval()\n",
        "      self.best_acc = acc\n",
        "      print('Load model from %s, Eval Acc: %.2f'%(config.pretrained, acc * 100))\n",
        "\n",
        "\n",
        "\n",
        "  def train(self):\n",
        "    for epoch in range(config.start_epoch, config.epoches):\n",
        "      self.train_epoch(epoch)\n",
        "      if (epoch + 1) % config.eval_interval == 0:\n",
        "        print('Start Evaluation')\n",
        "        acc = self.eval()\n",
        "\n",
        "        if acc > self.best_acc:\n",
        "          os.makedirs(config.checkpoints, exist_ok=True)\n",
        "          save_path = config.checkpoints+'epoch-%d_acc-%.2f.pth'%(epoch+1, acc)\n",
        "          self.save_model(save_path)\n",
        "          print('%s saved successfully...'%save_path)\n",
        "          self.best_acc = acc\n",
        "\n",
        "  def train_epoch(self, epoch):\n",
        "    total_loss = 0\n",
        "    corrects = 0\n",
        "    tbar = tqdm(self.train_loader)\n",
        "    self.model.train()\n",
        "    for i, (img, label) in enumerate(tbar):\n",
        "      img = img.to(self.device)\n",
        "      label = label.to(self.device)\n",
        "      self.optimizer.zero_grad()\n",
        "      pred = self.model(img)\n",
        "      loss = self.criterion(pred[0], label[:, 0]) + \\\n",
        "          self.criterion(pred[1], label[:, 1]) + \\\n",
        "          self.criterion(pred[2], label[:, 2]) + \\\n",
        "          self.criterion(pred[3], label[:, 3]) + \\\n",
        "          self.criterion(pred[4], label[:, 4])\n",
        "      total_loss += loss.item()\n",
        "      loss.backward()\n",
        "      self.optimizer.step()\n",
        "      temp = t.stack([\\\n",
        "            pred[0].argmax(1) == label[:, 0], \\\n",
        "            pred[1].argmax(1) == label[:, 1], \\\n",
        "            pred[2].argmax(1) == label[:, 2], \\\n",
        "            pred[3].argmax(1) == label[:, 3], \\\n",
        "            pred[4].argmax(1) == label[:, 4]\\\n",
        "        ], dim=1)\n",
        "      # if \n",
        "      corrects += t.all(temp, dim=1).sum().item()\n",
        "      if (i + 1) % config.print_interval == 0:\n",
        "        self.lr_scheduler.step()\n",
        "        tbar.set_description('loss: %.3f, acc: %.3f'%(loss/(i+1), corrects*100/((i + 1) * config.batch_size)))\n",
        "\n",
        "  def eval(self):\n",
        "    self.model.eval()\n",
        "    corrects = 0\n",
        "    with t.no_grad():\n",
        "      tbar = tqdm(self.val_loader)\n",
        "      for i, (img, label) in enumerate(tbar):\n",
        "        img = img.to(self.device)\n",
        "        label = label.to(self.device)\n",
        "        pred = self.model(img)\n",
        "\n",
        "        temp = t.stack([\n",
        "            pred[0].argmax(1) == label[:, 0], \\\n",
        "            pred[1].argmax(1) == label[:, 1], \\\n",
        "            pred[2].argmax(1) == label[:, 2], \\\n",
        "            pred[3].argmax(1) == label[:, 3], \\\n",
        "            pred[4].argmax(1) == label[:, 4]\\\n",
        "        ], dim=1)\n",
        "\n",
        "        corrects += t.all(temp, dim=1).sum().item()\n",
        "        tbar.set_description('Val Acc: %.2f'%(corrects * 100 /((i+1)*config.batch_size)))\n",
        "    self.model.train()\n",
        "    return corrects / (len(self.val_loader) * config.batch_size)\n",
        "\n",
        "  def save_model(self, save_path, save_opt=False, save_config=False):\n",
        "    dicts = {}\n",
        "    dicts['model'] = self.model.state_dict()\n",
        "    if save_opt:\n",
        "      dicts['opt'] = self.optimizer.state_dict()\n",
        "\n",
        "    if save_config:\n",
        "      dicts['config'] = {s: config.__getattribute__(s) for s in dir(config) if not s.startswith('_')}\n",
        "\n",
        "    t.save(dicts, save_path)\n",
        "\n",
        "  def load_model(self, load_path, save_opt=False, save_config=False):\n",
        "\n",
        "    dicts = t.load(load_path)\n",
        "    self.model.load_state_dict(dicts['model'])\n",
        "\n",
        "    if save_opt:\n",
        "      self.optimizer.load_state_dict(dicts['opt'])\n",
        "\n",
        "    if save_config:\n",
        "      for k, v in dicts['config'].items():\n",
        "        config.__setattr__(k, v)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaDkWOHmsjgP",
        "colab_type": "code",
        "outputId": "cec1fb1f-a51a-4fed-c86f-fd7dccddfe29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151,
          "referenced_widgets": [
            "e35ff927bc534065b2d0e228665d3acc",
            "294450336b9845ef97995a9c91b92a49",
            "07586eed6c444d09b2218e8599c51650",
            "9f662255456346cdb8df1994683e14e7",
            "10c960107a9643248c6dafed41680ba8",
            "34d1de474e12417991d0ea84ec70e4d8",
            "0081bd2e687a4544bc5c089dc6304139",
            "1ae04d065fae4081b5d47f1a42a96656",
            "ab061ef4d45a451d911dbb8f0e65ec56",
            "fca77898402045c68bd067a6cfd2b291",
            "63823b720d7e4dae96283741b5518711",
            "6acc91bcee0b4397b2aeef480670e65c",
            "95395f497efc47dbbf29424ad1598b1a",
            "d4924b001e2849fe829f0a664636b737",
            "474577188662429e9f69e80b9f71bc6e",
            "51447c38dce5443399b4dfaf60b9a204"
          ]
        }
      },
      "source": [
        "trainer = Trainer()\n",
        "trainer.train()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e35ff927bc534065b2d0e228665d3acc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=625.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Load model from /content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-30_acc-61.57.pth, Eval Acc: 61.57\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab061ef4d45a451d911dbb8f0e65ec56",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=1875.0), HTML(value='')))"
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVy24dEAZIyo",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mt6tq9H4_wuv",
        "colab_type": "text"
      },
      "source": [
        "## 预测并生成结果文件"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD2ISKAC_66H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predicts(model_path):\n",
        "  test_loader = DataLoader(DigitsDataset(data_dir['val_data'], None, aug=False), batch_size=config.batch_size, shuffle=False,\\\n",
        "                    num_workers=8, pin_memory=True, drop_last=False)\n",
        "  results = []\n",
        "  model = DigitsMobilenet(config.class_num).cuda()\n",
        "  model.load_state_dict(t.load(model_path)['model'])\n",
        "  print('Load model from %s successfully'%model_path)\n",
        "\n",
        "  tbar = tqdm(test_loader)\n",
        "  model.eval()\n",
        "  with t.no_grad():\n",
        "    for i, (img, img_names) in enumerate(tbar):\n",
        "      img = img.cuda()\n",
        "      pred = model(img)\n",
        "\n",
        "      results += [[name, code] for name, code in zip(img_names, parse2class(pred))]\n",
        "\n",
        "  # result.sort(key=results)\n",
        "  results = sorted(results, key=lambda x: x[0])\n",
        "\n",
        "  write2csv(results)\n",
        "  return results\n",
        "\n",
        "\n",
        "\n",
        "def parse2class(prediction):\n",
        "  \"\"\"\n",
        "  \n",
        "  Params:\n",
        "    prediction(tuple of tensor): \n",
        "\n",
        "  \n",
        "  \"\"\"\n",
        "  ch1, ch2, ch3, ch4, ch5 = prediction\n",
        "\n",
        "  char_list = [str(i) for i in range(10)]\n",
        "  char_list.append('')\n",
        "\n",
        "\n",
        "  ch1, ch2, ch3, ch4, ch5 = ch1.argmax(1), ch2.argmax(1), ch3.argmax(1), ch4.argmax(1), ch5.argmax(1)\n",
        "\n",
        "  ch1, ch2, ch3, ch4, ch5 = [char_list[i.item()] for i in ch1], [char_list[i.item()] for i in ch2], \\\n",
        "                  [char_list[i.item()] for i in ch3], [char_list[i.item()] for i in ch4], \\\n",
        "                  [char_list[i.item()] for i in ch5]\n",
        "\n",
        "  res = [c1+c2+c3+c4+c5 for c1, c2, c3, c4, c5 in zip(ch1, ch2, ch3, ch4, ch5)]             \n",
        "  return res\n",
        "\n",
        "\n",
        "def write2csv(results):\n",
        "  \"\"\"\n",
        "  \n",
        "  results(list):\n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  df = pd.DataFrame(results, columns=['file_name', 'file_code'])\n",
        "  save_name = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/results.csv'\n",
        "  df.to_csv(save_name, sep=',', index=None)\n",
        "  print('Results.saved to %s'%save_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1ukIqhOOq2d",
        "colab_type": "code",
        "outputId": "39d17719-cb9a-4461-b471-c11fd8460dcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "e22bf6e8c7264376ab8317a92634b9d5",
            "dcb9f2c408ab4814896c8a23ef09c19e",
            "5451270a46b446cb84ca04a412bbcc02",
            "db498ab40cca4129b79e1cdc581613f8",
            "40cb252ca7504d8ca19ede845e297a93",
            "8f0f563c0967447488cc2567bfb7e222",
            "34b2442401894c45827b87e2e8ef026d",
            "93de64aafb594a7aa71e4b6716088db1"
          ]
        }
      },
      "source": [
        "model_path = '/content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/'\n",
        "\n",
        "model_path = sorted(glob(model_path+'*.pth'), key=lambda x: float(x[-9:-4]))[-1]\n",
        "\n",
        "predicts(model_path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Load model from /content/drive/My Drive/Data/Datawhale-DigitsRecognition/checkpoints/epoch-30_acc-61.57.pth successfully\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e22bf6e8c7264376ab8317a92634b9d5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=625.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Results.saved to /content/drive/My Drive/Data/Datawhale-DigitsRecognition/results.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              " ['/content/data/mchar_val/000998.png', '326'],\n",
              " ['/content/data/mchar_val/000999.png', '66'],\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    }
  ]
}