self_supervised_contrastive_learning_on_pointcloud.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SINGHAMIT1/Self-Supervised-Contrastive-Learning-/blob/main/self_supervised_contrastive_learning_on_pointcloud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTqAbOIjjSXm"
      },
      "source": [
        "## Self-Supervised 3D Shape Representation Learning with PyTorch and ShapeNet Dataset\n",
        "\n",
        "###Developed a self-supervised learning model for 3D shape representation using a subset of the ShapeNet dataset in PyTorch. Implemented DynamicEdgeConv for graph construction and utilized InfoNCE loss for contrastive learning. Successfully separated different entities in the embedding space, demonstrating the model's effectiveness.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installation / Setup"
      ],
      "metadata": {
        "id": "2-pAk_rLhhiV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iy3-tXq3i8dO",
        "outputId": "bc41c25b-b9e5-4624-9f53-6dca5bcf7a24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m✨🍰✨ Everything looks OK!\n"
          ]
        }
      ],
      "source": [
        "# using conda for easier installation of PyG\n",
        "# If only using pip, it somehow takes forever to install on colab \n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hI0CtCFQhZaL"
      },
      "outputs": [],
      "source": [
        "# Installing torch geometric for point-cloud layers\n",
        "import torch\n",
        "version = f\"https://data.pyg.org/whl/torch-{torch.__version__}.html\"\n",
        "try: \n",
        "    import torch_geometric\n",
        "except:\n",
        "    !echo $version\n",
        "    !pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f $version\n",
        "    import torch_geometric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQDYyNK_-j--"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "Goal:\n",
        "- Self-Supervised Representation Learning of Shapes\n",
        "- Pointcloud = Set of unconnected nodes --> PyG\n",
        "- [ShapeNet Dataset](https://paperswithcode.com/dataset/shapenet) - I just use a subset of classes and act like I didn't have labels\n",
        "- I select 2k data points as otherwise I run out of memory on Colab\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtcN0Et2h21F",
        "outputId": "baff307f-1d7b-413e-a84a-e60e7108c939"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip\n",
            "Extracting ./shapenetcore_partanno_segmentation_benchmark_v0_normal.zip\n",
            "Processing...\n",
            "Done!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Samples:  2000\n",
            "Sample:  Data(x=[2723, 3], y=[2723], pos=[2723, 3], category=[1])\n"
          ]
        }
      ],
      "source": [
        "from torch_geometric.datasets import ShapeNet\n",
        "# Limit to 5000 samples, due to RAM restrictions\n",
        "dataset = ShapeNet(root=\".\", categories=[\"Table\", \"Lamp\", \"Guitar\", \"Motorbike\"]).shuffle()[:2000]\n",
        "print(\"Number of Samples: \", len(dataset))\n",
        "print(\"Sample: \", dataset[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Attribute Name  | Description\n",
        "-------------------|------------------\n",
        "Pos       | Normalized positions as 3D coordinates\n",
        "X       |  Normal vectors \n",
        "Y       | Class label "
      ],
      "metadata": {
        "id": "X05pyLpSiHlB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXjyMJP6B3bx",
        "outputId": "99f7c57d-a03e-4b77-823a-accad2698687"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0218, -0.1359,  0.0021],\n",
              "        [ 0.0165, -0.3779, -0.0896],\n",
              "        [-0.0258,  0.3684,  0.0162],\n",
              "        ...,\n",
              "        [ 0.0156, -0.2257,  0.0954],\n",
              "        [-0.0098, -0.3276, -0.1670],\n",
              "        [-0.0047, -0.3443, -0.1650]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset[1].pos"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*USING* plotly to inspect the data "
      ],
      "metadata": {
        "id": "SoaKy33ckunB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "XLuBtphVvlMZ",
        "outputId": "cab39b8c-7d2c-4e6b-f810-5246aee88e74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data points:  2815\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#!pip install plotly --quiet\n",
        "import plotly.express as px\n",
        "\n",
        "def plot_3d_shape(shape):\n",
        "    print(\"Number of data points: \", shape.x.shape[0])\n",
        "    x = shape.pos[:, 0]\n",
        "    y = shape.pos[:, 1]\n",
        "    z = shape.pos[:, 2]\n",
        "    fig = px.scatter_3d(x=x, y=y, z=z, opacity=0.3)\n",
        "    fig.show()\n",
        "\n",
        "# Pick a sample\n",
        "sample_idx = 2\n",
        "plot_3d_shape(dataset[sample_idx])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checkING the distribution of classes\n",
        "cat_dict = {key: 0 for key in dataset.categories}\n",
        "for datapoint in dataset: cat_dict[dataset.categories[datapoint.category.int()]]+=1\n",
        "cat_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_R4TFMbclLij",
        "outputId": "43aa5b07-2556-4ae3-f05f-b939f7e7bd78"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Table': 1375, 'Lamp': 389, 'Guitar': 191, 'Motorbike': 45}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEyNxSuPGrHX"
      },
      "source": [
        "## Data Preparation\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "tlOnmFOUd9OP"
      },
      "outputs": [],
      "source": [
        "from torch_geometric.loader import DataLoader\n",
        "import torch_geometric.transforms as T\n",
        "\n",
        "data_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "augmentation = T.Compose([T.RandomJitter(0.03), T.RandomFlip(1), T.RandomShear(0.2)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ce7gMHZQH54Q"
      },
      "source": [
        "Let's have a look at some samples ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "9rVbZMjDkjRm",
        "outputId": "76df3875-548f-41cc-b698-a9cb10c6e196"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data points:  2830\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ],
      "source": [
        "# Original data point\n",
        "sample = next(iter(data_loader))\n",
        "plot_3d_shape(sample[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "JU_NCJ1Zjw4F",
        "outputId": "71776656-e8bb-41ba-92f4-8f4ad9ed762e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data points:  2830\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Augmented data point\n",
        "transformered = augmentation(sample)\n",
        "plot_3d_shape(transformered[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lle1XiGfEsn7"
      },
      "source": [
        "## Model\n",
        "\n",
        "- using implementation of DynamicEdgeConv instead of pointnet\n",
        "- It uses the parameter k to detect the nearest neighbors which form a subgraph\n",
        "- In the paper they use 4 layers, here I just have 2\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "i3G7KOpFIhjZ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.nn import Linear\n",
        "import torch.nn.functional as F\n",
        "from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool\n",
        "\n",
        "\n",
        "class Model(torch.nn.Module):\n",
        "    def __init__(self, k=20, aggr='max'):\n",
        "        super().__init__()\n",
        "        # Feature extraction\n",
        "        self.conv1 = DynamicEdgeConv(MLP([2 * 3, 64, 64]), k, aggr)\n",
        "        self.conv2 = DynamicEdgeConv(MLP([2 * 64, 128]), k, aggr)\n",
        "        # Encoder head \n",
        "        self.lin1 = Linear(128 + 64, 128)\n",
        "        # Projection head (See explanation in SimCLRv2)\n",
        "        self.mlp = MLP([128, 256, 32], norm=None)\n",
        "\n",
        "    def forward(self, data, train=True):\n",
        "        if train:\n",
        "            # Get 2 augmentations of the batch\n",
        "            augm_1 = augmentation(data)\n",
        "            augm_2 = augmentation(data)\n",
        "\n",
        "            # Extract properties\n",
        "            pos_1, batch_1 = augm_1.pos, augm_1.batch\n",
        "            pos_2, batch_2 = augm_2.pos, augm_2.batch\n",
        "\n",
        "            # Get representations for first augmented view\n",
        "            x1 = self.conv1(pos_1, batch_1)\n",
        "            x2 = self.conv2(x1, batch_1)\n",
        "            h_points_1 = self.lin1(torch.cat([x1, x2], dim=1))\n",
        "\n",
        "            # Get representations for second augmented view\n",
        "            x1 = self.conv1(pos_2, batch_2)\n",
        "            x2 = self.conv2(x1, batch_2)\n",
        "            h_points_2 = self.lin1(torch.cat([x1, x2], dim=1))\n",
        "            \n",
        "            # Global representation\n",
        "            h_1 = global_max_pool(h_points_1, batch_1)\n",
        "            h_2 = global_max_pool(h_points_2, batch_2)\n",
        "        else:\n",
        "            x1 = self.conv1(data.pos, data.batch)\n",
        "            x2 = self.conv2(x1, data.batch)\n",
        "            h_points = self.lin1(torch.cat([x1, x2], dim=1))\n",
        "            return global_max_pool(h_points, data.batch)\n",
        "\n",
        "        # Transformation for loss function\n",
        "        compact_h_1 = self.mlp(h_1)\n",
        "        compact_h_2 = self.mlp(h_2)\n",
        "        return h_1, h_2, compact_h_1, compact_h_2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0QjhMfhJ8W7"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- I use InfoNCE / NT-Xent Loss implemented in pytorch metric learning library\n",
        "- Temperature allows to balance the similarity measure (make it more peaked)\n",
        "- Typical values are around 0.1 / 0.2 "
      ],
      "metadata": {
        "id": "fI18VkeQxadF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGxTDy0sLDTp",
        "outputId": "5747507d-1616-4739-e938-33ab2001f5ef"
      },
      "outputs": [
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          "name": "stdout",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m849.3/849.3 kB\u001b[0m \u001b[31m50.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m145.0/145.0 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.0/24.0 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.2/536.2 kB\u001b[0m \u001b[31m54.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for lit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# See https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#ntxentloss\n",
        "!pip install pytorch-metric-learning -q \n",
        "\n",
        "from pytorch_metric_learning.losses import NTXentLoss\n",
        "loss_func = NTXentLoss(temperature=0.10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "DDplPASCKNRD"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = Model().to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)\n",
        "\n",
        "\n",
        "data_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- No test dataset, as the evaluation can be done \"downstream\"\n",
        "- The compact representations go into the loss function\n",
        "- During test time no augmentations are applied and we can use the output representations"
      ],
      "metadata": {
        "id": "LASK3n1qz8Fx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXLT7Up7Kd9g",
        "outputId": "0720f585-a8a2-4466-bf2b-da04a41bcb38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 63/63 [02:26<00:00,  2.32s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 001, Loss: 1.4655\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 63/63 [02:25<00:00,  2.30s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 002, Loss: 0.8736\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 63/63 [02:25<00:00,  2.31s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 003, Loss: 0.6859\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 63/63 [02:25<00:00,  2.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 004, Loss: 0.4962\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import tqdm\n",
        "\n",
        "def train():\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    for _, data in enumerate(tqdm.tqdm(data_loader)):\n",
        "        data = data.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        # Get data representations\n",
        "        h_1, h_2, compact_h_1, compact_h_2 = model(data)\n",
        "        # Prepare for loss\n",
        "        embeddings = torch.cat((compact_h_1, compact_h_2))\n",
        "        # The same index corresponds to a positive pair\n",
        "        indices = torch.arange(0, compact_h_1.size(0), device=compact_h_2.device)\n",
        "        labels = torch.cat((indices, indices))\n",
        "        loss = loss_func(embeddings, labels)\n",
        "        loss.backward()\n",
        "        total_loss += loss.item() * data.num_graphs\n",
        "        optimizer.step()\n",
        "    return total_loss / len(dataset)\n",
        "\n",
        "for epoch in range(1, 5):\n",
        "    loss = train()\n",
        "    print(f'Epoch {epoch:03d}, Loss: {loss:.4f}')\n",
        "    scheduler.step()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J49zjaa5cxRe"
      },
      "source": [
        "## Evaluation of the Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "z97R1Vhou0uQ",
        "outputId": "c44a3985-8ed0-43f2-f6f5-4d612930f1d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get sample batch\n",
        "sample = next(iter(data_loader))\n",
        "\n",
        "# Get representations\n",
        "h = model(sample.to(device), train=False)\n",
        "h = h.cpu().detach()\n",
        "labels = sample.category.cpu().detach().numpy()\n",
        "\n",
        "# Get low-dimensional t-SNE Embeddings\n",
        "h_embedded = TSNE(n_components=2, learning_rate='auto',\n",
        "                   init='random').fit_transform(h.numpy())\n",
        "\n",
        "# Plot\n",
        "ax = sns.scatterplot(x=h_embedded[:,0], y=h_embedded[:,1], hue=labels, \n",
        "                    alpha=0.5, palette=\"tab10\")\n",
        "\n",
        "# Add labels to be able to identify the data points\n",
        "annotations = list(range(len(h_embedded[:,0])))\n",
        "\n",
        "def label_points(x, y, val, ax):\n",
        "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
        "    for i, point in a.iterrows():\n",
        "        ax.text(point['x']+.02, point['y'], str(int(point['val'])))\n",
        "\n",
        "label_points(pd.Series(h_embedded[:,0]), \n",
        "            pd.Series(h_embedded[:,1]), \n",
        "            pd.Series(annotations), \n",
        "            plt.gca()) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdEaaGAGY7X_"
      },
      "source": [
        "finding the most similar and most different data points.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwEq0v63Y4F4",
        "outputId": "c0e06a4b-afd5-43a9-d888-e7eb7685c2b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most similar data point in the embedding space for 17 is 18\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def sim_matrix(a, b, eps=1e-8):\n",
        "    \"\"\"\n",
        "    Eps for numerical stability\n",
        "    \"\"\"\n",
        "    a_n, b_n = a.norm(dim=1)[:, None], b.norm(dim=1)[:, None]\n",
        "    a_norm = a / torch.max(a_n, eps * torch.ones_like(a_n))\n",
        "    b_norm = b / torch.max(b_n, eps * torch.ones_like(b_n))\n",
        "    sim_mt = torch.mm(a_norm, b_norm.transpose(0, 1))\n",
        "    return sim_mt\n",
        "\n",
        "similarity = sim_matrix(h, h)\n",
        "max_indices = torch.topk(similarity, k=2)[1][:, 1]\n",
        "max_vals  = torch.topk(similarity, k=2)[0][:, 1]\n",
        "\n",
        "# Select index\n",
        "idx = 17\n",
        "similar_idx = max_indices[idx]\n",
        "print(f\"Most similar data point in the embedding space for {idx} is {similar_idx}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZJvNho8nQoW"
      },
      "source": [
        "Categories are: \"Table\", \"Lamp\", \"Guitar\", \"Motorbike\", \"Skateboard\"\n",
        "\n",
        "This is only based on the data in the current batch!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
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        "id": "qMv3cc_bb6ag",
        "outputId": "d97146db-d26d-4603-c441-a24c675aff5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data points:  2791\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "plot_3d_shape(sample[idx].cpu())"
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    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
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        "id": "i7Q86ALucDEx",
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      "outputs": [
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          "text": [
            "Number of data points:  2721\n"
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      "source": [
        "plot_3d_shape(sample[similar_idx].cpu())"
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      "source": [
        "This confirms that our embedding space has a proper arrangement and that our contrastive loss separated different entities successfully."
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