Efficientnet_sol_final.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M24QrQork-FZ"
      },
      "source": [
        "# CS182 Project: Introduction to EfficientNet\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ot2UEHsmDjx"
      },
      "source": [
        "### Part1: introduction\n",
        "EfficientNet is a family of convolutional neural networks that were designed to provide state-of-the-art accuracy on image classification tasks while maintaining a high level of efficiency. Developed by a team of researchers at Google, EfficientNet models use a novel compound scaling method to balance the number of parameters in the network with its depth and width, resulting in a highly optimized architecture that achieves superior performance with fewer computational resources. EfficientNet models have achieved top scores in various computer vision benchmarks, including the ImageNet dataset, and have been widely adopted for a range of applications, including object detection, segmentation, and transfer learning.\n",
        "\n",
        "In this HW, we're going to implement EfficientNet from scratch, and understand how EfficientNets are \"efficient\" in the sense of cost of compuation and number of parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yviRW1EmrXa"
      },
      "source": [
        "Imports and preparations: (Run the cell below if you're using Google Colab)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeiRRAQkAulz",
        "outputId": "8ec1899f-e8d4-4bf3-aa40-5a62598b8b3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "DRIVE_PATH = '/content/gdrive/My\\ Drive/cs182project_eq_efficientnet'\n",
        "DRIVE_PYTHON_PATH = DRIVE_PATH.replace('\\\\', '')\n",
        "if not os.path.exists(DRIVE_PYTHON_PATH):\n",
        "  %mkdir $DRIVE_PATH\n",
        "\n",
        "## the space in `My Drive` causes some issues,\n",
        "## make a symlink to avoid this\n",
        "SYM_PATH = '/content/cs182project_eq_efficientnet'\n",
        "if not os.path.exists(SYM_PATH):\n",
        "  !ln -s $DRIVE_PATH $SYM_PATH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhaO74GkpGiu",
        "outputId": "931eb82d-9273-493b-f175-9faf364a5f91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.1)\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.42.2-3build2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install graphviz \n",
        "!apt-get install graphviz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "50fXbzQ2AyX6"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from math import ceil\n",
        "\n",
        "# Torch stuff\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import random_split\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "import torch.nn.init as init\n",
        "\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "#training part\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "import copy\n",
        "\n",
        "import graphviz \n",
        "from itertools import tee"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Graphviz Utilities (run this)\n",
        "def generate_dwr(csf):\n",
        "  \"\"\"\n",
        "  Determins the depth, width and resolution from the scaling factor.\n",
        "  Alpha, beta, gamma are taken from the efficientnet paper.\n",
        "  \"\"\"\n",
        "  #From the paper\n",
        "  alpha = 1.2\n",
        "  beta = 1.1\n",
        "  gamma = 1.15\n",
        "  return (alpha ** csf, beta ** csf, gamma ** csf)\n",
        "\n",
        "def pairwise(iterable):\n",
        "  \"\"\"\n",
        "  Iterates through an iterable (list), pairwise.\n",
        "  a, b, c -> (a,b), (b,c)\n",
        "  \"\"\"\n",
        "  a, b = tee(iterable)\n",
        "  next(b, None)\n",
        "  return zip(a, b)\n",
        "\n",
        "def compose_edges(g, nodes):\n",
        "  \"\"\"\n",
        "  Forms the actual edges from a list of all the nodes, just sequentially.\n",
        "  \"\"\"\n",
        "  for a, b, in list(pairwise(nodes)):\n",
        "    g.edge(a, b, constraint='false')\n",
        "\n",
        "def num_layers(n):\n",
        "  # This is just a random constant\n",
        "  return max(int(0.8*n), 1)\n",
        "\n",
        "def generate_layers(g, rfactors, color, w_f, h_f, layer_name):\n",
        "  \"\"\"\n",
        "  Generates a colored 'layer' in the graph, this could result in several\n",
        "  Nodes being generated depending on the depth factor.\n",
        "  \"\"\"\n",
        "  d, w, r = rfactors\n",
        "  layers = num_layers(d)\n",
        "  items = []\n",
        "  for layer, index, in enumerate(range(layers)):\n",
        "    name = layer_name + str(index)\n",
        "    g.node(name, label = \" \", color = color, style = \"filled\", width = str(w*w_f), height = str(r*h_f))\n",
        "    items.append(name)\n",
        "  return items\n",
        "\n",
        "def generate_visualization(csf = 1):\n",
        "  factors = generate_dwr(csf)\n",
        "  d, w, r = factors\n",
        "  g = graphviz.Digraph('efficientNet', comment='efficientNet') \n",
        "  all_items = []\n",
        "  g.attr('node', shape='box')\n",
        "  g.node('input','input' ,color = '#ffffff')\n",
        "  all_items.append('input')\n",
        "  all_items += generate_layers(g, factors, color = '#b873bf', w_f = 0.1, h_f = 0.3, layer_name = 'first')\n",
        "  all_items += generate_layers(g, factors, color = '#e3c062', w_f = 0.2, h_f = 0.2, layer_name = 'second')\n",
        "  all_items += generate_layers(g, factors, color = '#62e3a2', w_f = 0.2, h_f = 0.4, layer_name = 'third')\n",
        "  all_items += generate_layers(g, factors, color = '#62dfe3', w_f = 0.02, h_f = 0.6, layer_name = 'fourth')\n",
        "  all_items += generate_layers(g, factors, color = '#e362d0', w_f = 0.02, h_f = 0.8, layer_name = 'fifth')\n",
        "  g.node('output', 'output', color = '#ffffff')\n",
        "  all_items.append('output')\n",
        "  compose_edges(g, all_items)\n",
        "  g.attr(label=r'EfficientNet Architecture Diagram \\n Compound Scaling Factor = %s' %(csf))\n",
        "  return g"
      ],
      "metadata": {
        "id": "0Elcw_ZZrI-R"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To better reinforce the intution about the compound scaling method, we have implemented a visualization generator function. The overall intution about just changing the compound scaling factor, then being able to affect overall change in the actual architecture, changing the depth, width, and resolution in a principled manner.\n",
        "\n",
        "* This is intended to provide intuition about efficientNet, this is not how efficientNet literally scales."
      ],
      "metadata": {
        "id": "kQHzG5LhrYUF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question: Play around with this function, at what point do you start to see new depth layers emerging? (You might also want to use this opportunity to check your intuition about the previous conceptual questions)."
      ],
      "metadata": {
        "id": "sc1plM7ZrcSP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generate_visualization(csf = 2) #Play around with this."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "CbBlOojKsMD4",
        "outputId": "a1a708d5-37cc-475f-a9c3-f4d0a3df2cf6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhibDcKWm88v"
      },
      "source": [
        "We're going to use CIFAR-10 dataset for this project. It is very commonly used while testing certain CV models. The dataset contains 60,000 32x32 color images in 10 different classes. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class. We're going to use the torchvision package to load the dataset. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.\n",
        "\n",
        "In this part we're going to implement a dataloader. The purpose of this is to build a convenient way to feed data from a dataset to a model during training or inference. With the DataLoader, users can easily handle large datasets and apply different data augmentation techniques to the input data. The PyTorch DataLoader is a flexible and efficient tool that has become a standard part of many deep learning workflows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FEiiw_KIBFbF"
      },
      "outputs": [],
      "source": [
        "train_transform = transforms.Compose(\n",
        "    [\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "        \n",
        "    ]\n",
        ")\n",
        "\n",
        "test_transform = transforms.Compose(\n",
        "    [\n",
        "        transforms.ToTensor(), \n",
        "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64mqlIB-Wa0J",
        "outputId": "caec61e1-2ee2-4823-a81f-73c89491df8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=train_transform)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                        download=True, transform=test_transform)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHv1wFh8OBb-",
        "outputId": "edfd7eec-be7c-4af0-f848-6bdd1bb65747"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_size, val_size = 40000, 10000\n",
        "train_ds, val_ds = random_split(trainset, [train_size, val_size])\n",
        "len(train_ds), len(val_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4kM7X8q-AF7"
      },
      "source": [
        "Question 1a): setup train_loader, train_dataset, val_loader, val_dataset, test_loader, test_dataset in the following block. You can use the code in the previous block as a reference.\n",
        "Hint: check out function: produce_dataloader_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "467s3tpEBJtv"
      },
      "outputs": [],
      "source": [
        "#######################\n",
        "# TODO: your code here#\n",
        "#######################\n",
        "trainloader = DataLoader(train_ds, batch_size=256,\n",
        "                            shuffle=True, num_workers=2)\n",
        "valloader = DataLoader(val_ds, batch_size=256, \n",
        "                            shuffle=True, num_workers=2)\n",
        "testloader = DataLoader(testset, batch_size=256,\n",
        "                            shuffle=False, num_workers=2)\n",
        "#######################\n",
        "#END OF YOUR CODE    #\n",
        "#######################"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "KsgBNtvxpGix",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "dc15683a-99b5-4620-e1bc-5b1dd9bf36a8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "'''\n",
        "You should be able to see some sample images from the training set if you correctly implemented the above code.\n",
        "'''\n",
        "def draw_sample_images(data, labels):\n",
        "    nrows = 4\n",
        "    ncols = 10\n",
        "    total_image = data.shape[0]\n",
        "    samples = np.random.choice(total_image, nrows*ncols)\n",
        "    plt.figure(figsize=(20, 5))\n",
        "    for i in range(nrows*ncols):\n",
        "        plt.subplot(nrows, ncols, i+1)\n",
        "        image = np.moveaxis(data[samples[i]].numpy(), 0, -1)\n",
        "        plt.imshow(image/2+0.5)\n",
        "        plt.title(trainset.classes[labels[samples[i]]])\n",
        "        plt.axis(\"off\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "data_iterator = iter(trainloader)\n",
        "images, labels = next(data_iterator)\n",
        "draw_sample_images(images, labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAG0AT-qPmjA",
        "outputId": "51daf4b6-3f38-4229-b13b-0f20433a844f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['airplane',\n",
              " 'automobile',\n",
              " 'bird',\n",
              " 'cat',\n",
              " 'deer',\n",
              " 'dog',\n",
              " 'frog',\n",
              " 'horse',\n",
              " 'ship',\n",
              " 'truck']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "classes = trainset.classes\n",
        "classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aUNsVP1-AF8"
      },
      "source": [
        "Question: What's the shape of data in train_loader for a sigle batch? (in terms of [N, C, H, W])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dEH-wldsN_0"
      },
      "source": [
        "### Part 2: Building the model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a12qpdF5sq-W"
      },
      "source": [
        "The \"highlight\" of EfficientNet is its use of compound scaling methods. Compound scaling in essence is to use a coefficient to uniformaly scale the 3 Dimensions (depth, width and resolution) of the model. The coefficient is denoted as $\\phi$ in the paper. The scaling method is as follows:\n",
        "\n",
        "$$\n",
        " \\text{depth}: d = \\alpha^\\phi \\\\\n",
        " \\text{width}: w = \\beta^\\phi \\\\\n",
        " \\text{resolution}: r = \\gamma^\\phi \\\\\n",
        " \\alpha \\cdot \\beta^2 \\cdot \\gamma ^2 \\approx 2\\\\\n",
        "\\alpha \\geq 1, \\beta \\geq 1, \\gamma \\geq 1\n",
        "$$ "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-R0zsi4-AF8"
      },
      "source": [
        "The author created a family of EfficientNet models with different $\\phi$ values, and the largest model is EfficientNet-B7 with $\\phi = 2.0$. In this HW, we're going to implement EfficientNet, with the ability to scale from $b0$ to $b7$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOFTJtgf-AF8"
      },
      "source": [
        "Firstly we'going to implement some tricks the author used that makes EfficientNet efficient. The first technique is called Squeeze and Excitation (SE). SE is very similiar to the attention mechanism. It is used to help the model to focus on the most important features. The SE module is implemented as follows: \n",
        "\n",
        "![image](https://github.com/charlietcheng/Intro-to-EfficientNet/blob/main/imgs/SE.jpeg?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "VbQ87NkFBfpo"
      },
      "outputs": [],
      "source": [
        "# Implement the Squueze and Excitation block down below\n",
        "# Note that though the image above shows we're using ReLu as the activation function, we're actually using SiLU (swish)\n",
        "# as the author mentioned in the paper that it performed better than ReLU. We also replace the avgpool with adaptiveavgpool\n",
        "# for the same reason.\n",
        "# Hint: use nn.AdaptiveAvgPool2d(1) to replace nn.AvgPool2d(1) and nn.SiLU() to replace nn.ReLU()\n",
        "class SqueezeExcitation(nn.Module):\n",
        "    def __init__(self, n_in, reduced_dim, fixed_params=False):\n",
        "        super(SqueezeExcitation, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(n_in, reduced_dim, kernel_size=1)\n",
        "        self.conv2 = nn.Conv2d(reduced_dim, n_in, kernel_size=1)\n",
        "        if fixed_params:\n",
        "          init.constant_(self.conv1.weight, 0.01)\n",
        "          init.constant_(self.conv1.bias, 0.0)\n",
        "          init.constant_(self.conv2.weight, 0.01)\n",
        "          init.constant_(self.conv2.bias, 0.0)\n",
        "        '''\n",
        "        Hints: follow the pipeline in the image above to implement the forward pass of the Squeeze and Excitation block.\n",
        "        use nn.Sequential() to build the block.\n",
        "        '''\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        self.se = nn.Sequential(\n",
        "            nn.AdaptiveAvgPool2d(1),\n",
        "            self.conv1,\n",
        "            nn.SiLU(),\n",
        "            self.conv2,\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################\n",
        "    \n",
        "    def forward(self, x):\n",
        "        '''\n",
        "        Hints: one line of code\n",
        "        '''\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        y = self.se(x)\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################\n",
        "        # Hint: consider why what the picture means by scaling and why we're multiplying here\n",
        "        return x * y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "5YHcJK2HpGiz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "049a520f-1a9b-4ace-9067-2fc3bab48822"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0.1196, 0.1235, 0.1471, 0.1510, 0.1274])\n"
          ]
        }
      ],
      "source": [
        "# unit test for se\n",
        "def test_se():\n",
        "  data_iterator = iter(testloader)\n",
        "  images, labels = next(data_iterator)\n",
        "  x = images[0]\n",
        "  se = SqueezeExcitation(3,2,fixed_params = True)\n",
        "  y = se(x)\n",
        "  y_test = y[0][0][:5].detach()\n",
        "  y_true = torch.tensor([0.1196, 0.1235, 0.1471, 0.1510, 0.1274])\n",
        "  assert torch.allclose(y_test,y_true,atol = 1e-3)\n",
        "test_se()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdUhAsTL-AF8"
      },
      "source": [
        "Next we're going to implement the trick: Stochastic Depth, which makes the entire training process much faster. The gist of it is to randomly drop a subset of layers and bypass them with the identity function during training. And a full network is used during testing/inference. \n",
        "\n",
        "The image below shows the implementation of Stochastic Depth, we suggest lo\n",
        "\n",
        "![image](https://github.com/charlietcheng/Intro-to-EfficientNet/blob/main/imgs/SD.jpeg?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "kU5a8MBO-AF8"
      },
      "outputs": [],
      "source": [
        "# Implement the Stochastic Depth block down below\n",
        "# Hint: use torch.rand() to generate a random number\n",
        "\n",
        "class StochasticDepth(nn.Module):\n",
        "    \n",
        "    def __init__(self, survival_prob = 0.8, fixed_params=False):\n",
        "        super(StochasticDepth, self).__init__()\n",
        "        self.fixed_params = fixed_params\n",
        "        self.p =  survival_prob\n",
        "        \n",
        "    def forward(self, x):\n",
        "        '''\n",
        "        Hints: what happens when self.training is True? What shall we do when self.training is False?\n",
        "        The idea is kind of similiar to Dropout and Masking.\n",
        "        '''\n",
        "        if self.fixed_params:\n",
        "          if torch.cuda.is_available():\n",
        "              torch.cuda.manual_seed(10)\n",
        "          else:\n",
        "              torch.manual_seed(10)\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        if not self.training:\n",
        "            return x\n",
        "        binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p\n",
        "        return torch.div(x, self.p) * binary_tensor\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "_UdegJfDpGiz"
      },
      "outputs": [],
      "source": [
        "# unit test for stochastic depth\n",
        "def test_sd():\n",
        "  data_iterator = iter(testloader)\n",
        "  images, labels = next(data_iterator)\n",
        "  x = images[0]\n",
        "  sd = StochasticDepth(fixed_params = True)\n",
        "  y = sd(x)\n",
        "  y_test = y[0][0][0][:5].detach()\n",
        "  y_true = torch.tensor([0.2990, 0.3088, 0.3676, 0.3775, 0.3186])\n",
        "  assert torch.allclose(y_test,y_true,atol = 1e-3)\n",
        "test_sd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "aVmgidXv-AF8"
      },
      "outputs": [],
      "source": [
        "# Here we provide you with the simple Conv-BatchNorm-Activation block for you\n",
        "# Note that we're using SiLU (swish) as the activation function instead of ReLU as the author mentioned in the paper \n",
        "# that it performed better than ReLU\n",
        "\n",
        "class ConvBnAct(nn.Module):\n",
        "    \n",
        "    def __init__(self, n_in, n_out, kernel_size = 3, stride = 1, \n",
        "                 padding = 0, groups = 1, bn = True, act = True,\n",
        "                 bias = False, fixed_params = False\n",
        "                ):\n",
        "        \n",
        "        super(ConvBnAct, self).__init__()\n",
        "        self.conv = nn.Conv2d(n_in, n_out, kernel_size = kernel_size,\n",
        "                              stride = stride, padding = padding,\n",
        "                              groups = groups, bias = bias\n",
        "                             )\n",
        "        if fixed_params:\n",
        "          init.constant_(self.conv.weight, 0.01)\n",
        "        if bias:\n",
        "          init.constant_(self.conv.bias, 0.0)\n",
        "          \n",
        "        self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity()\n",
        "        self.activation = nn.SiLU() if act else nn.Identity()\n",
        "        \n",
        "    def forward(self, x):\n",
        "        \n",
        "        x = self.conv(x)\n",
        "        x = self.batch_norm(x)\n",
        "        x = self.activation(x)\n",
        "        \n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cmt_NFdb-AF8"
      },
      "source": [
        "Finally here come the finally implementation of EfficientNet. Some additional tricks the author used here include Depthwise Separable Convolution, which is a combination of depthwise convolution and pointwise convolution. The depthwise convolution is used to extract features from each channel, and the pointwise convolution is used to combine the features from different channels.\n",
        "\n",
        "The image below shows the implementation of Depthwise Separable Convolution, we suggest referencing this image when performing the implementation:\n",
        "\n",
        "![image](https://github.com/charlietcheng/Intro-to-EfficientNet/blob/main/imgs/DSC.jpeg?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Zd3KN8Rj-AF8"
      },
      "outputs": [],
      "source": [
        "# We start by implementing Residual Bottleneck Block with Expansion Factor = N as defined in Mobilenet-V2 paper\n",
        "# with Squeeze and Excitation Block and Stochastic Depth. \n",
        "# The process of implementation: residual -> exapnd -> depthwise conv \n",
        "# -> squeeze and excitation -> pointwise conv -> skip connection\n",
        "\n",
        "class MBConvN(nn.Module):\n",
        "    def __init__(self, n_in, n_out, kernel_size = 3, \n",
        "                 stride = 1, expansion_factor = 6,\n",
        "                 reduction = 4, # Squeeze and Excitation Block\n",
        "                 survival_prob = 0.8, # Stochastic Depth\n",
        "                 fixed_params = False\n",
        "                ):\n",
        "        \n",
        "        super(MBConvN, self).__init__()\n",
        "        '''\n",
        "        Hints: self.skip_connection is True if stride == 1 and n_in == n_out\n",
        "\n",
        "        For self.expand, you can use nn.Identity() if expansion_factor == 1 else ConvBnAct(). Check the original paper \n",
        "        for more details for the parameter you are going to use.\n",
        "\n",
        "        For self.depthwise_conv, you can use ConvBnAct() with the correct parameters.\n",
        "\n",
        "        self.se is something you implemented above.\n",
        "\n",
        "        self.pointwise_conv is similar to self.depthwise_conv, but with different parameters.\n",
        "\n",
        "        self.drop_layers is something you implemented above as well.\n",
        "        '''\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        self.skip_connection = (stride == 1 and n_in == n_out) \n",
        "        intermediate_channels = int(n_in * expansion_factor)\n",
        "        padding = (kernel_size - 1)//2\n",
        "        reduced_dim = int(n_in//reduction)\n",
        "        \n",
        "        self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, intermediate_channels, kernel_size = 1, fixed_params = fixed_params)\n",
        "        self.depthwise_conv = ConvBnAct(intermediate_channels, intermediate_channels,\n",
        "                                        kernel_size = kernel_size, stride = stride, \n",
        "                                        padding = padding, groups = intermediate_channels, fixed_params = fixed_params\n",
        "                                       )\n",
        "        self.se = SqueezeExcitation(intermediate_channels, reduced_dim = reduced_dim, fixed_params = fixed_params)\n",
        "        self.pointwise_conv = ConvBnAct(intermediate_channels, n_out, \n",
        "                                        kernel_size = 1, act = False, fixed_params = fixed_params \n",
        "                                       )\n",
        "        self.drop_layers = StochasticDepth(survival_prob = survival_prob, fixed_params = fixed_params)\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################\n",
        "        \n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "        '''\n",
        "        Hints: if self.skip_connection is True, you should add residual to x before returning it.\n",
        "        Recollect the pipline of the MBConvN: residual -> exapnd -> depthwise conv -> squeeze and excitation \n",
        "        -> pointwise conv -> skip connection\n",
        "        '''\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        x = self.expand(x)\n",
        "        x = self.depthwise_conv(x)\n",
        "        x = self.se(x)\n",
        "        x = self.pointwise_conv(x)\n",
        "        \n",
        "        if self.skip_connection:\n",
        "            x = self.drop_layers(x)\n",
        "            x += residual\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "f80mmCDV-AF8"
      },
      "outputs": [],
      "source": [
        "# Here comes the acutal implmentation of EfficientNet\n",
        "class EfficientNet(nn.Module):\n",
        "    def __init__(self, width_mult = 1, depth_mult = 1, \n",
        "                dropout_rate = 0.2, num_classes = 1000, seed=42, fixed_params = False):\n",
        "        super(EfficientNet, self).__init__()\n",
        "        last_channel = ceil(1280 * width_mult)\n",
        "        self.features = self._feature_extractor(width_mult, depth_mult, last_channel, fixed_params)\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d(1)\n",
        "        self.fc1 = nn.Linear(last_channel, num_classes)\n",
        "        if fixed_params:\n",
        "          init.constant_(self.fc1.weight, 0.01)\n",
        "          init.constant_(self.fc1.bias, 0.0)\n",
        "          self.classifier = nn.Sequential(\n",
        "              self.fc1\n",
        "          )\n",
        "        else:\n",
        "          self.classifier = nn.Sequential(\n",
        "              nn.Dropout(dropout_rate),\n",
        "              self.fc1\n",
        "          )\n",
        "        \n",
        "        \n",
        "    def forward(self, x):\n",
        "        \n",
        "        x = self.features(x)\n",
        "        x = self.avgpool(x)\n",
        "        x = self.classifier(x.view(x.shape[0], -1))\n",
        "        \n",
        "        return x\n",
        "    \n",
        "    def _feature_extractor(self, width_mult, depth_mult, last_channel, fixed_params):\n",
        "        \n",
        "        channels = 4*ceil(int(32*width_mult) / 4)\n",
        "        layers = [ConvBnAct(3, channels, kernel_size = 3, stride = 2, padding = 1, fixed_params=fixed_params)]\n",
        "        in_channels = channels\n",
        "        \n",
        "        # These are from the paper\n",
        "        kernels = [3, 3, 5, 3, 5, 5, 3]\n",
        "        expansions = [1, 6, 6, 6, 6, 6, 6]\n",
        "        num_channels = [16, 24, 40, 80, 112, 192, 320]\n",
        "        num_layers = [1, 2, 2, 3, 3, 4, 1]\n",
        "        strides =[1, 2, 2, 2, 1, 2, 1]\n",
        "        \n",
        "        # Scale channels and num_layers according to width and depth multipliers.\n",
        "        scaled_num_channels = [4*ceil(int(c*width_mult) / 4) for c in num_channels]\n",
        "        scaled_num_layers = [int(d * depth_mult) for d in num_layers]\n",
        "\n",
        "        '''\n",
        "        Hints: save all layers in the list `layers` and we will use nn.Sequential to wrap them up.\n",
        "        You first use a for loop to iterate through all scaled number of layers. Then, for each iteration,\n",
        "        you use another for loop to iterate through all scaled number of channels. For each iteration, you\n",
        "        append a MBConvN block to the list `layers`. Note that the first MBConvN block in each iteration\n",
        "        should have a stride of `strides[i]` and the rest should have a stride of 1. Also, the first MBConvN\n",
        "        block in each iteration should have an input channel of `in_channels` and the rest should have an input\n",
        "        channel of `scaled_num_channels[i]`. After each iteration, you update `in_channels` to be\n",
        "        `scaled_num_channels[i]`.\n",
        "        '''\n",
        "        #######################\n",
        "        # TODO: your code here#\n",
        "        #######################\n",
        "        for i in range(len(scaled_num_channels)):\n",
        "            layers += [MBConvN(in_channels if repeat==0 else scaled_num_channels[i], \n",
        "                            scaled_num_channels[i],\n",
        "                            kernel_size = kernels[i],\n",
        "                            stride = strides[i] if repeat==0 else 1, \n",
        "                            expansion_factor = expansions[i],\n",
        "                            fixed_params = fixed_params\n",
        "                            )\n",
        "                    for repeat in range(scaled_num_layers[i])\n",
        "                    ]\n",
        "            in_channels = scaled_num_channels[i]\n",
        "        #######################\n",
        "        # End of your code    #\n",
        "        #######################\n",
        "        \n",
        "        layers.append(ConvBnAct(in_channels, last_channel, kernel_size = 1, stride = 1, padding = 0, fixed_params = fixed_params))\n",
        "    \n",
        "        return nn.Sequential(*layers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "pdS-qNxZpGi1"
      },
      "outputs": [],
      "source": [
        "# unit test for efficientnet\n",
        "def test_efficientnet():\n",
        "  data_iterator = iter(testloader)\n",
        "  images, labels = next(data_iterator)\n",
        "  x = images[:2]\n",
        "  net = EfficientNet(fixed_params=True)\n",
        "  y = net(x)\n",
        "  y_test = y[:,0].detach()\n",
        "  y_true = torch.tensor([-3.4425,  9.3575])\n",
        "  assert torch.allclose(y_test,y_true,atol = 1e-3)\n",
        "\n",
        "test_efficientnet()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "L54OyLZn-AF9"
      },
      "outputs": [],
      "source": [
        "# Compound scaling factors for efficient-net family.\n",
        "efficient_net_config = {\n",
        "    # tuple of width multiplier, depth multiplier, resolution, and Survival Prob\n",
        "    # from the paper\n",
        "    \"b0\" : (1.0, 1.0, 224, 0.2),\n",
        "    \"b1\" : (1.0, 1.1, 240, 0.2),\n",
        "    \"b2\" : (1.1, 1.2, 260, 0.3),\n",
        "    \"b3\" : (1.2, 1.4, 300, 0.3),\n",
        "    \"b4\" : (1.4, 1.8, 380, 0.4),\n",
        "    \"b5\" : (1.6, 2.2, 456, 0.4),\n",
        "    \"b6\" : (1.8, 2.6, 528, 0.5),\n",
        "    \"b7\" : (2.0, 3.1, 600, 0.5)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdPUGUqm-AF9"
      },
      "source": [
        "Finally we're going to train our implemented model. Follow the code instruction below to train the model. We recommend to use GPU to train the model. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "eNs7Yi15-AF9"
      },
      "outputs": [],
      "source": [
        "def calculate_loss_and_accuracy(model, dataloader, size_of_dataset, criterion):\n",
        "    \n",
        "    # Now set model to validation mode.\n",
        "    running_loss = 0\n",
        "    running_accuracy = 0\n",
        "    \n",
        "    # Processing the Test Loader\n",
        "    for (inputs, labels) in dataloader:\n",
        "        \n",
        "        # Load data to device.\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        # Outputs\n",
        "        outputs = model(inputs)\n",
        "        _ , preds = torch.max(outputs, 1)\n",
        "        \n",
        "        # Outputs\n",
        "        outputs = model(inputs)\n",
        "        _ , preds = torch.max(outputs, 1)\n",
        "        \n",
        "        # Loss and Backpropagation.\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Statistics\n",
        "        running_loss += loss.item()*inputs.size(0)\n",
        "        running_accuracy += torch.sum(preds == labels.data)\n",
        "        \n",
        "    epoch_loss = running_loss/size_of_dataset\n",
        "    epoch_accuracy = running_accuracy/size_of_dataset\n",
        "    \n",
        "    return epoch_loss, epoch_accuracy\n",
        "\n",
        "def train(model, criterion, optimizer, scheduler, num_of_epochs):\n",
        "    \n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    track_training_loss = []\n",
        "    track_val_loss = []\n",
        "    track_val_acc = []\n",
        "\n",
        "    for epoch in range(num_of_epochs):\n",
        "\n",
        "        print(f'\\nEpoch {epoch + 1}/{num_of_epochs}')\n",
        "        print('-'*30)\n",
        "\n",
        "        model.train() # Setting model to train.\n",
        "        running_loss = 0\n",
        "        running_accuracy = 0\n",
        "\n",
        "        # Processing the Train Loader\n",
        "        for (inputs, labels) in trainloader:\n",
        "\n",
        "            '''\n",
        "            Load data to device.\n",
        "            Hints: use .to(device) to load data to device\n",
        "            remember to zero the parameter gradients\n",
        "            '''\n",
        "            #######################\n",
        "            # TODO: your code here#\n",
        "            #######################\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad() # zero the parameter gradients\n",
        "            #######################\n",
        "            # End of your code    #\n",
        "            #######################\n",
        "\n",
        "            # Outputs\n",
        "            outputs = model(inputs)\n",
        "            _ , preds = torch.max(outputs, 1)\n",
        "\n",
        "            '''\n",
        "            Loss and Backpropagation.\n",
        "            Hints: use criterion to calculate loss\n",
        "            remember to perform backpropagation\n",
        "            '''\n",
        "            #######################\n",
        "            # TODO: your code here#\n",
        "            #######################\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            #######################\n",
        "            # End of your code    #\n",
        "            #######################\n",
        "            # Statistics\n",
        "            running_loss += loss.item()*inputs.size(0)\n",
        "            running_accuracy += torch.sum(preds == labels.data)\n",
        "        \n",
        "        scheduler.step()\n",
        "        epoch_loss = running_loss/len(trainset)\n",
        "        epoch_accuracy = running_accuracy/len(trainset)\n",
        "        track_training_loss.append(epoch_loss) # Loss Tracking\n",
        "\n",
        "        print(f'Training Loss: {epoch_loss:.4f} Training Acc.: {epoch_accuracy:.4f}')\n",
        "\n",
        "        # Now set model to validation mode.\n",
        "        model.eval()\n",
        "\n",
        "        val_loss, val_accuracy = calculate_loss_and_accuracy(model, valloader, len(val_ds), criterion)\n",
        "        track_val_loss.append(val_loss) \n",
        "        track_val_acc.append(val_accuracy)\n",
        "        if val_accuracy > best_acc:\n",
        "            print(\"Found better model...\")\n",
        "            print('Updating the model weights....\\n')\n",
        "            print(f'Val Loss: {val_loss:.4f} Val Acc.: {val_accuracy:.4f}\\n')\n",
        "\n",
        "            best_acc = val_accuracy\n",
        "            best_model_wts = copy.deepcopy(model.state_dict())\n",
        "     \n",
        "    model.load_state_dict(best_model_wts) # update model\n",
        "    \n",
        "    return  model, track_val_loss, track_val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm6WUbht-AF9",
        "outputId": "ae5417b9-aba9-425d-d6ba-a87abe5f6955"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/30\n",
            "------------------------------\n",
            "Training Loss: 1.6205 Training Acc.: 0.1915\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.7733 Val Acc.: 0.3218\n",
            "\n",
            "\n",
            "Epoch 2/30\n",
            "------------------------------\n",
            "Training Loss: 1.3543 Training Acc.: 0.2933\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.5587 Val Acc.: 0.4173\n",
            "\n",
            "\n",
            "Epoch 3/30\n",
            "------------------------------\n",
            "Training Loss: 1.2134 Training Acc.: 0.3558\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.4404 Val Acc.: 0.4756\n",
            "\n",
            "\n",
            "Epoch 4/30\n",
            "------------------------------\n",
            "Training Loss: 1.1185 Training Acc.: 0.3906\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3485 Val Acc.: 0.5083\n",
            "\n",
            "\n",
            "Epoch 5/30\n",
            "------------------------------\n",
            "Training Loss: 1.0529 Training Acc.: 0.4194\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2764 Val Acc.: 0.5401\n",
            "\n",
            "\n",
            "Epoch 6/30\n",
            "------------------------------\n",
            "Training Loss: 0.9913 Training Acc.: 0.4442\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2210 Val Acc.: 0.5585\n",
            "\n",
            "\n",
            "Epoch 7/30\n",
            "------------------------------\n",
            "Training Loss: 0.9393 Training Acc.: 0.4608\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1949 Val Acc.: 0.5712\n",
            "\n",
            "\n",
            "Epoch 8/30\n",
            "------------------------------\n",
            "Training Loss: 0.8939 Training Acc.: 0.4802\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1419 Val Acc.: 0.5876\n",
            "\n",
            "\n",
            "Epoch 9/30\n",
            "------------------------------\n",
            "Training Loss: 0.8509 Training Acc.: 0.4973\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0880 Val Acc.: 0.6113\n",
            "\n",
            "\n",
            "Epoch 10/30\n",
            "------------------------------\n",
            "Training Loss: 0.8141 Training Acc.: 0.5106\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0592 Val Acc.: 0.6200\n",
            "\n",
            "\n",
            "Epoch 11/30\n",
            "------------------------------\n",
            "Training Loss: 0.7720 Training Acc.: 0.5277\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0268 Val Acc.: 0.6368\n",
            "\n",
            "\n",
            "Epoch 12/30\n",
            "------------------------------\n",
            "Training Loss: 0.7339 Training Acc.: 0.5393\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0070 Val Acc.: 0.6448\n",
            "\n",
            "\n",
            "Epoch 13/30\n",
            "------------------------------\n",
            "Training Loss: 0.7065 Training Acc.: 0.5509\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9806 Val Acc.: 0.6524\n",
            "\n",
            "\n",
            "Epoch 14/30\n",
            "------------------------------\n",
            "Training Loss: 0.6781 Training Acc.: 0.5609\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9748 Val Acc.: 0.6589\n",
            "\n",
            "\n",
            "Epoch 15/30\n",
            "------------------------------\n",
            "Training Loss: 0.6512 Training Acc.: 0.5712\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9654 Val Acc.: 0.6659\n",
            "\n",
            "\n",
            "Epoch 16/30\n",
            "------------------------------\n",
            "Training Loss: 0.6267 Training Acc.: 0.5814\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9330 Val Acc.: 0.6745\n",
            "\n",
            "\n",
            "Epoch 17/30\n",
            "------------------------------\n",
            "Training Loss: 0.6008 Training Acc.: 0.5880\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9439 Val Acc.: 0.6756\n",
            "\n",
            "\n",
            "Epoch 18/30\n",
            "------------------------------\n",
            "Training Loss: 0.5773 Training Acc.: 0.5969\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9269 Val Acc.: 0.6829\n",
            "\n",
            "\n",
            "Epoch 19/30\n",
            "------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "Exception ignored in:   File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>self._shutdown_workers()\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "Traceback (most recent call last):\n",
            "    if w.is_alive():  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    \n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    self._shutdown_workers()assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "\n",
            "AssertionError:   File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "can only test a child process\n",
            "    if w.is_alive():\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "        self._shutdown_workers()assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "AssertionError    : if w.is_alive():can only test a child process\n",
            "\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    Exception ignored in: assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "AssertionError  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            ":     can only test a child processself._shutdown_workers()\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "Exception ignored in:     if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "\n",
            "    Traceback (most recent call last):\n",
            "if w.is_alive():\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "        if w.is_alive():\n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'      File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionErrorAssertionError: : can only test a child processcan only test a child process\n",
            "\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>Exception ignored in: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "if w.is_alive():        assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss: 0.5604 Training Acc.: 0.6030\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9144 Val Acc.: 0.6842\n",
            "\n",
            "\n",
            "Epoch 20/30\n",
            "------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "Exception ignored in:     <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "Traceback (most recent call last):\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "AssertionError    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            ":   File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    can only test a child process\n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: \n",
            "can only test a child processException ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "    self._shutdown_workers()  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "\n",
            "    if w.is_alive():Traceback (most recent call last):\n",
            "\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError    self._shutdown_workers(): can only test a child process\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "AssertionError  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            ": can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss: 0.5360 Training Acc.: 0.6127\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Exception ignored in: Traceback (most recent call last):\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "Traceback (most recent call last):\n",
            "    self._shutdown_workers()  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "    AssertionErrorif w.is_alive():: \n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "can only test a child processAssertionError\n",
            ": can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Exception ignored in: Traceback (most recent call last):\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "    Traceback (most recent call last):\n",
            "self._shutdown_workers()  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "self._shutdown_workers()    if w.is_alive():\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "\n",
            "      File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "if w.is_alive():    \n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "    AssertionErrorassert self._parent_pid == os.getpid(), 'can only test a child process': \n",
            "can only test a child processAssertionError\n",
            ": Exception ignored in: can only test a child process<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "Exception ignored in:   File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>    \n",
            "if w.is_alive():Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "        assert self._parent_pid == os.getpid(), 'can only test a child process'assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError\n",
            "AssertionError: : can only test a child processcan only test a child process\n",
            "\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9070 Val Acc.: 0.6954\n",
            "\n",
            "\n",
            "Epoch 21/30\n",
            "------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    Exception ignored in: if w.is_alive():<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "        self._shutdown_workers()assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():AssertionError\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process': \n",
            "AssertionErrorcan only test a child process: \n",
            "can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>Exception ignored in: \n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>Traceback (most recent call last):\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "Traceback (most recent call last):\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "self._shutdown_workers()    \n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "self._shutdown_workers()    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError\n",
            ": can only test a child process\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "Exception ignored in:     assert self._parent_pid == os.getpid(), 'can only test a child process'<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "AssertionError:     can only test a child process\n",
            "self._shutdown_workers()Exception ignored in: \n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "Traceback (most recent call last):\n",
            "    if w.is_alive():\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "AssertionError    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: : can only test a child process\n",
            "can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>Exception ignored in: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()<function _MultiProcessingDataLoaderIter.__del__ at 0x7f2e9a326050>\n",
            "\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "Traceback (most recent call last):\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "if w.is_alive():    \n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "self._shutdown_workers()    \n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    \n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionErrorAssertionError: can only test a child process: \n",
            "can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss: 0.5195 Training Acc.: 0.6168\n",
            "\n",
            "Epoch 22/30\n",
            "------------------------------\n",
            "Training Loss: 0.4994 Training Acc.: 0.6243\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.8707 Val Acc.: 0.7037\n",
            "\n",
            "\n",
            "Epoch 23/30\n",
            "------------------------------\n",
            "Training Loss: 0.4795 Training Acc.: 0.6339\n",
            "\n",
            "Epoch 24/30\n",
            "------------------------------\n",
            "Training Loss: 0.4686 Training Acc.: 0.6354\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.8781 Val Acc.: 0.7060\n",
            "\n",
            "\n",
            "Epoch 25/30\n",
            "------------------------------\n",
            "Training Loss: 0.4583 Training Acc.: 0.6394\n",
            "\n",
            "Epoch 26/30\n",
            "------------------------------\n",
            "Training Loss: 0.4336 Training Acc.: 0.6477\n",
            "\n",
            "Epoch 27/30\n",
            "------------------------------\n",
            "Training Loss: 0.4254 Training Acc.: 0.6508\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.8840 Val Acc.: 0.7103\n",
            "\n",
            "\n",
            "Epoch 28/30\n",
            "------------------------------\n",
            "Training Loss: 0.3997 Training Acc.: 0.6605\n",
            "\n",
            "Epoch 29/30\n",
            "------------------------------\n",
            "Training Loss: 0.3868 Training Acc.: 0.6654\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9020 Val Acc.: 0.7118\n",
            "\n",
            "\n",
            "Epoch 30/30\n",
            "------------------------------\n",
            "Training Loss: 0.3816 Training Acc.: 0.6659\n"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda')\n",
        "\n",
        "NUM_OF_CLASSES = 10\n",
        "BATCH_SIZE = 32\n",
        "NUM_OF_EPOCHS = 30\n",
        "\n",
        "# Initialize Efficientnet model\n",
        "# We are training the b2 version here\n",
        "version = 'b2'\n",
        "width_mult, depth_mult, res, dropout_rate = efficient_net_config[version]\n",
        "model = EfficientNet(width_mult, depth_mult, dropout_rate, num_classes = NUM_OF_CLASSES)\n",
        "model = model.to(device) # Load model to device.\n",
        "\n",
        "# Criterion.\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.AdamW(model.parameters(), lr=1e-6, weight_decay=1e-2)\n",
        "\n",
        "\n",
        "exp_lr_scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-2,\n",
        "                                             steps_per_epoch=len(trainloader), epochs=NUM_OF_EPOCHS)\n",
        "\n",
        "# Training\n",
        "best_model = train(model = model,\n",
        "                   criterion = criterion,\n",
        "                   optimizer = optimizer,\n",
        "                   scheduler = exp_lr_scheduler,\n",
        "                   num_of_epochs = NUM_OF_EPOCHS\n",
        "                  )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question: What is the best accuracy you can get? What is the best accuracy you can get with the same number of parameters as the EfficientNet-B2 model? Feel free to use different models and find the one with the best validation accuracy."
      ],
      "metadata": {
        "id": "tE24zpzztRkP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(model):\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  optimizer = optim.AdamW(model.parameters(), lr=1e-6, weight_decay=1e-2)\n",
        "\n",
        "\n",
        "  exp_lr_scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-2,\n",
        "                                              steps_per_epoch=len(trainloader), epochs=NUM_OF_EPOCHS)\n",
        "\n",
        "  best_model, val_losses, val_accs = train(model = model,\n",
        "                    criterion = criterion,\n",
        "                    optimizer = optimizer,\n",
        "                    scheduler = exp_lr_scheduler,\n",
        "                    num_of_epochs = NUM_OF_EPOCHS\n",
        "                    )\n",
        "  \n",
        "  val_loss, val_accuracy = calculate_loss_and_accuracy(best_model, valloader, len(val_ds), criterion)\n",
        "  print(\"final validation statistics, loss : %s, accuracy : %s\" %(val_loss, val_accuracy))\n",
        "\n",
        "  return best_model, val_losses, val_accs"
      ],
      "metadata": {
        "id": "lOjej3zOtAan"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let us compare with some other recent architecture models, which torchvision conveniently packages. Generally, we want to consider 2 things, the number of parameters, and the actual performance of the architecture.\n",
        "\n",
        "For this, let us consider densenet121, mobilenetv2 and resNet50 which are all fairly recent models.\n",
        "\n",
        "**Warning: this part takes roughly half an hour to train**"
      ],
      "metadata": {
        "id": "Ap8IP4-ntWPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "densenet121 = torchvision.models.densenet121(weights = False).to(device)\n",
        "mobilenetv2 = torchvision.models.mobilenet_v2(weights = False).to(device)\n",
        "resNet50 = torchvision.models.resnet50(weights = False).to(device)"
      ],
      "metadata": {
        "id": "uXTwBnd9tYOg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee212894-7cce-4668-f9d0-3333b392dba3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_parameters(model, model_name):\n",
        "  num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "  print(\"%s has %s params\" %(model_name, num_params))\n",
        "  return num_params"
      ],
      "metadata": {
        "id": "L5HXLxqj1xzu"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dense_params = train_model(densenet121)"
      ],
      "metadata": {
        "id": "xDRODdnrtYRw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f16b7c3-074f-4e94-fcde-c6a1ca886a68"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/30\n",
            "------------------------------\n",
            "Training Loss: 1.4407 Training Acc.: 0.3436\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3945 Val Acc.: 0.4979\n",
            "\n",
            "\n",
            "Epoch 2/30\n",
            "------------------------------\n",
            "Training Loss: 0.9452 Training Acc.: 0.4592\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1795 Val Acc.: 0.5797\n",
            "\n",
            "\n",
            "Epoch 3/30\n",
            "------------------------------\n",
            "Training Loss: 0.8057 Training Acc.: 0.5131\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0660 Val Acc.: 0.6181\n",
            "\n",
            "\n",
            "Epoch 4/30\n",
            "------------------------------\n",
            "Training Loss: 0.7102 Training Acc.: 0.5477\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0133 Val Acc.: 0.6399\n",
            "\n",
            "\n",
            "Epoch 5/30\n",
            "------------------------------\n",
            "Training Loss: 0.6251 Training Acc.: 0.5800\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9901 Val Acc.: 0.6562\n",
            "\n",
            "\n",
            "Epoch 6/30\n",
            "------------------------------\n",
            "Training Loss: 0.5536 Training Acc.: 0.6054\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9931 Val Acc.: 0.6619\n",
            "\n",
            "\n",
            "Epoch 7/30\n",
            "------------------------------\n",
            "Training Loss: 0.4955 Training Acc.: 0.6258\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9511 Val Acc.: 0.6755\n",
            "\n",
            "\n",
            "Epoch 8/30\n",
            "------------------------------\n",
            "Training Loss: 0.4401 Training Acc.: 0.6461\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9420 Val Acc.: 0.6848\n",
            "\n",
            "\n",
            "Epoch 9/30\n",
            "------------------------------\n",
            "Training Loss: 0.3880 Training Acc.: 0.6657\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9484 Val Acc.: 0.6926\n",
            "\n",
            "\n",
            "Epoch 10/30\n",
            "------------------------------\n",
            "Training Loss: 0.3397 Training Acc.: 0.6822\n",
            "\n",
            "Epoch 11/30\n",
            "------------------------------\n",
            "Training Loss: 0.3089 Training Acc.: 0.6948\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0342 Val Acc.: 0.6936\n",
            "\n",
            "\n",
            "Epoch 12/30\n",
            "------------------------------\n",
            "Training Loss: 0.2762 Training Acc.: 0.7046\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 0.9825 Val Acc.: 0.7016\n",
            "\n",
            "\n",
            "Epoch 13/30\n",
            "------------------------------\n",
            "Training Loss: 0.2432 Training Acc.: 0.7171\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0064 Val Acc.: 0.7090\n",
            "\n",
            "\n",
            "Epoch 14/30\n",
            "------------------------------\n",
            "Training Loss: 0.2125 Training Acc.: 0.7267\n",
            "\n",
            "Epoch 15/30\n",
            "------------------------------\n",
            "Training Loss: 0.1968 Training Acc.: 0.7324\n",
            "\n",
            "Epoch 16/30\n",
            "------------------------------\n",
            "Training Loss: 0.1823 Training Acc.: 0.7375\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.0733 Val Acc.: 0.7095\n",
            "\n",
            "\n",
            "Epoch 17/30\n",
            "------------------------------\n",
            "Training Loss: 0.1643 Training Acc.: 0.7435\n",
            "\n",
            "Epoch 18/30\n",
            "------------------------------\n",
            "Training Loss: 0.1503 Training Acc.: 0.7481\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1304 Val Acc.: 0.7148\n",
            "\n",
            "\n",
            "Epoch 19/30\n",
            "------------------------------\n",
            "Training Loss: 0.1325 Training Acc.: 0.7538\n",
            "\n",
            "Epoch 20/30\n",
            "------------------------------\n",
            "Training Loss: 0.1276 Training Acc.: 0.7560\n",
            "\n",
            "Epoch 21/30\n",
            "------------------------------\n",
            "Training Loss: 0.1165 Training Acc.: 0.7589\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2096 Val Acc.: 0.7183\n",
            "\n",
            "\n",
            "Epoch 22/30\n",
            "------------------------------\n",
            "Training Loss: 0.1164 Training Acc.: 0.7585\n",
            "\n",
            "Epoch 23/30\n",
            "------------------------------\n",
            "Training Loss: 0.1036 Training Acc.: 0.7641\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2055 Val Acc.: 0.7229\n",
            "\n",
            "\n",
            "Epoch 24/30\n",
            "------------------------------\n",
            "Training Loss: 0.0983 Training Acc.: 0.7652\n",
            "\n",
            "Epoch 25/30\n",
            "------------------------------\n",
            "Training Loss: 0.0920 Training Acc.: 0.7681\n",
            "\n",
            "Epoch 26/30\n",
            "------------------------------\n",
            "Training Loss: 0.0883 Training Acc.: 0.7691\n",
            "\n",
            "Epoch 27/30\n",
            "------------------------------\n",
            "Training Loss: 0.0857 Training Acc.: 0.7701\n",
            "\n",
            "Epoch 28/30\n",
            "------------------------------\n",
            "Training Loss: 0.0786 Training Acc.: 0.7723\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2852 Val Acc.: 0.7279\n",
            "\n",
            "\n",
            "Epoch 29/30\n",
            "------------------------------\n",
            "Training Loss: 0.0750 Training Acc.: 0.7739\n",
            "\n",
            "Epoch 30/30\n",
            "------------------------------\n",
            "Training Loss: 0.0885 Training Acc.: 0.7694\n",
            "final validation statistics, loss : 1.2749816423416138, accuracy : tensor(0.7291, device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mobile_params = train_model(mobilenetv2)"
      ],
      "metadata": {
        "id": "bUdTM59Pteih",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd8c1564-1099-4eb7-bfa7-11ab569333a6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/30\n",
            "------------------------------\n",
            "Training Loss: 1.8835 Training Acc.: 0.1552\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 2.0235 Val Acc.: 0.2370\n",
            "\n",
            "\n",
            "Epoch 2/30\n",
            "------------------------------\n",
            "Training Loss: 1.5204 Training Acc.: 0.2223\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.8410 Val Acc.: 0.3111\n",
            "\n",
            "\n",
            "Epoch 3/30\n",
            "------------------------------\n",
            "Training Loss: 1.3917 Training Acc.: 0.2807\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.7202 Val Acc.: 0.3629\n",
            "\n",
            "\n",
            "Epoch 4/30\n",
            "------------------------------\n",
            "Training Loss: 1.2973 Training Acc.: 0.3196\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.6093 Val Acc.: 0.4031\n",
            "\n",
            "\n",
            "Epoch 5/30\n",
            "------------------------------\n",
            "Training Loss: 1.2253 Training Acc.: 0.3482\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.5614 Val Acc.: 0.4195\n",
            "\n",
            "\n",
            "Epoch 6/30\n",
            "------------------------------\n",
            "Training Loss: 1.1690 Training Acc.: 0.3655\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.5022 Val Acc.: 0.4445\n",
            "\n",
            "\n",
            "Epoch 7/30\n",
            "------------------------------\n",
            "Training Loss: 1.1272 Training Acc.: 0.3860\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.4881 Val Acc.: 0.4545\n",
            "\n",
            "\n",
            "Epoch 8/30\n",
            "------------------------------\n",
            "Training Loss: 1.0866 Training Acc.: 0.4036\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.4560 Val Acc.: 0.4766\n",
            "\n",
            "\n",
            "Epoch 9/30\n",
            "------------------------------\n",
            "Training Loss: 1.0474 Training Acc.: 0.4159\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.4084 Val Acc.: 0.4859\n",
            "\n",
            "\n",
            "Epoch 10/30\n",
            "------------------------------\n",
            "Training Loss: 1.0158 Training Acc.: 0.4294\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3947 Val Acc.: 0.4966\n",
            "\n",
            "\n",
            "Epoch 11/30\n",
            "------------------------------\n",
            "Training Loss: 0.9765 Training Acc.: 0.4469\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3693 Val Acc.: 0.5035\n",
            "\n",
            "\n",
            "Epoch 12/30\n",
            "------------------------------\n",
            "Training Loss: 0.9493 Training Acc.: 0.4555\n",
            "\n",
            "Epoch 13/30\n",
            "------------------------------\n",
            "Training Loss: 0.9173 Training Acc.: 0.4683\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3349 Val Acc.: 0.5245\n",
            "\n",
            "\n",
            "Epoch 14/30\n",
            "------------------------------\n",
            "Training Loss: 0.8915 Training Acc.: 0.4811\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3328 Val Acc.: 0.5246\n",
            "\n",
            "\n",
            "Epoch 15/30\n",
            "------------------------------\n",
            "Training Loss: 0.8708 Training Acc.: 0.4882\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3113 Val Acc.: 0.5304\n",
            "\n",
            "\n",
            "Epoch 16/30\n",
            "------------------------------\n",
            "Training Loss: 0.8381 Training Acc.: 0.5018\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3112 Val Acc.: 0.5364\n",
            "\n",
            "\n",
            "Epoch 17/30\n",
            "------------------------------\n",
            "Training Loss: 0.8128 Training Acc.: 0.5107\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3131 Val Acc.: 0.5393\n",
            "\n",
            "\n",
            "Epoch 18/30\n",
            "------------------------------\n",
            "Training Loss: 0.7921 Training Acc.: 0.5188\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2908 Val Acc.: 0.5476\n",
            "\n",
            "\n",
            "Epoch 19/30\n",
            "------------------------------\n",
            "Training Loss: 0.7663 Training Acc.: 0.5275\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2968 Val Acc.: 0.5490\n",
            "\n",
            "\n",
            "Epoch 20/30\n",
            "------------------------------\n",
            "Training Loss: 0.7510 Training Acc.: 0.5326\n",
            "\n",
            "Epoch 21/30\n",
            "------------------------------\n",
            "Training Loss: 0.7225 Training Acc.: 0.5429\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2887 Val Acc.: 0.5639\n",
            "\n",
            "\n",
            "Epoch 22/30\n",
            "------------------------------\n",
            "Training Loss: 0.7026 Training Acc.: 0.5484\n",
            "\n",
            "Epoch 23/30\n",
            "------------------------------\n",
            "Training Loss: 0.6856 Training Acc.: 0.5580\n",
            "\n",
            "Epoch 24/30\n",
            "------------------------------\n",
            "Training Loss: 0.6653 Training Acc.: 0.5634\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2922 Val Acc.: 0.5723\n",
            "\n",
            "\n",
            "Epoch 25/30\n",
            "------------------------------\n",
            "Training Loss: 0.6407 Training Acc.: 0.5747\n",
            "\n",
            "Epoch 26/30\n",
            "------------------------------\n",
            "Training Loss: 0.6336 Training Acc.: 0.5770\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3072 Val Acc.: 0.5755\n",
            "\n",
            "\n",
            "Epoch 27/30\n",
            "------------------------------\n",
            "Training Loss: 0.6135 Training Acc.: 0.5831\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2871 Val Acc.: 0.5804\n",
            "\n",
            "\n",
            "Epoch 28/30\n",
            "------------------------------\n",
            "Training Loss: 0.5895 Training Acc.: 0.5925\n",
            "\n",
            "Epoch 29/30\n",
            "------------------------------\n",
            "Training Loss: 0.5730 Training Acc.: 0.5980\n",
            "\n",
            "Epoch 30/30\n",
            "------------------------------\n",
            "Training Loss: 0.5619 Training Acc.: 0.6013\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2950 Val Acc.: 0.5811\n",
            "\n",
            "final validation statistics, loss : 1.301525839805603, accuracy : tensor(0.5754, device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_params = train_model(resNet50)"
      ],
      "metadata": {
        "id": "AxZq2N-OtbUb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a44e80d9-17fe-4450-8942-4b9bec075fde"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/30\n",
            "------------------------------\n",
            "Training Loss: 1.6549 Training Acc.: 0.2270\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.7677 Val Acc.: 0.3719\n",
            "\n",
            "\n",
            "Epoch 2/30\n",
            "------------------------------\n",
            "Training Loss: 1.2636 Training Acc.: 0.3439\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.5424 Val Acc.: 0.4548\n",
            "\n",
            "\n",
            "Epoch 3/30\n",
            "------------------------------\n",
            "Training Loss: 1.1139 Training Acc.: 0.4022\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.4477 Val Acc.: 0.4837\n",
            "\n",
            "\n",
            "Epoch 4/30\n",
            "------------------------------\n",
            "Training Loss: 1.0128 Training Acc.: 0.4433\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3754 Val Acc.: 0.5207\n",
            "\n",
            "\n",
            "Epoch 5/30\n",
            "------------------------------\n",
            "Training Loss: 0.9318 Training Acc.: 0.4712\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3383 Val Acc.: 0.5310\n",
            "\n",
            "\n",
            "Epoch 6/30\n",
            "------------------------------\n",
            "Training Loss: 0.8504 Training Acc.: 0.5005\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2753 Val Acc.: 0.5464\n",
            "\n",
            "\n",
            "Epoch 7/30\n",
            "------------------------------\n",
            "Training Loss: 0.7785 Training Acc.: 0.5271\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2191 Val Acc.: 0.5686\n",
            "\n",
            "\n",
            "Epoch 8/30\n",
            "------------------------------\n",
            "Training Loss: 0.7190 Training Acc.: 0.5498\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2306 Val Acc.: 0.5704\n",
            "\n",
            "\n",
            "Epoch 9/30\n",
            "------------------------------\n",
            "Training Loss: 0.6682 Training Acc.: 0.5673\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2245 Val Acc.: 0.5856\n",
            "\n",
            "\n",
            "Epoch 10/30\n",
            "------------------------------\n",
            "Training Loss: 0.6070 Training Acc.: 0.5870\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1786 Val Acc.: 0.6051\n",
            "\n",
            "\n",
            "Epoch 11/30\n",
            "------------------------------\n",
            "Training Loss: 0.5721 Training Acc.: 0.6017\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2021 Val Acc.: 0.6059\n",
            "\n",
            "\n",
            "Epoch 12/30\n",
            "------------------------------\n",
            "Training Loss: 0.5325 Training Acc.: 0.6144\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2007 Val Acc.: 0.6096\n",
            "\n",
            "\n",
            "Epoch 13/30\n",
            "------------------------------\n",
            "Training Loss: 0.4818 Training Acc.: 0.6327\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1854 Val Acc.: 0.6144\n",
            "\n",
            "\n",
            "Epoch 14/30\n",
            "------------------------------\n",
            "Training Loss: 0.4486 Training Acc.: 0.6452\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2104 Val Acc.: 0.6193\n",
            "\n",
            "\n",
            "Epoch 15/30\n",
            "------------------------------\n",
            "Training Loss: 0.4807 Training Acc.: 0.6320\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.1812 Val Acc.: 0.6262\n",
            "\n",
            "\n",
            "Epoch 16/30\n",
            "------------------------------\n",
            "Training Loss: 0.4154 Training Acc.: 0.6564\n",
            "\n",
            "Epoch 17/30\n",
            "------------------------------\n",
            "Training Loss: 0.3804 Training Acc.: 0.6697\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2178 Val Acc.: 0.6365\n",
            "\n",
            "\n",
            "Epoch 18/30\n",
            "------------------------------\n",
            "Training Loss: 0.3145 Training Acc.: 0.6924\n",
            "\n",
            "Epoch 19/30\n",
            "------------------------------\n",
            "Training Loss: 0.2865 Training Acc.: 0.7005\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2631 Val Acc.: 0.6464\n",
            "\n",
            "\n",
            "Epoch 20/30\n",
            "------------------------------\n",
            "Training Loss: 0.2863 Training Acc.: 0.7034\n",
            "\n",
            "Epoch 21/30\n",
            "------------------------------\n",
            "Training Loss: 0.2785 Training Acc.: 0.7042\n",
            "\n",
            "Epoch 22/30\n",
            "------------------------------\n",
            "Training Loss: 0.2461 Training Acc.: 0.7151\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.2902 Val Acc.: 0.6556\n",
            "\n",
            "\n",
            "Epoch 23/30\n",
            "------------------------------\n",
            "Training Loss: 0.2586 Training Acc.: 0.7105\n",
            "\n",
            "Epoch 24/30\n",
            "------------------------------\n",
            "Training Loss: 0.2531 Training Acc.: 0.7132\n",
            "\n",
            "Epoch 25/30\n",
            "------------------------------\n",
            "Training Loss: 0.2844 Training Acc.: 0.7038\n",
            "\n",
            "Epoch 26/30\n",
            "------------------------------\n",
            "Training Loss: 0.2342 Training Acc.: 0.7186\n",
            "\n",
            "Epoch 27/30\n",
            "------------------------------\n",
            "Training Loss: 0.1815 Training Acc.: 0.7375\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3696 Val Acc.: 0.6611\n",
            "\n",
            "\n",
            "Epoch 28/30\n",
            "------------------------------\n",
            "Training Loss: 0.1520 Training Acc.: 0.7474\n",
            "\n",
            "Epoch 29/30\n",
            "------------------------------\n",
            "Training Loss: 0.1557 Training Acc.: 0.7467\n",
            "Found better model...\n",
            "Updating the model weights....\n",
            "\n",
            "Val Loss: 1.3651 Val Acc.: 0.6666\n",
            "\n",
            "\n",
            "Epoch 30/30\n",
            "------------------------------\n",
            "Training Loss: 0.1483 Training Acc.: 0.7483\n",
            "final validation statistics, loss : 1.3941359251022338, accuracy : tensor(0.6586, device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "den_model, den_losses, den_acc = dense_params\n",
        "mobile_model, mobile_losses, mobile_acc = mobile_params\n",
        "res_model, res_losses, res_acc = res_params"
      ],
      "metadata": {
        "id": "qJSoFkRZtbb-"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "den_acc = np.array([i.to('cpu').numpy() for i in den_acc])\n",
        "mobile_acc = np.array([i.to('cpu').numpy() for i in mobile_acc])\n",
        "res_acc = np.array([i.to('cpu').numpy() for i in res_acc])"
      ],
      "metadata": {
        "id": "WA-F120rDIMM"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_model, efficientnet_val_losses, efficientnet_val_accs = best_model\n",
        "efficientnet_val_accs = np.array([i.to('cpu').numpy() for i in efficientnet_val_accs])"
      ],
      "metadata": {
        "id": "5eq3y5q3DUPd"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us see how EfficientNet performs against them!"
      ],
      "metadata": {
        "id": "G5QBGztvtk1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(NUM_OF_EPOCHS), efficientnet_val_accs, label = 'efficientnetb2')\n",
        "plt.plot(range(NUM_OF_EPOCHS), den_acc, label = 'densenet121')\n",
        "plt.plot(range(NUM_OF_EPOCHS), mobile_acc, label = 'mobilenetv2')\n",
        "plt.plot(range(NUM_OF_EPOCHS), res_acc, label = 'resNet50')\n",
        "plt.title(\"Validation Accuracy vs. Epochs on CIFAR, Models\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wRxvIpMOthyI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "0fd900f7-eb9e-4de1-9536-cecb144d999b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question: Looking at the accuracy of EfficientNet compared to other state-of-the-art model architectures, how does the validation accuracy compare? Anything else interesting you've noticed about these plots?"
      ],
      "metadata": {
        "id": "XlkkJVD9tqYA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now examine the number of parameters within the different models. We provide a calculate_parameters function to just sum all the parameters for a model. We also provide some code for a quick bar plot."
      ],
      "metadata": {
        "id": "S0QEeemrtqaY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_parameters(model, model_name):\n",
        "  num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "  print(\"%s has %s params\" %(model_name, num_params))\n",
        "  return num_params"
      ],
      "metadata": {
        "id": "ekndvASEth0e"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eff_num_params = calculate_parameters(best_model, \"efficientnetb2\")\n",
        "den_num_params = calculate_parameters(den_model, \"densenet121\")\n",
        "mob_num_params = calculate_parameters(mobile_model, \"mobilenetv2\")\n",
        "res_num_params = calculate_parameters(res_model, \"resNet50\")"
      ],
      "metadata": {
        "id": "BIWAElestuzy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f826e6ea-bd96-4197-82e3-6a07ad7586ff"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "efficientnetb2 has 4872811 params\n",
            "densenet121 has 7978856 params\n",
            "mobilenetv2 has 3504872 params\n",
            "resNet50 has 25557032 params\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(['efficientnetb2', 'densenet121', 'mobilenetv2', 'resNet50'], [eff_num_params, den_num_params, mob_num_params, res_num_params])\n",
        "plt.legend()\n",
        "plt.title(\"Number of Params vs. Model\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"Number of Parameters\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "F90YmY77tu7O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "fe41bd15-9f01-40fd-d2b9-169b211eba99"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question: Examine the number of parameters of the different models, does anything stand out to you? If we take this as context, is there anything you want to comment about the validation performance of the models?"
      ],
      "metadata": {
        "id": "Te7-HgPqtzX9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the end of the notebook. We hope you've learned something about EfficientNet!\n"
      ],
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      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## References\n",
        "\n",
        "[1] Mingxing Tan, Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. https://arxiv.org/abs/1905.11946"
      ]
    }
  ],
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