ColabMNISTExample.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running in colab, make sure to select a runtime using a GPU instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clone serotiny and install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serotiny is a deep learning library containing datamodules, networks and models specific to the AICS single cell image dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "ddlGXbvosve4"
   },
   "outputs": [],
   "source": [
    "# Delete serotiny if it exists\n",
    "!rm -r serotiny/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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   "source": [
    "# Clone the serotiny repo\n",
    "\n",
    "import os\n",
    "from getpass import getpass\n",
    "import urllib\n",
    "\n",
    "user = input('User name: ')\n",
    "password = getpass('Password: ')\n",
    "password = urllib.parse.quote(password) \n",
    "repo_name = input('Repo name: ')\n",
    "repo_username = input(\"Repo username: \")\n",
    "\n",
    "# If public (False)\n",
    "# cmd_string = 'git clone https://{0}:{1}@github.com/{3}/{2}.git'.format(user, password, repo_name, repo_username)\n",
    "\n",
    "# If private (True)\n",
    "cmd_string = 'git clone -b mnist_colab https://{0}:{1}@github.com/{3}/{2}.git'.format(user, password, repo_name, repo_username)\n",
    "\n",
    "os.system(cmd_string)\n",
    "cmd_string, password = \"\", \"\" # removing the password from the variable"
   ]
  },
  {
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    "id": "ZGy7MnD57jpl"
   },
   "outputs": [],
   "source": [
    "# Change directory\n",
    "import os\n",
    "os.chdir(\"./serotiny/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "MMZrO3vIpkeK",
    "outputId": "43337c52-3d32-42b1-ff48-54d52c415242"
   },
   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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    "outputId": "7c6edaea-0d8f-4630-f3b4-bec6a8f9d9a5"
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   "outputs": [],
   "source": [
    "# Due to different dependencies, this may require a runtime restart. If so, can ignore the previous steps after restart\n",
    "# If prompted, run pip install -U ipython before restart\n",
    "!pip install -e .[all]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the AICS MNIST datamodule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "executionInfo": {
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     "status": "ok",
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    "id": "-K3boubrlkFH"
   },
   "source": [
    "This is a dataset of 70000 2D single cell images (28*28 pixels) containing 3 channels (DNA, Membrane, Structure), where the structure channel can be one of 10 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30190,
     "status": "ok",
     "timestamp": 1614634490438,
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      "displayName": "Ritvik Vasan",
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    },
    "id": "I_PwZN21mx70",
    "outputId": "f4620237-7bda-45e4-8a62-3a58602e935c"
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   "outputs": [],
   "source": [
    "from serotiny.library.datamodules import AICS_MNIST_DataModule\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "data_dir = './'\n",
    "\n",
    "AICS_MNIST_DataModule.get_dataset(data_dir)\n",
    "\n",
    "df = pd.read_csv(data_dir + \"/aics_mnist_rgb.csv\")\n",
    "\n",
    "data_config = {\n",
    "    \"classes\": df['structure'].unique(),\n",
    "    \"id_fields\": [\"CellId\", \"CellIndex\", \"FOVId\"],\n",
    "    \"channels\": [\"membrane\", \"structure\", \"dna\"],\n",
    "    \"select_channels\": [\"membrane\", \"structure\", \"dna\"],\n",
    "}\n",
    "dm = AICS_MNIST_DataModule(\n",
    "    config=data_config,\n",
    "    batch_size=128,\n",
    "    num_workers=1,\n",
    "    data_dir=data_dir,\n",
    "    x_label=\"cell_image\",\n",
    "    y_label=\"structure\"\n",
    ")\n",
    "dm.setup()\n",
    "in_channels = dm.num_channels\n",
    "dimensions = dm.dims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load a simple 2D MLP network and the corresponding classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 28635,
     "status": "ok",
     "timestamp": 1614634490439,
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     },
     "user_tz": 480
    },
    "id": "FXkaSosKqzLB"
   },
   "outputs": [],
   "source": [
    "from serotiny.library.networks._2d import BasicNeuralNetwork\n",
    "network_config = {\n",
    "    \"in_channels\": in_channels,\n",
    "    \"num_classes\": len(data_config['classes']),\n",
    "    \"dimensions\": dimensions,\n",
    "}\n",
    "network = BasicNeuralNetwork(**network_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 27331,
     "status": "ok",
     "timestamp": 1614634490499,
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      "displayName": "Ritvik Vasan",
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     },
     "user_tz": 480
    },
    "id": "4XoFpC2Gq2f5"
   },
   "outputs": [],
   "source": [
    "from serotiny.library.models import ClassificationModel\n",
    "classification_model = ClassificationModel(\n",
    "    network,\n",
    "    x_label=dm.x_label,\n",
    "    y_label=dm.y_label,\n",
    "    in_channels=in_channels,\n",
    "    classes=data_config['classes'],\n",
    "    dimensions=dimensions,\n",
    "    lr=0.001,\n",
    "    optimizer=\"adam\",\n",
    "    scheduler=\"reduce_lr_plateau\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define a pytorch lightning trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1614634490500,
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    },
    "id": "0sFnc-ecq6Fa",
    "outputId": "fdbd346c-35f5-40c1-cfa1-ee991c436047"
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   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "\n",
    "output_path = \"./\"\n",
    "\n",
    "\n",
    "tb_logger = TensorBoardLogger(\n",
    "    save_dir=str(output_path) + \"/lightning_logs\",\n",
    "    version=\"version_\" + datetime.now().strftime(\"%d-%m-%Y--%H-%M-%S\"),\n",
    "    name=\"\",\n",
    ")\n",
    "\n",
    "# Initialize a trainer\n",
    "trainer = pl.Trainer(\n",
    "    logger=[tb_logger],\n",
    "#     accelerator=\"ddp\",\n",
    "#     replace_sampler_ddp=False,\n",
    "    gpus=1,\n",
    "    max_epochs=1,\n",
    "    progress_bar_refresh_rate=20,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train!"
   ]
  },
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    "trainer.fit(classification_model, dm)"
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  },
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