MTL_PyTorch_S.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MTL PyTorch S.ipynb",
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyMsBLded8D5SWjj6PH9rlt7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jeremy26/hydranets_course/blob/main/MTL_PyTorch_S.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Welcome to the Multi-Task Learning Workshop!\n",
        "In this workshop, you're going to learn how to build multi-output networks with PyTorch.<p>\n",
        "Something like this image:"
      ],
      "metadata": {
        "id": "twDGnszz2B4j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![IMG_0814.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "ehsNufft2Iwi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The good thing about Multi-Task Learning is that it's pretty **new**, and we **share weights**. Similarly to when transfer learning helps save time when we train our model, we're going to **save time on training, but also on inference!**. You no longer need to run 3x50 layers to have 3 tasks solved, **you can run one neural network**, and **change the heads**. With that, your network benefit from what the other tasks are learning; **it's like teaching to a class of 3 students who would share what they learn together**, instead of letting them figuring out on their own.<p>\n",
        "\n",
        "Here's how this workshop is going to happen: \n",
        "1.  We're going to **load a multi-task learning dataset** named [UTK Face](https://susanqq.github.io/UTKFace/). It contains 24k images of faces, along with 3 labels: age, gender, and race/ethnicity.\n",
        "2.  We are going to explore the dataset, and we'll realize that we need to solve **3 tasks**: binary classification (gender), multi-class classification (race), and regression (age).\n",
        "3.  With PyTorch, we're going to learn how to **create a DataLoader that returns multiple labels**.\n",
        "4.  To solve the tasks, we'll work with a **pretrained ResNet model**, **behead it**, and **create 3 new heads**.\n",
        "5.  Finally, we'll **train the model** on a training dataset, and **test on the validation dataset**.\n",
        "<p>\n",
        "\n",
        "If you're familiar with PyTorch, this workshop might look simple, but it will be challenging to **get a great accuracy with it**. If you're new to PyTorch, you're gonna love it: We're going to **create everything from scratch**.\n"
      ],
      "metadata": {
        "id": "JiVlhi7w2cwI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 — The UTK Face Dataset\n",
        "You can download the UTK Face Dataset [here](https://susanqq.github.io/UTKFace/), but I uploaded on Think Autonomous' servers. <p>\n",
        "**The following wget command will download our dataset.**"
      ],
      "metadata": {
        "id": "u9cKZUvf2fw2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://images.squarespace-cdn.com/content/v1/5d6567d1afafe900010b2c70/1567268409336-V7HQTOKOVGT6OYHAER5D/utk-1.jpg)"
      ],
      "metadata": {
        "id": "CjxFI0pu4nwC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Gvz3UGl16oW"
      },
      "outputs": [],
      "source": [
        "!wget https://hydranets-data.s3.eu-west-3.amazonaws.com/UTKFace.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!jar xf UTKFace.zip"
      ],
      "metadata": {
        "id": "EsLW_FNb2Cdx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import transforms\n",
        "import glob\n",
        "import os"
      ],
      "metadata": {
        "id": "5mKtlxWg2nzO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 — Very fast Data Exploration\n",
        "Based on the dataset, you can see that the entire thing is on the name of the images.<p>\n",
        "For example, the image **UTKFace/100_0_0_20170112213500903.jpg.chip.jpg** can be interpreted as follows:\n",
        "\n",
        "* UTKFace/ is a prefix\n",
        "* **100 is the age**\n",
        "* **0 is the gender** (0: male, 1: female)\n",
        "* **0 is the race** (0:White, 1:Black, 2:Asian, 3:Indian, 4:Other)\n",
        "* The rest is the date and the extension (jpg)\n",
        "\n",
        "So: **[age] _ [gender] _ [race] _ [date&time].jpg**\n",
        "\n",
        "\n",
        "The example above is the filename for image number 0. Let's pray the image we see is a very old white man.🙏🏻\n"
      ],
      "metadata": {
        "id": "145c068M2nOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_paths = sorted(glob.glob(\"UTKFace/*.jpg.chip.jpg\"))\n",
        "\n",
        "images = []\n",
        "ages = []\n",
        "genders = []\n",
        "races = []\n",
        "\n",
        "for path in image_paths:\n",
        "    filename = path[8:].split(\"_\")\n",
        "    if len(filename)==4:\n",
        "        images.append(np.array(Image.open(path)))\n",
        "        ages.append(int(filename[0]))\n",
        "        genders.append(int(filename[1]))\n",
        "        races.append(int(filename[2]))"
      ],
      "metadata": {
        "id": "smyHUqAB2vMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(images))"
      ],
      "metadata": {
        "id": "3gPAgyTI2xCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_dict = #TODO: Create a dictionary"
      ],
      "metadata": {
        "id": "rR_rFCw92yug"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idx = np.random.randint(len(images))# Hint: Try 19006 for someone who's gonna yell motherfucka at you!\n",
        "\n",
        "plt.imshow(images[idx])\n",
        "plt.show()\n",
        "\n",
        "print(\"Age: \"+str(ages[idx]))\n",
        "print(\"Gender: \"+str(dataset_dict['gender_id'][genders[idx]]))\n",
        "print(\"Race: \"+str(dataset_dict['race_id'][races[idx]]))"
      ],
      "metadata": {
        "id": "BEPuiyx120Tz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalization"
      ],
      "metadata": {
        "id": "AJP8d16A2upW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_age_value, max_age_value = min(ages), max(ages)\n",
        "log_age_values = np.log10(ages)\n",
        "max_age_log_value = log_age_values.max()\n",
        "print('MAX AGE VALUE', max_age_value)\n",
        "print('MIN AGE VALUE', min_age_value)\n",
        "print('MAX AGE LOG VALUE', max_age_log_value)"
      ],
      "metadata": {
        "id": "jgVF1ssa28-x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_normalized_age_value(original_age_value):\n",
        "    return (original_age_value - min_age_value)/(max_age_value - min_age_value)\n",
        "\n",
        "def get_log_age_value(original_age_value):\n",
        "    return np.log10(original_age_value)/max_age_log_value\n",
        "\n",
        "def get_original_age_from_log_value(log_age_value):\n",
        "    return np.exp(log_age_value) * max_age_log_value\n",
        "\n",
        "def get_original_age_value(normalized_age_value):\n",
        "    return normalized_age_value * (max_age_value - min_age_value) + min_age_value"
      ],
      "metadata": {
        "id": "YlkU2PGz2-43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## More Data Exploration\n",
        "\n",
        "Usually, we should seek for balance in the dataset, we should explore it. Let's not loose to much time here as it's not our #1 Priority.<p>\n",
        "👉 Kaggle has a competition open for this dataset, and they released [a starter for the data visualization](https://www.kaggle.com/svenknoblauch/utkface-data-exploration). Let's use it directly in our work!"
      ],
      "metadata": {
        "id": "aZn9mnMD3BqB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "d = {'age': ages, 'gender': genders, 'race': races}\n",
        "df = pd.DataFrame(data=d)"
      ],
      "metadata": {
        "id": "Msg6QSWR2_g4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(21, 7))\n",
        "fig.suptitle('age distribution for gender', fontsize=20)\n",
        "\n",
        "df_age_male = df.groupby('gender').get_group(0)\n",
        "df_age_female = df.groupby('gender').get_group(1)\n",
        "sns.histplot(data=df_age_male, x=\"age\", kde=True, color=\"red\", ax=ax1, bins=50)\n",
        "sns.histplot(data=df_age_female, x=\"age\", kde=True, color=\"orange\", ax=ax2, bins=50)\n",
        "ax1.title.set_text(\"male\")\n",
        "ax2.title.set_text(\"female\")\n",
        "\n",
        "\n",
        "sns.kdeplot(data=df_age_male, x=\"age\", color=\"red\", ax=ax3)\n",
        "sns.kdeplot(data=df_age_female, x=\"age\", color=\"orange\", ax=ax3)\n",
        "ax3.legend([\"male\", \"female\"], fontsize=\"large\")\n",
        "ax3.title.set_text(\"male vs female\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kiCk_kJY3Dp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pie, (ax1, ax2) = plt.subplots(1, 2, figsize=[10,6])\n",
        "df.gender.value_counts().plot(kind='pie', labels=[\"male\", \"female\"], pctdistance=0.5, ax = ax1)\n",
        "ax1.yaxis.set_visible(False)\n",
        "ax1.title.set_text('gender distribution')\n",
        "\n",
        "df.race.value_counts().plot(kind='pie', labels=[\"White\", \"Black\", \"Asian\", \"Indian\", \"Others\"], pctdistance=0.5, ax = ax2)\n",
        "ax2.yaxis.set_visible(False)\n",
        "ax2.title.set_text('race distribution')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V2NuaQUn3GaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize=(20, 5))\n",
        "fig.suptitle('age distribution for all races', fontsize=20, y=1.1)\n",
        "fig.tight_layout()\n",
        "\n",
        "df_race_white = df.groupby('race').get_group(0)\n",
        "sns.histplot(data=df_race_white, x=\"age\", kde=True, color=\"red\", ax=ax1, bins=40)\n",
        "ax1.title.set_text(\"white mean_age: \"+\"%.2f\" % df_race_white.mean()[\"age\"])\n",
        "\n",
        "df_race_black = df.groupby('race').get_group(1)\n",
        "sns.histplot(data=df_race_black, x=\"age\", kde=True, color=\"orange\", ax=ax2, bins=40)\n",
        "ax2.title.set_text(\"black mean_age: \"+\"%.2f\" % df_race_black.mean()[\"age\"])\n",
        "\n",
        "df_race_asian = df.groupby('race').get_group(2)\n",
        "sns.histplot(data=df_race_asian, x=\"age\", kde=True, color=\"blue\", ax=ax3, bins=40)\n",
        "ax3.title.set_text(\"asian\")\n",
        "ax3.title.set_text(\"asian mean_age: \"+\"%.2f\" % df_race_asian.mean()[\"age\"])\n",
        "\n",
        "df_race_indian = df.groupby('race').get_group(3)\n",
        "sns.histplot(data=df_race_indian, x=\"age\", kde=True, color=\"green\", ax=ax4, bins=40)\n",
        "ax4.title.set_text(\"indian mean_age: \"+\"%.2f\" % df_race_indian.mean()[\"age\"])\n",
        "\n",
        "df_race_other = df.groupby('race').get_group(4)\n",
        "sns.histplot(data=df_race_other, x=\"age\", kde=True, color=\"purple\", ax=ax5, bins=40)\n",
        "ax5.title.set_text(\"other mean_age: \"+\"%.2f\" % df_race_other.mean()[\"age\"])\n",
        "\n",
        "plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.4, hspace=0.4)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ewj0W8d83G--"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3 — Create a Multi-Task DataLoader with PyTorch\n",
        "\n",
        "In PyTorch, we usually need to create 2 elements:\n",
        "* A Dataset — That will do exactly what we've done right above\n",
        "* A Dataloader — That will convert our data in PyTorch format\n",
        "\n",
        "## Dataset Minimal Code\n",
        "A dataset needs to implement a class that inherits Dataset with at the minimum 3 things:\n",
        "```python\n",
        "def __init__(self):\n",
        "def __len__(self):\n",
        "def __getitem__(self):\n",
        "```\n",
        "Okay, there are two underscores. It doesn't mean anything bad, just that we're overwritting some functions in Python. \n",
        "* The __init__ function is what's called when we create a Dataset. \n",
        "* The __len__ function is what's returned when we check the length of our dataset (with len(dataset)). \n",
        "* The __getitem__ function is what's called when we call for one element of the dataset (with dataset[i])"
      ],
      "metadata": {
        "id": "35DotW3Q2i9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Just in case your images don't load properly\n",
        "from PIL import ImageFile\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True"
      ],
      "metadata": {
        "id": "PiRvwx7K3LAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class UTKFace(Dataset):\n",
        "    def __init__(self, image_paths):\n",
        "        # Mean and Std for ImageNet\n",
        "        mean=[0.485, 0.456, 0.406] # ImageNet\n",
        "        std=[0.229, 0.224, 0.225] # ImageNet\n",
        "\n",
        "        # Define the Transforms\n",
        "        self.transform = #TODO: Create 3 transforms: Resize, To Tensor, Normalize\n",
        "\n",
        "        # Set Inputs and Labels\n",
        "        self.image_paths = image_paths\n",
        "        self.images = []\n",
        "        self.ages = []\n",
        "        self.genders = []\n",
        "        self.races = []\n",
        "\n",
        "        for path in image_paths:\n",
        "            filename = path[8:].split(\"_\")\n",
        "            if len(filename)==4:\n",
        "                self.images.append(path)\n",
        "                self.ages.append(int(filename[0]))\n",
        "                self.genders.append(int(filename[1]))\n",
        "                self.races.append(int(filename[2]))\n",
        "    \n",
        "    def __len__(self):\n",
        "         return len(self.images)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        # Load an Image\n",
        "        img = #TODO: Load an Image\n",
        "        # Transform it\n",
        "        img = #TODO: Transform it\n",
        "\n",
        "        # Get the Labels\n",
        "        age = #TODO: \n",
        "        gender = #TODO: \n",
        "        race = #TODO: \n",
        "        \n",
        "        # Return the sample of the dataset\n",
        "        sample = #TODO: \n",
        "        return sample"
      ],
      "metadata": {
        "id": "LFHdq6sq3LC8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train/Test Split"
      ],
      "metadata": {
        "id": "2Jbjwr0x3QWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import random_split\n",
        "from torch.utils.data import DataLoader"
      ],
      "metadata": {
        "id": "nLusDMGM3Oc7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the train and val splits\n",
        "TRAIN_SPLIT = #TODO: \n",
        "VAL_SPLIT = #TODO: \n",
        "\n",
        "# set the device we will be using to train the model\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "Xv3W1W_53OfJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_train = round(TRAIN_SPLIT*len(image_paths))\n",
        "num_val = round(VAL_SPLIT*len(image_paths))\n",
        "\n",
        "print('No of train samples', num_train)\n",
        "print('No of validation Samples', num_val)"
      ],
      "metadata": {
        "id": "ZSmueQT-3Oh6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_dataset, valid_dataset) = random_split(image_paths,[num_train, num_val],generator=torch.Generator().manual_seed(42))"
      ],
      "metadata": {
        "id": "WmXKrIJ23VDi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataloader"
      ],
      "metadata": {
        "id": "HhWM2fRERnWT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = #TODO: \n",
        "\n",
        "train_dataloader = #TODO: \n",
        "val_dataloader = #TODO: \n",
        "\n",
        "train_steps = len(train_dataloader.dataset) // BATCH_SIZE\n",
        "val_steps = len(val_dataloader.dataset) // BATCH_SIZE"
      ],
      "metadata": {
        "id": "JHRZUgB23Wfc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(img):\n",
        "    #TODO: "
      ],
      "metadata": {
        "id": "l_Q_l_YU3Xwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample = next(iter(train_dataloader))\n",
        "\n",
        "imshow(sample['image'][0])\n",
        "plt.show()\n",
        "print(sample[\"age\"][0].item())\n",
        "print(get_normalized_age_value(sample[\"age\"][0].item()))\n",
        "\n",
        "\n",
        "print(dataset_dict['gender_id'][sample[\"gender\"][0].item()])\n",
        "print(dataset_dict['race_id'][sample[\"race\"][0].item()])"
      ],
      "metadata": {
        "id": "9Z7BMFKk3ZNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4 — Multi-Task Neural Network with PyTorch\n",
        "\n",
        "In this part, we want to:\n",
        "1. Define a Base Neural Network\n",
        "2. Create the Heads\n",
        "3. Train it\n",
        "4. Test it\n"
      ],
      "metadata": {
        "id": "5uIKTYob3c0L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define the Base Model\n",
        "In this part, we're going to load a pretrained Resnet18. **In this part, I'm going to teach you how to add layers to a pretrained network, no matter the nework, so if you'd like to load an Inception v3 and go all Dicaprio on this, be my guest!**\n"
      ],
      "metadata": {
        "id": "qy89rCIq3fbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision.models import resnet50, resnet101, resnet18, resnet34"
      ],
      "metadata": {
        "id": "MpDvthOm3a3T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net = #TODO:"
      ],
      "metadata": {
        "id": "duj0uY7sUw0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchviz\n",
        "from torchviz import make_dot\n",
        "\n",
        "#make_dot(net(sample[\"image\"].to(device)), params=dict(list(net.named_parameters()))).render(\"ResNet50\", format=\"png\")"
      ],
      "metadata": {
        "id": "JU6lwV27UmJT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create the HydraNet class\n",
        "\n",
        "\n",
        "The final layer is called net.fc. If we want to add layers to that, we can always change that layer, or add layers after this one.\n",
        "\n",
        "\n",
        "Here's an example when changing this final layer to a 10 neuron output:\n",
        "```python\n",
        "self.net = models.resnet18(pretrained=True)\n",
        "self.net.fc = nn.Linear(model.fc.in_features, 10)\n",
        "```\n",
        "\n",
        "We can also use the OrderedDict method and have a set of layers (a real head!):\n",
        "```python\n",
        "from collections import OrderedDict\n",
        "self.n_features = self.net.fc.in_features\n",
        "self.net.fc1 = nn.Sequential(OrderedDict([\n",
        "    ('linear', nn.Linear(self.n_features,self.n_features)),\n",
        "    ('relu1', nn.ReLU()),\n",
        "    ('final', nn.Linear(self.n_features, 1))\n",
        "    ]))\n",
        "```"
      ],
      "metadata": {
        "id": "gPAopiy83nic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import OrderedDict"
      ],
      "metadata": {
        "id": "vUnDAS5X3lKQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class HydraNetModified(nn.Module):\n",
        "    def __init__(self, net):\n",
        "        pass\n",
        "        #TODO: \n",
        "        \n",
        "    def forward(self, x):\n",
        "        pass\n",
        "        #TODO: "
      ],
      "metadata": {
        "id": "U3gGEPKX3ltY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = #TODO: \n",
        "model.to(device=device)\n",
        "\n",
        "race_loss = #TODO:\n",
        "gender_loss = #TODO:\n",
        "age_loss = #TODO: \n",
        "\n",
        "lr =#TODO: \n",
        "momentum = 0.09 # Meaning that we will go 9% with the previous direction\n",
        "optimizer = #TODO: "
      ],
      "metadata": {
        "id": "T4Y1KvOf3rcd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#make_dot(model(sample[\"image\"].to(device)), params=dict(list(model.named_parameters()))).render(\"HydraNet\", format=\"png\")"
      ],
      "metadata": {
        "id": "KBdvX9_mUy6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the Model\n",
        "Here's a typical training loop in PyTorch\n",
        "```python\n",
        "losses = []\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    for data in dataLoader:\n",
        "        images, labels = data\n",
        "        outputs = model(images)\n",
        "        loss = criterion_label(outputs, labels)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item() * images.size(0) \n",
        "\n",
        "    epoch_loss = running_loss / len(dataloaders['train'])\n",
        "    losses.append(epoch_loss)\n",
        "    plt.plot(losses)\n",
        "```\n",
        "\n",
        "In our case, it will be a bit different, as we have several types of losses to plot (3x2)"
      ],
      "metadata": {
        "id": "tkDAtQDW3wQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "\n",
        "n_epochs = #TODO: \n",
        "\n",
        "logger = {\"train_loss\": list(),\n",
        "          \"validation_loss\": list(),\n",
        "          \"train_gender_loss\": list(),\n",
        "          \"train_race_loss\": list(),\n",
        "          \"train_age_loss\": list(),\n",
        "          \"validation_gender_loss\": list(),\n",
        "          \"validation_race_loss\": list(),\n",
        "          \"validation_age_loss\": list(),\n",
        "          }"
      ],
      "metadata": {
        "id": "DehfMXfL7nyh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sig = nn.Sigmoid()\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    model.train()\n",
        "\n",
        "    total_training_loss = 0\n",
        "    total_validation_loss = 0\n",
        "    training_gender_loss = 0\n",
        "    training_race_loss = 0\n",
        "    training_age_loss = 0\n",
        "    validation_gender_loss = 0\n",
        "    validation_race_loss = 0\n",
        "    validation_age_loss = 0\n",
        "\n",
        "    for i, data in enumerate(train_dataloader):\n",
        "        inputs = #\n",
        "        age_label = #\n",
        "        gender_label = #\n",
        "        race_label = #\n",
        "\n",
        "        #TODO: Zero Grad\n",
        "        age_output, gender_output, race_output = #\n",
        "        \n",
        "        loss_1 = #\n",
        "        loss_2 = #\n",
        "        loss_3 = #\n",
        "\n",
        "        loss = #\n",
        "\n",
        "        #Backward\n",
        "        #Step\n",
        "        total_training_loss += loss\n",
        "        \n",
        "        training_race_loss += loss_1.item()\n",
        "        training_gender_loss += loss_2.item()\n",
        "        training_age_loss += loss_3.item()\n",
        "    print('EPOCH ', epoch+1)\n",
        "    print(\"Training Losses: Race: {}, Gender: {}, Age: {}\".format(loss_1, loss_2, loss_3))\n",
        "\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "\n",
        "        for i, data in enumerate(val_dataloader):\n",
        "            inputs = #\n",
        "            age_label = #\n",
        "            gender_label = #\n",
        "            race_label =  #\n",
        "            age_output, gender_output, race_output = #\n",
        "        \n",
        "            loss_1 = #\n",
        "            loss_2 = #\n",
        "            loss_3 = #\n",
        "\n",
        "            loss = #\n",
        "            total_validation_loss += loss\n",
        "\n",
        "            validation_race_loss += loss_1.item()\n",
        "            validation_gender_loss += loss_2.item()\n",
        "            validation_age_loss += loss_3.item()\n",
        "        print(\"Validation Losses: Race: {}, Gender: {}, Age: {}\".format(loss_1, loss_2, loss_3))\n",
        "\n",
        "    avgTrainLoss = total_training_loss / train_steps\n",
        "    avgValLoss = total_validation_loss / val_steps\n",
        "    \n",
        "    print('Average Losses — Training: {} | Validation {}'.format(avgTrainLoss, avgValLoss))\n",
        "    print() \n",
        "    avgTrainGenderLoss = training_gender_loss/len(train_dataloader.dataset)\n",
        "    avgTrainRaceLoss = training_race_loss/len(train_dataloader.dataset)\n",
        "    avgTrainAgeLoss = training_age_loss/len(train_dataloader.dataset)\n",
        "\n",
        "    avgValGenderLoss = validation_gender_loss/len(val_dataloader.dataset)\n",
        "    avgValRaceLoss = validation_race_loss/len(val_dataloader.dataset)\n",
        "    avgValAgeLoss = validation_age_loss/len(val_dataloader.dataset)\n",
        "\n",
        "    logger[\"train_loss\"].append(avgTrainLoss.cpu().detach().numpy())\n",
        "    logger[\"train_gender_loss\"].append(avgTrainGenderLoss)\n",
        "    logger[\"train_race_loss\"].append(avgTrainRaceLoss)\n",
        "    logger[\"train_age_loss\"].append(avgTrainAgeLoss)\n",
        "    \n",
        "    logger[\"validation_loss\"].append(avgValLoss.cpu().detach().numpy())\n",
        "    logger[\"validation_gender_loss\"].append(avgValGenderLoss)\n",
        "    logger[\"validation_race_loss\"].append(avgValRaceLoss)\n",
        "    logger[\"validation_age_loss\"].append(avgValAgeLoss)\n"
      ],
      "metadata": {
        "id": "mcw6i-f93uaR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), \"best_model.pth\")"
      ],
      "metadata": {
        "id": "AyeDm4lv4GhE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Show the Results"
      ],
      "metadata": {
        "id": "sDz_2R6833Ar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(logger[\"train_loss\"])\n",
        "plt.plot(logger[\"validation_loss\"])\n",
        "plt.legend(['Train','Valid'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Mc_pMH9G33JA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(logger[\"train_gender_loss\"])\n",
        "plt.plot(logger[\"validation_gender_loss\"])\n",
        "plt.legend(['Train','Valid'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fAQiwY783-lV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(logger[\"train_race_loss\"])\n",
        "plt.plot(logger[\"validation_race_loss\"])\n",
        "plt.legend(['Train','Valid'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OLQGIu1T3_NT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(logger[\"train_age_loss\"])\n",
        "plt.plot(logger[\"validation_age_loss\"])\n",
        "plt.legend(['Train','Valid'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "UsN8rjbL4A5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference"
      ],
      "metadata": {
        "id": "3uYXZ1vH4HJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "indexes = valid_dataset.indices\n",
        "test_indices = indexes[0:10]"
      ],
      "metadata": {
        "id": "qBXkJlIQ4JLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx in test_indices:\n",
        "    plt.figure()\n",
        "    plt.imshow(images[idx])\n",
        "    plt.show()\n",
        "\n",
        "    image_norm = #\n",
        "    image_norm = #\n",
        "\n",
        "    model.eval()\n",
        "    age, gender, race = #\n",
        "\n",
        "    predicted_age = #\n",
        "\n",
        "    print(\"Age:\", str(ages[idx]), \"| Predicted:\", str(int(predicted_age)))\n",
        "\n",
        "    sigmoid = nn.Sigmoid()\n",
        "    out_gender = #\n",
        "    gender_classes = [\"male\", \"female\"]\n",
        "    print(\"Gender:\", str(dataset_dict['gender_id'][genders[idx]]), \"| Predicted:\", str(gender_classes[out_gender]))\n",
        "\n",
        "    out_race = #\n",
        "    race_classes = [\"white\", \"black\", \"asian\", \"indian\", \"other\"]\n",
        "    print(\"Race:\", str(dataset_dict['race_id'][races[idx]]), \"| Predicted:\", str(race_classes[out_race]))\n",
        "    print('\\n')"
      ],
      "metadata": {
        "id": "xAOuNQm04K1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "hkRS32cgVOiA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rtm71tBDD9Ax"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}