Test_4_Dihedral.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "ZDu1ZcjwCnI6",
        "CAF6tsZtO1kw",
        "TZ7o8fN3C5n4",
        "FCiQ3y5Bd0K6",
        "FT_UhGR_eINB"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Import and Unzip the data"
      ],
      "metadata": {
        "id": "ZDu1ZcjwCnI6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9htYZ7IY0EQ8",
        "outputId": "5cd7bce7-34cf-4fd1-f336-4a32274a39b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the E2CNN Library\n",
        "!pip install -q e2cnn\n",
        "!pip install torchsummary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oN1XBFV1n9Qb",
        "outputId": "e903f3a2-a1a9-4dca-8a8c-fc8729318356"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/225.3 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/225.3 KB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.9/dist-packages (1.5.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!tar -xzvf '/content/drive/MyDrive/Test_4/lenses.tgz' -C \"/content/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJN8rpwO4KnW",
        "outputId": "8e63db96-9bec-4c76-83e1-84439e3b2ebc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "lenses/sub/image_96684666919532588417221490821469245309.jpg\n",
            "lenses/sub/image_96873196206509307672125296706359966347.jpg\n",
            "lenses/sub/image_96933556270913437567537882763439418351.jpg\n",
            "lenses/sub/image_96987671860235759736800807251288887063.jpg\n",
            "lenses/sub/image_97103476945841877643790846506997562135.jpg\n",
            "lenses/sub/image_97144344876657990047963795389867731925.jpg\n",
            "lenses/sub/image_97191732290108221896459726998576656537.jpg\n",
            "lenses/sub/image_97251570811889918274790871690193708162.jpg\n",
            "lenses/sub/image_97335533895512771659011717201697065862.jpg\n",
            "lenses/sub/image_97371478694686993649140492019560834048.jpg\n",
            "lenses/sub/image_97386244403592401669369624618912472699.jpg\n",
            "lenses/sub/image_97394806728895034836938307470892080719.jpg\n",
            "lenses/sub/image_97423758318383847254033082699603750897.jpg\n",
            "lenses/sub/image_97435162452010868731473611018969420987.jpg\n",
            "lenses/sub/image_97436183165743643149545602511262122758.jpg\n",
            "lenses/sub/image_97464978128549921210083877489406126944.jpg\n",
            "lenses/sub/image_97557613818185673618464714173067277929.jpg\n",
            "lenses/sub/image_97690420994199445413639504752225809407.jpg\n",
            "lenses/sub/image_97753072273315517797577291914984913590.jpg\n",
            "lenses/sub/image_97977187304319504582309355530252495640.jpg\n",
            "lenses/sub/image_9800142300831076222538167490843673196.jpg\n",
            "lenses/sub/image_98045306135783735923143535812058966414.jpg\n",
            "lenses/sub/image_98141122350614351435765641013606590231.jpg\n",
            "lenses/sub/image_98153887807582696660071910389126425908.jpg\n",
            "lenses/sub/image_98172911854869521726024140898181995794.jpg\n",
            "lenses/sub/image_98304104639571077350973230032748820685.jpg\n",
            "lenses/sub/image_98384555989932584066576364871232376615.jpg\n",
            "lenses/sub/image_98445326222691786611517989831880761218.jpg\n",
            "lenses/sub/image_98559248413491994500694691485911783793.jpg\n",
            "lenses/sub/image_98625569148681209292102452565051134064.jpg\n",
            "lenses/sub/image_9870332380275187490518432026106874214.jpg\n",
            "lenses/sub/image_98990983810395417934500089470542282040.jpg\n",
            "lenses/sub/image_99064944170579920217602923082404186608.jpg\n",
            "lenses/sub/image_99106175962858877283983735899943052833.jpg\n",
            "lenses/sub/image_99133621793754494116450493096507659542.jpg\n",
            "lenses/sub/image_99176817852747447490587604539924382600.jpg\n",
            "lenses/sub/image_99432095710472625233850338696261050520.jpg\n",
            "lenses/sub/image_99449674582097533953579050519339287627.jpg\n",
            "lenses/sub/image_99451712819443622027972315433758903835.jpg\n",
            "lenses/sub/image_99540015600735655027894142919455069889.jpg\n",
            "lenses/sub/image_99682062958402369970171907945798683216.jpg\n",
            "lenses/sub/image_99725783689686563917607021095179778079.jpg\n",
            "lenses/sub/image_99843932254051245579691064551667857092.jpg\n",
            "lenses/sub/image_99864647887802251796855073977781723271.jpg\n",
            "lenses/sub/image_99874175613519980565905034998860263915.jpg\n",
            "lenses/sub/image_99915530765752266283926183291909304430.jpg\n",
            "lenses/sub/image_99926806521126455549172676205588725027.jpg\n",
            "lenses/sub/image_99992002121954128492573610442289284547.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Essential Imports"
      ],
      "metadata": {
        "id": "CAF6tsZtO1kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import random\n",
        "import shutil\n",
        "\n",
        "import cv2\n",
        "\n",
        "from e2cnn import gspaces, nn\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision.transforms import RandomRotation, Pad, Resize, ToTensor, Compose\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.transforms.functional import InterpolationMode\n",
        "from torchsummary import summary\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, auc\n",
        "from matplotlib import pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "mvTjLbvIOszV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split data into Train and Test set"
      ],
      "metadata": {
        "id": "TZ7o8fN3C5n4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lenses_path = '/content/lenses/'\n",
        "\n",
        "train_path = os.path.join(lenses_path, 'train')\n",
        "test_path = os.path.join(lenses_path, 'test')\n",
        "\n",
        "if not os.path.exists(train_path):\n",
        "    os.mkdir(train_path)\n",
        "if not os.path.exists(test_path):\n",
        "    os.mkdir(test_path)\n",
        "\n",
        "# Set the ratio for train:test split\n",
        "split_ratio = 0.9\n",
        "\n",
        "for sub in ['no_sub', 'sub']:\n",
        "\n",
        "    sub_path = os.path.join(lenses_path, sub)\n",
        "    \n",
        "    train_sub_path = os.path.join(train_path, sub)\n",
        "    test_sub_path = os.path.join(test_path, sub)\n",
        "    if not os.path.exists(train_sub_path):\n",
        "        os.makedirs(train_sub_path)\n",
        "    if not os.path.exists(test_sub_path):\n",
        "        os.makedirs(test_sub_path)\n",
        "    \n",
        "    img_files = os.listdir(sub_path)\n",
        "    img_files = [os.path.join(sub_path, img_file) for img_file in img_files if img_file.endswith('.jpg')]\n",
        "    \n",
        "    random.shuffle(img_files)\n",
        "    \n",
        "    # Index to split the image files\n",
        "    split_index = int(len(img_files) * split_ratio)\n",
        "    \n",
        "    # Split into train and test sets\n",
        "    train_images = img_files[:split_index]\n",
        "    test_images = img_files[split_index:]\n",
        "    \n",
        "    for image in train_images:\n",
        "        shutil.copy(image, train_sub_path)\n",
        "    \n",
        "    for image in test_images:\n",
        "        shutil.copy(image, test_sub_path)"
      ],
      "metadata": {
        "id": "CxBSryCBAi6V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir_path = '/content/lenses/test/sub/'\n",
        "\n",
        "files = os.listdir(dir_path)\n",
        "\n",
        "num_images = len([f for f in files if f.endswith('.jpg') or f.endswith('.png')])\n",
        "\n",
        "print(\"Number : \", num_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYnzfsuO7HmL",
        "outputId": "d925ed8f-3da5-4b56-c5a0-0b6eb756edec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number :  500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture for General E(2)-Equivariant Steerable CNNs"
      ],
      "metadata": {
        "id": "EB3djafjFjV_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We build a model based on Dihedral group $D_2$$_N$ which is group of *N* discrete planar rotations and reflections. $D_N$ has an order of *2N*. We implement $D_4$ in this notebook which expand to 4 rotations and 4 reflections."
      ],
      "metadata": {
        "id": "6gf6N2WrF7bv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The input model type is a *scalar field* since the images are gray-scale We use *Regular fields* inside the network. Group pooling is finally used to build invariant features for final classification.  "
      ],
      "metadata": {
        "id": "V0ML7PPTcM7A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The network was originally built for multi-class classification applications, hence for the purpose of Binary Classification, the last activation layer is set to be a *sigmoid* activation layer."
      ],
      "metadata": {
        "id": "PHXxCgHLc6-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "group = 'Dihedral' # Symmetry group : Cyclic' / 'Dihedral'\n",
        "N = 4 # Order of the symmetry group"
      ],
      "metadata": {
        "id": "q9MUjnQTTYiZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EquivariantNN(torch.nn.Module):\n",
        "    \n",
        "    def __init__(self, n_classes=2, group = group, N=N):\n",
        "        \n",
        "        super(EquivariantNN, self).__init__()\n",
        "        \n",
        "        # the model is equivariant under rotations by 45 degrees (2pi/8), modelled by C8 (Cyclic group of order N) and D4 (Dihedral group of order 2N (N=4 in this case))\n",
        "        \n",
        "        # Circular Equivariance\n",
        "        if group == 'Cyclic':\n",
        "            self.r2_act = gspaces.Rot2dOnR2(N=N)\n",
        "\n",
        "        # Dihedral Equivariance\n",
        "        if group == 'Dihedral':\n",
        "            self.r2_act = gspaces.FlipRot2dOnR2(N=N)\n",
        "        \n",
        "        # the input image is a scalar field, corresponding to the trivial representation\n",
        "        in_type = nn.FieldType(self.r2_act, [self.r2_act.trivial_repr])\n",
        "        \n",
        "        # we store the input type for wrapping the images into a geometric tensor during the forward pass\n",
        "        self.input_type = in_type\n",
        "        \n",
        "        # convolution 1\n",
        "        # first specify the output type of the convolutional layer\n",
        "        # we choose 24 feature fields, each transforming under the regular representation\n",
        "        out_type = nn.FieldType(self.r2_act, 24*[self.r2_act.regular_repr])\n",
        "        self.block1 = nn.SequentialModule(\n",
        "            nn.MaskModule(in_type, 149, margin=1),\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=7, padding=1, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        \n",
        "        # convolution 2\n",
        "        # the old output type is the input type to the next layer\n",
        "        in_type = self.block1.out_type\n",
        "        # the output type of the second convolution layer are 48 regular feature fields\n",
        "        out_type = nn.FieldType(self.r2_act, 48*[self.r2_act.regular_repr])\n",
        "        self.block2 = nn.SequentialModule(\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        self.pool1 = nn.SequentialModule(\n",
        "            nn.PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=2)\n",
        "        )\n",
        "        \n",
        "        # convolution 3\n",
        "        # the old output type is the input type to the next layer\n",
        "        in_type = self.block2.out_type\n",
        "        # the output type of the third convolution layer are 48 regular feature fields\n",
        "        out_type = nn.FieldType(self.r2_act, 48*[self.r2_act.regular_repr])\n",
        "        self.block3 = nn.SequentialModule(\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        \n",
        "        # convolution 4\n",
        "        # the old output type is the input type to the next layer\n",
        "        in_type = self.block3.out_type\n",
        "        # the output type of the fourth convolution layer are 96 regular feature fields\n",
        "        out_type = nn.FieldType(self.r2_act, 96*[self.r2_act.regular_repr])\n",
        "        self.block4 = nn.SequentialModule(\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        self.pool2 = nn.SequentialModule(\n",
        "            nn.PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=2)\n",
        "        )\n",
        "        \n",
        "        # convolution 5\n",
        "        # the old output type is the input type to the next layer\n",
        "        in_type = self.block4.out_type\n",
        "        # the output type of the fifth convolution layer are 96 regular feature fields\n",
        "        out_type = nn.FieldType(self.r2_act, 96*[self.r2_act.regular_repr])\n",
        "        self.block5 = nn.SequentialModule(\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        \n",
        "        # convolution 6\n",
        "        # the old output type is the input type to the next layer\n",
        "        in_type = self.block5.out_type\n",
        "        # the output type of the sixth convolution layer are 64 regular feature fields\n",
        "        out_type = nn.FieldType(self.r2_act, 64*[self.r2_act.regular_repr])\n",
        "        self.block6 = nn.SequentialModule(\n",
        "            nn.R2Conv(in_type, out_type, kernel_size=5, padding=1, bias=False),\n",
        "            nn.InnerBatchNorm(out_type),\n",
        "            nn.ReLU(out_type, inplace=True)\n",
        "        )\n",
        "        self.pool3 = nn.PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=1, padding=0)\n",
        "        \n",
        "        self.gpool = nn.GroupPooling(out_type)\n",
        "        \n",
        "        # number of output channels\n",
        "        c = self.gpool.out_type.size\n",
        "        \n",
        "        # Fully Connected\n",
        "        self.fully_net = torch.nn.Sequential(\n",
        "            torch.nn.Linear(61504, 64), # mat1 and mat2 shapes\n",
        "            torch.nn.BatchNorm1d(64),\n",
        "            torch.nn.ELU(inplace=True),\n",
        "            torch.nn.Linear(64, 1),\n",
        "            torch.nn.Sigmoid(),\n",
        "        )\n",
        "    \n",
        "    def forward(self, input: torch.Tensor):\n",
        "        # wrap the input tensor in a GeometricTensor\n",
        "        # (associate it with the input type)\n",
        "        x = nn.GeometricTensor(input, self.input_type)\n",
        "        \n",
        "        # apply each equivariant block\n",
        "        \n",
        "        # Each layer has an input and an output type\n",
        "        # A layer takes a GeometricTensor in input.\n",
        "        # This tensor needs to be associated with the same representation of the layer's input type\n",
        "        #\n",
        "        # The Layer outputs a new GeometricTensor, associated with the layer's output type.\n",
        "        # As a result, consecutive layers need to have matching input/output types\n",
        "        x = self.block1(x)\n",
        "        x = self.block2(x)\n",
        "        x = self.pool1(x)\n",
        "        \n",
        "        x = self.block3(x)\n",
        "        x = self.block4(x)\n",
        "        x = self.pool2(x)\n",
        "        \n",
        "        x = self.block5(x)\n",
        "        x = self.block6(x)\n",
        "        \n",
        "        # pool over the spatial dimensions\n",
        "        x = self.pool3(x)\n",
        "        \n",
        "        # pool over the group\n",
        "        x = self.gpool(x)\n",
        "\n",
        "        # unwrap the output GeometricTensor\n",
        "        # (take the Pytorch tensor and discard the associated representation)\n",
        "        x = x.tensor\n",
        "        \n",
        "        # classify with the final fully connected layers)\n",
        "        x = self.fully_net(x.reshape(x.shape[0], -1))\n",
        "        \n",
        "        return x"
      ],
      "metadata": {
        "id": "Nj0WNRehn3cK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Intialize the model"
      ],
      "metadata": {
        "id": "FCiQ3y5Bd0K6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model= EquivariantNN(group = group, N=N).to(device)"
      ],
      "metadata": {
        "id": "LsBrzn0h7OV-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cae309dc-deae-41fe-e678-a926ea899388"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py:80: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at ../aten/src/ATen/native/IndexingUtils.h:27.)\n",
            "  full_mask[mask] = norms.to(torch.uint8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(model, input_size=(1, 149, 149))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-3rH2r4eDzb",
        "outputId": "256b8c6f-25c1-436d-e395-2943449f717f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "        MaskModule-1          [-1, 1, 149, 149]               0\n",
            "SingleBlockBasisExpansion-2             [-1, 8, 1, 49]               0\n",
            "BlocksBasisExpansion-3                [-1, 1, 49]               0\n",
            "            R2Conv-4        [-1, 192, 145, 145]               0\n",
            "       BatchNorm3d-5      [-1, 24, 8, 145, 145]              48\n",
            "    InnerBatchNorm-6        [-1, 192, 145, 145]               0\n",
            "              ReLU-7        [-1, 192, 145, 145]               0\n",
            "  SequentialModule-8        [-1, 192, 145, 145]               0\n",
            "SingleBlockBasisExpansion-9             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-10             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-11             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-12             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-13             [-1, 8, 8, 25]               0\n",
            "BlocksBasisExpansion-14              [-1, 192, 25]               0\n",
            "           R2Conv-15        [-1, 384, 145, 145]               0\n",
            "      BatchNorm3d-16      [-1, 48, 8, 145, 145]              96\n",
            "   InnerBatchNorm-17        [-1, 384, 145, 145]               0\n",
            "             ReLU-18        [-1, 384, 145, 145]               0\n",
            " SequentialModule-19        [-1, 384, 145, 145]               0\n",
            "PointwiseAvgPoolAntialiased-20          [-1, 384, 73, 73]               0\n",
            " SequentialModule-21          [-1, 384, 73, 73]               0\n",
            "SingleBlockBasisExpansion-22             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-23             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-24             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-25             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-26             [-1, 8, 8, 25]               0\n",
            "BlocksBasisExpansion-27              [-1, 384, 25]               0\n",
            "           R2Conv-28          [-1, 384, 73, 73]               0\n",
            "      BatchNorm3d-29        [-1, 48, 8, 73, 73]              96\n",
            "   InnerBatchNorm-30          [-1, 384, 73, 73]               0\n",
            "             ReLU-31          [-1, 384, 73, 73]               0\n",
            " SequentialModule-32          [-1, 384, 73, 73]               0\n",
            "SingleBlockBasisExpansion-33             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-34             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-35             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-36             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-37             [-1, 8, 8, 25]               0\n",
            "BlocksBasisExpansion-38              [-1, 384, 25]               0\n",
            "           R2Conv-39          [-1, 768, 73, 73]               0\n",
            "      BatchNorm3d-40        [-1, 96, 8, 73, 73]             192\n",
            "   InnerBatchNorm-41          [-1, 768, 73, 73]               0\n",
            "             ReLU-42          [-1, 768, 73, 73]               0\n",
            " SequentialModule-43          [-1, 768, 73, 73]               0\n",
            "PointwiseAvgPoolAntialiased-44          [-1, 768, 37, 37]               0\n",
            " SequentialModule-45          [-1, 768, 37, 37]               0\n",
            "SingleBlockBasisExpansion-46             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-47             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-48             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-49             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-50             [-1, 8, 8, 25]               0\n",
            "BlocksBasisExpansion-51              [-1, 768, 25]               0\n",
            "           R2Conv-52          [-1, 768, 37, 37]               0\n",
            "      BatchNorm3d-53        [-1, 96, 8, 37, 37]             192\n",
            "   InnerBatchNorm-54          [-1, 768, 37, 37]               0\n",
            "             ReLU-55          [-1, 768, 37, 37]               0\n",
            " SequentialModule-56          [-1, 768, 37, 37]               0\n",
            "SingleBlockBasisExpansion-57             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-58             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-59             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-60             [-1, 8, 8, 25]               0\n",
            "SingleBlockBasisExpansion-61             [-1, 8, 8, 25]               0\n",
            "BlocksBasisExpansion-62              [-1, 768, 25]               0\n",
            "           R2Conv-63          [-1, 512, 35, 35]               0\n",
            "      BatchNorm3d-64        [-1, 64, 8, 35, 35]             128\n",
            "   InnerBatchNorm-65          [-1, 512, 35, 35]               0\n",
            "             ReLU-66          [-1, 512, 35, 35]               0\n",
            " SequentialModule-67          [-1, 512, 35, 35]               0\n",
            "PointwiseAvgPoolAntialiased-68          [-1, 512, 31, 31]               0\n",
            "     GroupPooling-69           [-1, 64, 31, 31]               0\n",
            "           Linear-70                   [-1, 64]       3,936,320\n",
            "      BatchNorm1d-71                   [-1, 64]             128\n",
            "              ELU-72                   [-1, 64]               0\n",
            "           Linear-73                    [-1, 1]              65\n",
            "          Sigmoid-74                    [-1, 1]               0\n",
            "================================================================\n",
            "Total params: 3,937,265\n",
            "Trainable params: 3,937,265\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.08\n",
            "Forward/backward pass size (MB): 812.64\n",
            "Params size (MB): 15.02\n",
            "Estimated Total Size (MB): 827.74\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data using DataLoader"
      ],
      "metadata": {
        "id": "FT_UhGR_eINB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_path = '/content/lenses/train/'\n",
        "test_path = '/content/lenses/test/'"
      ],
      "metadata": {
        "id": "Q477492i8d1d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# images are padded to have shape 129x129.\n",
        "# this allows to use odd-size filters with stride 2 when downsampling a feature map in the model\n",
        "pad = Pad((0, 0, 1, 1), fill=0)\n",
        "\n",
        "# to reduce interpolation artifacts (e.g. when testing the model on rotated images),\n",
        "# we upsample an image by a factor of 3, rotate it and finally downsample it again\n",
        "resize1 = Resize(447)\n",
        "resize2 = Resize(149)\n",
        "\n",
        "totensor = ToTensor()\n",
        "togray = transforms.Grayscale(num_output_channels=1)\n",
        "\n",
        "# apply different transfomations to the train and test data\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomCrop(148),\n",
        "    pad,\n",
        "    resize1,\n",
        "    RandomRotation(180, interpolation=InterpolationMode.BILINEAR, expand=False),\n",
        "    resize2,\n",
        "    totensor,\n",
        "    togray,\n",
        "])\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.RandomCrop(148),\n",
        "    pad,\n",
        "    totensor,\n",
        "    togray,\n",
        "   \n",
        "])\n",
        "trainset = torchvision.datasets.ImageFolder(root=train_path,  transform=transform_train)\n",
        "testset = torchvision.datasets.ImageFolder(root=test_path,  transform=transform_test)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(trainset, batch_size=32,shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(testset, batch_size=32)\n",
        "\n",
        "# use binary cross-entropy for binary classification task\n",
        "loss_function = torch.nn.BCELoss()\n",
        "# use adam optimizer with specific Learnng rate and weight decay\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=5e-5, weight_decay=1e-5)"
      ],
      "metadata": {
        "id": "QuCbybgDBJsU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "9Zl80AyveQ67"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_interval = 100 # interval (no. of iterations) at which to print the current loss during training.\n",
        "for epoch in range(5):\n",
        "    model.train()\n",
        "    for i, (x, t) in enumerate(train_loader):        \n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        x = x.to(device)\n",
        "        t = t.to(device)\n",
        "\n",
        "        y = model(x)\n",
        "        # reshape y into a 1-D tensor and convert to float64 to prevent numerical issues during training\n",
        "        y = y.flatten().to(torch.float64) \n",
        "        \n",
        "        loss = loss_function(y, t.to(torch.float64))\n",
        "        if i % log_interval == 0:\n",
        "          # current loss every log_interval batch to track training progress\n",
        "          print('Loss : ',loss.item())\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "    \n",
        "    # print accuracy after every epoch\n",
        "    if epoch % 1 == 0:\n",
        "        total = 0 # total no. of test samples\n",
        "        correct = 0 # no. of correctly classified test samples\n",
        "        with torch.no_grad():\n",
        "            model.eval()\n",
        "            for i, (x, t) in enumerate(test_loader):\n",
        "                x = x.to(device)\n",
        "                t = t.to(device)\n",
        "                y = model(x)\n",
        "                y = y.flatten().to(torch.float64)\n",
        "\n",
        "                # compute predicted label for each test sample\n",
        "                # threshold the output 'y' at 0.5\n",
        "                # results in boolean tensor \n",
        "                prediction = (y > 0.5).to(torch.int64)\n",
        "\n",
        "                # increment total by batch_size (first dimension of target tensor t)\n",
        "                total += t.shape[0]\n",
        "\n",
        "                # increment correct by no. of correctly classified test samples\n",
        "                # to check if the predicted label matches the true label\n",
        "                # add the total no. of matching elements\n",
        "                correct += (prediction == t).sum().item()\n",
        "\n",
        "        print(f\"epoch {epoch} | test accuracy : {correct/total*100.}\")"
      ],
      "metadata": {
        "id": "4GGsspiu7RSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0db18bf-699b-4766-9520-bcb4990aa7e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss :  0.7237093390246144\n",
            "Loss :  0.4479772750624743\n",
            "Loss :  0.16012789917191772\n",
            "epoch 0 | test accuracy : 82.19999999999999\n",
            "Loss :  0.14226743472466657\n",
            "Loss :  0.062088974899040555\n",
            "Loss :  0.09340598099896785\n",
            "epoch 1 | test accuracy : 96.7\n",
            "Loss :  0.0515542157604818\n",
            "Loss :  0.06332965085735648\n",
            "Loss :  0.025277679644900186\n",
            "epoch 2 | test accuracy : 95.89999999999999\n",
            "Loss :  0.05501300028771604\n",
            "Loss :  0.023335949710303304\n",
            "Loss :  0.038237102679103865\n",
            "epoch 3 | test accuracy : 99.2\n",
            "Loss :  0.026609541741654832\n",
            "Loss :  0.020701824422535278\n",
            "Loss :  0.026292954238617303\n",
            "epoch 4 | test accuracy : 99.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), 'lenses_model_D.pth')\n",
        "torch.save(model.state_dict(), '/content/drive/MyDrive/Test_4/lensesModel_D.pth')"
      ],
      "metadata": {
        "id": "-TzZWfItUolt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test the model and plot the ROC Curve"
      ],
      "metadata": {
        "id": "2750JlqtedcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model.load_state_dict(torch.load('/content/drive/MyDrive/models/lenses_model.pth', map_location=torch.device('cpu')))"
      ],
      "metadata": {
        "id": "CVh1XwnhhbjU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "\n",
        "    model.eval()\n",
        "    target = [] # list to store target values\n",
        "    pred = [] # list to store predicted values\n",
        "    for i, (x, t) in enumerate(test_loader):\n",
        "\n",
        "        x = x.to(device)\n",
        "        t = t.to(device)\n",
        "        y = model(x)\n",
        "        y = y.flatten().to(torch.float64)\n",
        "\n",
        "        prediction = (y > 0.5).to(torch.int64)\n",
        "        total += t.shape[0]\n",
        "        correct += (prediction == t).sum().item()\n",
        "        \n",
        "        target.append(t.cpu().numpy())\n",
        "        pred.append(y.cpu().detach().numpy())\n",
        "        \n",
        "    print(f\"epoch {epoch} | test accuracy : {correct/total*100.}\")"
      ],
      "metadata": {
        "id": "eZObwRd48Sj4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dde94be6-9f8e-4872-8cab-10575f0dcc73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 | test accuracy : 99.35000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# flatten the list to 1-D\n",
        "target_f = np.concatenate([np.ravel(a) for a in target])\n",
        "pred_f = np.concatenate([np.ravel(b) for b in pred])\n",
        "              \n",
        "# create a reference model with no-skill / random guessing behaviour\n",
        "ns_probs = [0 for _ in range(len(np.asarray(target_f)))]\n",
        "\n",
        "ns_auc = roc_auc_score(np.asarray(target_f), ns_probs)\n",
        "lr_auc = roc_auc_score(np.asarray(target_f),np.asarray(pred_f))\n",
        "\n",
        "# summarize scores\n",
        "print('No Skill: ROC AUC=%.2f' % (ns_auc))\n",
        "print('Logistic: ROC AUC=%.2f' % (lr_auc))\n",
        "\n",
        "# calculate roc curves\n",
        "ns_fpr, ns_tpr, _ = roc_curve(np.asarray(target_f), ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(np.asarray(target_f),np.asarray(pred_f))\n",
        "\n",
        "# plot the roc curve for the model\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')\n",
        "\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "\n",
        "# show title\n",
        "plt.title('ROC Curve using Dihedral Equivariance')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "EkeGBlqrVo4-",
        "outputId": "1566769c-5146-4a54-cfa9-5bc710196f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Skill: ROC AUC=0.50\n",
            "Logistic: ROC AUC=1.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_classes = 2\n",
        "target_f = []\n",
        "pred_f = [] \n",
        "\n",
        "for i in range(n_classes):\n",
        "    # true labels and predicted labels for the class\n",
        "    target_class_i = np.concatenate([np.ravel(a[i]) for a in target])\n",
        "    pred_class_i = np.concatenate([np.ravel(b[i]) for b in pred])\n",
        "    target_f.append(target_class_i)\n",
        "    pred_f.append(pred_class_i)\n",
        "\n",
        "# calculate roc curves and auc scores for each class and plot them\n",
        "fpr = {}\n",
        "tpr = {}\n",
        "roc_auc = {}\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(target_f[i], pred_f[i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "plt.figure()\n",
        "lw = 2\n",
        "colors = ['green', 'pink']\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
        "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
        "             ''.format(i, roc_auc[i]))\n",
        "\n",
        "# plot No-skill model\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=lw) \n",
        "\n",
        "# axis labels\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "\n",
        "# plot title\n",
        "plt.title('Receiver Operating Characteristic for each class using Dihedral Equivariance')\n",
        "\n",
        "# show the legend\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "#show the plot\n",
        "plt.show()\n",
        "\n",
        "# print auc score for each class\n",
        "for i in range(n_classes):\n",
        "    print(\"AUC score for class {}: {}\".format(i, roc_auc[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "sYPGPI9wV7zo",
        "outputId": "11a24af0-3715-4b51-8430-5fd651f188f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score for class 0: 1.0\n",
            "AUC score for class 1: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lm6KLNnYe6oA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}