CNN/Poseminar_CNN_21_06.ipynb

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  "cells": [
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      "source": [
        "# Installing required Packages"
      ],
      "metadata": {
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      }
    },
    {
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      "source": [
        "!pip install torchinfo\n",
        "!pip install comet_ml"
      ],
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        "id": "yLQjM-Whtoas",
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      "execution_count": 3,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchinfo\n",
            "  Downloading torchinfo-1.8.0-py3-none-any.whl (23 kB)\n",
            "Installing collected packages: torchinfo\n",
            "Successfully installed torchinfo-1.8.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting comet_ml\n",
            "  Downloading comet_ml-3.33.4-py3-none-any.whl (534 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m534.7/534.7 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: jsonschema!=3.1.0,>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from comet_ml) (4.3.3)\n",
            "Collecting python-box<7.0.0 (from comet_ml)\n",
            "  Downloading python_box-6.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting requests-toolbelt>=0.8.0 (from comet_ml)\n",
            "  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.10/dist-packages (from comet_ml) (2.27.1)\n",
            "Collecting semantic-version>=2.8.0 (from comet_ml)\n",
            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
            "Collecting sentry-sdk>=1.1.0 (from comet_ml)\n",
            "  Downloading sentry_sdk-1.25.1-py2.py3-none-any.whl (206 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m206.7/206.7 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting simplejson (from comet_ml)\n",
            "  Downloading simplejson-3.19.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (137 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.9/137.9 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from comet_ml) (1.16.0)\n",
            "Collecting websocket-client<1.4.0,>=0.55.0 (from comet_ml)\n",
            "  Downloading websocket_client-1.3.3-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.2 in /usr/local/lib/python3.10/dist-packages (from comet_ml) (1.14.1)\n",
            "Collecting wurlitzer>=1.0.2 (from comet_ml)\n",
            "  Downloading wurlitzer-3.0.3-py3-none-any.whl (7.3 kB)\n",
            "Collecting everett[ini]<3.2.0,>=1.0.1 (from comet_ml)\n",
            "  Downloading everett-3.1.0-py2.py3-none-any.whl (35 kB)\n",
            "Collecting dulwich!=0.20.33,>=0.20.6 (from comet_ml)\n",
            "  Downloading dulwich-0.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (510 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.1/510.1 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: rich>=13.3.2 in /usr/local/lib/python3.10/dist-packages (from comet_ml) (13.3.4)\n",
            "Collecting urllib3>2.0.0 (from comet_ml)\n",
            "  Downloading urllib3-2.0.3-py3-none-any.whl (123 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m123.6/123.6 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting configobj (from everett[ini]<3.2.0,>=1.0.1->comet_ml)\n",
            "  Downloading configobj-5.0.8-py2.py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema!=3.1.0,>=2.6.0->comet_ml) (23.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema!=3.1.0,>=2.6.0->comet_ml) (0.19.3)\n",
            "INFO: pip is looking at multiple versions of requests to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting requests>=2.18.4 (from comet_ml)\n",
            "  Downloading requests-2.31.0-py3-none-any.whl (62 kB)\n",
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            "Installing collected packages: everett, wurlitzer, websocket-client, urllib3, simplejson, semantic-version, python-box, configobj, sentry-sdk, requests, dulwich, requests-toolbelt, comet_ml\n",
            "  Attempting uninstall: websocket-client\n",
            "    Found existing installation: websocket-client 1.5.1\n",
            "    Uninstalling websocket-client-1.5.1:\n",
            "      Successfully uninstalled websocket-client-1.5.1\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.26.15\n",
            "    Uninstalling urllib3-1.26.15:\n",
            "      Successfully uninstalled urllib3-1.26.15\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.27.1\n",
            "    Uninstalling requests-2.27.1:\n",
            "      Successfully uninstalled requests-2.27.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed comet_ml-3.33.4 configobj-5.0.8 dulwich-0.21.5 everett-3.1.0 python-box-6.1.0 requests-2.31.0 requests-toolbelt-1.0.0 semantic-version-2.10.0 sentry-sdk-1.25.1 simplejson-3.19.1 urllib3-2.0.3 websocket-client-1.3.3 wurlitzer-3.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install lightning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbC7gz6wy7iO",
        "outputId": "dd63af9d-30aa-4fff-8c82-12c25bb64cd3"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hCollecting frozenlist>=1.1.1 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2024.0,>=2022.5.0->lightning)\n",
            "  Downloading frozenlist-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (149 kB)\n",
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            "\u001b[?25hCollecting aiosignal>=1.1.2 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2024.0,>=2022.5.0->lightning)\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette->lightning) (1.3.0)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py<3.0.0,>=2.2.0->rich<15.0,>=12.3.0->lightning) (0.1.2)\n",
            "Requirement already satisfied: setuptools>=41.0 in /usr/local/lib/python3.10/dist-packages (from readchar>=3.0.6->inquirer<5.0,>=2.10.0->lightning) (67.7.2)\n",
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            "Installing collected packages: python-editor, websockets, readchar, python-multipart, pyjwt, ordered-set, multidict, lightning-utilities, h11, frozenlist, blessed, async-timeout, yarl, uvicorn, starlette, inquirer, deepdiff, dateutils, croniter, arrow, aiosignal, starsessions, fastapi, aiohttp, lightning-cloud, torchmetrics, pytorch-lightning, lightning\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 arrow-1.2.3 async-timeout-4.0.2 blessed-1.20.0 croniter-1.3.15 dateutils-0.6.12 deepdiff-6.3.0 fastapi-0.88.0 frozenlist-1.3.3 h11-0.14.0 inquirer-3.1.3 lightning-2.0.3 lightning-cloud-0.5.36 lightning-utilities-0.8.0 multidict-6.0.4 ordered-set-4.1.0 pyjwt-2.7.0 python-editor-1.0.4 python-multipart-0.0.6 pytorch-lightning-2.0.3 readchar-4.0.5 starlette-0.22.0 starsessions-1.3.0 torchmetrics-0.11.4 uvicorn-0.22.0 websockets-11.0.3 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing required Packages"
      ],
      "metadata": {
        "id": "OhsRA_JgiIJX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import comet_ml\n",
        "import torch.nn as nn\n",
        "import pytorch_lightning as pl\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from pytorch_lightning import Trainer\n",
        "from pytorch_lightning.loggers import CometLogger\n",
        "from pytorch_lightning import loggers\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "from torchvision.datasets import MNIST\n",
        "import torchvision\n",
        "\n",
        "from torchinfo import summary\n",
        "\n",
        "from PIL import Image\n",
        "from matplotlib.pyplot import imshow"
      ],
      "metadata": {
        "id": "pZ1j3TKeiKfi"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining our Model"
      ],
      "metadata": {
        "id": "1luPpT9SiNAi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(pl.LightningModule):\n",
        "  def __init__(self, number_of_filters, learning_rate):\n",
        "    super().__init__()\n",
        "\n",
        "    self.save_hyperparameters()\n",
        "\n",
        "    self.learning_rate = learning_rate\n",
        "\n",
        "    self.val_correct_counter = 0\n",
        "    self.val_total_counter = 0\n",
        "    self.val_batch_counter = 0\n",
        "\n",
        "    self.loss = nn.NLLLoss()\n",
        "\n",
        "    self.hidden0 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=1, out_channels=number_of_filters, kernel_size=4),\n",
        "        nn.LeakyReLU(0.2)\n",
        "    )\n",
        "\n",
        "    self.hidden1 = nn.Sequential(\n",
        "        nn.Conv2d(number_of_filters, number_of_filters * 2, kernel_size=4),\n",
        "        nn.LeakyReLU(0.2),\n",
        "        nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "    )\n",
        "\n",
        "    self.hidden2 = nn.Sequential(\n",
        "        nn.Conv2d(number_of_filters * 2, number_of_filters * 4, kernel_size=4),\n",
        "        nn.LeakyReLU(0.2),\n",
        "    )\n",
        "\n",
        "    self.hidden3 = nn.Sequential(\n",
        "        nn.Conv2d(number_of_filters * 4, number_of_filters * 8, kernel_size=4),\n",
        "        nn.LeakyReLU(0.2),\n",
        "        nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "        nn.Flatten()\n",
        "    )\n",
        "\n",
        "    self.hidden4 = nn.Sequential(\n",
        "        nn.Linear(number_of_filters * 8 * 4, 100),\n",
        "        nn.LeakyReLU(0.2),\n",
        "    )\n",
        "\n",
        "    self.hidden5 = nn.Sequential(\n",
        "        nn.Linear(100, 10),\n",
        "        nn.LogSoftmax()\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "        #print(x.shape)\n",
        "        x = self.hidden0(x)\n",
        "        #print(x.shape)\n",
        "        x = self.hidden1(x)\n",
        "        #print(x.shape)\n",
        "        x = self.hidden2(x)\n",
        "        #print(x.shape)\n",
        "        x = self.hidden3(x)\n",
        "        #print(x.shape)\n",
        "        x = self.hidden4(x)\n",
        "        #print(x.shape)\n",
        "        x = self.hidden5(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "      x, y = batch\n",
        "\n",
        "      predicted = self.forward(x)\n",
        "      loss = self.loss(predicted, y)\n",
        "\n",
        "      self.logger.log_metrics({\"train_loss\": loss}, step=batch_idx)\n",
        "      return loss\n",
        "\n",
        "  def validation_step(self, batch, batch_idx):\n",
        "      x, y = batch\n",
        "\n",
        "      predicted = self.forward(x)\n",
        "      loss = self.loss(predicted, y)\n",
        "\n",
        "      # to calculate the test accuracy\n",
        "      self.val_correct_counter += int((torch.argmax(predicted, 1).flatten() == y).sum())\n",
        "      self.val_total_counter += y.size(0)\n",
        "\n",
        "      self.logger.log_metrics({\"val_loss\": loss}, step=batch_idx)\n",
        "      return loss\n",
        "\n",
        "  def on_validation_epoch_end(self):\n",
        "      # outputs is an array with what you returned in validation_step for each batch\n",
        "      # outputs = [{'loss': batch_0_loss}, {'loss': batch_1_loss}, ..., {'loss': batch_n_loss}]\n",
        "\n",
        "      avg_acc = 100 * self.val_correct_counter / self.val_total_counter\n",
        "\n",
        "      self.val_correct_counter = 0\n",
        "      self.val_total_counter = 0\n",
        "\n",
        "      self.logger.log_metrics({\"val_acc\": avg_acc}, step=self.val_batch_counter)\n",
        "\n",
        "      self.val_batch_counter += 1\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "      return torch.optim.Adam(self.parameters(), lr=self.learning_rate)"
      ],
      "metadata": {
        "id": "zQcnDBOBicoC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Initalisation and Hyperparameters\n"
      ],
      "metadata": {
        "id": "eVkkXEDyigod"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters\n",
        "hparam = {\n",
        "    \"learning_rate\": 0.001,\n",
        "    \"number_of_filters\": 16, # Needs to be 16 else switch linear Layer\n",
        "    \"batch_size\": 128 * 6,\n",
        "    \"optimizer\": \"Adam\",\n",
        "    \"seed\": 42\n",
        "}\n",
        "\n",
        "\n",
        "# Parameters for logging\n",
        "experiment_name = \"MAIN_CNN_21_06\"\n",
        "checkpoint_folder = \"./\" + experiment_name + \"_checkpoints/\"\n",
        "dataset_name = \"MNIST\"\n",
        "tags = [\"CNN\"]"
      ],
      "metadata": {
        "id": "sV0-uK-lipix"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Init Model\n",
        "model = Model(hparam[\"number_of_filters\"], hparam[\"learning_rate\"])"
      ],
      "metadata": {
        "id": "dM92T-F6i0ZD"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The size of our Model"
      ],
      "metadata": {
        "id": "RSHah1YIjWbM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(summary(model, input_size=(1, 1, 28, 28)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7vqkgC_i18R",
        "outputId": "bdca9800-2c02-47a6-8aac-8d1d3ba438ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==========================================================================================\n",
            "Layer (type:depth-idx)                   Output Shape              Param #\n",
            "==========================================================================================\n",
            "Model                                    [1, 10]                   --\n",
            "├─Sequential: 1-1                        [1, 16, 25, 25]           --\n",
            "│    └─Conv2d: 2-1                       [1, 16, 25, 25]           272\n",
            "│    └─LeakyReLU: 2-2                    [1, 16, 25, 25]           --\n",
            "├─Sequential: 1-2                        [1, 32, 11, 11]           --\n",
            "│    └─Conv2d: 2-3                       [1, 32, 22, 22]           8,224\n",
            "│    └─LeakyReLU: 2-4                    [1, 32, 22, 22]           --\n",
            "│    └─MaxPool2d: 2-5                    [1, 32, 11, 11]           --\n",
            "├─Sequential: 1-3                        [1, 64, 8, 8]             --\n",
            "│    └─Conv2d: 2-6                       [1, 64, 8, 8]             32,832\n",
            "│    └─LeakyReLU: 2-7                    [1, 64, 8, 8]             --\n",
            "├─Sequential: 1-4                        [1, 512]                  --\n",
            "│    └─Conv2d: 2-8                       [1, 128, 5, 5]            131,200\n",
            "│    └─LeakyReLU: 2-9                    [1, 128, 5, 5]            --\n",
            "│    └─MaxPool2d: 2-10                   [1, 128, 2, 2]            --\n",
            "│    └─Flatten: 2-11                     [1, 512]                  --\n",
            "├─Sequential: 1-5                        [1, 100]                  --\n",
            "│    └─Linear: 2-12                      [1, 100]                  51,300\n",
            "│    └─LeakyReLU: 2-13                   [1, 100]                  --\n",
            "├─Sequential: 1-6                        [1, 10]                   --\n",
            "│    └─Linear: 2-14                      [1, 10]                   1,010\n",
            "│    └─LogSoftmax: 2-15                  [1, 10]                   --\n",
            "==========================================================================================\n",
            "Total params: 224,838\n",
            "Trainable params: 224,838\n",
            "Non-trainable params: 0\n",
            "Total mult-adds (M): 9.58\n",
            "==========================================================================================\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.26\n",
            "Params size (MB): 0.90\n",
            "Estimated Total Size (MB): 1.17\n",
            "==========================================================================================\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:139: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading MNIST Dataset"
      ],
      "metadata": {
        "id": "aW-Uoj6wjdMW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "compose = transforms.Compose([\n",
        "          transforms.ToTensor(),\n",
        "          transforms.Normalize((0.5,), (0.5,))\n",
        "      ])"
      ],
      "metadata": {
        "id": "-Irq3qhDjlwq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Data\n",
        "train_ds = MNIST(\n",
        "    \"data\", train=True, download=True, transform=compose\n",
        ")\n",
        "\n",
        "train_loader = DataLoader(train_ds,  shuffle=True, batch_size=hparam[\"batch_size\"])\n",
        "\n",
        "eval_ds = MNIST(\n",
        "    \"data\", train=False, download=True, transform=compose\n",
        ")\n",
        "\n",
        "eval_loader = DataLoader(train_ds, batch_size=hparam[\"batch_size\"])"
      ],
      "metadata": {
        "id": "0HU3aHtSjO2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize our Logger\n",
        "\n",
        "I use as logger comet.ml, if you want to use this logger, create a text file with the name \"api_key.txt\" in the same folder and enter your comet.ml api key.\n",
        "If you do not want to use this logger switch the logger where you start training the model."
      ],
      "metadata": {
        "id": "gdLZ72gvjuR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get the api key\n",
        "with open(\"api_key.txt\", \"r\") as f:\n",
        "    f_api_key = f.read()\n",
        "\n",
        "# set global seed\n",
        "torch.manual_seed(hparam[\"seed\"])\n",
        "\n",
        "# init logger\n",
        "comet_logger = loggers.CometLogger(\n",
        "    api_key= f_api_key,\n",
        "    project_name=\"mnist-proseminar\",\n",
        "    experiment_name=experiment_name,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onIAl1cojhnW",
        "outputId": "ffb458c0-df46-4428-a989-7d8a6ee03027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.loggers.comet:CometLogger will be initialized in online mode\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#logging\n",
        "comet_logger.experiment.set_model_graph(str(model))\n",
        "comet_logger.experiment.add_tags(tags=tags)\n",
        "comet_logger.experiment.log_dataset_info(name=dataset_name)\n",
        "comet_logger.experiment.log_parameter(name=\"learning_rate\", value=hparam[\"learning_rate\"])\n",
        "comet_logger.experiment.log_parameter(name=\"batch_size\", value=hparam[\"batch_size\"])\n",
        "comet_logger.experiment.log_parameter(name=\"ndf\", value=hparam[\"number_of_filters\"])\n",
        "comet_logger.experiment.log_parameter(name=\"seed\", value=hparam[\"seed\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmJGYdHYlcBr",
        "outputId": "e463247b-1863-4de6-f908-4a563250b7fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/luposx/mnist-proseminar/09dd4b2b610849049034aef1430e2768\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the NN"
      ],
      "metadata": {
        "id": "wqv1_femlfbm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.train()\n",
        "\n",
        "# Initalize the trainer\n",
        "checkpoint_callback = ModelCheckpoint(dirpath=checkpoint_folder)\n",
        "trainer = pl.Trainer(# fast_dev_run=True,\n",
        "                      callbacks=checkpoint_callback,\n",
        "                      accelerator=\"gpu\",\n",
        "                      max_epochs=5,\n",
        "                      logger=comet_logger)\n",
        "trainer.fit(model, train_loader, eval_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952,
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        "id": "86K2ivaIldj2",
        "outputId": "4b89e40f-8586-4732-914c-638834506e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name    | Type       | Params\n",
            "---------------------------------------\n",
            "0 | loss    | NLLLoss    | 0     \n",
            "1 | hidden0 | Sequential | 272   \n",
            "2 | hidden1 | Sequential | 8.2 K \n",
            "3 | hidden2 | Sequential | 32.8 K\n",
            "4 | hidden3 | Sequential | 131 K \n",
            "5 | hidden4 | Sequential | 51.3 K\n",
            "6 | hidden5 | Sequential | 1.0 K \n",
            "---------------------------------------\n",
            "224 K     Trainable params\n",
            "0         Non-trainable params\n",
            "224 K     Total params\n",
            "0.899     Total estimated model params size (MB)\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Set model graph ignored; already called. Call with overwrite=True to replace graph definition\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1cc26c471b46479d973a2e32504df6a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a725b88f5b0a4e0caad99bfe4d86a0e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e845f2f4508d4a968f4d70f317f397fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "900fc3c6a713457daf340fd48c7a8f7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bca9942583043e28767a7109a1247aa"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c74289898428463c84d0572d77bd9b89"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f6ece9136f04f81a86cfafeb122bb19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/luposx/mnist-proseminar/09dd4b2b610849049034aef1430e2768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [35]        : (0.025587623938918114, 1.1600921154022217)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss [395] : (0.011664967983961105, 2.305577278137207)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_acc [6]      : (7.8125, 98.96166666666667)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_loss [397]   : (0.005283661186695099, 2.30991530418396)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name         : MAIN_CNN_21_06\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dataset_info : MNIST\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size        : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     learning_rate     : 0.001\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndf               : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     number_of_filters : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed              : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# log checkpoints, make sure we log everything before we shut down the logger\n",
        "comet_logger.experiment.log_asset_folder(folder=checkpoint_folder)\n",
        "comet_logger.experiment.end()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mzp61VgAlhrF",
        "outputId": "21942df3-1f5a-4bed-ea1e-46bc509f5759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/luposx/mnist-proseminar/09dd4b2b610849049034aef1430e2768\n",
            "\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m The default value for the log_file_name parameter will change from False to True in a future version. Explicitly pass log_file_name=True or log_file_name=False to disable this warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml ExistingExperiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/luposx/mnist-proseminar/09dd4b2b610849049034aef1430e2768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Created from : pytorch-lightning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name         : MAIN_CNN_21_06\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset : 1 (2.59 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 2.43 MB/2.59 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exporting the Model as onx file\n"
      ],
      "metadata": {
        "id": "OFMhcehg5u32"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install onnx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wX7coA585xRW",
        "outputId": "0aa7cd46-db48-44ef-e9e2-105bcd4d4d5d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting onnx\n",
            "  Downloading onnx-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.6/14.6 MB\u001b[0m \u001b[31m70.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from onnx) (1.22.4)\n",
            "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from onnx) (3.20.3)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.10/dist-packages (from onnx) (4.5.0)\n",
            "Installing collected packages: onnx\n",
            "Successfully installed onnx-1.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.onnx.export(model, torch.rand(1, 1, 28, 28), 'model.onnx', input_names=[\"Input Image\"], output_names=[\"Image Class Labels\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKMHjiI85zGS",
        "outputId": "d89173cd-bdd3-42f6-9bc7-1b8855fcd4eb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  return forward_call(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference of the Model"
      ],
      "metadata": {
        "id": "JsoBhnrolskR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure = plt.figure(figsize=(8, 8))\n",
        "data = []\n",
        "for i in range(1, 5):\n",
        "    sample_idx = torch.randint(len(train_ds), size=(1,)).item()\n",
        "    img, label = train_ds[sample_idx]\n",
        "    data.append(img)\n",
        "\n",
        "# create grid of images\n",
        "img_grid = torchvision.utils.make_grid(data)\n",
        "\n",
        "# show images\n",
        "imshow(img_grid.permute(1, 2, 0), cmap='Greys')"
      ],
      "metadata": {
        "id": "BBRaVfNjnFzN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = next(iter(train_ds))[0]\n",
        "plt.imshow(data.permute(1, 2, 0), cmap='Greys')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "1teGnSAolr8n",
        "outputId": "b7d24329-bbf9-453e-d9f2-ba14a4f1e016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb5fef623e0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.size())\n",
        "# We need one extra dimesnion to emulate batch size.\n",
        "data = data.view(1, 1, 28, 28)\n",
        "print(data.size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o77jRmFAlxvP",
        "outputId": "d7ccfb83-28ae-44fe-e33d-60672ffdd500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n",
            "torch.Size([1, 1, 28, 28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model.load_from_checkpoint(checkpoint_folder + \"/epoch=4-step=395.ckpt\")\n",
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "   y_hat = model(data.cuda())\n",
        "\n",
        "print(\"Results for Inference\")\n",
        "print(\"---------------------------\")\n",
        "print(\" \")\n",
        "print(\"Probabilities for Predicted-Labels: \", y_hat)\n",
        "print(\" \")\n",
        "print(\"Predicted Label: \", y_hat.argmax())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddyCNfuJl4Z1",
        "outputId": "d0479d38-b2d8-40f8-e6ad-c98bd46e8465"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for Inference\n",
            "---------------------------\n",
            " \n",
            "Probabilities for Predicted-Labels:  tensor([[-2.1434e+01, -1.6651e+01, -2.0672e+01, -5.2847e+00, -2.3566e+01,\n",
            "         -5.0846e-03, -2.1610e+01, -2.1795e+01, -1.4039e+01, -1.3095e+01]],\n",
            "       device='cuda:0')\n",
            " \n",
            "Predicted Label:  tensor(5, device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JE3RJsPa1waH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}