LeNet.ipynb

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          "model_module": "@jupyter-widgets/controls",
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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/teang1995/Algorithm_study/blob/master/LeNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDSEGPTaCPpm",
        "outputId": "999b9f27-b9dd-4bc9-e0cc-49ce5d31390f"
      },
      "source": [
        "!pip install torchtext==0.8.0 torch==1.7.1 pytorch-lightning==1.2.2 torchmetrics torchvision"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchtext==0.8.0 in /usr/local/lib/python3.7/dist-packages (0.8.0)\n",
            "Requirement already satisfied: torch==1.7.1 in /usr/local/lib/python3.7/dist-packages (1.7.1)\n",
            "Requirement already satisfied: pytorch-lightning==1.2.2 in /usr/local/lib/python3.7/dist-packages (1.2.2)\n",
            "Requirement already satisfied: torchmetrics in /usr/local/lib/python3.7/dist-packages (0.5.1)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (0.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torchtext==0.8.0) (4.62.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchtext==0.8.0) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchtext==0.8.0) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.7.1) (3.7.4.3)\n",
            "Requirement already satisfied: future>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning==1.2.2) (0.18.2)\n",
            "Requirement already satisfied: fsspec[http]>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning==1.2.2) (2021.9.0)\n",
            "Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning==1.2.2) (2.6.0)\n",
            "Requirement already satisfied: PyYAML!=5.4.*,>=5.1 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning==1.2.2) (5.3.1)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from fsspec[http]>=0.8.1->pytorch-lightning==1.2.2) (3.7.4.post0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.8.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.6.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.37.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (57.4.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.0.1)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.40.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (3.3.4)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (3.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.4.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.4->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.15.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (4.7.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (4.2.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (4.8.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (0.4.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext==0.8.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext==0.8.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext==0.8.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext==0.8.0) (2021.5.30)\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>=2.2.0->pytorch-lightning==1.2.2) (3.1.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from torchmetrics) (21.0)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision) (7.1.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]>=0.8.1->pytorch-lightning==1.2.2) (1.6.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]>=0.8.1->pytorch-lightning==1.2.2) (21.2.0)\n",
            "Requirement already satisfied: async-timeout<4.0,>=3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]>=0.8.1->pytorch-lightning==1.2.2) (3.0.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->fsspec[http]>=0.8.1->pytorch-lightning==1.2.2) (5.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning==1.2.2) (3.5.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->torchmetrics) (2.4.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vn8vj11xCUAd"
      },
      "source": [
        "import torch\n",
        "from torchmetrics import Metric\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import random_split\n",
        "from torchvision.datasets import MNIST\n",
        "from torchvision import transforms\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning import LightningDataModule\n",
        "from typing import Optional"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2przXTfCUZt"
      },
      "source": [
        "class MNISTDataModule(LightningDataModule):\n",
        "    def __init__(self, data_dir: str = \"path/to/dir\", batch_size: int = 32):\n",
        "        super().__init__()\n",
        "        self.data_dir = data_dir\n",
        "        self.batch_size = batch_size\n",
        "        self.transform = transforms.Compose([transforms.ToTensor()])\n",
        "        \n",
        "    def prepare_data(self):\n",
        "        \"\"\"\n",
        "            - download\n",
        "            - tokenize\n",
        "            - etc ...\n",
        "        \"\"\"\n",
        "        MNIST(self.data_dir, train=True, download=True) # just download at data_dir\n",
        "        MNIST(self.data_dir, train=False, download=True) # just download at data_dir\n",
        "    def setup(self, stage:Optional[str]) -> None:\n",
        "        \"\"\"\n",
        "            - count number of classes\n",
        "            - build vocabulary\n",
        "            - perform train/val/test split\n",
        "            - apply transforms\n",
        "            - etc...\n",
        "        \"\"\"\n",
        "        mnist_full = MNIST(self.data_dir, train=True, transform=self.transform) \n",
        "        self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000]) # split data\n",
        "        self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(self.mnist_train, batch_size=self.batch_size, shuffle=True)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(self.mnist_val, batch_size=self.batch_size, shuffle=False)\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return DataLoader(self.mnist_test, batch_size=self.batch_size, shuffle=False)\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z90dRxLyCUqX"
      },
      "source": [
        "mnist_datamodule = MNISTDataModule(data_dir='./', batch_size=32)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAN3RbxeCU46"
      },
      "source": [
        "class LeNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LeNet, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels=1,\n",
        "                           out_channels=3,\n",
        "                           kernel_size=3,\n",
        "                           stride=1,\n",
        "                           padding=1,\n",
        "                           bias=True)\n",
        "        self.conv2 = nn.Conv2d(in_channels=3,\n",
        "                           out_channels=6,\n",
        "                           kernel_size=3,\n",
        "                           stride=1,\n",
        "                           padding=1,\n",
        "                           bias=True)\n",
        "        self.subsample = nn.MaxPool2d(kernel_size=2)\n",
        "        self.fc1 = nn.Linear(294, 512)\n",
        "        self.fc2 = nn.Linear(512, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        bs = x.shape[0]\n",
        "        fm1 = self.conv1(x)\n",
        "        fm2 = self.subsample(fm1)\n",
        "        fm3 = self.conv2(fm2)\n",
        "        fm4 = self.subsample(fm3)\n",
        "        flattend = fm4.view(bs, -1)\n",
        "        em1 = self.fc1(flattend)\n",
        "        em2 = self.fc2(em1)\n",
        "        return torch.log_softmax(em2, dim=1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXl33fQQCcvd"
      },
      "source": [
        "class LeNetModule(pl.LightningModule):\n",
        "    def __init__(self,\n",
        "               init_lr : int = 1e-2):\n",
        "        super().__init__()\n",
        "        self.init_lr = init_lr\n",
        "        self.net = LeNet()\n",
        "        print(\"init done!\")\n",
        "    \n",
        "    def forward(self, X):\n",
        "        return self.net(X)\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        X, y = batch\n",
        "        prediction = self.forward(X)\n",
        "        loss = F.cross_entropy(prediction, y)\n",
        "        return {'loss': loss}\n",
        "  \n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        X, y = batch\n",
        "        prediction = self.forward(X)\n",
        "        loss = F.cross_entropy(prediction, y)\n",
        "        \n",
        "        acc = torch.sum(prediction.max(1, keepdim=True)[1] == y)\n",
        "        metrics = {'val_acc': acc, 'val_loss': loss}\n",
        "        self.log_dict(metrics)\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        X, y= batch\n",
        "        prediction = self.forward(X)\n",
        "        loss = F.cross_entropy(prediction, y)\n",
        "        acc = torch.sum(prediction.max(1)[1] == y) * 100 / len(y)\n",
        "        metrics = {'test_acc': acc, 'test_loss': loss}\n",
        "        self.log_dict(metrics)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.SGD(self.parameters(), lr=self.init_lr)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkvuB24YZPFc",
        "outputId": "bdc1ee32-45ed-431c-9549-3dd34118a44c"
      },
      "source": [
        "import numpy as np\n",
        "a = [[1 if j % 10 == i else 0 for i in range(10)] for j in range(32)]\n",
        "y = [i % 10 for i in range(32)]\n",
        "y = np.array(y)\n",
        "y = torch.tensor(y)\n",
        "a = np.array(a)\n",
        "b = torch.tensor(a)\n",
        "c = b.max(1)[1]\n",
        "result = (y==c)\n",
        "result.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([32])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-cqQfhnCcta",
        "outputId": "54a137b8-7c5e-4f10-fd47-a276095d1c25"
      },
      "source": [
        "lenet_module = LeNetModule(init_lr=1e-2)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "init done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
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        "outputId": "d5600761-7fd9-4d0d-de11-7e250c295fed"
      },
      "source": [
        "device = 1 if torch.cuda.is_available() else 0\n",
        "max_epochs=10\n",
        "\n",
        "trainer = pl.Trainer(gpus=device,\n",
        "                     max_epochs=max_epochs,\n",
        "                     progress_bar_refresh_rate=20,\n",
        "                     num_sanity_val_steps=0)\n",
        "\n",
        "trainer.fit(model=lenet_module, datamodule=mnist_datamodule)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: False, used: False\n",
            "TPU available: None, using: 0 TPU cores\n",
            "\n",
            "  | Name | Type  | Params\n",
            "-------------------------------\n",
            "0 | net  | LeNet | 156 K \n",
            "-------------------------------\n",
            "156 K     Trainable params\n",
            "0         Non-trainable params\n",
            "156 K     Total params\n",
            "0.625     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
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              "model_id": "1c451d0940134de78bf89e96d11e6d83",
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            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
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        },
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            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
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          "metadata": {},
          "execution_count": 9
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      },
      "source": [
        "trainer.test(lenet_module, datamodule=mnist_datamodule, verbose=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7c67bf7740594022b6905e986ed270a3",
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              "Testing: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'test_acc': 97.33999633789062, 'test_loss': 0.08329261839389801}\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_acc': 97.33999633789062, 'test_loss': 0.08329261839389801}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}